Keynote Speech

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Keynote I     Speech Title: Electronic Medical Record (EMR) traceability with Federated Learning and Blockchain

Abstract: The healthcare industry has been at the cutting edge of technology since time immemorial. Hardware, software, medication, surgical procedures; the quality of care available to patients in 2021 has never been better. And yet, the administration and data management underpinning that care is severely lagging behind. In this talk, we will examine how Federated Learning and Blockchain Technology may be a game changer for the Medical profession. These technologies most commonly associated with Bitcoin and the Internet of Things (IoT) could actually transform how we manage electronic medical records (EMR). Deploying EMR using blockchain and federated learning has the potential to fundamentally disrupt the healthcare industry for good. During this talk, we will look at the pros as well as the cons of such implementations. We will dive into current research that is ongoing in this growing field and try to predict future directions this research may take.

  • Speaker: Prof. Gautam Srivastava, Brandon University, Canada.
  • Gautam Srivastava was awarded his B.Sc. degree from Briar Cliff University in U.S.A. in the year 2004, followed by his M.Sc. and Ph.D. degrees from the University of Victoria in Victoria, British Columbia, Canada in the years 2006 and 2012, respectively. In 2014, he joined a tenure-track position at Brandon University in Brandon, Manitoba, Canada, where he currently is active in various professional and scholarly activities. He was promoted to the rank Associate Professor in January 2018. Dr. G, as he is popularly known, is active in research in the fields of Security, Privacy, Data Mining and Artificial Intelligence. In his 8-year academic career, he has published a total of 170 papers in high-impact conferences in many countries and in high-status journals (SCI, SCIE) and has also delivered invited guest lectures on Big Data, Cloud Computing, Internet of Things, and Cryptography at many Czech universities. He is an Editor of several international scientific research journals. He currently has active research projects with other academics in Singapore, Canada, Czech Republic, Poland and U.S.A. He is constantly looking for collaboration opportunities with foreign professors and students. He is an IEEE Senior Member and his research is currently funded by Mathematics of Information Technology and Complex Systems (MITACS) as well as the Natural Sciences and Engineering Research Council of Canada (NSERC).

    Keynote II      Speech Title: Input Space Partitioning for Machine Learning

    Abstract:This talk introduces an input attribute grouping method to improve the performance of learning. During training for a specific problem, the input attributes are partitioned into groups according to the degree of inter‐attribute promotion or correlation that quantifies the supportive or negative interactions between attributes. After partitioning, multiple sub‐networks are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each sub‐network. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.

  • Speaker: Prof. Steven Guan, Xi’an Jiaotong-Liverpool University, China.
  • Steven Guan received his BSc. from Tsinghua University and M.Sc. (1987) & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a Professor and the Director for Research Institute of Big Data Analytics at Xi'an Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK. Prof. Guan’s research interests include: machine learning, computational intelligence, big data analytics, mobile commerce, modeling, networking, personalization, security, and pseudorandom number generation. He has published extensively in these areas, with 130+ journal papers and 180+ book chapters or conference papers. He has chaired, delivered keynote speech for 80+ international conferences and served in 180+ international conference committees and 20+ editorial boards. There are quite a few inventions from Prof. Guan including Generalized Minimum Distance Decoding for Majority Logic Decodable Codes, Prioritized Petri Nets, Self-Modifiable Color Petri Nets, Dynamic Petri Net Model for Iterative and Interactive Distributed Multimedia Presentation, Incremental Feature Learning, Ordered Incremental Input/Output Feature Learning, Input/Output Space Partitioning for Machine Learning, Recursive Supervised Learning, Reduced Pattern Training using Pattern Distributor, Contribution Based Feature Selection, Incremental Genetic Algorithms, Incremental Multi-Objective Genetic Algorithms, Decremental Multi-objective Optimization, Multi-objective Optimization with Objective Replacement, Incremental Hyperplane Partitioning for Classification, Incremental Hyper-sphere Partitioning for Classification, Controllable Cellular Automata for Pseudorandom Number Generation, Self Programmable Cellular Automata, Configurable Cellular Automata, Layered Cellular Automata, Transformation Sequencing of Cellular Automata for Pseudorandom Number Generation, Open Communication with Self-Modifying Protocols, etc.

