RESEARCH KEYNOTE SERIES
(Professor, University of California Santa Barbara)
Bio: Behrooz Parhami (Ph.D. in computer science from University of California, Los Angeles, 1973) is Professor of Electrical and Computer Engineering, and former Associate Dean for Academic Personnel, College of Engineering, at University of California, Santa Barbara. He has research interests in computer arithmetic, parallel processing, and dependable computing. In his previous position with Sharif (formerly Arya-Mehr) the University of Technology in Tehran, Iran (1974-1988), he was also involved in educational planning, curriculum development, standardization efforts, technology transfer, and various editorial responsibilities, including a five-year term as Editor of Computer Report, a Persian-language computing periodical. His technical publications include over 300 papers in peer-reviewed journals and international conferences, a Persian-language textbook, and an English/Persian glossary of computing terms. Among his publications are three textbooks on parallel processing (Plenum, 1999), computer arithmetic (Oxford, 2000; 2nd ed. 2010), and computer architecture (Oxford, 2005). Professor Parhami is a Life Fellow of IEEE, a Fellow of IET, a Chartered Fellow of the British Computer Society, a member of the Association for Computing Machinery and American Society for Engineering Education, and a Distinguished Member of the Informatics Society of Iran for which he served as a founding member and President during 1979-84. Professor Parhami has served on the editorial boards of IEEE Trans. Sustainable Computing (since 2016), IEEE Trans. Computers (2009-2014; 2016-now), IEEE Trans. Parallel and Distributed Systems (2006-2010), and International J. Parallel, Emergent and Distributed Systems (2006-2012). He also chaired IEEE's Iran Section (1977-1986), received the IEEE Centennial Medal in 1984, and was honored with a most-cited paper award from J. Parallel & Distributed Computing in 2010. His consulting activities cover the design of high-performance digital systems and associated intellectual property issues.
Title of the Talk: Hybrid Digital-Analog Number Representation in Computing and in Nature.
Abstract: The discovery that mammals use a multi-modular method akin to residue number system (RNS), but with continuous residues or digits, to encode position information led to the award of the 2014 Nobel Prize in Medicine. After a brief review of the evidence in support of this hypothesis, and how it relates to RNS, I discuss the properties of continuous-digit RNS, and discuss results on the dynamic range, representational accuracy, and factors affecting the choice of the moduli, which are themselves real numbers. I then take a step back and briefly explore hybrid digital-analog number representations and their robustness and noise-immunity advantages more generally. I conclude with suggestions for further research on important open problems in the domain of hybrid digital-analog number representation and processing.
(Professor, University of Calgary)
Bio: Dr. Barry Sanders is Director of the Institute for Quantum Science and Technology at the University of Calgary, Lead Investigator of the Alberta Major Innovation Fund Project on Quantum Technologies, a Distinguished Chair Professor at the University of Science and Technology China and a Vajra Visiting Faculty member of the Raman Research Institute in India. He received his Bachelor of Science degree from the University of Calgary in 1984 and a Diploma of Imperial College supervised by Professor Sir Thomas W. B. Kibble in 1985. He completed a PhD in 1987 at Imperial College London supervised by Professor Sir Peter Knight. His postdoctoral research was at the Australian National University, the University of Queensland and the University of Waikato. Dr. Sanders was on the Macquarie University faculty from 1991 until moving to Calgary in 2003.
Dr. Sanders is especially well known for seminal contributions to theories of quantum-limited measurement, highly nonclassical light, practical quantum cryptography and optical implementations of quantum information tasks. His current research interests include quantum algorithms and implementations of quantum information tasks.
Dr. Sanders is a Fellow of the Institute of Physics (U.K.), the Optical Society of America, the Australian Institute of Physics, the American Physical Society and the Royal Society of Canada, and a Senior Fellow of the Canadian Institute for Advanced Research. He is a past President of the Australian Optical Society, past Founding Co-Chair of the Canadian Association of Physicists Division of Atomic, Molecular and Optical Physics and former Leader of the Optical Society of America Quantum Optical Science and Technology Technical Group. In 2016 Sanders was awarded the Imperial College London Doctor of Science (DSc) degree.
Dr. Sanders is Editor-in-Chief of New Journal of Physics, a former Associate Editor of Physical Review A, a former Editor of Optics Communications and a former Editor of Mathematical Structures of Computer Science.
