Prof. Jianguo Ma
Zhejiang Lab, China
Biography: Jianguo Ma received the doctoral degree in engineering in 1996 from Duisburg University, Duisburg, Germany. He was a faculty member of Nanyang Technological University (NTU) of Singapore from Sept 1997 to Dec. 2005 after his post-doctoral fellowship with Dalhousie University of Canada in Apr 1996 – Sept 1997. He was with the University of Electronic Science and Technology of China in Jan 2006 – Oct 2009 and he served as the Dean for the School of Electronic Information Engineering and the founding director of the Qingdao Institute of Oceanic Engineering of Tianjin University in Oct. 2009 – Aug 2016; he joined Guangdong University of Technology as a distinguished professor in Sept 2016 – Aug 2021. Dr. Ma served as the Vice Dean for the School of Micro-Nano Electronics of Zhejiang University in Sept, 2021 – Oct 2022, Starting from 1 Nov 2022 he joins the Zhejiang Lab. His research interests are: Microwave Electronics; RFIC Applications to Wireless Infrastructures; Microwave and THz Microelectronic Systems;
He served as the Associate Editor for IEEE Microwave and Wireless Components Letters in 2003 –2005; He was the member for IEEE University Program ad hoc Committee (2011~2013).
Dr. Ma was the Member of the Editorial Board for Proceedings of IEEE in 2013-2018
He is Fellow of IEEE for the Leadership in Microwave Electronics and RFICs Applications
Dr. Ma was serving as the Editor-in-Chief of IEEE Transactions on Microwave Theory and Techniques in 2020 –2022.
Speech Title: AI Empowered Microwave Power-Amplifier Designs and Optimizations
Abstract: Microwave power amplifiers have been and are still paying critical roles in any wireless communication systems and radar systems. The designs of microwave power-amplifiers are strongly experience-dependent because of the strong nonlinearities. It is time-consuming and very hard for getting optimal designs. Conceptual-wise the design procedures of microwave power-amplifiers are AI algorithms, it is straightforward to make use of AIs as tools for optimizing power-amplifier designs. The design challenging is discussed firstly followed by examples of using ANNs. PA-GPT will be the trend.
Prof. Guoyin Wang
Chongqing University of Posts
and Telecommunications, China
Biography: Professor Guoyin Wang received the B.E. degree in computer software, the M.S. degree in computer software, and the Ph.D. degree in computer organization and architecture from Xian Jiaotong University, China, in 1992, 1994, and 1996, respectively. He worked with the University of North Texas, USA, and the University of Regina, Canada, as a Visiting Scholar from 1998 to 1999. Since 1996, he has been working with the Chongqing University of Posts and Telecommunications, Chongqing, China, where he is currently a Professor, a Vice-President of the University. He is the Director of the Chongqing Key Laboratory of Computational Intelligence, the Key Laboratory of Tourism Multi-source Data Perception and Decision Technology of the Ministry of Culture and Tourism, and the Key Laboratory of Cyberspace Big Data Intelligent Security of the Ministry of Education. His research interests include data mining, machine learning, rough sets, granular computing, and cognitive computing. He was the President of International Rough Set Society (IRSS, 2014-2017). He is currently a Vice-President of the Chinese Association for Artificial Intelligence (CAAI) and a Council Member of the China Computer Federation (CCF). He is Fellow of IRSS, I2CICC, CAAI and CCF.
Speech Title: Multi-Granularity Cognitive Computing with Applications
Abstract: Cognitive computing is an interdisciplinary research direction in the field of artificial intelligence which aims to develop a coherent, unified, universal intelligent computing mechanism with inspiration of mind's capabilities. Granular human thinking is a kind of cognition mechanism for human problem solving. In this talk, Multi-Granularity Cognitive Computing (MGCC) is proposed to integrate the information transformation mechanism of traditional intelligent information processing systems and the multi-granularity cognitive law of human brain. The theoretical research issues and some applications about MGCC are discussed in this talk.
Prof. Xiangjian (Sean) He
University of Nottingham Ningbo,
Biography: Professor Xiangjian (Sean) He received his PhD in Computer Science from the University of Technology Sydney in 1999. He is currently the Deputy Head of Computer Science School and the Director of Computer Vision and Intelligent Perception Laboratory at the University of Nottingham Ningbo China (UNNC).
He is in list of the 'World Top 2% Scientists' reported by Stanford University in 2022.
He was the Professor of Computer Science and the Leader of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC) at the University of Technology Sydney (UTS) from 2011-2022. He was an IEEE Signal Processing Society Student Committee member. He was involved in a team receiving a UTS Chancellor's Award for Research Excellence through Collaboration in 2018. He has been awarded 'Internationally Registered Technology Specialist' by International Technology Institute (ITI). He led the UTS and Hong Kong Polytechnic University (PolyU) joint research project teams winning the 1st Runner-Up prize for the 2017 VIP Cup, and the champion for the 2019 VIP Cup, awarded by IEEE Signal Processing Society. In 2021, the team, PolyUTS, led by Prof Lam of PolyU and co-led by Prof He of UTS again won the 1st Runner-Up award for the 2021 VIP Cup.
He has been carrying out research mainly in the areas of computer vision, data analytics and machine learning in the previous years. He has recently been leading his research teams for deep-learning-based research for human behavious recognition, human counting and density estimation, tiny object detection, biomedical applications, saliency detection, natural language processing, cybersecurity, face and face expression recognition, road sign detection, license plate recognition, etc. He has played various chair roles in many international conferences such as ACM MM, MMM, ICDAR, IEEE BigDataSE, IEEE BigDataService, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE ICPR and IEEE ICARCV.
