2021国际计算智能最新进展会议受邀专家

此次会议目前共邀请到计算智能领域专家29人，其中院士16人

专家 （按字母排序）	姓名	职称/职务	工作单位	报告标题	备注
	Ah-Hwee Tan	教授、IEEE Transactions on Neural Networks and Learning Systems和IEEE Transactions on SMC Systems副主编	新加坡管理大学	Cognitive Computing: A New Wave of Computational Intelligence?
	A. Murat Tekalp	土耳其科学院院士、欧洲科学院院士、IEEE会士	科奇大学（土耳其）	Deep Learning for Image and Video Restoration/Super-Resolution
	Andries Engelbrecht	教授、IEEE Transactions on Evolutionary Computation副主编	斯泰伦博斯大学 （南非）	Forecasting Non-Stationary Time Series without Recurrent Connections
	Boris Mirkin	欧洲科学院院士、英国工程与物理科学研究委员会委员、英国船级社委员	莫斯科高等经济学院（俄罗斯）	Modeling conceptual generalization over taxonomies
	Chin-Teng Lin	教授、IEEE会士、IFSA会士、IEEE Transactions on Fuzzy Systems前主编	悉尼科技大学 （澳大利亚）	Brain Computer Interface in Human-Autonomy Teaming
	C.L.Philip Chen	欧洲科学与艺术学院院士、欧洲科学院外籍院士、IEEE会士、AAAS 会士、IAPR会士、CAA会士、HKIE会士、IEEE Transactions on Cybernetics主编	华南理工大学	A Dynamic Neural Network Structure and its Application for Continuous Learning in an Open Environment
	Derong Liu	欧洲科学院外籍院士、IEEE会士、Artificial Intelligence Review主编	广东工业大学	AI and Machine Learning for Optimal Control of Complex Nonlinear Systems
	De-Shuang Huang	教授、IEEE会士、IAPR会士、国家高层次人才特殊支持计划入选者	同济大学	Deep Neural Networks Based Motifs Mining in Biological Binding Sequences
	El-Sayed M. El-Alfy	教授、IEEE高级会员、IEEE Transactions on Neural Networks and Learning Systems副主编	法赫德国王石油与矿业大学（沙特阿拉伯）	Machine Learning Powered Applica- tions in IoT: Opportunities and Challenges
	Georgios N.Yannakakis	教授、IEEE Transactions on Games主编、 IEEE Transactions on Evolutionary Computation副主编、马耳他大学数字游戏学院院长	马耳他大学	AI and Games: The Virtuous Cycle
	Jacek Mańdziuk	教授、波兰科学院信息委员会委员、IEEE Transactions on Neural Networks and Learning Systems副主编	华沙工业大学 （波兰）	The many faces of the Vehicle Routing Problem. Problem formulations, solution methods, challenges
	James M. Keller	教授、IEEE终身会士、国际计算智能协会（IEEE CIS）主席	密苏里大学（美国）	Streaming Clustering and Predictive Health Alerts
	Janusz Kacprzyk	波兰科学院院士、欧洲科学与艺术学院院士、欧洲科学院院士、IEEE会士	波兰科学院系统研究所	To be confirmed
	Jinde Cao	欧洲科学院院士、俄罗斯自然科学院外籍院士、俄罗斯工程院外籍院士、非洲科学院院士、欧洲科学与艺术学院院士、巴基斯坦科学院院士、IEEE会士	东南大学	Swarm Intelligence Network and Mathematics
	Jun Wang	欧洲科学院外籍院士、IEEE会士	香港城市大学	Advances in Collaborative Neurodynamic Optimization
	Kay-Chen Tan	教授、IEEE会士、国际计算智能协会（IEEE CIS）副主席	香港理工大学	Advances in Evolutionary Transfer Optimization
	Leszek Rutkowski	波兰科学院院士、IEEE会士、Journal of Artificial Intelligence and Soft Computing Research主编	琴希托霍瓦工业大学（波兰）	Challenging problems in stream data mining
	Marco Dorigo	蚁群优化创始人、欧洲科学院院士、IEEE会士、Swarm Intelligence创刊主编	布鲁塞尔自由大学（比利时）	Towards robust and secure robot swarms
	Nikhil R. Pal	印度科学院院士、印度工程院院士、IEEE会士	印度统计研究所	Driver's Cognitive States: Monitoring Them and Revealing Associated Information Flow in the Brain
	Pedro Larranaga	欧洲科学院院士、欧洲人工智能协会会士	马德里理工大学 （西班牙）	Bayesian Networks for Interpretable Machine Learning and Optimization
	Radu-Emil Precup	罗马尼亚科学院通讯院士、International Journal of Artificial Intelligence主编	蒂米什瓦拉理工大学（罗马尼亚）	Evolving Fuzzy Models and Applications
	Sergey Ablameyko	白俄罗斯科学院院士、欧洲科学院院士、IAPR会士	白俄罗斯国立大学	Dynamic objects motion analysis in video by using optical flow and neural networks
	Slim Bechikh	IEEE Transactions on Evolutionary Computation 和 Swarm and Evolutionary Computation副主编	突尼斯大学	To be confirmed
	Tingwen Huang	欧洲科学与艺术学院院士、IEEE会士、AAIA会士、国家高层次人才特殊支持计划入选者	德克萨斯A&M大学卡塔尔分校 （卡塔尔）	Efficient Computational Approaches and Applications to Some Optimization Problems in Smart Grid
	Tufan Kumbasar	IEEE Transactions on Fuzzy Systems副主编	伊斯坦布尔科技大学（土耳其）	Why and How to fuse Deep Learning and Fuzzy Logic Systems?
	Witold Pedrycz	加拿大皇家科学院院士、波兰科学院外籍院士、IEEE会士、Information Sciences主编	阿尔伯塔大学 （加拿大）	Computing with Information Granules: Building a Synergistic Environment of Computational Intelligence
	Xin Yao	教授、IEEE会士、国家高层次人才特殊支持计划入选者、IEEE Transactions on Evolutionary Computation前主编	南方科技大学	Ensemble Approaches to Class Imbalance Learning
	Yaochu Jin	欧洲科学院院士、IEEE会士、IEEE Transactions on Cognitive and Developmental Systems主编	比勒费尔德大学 （德国）	Privacy-Preserving Data-Driven Evolutionary Optimization
	Zengguang Hou	教授、国家杰出青年基金获得者、国家高层次人才特殊支持计划入选者、IEEE会士	中国科学院自动化研究所	Human-Computer Interaction Control of Robots

Cognitive Computing: A New Wave of Computational Intelligence?

Ah-Hwee Tan, PhD & Professor

Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems and the IEEE Transactions on Systems, Man, and Cybernetics: Systems

Singapore Management University, Singapore City, Singapore

Abstract: Although recent development in machine learning techniques, in particular deep learning, has raised much expectation in Artificial Intelligence (AI), data-driven learning-based AI systems are typically designed to solve specific problems by learning from a massive amount of training data. Human cognition, on the other hand, involves a complex interplay of many high level functions, notably self-awareness, memory, reasoning, learning, and problem solving. In this talk, we shall review a family of biologically-inspired self-organizing neural network models, collectively known as fusion Adaptive Resonance Theory (fusion ART). By extending the Adaptive Resonance Theory (ART) into a multi-channel network architecture, fusion ART unifies a number of important neural models developed over the past decades, including Adaptive Resonance Theory (ART) networks for unsupervised learning, Adaptive Resonance Associative Map (ARAM) for supervised learning, and Fusion Architecture for Learning and Cognition (FALCON) for reinforcement learning. Following the notion of embodied cognition, fusion ART, encompassing a set of universal neural coding and adaptation principles, can be used as a building block for cognitive computation and intelligent behaviours, notably real-time reinforcement learning, reasoning, and decision making. In this talk, case studies will be presented, illustrating how such cognitive systems may be manifested as Non-Player Characters (NPC) in virtual game environment and Computer Generated Forces (CGF) in combat simulation.

Bio-Sketch: Dr Ah-Hwee Tan received Ph.D. in Cognitive and Neural Systems from Boston University, Master of Science and Bachelor of Science (First Class Honors) in Computer and Information Science from the National University of Singapore. He is currently Professor of Computer Science and Associate Dean (Research) at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). Before joining SMU, he was Professor of

Computer Science at the School of Computer Science and Engineering (SCSE), Nanyang Technological University, where he last served as the Associate Chair (Research) of the school. Prior to NTU, he was a Senior Member of Research Staff at the A*STAR Institute for Infocomm Research(12R), spearheading the Text Mining and Intelligent Agents research programmes. His current research interests include cognitive and neural systems, brain-inspired intelligent agents, machine learning, and text mining. Dr. Tan has published over 200 technical papers in major international journals and conferences of his fields, in addition to six edited books and proceedings. He holds two US patents, five Singapore patents, and has spearheaded several A*STAR projects in commercializing a suite of knowledge management and text mining software. He has served as Associate Editor/Editorial Board Member of several journals, including IEEE Transaction on Neural Networks and Learning, IEEE Transactions on SMC Systems, and IEEE Access. He is a Senior Member of IEEE, Member of Web Intelligence (WI) Technical Committee and Web Intelligence (WI) Conference Steering Committee, Vice Chair of IEEE CIS Task Force on Towards Human-Like Intelligence, and General Co-Chair of 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022).

