1. 报告题目：Neurodynamics-based Parallel Data Selection in the Era of Big Data
2. 报告题目：Generative and Discriminative Learnings: A Fuzzy Restricted Boltzmann Machine and a Novel Broad Learning System
报告人：C. L. Philip Chen（澳门大学，Dean and Chair Professor）
3. 报告题目：Imbalanced Learning in Big Data
报告人：何海波 (美国罗德岛大学，Chair Professor)
报告题目：Neurodynamics-based Parallel Data Selection in the Era of Big Data
Abstract–In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed.
Jun Wang is the Chair Professor Computational Intelligence in the Department of Computer Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Dalian University of Technology, Huazhong University of Science and Technology, and Shanghai Jiao Tong University (Changjiang Chair Professor). He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology and his Ph.D. degree in systems engineering from Case Western Reserve University. His current research interests include neural networks and their applications. He published about 200 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial board of Neural Networks (2012-2014), editorial advisory board of International Journal of Neural Systems (2006-2013. He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee; IEEE Computational Intelligence Society Awards Committee; IEEE Systems, Man, and Cybernetics Society Board of Governors, He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.
报告题目：Generative and Discriminative Learnings: A Fuzzy Restricted Boltzmann Machine and a Novel Broad Learning System
In recent years, deep learning caves out a research wave in machine learning. With its outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. This talk will introduce a fuzzy generative deep learning algorithm and a novel broad learning systems. A fuzzy generative learning -- Fuzzy Restricted Boltzmann Machine (FRBM) -- is developed by replacing real-valued weights and bias terms with symmetric triangular fuzzy numbers (STFNs) or Gaussian fuzzy numbers and corresponding learning algorithms. A theorem is concluded that all FRBMs with symmetric fuzzy numbers will have identical learning algorithm to that of FRBMs with STFNs. The second part of the talk is to discuss a very fast and efficient discriminative learning -- “Broad Learning”. Without stacking the layer-structure, the designed neural networks expand the neural nodes broadly and update the weights of the neural networks incrementally when additional nodes are needed and when the input data entering to the neural networks continuously. The designed network structure and learning algorithm are perfectly suitable for modeling and learning big data environment. Experiments results in MNIST and handwriting recognition and NORB database indicate that the proposed BLS significantly outperforms existing deep structures in learning accuracy and generalization ability.
Dr. Chen is currently the Dean of the Faculty of Science and Technology, University of Macau, Macau, China and a Chair Professor of the Department of Computer and Information Science since 2010. He worked at U.S. for 23 years as a tenured professor, a department head and associate dean in two different universities.
Dr. Chen’s research areas are in systems, cybernetics and computational intelligence. He is a Fellow of the IEEE and AAAS. He was the President of IEEE Systems, Man, and Cybernetics Society (SMCS) (2012-2013). Currently, he is the Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-). He has been an Associate Editor of several IEEE Transactions, and currently he is an Associate Editor of IEEE Trans on Fuzzy Systems, IEEE Trans on Cybernetics, and IEEE/CAA Automatica Sinica. He was the Chair of TC 9.1 Economic and Business Systems of IFAC, 2015-2017. He is also a Fellow of CAA and Fellow of HKIE and an Academician of International Academy of Systems and Cybernetics Science (IASCYS). In addition, he is an ABET (Accreditation Board of Engineering and Technology Education, USA) Program Evaluator for Computer Engineering, Electrical Engineering, and Software Engineering programs.
Dr. Chen he received Outstanding Electrical and Computer Engineering Award in 2016 from his alma mater, Purdue University, West Lafayette, where he received his Ph.D. degree in 1988, after he received his M.S. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1985.
报告题目：Imbalanced Learning in Big Data
Big data has become an important topic worldwide over the past several years. Among many aspects of the big data research and development, imbalanced learning has become a critical component as many data sets in real-world applications are imbalanced, ranging from surveillance, security, Internet, finance, social network, to medical and healthy related data analysis. In general, the imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews, which is a typical case for many of the medical diagnosis data analysis, such as cancerous classification. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently and effectively into information and knowledge representation. In this talk, I will start with an overview of the nature and foundation of the imbalanced learning, and then focus on the state-of-the-art methods and technologies in dealing with the imbalanced data, followed by a systematic discussion on the assessment metrics to evaluate learning performance under the imbalanced learning scenario. I will also present the latest research development in our group that we have developed and tested on various imbalanced data sets. Finally, as a relatively new challenge to the community, I will highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data facing the big data era.
Haibo He is the Robert Haas Endowed Chair Professor and the Director of the Computational Intelligence and Self-Adaptive (CISA) Laboratory at the University of Rhode Island, Kingston, RI, USA. His primary research interests include computational intelligence, machine learning and data mining, cyber security, and various application domains. He has published one sole-author book (Wiley), edited 1 book (Wiley-IEEE) and 6 conference proceedings (Springer), and authored/coauthors over 250 peer-reviewed journal and conference papers, including several highly cited papers in IEEE Transactions on Neural Networks and IEEE Transactions on Knowledge and Data Engineering, Cover Page Highlighted paper in IEEE Transactions on Information Forensics and Security, and Best Readings of the IEEE Communications Society. He has delivered more than 50 invited/keynote/plenary talks around the globe. He was the Chair of IEEE Computational Intelligence Society (CIS) Emergent Technologies Technical Committee (ETTC) (2015) and the Chair of IEEE CIS Neural Networks Technical Committee (NNTC) (2013 and 2014). He served as the General Chair of 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI’14, Orlando, Florida). He is currently the Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems. He was a recipient of the IEEE International Conference on Communications (ICC) “Best Paper Award” (2014), IEEE CIS “Outstanding Early Career Award” (2014), National Science Foundation “Faculty Early Career Development (CAREER) Award” (2011), and Providence Business News (PBN) “Rising Star Innovator” Award (2011). More information can be found at: http://www.ele.uri.edu/faculty/he/