Professor Bo Ai
IEEE Fellow，Beijing Jiaotong University
Professor Ai is a national Outstanding Youth, Excellent Youth, Newton Senior Scholar Fund, "National Science and Technology Talents Program", "Qiushi Outstanding Youth Award" of China Association for Science and Technology; "Outstanding Young Scholar of China Engineering Frontier", Chinese Academy of Engineering. Currently, he is Chairman of IEEE BTS Xi 'an Branch, Vice Chairman of IEEE VTS Beijing Branch, distinguished lecturer of IEEE VTS, member of Chinese Electronics Society, member of Chinese Communication Society, supervisor of Chinese Communication Society Supervisory Board, and expert of National 6G General Group.
He has published more than 150 papers in IEEE journals, more than 11,000 academic citations in Google, and 13 paper awards from IEEE Globecom and other international conferences. It has obtained 32 authorized invention patents; It has won 9 provincial and ministerial science and technology awards. The research results have been included in five national industry standards, and the results have been applied to Beijing-Shanghai high-speed railways and the construction of over 100 railway lines covering more than 30,000 kilometers.
Tilte: Feeder Communications For Future Integrated Networks
The integrated communication system has received increasing attention to establish a fully-connected world in the eras of beyond the fifth-generation wireless communication (B5G) and the sixthgeneration wireless communication (6G) systems, where a wide range of services with distinct requirements needs to be accommodated, covering distinct scenarios such as terrestrial, ocean, outer space and underwater. In this talk, we first present the idea of feeder communication, which aims to provide truly seamless and ubiquitous on-demand coverage for any human activity. Compared with the traditional integrated networks, the feeder communication focuses on the reliable data transmission over multiple propagation mediums such as outer space, stratosphere, troposphere, and underwater. Due to the significant differences of radio propagation characteristics in different mediums, feeder communication requires a large number of feeder nodes for inter-medium transmissions, a well-designed network structure, and intelligent wireless transmission and control schemes. The basic concepts, features, application scenarios, network architecture, and the corresponding wireless transmission technologies of feeder communication are presented with details. Finally, challenges and future directions are pointed out.
Professor Ljiljana Trajkovic
IEEE Fellow. Simon Fraser University, Canada
Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director and President of the IEEE Systems, Man, and Cybernetics Society and the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She is a Distinguished Lecturer of the IEEE Circuits and System Society and the IEEE Systems, Man, and Cybernetics Society. Dr. Trajkovic is a Professional Member of IEEE-HKN and a Life Fellow of the IEEE.
Tilte: Machine Learning for Detecting Internet Traffic Anomalies
Border Gateway Protocol (BGP) enables the Internet data routing. BGP anomalies may affect the Internet connectivity and cause routing disconnections, route flaps, and oscillations. Hence, detection of anomalous BGP routing dynamics is a topic of great interest in cybersecurity. Various anomaly and intrusion detection approaches based on machine learning have been employed to analyze BGP update messages collected from RIPE and Route Views collection sites. Survey of supervised and semi-supervised machine learning algorithms for detecting BGP anomalies and intrusions is presented. Deep learning, broad learning, and gradient boosting decision tree algorithms are evaluated by creating models using collected datasets that contain Internet worms, power outages, and ransomware events.
Professor Yonghui Li
ARC Future Fellow, IEEE Fellow. School of Electrical and Information Engineering, The University of Sydney, Australia
Yonghui Li received his PhD degree in November 2002. Since 2003, he has been with the Centre of Excellence in Telecommunications, the University of Sydney, Australia. Li is now a Professor and Director of Wireless Engineering Laboratory in School of Electrical and Information Engineering, University of Sydney. He is the recipient of the Australian Research Council (ARC)Queen Elizabeth II Fellowship in 2008 and ARC Future Fellowship in 2012. He is an IEEE Fellow for contributions to cooperative communications technologies.
Tilte: uRLLC for Industrial Internet
The world is currently witnessing the rise of many mission critical applications such as tele-surgery, intelligent transportation, industry automation, virtual reality and augmented reality, vehicular communications, etc. Some of these applications will be enabled by the fifth-generation of cellular networks (5G), which will provide the required ultra-reliable low latency communication (URLLC). However, guaranteeing these stringent reliability and end-to-end latency requirements continues to prove to be quite challenging, due to the significant shift in paradigms required in both theoretical fundamentals of wireless communications as well as design principles. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Addressing these challenges requires the development of new wireless technologies, underlying network protocols, and signal processing techniques. In this talk, we will present the key challenges and potential solutions for 5G and beyond 5G to support URLLC for industrial internet, in terms of error control coding improving reliability, channel access protocols for reducing latency, and multi-connectivity for improving network availability.
Professor Xinde Li
Academician of Russian Academy of Natural Sciences(RAEN). The member of IEEE. Southeast University, China.
