Faculty of Science and Technology | University of Macau

Magical Mathematics - Mathematical Magic
2017-03-01 15:30 - 16:30 (Asia/Macau) | E4-G053

Towards Processing of Big Graphs
2017-03-24 15:00 - 16:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Xuemin LIN
The University of New South Wales, Australia
Abstract
Graphs are very important parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications such as bioinformatics, web search, social network, road network, etc. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analysing graph data. In this talk, I will present some of our recent research efforts in processing big graphs including scalable processing theory and techniques, distributed computation, and system framework.
Biography
Xuemin Lin is a UNSW Scientia Professor and the head of database group in the school of computer science and engineering at UNSW, Australia. Xuemin’s research interests lie in databases, algorithms, and complexities. Specifically, he is working in the area of scalable data processing covering graph data, spatial-temporal data, streaming data, uncertain data, text data, etc. Xuemin was an associate editor of ACM TODS (2008-2014), IEEE TKDE (Feb 2013- Jan 2015), and an associate editor-in-Chief of IEEE TKDE (2015-2016). He is currently the Editor-in-Chief of IEEE TKDE (2017 Jan – Now). Xuemin has published over 130 papers in the top venues such as SIGMOD, SIGIR, SIGKDD, ACM MM, VLDB, PODS, ICDE, IJAIC, IEEE TKDE, VLDB J, and ACM TODS. Xuemin co-authored 16 best papers in the international conferences, including the best paper award in ICDE2016 and best student paper award in ICDE2007. Xuemin Lin was selected as one of the National Thousand Distinguished Overseas Scholars in China in 2010. He is an IEEE Fellow.

The Comprehensive Language Knowledge Base and its development綜合型語言知識庫及其發展
2017-04-12 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Yu Shiwen
Professor
School of Electronics Engineering and Computer Science
Peking University
Beijing
China
Abstract
任何一個自然語言處理系統都需要一個適配的語言知識庫。北京大學計算語言學研究所積30年之努力，建成以《現代漢語語法資訊詞典》為基礎的綜合型語言知識庫。本報告介紹綜合型語言知識庫的主要內容及其發展。當前語言計算研究已深入到面向語言大資料的語義計算。詞彙、詞法、句法層面的語言知識庫對語言淺層分析技術的發展起到了至關重要的作用，這個成功的經驗對當前的語義計算研究有啟示意義。一項新的語義知識資源建設的任務是確定句子中的謂詞並標注其論元的語義角色。本報告介紹這項任務的概況，論述對這項語義知識資源工程的認知並初步總結參與這項工程的實踐經驗。
Biography
俞士汶, 男, 1938年12月出生。北京大學資訊科學技術學院教授。1964年自北大計算數學專業畢業後一直在北大從事電腦學科的研究與教學工作。1986年起致力於計算語言學和自然語言處理的研究。作為第一完成人的主要研究成果有以《現代漢語語法資訊詞典》為基礎的“綜合型語言知識庫”，在獲得中國政府部門、中國全國性學術團體以及北京大學的多項獎勵後，於 2011年獲中國國家科學技術進步獎二等獎。同年獲中國中文資訊學會成立30年來首次頒發的終身成就獎。著作8本（其中2003年出版的《計算語言學概論》2016年獲評北京大學優秀教材），第一作者論文約110篇。作為博士生、碩士生的導師和博士後、訪問學者的合作教師，培養了一大批計算語言學領域的高端人才，於2012年獲北京大學訪問學者優秀導師榮譽。“綜合型語言知識庫”於2013年再次獲北京大學首屆產學研結合特別貢獻獎。當前參加中國國家重點基礎研究項目之973課題“融合三元空間的中文語言知識與世界知識獲取和組織”的研究工作。

Numbers and Games, Symmetry and Groups: My Life in Mathematics
2017-04-19 10:30 - 11:30 | E11-G015

