Miau Wiki - A felhasználó közreműködései [hu]

GYIK:COCO Hogyan

2026-02-08T16:36:41Z

Jkv1:

A COCO-elemzés technikai megvalósításáról ad tájékoztatást ez a két prezentáció:
* [http://miau.my-x.hu/temp/tananyag/ginf/coco_demo.pptx PPTX-verzió] - [http://miau.my-x.hu/temp/tananyag/ginf/coco_demo.pdf PDF-verzió] (Szerző: Pető István, SZIE GTK TKI - frissítve: 2010.03.24.)
* [http://miau.my-x.hu/temp/javitott/pl/coco_ppt_bovitett.ppt PPT-verzió] - [http://miau.my-x.hu/temp/javitott/pl/coco_ppt_bovitett.pdf PDF-verzió] (Szerző: Pető Krisztina, SZIE GTK GAM - frissítve: 2007.12.05.)
[[Kategória:GYIK]]

CT 00

2026-01-09T05:13:00Z

Jkv1: /* Title */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history
History of expectations: https://miau.my-x.hu/mediawiki/index.php/BPROF:zarovizsga -
especially: https://miau.my-x.hu/bprof/kje_bprof_szakdolgozat_specialitasok.docx
+ https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx)
+ https://miau.my-x.hu/temp/2025tavasz/rules/ (steps toward robot-lector:-)

=General rules=
*it is worth following the projects of the other students because the final exam is an exam, where EACH Student should be informed about ALL projects!!!
*each final thesis must be seen as a business activity (c.f. start-up): therefore, it must be given: targeted groups+utilities = a GOOD business model in order to have massive argumentations why this project is really relevant to realize...
*LLMs must be used for ALL thesis (quasi for each relevant aspects: planning, coding, etc.)
*...

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?

=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

Manager of universities:
*Benchmark: naive approach for daily marketing for motivating more Students to attendance
**Costs: basically wages (where employees/experts are writting messages for the social media)
**Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
**Expectation: the income through the human activities must be higher than the costs of the human activities, atl least zero (0 EUR)
*AI-driven support:
**Costs: redurced wages, but licence fees for AI (concept testing) - human experts produce concepts based on the particular data, robots are verifying concepts
**Costs of the AI-oriented development (10.000 EUR/licence)
**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions through the most realistic understanding of the marketing systems (e.g. 10.000 EUR/year)
*Conclusion: the investition into the AI-oriented development can be covered within 1 year

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
*old/new [2]
*English/other (Hungarian/other) [2]
*article/other (e.g.wikipedia) [2]
*KJU-affected/other [2]
*=2*2*2*2=at least 16 references (or more) following the above-mentioned types
*min 1 publication for each type

==Chapter#.8.4. Conversations with LLMs==
Each final thesis have to present an annex with the appropriate LLM-documentation: exact prompts + exact LLM-outputs for relevant parts of the development

CT 00

2026-01-09T05:08:13Z

Jkv1: /* Chapter#.8.4. Conversations with LLMs */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history
History of expectations: https://miau.my-x.hu/mediawiki/index.php/BPROF:zarovizsga -
especially: https://miau.my-x.hu/bprof/kje_bprof_szakdolgozat_specialitasok.docx
+ https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx)
+ https://miau.my-x.hu/temp/2025tavasz/rules/ (steps toward robot-lector:-)

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

Manager of universities:
*Benchmark: naive approach for daily marketing for motivating more Students to attendance
**Costs: basically wages (where employees/experts are writting messages for the social media)
**Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
**Expectation: the income through the human activities must be higher than the costs of the human activities, atl least zero (0 EUR)
*AI-driven support:
**Costs: redurced wages, but licence fees for AI (concept testing) - human experts produce concepts based on the particular data, robots are verifying concepts
**Costs of the AI-oriented development (10.000 EUR/licence)
**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions through the most realistic understanding of the marketing systems (e.g. 10.000 EUR/year)
*Conclusion: the investition into the AI-oriented development can be covered within 1 year

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
*old/new [2]
*English/other (Hungarian/other) [2]
*article/other (e.g.wikipedia) [2]
*KJU-affected/other [2]
*=2*2*2*2=at least 16 references (or more) following the above-mentioned types
*min 1 publication for each type

==Chapter#.8.4. Conversations with LLMs==
Each final thesis have to present an annex with the appropriate LLM-documentation: exact prompts + exact LLM-outputs for relevant parts of the development

CT 00

2025-10-28T03:18:32Z

Jkv1:

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history
History of expectations: https://miau.my-x.hu/mediawiki/index.php/BPROF:zarovizsga -
especially: https://miau.my-x.hu/bprof/kje_bprof_szakdolgozat_specialitasok.docx
+ https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx)
+ https://miau.my-x.hu/temp/2025tavasz/rules/ (steps toward robot-lector:-)

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

Manager of universities:
*Benchmark: naive approach for daily marketing for motivating more Students to attendance
**Costs: basically wages (where employees/experts are writting messages for the social media)
**Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
**Expectation: the income through the human activities must be higher than the costs of the human activities, atl least zero (0 EUR)
*AI-driven support:
**Costs: redurced wages, but licence fees for AI (concept testing) - human experts produce concepts based on the particular data, robots are verifying concepts
**Costs of the AI-oriented development (10.000 EUR/licence)
**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions through the most realistic understanding of the marketing systems (e.g. 10.000 EUR/year)
*Conclusion: the investition into the AI-oriented development can be covered within 1 year

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
*old/new [2]
*English/other (Hungarian/other) [2]
*article/other (e.g.wikipedia) [2]
*KJU-affected/other [2]
*=2*2*2*2=at least 16 references (or more) following the above-mentioned types
*min 1 publication for each type

==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-09-30T00:52:12Z

Jkv1:

