SPEAKERS
Prof. Juyang Weng IEEE Life Fellow Brain-Mind Institute and GENISAMA, USA | Brief Introduction: Prof. Juyang Weng received the BS degree from Fudan University, in 1982, M. Sc. and PhD degrees from the University of Illinois at Urbana-Champaign, in 1985 and 1989, respectively, all in computer science. He is a former faculty member of Department of Computer Science and Engineering, faculty member of the Cognitive Science Program, and faculty member of the Neuroscience Program at Michigan State University, East Lansing. He was a visiting professor at the Computer Science School of Fudan University, Nov. 2003 - March 2014, and did sabbatical research at MIT, at Media Lab Fall 1999 – Spring 2000; and at Department of Brain and Cognitive Science Fall 2006-Spring 2007 and taught BCS9.915/EECS6.887 Computational Cognitive and Neural Development during Spring 2007. Since the work of Cresceptron (ICCV 1993) the first deep learning neural networks for 3D world without post-selection misconduct, he expanded his research interests in biologically inspired systems to developmental learning, including perception, cognition, behaviors, motivation, machine thinking, and conscious learning models. He has published over 300 research articles on related subjects, including task muddiness, intelligence metrics, brain-mind architectures, emergent Turing machines, autonomous programing for general purposes (APFGP), Post-Selection flaws in “deep learning”, vision, audition, touch, attention, detection, recognition, autonomous navigation, and natural language understanding. He published with T. S. Huang and N. Ahuja a research monograph titledMotion and Structure from Image Sequences. He authored a book titledNatural and Artificial Intelligence: Computational Introduction to Computational Brain-Mind. Dr. Weng is an Editor-in-Chief of theInternational Journal of Humanoid Robotics, the Editor-in-Chief of theBrain-Mind Magazine, and an associate editor of theIEEE Transactions on Autonomous Mental Development (now Cognitive and Developmental Systems). With others’ support, he initiated the series ofInternational Conference on Development and Learning (ICDL), theIEEE Transactions on Autonomous Mental Development, the Brain-Mind Institute, and the startup GENISAMA LLC. He was an associate editor of theIEEE Transactions on Pattern Recognition and Machine Intelligence and theIEEE Transactions on Image Processing. Speech Title: The First Conscious Learning Algorithm Avoids “Deep Learning” Misconduct Abstract:From a fruit fly to a human, with many animal species in between, do they share a set of biological mechanisms to regulate the lifelong development of the brains? We have seen very impressive advances in understanding the principles of neuroscience. However, what is still missing is a holistic algorithm that is both broad and deep. By broad, we mean it approximates such mechanisms across a range of species. By deep, we mean that it specifies sufficient details so that the algorithm can be biologically and computationally verified and possibly corrected across a deep hierarchy of scales, from neurotransmitters, to cells, to brain patterns, to behaviors, to intelligence, to consciousness across the time span of a life. This talk outlines such a conscious learning algorithm, the first in the category as far as the presenter is aware of, called Developmental Network 3 (DN-3). All its predecessors, Cresceptron, IHDR, DN-1 and DN-2 were not capable of conscious learning till DN-3. A major extension from DN-2 to DN-3 is that the model starts from a single cell inside the skull so that brain patterning is fully automatic in a coarse to fine way. This biological model has been supported by computational experiments with real sensory data for vision, audition, natural languages, and planning, to be presented during the talk. This first ever algorithm for conscious learning is free from “deep learning” misconduct, including ChatGPT. |
Prof. James Tin-Yau KWOK IEEE Fellow Hong Kong University of Science and Technology, China | Brief Introduction: James Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow. Prof Kwok received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. He then joined the Department of Computer Science, Hong Kong Baptist University as an Assistant Professor. He returned to the Hong Kong University of Science and Technology and is now a Professor in the Department of Computer Science and Engineering. He is serving as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Neurocomputing, Artificial Intelligence Journal, International Journal of Data Science and Analytics, and on the Editorial Board of Machine Learning. He is also serving / served as Senior Area Chairs of major machine learning / AI conferences including NeurIPS, ICML, ICLR, IJCAI, and as Area Chairs of conferences including AAAI and ECML. He is on the IJCAI Board of Trustees. He is recognized as the Most Influential Scholar Award Honorable Mention for "outstanding and vibrant contributions to the field of AAAI/IJCAI between 2009 and 2019". Prof Kwok will be the IJCAI-2025 Program Chair. |
Prof. Shahram Latifi IEEE Fellow University of Nevada,USA | Brief Introduction: Shahram Latifi, an IEEE Fellow, received the Master of Science egree in Electrical Engineering from Fanni, Teheran University, Iran in 1980. He received the Master of Science and the PhD degrees both in Electrical and Computer Engineering from Louisiana State University, Baton Rouge, in 1986 and 1989, respectively. He is currently a Professor of Electrical Engineering at the University of Nevada, Las Vegas. Dr. Latifi is the director of the Center for Information and Communication Technology (CICT) at UNLV. He has designed and taught graduate courses on Bio-Surveillance, Image Processing, Computer Networks, Fault Tolerant Computing, and Data Compression in the past twenty years. He has given seminars on the aforementioned topics all over the world. He has authored over 200 technical articles in the areas of image processing, biosurveillance, biometrics, document analysis, computer networks, fault tolerant computing, parallel processing, and data compression. His research has been funded by NSF, NASA, DOE, Boeing, Lockheed and Cray Inc. Dr. Latifi was an Associate Editor of the IEEE Transactions on Computers (1999-2006) and Co-founder and General Chair of the IEEE Int'l Conf. on Information Technology. He is also a Registered Professional Engineer in the State of Nevada. |
Prof. P. Takis Mathiopoulos IEEE Senior Member University of Athens, Greece | Brief Introduction: P. Takis Mathiopoulos received the Ph.D. degree in digital communications from the University of Ottawa, Ottawa, Canada, in 1989. From 1982 to 1986, he was with Raytheon Canada Ltd., working in the areas of air navigational and satellite communications.