Motion trajectories for robotic task teaching
2017-04-27 15:30 - 16:30 | E11-1012
Instructors/Speakers
Prof. You-Fu LI
Professor
Department of Mechanical and Biomedical Engineering
City University of Hong Kong
Abstract
Motion trajectories play an important role in characterizing human or robot actions and behaviours. Effective ways to track and describe them are lacking that can fully depict spatial trajectories in 3D. In this work, I will present our research in visual tracking and trajectory description. An invariant descriptor proposed for free form trajectory description in Euclidean space. The signature admits rich invariants due to the computational locality. By implementing the approximate signature, the noise-sensitive high order derivatives are avoided. The trajectory can then be recognized based on the customized signatures similarity metric. Case studies will be given to show the signature’s effectiveness and robustness in 3-D trajectory description and recognition.
Biography
You-Fu Li received the PhD degree in robotics from the Department of Engineering Science, University of Oxford in 1993. From 1993 to 1995 he was a research staff in the Department of Computer Science at the University of Wales, Aberystwyth, UK. He joined City University of Hong Kong in 1995 and is currently professor in the Department of Mechanical and Biomedical Engineering. His research interests include robot sensing, robot vision, and visual tracking. He has served as an Associate Editor for IEEE Transactions on Automation Science and Engineering (T-ASE), Associate Editor and Guest Editor for IEEE Robotics and Automation Magazine (RAM), and Editor for CEB, IEEE International Conference on Robotics and Automation (ICRA).

Recent developments in experimental micromechanics for geomechanics applications
2017-05-15 10:30 - 12:00 | E11-1006
Instructors/Speakers
Prof. Kostas Senetakis
Assistant Professor
Department of Architecture and Civil Engineering
City University of Hong Kong
Hong Kong
Abstract
Recent developments in discrete element modeling (DEM) have enhanced our knowledge on the behavior of granular materials including soils, which are complex in nature. This complexity is partly attributed to the interactions of grains at their contacts. However, there has been less progress in experimental methods to measure different micro-quantities at the scale of a grain, particularly quantifying, friction, stiffness and grain surface damage, which would lead to a better understanding of the fundamental micro-mechanisms that control the macro-scale behavior of geo-materials. This would advance our numerical simulations providing realistic input to be used by DEM modelers, therefore improving our predictive models for the safer design and assessment of important infrastructures. In this presentation, recent developments in micromechanical experimental methods will be presented and discussed with a focus on the performance of a new generation apparatus developed at the City University of Hong Kong which is capable of measuring micro-quantities of real soil grain contacts. Applications to real engineering problems will be discussed and a strong link between micro-scale testing results and macro-scale behavior of soils under dynamic loads will be presented enhanced from recent research studies by the presenter.
Biography
Dr. Senetakis joined City University of Hong Kong in December 2016 as an Assistant Professor. Prior to his new appointment, he worked for two years as a Post-Doctoral Research Fellow at City University of Hong Kong (2011-2013) and three years as a Lecturer at Thammasat University in Bangkok Thailand (2013-2014) and UNSW in Sydney Australia (2014-2016). He holds a Diploma in Civil Engineering and he completed his MSc in Earthquake Engineering and Structural Dynamics in 2006 and PhD in Soil Dynamics in 2011 at the University of Thessaloniki Greece. Dr Senetakis research interests focus in the fields of experimental soil dynamics, micromechanics, geo-synthetics and recycled aggregates in geotechnics. Dr Senetakis worked for about five years as a consultant engineer in earthquake and geotechnical engineering projects and currently he is in charge of the Soil Mechanics Laboratory of the City University of Hong Kong which is one of the most well-equipped laboratories for advanced element and micro-mechanical testing for soils and weak rocks.

Statistical Arbitrage under the Efficient Market Hypothesis
2017-05-24 10:30 - 11:30 | E11-G015

