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X-WR-CALDESC:Events for Faculty of Science and Technology | University of Macau
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DTSTART:20170101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170609T110000
DTEND;TZID=Asia/Macau:20170609T120000
DTSTAMP:20260511T100901
CREATED:20170609T030006Z
LAST-MODIFIED:20220927T044400Z
UID:6114-1497006000-1497009600@www.fst.um.edu.mo
SUMMARY:Extreme Learning Machines (ELM): Enabling Pervasive Learning and Pervasive Intelligence in Internet of Intelligent Things
DESCRIPTION:Instructors/Speakers\nProf. Guang-Bin HUANG\nNanyang Technological University\nSingapore \nAbstract\nThis talk will analyse the differences and relationships among artificial intelligence and machine learning\, and also advocates the intelligence revolution and show its potential impact will be much more influential than agriculture revolution and industrial revolution. ELM theories may have explained the reasons why the brains are globally ordered but may be locally random. This talk will share with audience ELM’s direct biological evidences. Finally this talk will share with audiences the trends of machine learning in which ELM may play some important roles: 1) convergence of machine learning and biological learning; 2) from human and (living) thing intelligence to machine intelligence; 3) from cloud intelligence to local intelligence; 4) from Internet of Things (IoT) to Internet of Intelligent Things and Society of Intelligent Things; 5) pervasive learning and pervasive intelligence will come true. \nBiography\nGuang-Bin Huang is a Full Professor in the School of Electrical and Electronic Engineering\, Nanyang Technological University\, Singapore. He is a member of Elsevier’s Research Data Management Advisory Board. He is one of three Expert Directors for Expert Committee of China Big Data Industry Ecological Alliance organized by China Ministry of Industry and Information Technology\, and a member of International Robotic Expert Committee for China. He was a Nominee of 2016 Singapore President Science Award\, was awarded Thomson Reuters’s 2014 “Highly Cited Researcher” (Engineering)\, Thomson Reuters’s 2015 “Highly Cited Researcher” (in two fields: Engineering and Computer Science)\, and listed in Thomson Reuters’s “2014 The World’s Most Influential Scientific Minds” and “2015 The World’s Most Influential Scientific Minds.” He received the best paper award from IEEE Transactions on Neural Networks and Learning Systems (2013). \nHe serves as an Associate Editor of Neurocomputing\, Cognitive Computation\, neural networks\, and IEEE Transactions on Cybernetics. \nHe is Principal Investigator of BMW-NTU Joint Future Mobility Lab on Human Machine Interface and Assisted Driving\, Principal Investigator (data and video analytics) of Delta – NTU Joint Lab\, Principal Investigator (Scene Understanding) of ST Engineering – NTU Corporate Lab\, and Principal Investigator (Marine Data Analysis and Prediction for Autonomous Vessels) of Rolls Royce – NTU Corporate Lab. He has led/implemented several key industrial projects (e.g.\, Chief architect/designer and technical leader of Singapore Changi Airport Cargo Terminal 5 Inventory Control System (T5 ICS) Upgrading Project\, etc). \nOne of his main works is to propose a new machine learning theory and learning techniques called Extreme Learning Machines (ELM)\, which fills the gap between traditional feedforward neural networks\, support vector machines\, clustering and feature learning techniques. ELM theories have recently been confirmed with biological learning evidence directly\, and filled the gap between machine learning and biological learning. ELM theories have also addressed “Father of Computers” J. von Neumann’s concern on why “an imperfect neural network\, containing many random connections\, can be made to perform reliably those functions which might be represented by idealized wiring diagrams.” \n 
URL:https://www.fst.um.edu.mo/event/extreme-learning-machines-elm-enabling-pervasive-learning-and-pervasive-intelligence-in-internet-of-intelligent-things/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170621T110000
DTEND;TZID=Asia/Macau:20170621T120000
DTSTAMP:20260511T100901
CREATED:20170621T030011Z
LAST-MODIFIED:20220927T044359Z
UID:6118-1498042800-1498046400@www.fst.um.edu.mo
SUMMARY:Big Data Analytics on Big Spatial Database
DESCRIPTION:Instructors/Speakers\nProf. Raymond Chi-Wing WONG\nThe Hong Kong University of Science and Technology \nAbstract\nNowadays\, location-based services (LBSs)\, which refer to those services that are based on location (or spatial) data\, are broadly used in our daily life. In this talk\, we will talk about the recent development of LBSs. Some examples are “Search-nearby”\, “Spatial Crowdsourcing”\, “Trace Tracking” and “Shortest Distance”. We will focus on presenting some important results about shortest distance queries\, one fundamental LBS\, in the new context of the three-dimensional spatial database which receives a lot of attention from both the academic community and the industry community like Microsoft’s Bing Maps and Google Earth.  \nBiography\nRaymond Chi-Wing Wong is an Associate Professor in Computer Science and Engineering (CSE) of The Hong Kong University of Science and Technology (HKUST). He was the director of the Computer Engineering (CPEG) program from 2014 to 2016 and was the associate director of the Computer Engineering (CPEG) program from 2012 to 2014. He received the BSc\, MPhil and PhD degrees in Computer Science and Engineering in the Chinese University of Hong Kong (CUHK) in 2002\, 2004 and 2008\, respectively. In 2004-2005\, he worked as a research and development assistant under an R&D project funded by ITF and a local industrial company called Lifewood. \nHe received 28 awards. He published 54 conference papers (e.g.\, SIGMOD\, SIGKDD\, VLDB\, ICDE and ICDM)\, 23 journal/chapter papers (e.g.\, TODS\, DAMI\, TKDE\, VLDB journal and TKDD) and 1 book. He reviewed papers from conferences and journals related to data mining and database\, including VLDB conference\, SIGMOD\, TODS\, VLDB Journal\, TKDE\, TKDD\, ICDE\, SIGKDD\, ICDM\, DAMI\, DaWaK\, PAKDD\, EDBT and IJDWM. He is a program committee member of conferences\, including SIGMOD\, VLDB\, ICDE\, KDD\, ICDM and SDM\, and a referee of journals\, including TODS\, VLDBJ\, TKDE\, TKDD\, DAMI and KAIS. \n 