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history
History of expectations: https://miau.my-x.hu/mediawiki/index.php/BPROF:zarovizsga -
especially: https://miau.my-x.hu/bprof/kje_bprof_szakdolgozat_specialitasok.docx
+ https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx)

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

Manager of universities:
*Benchmark: naive approach for daily marketing for motivating more Students to attendance
**Costs: basically wages (where employees/experts are writting messages for the social media)
**Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
**Expectation: the income through the human activities must be higher than the costs of the human activities, atl least zero (0 EUR)
*AI-driven support:
**Costs: redurced wages, but licence fees for AI (concept testing) - human experts produce concepts based on the particular data, robots are verifying concepts
**Costs of the AI-oriented development (10.000 EUR/licence)
**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions through the most realistic understanding of the marketing systems (e.g. 10.000 EUR/year)
*Conclusion: the investition into the AI-oriented development can be covered within 1 year

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
*old/new [2]
*English/other (Hungarian/other) [2]
*article/other (e.g.wikipedia) [2]
*KJU-affected/other [2]
*=2*2*2*2=at least 16 references (or more) following the above-mentioned types
*min 1 publication for each type

==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-09-30T00:29:22Z

Jkv1: /* Chapter#.8.3. References */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

Manager of universities:
*Benchmark: naive approach for daily marketing for motivating more Students to attendance
**Costs: basically wages (where employees/experts are writting messages for the social media)
**Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
**Expectation: the income through the human activities must be higher than the costs of the human activities, atl least zero (0 EUR)
*AI-driven support:
**Costs: redurced wages, but licence fees for AI (concept testing) - human experts produce concepts based on the particular data, robots are verifying concepts
**Costs of the AI-oriented development (10.000 EUR/licence)
**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions through the most realistic understanding of the marketing systems (e.g. 10.000 EUR/year)
*Conclusion: the investition into the AI-oriented development can be covered within 1 year

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
*old/new [2]
*English/other (Hungarian/other) [2]
*article/other (e.g.wikipedia) [2]
*KJU-affected/other [2]
*=2*2*2*2=at least 16 references (or more) following the above-mentioned types
*min 1 publication for each type

==Chapter#.8.4. Conversations with LLMs==

Vita:CT 00

2025-05-26T12:21:44Z

Jkv1:

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

'''Recommended rules waiting for being integrated: https://miau.my-x.hu/temp/2025tavasz/rules/'''
Experimental interpretations (c.f. KNUTH: https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth) concerning automation challenges:
https://miau.my-x.hu/miau/323/robot_lektor/

Worth knowing: https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure
+ https://miau.my-x.hu/mediawiki/index.php/BPROF:zarovizsga

Recommended literature: https://www.facebook.com/groups/263175136143745/permalink/725750149886239/?rdid=KQzoaiQuSkaMLEzd#

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

When breaking a title into multiple lines with a line break, it is expected that words representing a single thought unit should be included in one line.

CORRECT:
*When breaking a title into multiple lines with a line break,
*it is expected that words representing a single thought unit should be included in one line.

INCORRECT:
*When breaking a title into multiple lines with a line break, it is
*expected that words representing a single thought unit should be included in one line.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
The chapter "discussions" should present a kind of self-critical interpretation of the publication.

=Chapter#5. Conclusions=
The chapter "conclusions" should present a kind of negotiation concerning the previous self-critical interpretation of the publication.

=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-05-23T14:50:52Z

Jkv1:

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

'''Recommended rules waiting for being integrated: https://miau.my-x.hu/temp/2025tavasz/rules/'''
Experimental interpretations (c.f. KNUTH: https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth) concerning automation challenges:
https://miau.my-x.hu/miau/323/robot_lektor/

Worth knowing: https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure
+ https://miau.my-x.hu/mediawiki/index.php/BPROF:zarovizsga

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

When breaking a title into multiple lines with a line break, it is expected that words representing a single thought unit should be included in one line.

CORRECT:
*When breaking a title into multiple lines with a line break,
*it is expected that words representing a single thought unit should be included in one line.

INCORRECT:
*When breaking a title into multiple lines with a line break, it is
*expected that words representing a single thought unit should be included in one line.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
The chapter "discussions" should present a kind of self-critical interpretation of the publication.

=Chapter#5. Conclusions=
The chapter "conclusions" should present a kind of negotiation concerning the previous self-critical interpretation of the publication.

=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-05-03T17:03:24Z

Jkv1: /* Chapter#5. Conclusions */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

'''Recommended rules waiting for being integrated: https://miau.my-x.hu/temp/2025tavasz/rules/'''
Experimental interpretations (c.f. KNUTH: https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth) concerning automation challenges:
https://miau.my-x.hu/miau/323/robot_lektor/

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

When breaking a title into multiple lines with a line break, it is expected that words representing a single thought unit should be included in one line.

CORRECT:
*When breaking a title into multiple lines with a line break,
*it is expected that words representing a single thought unit should be included in one line.

INCORRECT:
*When breaking a title into multiple lines with a line break, it is
*expected that words representing a single thought unit should be included in one line.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
The chapter "discussions" should present a kind of self-critical interpretation of the publication.

=Chapter#5. Conclusions=
The chapter "conclusions" should present a kind of negotiation concerning the previous self-critical interpretation of the publication.

=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-05-03T17:01:55Z

Jkv1: /* Chapter#4. Discussions */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

'''Recommended rules waiting for being integrated: https://miau.my-x.hu/temp/2025tavasz/rules/'''
Experimental interpretations (c.f. KNUTH: https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth) concerning automation challenges:
https://miau.my-x.hu/miau/323/robot_lektor/

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

When breaking a title into multiple lines with a line break, it is expected that words representing a single thought unit should be included in one line.

CORRECT:
*When breaking a title into multiple lines with a line break,
*it is expected that words representing a single thought unit should be included in one line.

