Keynote Speaker 1: Prof. Witold Pedrycz
Title: New Pursuits of Machine Learning Through Data and Knowledge Environment
Abstract: Over the recent years, we have been witnessing truly remarkable progress in Machine Learning (ML) with highly visible accomplishments encountered, in particular, in natural language processing and computer vision impacting numerous areas of human endeavours. Driven inherently by the technologically advanced learning and architectural developments, ML constructs are highly impactful coming with far reaching consequences; just to mention autonomous vehicles, control, health care imaging, decision-making in critical areas, among others. Data are central and of paramount relevance to the design methodology and algorithms of ML. While they are behind successes of ML, there are also far-reaching challenges that require urgent attention especially with the growing importance of requirements of interpretability, transparency, credibility, stability, and explainability. As a new direction, data-knowledge ML concerns a prudent and orchestrated involvement of data and domain knowledge used holistically to realize learning mechanisms and support the formation of the models.
The objective of this talk is to identify the challenges and develop a unique and comprehensive setting of data-knowledge environment in the realization of the development of ML models. We review some existing directions including concepts arising under the name of physics informed ML. We investigate the representative topologies of ML models identifying data and knowledge functional modules and interactions among them. We also elaborate on the central role of information granularity in this area.
Bio: Witold Pedrycz (IEEE Life Fellow) is Professor 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, and Machine Learning, among others.
Professor Pedrycz serves as 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).
Keynote Speaker 2: Prof. Lihua Xie
Title: Learning-based Control and Planning for Unmanned Systems
Abstract: Reinforcement learning where the agent learns a policy to optimize a pre-defined reward by interacting with the environment has undergone rapid development and found applications in many areas such as robot navigation and manipulation. In this talk, we focus on inverse reinforcement learning (IRL) where agent learns an appropriate cost function, leading to desired behaviours, based on demonstration data. We introduce a differential dynamic programming (DDP)-based framework for IRL with both open-loop and closed-loop costs and demonstrate that the closed-loop method is better than the open-loop one. We further discuss a learning-based dynamic weight adjustment scheme for robots operating in human-dense environments. The applications of the learning-based framework in UAVs and ground robots will be demonstrated.
Bio: Prof. Xie obtained PhD degree from the University of Newcastle, Australia, in 1992. He is currently a professor with the School of Electrical and Electronic Engineering, Nanyang Technological University and Director, Center for Advanced Robotics Technology Innovation (CARTIN). He has served as Head of Control and Instrumentation Division (2011-2014) and Director of Delta-NTU Corporate Laboratory for Cyber-Physical Systems (2016-2021). His research areas include robust control, multi-agent systems, unmanned systems and AI. He has authored and co-authored 10 monographs and over 500 journal articles, and was listed as a highly cited researcher. He is currently an Editor-in-Chief of Unmanned Systems and has served as an Editor of IET Book Series on Control and Associate Editor of IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Control Systems Technology, IEEE Transactions on Control of Network Systems, etc. He was an IEEE Distinguished Lecturer (2011-2014), a member of Board of Governors of IEEE Control System Society (2016-2018) and the General Chair of the 62nd IEEE Conference on Decision and Control. He is currently Vice-President of IEEE Control System Society. Professor Xie is Fellow of Academy of Engineering Singapore and Fellow of IEEE, IFAC, CAA and AAIA.
Keynote Speaker 3: Prof. Qingfu Zhang
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Bio: Prof. Zhang (IEEE Fellow) received the B.Sc. degree in mathematics from Shanxi University, Taiyuan, China, in 1984, and the M.Sc. degree in applied mathematics and the Ph.D. degree in information engineering from Xidian University, Xi’an, China, in 1991 and 1994, respectively. He is a Chair Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong. His main research interests include evolutionary computation, optimization, neural network, data analysis, and their applications. Prof. Zhang has been a Highly Cited Researcher in Computer Science, since 2016. He is an Associate Editor of the IEEE TRANSACTIONS ON CYBERNETICS. He is also an editorial board member of three other international journals.
