Speakers
Prof. Yan Zhang IEEE Fellow Member, Academia Europaea Member, Royal Norwegian Society of Sciences and Letters Academy Member, Norwegian Academy of Technological Sciences "Highly Cited Researcher" University of Oslo, Norway | Profile: Yan Zhang is currently a Full Professor with the Department of Informatics, University of Oslo, Norway. His research interests include next-generation wireless networks leading to 6G, green and secure cyber-physical systems. Dr. Zhang is an Editor for several IEEE transactions/magazine. Since 2018, Prof. Zhang has been listed as a Highly Cited Researcher by Clarivate Analytics (i.e., Web of Science). He is Fellow of IEEE, Fellow of IET, elected member of Academia Europaea (MAE), elected member of the Royal Norwegian Society of Sciences and Letters (DKNVS), and elected member of Norwegian Academy of Technological Sciences (NTVA). Speech Title: Efficiency and Privacy in Distributed Federated Learning Abstract: In this talk, we mainly introduce our recent major contributions in the field of distributed federated learning. We propose new solutions to address the computation, communications, and energy efficiency problems in federated learning. We exploit Blockchain to address the parameter/model privacy preservation in federated learning, while “Blockchain + federated learning” is currently a very active research field. We also present energy-efficient scheme when federated learning as distributed computing tasks in computing power networks. Several open issues have been pointed out as well in the related field. |
Prof. Qun Jin Foreign fellow of the Engineering Academy of Japan (EAJ) IEEE Senior Member Waseda University, Japan | Profile: Qun Jin is a professor in the Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. He has been extensively engaged in research works in the fields of computer science, information systems, and human informatics, with a focus on understanding and supporting humans through convergent research. His recent research interests cover behavior and cognitive informatics, artificial intelligence and machine learning, big data, personal analytics and individual modeling, trustworthy platforms for data federation, sharing, and utilization, cyber-physical-social systems, and applications in healthcare and learning support and for the realization of a carbon-neutral society. He authored or co-authored several monographs and more than 400 refereed papers published in academic journals and international conference proceedings. He served as a general chair, program chair, and keynote speaker for numerous IEEE/ACM sponsored international conferences. He served as a guest editor in recent years for IEEE Transactions on Industrial Informatics, IEEE Transactions on Computational Social Systems, IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Transactions on Emerging Topics in Computing, IEEE MultiMedia, and IEEE Cloud Computing. He is a foreign fellow of the Engineering Academy of Japan (EAJ). More information can be found athttps://researchmap.jp/jinqun/?lang=en. Speech Title: Understanding and Supporting Humans through Convergent Research and Technology Convergence Abstract: The grand challenges of today, such as protecting human health, cannot be solved by one discipline alone. It becomes essential and effective to make comprehensive use of the convergence knowledge, the merging of technologies, approaches, and insights from widely diverse fields through convergent research, to accurately respond to various societal issues. In this talk, after introducing the promising paradigm of convergent research and the concept of convergence knowledge and technology convergence, we will depict our vision on technology for the common good and computing for human well-being. We will further present our recent work on understanding and supporting humans through convergent research and technology convergence of AI, big data and IoT, and discuss important issues on privacy-preserving data analytics and solutions to promote human well-being. |
Prof. Zhaolong Ning IEEE Senior Member Chongqing University of Posts and Telecommunications, China | Profile: Dr. Ning received the MS and PhD degrees from Northeastern University, China. From 2013 to 2014, he was a research assist at the Kyushu University, Japan. From 2014 to 2020, he was an assistant and associate professor at the Dalian University of Technology, China. From 2019 to 2021, he was a Hong Kong Scholar at The University of Hong Kong. Currently, he is a full professor with the Chongqing University of Posts and Telecommunications, China. He has published more than 150 scientific papers in international journals and conferences, such as TMC, JSAC, Mobicom, and so on. Two journal papers win the best paper award of IEEE TVT and IEEE Systems Journals, and 6 conference papers win the best paper award. His research interests include Internet of things, vehicular edge computing, and artificial intelligence. He is a Highly Cited Researcher (Clarivate) and a Chinese Highly Cited Researcher (Elsevier) since 2020. He is the associate editor or lead guest editor of 8 journals. He is an IET Fellow, an EAI Fellow, and a Senior Member of IEEE. Speech Title: User Association and Trajectory Optimization for IRS-Assisted UAV Communications Abstract: Due to the flexibility, low cost, and easy deployment characteristics of UAVs, they are widely utilized in wireless communication networks and can provide temporary communication services in areas with weak or congested network coverage. However, due to the complexity of the communication environment, there might be obstructions between UAVs and users. Intelligent Reflecting Surfaces (IRS), as one of the significant new technologies in future 6G, play a role in constructing virtual Line-of-Sight (LoS) paths, bringing a new network paradigm to future communications aimed at creating an intelligently controllable wireless communication environment. To fully leverage IRS resources in IRS-assisted UAV communication networks, this talk explores the application of multiple IRS-assisted UAV communication networks in suburban and urban scenarios. |
