Dr. Vilem Novak, ProfessorUniversity of Ostrava, Institute for Research and Applications of Fuzzy Modeling, Ostrava, Czech Republic
Speech Title: Non-statistical methods for analysis, forecasting and mining time series
Abstract: In this lecture, we will overview results obtained in our institute over several years in the analysis, forecasting, and mining of information from time series using methods that predominantly have non-statistical character. We argue that our methods can be pretty successful in time series processing. In addition to that, they can also provide information that can hardly be obtained using statistical methods. Let us emphasize, however, that our goal is not to beat statistical methods but rather to extend the power of time series processing methods and benefit from the mutual synergy.
We will present special techniques of fuzzy modeling suitable for applications in time series processing, namely the Fuzzy Transform (F-transform) and selected methods of Fuzzy Natural Logic (FNL). The F-transform is applied especially to estimation of the trend or trend-cycle of time series, and also to estimation of the slope of time series over an imprecisely specified area. These results are then applied in the methods of FNL using which we are able to forecast the time series, provide explanation of the forecast in natural language and also provide comments to the slope of time series and detect possible structural breaks in it. Other applications of our methods include: reduction of the dimensionality, detection of ``bull and bear'' phases of financial time series, measures of similarity between time series, automatic summarization of knowledge about time series, detection of perceptional important points, and a few other ones.
Biography: Prof. Vilem Novak, Ph.D., DSc. is founder and former director of the Institute for Research and Applications of Fuzzy Modeling of the University of Ostrava, Czech Republic. The institute (established in 1996) is one of the world-renowned scientific workplaces that significantly contributed to the theory and applications of fuzzy modeling.
V. Novak obtained a PhD in mathematical logic at Charles University, Prague in 1988; DSc. (Doctor of Sciences) in computer science in the Polish Academy of Sciences, Warsaw in 1995; full professor at Masaryk University, Brno in 2001. His research activities include mathematical fuzzy logic, approximate reasoning, mathematical modeling of linguistic semantics, fuzzy control, analysis and forecasting of time series, and various kinds of fuzzy modeling applications. He belongs among pioneers of the fuzzy set theory.
He was general chair of the VIIth IFSA' 97 World Congress, Prague and of the international conferences EUSFLAT 2007, Ostrava and EUSFLAT 2019, Prague. He is a member of editorial boards of several scientific journals. He is often invited to give plenary talks at international conferences and to give university lectures all over the world. He is the author or co-author of 5 scientific monographs, two edited monographs, and over 300 scientific papers with over 7000 citations. He was awarded in the International Conference FLINS 2010 in China and obtained the title "IFSA fellow" in 2017 for his scientific achievements. He is currently the vice-president of IFSA.
Dr. Dmitry Zaitsev, ProfessorDepartment of Information Technology, Odessa State Environmental University, Ukraine
Speech Title: High Performance and Unconventional Computing for Fuzzy Systems and Data Mining
Abstract: Fuzzy systems and big data mining require considerable computational resources. Traditionally high performance computing is involved represented by supercomputers and clusters. A classification of fuzzy systems and data mining tasks is provided with regard to required computing capacity, solution time limitations, concurrent algorithms and others. An overview of conventional high performance computing systems is presented, including recent most powerful computers, their architecture and programming technology for multicore distributed nodes supplied with graphical processing units. Since traditional architecture suffers from memory-processor bottlenecks, unconventional computing models are in demand such as Sleptsov nets, cellular automata, spiking neuron systems, neuron networks and others. Their implementation, especially in the form of dedicated hardware, promises hyper-performance at the expense of mass parallelism and fine granulation provided by computing memory.
Biography: Dmitry A. Zaitsev received the Eng. degree in Applied Mathematics from Donetsk Polytechnic Institute, Donetsk, Ukraine, in 1986, the Ph.D. degree in Automated Control from the Kiev Institute of Cybernetics, Kiev, Ukraine, in 1991, and the Dr.Sc. degree in Telecommunications from the Odessa National Academy of Telecommunications, Odessa, Ukraine, in 2006. He has been a Professor of Information Technology at Odessa State Environmental University, Ukraine, since 2019. He developed the analysis of infinite Petri nets with regular structure, the decomposition of Petri nets in clans, generalized neighborhood for cellular automata, and the method of synthesis of fuzzy logic function given by tables. He developed Opera-Topaz software for manufacture operative planning and control; a new stack of networking protocols E6 and its implementation within Linux kernel; Petri net analysis software Deborah, Adriana, and ParAd; models of TCP, BGP, IOTP protocols, Ethernet, IP, MPLS, PBB, and Bluetooth networks. His current research interests include Petri net theory and its application in networking, computing and automated manufacture. Recently he started working in the area of exascale computing applying his theory of clans to speed-up solving sparse linear systems on parallel and distributed architectures. He was a co-director of joint projects with China and Austria. Recently he has been a visiting professor to Technical University of Dortmund, Germany on DAAD scholarship, to University of Tennessee Knoxville, USA on Fulbright scholarship and to Eindhoven University of Technology, Netherlands. He published a monograph, 3 book chapters and more than a hundred of papers including issues listed in JCR. He is a senior member of ACM and IEEE. Additional information including papers, software, models, video-lectures in put on personal website via http://daze.ho.ua
M-Tahar Kechadi, ProfessorSchool of Computer Science, University College Dublin (UCD), Ireland.
