FIEEE, FIET, CEng, Hon. FSIET
Associate Chair (Research)
School of Computer Science and Engineering
Nanyang Technological University, Singapore

Keynote: "Deep-learnt Features to Facilitate Image Compression and Computer Vision with an Integrated Framework"

Abstract: It has been long for image (or video) compression and computer vision to be two largely separate domains, and as a result, a computer vision task typically can only start after a whole image is decoded. This talk explores for intermediate deep-learnt visual features (rather than whole image/video) to be extracted and then coded, and this facilitates integration of signal compression and computer vision, accurate feature extraction, privacy preservation, flexible load distribution between edge and cloud, and green visual computing. It is hoped that the presentation can trigger more R&D in the related fields due to the nature of fundamental paradigm-shift in the proposed framework.

Short bio:
Lin Weisi is an active researcher in intelligent image processing, perception-based signal modelling and assessment, video compression, and multimedia communication. He had been the Lab Head, Visual Processing, in Institute for Infocomm Research (I2R)，Singapore. He is a Professor in School of Computer Science and Engineering, Nanyang Technological University, where he also served as the Associate Chair (Research).

He is a Fellow of IEEE and IET, and has been awarded Highly Cited Researcher 2019 and 2020 by Clarivate Analytics. He has elected as a Distinguished Lecturer in both IEEE Circuits and Systems Society (2016-17) and Asia-Pacific Signal and Information Processing Association (2012-13), and given keynote/invited/tutorial/panel talks in 30+ international conferences. He has been an Associate Editor for IEEE Trans. Image Process., IEEE Trans. Circuits Syst. Video Technol., IEEE Trans. Multimedia, IEEE Signal Process. Lett., Quality and User Experience, and J. Visual Commun. Image Represent. He also chaired the IEEE MMTC QoE Interest Group (2012-2014); he has been a TP Chair for IEEE ICME 2013, QoMEX 2014, PV 2015, PCM 2012 and IEEE VCIP 2017. He believes that good theory is practical, and has delivered 10+ major systems and modules for industrial deployment with the related technology developed.

IEEE Life Fellow
Canada Research Chair (CRC)
Computational Intelligence in the Department of Electrical and Computer Engineering
University of Alberta, Canada

Keynote: " Federated Learning, Knowledge Transfer, and Knowledge Distillation: Developments with Information Granules"

Abstract: The visible trends of Machine Learning addressing the emerging needs of coping with the diversity of real-world motivated learning scenarios involve federated learning, transfer learning, and knowledge distillation.
We advocate that to conveniently address these challenges encountered in these directions, it becomes beneficial to engage the fundamental framework of Granular Computing to enhance the above approaches or to establish new and augmented methodologies. We demonstrate that various ways of conceptualization of information granules in the form of fuzzy sets, sets, rough sets, among others, lead to efficient solutions.
To establish a sound conceptual modeling framework, we include a brief discussion of concepts of information granules and Granular Computing. In the sequel, a concise information granules-oriented design of rule-based architectures is discussed. A way of forming the rules through unsupervised federated learning is investigated along with algorithmic developments. A granular characterization of the model formed by the server vis-a-vis data located at individual clients is presented. It is demonstrated that the quality of the rules at the client’s end is described in terms of granular parameters and subsequently the global model becomes represented as a granular model. Subsequently, the roles of granular augmentations of models in the setting of granular knowledge transfer and knowledge distillation, in particular, are discussed.

Short bio:
Dr. Witold Pedrycz (IEEE Life Fellow, 2021) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering. Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Int. J. of Granular Computing (Springer). He currently serves on the Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals.　
 

IEEE Fellow, IET Fellow
Wilmot J. Nicholson Family Chair Professor
Chair, Department of Computer Science and Engineering
Santa Clara University, USA

Keynote: "Visual Coding – From Traditional Approach to Deep Learning Approach"

Abstract: In today’s Internet, visual data are everywhere. Eighty percent of all Internet traffic are from video data. The immensity of the size and amount of visual data dictates the need of efficient coding technology to effectively compress them. From the first video coding standard in the mid-1980s to the latest VVC/H.266 in 2020, coding efficiency has improved a lot. Traditional video codec has been based on a block-based hybrid codec structure. With the advancement of deep learning technology, video coding can be assisted by deep learning tools or/and use deep learning-based neural network as the backbone. The improvement in coding efficiency comes with huge computational complexity associated with deep approaches and the need of a more appropriate visual quality metric. Image coding is similar. From the early JPEG to BPG to VVC intra, coding efficiency has improved a lot. Deep learning approaches further improve this but often come with a high computational complexity. In this talk, we first discuss the key tools in the block-based hybrid codec structure, then discuss deep learning-based approaches, from autoencoder to the use of generative adversarial network (GAN). Finally, we highlight a couple of our on-going projects based on GANs, including the use of GANs in image coding and in video coding.