Prof. Chenglizhao Chen China University of Petroleum (East China) | Profile: Chenglizhao Chen received his Ph.D. degree from the State Key Laboratory of Virtual Reality and Systems in Beihang University (2017). Before that, he was also a joint Ph.D. candidate at Stony Brook University (2015-2016). After graduation, he joined the Qingdao University, as an Assistant Professor (2017-2019), an Associate Professor, tenure-track Professor, and the Vice Director of Computer Vision Laboratory (2019-2021). In Nov. 2021, he joined the College of Computer Science and Technology, China University of Petroleum (East China) as a Professor. He has published more than 50 papers in international journals and conferences, including reputable international journals such as TIP, TVCG, TMM, TCSVT, TOMM, TGRS, PR, INS, KBS, NC and top level international conferences like CVPR, AAAI, MM, IJCAI, and ISMAR. Speech Title: Weakly-supervised 360-degree Video Navigation Abstract: Different to the conventional 2D videos, the 360-degree videos enable the users to explore video content in the way with free camera angles, making its down-stream tasks to face a relatively large problem domain. As one of the most representative down-stream tasks, the primary objective of the 360-degree video navigation is to automatically formulate the camera-views frame-by-frame. By taking the full use of these camera-views, the users would be able to dynamically control the immersing interactions, suppressing the visual-physical incongruity, thus achieving better user experiences. Compared with the 2D videos, the currently available 360-degree videos are far less in number; even worse, the human annotated navigations are clearly more difficult to obtain. As a result, after entering the deep learning era, the fully-supervised training scheme, which has been widely-used in the 2D field, is clearly not suitable for the 360-degree video navigation. Following the key rationale of the weakly-supervised learning, this speech will introduce a series of research activities to conquer the common technical challenges of applying the deep learning technologies over the 360-degree video navigation task. |
Assoc. Prof. Por Lip Yee University of Malaya, Malaysia | Profile: Lip Yee received his Ph.D. from the University of Malaya, Malaysia under the supervision of Prof. Abdullah bin Gani in 2012. Currently, he is an Assoc. Professor at the Department of System and Computer Technology, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia. He is also a senior member of IEEE. Lip Yee and his team were the first few pioneers who received IRPA, E-Science, FRGS, ERGS, PRGS, HIR and IIRG grants. He was the first person who managed to secure 2 E-Science funds with the role of PI in 2008. He was also the first person at the FCSIT who managed to secure the PRGS and ERGS grants. Besides collaborators from Malaysia, Lip Yee also has international collaborators from France, UK New Zealand, Turkey, Thailand and China. He also established his connections with his national and international collaborators with some industrial partners in Malaysia and other countries. Speech Title: An Advanced Algorithm for Graphical Authentication: Enhancing Cognitive Usability and Mitigating Shoulder-Surfing Attacks Abstract: This research explores innovative strategies in graphical authentication to combat vulnerabilities from shoulder-surfing attacks. In the contemporary data security landscape, developing robust yet user-friendly authentication mechanisms is essential. Traditional alphanumeric passwords are susceptible to brute-force attacks and can be difficult to manage due to their complexity. Graphical passwords present a compelling alternative by leveraging human cognitive abilities to recognize and recall images, patterns, and symbols. Our study assesses the effectiveness of graphical passwords in preventing shoulder-surfing threats, where malicious actors steal login credentials by observing legitimate users, posing significant privacy and security risks. We introduce advanced algorithms that balance security and usability effectively. Our empirical findings demonstrate that the proposed approach not only enhances security but also reduces login times compared to traditional methods, underscoring the efficiency and practical applicability of user authentication. In summary, this research introduces advanced innovations in graphical authentication to address shoulder-surfing challenges, streamline the user experience, and prioritize accessibility and inclusivity in modern digital settings. |