Keynote Speaker 4: Prof. Changchun Hua
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Bio: Prof. Hua (IEEE Fellow) received the Ph.D. degree in electrical engineering from Yanshan University, Qinhuangdao, China, in 2005. From 2006 to 2007, he was a Research Fellow with the National University of Singapore, Singapore. From 2007 to 2009, he was with Carleton University, Ottawa, ON, Canada, funded by the Province of Ontario Ministry of Research and Innovation Program. From 2009 to 2010, he was with the University of Duisburg-Essen, Essen, Germany, funded by Alexander von Humboldt Foundation. He was involved in more than 10 projects supported by the National Natural Science Foundation of China, National Education Committee Foundation of China, and other important foundations. He has authored or coauthored more than 80 papers in mathematical, technical journals, and conferences. His research interests include nonlinear control systems, control systems design over network, teleoperation systems, and intelligent control.
Keynote Speaker 5: Prof. Peter Sinčák
Title: Digital Intelligence – Integration of Intelligent System of the Future
Abstract: In an era where technology rapidly evolves, Digital Intelligence stands at the forefront, heralding a new age of intelligent systems. This talk delves into the intricate world of integrating Artificial Intelligence (AI) and Deep Learning, laying the groundwork for systems that not only compute but also comprehend and communicate. At the core of this integration is the use of Fuzzy Logic and Fuzzy Inference Systems, which imbue machines with the ability to make decisions amidst ambiguity, closely mirroring human judgment.
Moreover, we will explore the role of Evolutionary Computing as a pivotal search-based technique that draws inspiration from the natural selection process, enabling the optimization of solutions in complex, dynamic environments. Alongside these, we will discuss various mathematical and structural approaches that contribute to the robustness of intelligent systems. Venturing into the realm of semantic AI, we spotlight the emergence of Capsule Neural Networks and Graph-Based Neural Networks. These innovative architectures are not just advancing data processing capabilities but are pivotal in the development of explanation engines. Such engines, powered by Natural Language Processing (NLP) and generative conversational models, promise to revolutionize how intelligent systems communicate their reasoning, making their decisions more transparent and understandable to users.
This presentation aims to unfold the potential of Digital Intelligence as a key integrator for the intelligent systems of the future, showcasing how the fusion of these diverse technologies paves the way for smarter, more intuitive, and interactively explainable AI.
Bio: Professor Peter Sinčák has contributed to the field of Artificial Intelligence (AI) at the Technical University of Košice, Slovakia. With a PhD from the Academy of Sciences in Prague, Sinčák has advanced through academic ranks to become a leading figure in AI education and research. He has been integral in developing AI-focused academic programs and has guided 18 PhD students to successful thesis defenses. His role in facilitating international academic exchanges and organizing significant events, like the European Summer School for machine learning, highlights his dedication to the global AI community. Sinčák's work extends beyond academia into industry collaborations, reflected in his tenure with the Slovak AI Society and Košice IT Valley. His research, focusing on the integration of Cloud Computing, AI, and Robotics, emphasizes practical applications of "DIGITAL INTELLIGENCE" in creating intelligent systems. He is also known for his contributions to international projects and conferences, showcasing his commitment to fostering knowledge exchange and innovation in AI.
As he moves towards an emeritus period, Sinčák's legacy in AI education, research, and industry international collaboration continues to inspire future generations in the field.
Keynote Speaker 6: Prof. Angelo Cangelosi
Title: Developmental Robotics for Language Learning, Trust and Theory of Mind
Abstract: Growing theoretical and experimental research on action and language processing and on number learning and gestures clearly demonstrates the role of embodiment in cognition and language processing. In psychology and neuroscience, this evidence constitutes the basis of embodied cognition, also known as grounded cognition (Pezzulo et al. 2012). In robotics and AI, these studies have important implications for the design of linguistic capabilities in cognitive agents and robots for human-robot collaboration, and have led to the new interdisciplinary approach of Developmental Robotics, as part of the wider Cognitive Robotics field (Cangelosi & Schlesinger 2015; Cangelosi & Asada 2022). During the talk we will present examples of developmental robotics models and experimental results from iCub experiments on the embodiment biases in early word acquisition and grammar learning (Morse et al. 2015; Morse & Cangelosi 2017) and experiments on pointing gestures and finger counting for number learning (De La Cruz et al. 2014). We will then present a novel developmental robotics model, and experiments, on Theory of Mind and its use for autonomous trust behavior in robots (Vinanzi et al. 2019, 2021). The implications for the use of such embodied approaches for embodied cognition in AI and cognitive sciences, and for robot companion applications will also be discussed.
