Speakers

Prof. Kun Zhang

Hainan Normal University, China

Biography: Hainan Normal University's School of Information Science and Technology, Department of Cybersecurity: professor, Ph.D., doctoral supervisor, Hainan Province high-level talent "Top Talent", Hainan Province " Nanhai Masters" youth project talent, Hainan Province "515" Engineering Talent Project third level candidate, Hainan Province Science and Technology expert, Guangdong Province Science and Technology expert, Guangxi Science and Technology expert, Sanya Municipal Party Committee key expert, CCF (China Computer Federation) senior member, CCF Computer Application Professional Committee executive member, CIE (Chinese Institute of Electronics) senior member, IEEE member, ACM member; presided over more than 40 vertical and horizontal projects, including National Natural Science Foundation projects, Hainan Provincial Natural Science Foundation, Hainan Provincial Key R&D Plan projects; published over 200 papers, of which more than 30 are SCI-indexed; obtained 17 national invention patents and 2 international invention patents; serves as an editorial board member of the SCI journal Physical Communication and editor-in-chief of the international journal Distributed Processing System; won 1 first prize and 1 second prize in Hainan Provincial Science and Technology Progress Awards.   

Speech Title: Research on Information Security, Network Security, and Privacy Protection in the New Era

Abstract: The development of the world today is approaching the era of rapid development of information technology. From a positive perspective, the deep integration of the Internet and various industries has enabled China to successfully achieve rapid breakthroughs. From the opposite perspective, the problems of information security, network security, and privacy protection brought about by the prosperity and development of the Internet remain severe, and enterprise data, personal privacy, and other issues face widespread infringement. Further exploration is needed on issues such as information security, network security, and privacy protection. By strengthening control measures, optimizing basic conditions, innovating encryption technologies, and raising security awareness, information security and network security can be enhanced.

Prof. Xiangjie Kong

Zhejiang University of Technology, China

Biography: Dr. Xiangjie Kong is currently a Full Professor in the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), China. Previously, he was an Associate Professor in School of Software, Dalian University of Technology (DUT), China, where he was the Head of the Department of Cyber Engineering. He is the Founding Director of City Science of Social Computing Lab (The CSSC Lab) (http://www.cssclab.cn). He is/was on the Editorial Boards of 6 International journals. He has served as the General Co-Chair, Workshop Chair, Publicity Chair or Program Committee Member of over 30 conferences. Dr. Kong has authored/co-authored over 140 scientific papers in international journals and conferences including IEEE TKDE, ACM TKDD, IEEE TNSE, IEEE TII, IEEE TITS, IEEE NETW, IEEE COMMUN MAG, IEEE TVT, IEEE IOJ, IEEE TSMC, IEEE TETC, IEEE TASE, IEEE TCSS, WWWJ, etc.. 5 of his papers is selected as ESI- Hot Paper (Top 1‰), and 16 papers are ESI-Highly Cited Papers (Top 1%).  His research has been reported by Nature Index and other medias. He has been invited as Reviewers for numerous prestigious journals including IEEE TKDE, IEEE TMC, IEEE TNNLS, IEEE TNSE, IEEE TII, IEEE IOTJ, IEEE COMMUN MAG, IEEE NETW, IEEE TITS, TCJ, JASIST, etc.. Dr. Kong has authored/co-authored three books (in Chinese). He has contributed to the development of 14 copyrighted software systems and 20 filed patents. He has an h-index of 42 and i10-index of 104, and a total of more than 6200 citations to his work according to Google Scholar. He is named in the 2019 and 2020 world’s top 2% of Scientists List published by Stanford University. Dr. Kong received IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, and The Natural Science Fund of Zhejiang Province for Distinguished Young Scholars. He has been invited as Keynote Speaker at 2 international conferences, and delivered a number of Invited Talks at international conferences and many universities worldwide.  His research interests include big data, network science, and computational social science. He is a Distinguished Member of CCF, a Senior Member of IEEE, a Full Member of Sigma Xi, and a Member of ACM.

Speech Title: Mobile Crowdsourcing: From Urban Big Data to Smart Cities

Abstract: Leveraging new communication technologies and Internet of Things (IoT) applications, multi-sourced urban big data can be collected. Local administrations and governments aim at managing the city infrastructures and optimize the public services in an efficient and sustainable manner. Furthermore, they adopt intelligent and cost-effective mobile applications to deal with natural disasters, such as pollution and traffic congestion. Mobile crowdsourcing (MCS) is an emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with ubiquitous mobile devices to provide decentralized applications and services. Using MCS solutions, residents play the role of an active worker to generate a wealth of crowdsourced data which can significantly promote the development of smart cities. This talk highlight research challenges in computing and analyzing mobile crowdsourced data generated by large amount of participants/devices, and fusing multi-sourced and heterogeneous urban big data to facilitate applications towards smart cities.