Numerical Simulation of Polymer Flooding with MRST
2017-05-25 10:30 - 11:15 | E11-4045 (University of Macau)
Instructors/Speakers
Dr. Kai BAO
Department of Mathematics & Cybernetics at SINTEF
Oslo
Norway
Abstract
In this talk, main topic will be the development of a fully-implicit polymer flooding simulator within MATLAB Reservoir Simulation Toolbox (MRST). We will introduce the background of the reservoir simulation, water-flooding and why we need to use polymer to change the flooding fluid property. Physical equations are presented to describe the polymer flooding process. Then we introduces the key features of MRST, such as its modular design, vectorized implementation, support for general unstructured grids, and automatic differentiation framework, which makes it a very powerful prototyping and experimentation platform for development of new flow models for reservoir simulation. Certain implementation details are discussed and verification against commercial simulators are provided. Application of the simulator to different scenarios is presented. And finally, we will introduce briefly the open-source development activities in Computational Geosciences group in SINTEF Digital.
Biography
Kai Bao is a Research Scientist in the Department of Mathematics & Cybernetics at SINTEF, Oslo, Norway. Before he joined SINTEF in March 2014, he worked for over 3 years as a postdoctoral fellow in King Abdullah University of Science and Technology, Saudi Arabia. He holds a BE degree in thermal engineering from Xi’an Jiaotong University and a PhD degree in computer applied technology from Institute of Software, Chinese Academy Sciences. He is a member of the Society of Petroleum Engineers (SPE) and European Association of Geoscientists and Engineers (EAGE) .
Kai’s research interests include reservoir simulation, parallel computing, chemical enhanced oil recovery, computational fluid dynamics and physically based fluid animation. He has published papers through journals and conferences on above fields. He is actively involved in the development of the open-source reservoir simulators, Open Porous Media (OPM, http://opm-project.org/) and MATLAB Reservoir Simulation Toolbox (MRST, http://www.sintef.no/projectweb/mrst/).

Predicting the fate of a migrating fluid using spill-point analysis, with application to CO2 storage modelling
2017-05-25 11:15 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Dr. Rebecca ALLEN
Mathematics and Cybernetics Department of SINTEF Digital
Abstract
To reduce the amount of anthropogenic carbon dioxide (CO2) that is released to the atmosphere, CO2 can be captured from its point source and injected into subsurface saline aquifers for long-term storage. This concept is known as Carbon Capture and Storage (CCS), and has been put into practice for more than 20 years in Norway. An important question to answer before starting a storage project is how much CO2 may be adequately trapped in a saline aquifer. To estimate this storage capacity, one must consider the flow dynamics involved during injection and post-injection, and simulation software plays an important role in this regard. During the injection period, CO2 is primarily driven by pressure gradients, however after injection has stopped, CO2 is primarily driven by gravity forces and its migration is strongly influenced by the shape of the aquifer’s top-surface. As such, spill-point analysis can be used to help predict the long-term migration of CO2 within the aquifer. This reduces the need to perform computationally intensive simulations for thousands of years, yet still captures the amount of CO2 destined to remain within the aquifer.
Biography
Rebecca Allen is a Post Doctorate Fellow at the Computational Geosciences group of the Mathematics and Cybernetics department of SINTEF Digital. She obtained her BEng in Civil Engineering from McMaster University in Canada in 2009. Between 2009 and 2015, she completed her MSc in Environmental Science and Engineering and her PhD in Earth Science and Engineering from King Abdullah University of Science and Technology in Saudi Arabia. She is a member of the Society of Petroleum Engineers (SPE), the International Society for Porous Media (InterPore) and the IEAGHG modelling network. In 2013, she was co-awarded as an outstanding student at IEAGHG’s International Summer School on Carbon Capture and Storage, and was invited to be a student mentor at the following year’s summer school.
Rebecca’s current research activities are related to modelling large-scale storage of CO2 in geological formations, in particular well optimization, model calibration, and capacity estimation. She has published work in Geofluids, Energy Procedia, SPE Journal, and Progress in Computational Fluid Dynamics. She has also presented work at various conferences including GHGT, InterPore, SPE Reservoir Simulation Symposium, and ECMOR.

Uncertainty principle for 2D discrete signals
2017-05-26 10:30 - 11:30 | E11-1035 (University of Macau)
Instructors/Speakers
Prof. Yan YANG
Associate Professor
School of Mathematics
Sun Yat-Sen University
China
Abstract
In this talk, the uncertainty principle for 2D discrete signals associated with quaternion Fourier transform is obtained. As an application, it explains an interesting phenomena in signal recovery problems where there is an interplay of missing data and time-limiting.
Biography
Prof Yan Yang is an Associate Professor in Sun Yat-Sen University, China. Her Research interest includes Complex Variables，Clifford Analysis，Signal analysis in higher dimensional spaces. Prof. Yan has published more than 25 papers on MMAS, Acta Math Scientia, Integral Operator and Special Functions and so on.