INCORRECT:
*When breaking a title into multiple lines with a line break, it is
*expected that words representing a single thought unit should be included in one line.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
The chapter "discussions" should present a kind of self-critical interpretation of the publication.

=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-28T05:58:21Z

Jkv1:

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

'''Recommended rules waiting for being integrated: https://miau.my-x.hu/temp/2025tavasz/rules/'''
Experimental interpretations (c.f. KNUTH: https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth) concerning automation challenges:
https://miau.my-x.hu/miau/323/robot_lektor/

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

When breaking a title into multiple lines with a line break, it is expected that words representing a single thought unit should be included in one line.

CORRECT:
*When breaking a title into multiple lines with a line break,
*it is expected that words representing a single thought unit should be included in one line.

INCORRECT:
*When breaking a title into multiple lines with a line break, it is
*expected that words representing a single thought unit should be included in one line.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-26T18:07:41Z

Jkv1:

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

'''Recommended rules waiting for being integrated: https://miau.my-x.hu/temp/2025tavasz/rules/'''

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

When breaking a title into multiple lines with a line break, it is expected that words representing a single thought unit should be included in one line.

CORRECT:
*When breaking a title into multiple lines with a line break,
*it is expected that words representing a single thought unit should be included in one line.

INCORRECT:
*When breaking a title into multiple lines with a line break, it is
*expected that words representing a single thought unit should be included in one line.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-26T17:18:55Z

Jkv1: /* Title */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

When breaking a title into multiple lines with a line break, it is expected that words representing a single thought unit should be included in one line.

CORRECT:
*When breaking a title into multiple lines with a line break,
*it is expected that words representing a single thought unit should be included in one line.

INCORRECT:
*When breaking a title into multiple lines with a line break, it is
*expected that words representing a single thought unit should be included in one line.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-26T10:36:01Z

Jkv1: /* Title */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

A cím sortöréssel több sorba tördelése kapcsán elvárás, hogy egy sorba egy gondolati egységet jelentő szavak kerüljenek.

HELYES:
*A cím sortöréssel több sorba tördelése kapcsán elvárás,
*hogy egy sorba egy gondolati egységet jelentő szavak kerüljenek.

HELYTELEN:
*A cím sortöréssel több sorba tördelése kapcsán elvárás, hogy egy
*sorba egy gondolati egységet jelentő szavak kerüljenek.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-26T10:31:06Z

Jkv1: /* Title */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text

A cím sortöréssel több sorba tördelése kapcsán elvárás, hogy egy sorba egy gondolati egységet jelentő szavak kerüljenek.

=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-20T06:43:05Z

Jkv1: /* Chapter#3. Own developments */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility - https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking8.docx)

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-20T06:41:37Z

Jkv1: /* Chapter#3. Own developments */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

Application of Category Types in Real-Life Projects: This main chapter bridges theory and practice by illustrating how category types (e.g., KPIs, testing frameworks) are applied in case studies (e.g., e-car log analysis, student projects). Include: Step-by-step workflows (e.g., task1 → task4 from Chapter 1.2 - incl. subtasks!) / Automation scripts (e.g., COCO Y0 for concept testing) / Student assignments / etc.

The main chapter about the own developments illustrates how category types are applied in real-life projects, including case studies from student assignments adapting industry examples being describe in chapter#2. Each example will highlight the decision-making process and outcomes (c.f. reproducibility).

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-20T06:36:57Z

Jkv1: /* Chapter#2. Literature */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking7.docx)

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-20T06:35:56Z

Jkv1: /* Chapter#2. Literature */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).

Rule: Consistent Use of Parenthetical Citations for Sources

Description: All citations in the text must use the footnote-section OR the parenthetical format (e.g., (Source: https://example.com)) immediately following the cited information, including a brief source description and URL where applicable.

Rationale: The wiki-based document here and now uses parenthetical citations (e.g., “Source: https://en.wikipedia.org/wiki/Software_testing” in Chapter#2.1), but Vita:CT_00 for the time being still lacks a formal rule standardizing this format (because this is the task of Students who are writing the own rules for themselves). A consistent citation style improves readability and verifiability, similar to how bullet points standardize example presentation. The subchapter about the list of references must follow one of the so called standardr formats: https://pitt.libguides.com/citationhelp

==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-20T06:29:59Z

Jkv1: /* Chapter#.8.2. Figures */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).
==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations (e.g. vertical/horizontal, left/right interpretations, arrows and their directions, colours, shapes, etc.) are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-20T06:28:57Z

Jkv1: /* Chapter#.8.2. Figures */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).
==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

Every figure must include:
*Number/Tag: "Figure X:" (sequential numbering) - and this number should be mentioned at least one single time in the main text, where the explanations are.
*Descriptive Title: Clearly state the figure’s purpose (e.g., "Flow Chart of Risk Assessment").
*Source:
**Author-generated: "Source: Author’s own work, 2025."
**AI-assisted: "Source: ChatGPT-4 simulation; validated via Excel."
**External: "Source: [MIAU_XLSX_URL]."
**Validation Note: Briefly explain verification (e.g., "Data confirmed via COCO Y0 analysis in Section 3.1.6").
(c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking6.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-19T19:48:43Z

Jkv1: /* Chapter#1.6. About the structure of the publication */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, but never use blank lines, double spaces, tabulators, etc. - it means the old "type-writer-solutions"...
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).
==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-19T19:46:15Z

Jkv1: /* Chapter#1.6. About the structure of the publication */

This discussion page helps to interpret what should be done in a particular chapter and why...
Final product: https://miau.my-x.hu/mediawiki/index.php/CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Optimization challenge: the better is a title the more is the number of keywords but the less is the length of the text
=Subtitle=
(a new aspect can be visualized based on the same principle as before in case of the title)
=Authors=
names (https://orcid.org/...),
=Institutions=
expected cover-sheet-elements incl. university, department, etc.
=Abstract=
One-pager-like chapter for conferences (c.f. IKSAD/Türkiye: https://miau.my-x.hu/miau2009/index_en.php3?x=e080). The abstract is not the summary, where citations might quasi only be listed about the most relevant statements of the publication. The abstract should deliver a kind of motivating information package - quasi without any citations: e.g. history of the project, descriptive core information about the problem, own steps and their results, future - maximal one single page.