Title of the Talk: Security for Quantum Networks
Abstract: Quantum networks enhance information-and-communication technology through applications such as secure quantum computing on the cloud, quantum-enhanced high-precision clock synchronization and quantum-enhanced key distribution. Reliable and efficient functioning of a quantum network depends on identifying and mitigating security risks originating within and outside the network. We introduce a comprehensive framework for developing and assessing secure quantum networks. In particular, we articulate issues for making quantum networks secure in general, summarise the state of the art and identify priority directions for further investigation. Our analysis builds on secure communication protocols developed for classical layered network architectures such as the open-systems interconnection (OSI) model and the transmission control protocol/internet protocol (TCP/IP) model. Our work could enable development of a hardware-independent framework for securing general quantum networks that allows developers to identify mandatory security mechanisms and incorporate additional security requirements for clients during network design.
(Professor, University of Toronto)
Bio: Dr. Gerald Penn is a Professor of Computer Science at the University of Toronto, and a Fellow of Computer Science at the University of Trinity College. His research interests are spoken language processing and computational linguistics. He is a senior member of IEEE and AAAI, and a past recipient of the Ontario Early Researcher Award. His joint work with Geoffrey Hinton and Hui Jiang on signal processing with neural networks revolutionized acoustic modelling for speech recognition systems, and received the IEEE Signal Processing Society's Best Paper Award. He has led numerous research projects, including those funded by Avaya, Bell Canada, CAE, the Connaught Fund, Microsoft, NSERC, the German Ministry for Training and Research, SMART Technologies, the U.S. Army and the U.S. Office of the Director of National Intelligence.
Title of the Talk: "Understanding Language'' with Deep Learning
Abstract: The last ten years have witnessed an enormous increase in the application of deep-learning methods to both spoken and textual natural language processing, often with hand-wavy appeals to cognitive
neuroscience. Have they actually advanced the state of the art in "understanding" language? Initial successes with respect to some well-defined tasks such as language modelling and acoustic modelling cleared the way for such claims, but attempts to extend this progress to other feats that might follow from genuine natural language understanding have been more problematic --- often because of sloppy evaluations or poorly chosen baselines.
This talk will consider three of these feats in detail: grammatically judgements, acoustic filter banks, and text-to-speech synthesis. We will discuss the consequences of empirically grounding the evaluation and the choice of inferential statistics versus more engineering-oriented correlation and accuracy scores.
Marina L. Gavrilova
(Professor, University of Calgary)
Bio: Marina L. Gavrilova is a Full Professor in the Department of Computer Science, University of Calgary, a head of the Biometric Technologies Laboratory and a Board Member of ISPIA. Her publications include over 200 journal and conference papers, edited special issues, books and book chapters in the areas of image processing, pattern recognition, machine learning, biometric and online security. She has founded ICCSA – an international conference series with LNCS/IEEE, co- chaired a number of top international conferences, and is Founding Editor-in- Chief of LNCS Transactions on Computational Science Journal. Dr. Gavrilova is on the Editorial Boards of the Visual Computer, International Journal of Biometrics, and six other journals. She has given over 50 keynotes, invited lectures and tutorials at major scientific gatherings and industry research centers, including at Stanford University, SERIAS Center at Purdue, Microsoft Research USA, Oxford University UK, Samsung Research South Korea and others. Dr. Gavrilova currently serves as an Associate Editor for IEEE Access, IEEE Transactions on Computational Social Systems, the Visual Computer and the International Journal of Biometrics, and was appointed by the IEEE Biometric Council to serve on IEEE Transactions on Biometrics, Behavior, and Identity Science Committee.
Title of the Talk: New Generation of Social Context-based Biometric Multi-modal Systems
Abstract: Human identity recognition is one of the key mechanisms of ensuring proper asset and information access to individuals, which is the base of many government, social services, consumer, financial and recreational activities in the society. Biometrics are also increasingly used in a cybersecurity context to mitigate vulnerabilities and to ensure protection against an unauthorized access or estimate risk level.
This keynote lecture discusses a new type of biometric information – Social context-based biometrics, that can be harvested from publicly available data in an on-line domain. Using advanced machine learning and pattern recognition methods, a fairly accurate profile of social network users can be established and then used to identify a user, their preferences or even their gender. The lecture will describe the developed multi-modal prototype system that can extracts specific spatial, temporal, behavioral, soft, and even aesthetic information based on social networks activities. It will conclude with practical case studies and discussion of challenges in the area of social biometrics.