Bio: Angelo Cangelosi is Professor of Machine Learning and Robotics at the University of Manchester (UK) and co-director and founder of the Manchester Centre for Robotics and AI. He was selected for the award of the European Research Council (ERC) Advanced grant (funded by UKRI). His research interests are in cognitive and developmental robotics, neural networks, language grounding, human robot-interaction and trust, and robot companions for health and social care. Overall, he has secured over £38m of research grants as coordinator/PI, including the ERC Advanced eTALK, the UKRI TAS Trust Node and CRADLE Prosperity, the US AFRL project THRIVE++, and numerous Horizon and MSCAs grants. Cangelosi has produced more than 300 scientific publications. He is Editor-in-Chief of the journals Interaction Studies and IET Cognitive Computation and Systems, and in 2015 was Editor-in-Chief of IEEE Transactions on Autonomous Development. He has chaired numerous international conferences, including ICANN2022 Bristol, and ICDL2021 Beijing. His book “Developmental Robotics: From Babies to Robots” (MIT Press) was published in January 2015, and translated in Chinese and Japanese. His latest book “Cognitive Robotics” (MIT Press), coedited with Minoru Asada, was recently published in 2022.
Keynote Speaker 7: Prof. Jian Sun
Title: Data-driven control of networked systems
Abstract: With the development of information technologies, control systems are becoming more intelligent and interconnected. Accurate modeling of a control system has become increasingly difficult. For systems that are difficult to accurately model, traditional model-based control theories and methods are difficult to achieve ideal control performance. Data-driven control refers to the control method of designing controllers based solely on the offline/online data when the mathematical model and parameters of the control system are unknown. Data-driven control methods are independence of precise models and have broad applications. This talk will introduce the recent progress of data-driven control methods for networked systems, including data-driven event-triggered control and self-triggered control, data-driven resilient control under DoS attacks, data-driven self-triggered control based on trajectory prediction, data-driven robust LQG control, and data-driven output regulation of networked systems.
Bio: Prof. Sun received the Bachelor’s degree from the Department of Automation and Electric Engineering at Jilin Institute of Technology, Changchun, China, in 2001, the Master’s degree from Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CAS), Changchun, China, in 2004, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, in 2007. He was a research fellow at Faculty of Advanced Technology, University of Glamorgan, UK, from April 2008 to October 2009. He was a postdoctoral research fellow at Beijing Institute of Technology, Beijing, China, from December 2007 to May 2010. In May 2010, he joined the School of Automation, Beijing Institute of Technology, where he has been a professor since 2013. His current research interests include autonomous unmanned systems, networked control systems, and data-driven control.
He is a member of the Editorial Boards of several journals including IEEE Transactions on Systems, Man & Cybernetics: System, Science China Information Sciences, Journal of Systems Science & Complexity and ACTA AUTOMATICA SINICA.
Keynote Speaker 8: Prof. Zhengguang Wu
Title: Cyber-Attack Design and Detection of Cyber-Physical Power Systems
Abstract: Compared to the traditional power grid, the Cyber-Physical Power System (CPPS) has been shown to be of significant benefits in terms of enhancing operational efficiency and monitoring operation states. Particularly, with the development of Internet of Things and 5G technology, CPPS deeply integrates both cyber and physical systems with intelligent sensing devices. While these characteristics provide further ideas for realizing a smart grid, they also bring new challenges to operational safety and stability. Malicious cyber-attacks by adversaries can directly impact control center’s judgment and decision-making on CPPS. Therefore, the research on cyber-attack strategies and defense methods for CPPS is crucial in both theoretical and engineering fields, becoming one of the hottest topics in CPPS today. The analysis of threats in CPPS not only requires full consideration of their attack capabilities as well as data analysis and information mining abilities from attackers’ perspectives, but also necessitates designing detection methods that enhance CPPS security from defenders’ perspectives. Thus, this report discusses CPPS cyber security from two aspects: cyber-attack strategy and detection method.