Deep Learning for Image and Video Restoration/Super-Resolution

A. Murat Tekalp, PhD & Professor

Member of Turkish Academy of Sciences, Member of Academia Europaea, IEEE Fellow, Lifetime Achievement Award of IEEE Turkey Section

Ko¸c University, Istanbul, Turkey

Abstract: Deep learning has made a significant impact not only on computer vision and natural language processing but also on classical signal processing problems such as image/video restoration/SR and compression. Recent advances in neural architectures led to significant improvements in the performance of learned image/video restoration and SR.  We can consider learned image restoration and SR as learning either a mapping from the space of degraded images to ideal images based on the universal approximation theorem, or a generative model that captures the probability distribution of ideal images.   An important benefit of data-driven deep learning approach is that neural models can be optimized for any differentiable loss function, including visual perceptual loss functions, leading to perceptual video restoration and SR, which cannot be easily handled by traditional model-based approaches.  I will discuss loss functions and evaluation criteria for image/video restoration and SR, including fidelity and perceptual criteria, and the relation between them, where we briefly review the perception vs. fidelity (distortion) trade-off. We then discuss practical problems in applying supervised training to real-life restoration and SR, including overfitting image priors and overfitting the degradation model and some possible ways to deal with these problems.

Bio-Sketch: A. Murat Tekalp received Ph.D. degree in Electrical, Computer, and Systems Engineering from Rensselaer Polytechnic Institute (RPI), Troy, New York, in 1984, He has been with Eastman Kodak Company, Rochester, New York, from 1984 to 1987, and with the University of Rochester, Rochester, New York, from 1987 to 2005, where he was promoted to Distinguished University Professor. He is currently Professor at Koç University, Istanbul, Turkey. He served as Dean of Engineering between 2010-2013. His research interests are in digital image and video processing, including video compression and streaming, video networking, and image/video processing using deep learning. He is a Fellow of IEEE and a member of Academia Europaea. He served as an Associate Editor for IEEE Trans. on Signal Processing (1990-1992) and IEEE Trans. on Image Processing (1994-1996). He chaired the IEEE Signal Processing Society Technical Committee on Image and Multidimensional Signal Processing (Jan. 1996 - Dec. 1997).  He was the Editor-in-Chief of the EURASIP journal Signal Processing: Image Communication published by Elsevier between 1999-2010. He was appointed as the General Chair of IEEE Int. Conf. on Image Processing (ICIP) at Rochester, NY in 2002, and as Technical Program Co-Chair for ICASSP 2000, EUSIPCO 2005, and ICIP 2020.  He was on the Editorial Board of the IEEE Signal Processing Magazine (2007-2010) and the Proceedings of the IEEE (2014-2020). He is now serving in the Editorial Board of Wiley-IEEE Press (since 2018).   He served in the ERC Advanced Grants Evaluation Panel (2009-2015). Dr. Tekalp has authored the Prentice Hall book Digital Video Processing (1995), a completely rewritten second edition of which is published in 2015.

Forecasting Non-Stationary Time Series without Recurrent Connections

Andries Engelbrecht, PhD & Professor

Associate Editor of the IEEE Transactions on Evolutionary Computation

Stellenbosch University, Matieland, South Africa

Abstract: Artificial neural networks (NNs) are widely used in modeling and forecasting time series. Since most practical time series are non-stationary, NN forecasters are often implemented using recurrent/delayed connections to handle the temporal component of the time varying sequence. These recurrent/delayed connections increase the number of weights required to be optimized during training of the NN. Particle swarm optimization (PSO) has become an established method for training NNs, and was shown in several studies to outperform the classical backpropagation training algorithm. The original PSO was, however, designed for static environments. In dealing with non-stationary data, modified versions of PSOs for optimization in dynamic environments are used. These dynamic PSOs have been successfully used to train NNs on classification problems under non-stationary environments. This talk formulates training of a NN forecaster as dynamic optimization problem to investigate if recurrent/delayed connections are necessary in a NN time series forecaster when a dynamic PSO is used as the training algorithm. Eight forecasting problems are used to show that FNNs trained with the dynamic PSO significantly outperform various recurrent NNs. These findings highlight that recurrent/delayed connections are not necessary in NNs used for time series forecasting (for the time series considered in this talk) as long as a dynamic PSO algorithm is used as the training method.

Bio-Sketch: Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to 2019, he was appointed in the Department of Computer Science, University of Pretoria (1998-2018), where he served as the head of the department (2008–2017), South African Research Chair in Artificial Intelligence (2007–2018), and Director of the Institute for Big Data and Data Science (2017–2018). His research interests include swarm intelligence, evolutionary computation, artificial neural networks, artificial immune systems, machine learning, data analytics, and the application of these Artificial Intelligence paradigms to data mining, games, bioinformatics, finance, and difficult optimization problems. He is author of two books, “Computational Intelligence: An Introduction” and “Fundamentals of Computational Swarm Intelligence”.

Modeling conceptual generalization over taxonomies

Boris Mirkin, PhD & Professor

Member of Academia Europaea, Member of the UK EPSRC Computing Peer Review College, Member of the British Classification Society

Higher School of Economics, Moscow, RF and Birkbeck, University of London, UK

Abstract: Deep learning successfully generalizes features of records and images. Here is an effort on the opposite side, in mental generalizations. We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a domain taxonomy. This generalization lifts the thematic set to a “head subject” in the higher ranks of the taxonomy to “tightly” cover the set, possibly bringing in some errors, both “gaps” and “offshoots”. We developed algorithms for both maximum parsimony and maximum likelihood criteria. Our approach involves two more automated analysis techniques: a fuzzy clustering method, FADDIS, using both additive and spectral properties, and a purely structural string-to-text relevance measure based on suffix trees annotated by symbol frequencies. We apply this in two domains: (a) extracting research tendencies from collections of research papers in Data Science; (b) extending sizes of internet advert targeted audiences. (Joint work with S. Nascimento,T. Fenner, D. Frolov, and Z. Ayrapetyan.)

Bio-Sketch: Boris Mirkin holds a PhD in Computer Science (Mathematics) and DSc in Systems Analysis (Technology) degrees from Russian Universities. He was one of the leaders in developing clustering and data analysis research in Russia and the USSR.

In 1991-2010, he travelled through long-term visiting appointments in France, Germany, USA, and a teaching appointment as Professor of Computer Science, Birkbeck University of London, UK (2000-2010).

He develops methods for clustering and interpretation of complex data within the “data recovery” perspective. Currently these approaches are being extended to automation of text analysis problems including the development and use of hierarchical ontologies. He published a hundred of refereed papers and a dozen of monographs.

Brain Computer Interface in Human-Autonomy Teaming

Chin-Teng Lin, PhD & Professor

IEEE Fellow, IFSA Fellow, Editor in Chief, IEEE Transactions on Fuzzy Systems

University of Technology Sydney, NSW, Australia

Abstract: BCI is widely considered a ‘disruptive technology’ for the next-generation human-computer interface in wearable computers and devices. In particular, there are incredible potential real-life applications of BCI in augmenting human performance for people in health and aged care. Despite this, there are limitations. Human cognitive functions, such as action planning, intention, preference, perception, attention, situational awareness, and decision-making, although omnipresent in our daily lives, are complex and hard to emulate. Yet, by studying the brain and behaviour at work, a BCI plays an incredibly important role natural cognition. Discover the latest thinking in the realm of the Brain-Computer Interface in this lecture. Listen the current status of BCI and discusses its three major obstacles: the shortage of wearable EEG devices, the various forms of noise contamination that hinder BCI performance, and the lack of suitable adaptive cognitive modelling. This talk will introduce the fundamental physiological changes of human cognitive functions in the interaction with autonomous machines (autonomy) and explain how to combine the bio-findings and AI techniques to develop monitoring and feedback systems to enhance the cooperation of human and autonomy.

Bio-Sketch: Chin-Teng Lin received the B.S. degree from the National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, West Lafayette, Indiana, U.S.A. in 1989 and 1992, respectively. He is currently a Distinguished Professor, Co-Director of Centre for AI, and Director of CIBCI Lab, FEIT, UTS. He is also invited as the International Faculty of the University of California at San Diego (UCSD) from 2012 and Honorary Professorship of University of Nottingham from 2014.

Prof. Lin’s research focuses on machine-intelligent systems and brain computer interface, including algorithm development and system design. He has published over 380 journal papers (H-Index 76 based on Google Scholar), and is the co-author of Neural Fuzzy Systems (Prentice-Hall) and author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). Dr. Lin served as Editor-in-Chief of IEEE Transactions on Fuzzy Systems from 2011 to 2016, and has served on the Board of Governors of IEEE Circuits and Systems Society, IEEE Systems, Man, and Cybernetics Society, and IEEE Computational Intelligence Society. Dr. Lin is an IEEE Fellow, and received the IEEE Fuzzy Pioneer Award in 2017.

A Dynamic Neural Network Structure and its Application for Continuous Learning in an Open Environment

C.L.Philip Chen, PhD & Professor

Member of the European Academy of Sciences and Arts, Foreign Member of Academia Europaea, IEEE Fellow, AAAS Fellow, IAPR Fellow, CAA Fellow, HKIE Fellow, Editor in Chief, IEEE Transactions on Cybernetics

South China University of Technology, Shenzhen, China

Abstract: Learning in neural networks suffers from the fixed structure of the network with a given number of layers and neurons when gather information for training. When the learning is unsatisfied, a new structure is re-designed and the process is repeated. In this talk, a dynamic neural network structure via Stacked Broad Learning Systems (BLS) will be discussed. The BLS has been proved to be effective and efficient lately. The proposed dynamic model is a novel incremental stacking of BLS. This invariant inherits the efficiency and effectiveness of BLS that the structure and weights of lower layers of BLS are fixed when the new blocks are added. The modified incremental stacking algorithm only computes the connection weights within the BLS block while the connecting weights of the lower stacks remains fixed, making the learning process very efficient. Experimental results on UCI datasets, MNIST dataset, NORB dataset, CIFAR-10 dataset, SVHN dataset, and CIFAR-100 dataset indicate that the proposed method outperforms the other state-of-the-art methods on both accuracy and training speed, such as Deep Residual Networks. In addition, taking the advantages of BLS that can accumulate and reuse the learned knowledge, applications of BLS, Federated Broad Learning, in an open environment will be discussed. Federated Broad Learning supports learning from streaming data continuously, so it can adapt to the environment changes and provide better real-time performance. The broad learning enabled systems have been rigorously established in theory and tested in both simulation and experimental studies.

