Speaker: Prof. Steven Guan, Director of the Research Institute of Big Data Analytics (RIBDA), Xi’an Jiaotong-Liverpool University, China
Title: Incremental Hyperplane Partitioning Approach for Classification
Abstract: An incremental hyperplane partitioning approach is proposed for classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm (GA). A new method – Incremental Linear Encoding based Genetic Algorithm (ILEGA) is proposed for that purpose. We solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. The algorithm is tested with six datasets.The experimental results show that ILEGA outperform in both lower- and higher-dimensional problems compared with the original GA. A variation of the incremental hyperplane partitioning approach is also presented, namely incremental hypersphere partitioning.
Biography: Steven Guan received his BSc. from Tsinghua University (1979) and M.Sc. (1987) & PhD (1989) 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 as 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 has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the Computing Center and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor for eight years. 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 speeches 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 MultiObjective 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.
Speaker: Prof. Danny Hughes, Department of Computer Science of KU Leuven, Belgium
Title: Retrofitting Your Assets with the Industrial IoT: Opportunities and Challenges
Abstract: This talk will describe industrial experiences of applying IoT technologies to gather real-time telemetry from a wide range of industrial assets in order to improve product quality, eliminate downtime and reduce waste. This talk will first review key building blocks of this solution, including: plug-and-play sensors, reliable wireless networks and nuts-and-bolts data analytics. The talk will then discuss how core IoT technologies can be combined to solve real business solutions. This presentation will be illustrated throughout with examples of real-world industrial IoT systems that are generating value today for customers in domains as diverse as: ore processing, automotive manufacture and fast moving consumer goods.
Biography: Dr. Danny Hughes is a Professor with the Department of Computer Science of KU Leuven (Belgium), where he is a member of the DistriNet (Distributed Systems and Computer Networks) research group and leads the Networked Embedded Software task-force. He has a PhD from Lancaster University (UK) and has previously worked at the University of California at Berkeley (USA), the University of Sao Paulo (Brazil) and Xi’an Jiaotong-Liverpool University (China).