URL:https://www.fst.um.edu.mo/event/big-data-analytics-on-big-spatial-database/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170626T110000
DTEND;TZID=Asia/Macau:20170626T120000
DTSTAMP:20260511T100901
CREATED:20170626T030025Z
LAST-MODIFIED:20220927T044359Z
UID:6121-1498474800-1498478400@www.fst.um.edu.mo
SUMMARY:Determining the Impact Regions of Competing Options in Preference Space
DESCRIPTION:Instructors/Speakers\nProf. Man Lung YIU\nThe Hong Kong Polytechnic University \nAbstract\nIn rank-aware processing\, user preferences are typically represented by a numeric weight per data attribute\, collectively forming a weight vector. \nThe score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting\, the user input is a vector (equivalently\, a point) in a d-dimensional preference space\, where d is the number of data attributes. \nIn this work\, we study the problem of determining in which regions of the preference space the weight vector should lie so that a given option focal record is among the top-k score-wise. In effect\, these regions capture all possible user profiles for which the focal record is highly preferable\, and are therefore essential in market impact analysis\, potential customer identification\, profile-based marketing\, targeted advertising\, etc. We refer to our problem as k-Shortlist Preference Region identification\, and exploit its computational geometric nature to develop a framework for its efficient (and exact) processing. Using real and synthetic benchmarks\, we show that our most optimized algorithm outperforms by three orders of magnitude a competitor we constructed from previous work on a different problem. \nBiography\nMan Lung Yiu received the bachelor’s degree in computer engineering and the PhD degree in computer science from the University of Hong Kong in 2002 and 2006\, respectively. Prior to his current post\, he worked at Aalborg University for three years starting in the Fall of 2006. He is now an associate professor in the Department of Computing\, Hong Kong Polytechnic University. His research focuses on the management of complex data\, in particular query processing topics on spatiotemporal data and multidimensional data. \n 
URL:https://www.fst.um.edu.mo/event/determining-the-impact-regions-of-competing-options-in-preference-space/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170630T153000
DTEND;TZID=Asia/Macau:20170630T163000
DTSTAMP:20260511T100901
CREATED:20170630T073051Z
LAST-MODIFIED:20220927T044358Z
UID:6124-1498836600-1498840200@www.fst.um.edu.mo
SUMMARY:Diagnosing Un-occurred Diseases by Dynamic Network Biomarkers --- Detecting the Tipping Points of Biological Processes by Big Data
DESCRIPTION:Instructors/Speakers\nProf. Luonan CHEN\nProfessor\nCAS Key Laboratory of System Biology\nInstitute of Biochemistry and Cell Biology\nShanghai Institutes for Biological Sciences\, CAS\, Shanghai\nChina \nAbstract\nConsiderable evidence suggests that during the progression of complex diseases\, the deteriorations are not necessarily smooth but are abrupt\, and may cause a critical transition from one state to another at a tipping point. Here\, we develop a model-free method to detect early-warning signals of such critical transitions (or un-occurred diseases)\, even with only a small number of samples. Specifically\, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent sudden deterioration before the critical transition occurs. Based on theoretical analyses\, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample\, e.g.\, high-throughput data. We employ gene expression data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data (e.g.\, liver cancer\, lung injury\, influenza\, type-2 diabetes) and functional analysis. DNB can also be used for the analysis of nonlinear biological processes\, e.g.\, cell differentiation process. \nBiography\nProf. Luonan Chen received BS degree in the Electrical Engineering\, from Huazhong University of Science and Technology\, and the M.E. and Ph.D. degrees in the electrical engineering\, from Tohoku University\, Sendai\, Japan\, in 1984\, 1988 and 1991\, respectively. From 1997\, he was an associate professor of the Osaka Sangyo University\, Osaka\, Japan\, and then a full Professor. Since 2010\, he has been a professor and executive director at Key Laboratory of Systems Biology\, Shanghai Institutes for Biological Sciences\, Chinese Academy of Sciences. He was the founding director of Institute of Systems Biology\, Shanghai University\, and is also research professor at the University of Tokyo since 2010. He was elected as the founding president of Computational Systems Biology Society of OR China\, and Chair of Technical Committee of Systems Biology at IEEE SMC Society. He serves as editor or editorial board member for major systems biology related journals. In recent years\, he published over 280 SCI journal papers and two monographs (books) in the area of systems biology. \n 
URL:https://www.fst.um.edu.mo/event/diagnosing-un-occurred-diseases-by-dynamic-network-biomarkers-detecting-the-tipping-points-of-biological-processes-by-big-data/
LOCATION:E11-1006
CATEGORIES:event_list,seminarslectures
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