Biography: Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering. Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Int. J. of Granular Computing (Springer). He currently serves on the Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals.
Speech: The quality of Machine Learning (ML) architectures and credibility of generated results are inherently implied by the nature, quality, and amount of available data. This is of particular importance when considering prediction or classification problems in management, modeling software engineering processes and products. The credibility of ML models and confidence quantified their results are also of paramount concern to any critical application.
We advocate that the credibility (confidence) of results produced by ML constructs is inherently expressed in the form of information granules; several development scenarios are revisited including those involving constructs in statistics (confidence and prediction intervals), probability (Gaussian process models), and granular parameters (fuzzy sets techniques).
We carefully revisit the two commonly encountered and challenging category of applications of ML where the aspect of quality requires a thorough assessment. The first category concerns Federated Learning where in light of privacy requirement, the developed ML models have to be carefully evaluated. The second concerns knowledge transfer (by demonstrating on how a thoughtful and prudently arranged knowledge reuse supports energy-aware ML computing).
Biography: Enrique Herrera-Viedma is Professor of the Dept. of Computer Science and Artificial Intelligence at the University of Granada (UGR) and he is currently serving as Vice-Rector for Research and Knowledge Transfer at the UGR. He is Fellow IEEE and Fellow IFSA and Doctor Honoris Causa by Oradea University.
He was Vice-President (VP) for Publications in IEEE System Man and Cybernetics Society and now he is VP for Cybernetics, one of the founders of the IEEE Trans. in Artificial Intelligence, and Highly Cited Researcher by Clarivate Analytics in Computer Science and Engineering in 2014-2021. He has published more than 300 papers in JCR journals, his h-index is 111 in Google Scholar (>50000 citations) and 87 in WoS (>29000 citations). In 2013 he published in the prestigious journal SCIENCE about the new role of digital libraries in the era of the information society. He is a member of the panel of experts of the national project evaluation agencies in Portugal, Switzerland, France, and Kazastan; and Member of the European Committee of Experts for the evaluation of strategic information infrastructure projects in Europe (ESFRI- European Strategy Forum on Research Infrastructures), since November 2017. He has also been guest lecturer in plenary lectures and tutorials in multiple national and international conferences related to Artificial Intelligence such as: 4th Int. Workshop on Preferences and Decisions, 2003, Trento (Italy); Modeling Decision for Artificial Intelligence. 2004, Barcelona (Spain); AGOP 2005, Lugano; 4th EUSFLAT & 11th LFA Conference, Barcelona, 2005; Third Int. Workshop of Artificial Intelligence. ; ESTYLF 2010, Huelva; ; Int. IEEE Intelligent Systems 2014, Poland; IEEE SMC 2014; EUSFLAT 2017, Poland; SOMET 2017, Japan; PIC 2018, Nanjing, China; BAFI 2018, Chile; IPMU 2018, Cadiz. He .is Associated Editor in several AI journals like IEEE TFS, IEEE ITS, IEEE TSMC-Syst, Knosys, ASOC, Fuzzy Opt. and Decision Making, Information Sciences, Soft Computing.
Biography: Dr. Yan-Fu Li is currently a full professor at the Department of Industrial Engineering (IE), Tsinghua University. He is the Director of the Institute for Quality & Reliability of Tsinghua University .He received his Ph.D in Industrial Engineering from National University of Singapore in 2010. He was a faculty member at Laboratory of Industrial Engineering at CentraleSupélec, France, from 2011 to 2016. His research areas include RAMS (reliability, availability, maintainability, safety) assessment and optimization with the applications onto telecom systems, energy systems, transport systems, etc. He is the Principal Investigator (PI) of several government projects including the key project funded by National Natural Science Foundation of China, the project in National Key R&D Program of China. He is also experienced in industrial research, the partners include Huawei, Volkswagen, Mitsubishi Heavy Industries, EDF, ALSTOM, etc. Dr. Li has published more than 120 research papers, including more than 70 peer-reviewed international journal papers with H-index 33. He is the Elsevier Highly Cited Chinese Researcher from 2019-2021. He is currently an Associate Editor of IEEE Transactions on Reliability, Guest editor of IEEE Transactions on Industrial Informatics and Reliability Engineering & Systems Safety, a senior member of IEEE and IISE. He is a vice president of the System Reliability Chapter of System Engineering Society of China.
Speech: The telecommunication network is one of the largest and most complex engineering systems, in which various advanced technologies are rapidly adopted. However, the maintenance of the telecom network is still at relatively primitive stage, where a large number of components are still undergone corrective maintenance. With the large-scale construction and implementation of 5G base stations in worldwide, operation and maintenance (O&M) companies and agents have been working under huge pressure to control the O&M cost, which have already accounted for nearly 30% of telecom operators' expenses. A systematic solution is therefore urgently needed. Power grids transmit energy, and telecom networks transmit information. Both of these infrastructures are critical to human society, and there are certain similarities between them. The reliability-centered maintenance (RCM) of the power grid developed earlier, and a matured reliability index system has been developed to guide the O&M practice. This representation aims to promote the creation and implementation of the telecom network reliability index system by reviewing and learning from the development history of power grid reliability indices. The differences between the evolution paths of the reliability index systems of the two networks are shown and analyzed. To bridge the gap in telecom network reliability, we proposed a new set of reliability indices from the perspective of customer demand satisfaction, and performed empirical validations using real data. Finally, we show that this work could point out an important direction for future research as well as practice of telecom network reliability.