Keynote Speakers

Prof. Irwin King
IEEE Fellow
The Chinese University of Hong Kong, China

Prof. Irwin King is the Chair and Professor of Computer Science & Engineering at The Chinese University of Hong Kong. His research interests include machine learning, social computing, AI, data mining, and multimedia information processing. He is an IEEE Fellow and an ACM Distinguished Member. He is the recipient of numerous awards and recognitions, including the Test of Time Awards, Best Paper Award, Global AI List, and Outstanding Achievement Award for his contributions to social computing with machine learning. He received his B.Sc. degree from the California Institute of Technology (Caltech), Pasadena, and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California (USC), Los Angeles.

Speech Title: Social Recommendations—a Historical Perspective and Recent Advancements
With the exponential growth of information generated on the Internet, social recommendation has been a hot research topic in social computing after the popularization of social media as filtered suggestions (news, music, web pages, tags, etc.) are highly desirable to cope with the information explosion problem. In this keynote, I plan to take a walk down memory lane by presenting some of our seminal and pioneering work in social and location recommendation based on the matrix factorization framework. I will outline novel ways to use social ensemble, trust relations, tags, click-through rate, etc. to improve social and location recommender systems for a wide range of applications and services. I plan also to elucidate some recent works that suggest potential future directions in social recommendations.

Prof. Alex Kot
IEEE Fellow
Nanyang Technological University, Singapore

Alex Kot (IEEE Fellow) has been with the Nanyang Technological University (NTU), Singapore since 1991. He headed the Division of Information Engineering at the School of Electrical and Electronic Engineering (EEE) for eight years. He was the Vice Dean Research and Associate Chair (Research) for the School of EEE for three years, overseeing the research activities for the School with over 200 faculty members. He was the Associate Dean (Graduate Studies) for the College of Engineering (COE) for eight years. He is currently the Director of ROSE Lab [Rapid(Rich) Object SEearch Lab) and the Director of NTU-PKU Joint Research Institue . He has published extensively with over 300 technical papers in the areas of signal processing for communication, biometrics recognition, authentication, image forensics, machine learning and AI. He has two USA and one Singapore patents granted.

Dr. Kot served as Associate Editor for a number of IEEE transactions, including IEEE TSP, IMM, TCSVT, TCAS-I, TCAS-II, TIP, SPM, SPL, JSTSP, JASP, TIFS, etc. He was a TC member for several IEEE Technical Committee in SPS and CASS. He has served the IEEE in various capacities such as the General Co-Chair for the 2004 IEEE International Conference on Image Processing (ICIP) and area/track chairs for several IEEE flagship conferences. He also served as the IEEE Signal Processing Society Distinguished Lecturer Program Coordinator and the Chapters Chair for IEEE Signal Processing Chapters worldwide. He received the Best Teacher of The Year Award at NTU, the Microsoft MSRA Award and as a co-author for several award papers. He was elected as the IEEE CAS Distinguished Lecturer in 2005. He was a Vice President in the Signal Processing Society and IEEE Signal Processing Society Distinguished Lecturer. He is now a Fellow of the Academy of Engineering, Singapore, a Fellow of IEEE and a Fellow of IES.

Speech Title: Face Anti-Spoofing in Practical Scenarios
Face recognition has become very popular as a convenient biometric for identity verification in recent years. The presentation attack is a serious threat hindering the application of face recognition systems. Face presentation attack detection (PAD) is an essential anti-spoofing measure to enhance the security of face recognition systems by discriminating presentation attacks from bona fide attempts. However, existing methods have achieved good performance in intra-domain testing but not in a new target domain.
In this talk, we introduce Asymmetric Modality Translation for Face Presentation Attack Detection to improve generalization capability. We also propose the One-Class Knowledge Distillation for Face Presentation Attack Detection by using only Bona-Fide (one-class) example to efficiently fine-tune a model in the target domain. We also propose Rehearsal-Free Domain Continual Face Anti-Spoofing to continually fine-tune a pre-trained model to tackle continuous challenges of different domain data.