In 1989, he joined the Department of Electrical and Computer Engineering (ECE), University of British Columbia (UBC), Vancouver, Canada, as an Assistant Professor and where he was a faculty member until 2003, holding the rank of Professor from 2000 to 2003. From 2000 to 2014, he was the Director (2000 - 2004) and then the Director of Research of the Institute for Space Applications and Remote Sensing (ISARS), National Observatory of Athens (NOA). Since 2014, he is Professor of Telecommunications at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece. He also held visiting faculty long term honorary academic appointments as Guest Professor at South West Jiao Tong University (SWJTU), Chengdu, China, and Guest (Global) Professor at Keio University, Tokyo, Japan.
His research activities and contributions have dealt with wireless terrestrial and satellite communication systems and network as well as in remote sensing, LiDAR systems, and information technology, including blockchain systems. In these areas, he has coauthored 140 journal papers published mainly in various IEEE journals, 1 book (edited), 5 book chapters, and more than 140 conference papers. Dr. Mathiopoulos has been or currently serves on the editorial board of several archival journals, including the IET Communications as an Area Editor, the IEEE Transactions on Communications, the Remote Sensing Journal, and as Specialty Chief Editor for the Arial and Space Network Journal of Frontiers.
From 2001 to 2014, he has served as a Greek Representative to high-level committees in the European Commission and the European Space Agency. He has been a member of the Technical Program Committees (TPC) for numerous IEEE and other international conferences and has served as TPC Vice Chair of several IEEE conferences. He has delivered numerous invited presentations, including plenary and keynote lectures, and has taught many short courses all over the world. As a faculty member UBC, he has been awarded an Advanced Systems Institute (ASI) Fellowship as well as a Killam Research Fellowship. He is also the co-recipient of two best conference paper awards and has received by the IEEE Communication Society the Satellite and Space Communication Technical Committee “2017 Distinguished Service Award” for outstanding contributions in the field of Satellite and Space Communications. Speech Title: (Optimal) Detection for (Fast) Fading Channels Abstract: We will be reviewing the most important fading channel models and then present the most important associated receiver structures used in wireless digital communication systems. Emphasis will be given to the maximum likelihood sequence estimation receiver which lead to the so-called multiple differential detection (MDD) receiver structure for fast fading channels. Then we will be presenting the most important diversity receiver techniques and their application to “new” classes of statistical fading channels, such as the Weibull (short-term fading), the Lognormal (long-term fading) and the Generalized-K (composite fading) models. We will also be discussing some open research problems promising for future investigation. |
Prof. Li Chen IEEE Senior Member Sun Yat-sen University, China | Brief Introduction: Li Chen was awarded his PhD by Newcastle University in 2008 and now is a Professor of the School of Electronics and Information Engineering, Sun Yat-sen University. From Aug. 2017 to Mar. 2020, he was the Deputy Dean of the School of Electronics and Communication Engineering. He specializes in channel coding, particularly in algebraic coding theory and techniques. From Jul. 2015 to Jun. 2016, he took sabbatical, visiting Ulm University in Germany and University of Notre Dame in U.S. He has also visited the Institute of Network Coding, the Chinese University of Hong Kong for several occasions. He is a member of the IEEE Information Theory Society Board of Governors Conference Committee and chairing the Conference Committee. He founded and chairs the IEEE Information Theory Society Guangzhou Chapter, which was awarded 2021 Chapter of the Year of the IEEE Information Theory Society. He was awarded The Chinese Information Theory Young Researcher award by the Chinese society of Electronics. He is an Associate Editor of the IEEE Transactions on Communications. He has been organizing several international conferences and workshops, including the 2018 IEEE Information Theory Workshop (ITW) in Guangzhou and the 2022 IEEE East Asian School of Information Theory (EASIT) in Shenzhen, for which he is the General Co-Chair. He is also the TPC Co-Chair of the 2022 IEEE/CIC International Conference on Communications in China (ICCC) in Foshan. He will organize ISIT 2026 in Guangzhou China, which is the first time that ISIT lands on mainland China. Speech Title: U-UV Codes -- The Good Short-to-Medium Length Codes Abstract: Competent short-to-medium length channel codes will play an important role in future communication systems. This talk introduces a good performing short-to-medium length code: the UUV codes. U-UV codes are constructed by a number of component codes in the (U | U + V) recursive structure, where the U codes and V codes are component codes. This construction is known as the Plotkin construction and the generalized concatenated codes with inner polar codes. Good performing U-UV codes can be designed for a targeted transmission rate. The successive cancellation list (SCL) decoding of the U-UV codes is substantiated by the list decoding of the component codes. This talk also shows that SCL decoding of U-UV codes can provide competent error-correction performance, and they can outperform other competent short-to-medium length codes. |