Bio-Sketch: C. L. Philip Chen (F’07) is the Chair Professor and Dean of the College of Computer Science and Engineering, South China University of Technology and was a Chair Professor of the Faculty of Science and Technology, University of Macau, where he was the former Dean (2010-2017), after he left a professorship position in the USA for 23 years. He is a Fellow of IEEE, AAAS, IAPR, CAA, and HKIE; a member of Academia Europaea (AE), European Academy of Sciences and Arts (EASA). He received IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learnings; and IEEE Joseph G. Wohl Career Award for his contributions in SMCS and IEEE in 2021. He was a recipient of the 2016 Outstanding Electrical and Computer Engineers Award from his alma mater, Purdue University. He received IEEE Transactions on Neural Networks and Learning Systems best transactions paper award two times for his papers in 2014 and 2018, Franklin Taylor best conference paper award in IEEE Int’l Conf. on SMC 2019. He is a highly cited researcher by Clarivate Analytics from 2018 to 2021 continuously.

Currently, he is the Editor-in-Chief of the IEEE Transactions on Cybernetics after he completed his term as the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-2019). He was the IEEE Systems, Man, and Cybernetics Society President from 2012 to 2013, an Associate Editor of the IEEE Transactions on AI, IEEE Trans on SMC: Systems, and IEEE Transactions on Fuzzy Systems, an Associate Editor of China Sciences: Information Sciences. He received Macau FDCT Natural Science Award three times and a First-rank Guangdong Province Scientific and Technology Advancement Award in 2019. His current research interests include cybernetics, computational intelligence, and systems.

AI and Machine Learning for Optimal Control of Complex Nonlinear Systems

Derong Liu, PhD & Professor

Foreign member of the European Academy of Sciences, IEEE Fellow, Editor in Chief, Artificial Intelligence Review

Guangdong University of Technology, Guangzhou, China

Abstract: Researchers have been searching for novel control methods to handle the complexity of modern industrial processes. Artificial intelligence and especially machine learning approaches might provide a solution for the next generation of control methodologies that can handle the level of complexities in many modern industrial processes. It has been shown by many researchers reinforcement learning can do a very good job approximating optimal control actions and provide a nearly optimal solution for the control of complex nonlinear systems. It requires a combination of function approximation structures such as neural networks and optimal control techniques such as dynamic programming. Theoretical development has been on a fast-track in the past ten years. On the other hand, parallel control, cloud control, as well as agent-based control have been studied as alternatives for handling complex nonlinear systems. This lecture will review the development of these methodologies to summarize the inherent relationship among these developments.

Bio-Sketch: Derong Liu received the PhD degree in electrical engineering from the University of Notre Dame, USA, in 1994. He became a Full Professor of Electrical and Computer Engineering and of Computer Science at the University of Illinois at Chicago in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2016. He has published 19 books. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2105. He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, a Fellow of the International Association of Pattern Recognition, and a Member of Academia Europaea (The Academy of Europe).

Deep Neural Networks Based Motifs Mining in Biological Binding Sequences

De-Shuang Huang, PhD & Professor

IEEE Fellow, IAPR Fellow, Editorial board of, IEEE/ACM Transactions on Computational Biology & Bioinformatics

Tongji University, Shanghai, China

Abstract: Transcription factor/Translation factor (TF) play a central role in gene regulation. Knowing the binding specificities of TFs is essential for developing models of the regulatory processes in biological systems and for deciphering the mechanism of gene expression. In this talk, I will first present the fundamental issue for motif prediction of biological sequences, then systematically present motif prediction of biological sequences in combination with the popular emerging technology “Deep Neural Networks”. Firstly, several classical models for deep neural network and the research status of biological sequence motif prediction will be briefly introduced, and the existing shortcomings of deep-learning based motif prediction is discussed, some motif prediction methods including high-order convolutional neural network architecture, weakly-supervised convolutional neural network architecture, deep-learning based sequence + shape framework and bidirectional recurrent neural network for DNA motif prediction are briefly overviewed. Secondly, some latest results are importantly presented. Finally, some new research problems in this aspect will be pointed out and over-reviewed.

Bio-Sketch: De-Shuang Huang is a Professor in Department of Computer Science and Director of Institute of Machine Learning and Systems Biology at Tongji University, China. He is currently the Fellow of the International Association of Pattern Recognition (IAPR Fellow), Fellow of the IEEE (IEEE Fellow) and Senior Member of the INNS, Bioinformatics and Bioengineering Technical Committee Member of IEEE CIS, Neural Networks Technical Committee Member of IEEE CIS, the member of the INNS, Co-Chair of the Big Data Analytics section within INNS, and associated editors of IEEE/ACM Transactions on Computational Biology & Bioinformatics, and Neural Networks, etc. He founded the International Conference on Intelligent Computing (ICIC) in 2005. ICIC has since been successfully held annually with him serving as General or Steering Committee Chair. He also served as the 2015 International Joint Conference on Neural Networks (IJCNN 2015) General Chair, July 12-17, 2015, Killarney, Ireland, the 2014 11th IEEE Computational Intelligence in Bioinformatics and Computational Biology Conference (IEEE-CIBCBC) Program Committee Chair, May 21-24, 2014, Honolulu, USA. His main research interest includes neural networks, pattern recognition and bioinformatics.

Machine Learning Powered Applications in IoT: Opportunities and Challenges

El-Sayed El-Alfy, PhD & Professor

IEEE Senior Member, Associate Editor of，IEEE Transactions on Neural Networks and Learning Systems

King Fahd University of Petroleum and Minerals, Saudi Arabia

Abstract: Recent years have witnessed a rapid development and evolution of the Internet-of-Things (IoT) due to modern advancements in computing and communication hardware and software technologies. The amalgamation of machine learning with IoT has enabled smart and cost-effective applications penetrating many facets of our daily life activities by automatically making insights and valuable inferences of the massive amounts of data produced by people and machines at the network edge. The global sensor market is estimated to steadily increase reaching over 350 billion USD by 2027 with ubiquitous deployment in cyber-physical systems in many domains such as home appliances, transportation, healthcare, energy and utilities, manufacturing, agriculture, defense and cybersecurity. In this talk, we will provide a concise overview of the IoT architecture and the evolution of sensing technologies. Moreover, we will discuss the potential benefits of machine learning in IoT applications with demonstration of some use cases.

Bio-Sketch: EL-SAYED M. EL-ALFY is currently a professor and affiliated researcher the intelligent secure systems research center, King Fahd University of Petroleum and Minerals. He has more than 25 years of experience in industry and academia, involving research, teaching, supervision, curriculum design, program assessment, and quality assurance in higher education. He is an approved ABET/CSAB Program Evaluator (PEV), NCAAA reviewer, and consultant for several universities and research agents in various countries. He is an active researcher with interests in fields related to machine learning and nature-inspired computing and their applications to intelligent systems and cybersecurity analytics. His work has been internationally recognized and received a number of awards. He has published numerously in peer-reviewed journals and conferences, edited a number of books published by reputable international publishers, attended and contributed in the organization of many world-class conferences, and supervised master and Ph.D. students.  Dr. El-Alfy is a senior member of IEEE and was also a member of ACM, IEEE Computational Intelligence Society, IEEE Computer Society, IEEE Communication Society, and IEEE Vehicular Technology Society. He has served as a Guest Editor for a number of special journal issues, and in the editorial board of a number of international journals, including IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Neural Networks and Learning Systems, International Journal of Trust Management in Computing and Communications, and Journal of Emerging Technologies in Web Intelligence (JETWI).

AI and Games: The Virtuous Cycle

Georgios N. Yannakakis, PhD & Professor

Editor in Chief, IEEE Transactions on Games, Associate Editor of, IEEE Transactions on Evolutionary Computation, Dean of the School of Digital Games, University of Malta

University of Malta, Msida, Malta

Abstract: Ever since the birth of the idea of artificial intelligence (AI), games have been helping AI research to advance. Games not only pose interesting and complex problems for AI to solve, they also offer a rich canvas for creativity and expression. This rare domain where science meets art and interaction offers unique properties for the study of AI and is the key driver of technical progress and AI breakthroughs including deep learning and artificial general intelligence. It is not only AI that advances through games, however; AI has been helping games to advance across several fronts: in the way we play them, in the way we understand their inner functionalities, in the way we design them, and in the way we understand play, interaction and creativity. As games get increasingly richer and more complex through creative AI processes, AI advances further and in turn, it advances the environments it is trained in a continuous co-(r)evolutionary virtuous loop. Video games are arguably the most important domain to develop AI for, while AI is arguably the most important technological leap forward for games.

Bio-Sketch: Georgios N. Yannakakis is a Professor and Director of the Institute of Digital Games, University of Malta and co-founder of modl.ai. He does research at the crossroads of artificial intelligence, computational creativity, affective computing, game technology, and human-computer interaction and he has published over 260 journal and conference papers in the aforementioned fields (h-index 57). His research has been supported by numerous national and European grants and has appeared in Science Magazine and New Scientist among other venues. He has been involved in a number of journal editorial boards; he is the upcoming Editor in Chief of the IEEE Transactions on Games and an Associate Editor of the IEEE Transactions on Evolutionary Computation. Prof. Yannakakis has been the General Chair of key conferences in the area of game artificial intelligence (IEEE CIG 2010) and games research (FDG 2013, FDG 2020). He is the co-author of the Artificial Intelligence and Games textbook and the co-organiser of the Artificial Intelligence and Games summer school series.

