Leilani H. Gilpin, Massachusetts Institute of Technology, USA, lhg@mit.edu
Anomaly Detection Through Explanations
Under most conditions, complex systems are imperfect. When errors occur, as they inevitably will, systems need to be able to (1) localize the error and (2) take appropriate action to mitigate the repercussions of that error. In this talk, I present new methodologies for detecting and explaining errors in complex systems. My novel contribution is a system-wide monitoring architecture, which is composed of introspective, overlapping committees of subsystems. Each subsystem is encapsulated in a “reasonableness” monitor…more>>>
Prof. María Ángeles Gil, University of Oviedo, Spain, magil@uniovi.es
Fuzzy Rating Scales of measurement: a psychometric tool capturing imprecisión and individual differences
In introducing Fuzzy Sets, Lotfi A. Zadeh anticipated that they would play an important role in human thinking. Fuzzy rating scales were developed as a psychometric tool aiming to appropriately capture the natural imprecision involved in measuring many variables in Social Sciences. Furthermore, these scales also cope with the individual differences in such a measurement, so they look to be more informative from a statistical perspective…more>>>
Prof. Sven Dickinson, University of Toronto, sven@cs.toronto.edu
The Role of Symmetry in Human and Computer Vision
Symmetry is one of the most ubiquitous regularities in our natural world. For almost 100 years, human vision researchers have studied how the human vision system has evolved to exploit this powerful regularity as a basis for grouping image features. While computer vision is a much younger discipline, the trajectory is similar, with symmetry playing a major role in both perceptual grouping and object representation. After briefly reviewing some of the milestones in symmetry-based perceptual grouping and object…more>>>
Prof. Michael Smithson, Australian National University, Australia, Michael.Smithson@anu.edu.au
Univariate and Multivariate Distributions for Fuzzy Membership Data
Fuzzy data can take many different forms, but perhaps the most common kind include fuzzy membership values on the closed unit interval, [0,1]. Appropriate statistical analyses of such data treat them as random variables on the unit interval, which motivates the topic for this talk. A survey of distributions for random variables on the unit interval reveals several practical problems and (in some instances) their solutions…more>>>
Prof. Jerry M. Mendel, University of Southern California, USA, jmmprof@me.com
Why Have Fuzzy Sets Made Almost No Impact on AI, and Can This be Changed?
This keynote talk answers two questions that should be of great importance to the attendees of FUZZ-IEEE 2021:- Why have fuzzy sets made almost no impact on AI?; and,
- Can this be changed?
Prof. Lorna McGregor, University of Essex, UK, lmcgreg@essex.ac.uk