He was a vice director of Intelligent Robot Committee of Chinese Association for Artificial Intelligence from 2017, a vice director of Intelligent Products and Industry Working Committee of Chinese Association for Artificial Intelligence from 2019. His research interests include Artificial Intelligence, Intelligent Robot, Machine Perception and Understanding, and human-robot interaction, etc. He has undertaken many national key projects, i.e. National 863 key project, JKW key project, etc. and has published more than 80 high quality papers and 2 books, and owns 17 national invention patents. He also won many prizes, i.e. international contribution prize, Scientific and Technological Progress Award in CAA, etc.
Tilte: Robot Occasion Cognition from person and place
With the advances of artificial intelligence, robots have been widely used in many service sectors and close to human life. However, constructing a harmonious and natural human-robot interaction environment puts forward high requirements for humanoid performance of robot. In this presentation, we first summarize the natural person and place in the robot interaction environment as the occasion, proposing the concept of robot occasion cognition. Then, according to the challenges of robot cognition for human and place in the occasion, I will report the recent researches about human identification and emotion recognition, place perception and understanding, in which we explore a new paradigm of robot occasion cognition towards various people and local conditions. Finally, I will give some prospects for the future progress of this topic.
Professor Qingliang Chen
Department of Computer Science, Jinan University, China
Qingliang Chen received the Ph.D. degree in computer science from Sun Yat-Sen University, China. He is currently a professor with Department of Computer Science, Jinan University and an adjunct professor with Institute for Integrated and Intelligent Systems (IIIS), Griffith University. He was a Postdoctoral Fellow with Peking University, from 2010 to 2012. His research interests are machine learning and pattern recognition, and he has published more than 30 articles in peer-reviewed international journals and conferences such as AAAI, IJCAI and ECCV, and has served as the principal investigator for three research grants from National Natural Science Foundation of China.
Tilte: Domain-Level Pairwise Semantic Interaction for Aspect-Based Sentiment Classification
Aspect-based sentiment classification (ABSC) is a very challenging subtask of sentiment analysis (SA) and suffers badly from the class-imbalance. Existing methods only process sentences independently, without considering the domain-level relationship between sentences, and fail to provide effective solutions to the problem of class-imbalance. From an intuitive point of view, sentences in the same domain often have high-level semantic connections. The interaction of their high-level semantic features can force the model to produce better semantic representations, and find the similarities and nuances between sentences better. Driven by this idea, this talk will prepsent a plug-and-play Pairwise Semantic Interaction (PSI) module, which takes pairwise sentences as input, and obtains interactive information by learning the semantic vectors of the two sentences. Subsequently, different gates are generated to effectively highlight the key semantic features of each sentence. Finally, the adversarial interaction between the vectors is used to make the semantic representation of two sentences more distinguishable. Experimental results on four ABSC datasets show that, in most cases, PSI is superior to many competitive state-of-the-art baselines and can significantly alleviate the problem of class-imbalance.
Professor Yi Fang
Guangdong University of Technology/Communication Engineering, China
Yi Fang is a full professor with the Guangdong University of Technology, China, where he serves as the Vice Dean for the School of Information Engineering. He received the Ph.D. degree in communication engineering, Xiamen University, China, in 2013. He was a Postdoctoral Fellow with the Chinese University of Hong Kong, a Research Fellow with the Nanyang Technological University, Singapore, a Visiting Scholar with the University College London, UK, a Research Assistant with the Hong Kong Polytechnic University, Hong Kong from 2012 to 2018. He has published over 120 research papers, including more than 70 IEEE journal papers and 5 ESI hot /highly-cited papers. He also holds 20 granted patents related to channel coding and modulation. He has served as an Associate Editor for Frontiers in Signal Processing, and Frontiers in Communications and Networks. In addition, He served as the Symposium/Workshop/Organizing/Publicity/Local Arrangement Co-chair for various international conferences, including IEEE/CIC ICCC 2022/ISTC 2018/ISWCS 2019. He is a Senior Member of IEEE. His currently research interests include information and coding theory (especially LDPC codes), spread-spectrum modulation, and cooperative communications.
Tilte: Protograph LDPC Codes: State-of-the-Art and Future Challenges
Low-density parity-check (LDPC) codes have attracted much attention over the past two decades since they can asymptotically approach the Shannon capacity in a myriad of data transmission and storage scenarios. As a type of promising structured LDPC codes, the protograph LDPC codes not only inherit the advantage of conventional LDPC codes, i.e., excellent error performance, but also possess simple representations to realize fast encoding and efficient decoding. In this talk, we will first present the recent research progresses in protograph LDPC code design and analysis under different channel conditions, including the additive white Gaussian noise (AWGN) channels, fading channels, data-storage channels, and Poisson pulse-position modulation (PPM) channels. Afterwards, we will provide several valuable research challenges which have not been adequately addressed in the open literature, but deserve further investigation.
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2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE 2024) http://iccece.org/