=Chapter#1. Introduction=
Between to chapters (e.g. 1. and 1.1.), it is necessary to have explaining texts - mostly about a kind of detailed/specific table of content with argumentations...
==Chapter#1.1. Aims/objectives==
A final thesis (a publication in general) might not formulate more aims than being really covered through the publication. In order to avoide the suspicions about potential realistic, but not realized aims/objectives, each promise should have a "CHAPTER#..."-sign, where the Readers will be capable of checking the real performaces behind each promise - immediately. These chapter#-signs can be empty, if the whole structure (table of content) is still fluid. But each empty sing should be filled with the appropriate numbers, if the details behind a promise could be formulated as a finally existing subchapter (mostly in chapter#3 - own development).

The aims may only be listed, if the basic definitions are given. The keywords of the (sub)title and each further relevant term should always and immediately be defined after the first using.

It is not forbidden to work with arbitrary high complexity. In this case: the Readers have to understand the XLSX-files before going on...

Important and general rule: if a plural form is used, then it is necessary to present examples: e.g. philosophycal challenges (e.g. automation, nature/level of vierification), or arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers) or relevant keywords ... (c.f. concepts, verification, partial log-data) or different steps (task1, task2, task3, task4:interpretation of the hidden file), etc. Examples for plural terms can be formatted as numbered lists or list with bullet point or even with parentheses: (e.g., 1. Example 1, 2. Example 2). (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking3.docx)

Recommended literature about keyhole-challenges: https://miau.my-x.hu/myx-free/index_en.php3?x=fbl, https://miau.my-x.hu/myx-free/index_en.php3?x=iq

==Chapter#1.2. Tasks==
Rules: The list of the particular tasks should deliver a clear classification of steps without any overlapping effects and/or without any lacks. BTW: this expectation is valid for each lists in general. Tasks are also promises (as aims/objectives), therefore, it is also necessary to have a chapter#-sign for each task. Tasks are decisions of the Author(s), therefore, it is necessary to deliver argumentations for the rationality of these decisions.

==Chapter#1.3. Targeted groups==
Rules: each potential targeted groups should be listed (see without overlapping and lacks) - and the argumentations must be given: why is a listed element rational? Targeted groups are potential customers, who should be capable of paying for the results of this project based on a real information added-value estimated by the Authors themselfes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
Rules: For each targeted group should be made an as far as exact estimation in USD or EUR about the information added-value. It is expected, that the estimations are positive values! Negative values means: parasitism through the IT-experts concerning their customers! Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.

==Chapter#1.5. Motivation==
Quasi arbitrary argumentation (in dependence with targeted groups AND informational added-values)...

==Chapter#1.6. About the structure of the publication==
In this subchapter, it is necessary to write about ALL aspects which will only be mentioned but without deep details.

In this subchapter, it is also necessary to clarify ALL the used formats.

In this subchapter, the structure of the publication must be defined in advance.

In personalized cases, such as student assignments or context-specific case studies, examples must be listed in bullet point format to ensure clarity and flexibility for descriptive, non-sequential items. For example:
*Concept A: Energy efficiency analysis based on e-car log data.
*Concept B: IT-security evaluation for educational platforms. To ensure consistency across Vita:CT_00 and CT_00, the following additional formatting guidelines apply:
*Numbered lists (1., 2., etc.) must be used for sequential or prioritized examples, such as structured analyses in CT_00.
*Lettered listings (a), (b), etc., should be avoided unless referring to pre-defined categories labeled with letters.
*Use bold for key terms or section headers, and italics for emphasis (e.g., cautionary notes or highlights). Avoid underlines unless required (e.g., for links).
*All formatting choices must be applied consistently throughout the article to enhance readability and avoid confusion. These rules complement CT_00’s formatting guidelines (see CT_00, Chapter#1.6), ensuring alignment between formal and discussion content. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking2.docx)

Recommendation Font and Size: For readability and professionalism, especially if exported to a Word document or PDF, the following are recommended:
*Font: Use a clean, professional font such as Times New Roman, Arial, or Calibri. Calibri is modern and widely accepted in academic and professional settings.
*Font Size: Use 12-point font for body text and 14-point for headings to ensure readability. Subheadings can use 12-point bold to differentiate from body text.
*Line Spacing: Use 1.15 or 1.5 line spacing to improve readability, with a single blank line between paragraphs.
*Justification: Use justified text alignment for a polished look, ensuring consistent spacing. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking4.docx)

=Chapter#2. Literature=
Between two chapter-titles, it is necessary to have explanations about the structure of the particular subchapters.

This chapter is dedicated for all definitions, which are necessary to understand the own development, results. It is forbidden to have subchapters without any citation(s). Here, it is important to use citations with sources. Between two citations, it is expected, that the Author(s) deliver(s) argumentations about each citation: is a citation is to integrated or even to avoid? More precisely: each citation should be evaluated by the Author(s): either in a positive way (the particural statement of the citation will be integrated: chapter#... or in a negative way: the particural statement of the citation is to avoide).
==Chapter#2.1. ...==
==Chapter#2.2. ...==
==Chapter#2.3. ...==
==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
The presentation of the own developments, experiments, idea, etc. must strict use the keywords inroduced (mostly based on citations, but also in form of own interpretation to the definition in the literature in chapter#2.