Bio: Prof. Wu (Member, IEEE) received the B.S. and M.S. degrees in mathematics from Zhejiang Normal University, Jinhua, China, in 2004 and 2007, respectively, and the Ph.D. degree in control science and engineering from Zhejiang University, China, in 2011. He has authored or coauthored more than 200 articles in IEEE Trans. Journals and Automatica. His current research interests include multi-agent systems, Markov jump systems, smart grids, and cyber-physical systems. He serves as an Associate Editor/an Editorial Board Member for some international journals including IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS. He was named as a Highly Cited Researcher (Clarivate Analytics).
Keynote Speaker 9: Prof. Yixin Zhong
Title: Paradigm Revolution in Artificial Intelligence
Abstract: A new theory, named as “Paradigm Revolution in AI”, is presented in this keynote, which may give significant implication to the direction of AI research. As is well known, AI is a kind of complex information system and should follow the paradigm for information discipline (PID). According to the theory presented, however, the paradigm followed in AI research in practice is not PID, but the paradigm for physical discipline (PPD). As results, AI research should change its paradigm from PPD to PID. This is called Paradigm Revolution in AI. The consequence of the paradigm changed in AI research, a general theory of AI, much superior than the current AI theories, is achieved.
Bio: Professor Zhong from AI School, Beijing University of Posts and Telecommunications (BUPT), Beijing, China. He was president of Chinese Association for Artificial Intelligence (CAAI) from 2001 to 2010. He is now Fellow of Academy of Engineering and Technology for Developing World and Fellow of International Academy for Information Study. His most recent achievements in AI include the manuscript “General Theory of Intelligence” published in 2023 by Science Press, China and “Mechanism-based AI Theory” published in 2021 by BUPT Press, China.
Keynote Speaker 10: Prof. Masaaki Ida
Title: Data Science and Open Data for Social Science
Abstract: In recent years, data science has rapidly spread to a vast range of practical social science fields related to economics, finance, business, and education. When performing data analysis in such fields, a major issue is that it takes a large amount of time and effort to collect data and maintain databases. In this talk, we will review publicly available government data, finance data, and education data of higher education institutions such as universities as typical social science databases. Then, we will examine procedures for practical data analysis using these databases, how to use Web API, programming and visualization method. In addition, in order to develop human resource for data analysis in social science field, we will examine the skills required for data scientists and consider the training methods for data scientists using various open databases.
Bio: Prof. Masaaki Ida received the B.E., M.E, and Ph.D. degrees, all in engineering, from Kyoto University, Kyoto, Japan. He is currently a professor of National Institution for Academic Degrees and Quality Enhancement of Higher Education, Japan. He has been a board member of Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) four times as editor-in-chief, academic award committee chair, archives committee chair, and now serving as the vice president of SOFT. His current research interests are in computer science, especially in advanced database and communication technology in higher education. He has been responsible for works related to university evaluation and quality enhancement of higher education, as well as the related system development of numerous practical information systems.
Keynote Speaker 11: Prof. Yanjun Huang
Title: A self-evolution method for autonomous driving systems
Abstract: A series of accidents indicate that mass-production cars equipped with autonomous driving systems still cannot operate safely in real-world environments, especially when faced with unknown scenarios. Therefore, ensuring safer driving with autonomous systems has become a challenge. Humans possess a general self-evolution capability, allowing them to gradually learn how to drive through limited scenarios like driving tests, eventually adapting to the infinite complexity of the real world. Over time, they group up from novices to experienced drivers. Similarly, if designed autonomous vehicles are endowed with similar capabilities, they could adapt to the infinite and unprecedented scenarios encountered in the real world, significantly reducing accidents. Therefore, this presentation proposes a self-evolution algorithm for autonomous driving to enable the progress of autonomous driving systems from "novices" to "experts."
Bio: Dr. Yanjun Huang is a professor, recognized as a national-level young talent, a talent team leader of MoE. He has led major national projects such as the KSFC Key Projects and Key R&D Programs. In the past three years, he has published 9 ESI Highly Cited Papers and 1 Hot Paper. He has received several Best Paper Awards, including IEEE TVT’s 2019 Best Paper, IEEE TIV’s 2024 Best Paper, etc.
He has been invited to serve as an AE for several top journals such as IEEE TITS and Proc IMechE, Part D. Additionally, he was invited to chair the first "Artificial Intelligence and Autonomous Driving" Global Youth Forum organized by the International Federation of Automotive Engineering Societies (FISITA). He has also led his team to win first place in the China Intelligent and Connected Vehicle Algorithm Competition and has guided students to achieve numerous national and provincial awards in innovation competitions.
