Confirmed plenary talks:
Microsoft Research, Redmond, WA, USA
Title: Impugning Alleged Randomness
According to a 1985 issue of New York Times, "The New Jersey Supreme Court today caught up with the Essex County Clerk and a Democrat who has conducted drawings for decades that have given Democrats the top ballot line in the county 40 times out of 41 times." But the clerk wasn't punished. Here's another case of that sort, from Israel. In the 1980s the Israeli tax authorities encouraged the public to request invoices from plumbers, painters, etc. and send the invoices in; big prices were ruffled off. It turned out that the winner was none other than the Director of Customs and VAT at the time. The operation collapsed but the director wasn't punished.You may be convinced that such lotteries are rigged, but how would you argue that in the court of law? Yes, the probability of the suspicious outcome is negligible. However the probability of any particular outcome is negligible. What can you say? We attempt to furnish you with an argument.
YYuri Gurevich is Principal Researcher at Microsoft Research in Redmond, WA. He is also Professor Emeritus at the University of Michigan, a Fellow of ACM, the Guggenheim Foundation and EATCS, a member of Academia Europaea, and Dr. Honoris Causa of a Belgian and a Russian university.
University of Texas at El Paso, TX, USA
Title: Formalizing the Informal, Precisiating the Imprecise: How Fuzzy Logic Can Help Mathematicians and Physicists by Formalizing Their Intuitive Ideas (based on joint work with Olga Kosheleva and Renata Reiser)
Fuzzy methodology transforms expert ideas — formulated in terms of words from natural language — into precise rules and formulas. In this talk, we show that by applying this methodology to intuitive physical and mathematical ideas, we can get known fundamental physical equations and known mathematical techniques for solving these equations. This fact makes us confident that in the future, fuzzy techniques will help physicists and mathematicians to transform their imprecise ideas into new physical equations and new techniques for solving these equations.
Vladik Kreinovich received his MS in Mathematics and Computer Science from St. Petersburg University, Russia, in 1974, and PhD from the Institute of Mathematics, Soviet Academy of Sciences, Novosibirsk, in 1979. From 1975 to 1980, he worked with the Soviet Academy of Sciences; during this time, he worked with the Special Astrophysical Observatory (focusing on the representation and processing of uncertainty in radioastronomy). For most of the 1980s, he worked on error estimation and intelligent information processing for the National Institute for Electrical Measuring Instruments, Russia. In 1989, he was a visiting scholar at Stanford University. Since 1990, he has worked in the Department of Computer Science at the University of Texas at El Paso. In addition, he has served as an invited professor in Paris (University of Paris VI), France; Hong Kong; St. Petersburg, Russia; and Brazil.
His main interests are the representation and processing of uncertainty, especially interval computations and intelligent control. He has published six books, eleven edited books, and more than 1,000 papers. Vladik is a member of the editorial board of the international journal "Reliable Computing" (formerly "Interval Computations") and several other journals. In addition, he is the co-maintainer of the international Web site on interval computationshttp://www.cs.utep.edu/interval-comp.
Vladik is Vice President for Publications of IEEE Systems, Man, and Cybernetics Society; he served as President of the North American Fuzzy Information Processing Society 2012-14; is a foreign member of the Russian Academy of Metrological Sciences; was the recipient of the 2003 El Paso Energy Foundation Faculty Achievement Award for Research awarded by the University of Texas at El Paso; and was a co-recipient of the 2005 Star Award from the University of Texas System.
University of Edmonton, AB, Canada
Title: Fuzzy-based Methods for Processing Hybrid and Temporal Data in Linked Open Data Environment
The web becomes a huge repository of data and information. In order to deal with large quantities of data, the users need intelligent-based tools and methods to access data, process it, and make it useful for variety of purposes. Proposed as a part of Semantic Web, Resource Description Framework (RDF) is an important way of representing information. Its intrinsic feature of high connectivity creates an opportunity to process web data in a way that information and knowledge embedded in it can be extracted and absorbed. RDF has become a building block of Linked Open Data.
In this talk, we show how fuzzy-based technologies can be used to process RDF data and provide the users with capabilities to cope with versatile and complex information. These technologies, when employed, are able to ensure a human-like way of interacting with the web when looking for useful facts and information.
At the beginning, we focus on methods suitable for dealing with hybrid, i.e., numerical and symbolic, data in the distributed environment. We adopt a linguistic representation model to identify items that match a given reference to the highest possible degree, and satisfying – at the same time – variety of criteria. The proposed methodology deals with different types of features describing items, and utilizes comparison mechanisms most suitable for each feature type.