Extreme Learning Machines (ELM): Enabling Pervasive Learning and Pervasive Intelligence in Internet of Intelligent Things
2017-06-09 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Guang-Bin HUANG
Nanyang Technological University
Singapore
Abstract
This 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.
Biography
Guang-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).
He serves as an Associate Editor of Neurocomputing, Cognitive Computation, neural networks, and IEEE Transactions on Cybernetics.
He 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).
One 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.”

Big Data Analytics on Big Spatial Database
2017-06-21 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Raymond Chi-Wing WONG
The Hong Kong University of Science and Technology
Abstract
Nowadays, 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.
Biography
Raymond 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.
He 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.

Determining the Impact Regions of Competing Options in Preference Space
2017-06-26 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Man Lung YIU
The Hong Kong Polytechnic University
Abstract
In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector.
The 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.
In 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.
Biography
Man 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.

Diagnosing Un-occurred Diseases by Dynamic Network Biomarkers --- Detecting the Tipping Points of Biological Processes by Big Data
2017-06-30 15:30 - 16:30 | E11-1006
Instructors/Speakers
Prof. Luonan CHEN
Professor
CAS Key Laboratory of System Biology
Institute of Biochemistry and Cell Biology
Shanghai Institutes for Biological Sciences, CAS, Shanghai
China
Abstract
Considerable 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.
Biography
Prof. 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.

New Nonlinear Control Designs and Applications for Better Performance and Easier Implementation
2017-07-18 15:00 - 16:00 | E11-1006
Instructors/Speakers
Prof. Chunjiang QIAN
Mary Lou Clarke Endowed Distinguished Professor, Department Chair
Department of Electrical and Computer Engineering
University of Texas, San Antonio, USA
Abstract
In the past, to design controllers for nonlinear systems, the intrinsic nonlinear structures are often ignored or destroyed by linearization or feedback linearization methods. However, these controllers, including PID controllers and even some nonlinear controllers based on feedback linearization, are no longer adequate to accommodate the increasing requirement for improved performances and enlarged operating regions of modern nonlinear control systems. This talk will discuss new perspectives in handling nonlinear systems and sketch recent progresses in nonlinear controller design including finite-time control, nonlinear observer, domination approach, and sampled-data control. In addition, these talk shows that the new controllers can ensure improved performance and easier implementation for several practical systems, such as marine surface vehicles, electric vehicles driven by in-wheel motors, and hydraulic turbine systems.
Biography
Prof. Chunjiang Qian received his B.S. and M.S degrees in Control Theory from Fudan University in 1992 and 1994 respectively, and the Ph.D. degree in Electrical Engineering from Case Western Reserve University, 2001. Since August 2001, he has been with the Department of Electrical and Computer Engineering, University of Texas at San Antonio, where he is currently Mary Lou Clarke Endowed Distinguished Professor and serving as the Department Chair. His current research interests include robust and adaptive control, nonlinear system theory, optimal control, network control system, power plant control, and biomedical applications. In those areas, he has published one monograph and more than 180 papers.
Prof. Qian is a recipient of 2003 U.S. National Science Foundation (NSF) CAREER Award and one of the inaugural recipients of the University of Texas System Regents’ Outstanding Teaching Award in 2009. He received the 3rd Best Paper Award in the ISA (International Society of Automation) Power Industry Division Symposium (2011) and the Best Poster Paper Award in the 3rd IFAC International Conference on Intelligent Control and Automation Science (2013). He currently serves as an Associate Editor for Automatica and a Subject Editor for International Journal of Robust and Nonlinear Control.