The many faces of the Vehicle Routing Problem. Problem formulations, solution methods, challenges

Jacek Mańdziuk, PhD & Professor

Member of the Information Committee of the Polish Academy of Sciences, General Co-Chair of the 2021 IEEE Congress on Evolutionary Computation, AE of IEEE Transactions on Neural Networks and Learning Systems

Warsaw University of Technology, Warsaw, Poland

Abstract: The Vehicle Routing Problem belongs to the most widely researched problems in the domain of Operations Research, mainly due to its practical relevance and combinatorial complexity. The importance of the VRP stems from its direct application to everyday business routines of distribution / service-providing companies. Due to a huge variety of practical implementations of the problem, the VRP literature covers a broad range of possible extensions to the classical problem formulation. The list of newly proposed or currently enjoying popularity VRP formulations includes: last mile and same day delivery, crowd shipping, bike sharing systems, post-disaster response plans, local routing in large production or cargo plants, autonomous delivery, UAV delivery, Green VRP, Waste Collection VRP or Rich VRP. Simultaneously, an adequate increase of interest in the application of traditional Computational Intelligence methods (e.g. genetic, memetic, ant colony or particle swarm optimization, simulated annealing, or their various hybrid versions) can be observed in the VRP domain. At the same time, approaches proven efficient in other optimization areas (e.g. methods based on Monte Carlo simulations or algorithms rooted in game theory) have lately entered the VRP field and become a viable alternative to more traditional techniques. This talk will start with presentation of the baseline versions of the problem along with adequate population-based solutions, followed by an overview of new problem formulations and recently-proposed solving methods.

Bio-Sketch: Jacek Mańdziuk, Ph.D., D.Sc. (Senior Member IEEE) received the M.Sc. (Honors) and Ph.D. degrees in applied mathematics from the Warsaw University of Technology, Warsaw, Poland, in 1989 and 1993, resp. and the D.Sc. degree in computer science from the Systems Research Institute, Polish Academy of Sciences, Warsaw, in 2000. He is currently Professor with the Faculty of Mathematics and Information Science, Warsaw University of Technology, where he is also the Head of the Division of Artificial Intelligence and Computational Methods and the Head of the Doctoral Program in computer science. He has authored three books (including Knowledge-free and Learning-based Methods in Intelligent Game Playing, Springer) and 160+ research papers. His research interests include application of Computational Intelligence and Artificial Intelligence to games, dynamic and bilevel optimization problems, human machine co-learning and cooperation in problem solving, and development of general-purpose human-like learning and problem-solving methods which involve intuition, creativity and multitasking. Prof. Mańdziuk was a recipient of the Fulbright Senior Research Award (UC Berkeley and ICSI Berkeley, USA) and the Robert Schuman Foundation Fellowship (CNRS, Besaçon, France). Recently he has been a visiting professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore (2015-2017), General Co-Chair of the 2021 IEEE Congress on Evolutionary Computation (CEC’21), Krakow, Poland, and Chair of the annual IEEE SSCI Symposium on Computational Intelligence for Human-like Intelligence 2013-2021. He is the Founding Chair of the IEEE ETTC Task Force on Toward Human-like Intelligence and serves/served as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems and the IEEE Transactions on Computational Intelligence and AI in Games.

Streaming Clustering and Predictive Health Alerts

James M. Keller, PhD & Professor

IEEE Life Fellow, IFSA Fellow, President-Elect of CIS, receives the 2021 Frank Rosenblatt Award from the IEEE

University of Missouri, Michigan, U.S

Abstract: As one who has been involved in research and applications of clustering for many years, I’ve come to view the clustering enterprise through three basic questions. 1. Do you believe there are any clusters in your data? 2. If so, can you come up with a technique to find the natural grouping of your data? 3. Are the clusters you found good groupings of the data? These questions have fueled many advances to both feature vector analytics and relational data analytics. Question1 probably draws the least attention since us clustering folk want to get about our business. However, for example, some nice visualization techniques have been advanced to assist with this assertion. A side benefit of not skipping this aspect of the problem is that the methods to provide an idea of whether the data has natural clusters also give hints about the big question of how many clusters to search for. There are hundreds, perhaps thousands, of answers to question 2, and always room for more. Question 3 looks at the issue of cluster validity, usually optimizing the number of clusters to provide compact and well separated groups of data.

With the explosion of ubiquitous continuous sensing (something Lotfi Zadeh predicted as one of the pillars of Recognition Technology in the late 1990s), on-line streaming clustering is attracting more and more attention. I was drawn into this world mainly due to our desire to continuously monitor the activities, and health conditions, of older adults in a large interdisciplinary eldercare research group. Roughly, the requirements are that the streaming clustering algorithm recognize and adapt clusters as the data evolves, that anomalies are detected, and that new clusters are automatically formed as incoming data dictate. A practical advantage of a streaming model is that data trends can be examined as they occur, and alerts could be generated as feature vectors representing activity move “toward” cluster boundaries. The purpose of this talk is to examine my thoughts on streaming clustering in general, and a new early warning approach of health changes in eldercare monitoring, based on the MU streaming clustering algorithm.

Bio-Sketch: James M. Keller received the Ph.D. in Mathematics in 1978. He is now the Curators’ Distinguished Professor Emeritus in the Electrical Engineering and Computer Science Department at the University of Missouri. Jim is an Honorary Professor at the University of Nottingham. His research interests center on computational intelligence: fuzzy set theory and fuzzy logic, neural networks, and evolutionary computation with a focus on problems in computer vision, pattern recognition, and information fusion including bioinformatics, spatial reasoning in robotics, geospatial intelligence, sensor and information analysis in technology for eldercare, and landmine detection. His industrial and government funding sources include the Electronics and Space Corporation, Union Electric, Geo-Centers, National Science Foundation, the Administration on Aging, The National Institutes of Health, NASA/JSC, the Air Force Office of Scientific Research, the Army Research Office, the Office of Naval Research, the National Geospatial Intelligence Agency, the U.S. Army Engineer Research and Development Center, the Leonard Wood Institute, and the Army Night Vision and Electronic Sensors Directorate. Professor Keller has coauthored over 500 technical publications.

Jim is a Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the International Fuzzy Systems Association (IFSA), and a past President of the North American Fuzzy Information Processing Society (NAFIPS). He received the 2007 Fuzzy Systems Pioneer Award and the 2010 Meritorious Service Award from the IEEE Computational Intelligence Society (CIS). Jim won the 2021 IEEE Frank Rosenblatt Technical Field Award. He has been a distinguished lecturer for the IEEE CIS and the ACM. Jim finished a full six years term as Editor-in-Chief of the IEEE Transactions on Fuzzy Systems, followed by being the Vice President for Publications of the IEEE Computational Intelligence Society from 2005-2008, then as an elected CIS Adcom member, and finished another term as VP Pubs (2017-2020). He is President-Elect of CIS in 2021 with his term as President in 2022 - 2023. He was the IEEE TAB Transactions Chair as a member of the IEEE Periodicals Committee, and was a member of the IEEE Publication Review and Advisory Committee from 2010 to 2017. Among many conference duties over the years, Jim was the general chair of the 1991 NAFIPS Workshop, the 2003 IEEE International Conference on Fuzzy Systems, and co-general chair of the 2019 IEEE International Conference on Fuzzy Systems.

To be confirmed

Janusz Kacprzyk, PhD & Professor

Full Member, Polish Academy of Sciences, Member, Academia Europaea, Member, European Academy of Sciences and Arts, Member, European Academy of Sciences, Member, International Academy for Systems and Cybernetics Sciences (IASCYS), Foreign Member, Bulgarian Academy of Sciences, Foreign Member, Spanish Royal Academy of Economic and Financial Sciences (RACEF), Foreign Member, Finnish Society of Sciences and Letters, Foreign Member, Royal Flemish Academy of Belgium for Science and the Arts (KVAB), Foreign Member, National Academy of Sciences of Ukraine, Foreign Member, Lithuanian Academy of Sciences, President, Polish Operational and Systems, Fellow of IEEE, IET, IFSA, EurAI, IFIP, AAIA, SMIA

Institute of Polish Academy of Sciences, Warsaw, Poland

Abstract: ……

Bio-Sketch: ……

Swarm Intelligence Network and Mathematics

Jinde Cao, PhD & Professor

Member of Academia Europaea, Foreign Member of the Russian Academy of Natural Sciences, Foreign Member of the Russian Academy of Engineering, Member of the African Academy of Sciences, Member of the Pakistan Academy of Sciences, IEEE Fellow

Southeast University, Nanjing, China

Abstract: This report mainly introduces the related background of Swarm Intelligence Network, the main work progress at home and abroad, and future prospects and reflections.

Bio-Sketch: Cao Jinde, chief professor of Southeast University, winner of the National Innovation Award, and enjoys the special government allowance of the State Council. He was successively elected as a member of the European Academy of Sciences, a member of the Russian Academy of Engineering, a member of the Russian Academy of Natural Sciences, a member of the African Academy of Sciences, a member of the European Academy of Sciences and Arts, Member of the Pakistan Academy of Sciences, Member of the International Academy of Systems and Control Sciences, and IEEE Fellow.

He is a Chief Professor of Southeast University, Dean of the School of Mathematics, Director of the Department of Science, Director of the Key Laboratory of Network Group Intelligence of Jiangsu Province, Member of the Mathematics Professional Teaching Committee of the Ministry of Education, Standing Director of the Chinese Society of Industrial and Applied Mathematics, Director of the Jiangsu Society of Industrial and Applied Mathematics Chairman, Chairman of Jiangsu University Mathematical Union, etc. The winner of the National Innovation Competition Award enjoys a special government allowance from the State Council. He was successively elected as a member of the European Academy of Sciences, a member of the Russian Academy of Engineering, a member of the Russian Academy of Natural Sciences, a member of the European Academy of Sciences and Arts, a member of the African Academy of Sciences, a member of the Pakistan Academy of Sciences, a member of the International Academy of Systems and Control Sciences, and an IEEE Fellow. He has long been engaged in the research of complex networks and complex systems, neural dynamics and optimization, and engineering stability. He has presided over 1 national key research and development plan project, 9 national natural science fund projects (including 1 key project), and doctoral fund of the Ministry of Education 3 projects, the project results are included in the 2009 annual report of the National Natural Science Foundation of China as a tour of the results of the National Natural Science Foundation of China.