Further, we investigate interaction with data equipped with temporal features. We propose a fuzzy-based methodology for constructing and executing queries on RDF data that contains time related information. This methodology allows the users to inquiry about events and facts described with fuzzy temporal terms. A query interface that isolates the users from complexity of temporal RDF data is proposed and explained. A number of build-in predicates needed for constructing fuzzy temporal queries are described.
Marek Reformat received his MSc degree (with honors) from Technical University of Poznan, Poland, and a PhD degree from University of Manitoba, Canada.
His initial research projects involved different aspects related to computer networks, especially in the area of management and performance measurement. He co-authored several papers and reports regarding this topic. During his PhD studies, his research interests included distributed computing, with emphasis on fault-tolerant systems in such frameworks as Parallel Virtual Machine (PVM) and Message Passing Interface (MPI); optimization methods; and fuzzy sets and systems. His principle interest was related to evolutionary computing and its application to optimization problems. He proposed a new methodology for design of control systems, which relied on a combination of advanced system simulators and genetic computation. He applied this concept to the control design problem in the area of power systems. In 1997 he joined the Manitoba HVDC Research Centre, where he was a member of a simulation software development team. He was involved in improvement and development of an electromagnetic transients program for time-domain simulation, performed functional and structural testing of the software, and provided expert consulting services in the area of simulation and modeling internationally.
Marek has been with the Department of Electrical and Computer Engineering at University of Alberta since July 2000. He is an Associate Editor of a number of journals related to computational intelligence and software engineering. He has been a member of program committees of several conferences related to those areas. He is actively involved in North American Fuzzy Information Processing Society (NAFIPS). He is a member of the IEEE and ACM.
University of Urbino, Italy
Title: Recent results in fuzzy arithmetic and fuzzy calculus: theory and applications
Real fuzzy numbers/intervals are fuzzy sets on R (the set of real numbers) having a membership function that is normal, upper semicontinuous, quasi concave and has bounded support.
Well known representations are:
- LU (Lower-Upper), for the Lower and Upper branches defining the endpoints of the level-cuts (Goetschel-Voxman, 1986); and
- LR (Left-Right), for the Left and Right sides of its membership function (Dubois-Prade, 1987).
- A third representation will be shown, called ACF (from Average Cumulative Function, based on possibility theory).
Both LR and LU forms of fuzzy intervals have been investigated in order (a) to find families of parametric functions (also called shape functions), and (b) to define operators for efficient approximate fuzzy arithmetic operations and computations. Several parametric families of shape functions have been proposed.
Four related topics will be illustrated:
(1) approximation tools for fuzzy numbers by parametric (nonlinear) shape functions;
(2) efficient operators for fuzzy arithmetic with "shape preservation" properties;
(3) extended arithmetic operations, in particular extended di§erence and extended addition;
(4) applications to fuzzy calculus and to fuzzy differential equations.
Luciano Stefanini (1949) obtained his Laurea Degree in Mathematics at the University of Bologna in 1974. In 1982 he started his activity at the University of Urbino, Italy. In 1987 he became associate professor and in 1994 he was a winner of full professor position. At present he is permanent full professor, chair of Mathematics, at the Faculty of Economics of the University of Urbino. From 1993 to 2001 he has been the director of the Institute of Economic Sciences, University of Urbino and from 1992 to 2012 he has been the director of the Computing Centre of the Faculty of
Economics. He participated to and has been responsible of several regional, national and international research projects, in particular in the field of water resourse optimization, distribution management and logistics, public transportation systems. His research interests cover numerical analysis, computer graphics, game theory and optimisation, operations research, transportations reasearch, computational statistics and estimation, stochastic
processes and differential equations, decision theory, data processing in economics and finance, Fuzzy Mathematics and its Applications. He is member of AMASES - (Italian) Association of mathematics applied to economic and social sciences and EUSFLAT - European Society for Fuzzy Logic and Technology. Actually, he is a member of the editorial board of "Fuzzy Sets and Systems" and "Soft Computing".
Iona College, New Rochelle, NY, USA
Title: Aggregation Methods for Information Fusion and Decision Making
Intelligent decision-making requires the use of all available information. However the information used for decision-making generally comes from multiple sources and is expressed in various modalities. We are interested in the problem of multi-source information fusion in the case when the information provided has some uncertainty. In order to address this problem we need to provide methods for the representation of different types of uncertain information. Here we shall discuss some computational intelligence based approaches for attaining this capability. One approach we consider is the use of a set measure for the representation of uncertain information. We shall also look at some aggregation approaches for the fusion of this information.