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
Each publication must consist at least one figure from the literature and at least one own figure.

It is required to include full bibliographic data and licensing details for all image sources, especially those taken from external literature, to ensure proper attribution and maintain academic integrity. Visual elements such as charts and tables should be designed for maximum readability and clarity (incl. units). Each visual aid must include: A clear caption describing its content, Visible and labeled axes, columns, or rows, readable text and elements (e.g., font size, colors, line thickness), and proper spacing and contrast to ensure all data is distinguishable. (c.f. https://miau.my-x.hu/temp/2025tavasz/ct2/suggestion_frame_for_change_tracking.docx)

==Chapter#.8.3. References==
https://miau.my-x.hu/mediawiki/index.php/BPROF_Thesis_Structure / https://miau.my-x.hu/bprof/Deepl%20-%20Thesis%20specialities%20of%20the%20BPROF%20training%20at%20the%20KJE.docx / https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

==Chapter#.8.4. Conversations with LLMs==
Each publication must have at least one chapter, where relevant information units come from ChatGPT/Copilot (e.g. potential keywords, definitions, etc.). The entire conversations (prompts+ouputs) must be presented in the annex and the used details (citation) must be evaluated mostly in chapter#2.

Vita:CT 00

2025-04-19T19:32:47Z

Jkv1: /* Chapter#1.1. Aims/objectives */

Vita:CT 00

2025-04-19T19:31:46Z

Jkv1: /* Chapter#1.1. Aims/objectives */

Vita:CT 00

2025-04-19T19:22:13Z

Jkv1: /* Chapter#1.6. About the structure of the publication */

Vita:CT 00

2025-04-19T19:21:43Z

Jkv1: /* Chapter#1.6. About the structure of the publication */

Vita:CT 00

2025-04-19T17:44:02Z

Jkv1: /* Chapter#.8.2. Figures */

Vita:CT 00

2025-04-19T17:41:30Z

Jkv1: /* Chapter#.8.2. Figures */

CT 00

2025-04-07T11:50:44Z

Jkv1: /* Chapter#1.4. Utilities (estimation of informational added-values) */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

Manager of universities:
*Benchmark: naive approach for daily marketing for motivating more Students to attendance
**Costs: basically wages (where employees/experts are writting messages for the social media)
**Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
**Expectation: the income through the human activities must be higher than the costs of the human activities, atl least zero (0 EUR)
*AI-driven support:
**Costs: redurced wages, but licence fees for AI (concept testing) - human experts produce concepts based on the particular data, robots are verifying concepts
**Costs of the AI-oriented development (10.000 EUR/licence)
**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions through the most realistic understanding of the marketing systems (e.g. 10.000 EUR/year)
*Conclusion: the investition into the AI-oriented development can be covered within 1 year

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T11:42:12Z

Jkv1: /* Chapter#1.4. Utilities (estimation of informational added-values) */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

Manager of universities:
*Benchmark: naive approach for daily marketing for motivating more Students to attendance
**Costs: basically wages (where employees/experts are writting messages for the social media)
**Impacts: in ideal case, the share of the particular university is not decreasing compared to the competitive institutions
**Expectation: the
*AI-driven support:
**Costs: redurced wages
**Impacts: in ideal case, the share of the particular university is massive increasing compared to the competitive institutions

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T10:22:15Z

Jkv1: /* Chapter#2.3. KPIs */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!

Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*significancy: It could be important, but here and now, it is nor operationalized.
*efficiency: It could be important, but here and now, it is nor operationalized.
*
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T10:18:33Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:
*''"This paper aims to analyze these datasets to evaluate the accuracy of performance predictions and their implications on model efficiency."''
*''"Fact-estimate discrepancies were also evaluated, with lower values signifying better estimation accuracy."''
*''"*Model_A6*: Includes hidden attributes, achieving a high correlation (0.99) and strong estimation accuracy"''
*''" *Model_C6*: Poor correlation (0.80) and weak estimation accuracy, ranking the lowest among models."''
*''"Advanced Estimations: OAM, Y0, OAM_2, and Y0_2 The OAM worksheet evaluates model stability and accuracy through a COCO:Y0 engine estimation. "''
*''"Conclusion The dataset analysis reveals critical insights into the accuracy and efficiency of various e-car models. Models A6 and B6 exhibit the highest reliability based on correlation and estimation accuracy, while Model C6 underperforms significantly. "''
The term ''"underperforms significantly"'' is a logical trap: the significance should be important (c.f. special KPI), but the basic XLSX-file does not have any classic significance analyses. The term of ''"model efficiency"'' seems to be important, but there are buzzwords like "efficiency" which are empty bubbles if the operationalism/defining is not given. The interpretation/evaluation/ranking of the concept-variations (A-B-C) can be identified, but without an automatable flow-chart of the realistic detailed steps. The real role of the COCO Y0-models could not be derived - unfortunately. It is important, that concepts (A,B) could have the same "accuracy", while concept-C is definitely less robust (what robustness ever means).

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T10:07:50Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T10:07:33Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#6:
Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy" (c.f. source#1).
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).
On the other hand: source#6 delivers a LLM-based interpretation, where the basic XLSX-file are seen as a form of the complex communication contrary e.g. to MTMT-logic, but parallel to the MIAU.MY-X.HU-logic: c.f. ''"The Excel file is not merely a repository of data; it is a narrative of a systematic experimental approach."''

Source#7:

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T10:01:34Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#1&6&...:

Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy".
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).

Source#7:

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T10:01:17Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#1&6&...:
Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy".
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).