Civil Engineering Summer Camp 2017土木工程夏令營2017
2017-07-19 - 2017-07-21

Just-Noticeable Difference (JND) for Multimedia Signals
2017-07-20 15:00 - 16:00 | E11-1035 (University of Macau)
Instructors/Speakers
Prof. Weisi LIN
Nanyang Technological University
Singapore
Abstract
As a result of the evolution, the human has developed unique characteristics in perception of viewing, hearing, smelling, touching and tasting. Just Noticeable Difference (JND) refers to the minimal amount of “X” that must be changed for the difference to be sensed by the human, where X can be any signal, derived quantity from signals such as emotion and user-experience, or even technical specifications such as resolution, asynchrony, accuracy, etc. “Perception is reality”, so JND plays an important role both explicitly and implicitly throughout our work and life, from sound to smell and from engineering to marketing (e.g., advertisement, logo management, personalization, and recommendation). The scientific measurement and formulation for JND are the prerequisite for user-centric designs and for turning human perceptual sensitivities into many system advantages. In this seminar, a holistic view will be first presented on JND research and practice, followed by an in-depth case study in visual signals. JND modeling for visual signals has attracted much research interests so far, while those for audio, haptics, olfaction, gestation and other forms of signals are expected to intensify. In essence, factors to influence JND also include utility, culture and personality, as will be highlighted.
Biography
Dr Lin Weisi is a well-recognized researcher in image processing, perception-based signal modelling and assessment, video compression, and multimedia communication systems. In the said areas, he has published 180+ international journal papers and 230+ international conference papers, 7 patents, 9 book chapters, 2 authored books and 3 edited books, as well as excellent track record in leading and delivering more than 10 major funded projects (with over S$6.5m research funding). He earned his Ph.D from King’s College, University of London. He had been the Lab Head, Visual Processing, in Institute for Infocomm Research (I2R). Currently, he is an Associate Professor, School of Computer Science and Engineering, Nanyang Technological University, where he served as the Associate Chair (Graduate Studies) in 2013-2014.
He is a Fellow of IEEE and IET, and an Honorary Fellow of Singapore Institute of Engineering Technologists. He has been elected as a Distinguished Lecturer in both IEEE Circuits and Systems Society (2016-17) and Asia-Pacific Signal and Information Processing Association (2012-13), and given keynote/invited/tutorial/panel talks to 20+ international conferences during the past 10 years. He has been an Associate Editor for IEEE Trans. on Image Processing, IEEE Trans. on Circuits and Systems for Video Technology, IEEE Trans. on Multimedia, IEEE Signal Processing Letters, Quality and User Experience, and Journal of Visual Communication and Image Representation. He was also the Guest Editor for 7 special issues in international journals, and chaired the IEEE MMTC QoE Interest Group (2012-2014); he has been a Technical Program Chair for IEEE Int’l Conf. Multimedia and Expo (ICME 2013), International Workshop on Quality of Multimedia Experience (QoMEX 2014), International Packet Video Workshop (PV 2015), Pacific-Rim Conf. on Multimedia (PCM 2012) and IEEE Visual Communications and Image Processing (VCIP 2017). He has been awarded Zukunftskolleg Mentorship (2014) by University of Konstanz (Germany), Distinguished Overseas Professorship (2014) by Xidian University (China), and High Impact Research (HIR) Icon (2016) by University of Malaya. He has served as a voting member of 7 IEEE Technical Committees, and on the IEEE ICME Steering Committee (2014-2015). He has been also elected to the European Network on QoE in Multimedia Systems and Services (QUALINET) from a Non-COST Country Institution, based on scientific merits. He believes that good theory is practical, and has delivered 9 major systems and modules for industrial deployment with the technology developed.

