Title
Incremental and Multi-source Data Mining

Abstract
Along with the coming of the big data era, data usually come constantly and may be distributed in different sources for a big company. Incremental and multi-source data mining thus becomes more and more important. In this speech, I will thus introduce these issues for different kinds of knowledge. In the first part, I will describe the concepts of some incremental mining approaches, including FUP, negative border and pre-large itemsets, and propose efficient incremental mining algorithms based on them for different mining problems. Extension to data deletion and modification and to different data structures is discussed as well. Besides, many large organizations have multiple databases distributed at different branches. In the second part, I will thus consider extending the incremental concepts to data mining from multiple sources. Different from incremental mining, the amount of data at each data source is usually bigger than that from just updating. Thus, it is not the case of inserting a small amount of data into a database, but merging two or more big-size databases to mine knowledge. The concepts in incremental mining will be extended to designing good pruning strategies for reducing the execution time in multi-source mining. Experimental results are also shown to verify the effectiveness and efficiency of the above approaches.

Biodata
Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992. He served at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University from 1994 to 2001. He was in charge of the whole computerization and library planning for National University of Kaohsiung in Preparation from 1997 to 2000 and served as the first director of the library and computer center in National University of Kaohsiung from 2000 to 2001, as the Dean of Academic Affairs from 2003 to 2006, as the Administrative Vice President from 2007 to 2008, and as the Academic Vice President in 2010. He is currently a distinguished professor at the Department of Computer Science and Information Engineering and at the Department of Electrical Engineering, National University of Kaohsiung, and a joint professor at the Department of Computer Science and Engineering, National Sun Yat-sen University, Taiwan. He got the first national flexible wage award from Ministry of Education in Taiwan.

He has published more than 500 research papers in international/national journals and conferences and has planned more than fifty information systems. He is also the board member of more than forty journals and the program committee member of more than three hundred conferences. His current research interests include knowledge engineering, data mining, soft computing, management information systems, and www applications.

Title
Modelling and Simulation of Large Complex Systems

Abstract
Simulation model development is a process that involves producing computer models to approximate the behaviour of real processes. Although specification and requirements alter amongst problems, the standard methodology applied in the simulation model development process remains unchanged between projects.

In this talk I will focus on discrete event simulation. A discrete event simulation cannot only provide accurate prediction of the system behaviour (hard system thinking), it can also be used to facilitate problem understanding and management learning (soft system thinking). This talk will cover some case studies to put forward personal research experience in studying and dealing with large complex systems such as international airport security and the balance between complexity and risk. The baggage handling system (BHS) in airports plays an important role, ensuring bags are secure and delivered on time. It is the key component within major airports to ensure smooth transition of luggage and ensure a safe flying experience by preventing dangerous material from entering the plane. The performance of the baggage handling system is crucial to airport operation.

Biodata
Saeid Nahavandi received his BSc (Hons), MSc and PhD in Control Engineering from Durham University, UK in 1985, 1986 and 1991 respectively. Saeid is an Alfred Deakin Professor and the Director for the Centre for Intelligent Systems Research at Deakin University in Australia. Professor Nahavandi is a Fellow member of IET, IEAust and Senior Member of IEEE and has published over 550 refereed papers and been awarded several competitive Australian Research Council (ARC) grants over the past 18 years. He received the Research collaboration / initiatives award from Japan (2000) and Prince & Princess of Wales Science Award in 1994. He won the title of Young Engineer of the Year Award in 1996 and holds two patents. In 2002 Professor Nahavandi served as a consultant to the Jet Propulsion Lab (NASA) during his visit to JPL Labs. In 2006 he received the title of Alfred Deakin Professor, the highest honour at Deakin University for his contribution to fundamental research.

In modelling and simulation of complex systems he received awards from several organisations to focus on simulation based optimization of manufacturing processes, airport operations, logistics and distribution centres. He has carried out industry based research with several major international companies such as GM, Ford, Holden, Nissan, Bosch, Futuris, Boeing, Vestas just to name a few.

Professor Nahavandi was General Co-Chair for the IEEE SMC 2011. He also holds the position of Co-Editor-in-Chief for IEEE Systems Journal, Associate Editor: IEEE/ASME Mechatronics, Associate Editor: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, Associate Editor: IEEE SMC Magazine.