Source#7:

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T10:00:58Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

Source#1&6&...
Not all background materials (https://miau.my-x.hu/mediawiki/index.php?title=CT_01) are using the term of "accuracy".
''"The model sheets likely represent different iterations or configurations of the underlying analysis. Each model appears to test alternative assumptions or parameters regarding energy consumption. The consistent referencing of objects, attributes, and the notion of “steps” (as seen in the Hungarian “Lépcsôk”) suggests a systematic approach to evaluating model performance and reliability."'' The challenge can be identified in Source#6, but the problem about the accuracy seems to be lost in fram eof goals.
''"Pattern Recognition"'' is an important term, but the evaluation (goodness) of potential patterns could not be explained in a detailed way. This negative effects seems to be a conclusion of the chatgpt-impact (c.f. ''"In this essay, we explore the multifaceted layers of the Excel file while integrating insights from AI-assisted dialogues, demonstrating how tools like ChatGPT/Copilot can enrich the interpretative process."''). Further bubble-like text-elements (characteristical for chatgpt/copilot) can also be identified: e.g. ''"Validate Patterns: Multiple interactions confirmed recurring themes across the dataset, particularly regarding the consistency in the averaging process and the role of model sheets in testing various conceptual scenarios."'' All these formulations are without any real/deep/operationalized meaning - unfortunately. LLM-approaches are definitely not capable of rational hermeneutics (e.g. https://miau.my-x.hu/miau/320/tartalom_es_forma_szoveges_elvalasztasa_copilot_gyogypedagogia.docx).

Source#7:

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T09:46:26Z

Jkv1: /* Chapter#2.3. KPIs */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!
The own consolidation (system model, system plan) have to clarify an automatable process for testing/evaluating concepts.

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T09:43:51Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

Source#5:
*''"Multiple Tests for Accuracy: The three COCO STD datasets help ensure the rankings are reliable."''
*''"Simplify the Steps: Some calculations seem unnecessary and could be removed without losing accuracy."'' +''"While most steps make sense, some choices (like using 37 instead of 36) seem unusual."''
The expression of "multiple tests" means: the goodness must have different layers (and they should be aggregated in an optimzed way).
The ''""simplification""'' can be seen as a kind of discussion-layer.

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T09:38:49Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph in Source#2). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T09:37:55Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):

Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.

Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T09:37:36Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):
Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."''
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."''
The formulations talks about quantification, e.g. correlation.
Source#4:
*''"Error Dispersion: Elevated error metrics in the quasi-random outcomes underscored the impact of randomness on the predictive accuracy."''
*''"This combined approach improves prediction accuracy and helps pinpoint areas where model refinements are necessary, thereby advancing the overall robustness of the performance evaluation. "''
The mentioning of the ''randomness'' is important as on of the characteristic points of the concept testing as such. The mentioning of ''improving'' is a clear sing for the necessity of measuring of goodness. Such terms as ''robustness'' are disturbing: they are empty bubbles without any potential steps towards the ''KNUTH-principle'' (c.f. https://miau.my-x.hu/miau2009/index_tki.php3?_filterText0=*knuth)

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T08:53:35Z

Jkv1: /* Chapter#2.3. KPIs */

Final-thesis-like publication based on previous performances (see: https://miau.my-x.hu/mediawiki/index.php?title=CT_01)
Principles for editing: https://miau.my-x.hu/mediawiki/index.php/Vita:CT_00
History of the final product: https://miau.my-x.hu/mediawiki/index.php?title=CT_00&action=history
History of the discussion page: https://miau.my-x.hu/mediawiki/index.php?title=Vita:CT_00&action=history

=Title=
Which concepts can be verified based on partial data about log-information in an e-car?
=Subtitle=
(or a cooperative experiment, how to create e.g. the chapter2 about literature in a final thesis)
=Authors=
László Pitlik (https://orcid.org/0000-0001-5819-0319),
László Pitlik (Jr.) (https://orcid.org/0000-0002-8058-9577)
Mátyás Pitlik (https://orcid.org/0000-0002-1991-3008),
=Institutions=
MY-X research team
=Abstract=
History of the project: The software-testing as such from point of view of a praxis-oriented education has to enforce real testing experiences - especially about softwares being given day-by-day in the education (e.g. https://miau.my-x.hu/miau/320/moodle_neptun_tests/, https://miau.my-x.hu/miau/320/moodle_testing/, https://miau.my-x.hu/miau/320/teams_testing/). On the other hand, it is not correct, if the term of testing is only focusing on ergonomy, functionality in a trivial way. Therefore, specific aspects are also important: e.g. https://miau.my-x.hu/miau/320/moodle_cubes_logic/ about interpreting systems with seemingly correct functionalities and/or https://miau.my-x.hu/miau/320/moodle_webkincstar/ about legal aspects of potential damages based on testing results. Finally, the testing as such approximate the challenge of concept testing (c.f.
https://miau.my-x.hu/miau/320/concept_testing/), where the best concepts should be derived based on partial log-data about arbitrary systems (c.f. encryption/decryption tasks for unknown-cyphers).

Own objectives and results: This publication demonstrates a case about the negotiation process of 10+ experts concerning a tricky challenge, where partial (raw and derived) log-data of an e-car could be analyzed based on three concepts. 2 of them were totally correct from mathematical point of views, and one concept was a randomized set of potential interpretable numbers. The interpretation process had two levels: the first level made only a part of the existing data visible. On other level, all data could be seen. Parallel, to the case tadies based on human intuition processes, an AI-based approach must also be interpreted by human experts. The conclusions can be seen in this publication.

Future: The creation of the publication (as a kind of side effect) will also be used in the education to demonstrate a lot of rules concerning the writing process of a final thesis. On the other hand, the main motivation is always the automation: it is important, that human experts are capable of solving problems in an approximative way, but it is significantly more relevant to explore, how can we derive automations concerning the thinking processes of human experts.

