Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,026 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle