Speakers

Biography：Min Chen is a professor and a doctoral supervisor at the School of Computer Science and Engineering, South China University of Technology; he is an IEEE Fellow (Fellow of the International Institute of Electrical and Electronic Engineers), IET Fellow (Fellow of the Institution of Engineering and Technology, UK), with more than 39,500 citations from Google Scholar, H-index=94, and has been awarded the 2018, 2019, 2020, 2021, and 2022 Coreview Scholarships. He graduated from South China University of Technology (SCUT) at the age of 23 with a Ph.D. in Electronics and Communication Engineering, and worked as a postdoctoral fellow at Seoul National University in South Korea and the University of British Columbia in Canada; he taught at Seoul National University in 2009; he returned to China in 2009 as a high-level talent from overseas and founded the Embedded and Pervasive Computing Laboratory of Huazhong University of Science and Technology (HUST); and he is currently a professor and a Ph.D. director of the School of Computer Science at HUST. He has published more than 200 papers in IEEE JSAC, IEEE TNNLS, IEEE TPDS, IEEE TWC, IEEE TSC, INFOCOM, Science, Nature Communications, and other international authoritative journals and well-known academic conferences, and has been authorized more than 20 national invention patents. He has published 12 monographs and textbooks, among which the all-American English textbook "Big Data Analytics Applications" has been adopted by 40 famous schools such as Harvard and Stanford. He has been invited to give presentations in 16 international academic conferences, and several papers won the best conference papers, and was awarded the Fred W. Ellersick Prize of the IEEE Communications Society (2017), the Jack Neubauer Memorial Award of the IEEE Society for In-vehicle Technology (2019), and the Best Paper Award of the IEEE ComSoc Asia-Pacific Region (2022). Award (2022).

Title: Meta Fiberverse: Embodied Intelligence Evolution over Digital Intelligent Space Built on Meta Fabric

Abstract: Recently, advancements in large language models have provided powerful reasoning capabilities for embodied agents to interact dynamically with the environment. This offers hope for achieving robot-centric embodied intelligence and may pave the way for the realization of Artificial General Intelligence (AGI) in the future. This report proposes a fabric computing-based fabric metaverse. Due to the unique advantages of fabric-state perception in facilitating long-term, comfortable, and unobtrusive coexistence with humans, the intelligent digital space constructed from meta-fabrics could autonomously generate supportive tasks based on the understanding of user needs. Additionally, it can correct its value biases by capturing subtle behaviors in the daily lives of users . Finally, this report explores the potential of embodied agents in the fabric metaverse for evolving to open-space intelligence.

Biography：Ferrante Neri earned his Laurea and Ph.D. degrees in Electrical Engineering from the Politecnico di Bari, Italy, in 2002 and 2007, respectively. He further pursued advanced studies, obtaining a second Ph.D. in Scientific Computing and Optimization and a D.Sc. in Computational Intelligence from the University of Jyväskylä, Finland, in 2007 and 2010, respectively. From 2009 to 2014, he was an Academy Research Fellow with the Academy of Finland, leading a pioneering project on Algorithmic Design Issues in Memetic Computing. He subsequently held academic positions at De Montfort University, Leicester, UK (2012–2019), and the University of Nottingham, UK (2019–2022). Since 2022, Professor Neri has been a Full Professor of Machine Learning and Artificial Intelligence at the University of Surrey, Guildford. Formerly the Head of the Nature-Inspired Computing and Engineering (NICE) Research Group, he currently serves as the Associate Dean (International) for the Faculty of Engineering and Physical Sciences. Additionally, he is a Jiangsu Distinguished Professor at Nanjing University of Information Science and Technology. Professor Neri’s research centres on metaheuristic optimisation and its transformative applications in machine learning, reflecting his commitment to advancing innovation in computational intelligence.

Title: Optimisation at the Heart of Machine Learning: From Features to Architectures

Abstract: Machine Learning (ML), a subset of Artificial Intelligence (AI), focuses on constructing data-driven parametric predictive models. This presentation underscores the pivotal role of optimisation in ML, spanning multiple stages of the modelling process. It delves into three fundamental tasks: (1) selecting model variables through feature selection; (2) optimising model parameters via training; and (3) designing model architectures, with an emphasis on neural architecture search. These tasks collectively represent key aspects of neural model design, all aimed at maximising predictive accuracy. The talk will showcase recent research tackling these challenges, providing insights into cutting-edge optimisation techniques and their application within ML.

Biography：Professor Hongying Meng is associated with the Department of Electronic and Electrical Engineering at Brunel University London. Prior to this role, he held various research positions at several universities in the UK, including University College London (UCL), University of York, University of Southampton, University of Lincoln, and University of Dundee. He received his Ph.D. in Communication and Electronic Systems from Xi’an Jiaotong University and served as a lecturer in the Electronic Engineering Department at Tsinghua University in Beijing, China.

Professor Meng's research areas encompass biomedical engineering, computer vision, affective computing, artificial intelligence, neuromorphic computing, and the Internet of Things. His work is supported by various funding bodies, including the Engineering and Physical Sciences Research Council (EPSRC), EU Horizon 2020, the Royal Academy of Engineering, and the Royal Society. He has authored over 200 academic papers that have garnered more than 6,800 citations, with a Google Scholar h-index of 39.

Notably, he has developed two distinct emotion recognition systems that won international challenge competitions AVEC 2011 and AVEC 2013. Professor Meng is a Senior Member of the IEEE and serves as an associate editor for the IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) and the IEEE Transactions on Cognitive and Developmental Systems (TCDS). Additionally, he is the Associate Editor-in-Chief for Digital Twins and Applications by IET. In 2022, he was recognized as one of the AI 2000 Most Influential Scholars by Aminer.

Title: Generative AI and Its Applications

Abstract: In this talk,  the concept of generative AI will be briefly introduced. Then, some popular models will be discussed. After that, some related research work we have done will be reported especially. Specifically, some theoretical work on the models and its applications in signal generations such as image, sound, EEG, ECG, as well as multiple signal generation will be reported in details. Finally, its other possible applications will be discussed.

Quanbo Ge

Nanjing University of Information Science and Technology, China

Biography：Quanbo Ge received the bachelor's and master’s degrees from the College of Computer and Information Engineering, Henan University, Kaifeng, China, in 2002 and 2005, respectively, and the Ph.D. degree from Shanghai Maritime University, Shanghai, China, in 2008.

He was a Professor with the Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou, China. From 2008 to 2010, he was a Lecturer with the School of Automation, Hangzhou Dianzi University, where became an Associate Professor, in 2010. From 2009 to 2013, he was a Postdoctoral Fellow with the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China. From 2012 to 2013, he was a Visiting Scholar with the Optimization for Signal Processing and Communication Group, Department of Electrical and Computer Engineering, Twin Cities Campus, University of Minnesota, Minneapolis, MN, USA. He is currently a Professor with the School of Automation, Nanjing University of Information Science and Technology, Nanjing, China. He is also Associate Editors of IEEE TSMC: Systems and International Journal of Systems Science. His research interests include information fusion, autonomous unmanned system, and intelligent power system etc.

Title: Target Tracking for Unmanned Systems in Complex Environments Based on Multi-Machine Learning Fusion

Abstract: Accurate tracking and localization of unmanned systems in complex environments are critical for enhancing autonomous navigation, target recognition, and situational awareness. However, existing studies still face challenges such as low light conditions, target occlusion, scale variations, and high noise, which lead to reduced accuracy in target recognition, motion estimation, and tracking, making precise localization difficult to achieve. To address these issues, we conducted research on several innovative approaches, including the Relative Pose Estimation of UAVs Based on Weighted Fusion of Multi-Keypoint Detection, A Strong UAV Vision Tracker Based On Deep Broad Learning System and Correlation Filter, Pose Measurement of USVs in Low-Light Environments Based on Customized Ellipse Detection, Multi-attention and Adaptive Reparameterized Feature Pyramid Network for Small Target Detection on Water Surfaces, and the Research on the USV-VIO Method Based on Global Information Optimization. Simulation experiments validated the effectiveness of these methods in complex environments, significantly improving system robustness and accuracy.