Keynote Speaker 12: Prof. Wenkai Hu
Title: Advanced Data Analytics for Alarm Monitoring in Complex Industrial Facilities
Abstract: This presentation will introduce advanced data techniques, such as few-shot learning, pattern mining, and causality inference, for alarm monitoring in complex industrial facilities. Due to the intricate interactions and interdependencies within the system, anomalies can easily emerge and spread, triggering widespread effects and cause a series of alarms. This situation does not only escalate the workload for operators, but also has the potential to result in adverse outcomes. Consequently, it is crucial to advance technologies aimed at minimizing nuisance alarms, curbing alarm floods, and pinpointing the root causes of alarm events. The insights gained from these efforts would bolster decision-making for operators, enabling them to take prompt corrective measures and prevent further deterioration of the situation. Specifically, this talk will introduce how to mine interesting frequent alarm patterns by pattern mining, discover the abnormality propagation paths through causality inference, and identify the root causes of alarms using deep learning.
Bio: Dr. Wenkai Hu received the B.Eng. and M.Sc. degrees in Power and Mechanical Engineering from Wuhan University, Wuhan, Hubei, China, in 2010 and 2012, respectively, and the Ph.D. degree in Electrical and Computer Engineering from the University of Alberta in 2016. He was a Post-Doctoral Fellow from October 2016 to September 2018, and a Research Associate from November 2018 to February 2019 at the University of Alberta. He is currently a Professor with the School of Automation and the School of Future Technology in China University of Geosciences, Wuhan, China. His research interests include advanced alarm monitoring, data mining, causality inference, and process control for complex industrial processes. He has received several honors, including the Hubei Outstanding Young Scholar, Chutian Young Elite, and CUG Outstanding Young Talent. In addition, he has also earned numerous academic and teaching awards, including the 30th CPCC Zhang Zhongjun Excellent Paper Award, the IEEE-ICPS 2023 Best Paper Prize, the NCAA 2023 Best Paper Award, the First Award of CUG Young Faculties Teaching Competition in 2022, the ISCIIA 2022 Best Presentation Award, and the UofA Postdoctoral Travel Award. Furthermore, he has guided graduate students to win the Bronze Prize of PCIC 2021 Huawei Causal Inference Competition and the CPCC 2023 Student Best Paper Nominee Award.
Keynote Speaker 13: Prof. Yuki Nakagawa
Title: Robot Applications in Food Factories
Abstract: In the rapidly evolving landscape of food manufacturing, the integration of robotics and AI is becoming increasingly vital to enhance efficiency, ensure consistency, and maintain high standards of hygiene. This lecture will explore the current and future applications of robotics in food manufacturing plants, focusing on the roles robots play in automating repetitive and labor-intensive tasks, improving product quality, and addressing challenges such as labor shortages and rising operational costs. We will examine case studies showcasing successful implementations of robotic systems in various stages of food production, from raw material handling and processing to packaging. Additionally, the discussion will highlight the technological advancements that are driving innovation in this field, including the use of AI and machine learning to enable smarter, more adaptable robotic solutions. We can discuss how robotics can be leveraged to optimize operations in food manufacturing plants, reduce human error, and create a more sustainable and efficient production environment.
Bio: In 2005, she started RT Corporation, a company that develops, sells, and trains university students and professional engineers about service robots, and became its representative director. RT is also a Jetson Education Partner of NVIDIA. Concerning Google ADK production and sales, RT became the first Japanese company to be introduced at the Google I/O in 2011. In 2015, she was selected as one of the "25 Women in the World You Should Know in the Robotics Industry" by ROBOHUB in Silicon Valley, USA. In 2017, the company received investment from the Mirai Sousei Fund as a lead investor to develop Foodly, a humanoid collaborative robot for food factories. It is also attracting attention as a rapidly growing venture. She has published many papers and books, and writes a column for robotics or food magazines. She is also the executive director of the New Technology Foundation, which organizes Micromouse, a maze analysis robot contest, and ROS society as the director of Open Robotics in the USA and a board member of ROSConJP, a general incorporated association in Japan. In 2020, she received the Strait of Magellan Award. In 2022, she was certified as a fellow of robot education and food robotics from the Robotics Society of Japan.