Advances in Collaborative Neurodynamic Optimization

Jun Wang, PhD & Professor

Foreign Member of Academia Europaea, IEEE Fellow, IAPR Fellow, Editor-in-Chief of IEEE Transactions on Cybernetics

City University of Hong Kong, Hong Kong, China

Abstract: The past three decades witnessed the birth and growth of neurodynamic optimization which has emerged as a potentially powerful problem-solving tool for constrained optimization due to its inherent nature of biological plausibility and parallel and distributed information processing. Despite the success, almost all existing neurodynamic approaches work well only for optimization problems with generalized convex functions. Effective neurodynamic approaches to optimization problems with nonconvex functions and discrete variables are rarely available. In this talk, collaborative neurodynamic optimization approaches will be presented. In the collaborative neurodynamic optimization framework, multiple neurodynamic optimization models with diversified initial states are employed for scatter local search in parallel and a meta-heuristic rule (such as PSO) is used to reposition neuronal states upon their local convergence to escape local minima and move toward global optimal solutions. The efficacy of the proposed approaches will be substantiated with experimental results for, nonnegative matrix factorization, supervised learning, vehicle-task assignment, and portfolio selection, etc.

Bio-Sketch: Jun Wang is the Chair Professor Computational Intelligence in the Department of Computer Science and School of Data Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Dalian University of Technology, Huazhong University of Science and Technology, and Shanghai Jiao Tong University (Changjiang Chair Professor). He received a B.S. degree in electrical engineering and an M.S. degree from Dalian University of Technology and his Ph.D. degree from Case Western Reserve University. His current research interests include neural networks and their applications. He published over 270 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He was the Editor-in-Chief of the IEEE Transactions on Cybernetics (2014-2019) and served as an Associate Editor of the IEEE Transactions on Neural Networks, IEEE Transactions on Cybernetics and its predecessor, and IEEE Transactions on Systems, Man, and Cybernetics: Part C, as a member of the editorial board of Neural Networks, editorial advisory board of International Journal of Neural Systems. He was an organizer of many international conferences such as the General Chair of the 13th/25th International Conference on Neural Information Processing (2006/2018), the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the 2012 IEEE International Conference on Systems, Man, and Cybernetics. He is an IEEE Life Fellow, IAPR Fellow, CAAI Fellow, and a foreign member of Academia Europaea (The Academy of Europe). He was a Distinguished Lecturer of the IEEE Computational Intelligence Society (CIS) and the IEEE Systems, Man and Cybernetics Society (SMCS). In addition, he served in many professional organizations such as Asia Pacific Neural Network Assembly (APNNA) as President (2006), IEEE Fellow Committee, IEEE CIS Awards and Fellow Committees, IEEE SMCS Board of Governors and Fellow Committee. He is a recipient of the APNNA Outstanding Achievement Award, IEEE CIS Neural Networks Pioneer Award, CAAI Wu Wenjun AI Science and Technology Achievement Award, and IEEE SMCS Norbert Wiener Award and Outstanding Contribution Award, among other distinctions.

Advances in Evolutionary Transfer Optimization

Kay Chen Tan, PhD & Professor

IEEE Fellow, Editor-in-Chief of IEEE Transactions on Evolutionary Computation

Hong Kong Polytechnic University, Hong Kong, China

Abstract: It is known that the processes of learning and the transfer of what has been learned are central to humans in problem-solving. However, the study of optimization methodology which learns from the problem solved and transfer what have been learned to help problem- solving on unseen problems, has been under-explored in the context of evolutionary computation. This talk will touch upon the topic of evolutionary transfer optimization (ETO), which focuses on knowledge learning and transfer across problems for enhanced evolutionary optimization performance. I will first present an overview of existing ETO approaches for problem-solving in evolutionary computation. I will then introduce some of our recent work on ETO. It will end with a discussion on future ETO research directions, covering various topics ranging from theoretical analysis to real-world applications.

Bio-Sketch: Kay Chen Tan is currently a Chair Professor (Computational Intelligence) of the Department of Computing, The Hong Kong Polytechnic University. He has co-authored 7 books and published over 200 peer-reviewed journal articles. Prof. Tan is currently the Vice- President (Publications) of IEEE Computational Intelligence Society, USA. He was the Editor-in-Chief of IEEE Transactions on Evolutionary Computation from 2015-2020 (IF: 11.554) and IEEE Computational Intelligence Magazine from 2010-2013 (IF: 11.356). Prof. Tan is an IEEE Fellow, an IEEE Distinguished Lecturer Program (DLP) speaker since 2012, and an Honorary Professor at University of Nottingham in UK. He is also the Chief Co- Editor of Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications since 2020.

Challenging problems in stream data mining

Leszek Rutkowski, PhD & Professor

Member of the Polish Academy of Sciences, IEEE Fellow, Editor-in-chief, Journal of Artificial Intelligence and Soft Computing Research

University of Technology, Częstochowa, Poland

Abstract: This lecture presents a collection of original methods and algorithms for stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, the basic concepts of stream data mining are outlined with a special emphasis put on concept drift – the phenomenon describing the time-varying nature of streaming data.  Next, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. The material presented in the lecture is based on the recent book: Leszek Rutkowski, Maciej Jaworski, Piotr Duda, “Stream Data Mining: Algorithms and Their Probabilistic Properties”, Studies in Big Data, Springer 2020.

Bio-Sketch: Professor Leszek Rutkowski (IEEE Fellow and Academician - Polish Academy of Sciences) received the M.Sc. degree in cybernetics, the Ph.D. and D.Sc. degrees in automatic control/learning systems from the Wrocław University of Technology, Wrocław, Poland, in 1977, 1980, and 1986, respectively, and the honoris causa degree from the AGH University of Science and Technology, Cracow, Poland, in 2014. He has been with the Czestochowa University of Technology, Czestochowa, Poland, since 1980, and with the Academy of Social Sciences, Poland, since 2010; in both places he currently holds the position of a Full Professor. From 1987 to 1990, he held a visiting position with the School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA. In 2004, he was elected as a member of the Polish Academy of Sciences, Warsaw, Poland. He has authored/co-authored over 300 publications and 8 books. Professor Leszek Rutkowski was a recipient of the IEEE Transaction on Neural Networks Outstanding Paper Award and, as the Chairman of the Chapter, the Outstanding Chapter Award from the IEEE Computational Intelligence Society.  He was awarded by the IEEE Fellow Membership Grade for contributions to neurocomputing and flexible fuzzy systems in 2004. In 2015 Professor Leszek Rutkowski received a degree honoris causa from the prestigious AGH University of Science and Technology in Cracow “in recognition of outstanding scientific achievements in the field of artificial intelligence in particular neuro-fuzzy systems”. Professor Leszek Rutkowski is the Founding Chair of the Polish Chapter of the IEEE Computational Intelligence Society, and in 2019 was re-elected as the President of the Polish Neural Network Society. His current research interests include stream data mining, neural networks, deep learning, multiagent systems, fuzzy modeling, and pattern classification. He is editor-in-chief of the Journal of Artificial Intelligence and Soft Computing Research, and he is on the editorial board of the IEEE Transactions on Cybernetics, International Journal of Neural Systems, International Journal of Applied Mathematics and Computer Science, and Knowledge and Information Systems. He is a widely cited and internationally recognized scholar with H index equal to 53, 43, and 42, in Google Scholar, Web of Science, and Scopus databases, respectively.

Towards robust and secure robot swarms

Marco Dorigo, PhD & Professor

Member of Academia Europaea, Inventor of the ant colony optimization metaheuristic, IEEE Fellow, AAAI Fellow, ECAI Fellow, Editor-in-Chief of the journal “Swarm Intelligence”

University Libre De Bruxelles, Iridis, Belgium

Abstract: In the talk, I will present an approach named mergeable nervous system for robot swarms and show how it can be used to design and implement swarms of robots that are easier to control and that can self-repair.  I will also discuss some recent results of research aimed to improve security in robot swarms via the use of Merkle trees and of the blockchain.

Bio-Sketch: Marco Dorigo received the Laurea, Master of Technology, degree in industrial technologies engineering in 1986, and the Ph.D. degree in electronic engineering in 1992 from the Politecnico di Milano, Milan, Italy, and the title of Agr'eg'e de l’Enseignement Sup'erieur, from ULB, in 1995. From 1992 to 1993, he was a Research Fellow at the International Computer Science Institute, Berkeley, CA. In 1993, he was a NATO-CNR Fellow, and from 1994 to 1996, a Marie Curie Fellow. Since 1996, he has been a tenured Researcher of the FNRS, the Belgian National Funds for Scientific Research, and co-director of IRIDIA, the artificial intelligence laboratory of the ULB. He is the inventor of the ant colony optimization metaheuristic. His current research interests include swarm intelligence, swarm robotics, and metaheuristics for discrete optimization. He is the Editor-in-Chief of Swarm Intelligence, and an Associate Editor or member of the Editorial Boards of many journals on computational intelligence and adaptive systems. Dr. Dorigo is a Fellow of the AAAI, EurAI, and IEEE. He was awarded the Italian Prize for Artificial Intelligence in 1996, the Marie Curie Excellence Award in 2003, the Dr. A. De Leeuw-Damry-Bourlart award in applied sciences in 2005, the Cajastur International Prize for Soft Computing in 2007, an ERC Advanced Grant in 2010, the IEEE Frank Rosenblatt Award in 2015, and the IEEE Evolutionary Computation Pioneer Award, awarded in 2016.