=Chapter#1. Introduction=
In this chapter, it will be necessary to clarify the basic information about the project: aims/objectives, tasks, targeted groups, uitilities (estimation of information added-values), motivation, about the structure of the study.
==Chapter#1.1. Aims/objectives==
The title signalize more relevant keywords needing at least a short definition (c.f. concepts, verification, partial log-data).

The data asset for task-definitions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_task_level.xlsx. The whole analytical process can be interpreted here: https://miau.my-x.hu/miau/320/concept_testing/concept_testing_v1.xlsx
There are 3 task levels (for each level there is a separate sheet "task1", "task2", "task3" - see *task_level.xlsx). The entire complexity (see *_v1.xlsx - including data and analytical steps) was a hidden file during the task-periode. Further files concerning solutions can be seen here: https://miau.my-x.hu/miau/320/concept_testing/?C=M;O=D.

Based on the above-mentioned files, the expression partial data means: parts of a complex systems are presented as task in order to motivate for explanations/interpretations. The situation is the same, as somebody has to report about a room based on a view through one/more key-hole(s).

Concepts as keyword means: based on the raw data and further calculated data, there are 3 hidden formulas and only the results of these hidden formulas are known in frame of the tasks. The inputs of the tasks is only data positions without any formulas.

Verification as keyword means: what kind of analytical steps lead to a situation, where it is possible to classify concepts as potential realistic or even potential irrealistic.

Based on these short definitions, the publication try to present a case study where (see the entire publication as such), where different steps (task1, task2, task3, task4:interpretation of the hidden file) are interpreted in a detailed way.

The experiment based on the data delivered in task1 can be found in chapter#...

The experiment based on the data delivered in task2 can be found in chapter#...

The experiment based on the data delivered in task3 can be found in chapter#...

The experiment based on the data delivered in task4 can be found in chapter#...

The entire publication tries to deliver interpretation possibilities to the term "verification". Verification can be derived manually (see chapter#...) or even in an automated way (see chapter#...). The manual-driven steps can have such a traps, where automation becomes impossible (see chapter#...).

Summa summarum: the whole publication tries to have influence to the thinking methodology of the Students in order to see practical steps behind phylosophycal challenges (e.g. automation, nature/level of vierification). The publication can be evaluated as understood, if the Reader think, (s)he is capable of deriving classifications concerning arbitrary concepts and (s)he is capable of deciding about a concept whether it it is rather realistic or rather irrealistic. It is also important, that the Readers see the third output-level: namely, not each concept may be evaluated based on the partical given raw data (see chapter#...).

==Chapter#1.2. Tasks==
The aims/objective presented already the 3+1 tasks: 3 tasks are handling with concepts based on partial information. The last one (4th) demonstrates holistic/complete information.

Task1: Based on the particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task2: Based on further particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task3: Based on further new particular information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...)

Task4a: Based on holistic/complete information, which concept (A,B,C) seems to be rational or irrational? (see chapter#...) AND

Task4b: How can be automated the most complex (most consistent) verification process? (see chapter#...)

Argumentations: The new and newer futher information units try to support the understanding process concerning more and more complex verification strategies. The tasks sould be solved in a step-by-step-way in order to ensure didactical impacts/effects in Students.

==Chapter#1.3. Targeted groups==
The entire challenge is a didactical challenge. The step-wise progress is the learning process as such. The methodology is basing on trial-and-error-effects in individuals and in groups. Therefore, the targeted groups are individuals (as Students) and groups of Students. On the other hand: each learning material is a kind of support for teachers too. Therefore, teachers are also part of the targeted groups. Affected teachers are not only teachers having the same subject (c.f. testing), but each subject can also be supported through the phylosophycal (context free) aspects. Finally, instituions (management of institutions/universities) are also a kind of targeted group, because the castles of the sciences have to apply each teached knowledge in the own management processes.

==Chapter#1.4. Utilities (estimation of informational added-values)==
There are now 4 targeted groups: individuals as Students, groups of Students, individuals as Teachers, manager of universities. The informational added-value is the difference between impacts without and with the results of this project minus costs. In ideal case: the projects does cause more positive impacts than costs compared to the benchmark where the projects results are not given.
Estimations have two layers: incomes and costs in the bechmark situation AND incomes and costs based on the results of the projects.... (later)

==Chapter#1.5. Motivation==
This publication is an efficient case study concerning knowledge management, especially testing knowledge management processes among Students for better final theses and parallel, it is a real publication about a complex challenge: concept testing layers. Therefore, it is motivating to integrate to goals in one single action.

==Chapter#1.6. About the structure of the publication==
The publication will concern mathematical aspects (see similarity analyses), but without such level of details, where this publication could be used for learning about the complex system of the similerities. This challenge is complex enough in order to handle in an other publication.

This publication tries to follow the strict pattern predefined for final theses in general, and especially for BPROF-Students. In this publication one single expectation will not be worked out: the relationships between the subjects in the curriculum and the particular publication title. In order to have appropriate examples, please analyse the following URL: https://miau.my-x.hu/temp/2025tavasz/?C=M;O=D

The publication is just a quasi formatted text. Only chapters are defined in a more-layer-strucuture. The ''"citations"'' will be written as prescripted incl. the necessary sources - in this case in form of URLs pointing to specific parts of the background documentations: e.g. https://miau.my-x.hu/mediawiki/index.php?title=CT_01
Further formats (bold, underlined, footnotes, lists, etc.) are excluded.

