Bibliographic record
Abstract
This special issue of Fundamenta Informaticae focuses on the foundations and applications of Cognitive Informatics and Computational Intelligence (briefly, CI2).CI2 focuses on studies of human information processing as well as the byproducts of perception and cognition.Cognitive Informatics (CI) is a multidisciplinary study of cognition, computing and information sciences which investigates the information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing.Specifically, CI2 provides a coherent set of fundamental theories and contemporary mathematics that form the foundation for most science and engineering disciplines such as applied mathematics (e.g., perceptual forms of fuzzy sets, near sets and rough sets), computer science, cognitive science, computer engineering (e.g., computer vision), cybernetics (e.g., machine behavior), neuropsychology and pure mathematics (e.g., proximity spaces, topological spaces via near and far).This special issue presents some of the latest advances in cognitive informatics and cognitive computing.A total of 11 papers were accepted for publication.Each accepted paper has undergone a thorough review (at least two reviewers for each paper) and a second round of review and revision cycle.The paper by M.H-Herrero, P. Rabanal, I. Rodríguez, and F. Rubio on Comparing Problem Solving Strategies for NP-hard Optimization Problems, present analysis of performance of humans when solving NP-complete problems.These analyses are supported by experiments which include the human capability to compute good suboptimal solutions to these problems, and the authors try to identify the kind of problem instances which make humans compute the best and worst solutions (including the dependance of their performance on the size of problem instances).Finally, their performance with computational heuristics typically used to approximately solve these problems are compared, and participants in these experiments are also interviewed in order to infer the most typical strategies used by them, as well as how these strategies depend on the form and size of problem instances.The paper by G. Virginia and H.S. Nguyen on Lexicon-based Document Representation, is based on tolerance rough sets model(TRSM) to model document-term relations in text mining.Specifically, this representation maps the terms occurring in TRSM-representation to terms in the lexicon, hence the final representation of a document is a weight vector consisting only of terms that occurred in the lexicon (lexicon-representation).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.551 | 0.380 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".