||Professor Luiz Moutinho
Adam Smith Business School
University of Glasgow
West Quadrangle, Gilbert Scott Building
Glasgow G12 8QQ
Beyond Self-Reporting..Polymeasures: from Behavioural Genetics, Biomarketing and Human-Computer Interaction to Neurophysiology and Neuroscience.
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With a backdrop of the trend towards the sensorization of things, the limitations of research based on self-reporting measures are highlighted.An overview of the new concept of polymeasures is then introduced ,analysing techniques ranging from facial electromyography and voice prints to vein matching and pupillometrics.The presentation then will analyse issues related to the brain physiology and a dissection of the physio data.The importance of emotion in intelligent human functioning is stressed.There will be then the a discussion of signal-based evaluation tools,voice emotion response and utterance analysis.The emotional speech corpus and the core architecture of the interface of the Slogan Validator System will be assessed.In the area of neuroscience, I will address my research on emotional branding, covering concepts like fMRI,the BOLD signal, neuroscience paradigms,interoception and the model of emotions.Specific techniques beyond fMRI like fNIRS-DOT and Neuroelectrics like TMS will also be presented.Finally,an example of an artificial neural networks topology and bootstrapping using neuroscientific data will be shown.
Professor Moutinho completed his PhD at the University of Sheffield in 1982. He has been a Full Professor for 25 years and held posts at Cardiff Business School, University of Wales College of Cardiff, Cleveland State University, Ohio, USA, Northern Arizona University, USA and California State University, USA. He has held Visiting Professorship positions at Hubei University, China, Hubei University of Economics, China, Hebei United University, China, University of Vilnius, Lithuania, University of Innsbruck, Austria, Otago University, New Zealand, University of Aarhus, Denmark, Bled School of Management, Slovenia, University of Aveiro, Portugal, Gyor University, Hungary, Feng Chia University, Taiwan, University of Coimbra, Portugal, Technical University of Lisbon, Portugal, FGV-Sao Paulo, Brazil, Catholic University, Brazil, University of Los Andes, Colombia and University of Cyprus. Between 1987 and 1989 he was the Director of the Doctoral Programmes at the Confederation of Scottish Business Schools and at the Cardiff Business School between 1993 and 1996. He was Director of the Doctoral Programme at the University of Glasgow in Management between 1996 and 2004. He is the founding Editor of the Journal of Modelling in Management (JM2) and has another 4 Associate Editorships as well as being in the Editorial Boards of another 46 international academic journals.
His areas of research interest also encompass bio-marketing, neuroscience in marketing, evolutionary algorithms, human-computer interaction, the use of artificial neural networks in marketing, modelling consumer behaviour, marketing futurecast and tourism and marketing.
Other primary areas of Professor Moutinho's academic research are related to modelling processes of consumer behaviour. He has developed a number of conceptual models over the years in areas such as tourism destination decision processes, automated banking, supermarket patronage, among other areas. The testing of these research models has been based on the application of many different statistical, computer and mathematical modelling techniques ranging from multidimensional scaling, multinomial logit generalised linear models (GLMs) and linear structural relations to neural networks, ordered probit, simulated ammealing, tabu search, genetic algorithms, and fuzzy logic. He has over 130 articles published in refereed academic journals, 27 books, more than 5,500 academic citations, an h-index of 36 and an i10-index of 115.
||Dr Simon Fong
University of Macau
Big Data Stream Mining: Opportunities and Challenges
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Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, one of the root sources of Big Data is founded on data streams. We call this Big Data Stream, which is sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining or high-speed data analytics in a broad sense. A novel data stream mining methodology, called Stream-based Holistic Analytics and Reasoning in Parallel (SHARP) is proposed. SHARP is based on principles of incremental learning which span across a typical data-mining model construction process, from lightweight feature selection, one-pass incremental decision tree induction, and incremental swarm optimization. Each one of these components in SHARP is designed to function together aiming at improving the classification/prediction performance to its best possible. SHARP is scalable, that depends on the available computing resources during runtime, the components can execute in parallel, collectively enhancing different aspects of the overall SHARP process for mining data streams. It is believed that if Big Data Streams are being mined by incrementally learning a data mining model, one pass at a time on the fly, the large volume of such big data is no longer a technical issue, from the perspective of data analytics. Computer simulation experiments are demonstrated in this tutorial, pertaining to concepts of SHARP, for illustrating its opportunities and challenges.
Dr. Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now Associate Professor of the Computer and Information Science Department, University of Macau. He is one of the founding members of the Data Analytics and Collaborative Computing Research Group. Before joining the University of Macau, he worked as an Assistant Professor in the School of Computer Engineering, Nanyang Technological University, Singapore. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Melbourne, Hong Kong and Singapore. Some companies that he worked before include Hong Kong Telecom, Singapore Network Services, AES Pro-Data and United Oversea Bank, Singapore. Dr. Fong has published over 290 book chapters, international conference and peer-reviewed journal papers, mostly in the area of optimization algorithms and data-mining. Simon holds several editorial positions in SCIE and EI-indexed journals. Currently, he is the Intl. Coordinator (Asia-Pacific Region) of INNS-India Regional Chapter.