Prof.Guojun Han IEEE Senior Member Guangdong University of Technology, China | Brief Introduction: Prof. Guojun HAN received his Ph.D. from Sun Yatsen University, Guangzhou, China, and the M.E. degree from South China University of Technology, Guangzhou, China. From March 2011 to August 2013, he was a Research Fellow at the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. From October 2013 to April 2014, he was a Research Associate at the Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology. He is now a Full Professor and Dean at the School of Information Engineering, Guangdong University of Technology, Guangzhou, China. He has been a Senior Member of IEEE since 2014. His research interests are in the areas of memory devices and data storage systems, wireless communications, coding and information theory. He has more than 15 years’ experience on research and development of advanced channel coding and signal processing algorithms and techniques for various data storage and communication systems. Speech Title: Resource Management for Caching, Communication, and Computing in Vehicular Edge Systems Abstract: Effective resource management is pivotal in enhancing transmission efficiency across vehicular networks, particularly in the context of burgeoning multimedia content, computational demands, and latency-sensitive applications. Amidst the challenges of demand uncertainty, high dynamism, and task diversity, the complexity of resource management escalates, necessitating more advanced strategies. This presentation will delve into the evolution of technology standards within vehicular networks, shedding light on the ongoing progress in content distribution and computational task offloading within these systems. Our efforts encompass a dual-layer content caching strategy, designed to optimize transmission latency and enhance hit rates for content access. Moreover, we introduce a dynamic caching and offloading strategy tailored for program-associated tasks, alongside an offloading and resource allocation strategy specifically catered to collaborative sensing tasks. These two strategies synergistically refine resource management, operating at a systemic macro perspective and a multi-agent collaboration level, respectively. Furthermore, we have developed a prototype of an object-level cooperative perception system, promoting the practical implementation of cooperative perception in vehicular environments. |
Prof. Yong Yue IET Fellow Xi'an Jiaotong-Liverpool University, China | Brief Introduction: Yong Yue (BEng Northeastern China, PhD Heriot-Watt UK, CEng, FIET, FIMechE, FHEA) is currently a Professor and Director of the Virtual Engineering Centre (VEC). He was Head of Department of Computer Science and Software Engineering (2013-2019). Prior to joining XJTLU, he had held various positions in industry and academia in China and the UK, including Engineer, Project Manager, Professor, Director of Research and Head of Department. Professor Yue has experience in learning and teaching, research and enterprise as well as management. He has led a variety of research and professional projects supported by major funding bodies and industry. He has also lead curriculum development at both undergraduate and postgraduate levels. His Research interests include computer vision, robotics, virtual reality and operations research. He has over 250 peer-reviewed publications and supervised 27 PhD students to successful completion. Speech Title: Intelligent Real-Time Path Planning for Unmanned Surface Vehicle Abstract: Unmanned Surface Vehicles (USVs) play a key role in water environment monitoring. The backbone of USVs is intelligent path planning which is crucial for ensuring the safety, reliability and success of USVs amid challenges such as fluctuating currents and tides while detecting and avoiding obstacles. This talk will briefly introduce the USV, reviews contemporary techniques for path planning and present ongoing work on intelligent real-time path planning for the USV for water environment monitoring. The work covers a novel path-keeping algorithm based on artificial potential field (PK-APF), enhancing the USV ability to maintain its pre-set path under variable wind conditions; a novel riverbank following planner (RBFP) with point cloud data to realise autonomous navigation along riverbanks for surveying water environments; a self-supervised framework for autonomous USV docking without the need for traditional human labelling and camera calibration, leading to highly precise USV docking manoeuvres. |
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2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE 2024) http://www.iccisce.com/