Driver's Cognitive States: Monitoring Them and Revealing Associated Information Flow in the Brain

Nikhil R. Pal, PhD & Professor

Member of Indian Academy of Sciences, Member of Indian Academy of Engineering, IEEE Fellow

Head, Center for Artificial Intelligence and Machine Learning

Indian Statistical Institute, Calcutta, India

Abstract: Every year, in every country drowsy driving kills many people. Therefore, it is very important to assess the vigilance levels (cognitive states) of a driver when he/she is driving so that if the driver falls drowsy or gets distracted by some other reason, such as by a mobile phone call, he/she can be given an alert signal. In this talk I consider two important issues related to drowsy driving, when EEG is used as our source of information. 1. We discuss the design of an unsupervised system for detection of drowsiness in drivers in a subject and session independent manner and at the same time using just one EEG channel so that the driver’s comfort is not affected. The system does not need any pre-trained model. Every time any driver starts driving a fresh model of normal (alert) state of the driver is generated and a significant deviation from the model is viewed as a distraction. 2. We also analyse the changes in information flow between different brain regions when the vigilance level of a subject changes from alert to drowsy. For this we use transfer entropy as the vehicle. It indicates how the brain struggles, possibly by recruiting more neurons, to maintain the activity level when the cognitive state changes from alert to drowsy. It reveals some interesting findings that may help to understand the cortico-cortical communication during drowsy driving.

Bio-Sketch: Nikhil R. Pal (Fellow, IEEE) received the B.Sc. degree in physics and Master of Business Management degree from the University of Calcutta, Calcutta, India, and the Master of Technology and Ph.D. degrees from the Indian Statistical Institute, Calcutta, India. He is a Professor of Electronics and Communication Sciences Unit and the Head of the Centre for Artificial Intelligence and Machine Learning, Indian Statistical Institute, Kolkata, India. His current research interest includes brain science, computational intelligence, machine learning, and data mining.

Prof. Pal was a recipient of the 2015 IEEE Computational Intelligence Society (CIS) Fuzzy Systems Pioneer Award. He has given many plenary/keynote speeches in different premier international conferences in the area of computational intelligence. He was the Editor-in-Chief of the IEEE TRANSACTIONS ON FUZZY SYSTEMS from January 2005 to December 2010. He served/has been serving on the Editorial/Advisory Board/Steering Committee of several journals, including the International Journal of Approximate Reasoning, Applied Soft Computing, International Journal of Neural Systems, Fuzzy Sets and Systems, IEEE TRANSACTIONS ON FUZZY SYSTEMS, and the IEEE TRANSACTIONS ON CYBERNETICS. He has served as the general chair, the program chair, and the co-program chair of several conferences. He was a Distinguished Lecturer of the IEEE CIS from 2010 to 2012 and from 2016 to 2018, and a member of the Administrative Committee of the IEEE CIS from 2010 to 2012. He served as the Vice-President for Publications of the IEEE CIS from 2013 to 2016 as well as the President of the IEEE CIS from 2018 to 2019. He is a Fellow of the National Academy of Sciences, India, Indian National Academy of Engineering, Indian National Science Academy, International Fuzzy Systems Association, and The World Academy of Sciences.

Bayesian Networks for Interpretable Machine Learning and Optimization

Pedro Larrañaga, PhD & Professor

Member of the Academia Europaea, EurAI Fellow, Fellow of the Asia-Pacific Artificial Intelligence Association

Universidad Politécnica de Madrid, Madrid, Spain

Abstract: Artificial intelligence is being increasingly used for high-stakes applications, and it is becoming more and more important that the models used to be interpretable. Bayesian networks offer a paradigm for interpretable artificial intelligence that is based on probability theory. This talk discusses techniques and algorithms that have been developed in the field of Bayesian networks to facilitate the interpretation of machine learning models as well as the heuristic optimization process. With regard to machine learning models, several approaches will be shown that allow understanding the process of structural learning of a Bayesian network from data, to later discuss about the visualization options of said models and finally introduce methods to interpret different types of reasoning (diagnostic, intercausal, predictive, abductive, counterfactual, ...) that Bayesian networks are capable of providing. The heuristic optimization process that an evolutionary algorithm follows will be interpreted from the so-called estimation of distribution algorithms in which the crossover and mutation operators used in the genetic algorithms are replaced in each generation by the learning and simulation of a Bayesian network.

Bio-Sketch: Pedro Larrañaga is Full Professor in Computer Science and Artificial Intelligence at the Universidad Politécnica de Madrid. He received the MSc degree in Mathematics (Statistics) from the University of Valladolid and the PhD degree in Computer Science from the University of the Basque Country (Excellence Award). His research interests are primarily in the areas of probabilistic graphical models, metaheuristics for optimization, machine learning classification models, and real applications, like biomedicine, bioinformatics, neuroscience, industry 4.0 and sports. He has published more than 200 papers in high impact factor journals and has supervised more than 30 PhD theses. He is fellow of the European Association for Artificial Intelligence since 2012 and fellow of the Academia Europaea and of the Asia-Pacific Artificial Intelligence Association since 2018 and 2021 respectively. He has been awarded the 2013 Spanish National Prize in Computer Science, the prize of the Spanish Association for Artificial Intelligence in 2018 and the Amity Research Award in Machine Learning in New Delhi, in 2020.

Evolving Fuzzy Models and Applications

Radu-Emil Precup, PhD & Professor

Corresponding Member of the Romanian Academy，Editor-in-Chief, International Journal of Artificial Intelligence, Editorial board of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, Information Sciences (Elsevier), Applied Soft Computing (Elsevier)

Politehnica University of Timişoara, Romania

Abstract: As shown in the classical papers on evolving fuzzy systems (EFSs), evolving Takagi-Sugeno or Takagi-Sugeno-Kang fuzzy models are characterized by continuous online rule base learning. These fuzzy models are developed in terms of the application of online identification algorithms. The online identification algorithms continuously evolve the parameters of the fuzzy models, which are built online by adding new or removing old local models. This process is referred to as the adding mechanism. According to the recent classification of online identification algorithms (Dovžan, Logar and Škrjanc, 2015), three categories of online identification algorithms are considered, (1), (2) and (3): (1) adaptive algorithms – they start with the initial Takagi-Sugeno-Kang fuzzy model structure given by other algorithms or by user experience, the number of space partitions/clusters does not change over time, and these algorithms adapt just the parameters of the membership functions and the local models; (2) incremental algorithms – they implement only adding mechanisms; (3) evolving algorithms – these algorithm implement, besides the adding mechanism, also the removing and a part of them the merging and the splitting mechanisms. These specific features ensure a large area of applications. This speech highlights a part of the results obtained by the Process Control group of the Politehnica University of Timisoara, Romania. The presentation is focused on representative applications, implemented in our labs, with real-validated by experimental results. The results pointed out here include different lab equipment as pendulum-crane systems, twin rotor aerodynamic systems, magnetic levitation systems, anti-lock braking systems, and shape memory alloy systems. The scope of the development of these models is the model-based and data-driven model-free design and tuning of fuzzy controllers by the Process Control Group.

Keywords—applications, evolving fuzzy models, lab equipment, Takagi-Sugeno-Kang fuzzy models

Bio-Sketch: Radu-Emil Precup (M IEEE '03 - SM IEEE '07) was born in Lugoj, Romania, in 1963. He received the Dipl.Ing. (Hons.) degree in automation and computers from the "Traian Vuia" Polytechnic Institute of Timisoara, Timisoara, Romania, in 1987, the Diploma in mathematics from the West University of Timisoara, Timisoara, in 1993, and the Ph.D. degree in automatic systems from the "Politehnica" University of Timisoara, Timisoara, in 1996.

From 1987 to 1991, he was with Infoservice S.A., Timisoara. He is currently with the Politehnica University of Timisoara, Romania, where he became a Professor in the Department of Automation and Applied Informatics, in 2000, and he is currently a Doctoral Supervisor of automation and systems engineering. He is also an Adjunct Professor within the School of Engineering, Edith Cowan University, Joondalup, WA, Australia, and a Member of the Doctoral School of Applied Informatics with the Óbuda University (previously named Budapest Tech Polytechnical Institution), Budapest, Hungary. He is currently the Director of the Automatic Systems Engineering Research Centre with the Politehnica University of Timisoara, Romania. From 1999 to 2009, he held research and teaching positions with the Université de Savoie, Chambéry and Annecy, France, Budapest Tech Polytechnical Institution, Budapest, Hungary, Vienna University of Technology, Vienna, Austria, and Budapest University of Technology and Economics, Budapest, Hungary. He has been an Editor-in-Chief of the International Journal of Artificial Intelligence since 2008 and he is also on the editorial board of several other prestigious journals including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, Information Sciences (Elsevier), Engineering Applications of Artificial Intelligence (Elsevier), Applied Soft Computing (Elsevier), Expert Systems with Applications (Elsevier), Evolving Systems (Springer), Healthcare Analytics (Elsevier), Communications in Transportation Research (Elsevier) and Cogent Engineering (Taylor & Francis).

He is the author or coauthor of more than 300 papers published in various scientific journals, refereed conference proceedings, and contributions to books. His research interests include mainly development and analysis of new control structures and algorithms (conventional control, fuzzy control, data-based control, sliding mode control, neuro-fuzzy control, etc.), theory and applications of soft computing, computer-aided design of control systems, modeling, optimization (including nature-inspired algorithms), and applications to mechatronic systems (including automotive systems and mobile robots), embedded systems, control of power plants, servo systems, electrical driving systems.