Speech Title: Big Data, Machine Learning and Computer Vision
Abstract: Big data are in all science and engineering domains. Analysis of them requires novel learning techniques to address the various challenges. This talk will briefly introduce the basic concepts of machine learning and give a brief survey of the research on machine learning for big data processing. Some promising learning methods in recent studies will be highlighted. Then, the challenges and possible solutions of machine learning for big data will be presented. Following that, the applications in computer vision, image and signal processing, Internet of Things, etc. will be investigated and various deep learning network models will be demonstrated for various applications such as crowd counting, image segmentation, traffic prediction, object tracking, etc.
Prof. Philippe Fournier
Shenzhen University, China
Biography: Philippe Fournier-Viger (Ph.D) is a Canadian researcher, distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China in 2015 and became full professor after receiving a talent title from the National Science Foundation of China. He has published more than 375 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 13,000 citations. He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate editor-in-chief of the Applied Intelligence journal and has been keynote speaker for over 20 international conferences and co-edited four books for Springer. He is a cofounder of the UDML, PMDB and MLiSE series of workshops held at the ICDM, PKDD, DASFAA and KDD conferences. Website:http://www.philippefournier-viger.com
Speech Title: Advances and challenges for the automatic discovery of interesting patterns in data
Abstract: Intelligent systems and tools can play an important role in various domains such as for factory automation, e-business, and manufacturing. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex data and can yield large amounts of data such usage logs, images, videos, and data collected from industrial sensors. Managing data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems and other applications.The talk will first briefly review early study on designing algorithms for identifying frequent patterns. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF opensource software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques for intelligent systems will be discussed.
Prof. Fanghong Zhang
Chongqing National center for applied mathematics, China
Biography: Professor Fanghong Zhang. Ph. D. graduated from Belgium's National Ghent University (Top 100 in the world) , Major in big data analysis, Chongqing “Hongyan” high-level overseas talents, Chongqing“100 plan” experts, a leading 2021 in scientific and technological innovation in Chongqing, who enjoys government expert subsidies. She has presided over a number of mathematical theory research and medical algorithm applications, leading the biomedical team of the Chongqing National Application Center to cooperate in depth with the medical unit of the First Affiliated Hospital of the Army Medical University, Children's Hospital of the Chongqing Medical University University, and on behalf of the center and “Digital Medicine” alliance units signed a strategic cooperation agreement. The key technologies and applications of sample and data collaboration in Clinical Biological Resource Bank, the technology of block chain data sharing in Biological Resource Bank, the construction of Medical Knowledge Atlas, the development of small focus recognition model for kidney disease, and the recognition technology of perforation in chest and abdomen were studied, promote the “Mathematics + biomedicine” cross-disciplinary integration and development. He has published more than 20 academic papers in domestic and foreign journals, patented 6 inventions and won 3 provincial and ministerial awards. She was a big data expert at China Shipping Group Offshore Wind Power Co. , Ltd. and a data scientist at mckinsey & Company in the United States, mckinsey Shanghai hosts mckinsey's large health care data analysis program for the world's top 500 clients and government agencies. She is now a professor at the Chongqing National Applied Mathematics Center.
Speech Title: Intelligent decision-making system for nephrotic syndrome in Children
Abstract: Childhood interrenal syndrome refers to a general term for a group of kidney diseases, including many types of glomerular disease and renal tubular disease.In allusion to problems such as the variety of comprehensive interrenal treatment plans for children and difficulty in evaluating prognosis, various technical means such as natural language processing NLP algorithms, knowledge mapping algorithms, and machine learning were used to process and analyze medical record data. Besides, an auxiliary diagnosis and treatment system with medical record inquiry, cause speculation, treatment plan recommendation, and efficacy evaluation was constructed. At the same time, in response to the difficulty of diagnosing interrenal syndrome in children, a set of high-efficiency and high-precision image recognition algorithms for the lesion area was developed based on the characteristics of the lesion area in CT images of the kidney, and a CT image diagnosis scheme for diagnosing interrenal syndrome in children was constructed.
Dr. Boran Yang
Chongqing University of Technology, China
Biography: Boran Yang received his Ph.D. degree from Chongqing University of Posts and Telecommunications and joined Chongqing University of Technology, Chongqing, China as a faculty member/research secretary in the School of Artificial Intelligence in 2023. His research interest includes Artificial Intelligence of Things, edge intelligence, and network security. He published more than 20 technical papers, in top journals such as IEEE JSAC, TGCN, TVT, TMM, IOTJ, and GLOBECOM. He is a co-recipient of the Best Paper Awards from IEEE MSN 2020 and IEEE GreenCom 2019. He served as the Guest Editor for DCN, Sensors, and WEVJ and the reviewer for IEEE TCOM and IOTJ. He also served as a TPC member for IEEE Healthcom 2023. He is a member of IEEE.
Speech Title: A Fine-Grained Intrusion Protection System for Inter-Edge Trust Transfer
Abstract: The phenomenal popularity of smart mobile computing hardware is enabling pervasive edge intelligence and ushering us into a digital twin era. However, the natural barrier between edge equipment owned by different interested parties poses unique challenges for cross-domain trust management. In addition, the openness of radio access and the accessibility of edge services render edge intelligence systems vulnerable and put sensitive user data in jeopardy. This paper presents an intrusion protection mechanism for edge trust transfer to address the inter-edge trust management issue and the conundrum of detecting indistinguishable malevolent nodes launching weak attacks. First, an inter-edge reputation transfer framework is established to leverage the trust quality of different edges to retain the accumulated trust histories of users when they roam in multi-edge environments structurally. Second, a fine-grained intrusion protection system is proposed to reduce the negative impact of attacks on user interactions and improve the overall trust quality and system security of edge intelligence services. The experimental results validate the effectiveness and superior performance of the proposed intrusion protection for edge trust transfer in securing, enhancing, and consolidating edge intelligence services.