MétaCan
Menu
Retour à la cohorte
Enregistrement W7014588317

Proceedings of WILF 2021, the 13th International Workshop on Fuzzy Logic and Applications (WILF 2021)

2022· other· en· W7014588317 sur OpenAlexaboutno aff

Notice bibliographique

RevueCINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2022
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFuzzy logicFuzzy setFocus (optics)Set (abstract data type)Event (particle physics)Fuzzy control systemComputational intelligence
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The 13th International Workshop on Fuzzy Logic and Applications, WILF 2021, was held in \nVietri sul Mare, Italy during December 20-22, 2021. \nThis is the latest instance of an established series of interdisciplinary meetings. Organised by \nthe Italian community of researchers in fuzzy logic and soft computing, it started as a national \nworkshop, but rapidly evolved into an event with an international perspective, hosting renown \nscientists from all over the world in various capacities as delegates, organisers, keynote speakers. \nThe previous editions of WILF have been held in Naples (1995), Bari (1997), Genoa (1999), Milan \n(2001), Naples (2003), Crema (2005), Camogli (2007), Palermo (2009), Trani (2011), Genoa (2013), \nNaples (2016), and Genoa (2018). \nFor the 2021 edition, we wanted to provide an occasion for meeting each other and for person- \nto-person interaction, rather than one-directional communication. We opted for a format that \nemphasizes exchange of ideas and discussion. Contributions were of three types. Besides \nregular ones, aimed at presenting novel research results, we also encouraged brief, “highlight” \npresentations of mature research to give it higher visibility, and “ideas” that described research \nin an early or preliminary stage, to foster discussion and produce suggestions. In this way, the \nconference covered present, as well as past and future, research. The topical focus of this edition was on the relationship of Fuzzy Set theory and methods \nwith humans, society, and data-driven approaches to Computational Intelligence, that is to \nsay essentially Machine Learning. Nowadays, Artificial Intelligence has become an enabling \ntechnology that pervades many aspects of our daily life. Machine Learning is of course at \nthe forefront of this advancement. However, as the role of Artificial Intelligence becomes \nmore and more important, so does the need for reliable solutions to several issues that go well \nbeyond technological aspects. These include, among many others: accountability and explain- \nability; interaction between artificial and human intelligence, including issues of nonverbal \ncommunication; monitoring and minimising the effects of biases (gender, race, culture. . . ) on \nmachine-guided decision making. \nNotwithstanding their huge success, purely data-driven technologies are showing their limits \nprecisely in these areas. There is a growing need for methods that, in a tight interaction with \nthem, provide different degrees of control over the several facets of automated decision making, allowing the use of explicit knowledge beyond what can be extracted from data. The diversity \nand complementarity of Computational Intelligence techniques in addressing these issues is \nbound to play a crucial role. \nThe contributions we received were fully in line with these topics. After a rigorous peer-review \nprocess, among the submissions received from all Europe we selected 20 high-quality regular \nmanuscripts, 6 idea papers and 4 highlight abstracts. These were accepted for presentation at \nthe conference and are published in this volume. \nIn addition, the event hosted three very interesting keynote talks by high profile researchers: \n• Extensions of fuzzy integrals and applications to the computational brain – by Humberto \nBustince, full professor of Computer Science and Artificial Intelligence in the Public \nUniversity of Navarra (Spain) and Honorary Professor at the University of Nottingham \n(UK). \n• Fuzzy Logic and XAI: Past, Present, and Some Thoughts on the Future – by Scott Dick, pro- \nfessor at the Faculty of Engineering - Electrical & Computer Engineering Dept, University \nof Alberta (Canada). \n• Fuzzy sets: the legacy and its future – by Didier Dubois, Emeritus Research Advisor at \nIRIT, the Computer Science Department of Paul Sabatier University in Toulouse, France \nand French National Centre for Scientific Resarch (CNRS). \nFinally, two thematic round tables were held: \n• Computational Intelligence methods for Digital Health, INdAM-GNCS research day – chair \nGiovanna Castellano, University of Bari “Aldo Moro” (Italy) \n• Towards national laboratories on soft computing – chair Antonio di Nola, University of \nSalerno (Italy) \nDuring the past two years, due to the COVID-19 pandemic there have been many obstacles \nto the organisation of meetings. Some WILF 2021 delegates were not able to travel, and the \nevent was held in a mixed format, in presence and in teleconferencing. Still, the participation \nwas high, and there was a very rich social activity program. The success of this edition can be \nsummarised by the motto we chose since the very beginning: “Back together again!”. \nCredit for this success, however, is due to the contribution of many people, in particular the \nProgram Committee members for their commitment to providing high-quality, constructive \nreviews, the keynote speakers, the round table organisers, all the contributors and delegates, and \nlast but by no means least the local organising secretariat (IIASS, Dr. Tina Nappi) for making \neverything run smoothly and flawlessly.

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.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,062
Score d'incertitude au seuil0,207

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,009
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,002
Communication savante0,0060,004
Science ouverte0,0020,002
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0620,021

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.

Tête enseignante Opus0,044
Tête enseignante GPT0,288
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueCINECA IRIS Institutional Research Information System (University of Bari Aldo Moro)Travaux en français237 207