Ronald R. Yager has worked in the area of computational intelligence for over twenty-five years. He is Director of the Machine Intelligence Institute and Professor of Information Systems at Iona College. He is editor and chief of the International Journal of Intelligent Systems. He has published over 500 papers and edited over 30 books in areas related to fuzzy sets, human behavioral modeling, decision-making under uncertainty and the fusion of information. He is among the world’s top 1% most highly cited researchers with over 40,000 citations in Google Scholar. He was the recipient of the IEEE Computational Intelligence Society Pioneer award in Fuzzy Systems. He received the special honorary medal of the 50-th Anniversary of the Polish Academy of Sciences. He received the Lifetime Outstanding Achievement Award from International the Fuzzy Systems Association. He recently received honorary doctorate degrees, honoris causa, from the Azerbaijan Technical University and the State University of Information Technologies, Sofia Bulgaria. Dr. Yager is a fellow of the IEEE, the New York Academy of Sciences and the Fuzzy Systems Association. He has served at the National Science Foundation as program director in the Information Sciences program. He was a NASA/Stanford visiting fellow and a research associate at the University of California, Berkeley. He has been a lecturer at NATO Advanced Study Institutes. He was a program director at the National Science Foundation. He is a visiting distinguished scientist at King Saud University, Riyadh Saudi Arabia. He is an adjunct professor at Aalborg University in Denmark. He received his undergraduate degree from the City College of New York and his Ph. D. from the Polytechnic Institute New York University.
NorthWest Research Associates, Redmond, WA, USA
Title: Lower-Dimensional Features in climate models and their fuzzy modeling (based on joint work with B. Liepert)
Following common practice in data assimilation schemes, most diagnostic tools and metrics for intercomparison of reanalyses, correct a climate model forecast (hincast) or background field of continuous variables based on optimal minimization of the model variable with respect to observed values, summed over some or all grid points in a discretization. Evaluated properly, this procedure allows for effective utilization of innovation, increments, and residuals to improve parameterizations and physical understanding. The least squares difference is often used as a basic measure of accuracy that is then normalized to form an agreement index/metric and to quantify the correction needed. These metrics are most appropriate for continuous fields where the observed and model variables are commensurate (i.e., measured with the same units). They are, however, flawed when used in the presence of sharp gradients and discontinuities and when used to evaluate a model’s success in predicting or reproducing smaller scale lower dimensional features contained within a bulk simulation. These features, occur frequently in geophysical climate applications and often represent discontinuities that are associated with important climate physical and dynamic processes.
Ideally, a validation and intercomparison scheme should maintain the physical principles embodied in the model and be able to evaluate and utilize lower dimensional information (i.e., information contained within a bulk simulation even when not directly observed or represented by model variables). Nonetheless, physical principles are often violated, and lower dimensional information usually ignored. Conversely, models that resolve such information and the associated physics well, yet imprecisely are penalized by traditional schemes. This can lead to (perceived or real) poor model performance and predictability and can become deleterious in model improvements when observations are sparse, fuzzy, or irregular. It also impedes our ability to evaluate how well the models represent the relevant processes at different space and time scales, and what resolution is required to adequately simulate key processes. This talk intends to start a discussion on how address these issues.
Gad Levy has broad experience in meteorological, climate and statistical applications and methodology, mathematical and statistical methodology, Boundary Layer meteorology, Bayesian approaches, Technical and non-technical writing/editing, consulting with engineers, non-technical staff and managers and excellent communication skills. He has A Ph.D. in Atmospheric Sciences from the University of Washington, were he also attended graduate programs in Management of Science and Technology and Business Administration, and an M.S. in Atmospheric Sciences from the College of engineering at CSU. Currently, he is serving as the co-chair of the process studies and model implementation panel of the US Climate Variability and Predictability (CLIVAR) program, as well as on the Science Steering Committees of the US CLIVAR and the International Pan Ocean Remote Sensing (PORSEC) association (here he is also the vice president and president-elect). He is an avid collaborator and has collaborated nationally and internationally with diverse teams across different disciplines, cultures, and institutions. He has served as PI and referee for scientific journals, US EPA, NSF, The Israel Research Council, The National Oceanic and Atmospheric Administration, NASA, The Netherlands Remote Sensing Board (BCRS), The Chinese Academy of Sciences and the German Aerospace Center (DLR). He is lead and co-author and editor of over 75 refereed papers, book chapters, books, technical reports, and conference papers. He is an editor of the International Journal of Remote Sensing.
University of Saskatchewan, Saskatoon, SK, Canada
Title: Fuzzy Neural Networks: Theory and Applications