Title
Collective Neurodynamic Optimization Approaches to Nonnegative Matrix Factorization

Abstract
Nonnegative matrix factorization (NMF) is an advanced method for nonnegative feature extraction, with widespread applications. However, the NMF solution often entails to solve a global optimization problem with a nonconvex objective function and a nonnegativity constraint. To tackle this challenging problem, this paper presents a collective neurodynamic optimization approach by employing a population of recurrent neural networks (RNNs) at the lower level and particle swarm optimization (PSO) with wavelet mutation at the upper level. The RNNs act as search agents carrying out precise constrained local searches according to their neurodynamic equations and initial conditions. The PSO algorithm coordinates and guides the RNNs with updated intial states toward global optimal solution(s). A wavelet mutation operator is added in the optimization to enhance PSO exploration capability. Through iterative interaction and improvement of the locally best solutions of RNNs and global best positions of the whole population, the population-based neurodynamic systems is almost sure to achieve the global optimality for the NMF problem. The convergence of the group best state to the global optimal solution with probability one is proven. The experimental results substantiate the efficacy and superiority of the collective neurodynamic optimization approach to bound-constrained global optimization with several benchmark nonconvex functions and NMF-based clustering with benchmark datasets in comparison to the state-of-the-art algorithms.

Biodata
Jun Wang is a 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 Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Huazhong University of Science and Technology, and Shanghai Jiao Tong University as a Changjiang Chair Professor, and Dalian University of Technology as a National Thousand-Talent Chair Professor. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published over 170 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 since 2014 and a member of the editorial board of Neural Networks since 2012. 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 advisory board of International Journal of Neural Systems (2006-2013), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014), and International Journal of Fuzzy Systems (2010, 2011). 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 (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), 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, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.

Title
Big data in contemporary linguistic research. In search of optimum methods for language chronologization

Abstract
Work in linguistics in the 21st century is developing chiefly in the direction of experimental, corpus-based, quantitative research. Excerption, accumulation and observation, followed by computation – this is what practically all researchers today are doing. While the dominant issue in research before the big data era was simply the identification of oppositions, at present more and more time is being devoted to the collection of numerical data concerning particular linguistic objects.

The lecture will concern the theoretical and practical problems of analysing the mass of linguistic data which has arisen in conjunction with the development of many fields of life – including academic, for example relating to the growth in the total number of researchers, diversification of linguistic disciplines, etc., as well as the general development of technology, civilization, etc. Moreover, the universe of texts is growing every day – both forwards and backwards. Forwards because every new article, book, blog, e-mail or text message expands the set of existing texts; and backwards because the same set is also expanded whenever a scan is made of another historical text. Our knowledge about past times is growing by leaps and bounds. We are therefore particularly interested in the analysis of historical texts that can be carried out in the second decade of the 21stcentury.

To sum up – linguistics is becoming more and more quantitative. As a consequence, and with regard to the costs of human researchers, it is also becoming increasingly computerized. While most of the pool of actions known to us relating to automatic processing and searching of texts and extraction of information are carried out on our private home computers, the tasks being undertaken by linguists in the 21stcentury carry much higher operating requirements, which can be satisfied only with the use of supercomputers, clusters or grids. The same requirements currently apply to the analysis of historical texts.

The lecture will deal with contemporary problems relating to the processing of historical natural language from the point of view of a linguist who also has operational interests.

Biodata
Piotr Wierzchoń graduated in Poland at the Polish and Classical Languages Faculty of Adam Mickiewicz University in 1997. He went on to complete a doctorate at the Modern Languages Faculty of the same university under Professor Jerzy Bańczerowski (his doctoral thesis concerned problems in the description of inflection). He gained his habilitation qualification in 2004, and was awarded the title of Professor of Humanities in 2012/2013. His work concerns subjects including lexicology, corpusology, lexicography, philosophy of language, algorithm theory, axiomatic reconstruction of linguistic theories, contrastive linguistics, morphonotactics, phrasematics, chronologization of new Polish vocabulary, large derivation models of 20th-century vocabulary, and other related areas. He worked for four years at Hankuk University of Foreign Studies in South Korea.

He has been interested since a young age in science and technology, the history of inventions and their patenting. In the 1980s, with his parents’ support, he subscribed to the Polish magazines ABC Techniki, Kalejdoskop Techniki and Młody Technik.

He is the author of a large number of articles and more than a dozen books. He has recently taken a particular interest in the construction of production lines, including linguistic ones. The development of such solutions involves the testing of various possible scenarios (algorithms) and, on that basis, the optimization of every component process (task). A work that has been of great importance on this path to the creation of grammars of action is the article by T. Kotarbiński titled “A General Methodology of Action.”

One of his latest achievements is the theory of linguochronologization (TLCH), which was outlined in the work entitled “Photodocumentation. Chronologization. Emendation. The theory and practice of verification of lexical material in linguistic research” (Poznań, 2008). This is a theory in applied linguistics, oriented towards the chronologization of vocabulary from the 20th century, making it possible to obtain in a very economical manner a set of excerption and chronologization results from vast quantities of photographic documentation.

He is the Director of the Institute of Linguistics at Adam Mickiewicz University in Poznań, which offers several courses of study, including some specializing in less commonly studied modern languages such as Korean, Vietnamese, Hungarian, Modern Greek, Finnish, Indonesian and Malaysian, Lithuanian, and Latvian.