Prof. Precup is a corresponding member of The Romanian Academy, a Doctor Honoris Causa of the Óbuda University, Budapest, Hungary, a Doctor Honoris Causa of the Széchenyi István University, Győr, Hungary, a member of the Subcommittee on Computational Intelligence as part of the Technical Committee (TC) on Control, Robotics and Mechatronics in the Institute of Electrical and Electronics Engineers (IEEE) Industrial Electronics Society, the Task Force on Autonomous Learning Systems within the Neural Networks TC of the IEEE Computational Intelligence Society, the TCs on Computational Cybernetics and Cyber-Medical Systems of the IEEE Systems, Man, and Cybernetics Society, the Task Force on Adaptive and Evolving Fuzzy Systems within the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society, the International Federation of Automatic Control (IFAC) Technical Committee on Computational Intelligence in Control (previously named Cognition and Control), the Working Group WG 12.9 on Computational Intelligence of the Technical Committee TC12 on Artificial Intelligence of the International Federation for Information Processing (IFIP), the European Society for Fuzzy Logic and Technology (EUSFLAT), the Hungarian Fuzzy Association, and the Romanian Society of Control Engineering and Technical Informatics.

He was the recipient of the Elsevier Scopus Award for Excellence in Global Contribution (2017), the "Tudor Tănăsescu" Prize from the Romanian Academy for data-driven controller tuning techniques (2020), the "Grigore Moisil" Prize from the Romanian Academy, two times, in 2005 and 2016, for his contribution on fuzzy control and the optimization of fuzzy systems, the Spiru Haret Award from the National Grand Lodge of Romania in partnership with the Romanian Academy in 2016 for education, environment and IT, the Excellency Diploma of the International Conference on Automation, Quality & Testing, Robotics AQTR 2004 (THETA 14, Cluj-Napoca, Romania), two Best Paper Awards in the Intelligent Control Area of the 2008 Conference on Human System Interaction HSI 2008, Krakow (Poland), the Best Paper Award of 16th Online World Conference on Soft Computing in Industrial Applications WSC16 (Loughborough University, UK) in 2011, the Certificate of Appreciation for the Best Paper in the Session TT07 1 Control Theory of 39th Annual Conference of the IEEE Industrial Electronics Society IECON 2013 (Vienna, Austria), a Best Paper Nomination at 12th International Conference on Informatics in Control, Automation and Robotics ICINCO 2015 (Colmar, France), a Best Paper Award at 7th International Conference on Information Technology and Quantitative Management ITQM 2019 (Granada, Spain), a Best Paper Award at 8th International Conference on Information Technology and Quantitative Management ITQM 2020 & 2021 (Chengu, China), and was listed as one of the top 10 researchers in Artificial Intelligence and Automation (according to IIoT World as of July 2017).

Dynamic objects motion analysis in video by using optical flow and neural networks

Sergey V. Ablameyko, PhD & Professor

Member of Belarus Academy of Sciences, Member of Academia Europaea, IAPR Fellow, AAIA Fellow and Vice-President

Belarusian State University, Minsk, Republic of Belarus

Abstract: One of the important tasks in situation monitoring in video sequences is tracking of dynamic objects. Dynamic objects are physical bodies, devices, or set of interconnected bodies or devices such as moving cyclone, moving group of people, a growing cell or aggregation of cells, etc. Such an object has an internal structure consisting of interacting dynamic elements. Dynamic objects combine movement of objects as a whole with the movement of internal structures, their aggregation, dispersion, direct of chaotic movement of internal structures. The presentation will be devoted to dynamic objects motion analysis in video by using optical flow and neural networks. First, we determine the main types of motion which make it possible to separate the key moments of the object motion and describe the stages of their movement and their interaction with each other. We use basic optical flow to form integral optical flow and use it to separate background and foreground and obtain intensive motion regions. Based on information extracted from integral optical flow, we introduce notion of motion maps and show how it can be used for object motion analysis. In motion maps, we analyze pixel motions statistically for each frame to obtain quantity of pixels moving toward or away from each pixel and their comprehensive motion at each pixel. We then define and compute regional motion indicators to describe motions at region-level. These indicators are further used for analysis of dynamic object behavior. Motion maps allow to determine the moment at which the state starts to change and we classify the main types of motion in the objects’ population: directed motion, aggregation (motion towards each other), dispersion (motion in different directions with respect to the common center), division (formation of several new objects in the place where the old one was), and apoptosis (object destruction).

It was used to analyze objects’ motion by using the integral optical flow. Several tasks have been solved.

The first application is analysis of biomedical images. Analysis of stem cell behavior in microscopic video. Important part of living cell monitoring is the investigation of dynamical properties of cells, cell conglomerates and cellular interactions including detection of spatiotemporal localization of mitosis events. We show cell monitoring algorithm that analyze behavior of the cells’ population as a system of dynamical objects by using a concept of the integral optical flow. The presented results are used for monitoring and quantitative analysis of the development of cell cultures, estimating the dynamic changes taking place in them, and determining the population’s heterogeneity and viability.

A method for determining the characteristics of blood flow in the vessels of eye conjunctiva, such as linear and volumetric blood speed, and topological characteristics of vascular net was developed. To solve the problem of vessels network segmentation, we use a fully connected convolutional neural network. The peculiarity of its organization is that the usual convolution network is supplemented with layers in which the union operators are replaced by operators of increasing discretization, which leads to an increase in the resolution of the output image. Combining features with higher resolution from a narrowing area with an expanding output area allows to train convolutional layers to form a more accurate result at the output. We show the efficiency of our method in real eye vessels scenes.

Second, a method for monitoring dynamic wound changes in video sequence based on integral optical flow will be shown. Dynamic characteristics of wound tissue changing are introduced and calculated. We show the efficiency of our method in real wound scenes.

The second application is people and crowd behavior in video. We developed a new method to identify and track crowd motion in videos based on integral optical flow. During this process, three factors, quantity, intensity and direction of moving pixels, are to be considered. Based on integral optical flow, we can then define and construct four motion maps to together describe pixel motions at each position, i.e. statistical analysis of quantity and motion direction of pixels moving toward or away from each position. After that, we introduce regional motion indicators to analyze motion at region-level which is appropriate for group people motion analysis. At last, we use threshold segmentation to identify crowd motion. The method is based on crowd geometric structure formed by crowd motion in certain time period.

We also applied our approach to analyze cars movement in video and detect flow extreme situations in real-world videos. We defined and calculated parameters of moving car flow including direction, speed, density, and intensity without detecting and counting cars.

In presentation, many real videos with object motion analysis will be shown.

Bio-Sketch: Sergey Ablameyko (born in 1956, DipMath in 1978, PhD in 1984, DSc in 1990, Prof in 1992). He was a General Director of the United Institute of Informatics Problems of the National Academy of Sciences of Belarus (2002-2008), Rector (President) of the Belarusian State University (2008-2017) and now he is a Professor of BSU.

He has more than 650 publications including 25 authored/co-authored books (one published by Springer (UK), one - by SPIE (USA), one – by Exit (Poland) and 25 edited books (two published by IOS Press in NATO Science Series). In his academic career he was a visiting scientist in Italy, Japan, Sweden, Finland, England, Germany, UK, Greece, Spain, Australia, New Zealand, China. He was a chair/co-chair, member of Program Committees of numerous (more than 100) International conferences held worldwide.

He is in Editorial Board of Pattern Recognition and Image Analysis, Non-linear Phenomena in Complex Systems and many other international journals. His scientific interests are: Image analysis, pattern recognition, digital geometry, graphics recognition, knowledge-based systems, geographical information systems, medical imaging.

He is IEE Fellow (UK) (1995), IEEE Senior Member (USA) (1995), Academician of International Academy of Information Processes and Technologies (1995), Academician of Belarusian Academy of Engineering (1995), Fellow of International Association for Pattern Recognition (1998), Academician of the National Academy of Sciences of Belarus (2009), Academician of Spanish Royal Academy of Doctors (2009), Academician of European Academy of Economy and Enterprise Management (2010), Academician of Academia Europea (2011), Honorary professor of Grodno State University, Belarus (2012), Academician of Russian Space Academy (2012), Honorary professor of Dalian University of Technology, China (2013), Honorary professor of Moscow State University, Russia (2014), Honorary professor of Binh Duong University, Vietnam (2014), Academician of Russian Academy of Natural Sciences (2015), Academician of Spanish Royal Academy of Economics and Finance (2016), Honorary professor of Belgrade Alfa BK University, Serbia (2016).

For his activity he was awarded by State Prize of Belarus (highest national scientific award), Friendship Award of Russian Federation (2009), Friendship Award of Zhejiang Province of China (2018) and many other national and international awards.

To be confirmed

Slim Bechikh, PhD & Associate Professor

Associate Editors, IEEE Transactions on Evolutionary Computation, Swarm and Evolutionary Computation

University of Carthage, Tunisia

Abstract: ……

Bio-Sketch: ……

Efficient Computational Approaches and Applications to Some Optimization Problems in Smart Grid

Tingwen Huang, PhD & Professor

Member of Academia Europaea, IEEE Fellow, AAIA Fellow

Texas A&M University at Qatar, College Station, Texas, USA

Abstract: In a smart grid context, first we will look at the trading optimization problem among Microgrids which benefit all sides. A multi-leaders and multi-followers Stackelberg game model is proposed for energy trading problem in a multi-energy microgrid system. Then, we consider the optimization problem in energy wholesale market when incorporating renewable energy sources, and a two-stage Stackelberg-Cournot stochastic game model is established. The risk measurement technique, conditional value at risk (CVaR), is harnessed to estimate the overbidding risk. Third, we will consider the Plug-In Electric Vehicles (PEVs) Charging: Feeder Overload Control problem. The last optimization problem is to consider minimizing the total cost of the generators when they generating electricity satisfying the demand.