Short bio:
Nam Ling received the B.Eng. degree (Electrical Engineering) from the National University of Singapore and the M.S. and Ph.D. degrees (Computer Engineering) from the University of Louisiana, Lafayette, U.S.A. He is currently the Wilmot J. Nicholson Family Chair Professor (Endowed Chair) of Santa Clara University (U.S.A) (since 2020) and the Chair of its Department of Computer Science & Engineering (since 2010). From 2010 to 2020, he was the Sanfilippo Family Chair Professor (Endowed Chair) of Santa Clara University. From 2002 to 2010, he was an Associate Dean for its School of Engineering (Graduate Studies, Research, and Faculty Development). He is/was also a Distinguished Professor for Xi’an University of Posts & Telecommunications, a Cuiying Chair Professor for Lanzhou University, a Guest Professor for Tianjin University, a Chair Professor and Minjiang Scholar for Fuzhou University, a Guest Professor for Shanghai Jiao Tong University, a Guest Professor for Zhongyuan University of Technology (China), and a Consulting Professor for the National University of Singapore. He has more than 230 publications (including books) in video/image coding and systolic arrays. He also has seven adopted standards contributions and has been granted with more than 20 U.S./European/PCT patents. He is an IEEE Fellow due to his contributions to video coding algorithms and architectures. He is also an IET Fellow. He was named IEEE Distinguished Lecturer twice and was also an APSIPA Distinguished Lecturer. He received the IEEE ICCE Best Paper Award (First Place) and the IEEE Umedia Best/Excellent Paper Awards (three times). He received six awards from Santa Clara University, four at the University level (Outstanding Achievement, Recent Achievement in Scholarship, President’s Recognition, and Sustained Excellence in Scholarship) and two at the School/College level (Researcher of the Year and Teaching Excellence). He has served as Keynote Speaker for IEEE APCCAS, VCVP (twice), JCPC, IEEE ICAST, IEEE ICIEA, IET FC & U-Media, IEEE U-Media, Workshop at XUPT (twice), ICCIT, as well as a Distinguished Speaker for IEEE ICIEA. He is/was General Chair/Co Chair for IEEE Hot Chips, VCVP (twice), IEEE ICME, IEEE U-Media (five times), and IEEE SiPS. He was an Honorary Co-Chair for IEEE Umedia. He has also served as Technical Program Co Chair for IEEE ISCAS, APSIPA ASC, IEEE APCCAS, IEEE SiPS (twice), DCV, and IEEE VCIP. He was Technical Committee Chair for IEEE CASCOM TC and IEEE TCMM, and has served as Guest Editor/Associate Editor for IEEE TCAS I, IEEE J-STSP, Springer JSPS, Springer MSSP, and other journals. He has delivered more than 120 invited colloquia worldwide and has served as Visiting Professor/Consultant/Scientist for many institutions/companies. Recently, he organized and conducted an APSIPA panel on "The Future of Video Coding", with great response. 　
 

Nanyang Technological University,
Singapore

Short bio:
Dr. Mao Kezhi obtained his BEng, MEng and PhD from Jinan University, Northeastern University, and University of Sheffield in 1989, 1992 and 1998 respectively. He joined School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore in 1998, where he is now a tenured Associate Professor. Dr. Mao has over 20 years of research experience in artificial intelligence, machine learning, big data, image processing, natural language processing, and information fusion. Besides academic research, Dr. Mao is also active in development and consulting. He has successfully developed and delivered several intelligent systems and software tools to government agencies, hospitals and industries.

 

IEEE Fellow & OSA Fellow
The University of Sydney, Australia

Keynote: "Advances in Integrated Photonic Signal Processing "

Abstract: Photonic signal processing enables the realisation of functions that are difficult or not even possible to be achieved using electronic techniques. This exploits the inherent advantages of photonics including wide bandwidth and immunity to electromagnetic interference. Photonic signal processors can provide in-built signal conditioning for overcoming a range of challenging problems in the processing of high-speed signals. Recently, there has been a significant global drive to achieve integration of photonic signal processors on silicon platforms, especially since this leverages the CMOS fabrication technology to enable boosting the performance of future systems performing communications, sensing and deep learning with the potential for implementing high bandwidth, fast and complex functionalities. Advances in integrated photonic signal processing are presented. These include dense optical integration techniques for LIDAR on a chip systems, photonic neural networks for deep learning, multi-function signal processors, programmable integrated photonic processors and high-resolution integrated sensors for IoT. These photonic processors herald new capabilities for achieving high-performance signal processing.

Short bio:
Professor Minasian is a Chair Professor with the School of Electrical and Information Engineering at the University of Sydney, Australia. He is also the Founding Director of the Fibre-optics and Photonics Laboratory. His research has made key contributions to microwave photonic signal processing. He is recognized as an author of one of the top 1% most highly cited papers in his field worldwide. Professor Minasian has contributed over 390 research publications, including Invited Papers in the IEEE Transactions and Journals. He has 79 Plenary, Keynote and Invited Talks at international conferences. He has served on numerous technical and steering committees of international conferences, and is on the IEEE Fellow Evaluation Committee. . Professor Minasian was the recipient of the ATERB Medal for Outstanding Investigator in Telecommunications, awarded by the Australian Telecommunications and Electronics Research Board. He is a Life Fellow of the IEEE, and a Fellow of the Optical Society of America.