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
Abstract: 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
Abstract: 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
Abstract: 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
Abstract: 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.