Bio-Sketch: Tingwen Huang received his B.S. from Southwest Normal University, M.S. from Sichuan University and Ph.D. from Texas A&M University. Now, he is a professor at Texas A&M University at Qatar, Qatar. His research focuses on dynamics of nonlinear systems including neural networks, complex networks and multi-agent and their applications to smart grids and other areas. He has published some papers in these areas.

He is very actively involving in professional service. He serves/served as President (2020) for Asia Pacific Neural Network Society, as an action editor or associate editor for several international journals.

He is a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of IEEE and AAIA (Asia-Pacific Artificial Intelligence Association), a Changjiang Chair Professor.

Why and How to fuse Deep Learning and Fuzzy Logic Systems?

Tufan Kumbasar, PhD & Associate Professor

Area Editor-in-Chief, International Journal of Approximate Reasoning, Associate Editor of, IEEE Transaction on Fuzzy Systems

Istanbul Technical University, Maslak-Istanbul, Turkey

Abstract: It has been shown in various studies that Interval Type-2 (IT2) Fuzzy Sets (FSs) and Fuzzy Logic Systems (FLSs) can bring significant performance improvement in control, regression, and classification problems when compared to its Type-1 (T1) counterparts. However, the performance of FLSs depends on the number (and shape) of the MFs, the dimension size of the inputs and outputs, and the volume of data. The increasing number/ size of these factors naturally results in the issues, which are the curse of dimensionality and the increasing training complexity of FLSs since they have many learnable parameters. On the other hand, Deep Learning (DL) stands up as the jewel of machine learning when tackling these issues, especially from the representation power of deep neural networks. This talk will introduce the basic concepts of FLSs alongside DL by means of various case studies. I will explain how and why DL methods should be integrated into or combined with FLSs to enhance the learning performance of models. The talk will showcase various successful fusing applications of IT2-FLS and DL integration studies.

Bio-Sketch: Tufan Kumbasar received B.Sc., M.Sc., and Ph.D. degrees in Control and Automation Engineering from Istanbul Technical University. He is currently an Associate Professor in the Control and Automation Engineering Department and the director of Artificial Intelligence and Intelligent Systems (AI2S) Laboratory, Faculty of Electrical and Electronics Engineering, Istanbul Technical University.

He has currently authored more than 100 papers in international conferences, journals, and books. His major research interests are in computational intelligence, notably type-2 fuzzy logic, fuzzy control, neural networks, evolutionary algorithms, and intelligent systems. He is also interested in robotics, machine learning, intelligent control, and their real-world applications. He has served as a Publication Co-Chair, Panel Session Co-Chair, Special Session Co-Chair, PC, IPC, and TPC in various international and national conferences. Dr. Kumbasar is an Associate Editor for the IEEE Transactions on Fuzzy Systems and an Area Editor for the International Journal of Approximate Reasoning.

Dr. Kumbasar received the Best Paper Awards from the IEEE International Conference on Fuzzy Systems in 2015 and the 6th International Conference on Control Engineering & Information Technology in 2018. He was the recipient of the ODTÜ Mustafa N. Parlar Research and Education Foundation Research Incentive Award in 2020.

Computing with Information Granules: Building a Synergistic Environment of Computational Intelligence

Witold Pedrycz, PhD & Professor

Member of the Royal Society of Canada, Foreign Member of the Polish Academy of Sciences, IEEE Life Fellow, Editor in Chief, Information Sciences, Co-editor-in-Chief of Int. J. of Granular Computing

University of Alberta, Edmonton, Canada

Abstract: With enormous amounts of data come opportunities of building models of real-world systems that are instrumental in realizing a plethora of control, prediction, and classification tasks. The interpretability facet of ensuing models becomes highly relevant in light of designing autonomous systems and all those constructs supporting human-centric decision-making environments. To transform data to tangible and actionable pieces of knowledge and formulate a problem at hand at a suitable level of abstraction, a convenient way to proceed is to position the problem in the environment of Granular Computing.  We advocate that a systematic way of capturing knowledge residing within acquired data and encapsulating such knowledge in the form of interpretable models is supported by a suitable level of abstraction at which the data are to be represented. An abstraction mechanism is conveniently realized in the form of information granules. Information granules and Granular Computing deliver an operational and flexible setting in which granular models are built and analyzed. A formal characterization of information granules is introduced where they are concisely described as triple (G, I, R) capturing their underlying geometry in the data space (G), information content (I), and representation capabilities given the underlying experimental evidence (R).

A suite of design methods transforming data into information granules being articulated in various formal settings (e.g., intervals, fuzzy sets, rough sets) is analyzed and an array of generalizations is discussed (including collaborative ways of building granules in the presence of some auxiliary domain knowledge). Equally important is an evaluation of the quality of information granules sought as an abstraction mechanism of data with the help of a reconstruction criterion (degranulation).

In the sequel, it is shown how information granules regarded as functional modules are efficiently used in the construction of a vast array of interpretable models, especially rule-based architectures. It is discussed how learning mechanisms are carried out in a unified setting of knowledge-based learning completed in the presence of data and information granules.   

Bio-Sketch: Witold Pedrycz (IEEE Life Fellow) 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. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.

His main research directions involve Computational Intelligence, Granular Computing, knowledge discovery, data science, and knowledge-based neural networks among others.

Dr. Pedrycz is 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 Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer). 

Ensemble Approaches to Class Imbalance Learning

Xin Yao, PhD & Professor

IEEE Fellow, Royal Society Wolfson Research Merit Award, IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award

Southern University of Science and Technology, Shenzhen, China

Abstract: Many real-world classification problems have highly imbalanced and skew data distributions. In fault diagnosis and condition monitoring for example, there are ample data for the normal class, yet data for faults are always very limited and costly to obtain. It is often a challenge to increase the performance of a classifier on minority classes without sacrificing the performance on majority classes. This talk discusses some of the techniques and algorithms that have been developed for class imbalance learning, especially through ensemble learning. First, the motivations behind ensemble learning are introduced and the importance of diversity highlighted. Second, some of the challenges of multi-class imbalance learning and potential solutions are presented. What might have worked well for the binary case do not work for multiple classes anymore, especially when the number of classes increases. Third, online class imbalance learning will be discussed, which can be seen as a combination of online learning and class imbalance learning. Online class imbalance learning poses new research challenges that still have not been well understood., let alone solved, especially for imbalanced data streams with concept drift. Fourth, the natural fit of multi-objective learning to class imbalance learning is pointed out. The relationship between multi-objective learning and ensemble learning will be discussed. Finally, future research directions will be given.

Bio-Sketch: Xin Yao is a Chair Professor of Computer Science at the Southern University of Science and Technology, Shenzhen, China, and a part-time Professor of Computer Science at the University of Birmingham, UK. His major research interests include evolutionary computation, ensemble learning and search-based software engineering. He is an IEEE fellow, a former (2014-15) President of IEEE Computational Intelligence Society (CIS) and a former (2003-08) Editor-in-Chief of IEEE Transactions on Evolutionary Computation. His work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards at conferences. He received a Royal Society Wolfson Research Merit Award in 2012, the IEEE CIS Evolutionary Computation Pioneer Award in 2013, and the 2020 IEEE Frank Rosenblatt Award.

Privacy-Preserving Data-Driven Evolutionary Optimization

Yaochu Jin, PhD & Professor

Member of Academia Europaea, IEEE Fellow, Editor-in-Chief of IEEE Transaction on Cognitive and Development Systems, Complex & Intelligent Systems

Bielefeld University, North Rhine Westphalia, Germany

Abstract: Privacy preservation is a key concern in data-driven machine learning and decision-making. This talk begins with an introduction to privacy-preserving machine learning and data-driven evolutionary optimization. Then, we present one single-objective and one multi-objective data-driven evolutionary algorithms that can perform optimization based on data distributed on multiple devices, without requiring to collect and store the data on a single server. Empirical results show that the federated data-driven evolutionary algorithms perform comparably well with centralized data-driven algorithms, while being able to preserve the data privacy.

Bio-Sketch: He is an Alexander von Humboldt Professor for Artificial Intelligence endowed by the Germany Federal Minister of Education and Research, Chair of Nature Inspired Computing and Engineering, Faculty of Technology, Bielefeld University, Germany. He is also a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include evolutionary optimization, evolutionary learning, trustworthy machine learning, and evolutionary developmental systems.

Prof Jin is presently the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and the Editor-in-Chief of Complex & Intelligent Systems. He was an IEEE Distinguished Lecturer in 2013-2015 and 2017-2019, the Vice President for Technical Activities of the IEEE Computational Intelligence Society (2015-2016). He is the recipient of the 2018 and 2021 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Web of Science as “a Highly Cited Researcher” consecutively from 2019 to 2021. He is a Member of Academia Europaea and Fellow of IEEE.

Human-Computer Interaction Control of Robots

Zengguang Hou, PhD & Professor

IEEE Fellow, editorial board of IEEE Transactions on Cybernetics, Neural Networks

Chinese Academy of Sciences, Beijing, China

Abstract: Intelligent robots are entering our living and working spaces, but their popularization and application are also facing many challenges. Besides, high-efficiency, reliable, and safe human-computer interaction and control are an important challenge that hinders their development. The report combines the acquisition and processing of multi-modal biological signals and the active and passive control applications of rehabilitation robots, expounds the challenges in related fields, as well as the thinking and prospects for future development.

Bio-Sketch: Prof Hou is a researcher and doctoral supervisor at the Institute of Automation of the Chinese Academy of Sciences, and concurrently serves as the deputy director of the State Key Laboratory of Complex Systems Management and Control. He is an IEEE Fellow, a receiver of National Funds for Distinguished Young youths and the Ten Thousand Plan. He is currently the vice chairman of the Chinese Society of Automation, the vice chairman of the International Society of Asia-Pacific Neural Networks, the director of the International Society of Neural Networks, and the editorial board of IEEE Transactions on Cybernetics, Neural Networks.