Prof. Kwang-Cheng Chen
IEEE Fellow
University of South Florida, USA

Kwang-Cheng Chen has been a Professor at the Department of Electrical Engineering, University of South Florida, since 2016. From 1987 to 2016, Dr. Chen worked with SSE, Communications Satellite Corp., IBM Thomas J. Watson Research Center, National Tsing Hua University, HP Labs., and National Taiwan University in mobile communications and networks. He visited TU Delft (1998), Aalborg University (2008), Sungkyunkwan University (2013), and Massachusetts Institute of Technology (2012-2013, 2015-2016). He founded a wireless IC design company in 2001, which was acquired by MediaTek Inc. in 2004. He has been actively involving in the organization of various IEEE conferences and serving editorships with a few IEEE journals, together with various IEEE volunteer services to the IEEE, Communications Society, Vehicular Technology Society, and Signal Processing Society, such as founding the Technical Committee on Social Networks in the IEEE Communications Society. Dr. Chen also has contributed essential technology to various international standards, namely IEEE 802 wireless LANs, Bluetooth, LTE and LTE-A, 5G-NR, and ITU-T FG ML5G. He has authored and co-authored over 350 IEEE publications, 4 books published by Wiley and River (most recently, Artificial Intelligence in Wireless Robotics, 2020), and more than 26 granted US patents. Dr. Chen is an IEEE Fellow, AAIA Fellow, and has received a number of awards including 2011 IEEE COMSOC WTC Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award, and paper awards in conferences. Dr. Chen’s current research interests include quantum communications and computing, wireless networks, multi-agent systems and social networks, and cybersecurity.

Speech Title: Quantum Computations – Architecture and Implementation
Quantum entanglement that puzzled great minds like Einstein enables recent advances in quantum computers, computations, and various quantum information systems. In this talk, we will introduce the difference of logic implementation between quantum and classic computing, and then quantum gate-based computing architecture and adiabatic quantum computation while taking fault-tolerance into consideration. Quantum neural networks will be introduced to further illustrate unique aspects of quantum computations, technological advantages, and technical challenges. Quantum computations have been successfully applied to some computational problems that were not possible before, such as molecular biology, cancer research, and precision medicine. In addition, noisy and intermediate-scale quantum (NISQ) information systems will be introduced by incorporating fault-tolerant mechanisms.

Prof. Farid Meziane
University of Derby, UK

Farid Meziane is a professor of Data Science and the Head of the Data Science Research Centre at the University of Derby, UK. He obtained a PhD in Computer Science from the University of Salford, UK on his work on producing formal specification from Natural Language requirements. The work was considered at that time as pioneering in the area and paved the way for a large interest in automating the production of software specifications from informal requirements.
He has authored over 150 scientific papers and participated in many national and international research projects. He is the co-chair of the international conference on application of Natural Language to information systems; co-chair of the international conference on Information Science and Systems. He is serving the programme committee of over ten international conferences. He is an associate editor for the data and knowledge engineering (Elsevier) journal and the managing editor of the International Journal of Information Technology and Web Engineering (IDEA publishing). He was awarded the Highly Commended Award from the Literati Club, 2001 for his paper on Intelligent Systems in Manufacturing: Current Development and Future Prospects. His research expertise includes Natural Language processing, semantic computing, data mining and big data and knowledge Engineering.

Speech Title: Exploiting Web Resources to Support Automatic Course Design
With the rapid advances in E-learning systems, personalisation and adaptability have now become important features in the education technology. In this paper, we describe the development of an architecture for A Personalised and Adaptable ELearning System (APELS) that attempts to contribute to advancements in this field.
APELS aims to provide a personalised and adaptable learning environment to users from the freely available resources on the Web. An ontology was employed to model a specific learning subject and to extract the relevant learning resources from the Web based on a learner’s model (the learners background, needs and learning styles). The APELS system uses natural language processing techniques to evaluate the content extracted from relevant resources against a set of learning outcomes as defined by standard curricula to enable the appropriate learning of the subject. An application in the computer science field is used to illustrate the working mechanisms of the APELS system and its evaluation based on the ACM/IEEE computing curriculum. An experimental evaluation was conducted with domain experts to evaluate whether APELS can produce the right learning material that suits the learning needs of a learner. The results show that the produced content by APELS is of a good quality and satisfies the learning outcomes for teaching purposes.