=Chapter#2. Literature=
This chapter is dedicated for all definitions, which are necessary to understand the own development, results. Here, it is important to use citations with sources and between two citations, it is expected, that the Author(s) deliver argumentations about each citation: is a citation is to integrated or even to avoid? Relevant topics are: testing as such, proving as such, KPIs, correlations, regressions, similarity analyses, automation, ...
==Chapter#2.1. Testing==
''"Software testing is the act of checking whether software satisfies expectations."'' (Source: https://en.wikipedia.org/wiki/Software_testing)
This short definition is complex enough to deliver a relevant new keyword: ''"expectations"''. Before this abstraction is really involved, the term of "concept testing" should be defined. This definition may come from the Author(s), because here and now, only the goals of the Author(s) are relevant. Concepts are therefore patterns (formulas, systems, relationships, models, etc.) being seemingly capable of mirroring the connections between the known data (even they are partial from point of view of a holistic approach). ''"Expectations"'' are all measurable features being capable of monitoring the goodnees of the unknown connections. It is important: the human experts may not change the raw data if a concept seems not to be appropriate enough. Always the concepts should be changed till all raw data are covered through the mathematisms of the particular (best) concept. The problems of the arbitrariness of the human experts can be found listed in the book: Arthur Koestler, The Sleepwalkers! (more: https://en.wikipedia.org/wiki/The_Sleepwalkers:_A_History_of_Man%27s_Changing_Vision_of_the_Universe) Therefore, the goodness of the concepts let assume a scale: the one end of the scale is the set of the randomized generated concepts. The opposite end of this scale is the set of the error-free solutions (because it is possible two have alternative solutions with the same evaluation value).

==Chapter#2.2. Proving, goodness, objectivity==
As a direct logical step based on the subchapter#2.1. (about testing): Goodness as such is also concerned in the background publications: e.g. ''"This level of accuracy—where predicted values match actual ones—is a strong sign that A-Concept is successfully capturing meaningful patterns."'' (source: https://miau.my-x.hu/mediawiki/index.php/CT_01#A-Concept:_A_Rational_Framework - first paragraph). The background texts has 39 items about accuracy. All these mentionings should be consolidated in the chapter#3 in order to see, what kind of automatable system can be identified for concept testing as such. The statement in the above-mentioned citation about the accuracy means, goodness can be measured, if predicted (estimated) values are the same compared to the appropriate facts (matching). It is a relevant aspects of goodness, but it is a discrete scale (hit rate / contingency coefficient), where statistics about existing and not-existing matching-positions will be derived: e.g. 75% matching means: 3 of 4 facts have matching with the estimated values. The basic principle (direction) is valid for a hit rate: the more the more. BUT, not only hit rate is existing. The estimations could have numeric accuracy: e.g. difference(^2) between facts and estimations. Important assumption: quasi unlimited goodness-criteria can be defined and therefore, we need immediately a kind of aggregation process for all goodness-criteria. This aggregation may however not be arbitrary (see: weights and/or scores). The aggregation must be optimized! Conclusion: the best concept can only be derived in an automated way, if the goodness-criteria are complex and aggregated in an optimized (objective way). The last (4th) task in the concept testing process is given in order to enforce this optimized aggregation process based on a clear example...
Further interpretations about the goodness (c.f. key-term=accuracy, source=https://miau.my-x.hu/mediawiki/index.php?title=CT_01):
Source#3:
*''"The analytical summaries (e.g., "Átlag / rel. diff," "Maximum / rel. diff4") quantify the estimation process’s accuracy."'' (source#3)
*''"The ranking and COCO framework abstract this into testable units, validated by estimation models (A5-C6) that predict outcomes with high accuracy (e.g., correlations above 0.96 for A6, B6)."'' (source#3)
The formulations talks about quantification, e.g. correlation.

==Chapter#2.3. KPIs==
Matching-oriented KPIs:
*hit rates: ''"A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative)."'' (https://en.wikipedia.org/wiki/False_positives_and_false_negatives)
*further classifications: The matching can not only interpreted between already/really existing pairs of values. Artificial benchmarks can also be integrated into a goodness-structure: e.g. matching of dynamical processes (fact vs. extimations): increasing:increasing, decreasing:decreasing, increasing:decreasing, decreasing:increasing compared to the previous values. Benchmarks can be defined in quasi arbitrary ways.
*...
All matching-oriented KPIs are relevant!
Numeric KPIs:
*sum of absulote difference between facts and estimations:
*sum of quadratic difference between facts and estimations: e.g. Excel: SUMSQ() - ''"Returns the sum of the squares of the arguments."'' (https://support.microsoft.com/en-us/office/sumsq-function-e3313c02-51cc-4963-aae6-31442d9ec307) where the arguments are the differences between facts and estimations
*correlation: "''In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve.''" (https://en.wikipedia.org/wiki/Correlation) The correlation is a more complex abstraction, than e.g. SUMSQ.
*...
All numeric KPIs are relevant!

==Chapter#2.4. ...==
==Chapter#2.5. ...==
==Chapter#2.6. ...==
==Chapter#2.7. ...==

=Chapter#3. Own developments=
...
==Chapter#3.x Automation==
==Chapter#3.x Testing==
==Chapter#3.x IT-security aspects==

=Chapter#4. Discussions=
=Chapter#5. Conclusions=
=Chapter#6. Future=
=Chapter#7. Summary=
=Chapter#8. Annexes=
==Chapter#.8.1. Abbreviations==
==Chapter#.8.2. Figures==
==Chapter#.8.3. References==
==Chapter#.8.4. Conversations with LLMs==

CT 00

2025-04-07T08:26:41Z

Jkv1: /* Chapter#2.3. KPIs */

CT 00

2025-04-07T08:26:18Z

Jkv1: /* Chapter#2.3. KPIs */

CT 00

2025-04-07T08:25:05Z

Jkv1: /* Chapter#2.2. Proving, goodness, objectivity */

CT 00

2025-04-07T07:21:44Z

Jkv1: /* Chapter#2.3. KPIs */

CT 00

2025-04-07T06:40:07Z

Jkv1: /* Chapter#2.3. KPIs */

CT 00

2025-04-07T06:08:10Z

Jkv1: /* Chapter#2.3. ... */