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Record W7014588317

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

2022· other· en· W7014588317 on OpenAlexaboutno aff

Bibliographic record

VenueCINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFuzzy logicFuzzy setFocus (optics)Set (abstract data type)Event (particle physics)Fuzzy control systemComputational intelligence
DOInot available

Abstract

fetched live from 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0060.004
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0620.021

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.

Opus teacher head0.044
GPT teacher head0.288
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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