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Keynote Lectures

Multi-objective Evolutionary Federated Learning
Yaochu Jin, University of Surrey, United Kingdom

Interpretability and Explainability Facets of Data Analytics: Symbols and Information Granules
Witold Pedrycz, University of Alberta, Canada

Industry 4.0 and Society 5.0: Challenges from a Data Analysis Perspective
Ajith Abraham, Machine Intelligence Research Labs (MIR Labs), United States

Ling Liu, Georgia Institute of Technology, United States



Multi-objective Evolutionary Federated Learning

Yaochu Jin
University of Surrey
United Kingdom

Brief Bio
Yaochu Jin (Fellow, 2016) is currently a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a “Finland Distinguished Professor” of University of Jyvaskyla, Finland, a “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include data-driven evolutionary optimization, evolutionary learning, trustworthy machine learning, and morphogenetic self-organizing systems. 

Dr 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 and Vice President for Technical Activities of the IEEE Computational Intelligence Society. He was the General Co-Chair of the 2016 IEEE Symposium Series on Computational Intelligence and the Chair of the 2020 IEEE Congress on Evolutionary Computation. He is the recipient of the 2018 and 2021 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He was named by the Web of Science as “a Highly Cited Researcher” in 2019 and 2020. He is a Fellow of IEEE.

Federated learning is a powerful machine learning approach to privacy-preserving machine learning. In this talk, I am going to introduce the use of evolutionary algorithms for enhancing the performance of federated learning.  I’ll start with a brief introduction to evolutionary multi-objective machine learning, which is followed by a presentation of an evolutionary multi-objective federated learning algorithm for reducing communication cost and an algorithm for real-time multi-objective search of deep neural architectures based on a double sampling strategy. 



Interpretability and Explainability Facets of Data Analytics: Symbols and Information Granules

Witold Pedrycz
University of Alberta

Brief Bio

Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, 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, fuzzy modeling and Granular Computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He has published papers in these areas. He is also an author of 21 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering. 

Dr. Pedrycz is vigorously 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).  He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals. 

In data analytics, system modeling, and decision-making models, the aspects of interpretability and explainability are of paramount relevance, just to mention only explainable Artificial Intelligence (XAI). They are especially timely in light of the increasing complexity of systems one has to cope with.

We advocate that there are two factors that immensely contribute to the realization of the above important features, namely, a suitable level of abstraction in describing the problem and a logic fabric of the resultant construct. It is demonstrated that their conceptualization and the following realization can be conveniently carried out with the use of information granules (for example, fuzzy sets, sets, rough sets, and alike).
Concepts are building blocks forming the interpretable environment capturing the essence of data and key relationships existing there. The emergence of concepts is supported by a systematic and focused analysis of data. At the same time, their initialization is specified by stakeholders or/and the owners and users of data.   We present a comprehensive discussion of information granules-oriented design of concepts and their description by engaging an innovative mechanism of conditional (concept)-driven clustering. It is shown that the initial phase of the process is guided by the formulation of some generic (say, low profit) or some complex multidimensional concepts (say, poor quality of environment or high stability of network traffic) all of which are described by means of some information granules. In the sequel is explained by other variables through clustering focuses by the context. The description of concepts is delivered by a logic expression whose calibration is completed by a detailed learning of the associated logic neural network. The constructed network helps quantify contributions of individual information granules to the description of the underlying concept and facilitate a more qualitative characterization achieved with the aid of linguistic approximation. This form of approximation delivers a concise and interpretable abstract description through linguistic quantifiers. 
A detailed example of enhancement of interpretability of functional rule-based models with the rules in the form “if x is A then y =f(x)”. The interpretability mechanisms are focused on the elevation of interpretability of the conditions and conclusions of the rules. It is shown that augmenting interpretability of conditions is achieved by (i) decomposing a multivariable information granule into its one-dimensional components, (ii) their symbolic characterization, and (iii) linguistic approximation. A hierarchy of interpretation mechanisms is systematically established. We also discuss how this increased interpretability associates with the reduced accuracy of the rules and how sound trade-offs between these features are formed.



Industry 4.0 and Society 5.0: Challenges from a Data Analysis Perspective

Ajith Abraham
Machine Intelligence Research Labs (MIR Labs)
United States

Brief Bio
Dr. Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia. The Network with Head quarters in Seattle, USA has currently more than 1,000 scientific members from over 100 countries. As an Investigator / Co-Investigator, he has won research grants worth over 100+ Million US$ from Australia, USA, EU, Italy, Czech Republic, France, Malaysia and China.

Dr. Abraham works in a multi-disciplinary environment involving machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, data mining and applied to various real world problems. In these areas he has authored / coauthored more than 1,300+ research publications out of which there are 100+ books covering various aspects of Computer Science. One of his books was translated to Japanese and few other articles were translated to Russian and Chinese. About 1000+ publications are indexed by Scopus and over 800 are indexed by Thomson ISI Web of Science. Some of the articles are available in the ScienceDirect Top 25 hottest articles. He has 700+ co-authors originating from 40+ countries. Dr. Abraham has more than 36,000+ academic citations (h-index of 89 as per google scholar). He has given more than 100 plenary lectures and conference tutorials (in 20+ countries). For his research, he has won seven best paper awards at prestigious International conferences held in Belgium, Canada Bahrain, Czech Republic, China and India.

Since 2008, Dr. Abraham is the Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (which has over 200+ members) and served as a Distinguished Lecturer of IEEE Computer Society representing Europe (2011-2013). Currently Dr. Abraham is the editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves/served the editorial board of over 15 International Journals indexed by Thomson ISI. He is actively involved in the organization of several academic conferences, and some of them are now annual events. Dr. Abraham received Ph.D. degree in Computer Science from Monash University, Melbourne, Australia (2001) and a Master of Science Degree from Nanyang Technological University, Singapore (1998). More information at: http://www.softcomputing.net/

We are blessed with the sophisticated technological artifacts that are enriching our daily lives and the society. It is believed that the future Internet is going to provide us the framework to integrate, control or operate virtually any device, appliance, monitoring systems, infrastructures etc. Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies, which also includes a close integration of cyber-physical systems, the
Internet of things and cloud computing. In this talk, the concept of Industry 4.0 and Society 5.0 will be presented and then various research challenges
from several application perspectives will be illustrated. Some real world applications involving the analysis of complex data / applications would be the key focus.



Secure Object Detection on the Edge

Ling Liu
Georgia Institute of Technology
United States

Brief Bio
Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale big data-powered artificial intelligence (AI) systems, and machine learning (ML) algorithms and analytics, including performance, availability, privacy, security and trust. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals, including the editor in chief of IEEE Transactions on Service Computing (2013-2016) and currently, the editor in chief of ACM Transactions on Internet Computing (TOIT). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems and applications, Cloud Computing, Services Computing, Privacy, Security and Trust. Her current research is primarily supported by USA National Science Foundation under CISE programs and IBM.


Deep neural networks (DNNs) have fueled the wide deployment of object detection models in a number of mission-critical domains, such as traffic sign detection on autonomous vehicles, and intrusion detection on surveillance systems. Recent studies have revealed that deep object detectors can also be compromised under adversarial attacks, causing a victim detector to detect no object, fake objects, or wrong objects. However, very few studies how to guarantee the robustness of object detection against adversarial manipulations. This keynote presents an in-depth understanding of vulnerabilities of deep object detection systems by analyzing the adversarial robustness  under different DNN detector training algorithms, different attack strategies, different adverse effects and costs. Then I will describe a set of mitigation strategies and techniques for robust object detection by guaranteeing high adversarial robustness while maintaining high benign detection accuracy.