Prof. Yan Yang

Southwest Jiaotong University, 

China

Biography: Dr. Yan Yang is currently Professor and deputy dean of Computing and Artificial Intelligence, Southwest Jiaotong University. She holds an Academic and Technical Leader of Sichuan Province. Her mainly research interests include artificial intelligence, big data analysis and mining, ensemble learning and multi-view learning, etc. She has undertaken more than 10 high-level projects and published more than 230 papers, one of which was selected as one of the 100 most influential international academic papers in China in 2021. She won the special award of Zhan Tianyou Railway Science and Technology Award, and first prize of computer science and technology of Sichuan Province. She also serves as the Vice Chair of ACM Chengdu Chapter, and Vice-Chairman General of Sichuan Computer Society.

Speech Title: Urban Spatio-Temporal Data Prediction based on Deep Learning

Abstract: Urban spatial-temporal data encapsulates a wealth of information and intrinsic value, rendering it of paramount importance in transportation perception domains. Through the analysis and exploration of urban spatial-temporal data, it can address typical urban challenges such as traffic congestion, thus providing invaluable support for the construction of smart cities. In this talk, I will introduce deep learning, multi-view learning and transfer learning to fully extract the nonlinear and dynamic spatial-temporal dependencies within data. Novel deep learning models are devised and the effectiveness of the proposed models is shown through the demands of traffic prediction.

Assoc. Prof. Anwar P.P. 

Abdul Majeed

Xi'an Jiaotong-Liverpool 

University(XJTLU), China

Biography: Dr Anwar P.P. Abdul Majeed graduated with a first-class honours B.Eng. in Mechanical Engineering from Universiti Teknologi MARA (UiTM), Malaysia. He obtained an MSc. in Nuclear Engineering from Imperial College London, United Kingdom. He then received his PhD in Rehabilitation Robotics from the Universiti Malaysia Pahang (UMP). He is currently serving as an Associate Professor at the School of Robotics, XJTLU. Prior to joining XJTLU, he was a Senior Lecturer (Assistant Professor) and the Head of Programme (Bachelor of Manufacturing Engineering Technology (Industrial Automation)) at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMP. He is also currently serving as an adjunct lecturer at UCSI University, Malaysia. Dr Anwar is also a Visiting Research Fellow at EUREKA Robotics Centre, Cardiff Metropolitan University, UK.

Dr Anwar is a Chartered Engineer, registered with the Institution of Mechanical Engineers (IMechE), UK, a Member of the Institution of Engineering and Technology (IET), UK, as well as a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). He is an active research member at the Innovative Manufacturing, Mechatronics and Sports Laboratory (iMAMS), UMP. His research interest includes rehabilitation robotics, computational mechanics, applied mechanics, sports engineering, renewable and nuclear energy, sports performance analysis as well as machine learning.

He has authored over 60 papers in different journals, conference proceedings as well as books. He serves as a reviewer in a number of prolific journals, such as IEEE Access, Frontiers in Bioengineering and Biotechnology, SN Applied Sciences, PeerJ Computer Science, and Applied Computing and Informatics, amongst others. He has also served as a Guest Editor for SN Applied Sciences, MDPI, Frontiers, as well as an Editor for several Springer book series. He is currently serving as an Academic Editor for PLOS ONE, a Review Editor for Frontiers in Robotics and AI, an Associate Editor for Frontiers in Rehabilitation Sciences and a section editor for Mekatronika (UMP Press). Dr Anwar is also a member of the Young Scientists Network of the Academy of Sciences Malaysia (YSN - ASM). With regards to learned/civil society activities, he is an active member of the IET Malaysia Local Network as well as acting as a Liaison Officer for the Imperial College Alumni Association Malaysia.

Speech Title: The Classification of Medical Imaging and Bio-Signals: A Feature-Based Transfer Learning Approach

Abstract: This keynote presentation explores the employment of feature-based transfer learning through a series of medical imaging and bio-signals case studies. It introduces the concept of transfer learning and its benefits, focusing on the extraction and reuse of learned features from pre-trained models. The presentation showcases case studies in medical imaging as well as biologically driven signals such as EEG, amongst others. The keynote addresses challenges and considerations in transfer learning and emphasizes the importance of model selection, fine-tuning strategies, and evaluation metrics. Overall, it aims to inspire researchers and practitioners to leverage transfer learning to enhance performance and efficiency across diverse domains.