    Keynote III     Speech Title: Research Experiences and Deep Learning

    Abstract: In this presentation, the research experience and deep learning approach studied in the DB (Database and Bioinformatics) lab will be presented. First, the main research flow and contents of our laboratory over the past 35 years will be summarized. Then, we examine important keywords in machine learning and deep learning concepts, and explain how our recent research is applying real-world applications to deep learning methods. Finally, we introduce "Auto Adaptive Deep Learning Approach for Time-Dependent Domains", a study we are currently conducting.

  • Speaker: Prof. Keun Ho Ryu, Chungbuk National University, South Korea.
  • Keun Ho Ryu (M’82, LM’19) received the Ph.D. degree in computer science and engineering from Yonsei University, South Korea, in 1988. He has served at the Reserve Officers’ Training Corps (ROTC) of the Korean Army. He was with The University of Arizona, Tucson, AZ, USA, as a Postdoctoral and a Research Scientist, and also with the Electronics and Telecommunications Research Institute, South Korea, as a Senior Researcher. He is currently a Professor with the Faculty of Information Technology, Ton Duc Thang University, Vietnam, as well as an Emeritus and Endowed Chair Researcherwith Chungbuk National University, South Korea, and also an Adjunct Professor with Chiang Mai University, Thailand. He is also an Honorary Doctorate of the National University of Mongolia. He is not only the Leader of the Database and Bioinformatics Laboratory, South Korea, since 1986, but also the co-leader of Research Group, Data Science Laboratory, Vietnam, since March 1, 2019. He is the former Vice-President of the Personalized Tumor Engineering Research Center. He has published over 1000 referred technical articles in various journals and international conferences, in addition to authoring a number of books. His research interests include databases, big data analysis, data mining, deep learning, biomedical informatics, and bioinformatics. He has been a member of the IEEE in 1982 and a member of the ACM since 1983. He has served on numerous program committees, including roles as the Demonstration Co-Chair of the VLDB, as the Panel and Tutorial Co-Chair of the APWeb, and as the FITAT General Co-Chair. As supervisor to doctoral candidates, he directed 112 doctoral dissertations, and produced 112 Ph.Ds. He has found the FITAT in 2007 and been taken care of FITAT since 2007.

    Keynote IV      Speech Title: Signal Processing Approaches to Sensing without Sensors

    Abstract: Human activity recognition is the core technology that enables a wide variety of applications such as health care, smart homes, fitness tracking, and building surveillance. We recognize human activities using signals from commercial WiFi devices. Human bodies reflect wireless signals as they are mostly made of water. Different human activities cause different changes on wireless signals. Thus, by analyzing the changes in wireless signals, we can recognize the corresponding human activities that cause the changes. We classify human activities into macro activities, which involve mostly arm, leg, or body scale movements, and micro activities, which involve mostly finger or hand scale movements. Human activity recognition and monitoring is the enabling technology for various applications such as elderly/health care, building surveillance, human-computer interaction, health care, smart homes, and fitness tracking. In this talk, I will present our research results on this topic.

  • Speaker: Prof. Alex X. Liu, Ant Group, China
  • Alex X. Liu received his Ph.D. degree in Computer Science from The University of Texas at Austin in 2006, and is currently the Chief Scientist of Ant Group. Before that, he was a Professor of the Department of Computer Science and Engineering at Michigan State University. He received the IEEE & IFIP William C. Carter Award in 2004, a National Science Foundation CAREER award in 2009, the Michigan State University Withrow Distinguished Scholar (Junior) Award in 2011, and the Michigan State University Withrow Distinguished Scholar (Senior) Award in 2019. He has served as an Editor for IEEE/ACM Transactions on Networking and an Area Editor for Computer Communications. He is currently an Associate Editor for IEEE Transactions on Dependable and Secure Computing and IEEE Transactions on Mobile Computing. He has served as the TPC Co-Chair for ICNP 2014 and IFIP Networking 2019. He received Best Paper Awards from SECON-2018, ICNP-2012, SRDS-2012, and LISA-2010. His research interests focus on network security, dependable computing, data privacy, and Internet of Things. He is an IEEE Fellow, an IET Fellow, an AAIA Fellow, and an ACM Distinguished Scientist.