(Professor, Ryerson University )
Bio: Dr. Alex is a Prof. of Computer Science. He earned his PhD from the U of Waterloo, his MSc from the U of Guelph and his B. Tech from Ryerson. He serves as the Graduate Program Director (GPD) of The Master of Digital Media program and as the GPD of the graduate programs in Computer Science. He is the creator of over 30 Certificate and other programs in Ryerson's Chang School of Continuing Education.
His research focuses on "Computational Public Safety". He seeks out collaborations with individuals and groups in fields as varied as Archaeology, Law Enforcement, Physics, Disaster Management, Fire Protection, English, Early Childhood Education, Performance and Fashion. In 2019 his work in finding lost people living with dementia was featured at Toronto’s CRAM Festival. His research goal is to one day contribute to saving someone's life.
Title of the Talk: Algorithmic thinking for Computational Public Safety (CPS)
Abstract: Public Safety refers to the welfare and protection of the general public. It is usually expressed as a governmental responsibility involving organizations providing security, fire, and medical services. These services have always used informal algorithms to perform their functions but with the advent of cheap, readily available computation and digital network resources, increasingly these algorithms have become more complex and formalized to the point where they can be expressed in ways commonly used to describe computer programs. In this presentation, Dr. Ferworn will discuss some of the real world challenges he has faced in his work with CPS tasks and algorithms as varied as those for search and rescue, improvised explosive device modeling and neutralization and patient health record quality assurance.
(Directeur de recherche – CNRS)
Bio: Dr. Frederic Dufaux is a CNRS Research Director at Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes (L2S, UMR 8506), where he is head of the Telecom and Networking hub. He received his M.Sc. in physics and Ph.D. in electrical engineering from EPFL in 1990 and 1994 respectively.
Frederic is a Fellow of IEEE. He was Chair of the IEEE SPS Multimedia Signal Processing (MMSP) Technical Committee in 2018 and 2019. He is a member of the IEEE SPS Technical Directions Board. He was Vice General Chair of ICIP 2014, General Chair of MMSP 2018, and Technical Program co-Chair of ICIP 2019. He will be Technical Program co-Chair of ICIP 2021. He is also a founding member and the Chair of the EURASIP Technical Area Committee on Visual Information Processing. He was also Editor-in-Chief of Signal Processing: Image Communication from 2010 until 2019.
Frederic has been involved in the standardization of digital video and imaging technologies for more than 15 years, participating both in the MPEG and JPEG committees. He was co-chairman of JPEG 2000 over wireless (JPWL) and co-chairman of JPSearch. He is the recipient of two ISO awards for these contributions.
His research interests include image and video coding, 3D video, high dynamic range imaging, visual quality assessment, video surveillance, privacy protection, image and video analysis, multimedia content search and retrieval, and video transmission over wireless network. He is author or co-author of 3 books, more than 200 research publications and 20 patents issued or pending.
Title of the Talk: Hyper-Realistic Imaging for Enhanced Quality of Experience
Abstract: Producing truly realistic and immersive digital imaging is widely seen as the ultimate goal towards further improving Quality of Experience (QoE) for end users of multimedia services. The human visual system is able to perceive a wide range of colors, luminous intensities, and depth, as present in a real scene. However, current traditional imaging technologies cannot capture nor reproduce such a rich visual information. Recent research innovations have made it possible to address these bottlenecks in multimedia systems. As a result, new multimedia signal processing areas have emerged such as ultra-high-definition, high frame rate, high dynamic range imaging, light fields, and point cloud.
These technologies have the potential to bring a leap forward for upcoming multimedia systems. However, the effective deployment of hyper-realistic video technologies entails many technical and scientific challenges. In this talk, I will discuss recent research activities covering several aspects of hyper-realistic imaging, including point cloud compression, light field compression, and semantic-aware tone mapping for high dynamic range imaging.
|Full Paper Submission:||28th September 2020|
|Acceptance Notification:||9th October 2020|
|Final Paper Submission:||17th October 2020|
|Early Bird Registration:||16th October 2020|
|Presentation Submission:||25th October 2020|
|Conference:||4 - 7 November 2020|
• Conference Proceedings will be submitted for publication at IEEE Xplore® digital library .
• Best Paper Award will be given for each track.
• Conference Record No