Speech Title: Dimensionality Reduction with Bivariate and Multivariate Copulas
Abstract: Modelling high dimensional datasets is a challenging task, as often the datasets contain noise and redundant dimensions, which lead to misspecification of the models and poor results of the analysis. Many statistical methods have been proposed to deal with this issue, but most of them present very high computational complexity and / or produce poor results. In this presentation, I discuss two approaches based on copula to detect inter-correlations between dimensions. The two approaches both use copulas. The first approach is the direct application of copulas to describe and model the inter-correlation (also called dependence) between any two dimensions - bivariate analysis. The second uses multivariate copulas to model dependences between a number of dimensions. While the two approaches are similar, they use different algorithms to model the inter-correlations between dimensions. After explaining their concepts, I will show how to use them to detect redundant dimensions and then compare their computational complexity and the quality of their results.
Biography: M-Tahar Kechadi is professor in School of Computer Science, University College Dublin (UCD), Ireland. He was awarded PhD and a Masters degree in Computer Science from University of Lille 1, France. He is currently Principal Investigator in the INSIGHT Centre for Data Analytics and CONSUS, one of the biggest projects in Precision Agriculture. He is in the editorial board of the Journal of Future Generation of Computer Systems. He is full member at CERN and a visiting professor at Fuzhou University, Fujian, China. The core and central focus of my research in the last decade is how to manage and analyse data quickly and efficiently. Nowadays we live in digital world, we produce more data than we can analyse and exploit. This “big data” will continue to grow at rapid pace, will underpin new waves of innovation in nearly every sector of the economy worldwide, and will reshape the way we build and use computers (hardware and software). Currently, my research interests are primary in i) Big Data Analytics and its applications to real-world applications. ii) Big Data Applications: Digital Healthcare and digital agriculture (Precision Agriculture). iii)Distributed Mining techniques and models and their execution environments and applications. vi) Cloud/Grid computing and services for supporting data access, management, and mining processes, v) Digital Forensics and cybercrime investigations.
Dr. Bernard P. Zeigler, ProfessorDepartment of Electrical and Computer Engineering, University of Arizona, USA
Speech Title: Linking Cognitive Behavior to Neural Circuits Via DEVS Models and Their Minimal Realizations
Abstract: Perception and generation of temporal event patterns, such as order of arrival of distinct events, coincidence of such events, and equality of event patterns are interesting cognitive behaviors that are simple enough to connect to potential biological realizations. Here we discuss Discrete Event System Specification (DEVS) models that exhibit such behaviors and can be developed, observed and tested in computational form. In this talk, we review the basics of the systems theory underlying DEVS as a modeling and simulation abstraction. Then we show how DEVS represents individual neural elements and their compositions to realize temporal event patterns by having the necessary states, processing signals, and memory features while coordinating themselves in space and time. Mathematical system-theory proofs of such models’ canonical minimal nature support the claim that their structures must be embedded in any plausible biological model of cognitive behavior. Thus, we argue that discrete event models of this nature constitute waypoints in the search for implementations involving elements such as neurons, neural micro-circuits, glial-astrocyte-neuron conglomerates, or as yet-unknown components. Implications of this methodology for intelligent cyber-physical system implementations will be discussed.
Biography: Dr. Bernard P. Zeigler is Professor Emeritus of Electrical and Computer Engineering at the University of Arizona and the Chief Scientist at RTSync Corp (rtsync.com). He received a B.Eng. Physics from McGill, M.S. from MIT, and Ph.D. from the University of Michigan (1968). Prof. Zeigler is best known for his theoretical work concerning modeling and simulation based on systems theory and the DEVS formalism which he invented in 1976. His book “Theory of Modeling and Simulation” has become a classic in the field. Recently, he published the third edition of the book updated with the help of two young researchers. His R&D work in academia and industry has received recognition from numerous funding and professional agencies. Zeigler is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and The Society for Modeling and Simulation International (SCS). He is a member of the SCS Hall of Fame and received the Institute for Operations Research and Management Sciences (INFORMS) Simulation Society Lifetime Professional Achievement Award. His interests include Intelligent Systems, Knowledge Based System Design and Engineering, Cognitive behavior modeling and simulation, and cyber-physical systems Internet-of-Things realizations. His Wikipedia page is https://en.wikipedia.org/wiki/Bernard_P._Zeigler.