Fillers' application in concrete
2017-07-21 11:00 - 12:00 | E11-1043
Instructors/Speakers
Dr. Johnny Ching Ming HO
Senior Lecturer
School of Civil Engineering, University of Queensland
Australia
Abstract
Wet packing density defined as the maximum solid-to-container volume ratio is one of the fundamental concrete properties that governs the performance of fresh and hardened concrete. By increasing the wet packing density, the concrete performance improves in the following ways: (1) The densely packed particles decrease the porosity of concrete such that more loads are carried by the aggregates instead of paste that increases strength; (2) More water trapped in the interstitial void can be free up to increase workability; (3) Less paste is required to fill up the void between aggregates that increases dimensional stability. An effective way to increase the wet packing density of concrete is to broaden the particle size distribution, e.g. blending with fly ash and silica fume. However, these cementitious materials will hydrate with water and form paste, which decrease the dimensional stability of concrete. To this, inert (non-cementitious) fillers are advocated to improve the packing density and performance of concrete. In this seminar, the speaker will introduce the use of limestone and foundry sand to improve the performance of concrete by sharing some test results obtained at University of Queensland. Some recommendations on the use of Nano-material in concrete will be provided. Appropriate use of inert filler(s) can improve the performance of concrete, decrease cement usage and cost, as well as cutting down the greenhouse gas emission.
Biography
Dr. Johnny Ho is a Senior Lecturer in the School of Civil Engineering, The University of Queensland. Before joining the university in 2013, he worked as an Assistant Professor in The University of Hong Kong from 2007-2013. Practically, Dr. Ho worked in both Hong Kong and Brisbane offices of Arup on some large scale infrastructure projects such as The Stonecutters Bridge in Hong Kong and the Ipswich Motorway Upgrade (Wacol to Darra) in Brisbane. Dr. Ho’s research interests are on mix design of high-performance concrete with multi-sized fillers, rheology of cement paste and mortar, as well as their application in concrete-filled-steel-tube columns.

Computer Vision and Human-Machine Interaction Summer Camp 20172017計算機視覺與人機交互夏令營
2017-07-26 - 2017-07-27

Green Vehicle Summer Camp 2017環保交通工具夏令營 2017
2017-07-26 - 2017-07-28

Wireless Technology Study Summer Camp 2017無線技術研習夏令營 2017
2017-07-26 - 2017-07-28

Contract-Based Model of Evolving Architecture for Intelligent Cyber-Physical Systems
2017-07-26 15:00 - 16:00 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Zhiming LIU
Centre for Research and Innovation in Software Engineering (RISE)
Southwest University, Chongqing
China
Abstract
The sustainable development of most economies and the quality of life of their citizens largely depend on the development and application of evolutionary digital ecosystems. The characteristic features of these systems are reflected in the so called Internet of Things (IoT), Smart Cities, Cyber-Physical Systems (CPS), Systems of Human-Machine-Things, and Data Centers. Compared to the challenges in traditional ICT applications that engineers used to face, system and software development is to develop and integrate new components or subsystems, new applications and front end services that depends on the infrastructures of existing systems. This has to deal with the complexity of ever evolving architectures digital components, physical components, together with sensors and smart devices controlled and coordinated by software. The architectural components are designed with different technologies, run on different platforms and interact through different communication technologies. Software runs in these systems for data processing and analytics, computation, and intelligent control. However, the critical requirements of applications of these systems should not be compromised, and thus critical components need to be “provably correct”. In this talk we discuss, for development software in these emerging systems, the need of a foundation for the combination of traditional software engineering, AI (or knowledge-based engineering) and the emerging Big Data technologies. We show how an interface theory could play a core role in this foundation for seamless combination of different models, methods and tools for software development, AI and Big Data, as well as for system integration.
Biography
Zhiming Liu is known for his work on real-time and fault-tolerant program design, verification, and schedulability analysis by transformations that established a theory of real-time and fault-tolerant refinement; probabilistic duration calculus for system dependability specification and analysis, and his work on the rCOS formal method of model-driven design. The key bibliographic database lists over 130 of his peer reviewed publications in recognized outlets. These include papers published in the top journals on formal methods, ACM Transactions on Programming Languages, Formal Aspects of Computing, Science of Computer Programming and Theoretical Computer Science, as well as mainstream conferences. He received a 2nd prize in natural science of Science and Technology Award from Macao SAR in 2012 for his work on the rCOS method. He joined Southwest University in Chongqing (China) as a full-time professor in January 2016, and he is now leading to build the Centre for Research and Innovation in Software Engineering. Zhiming Liu received his PhD in 1991 from the University of Warwick (UK), and then worked there for three more years as a postdoc research fellow till 1994. After that, he worked during 1994-2005 at the University of Leicester as lecturer. Between 2002 and 2013, he worked for the United National University – International Institute for Software Technology (UNU-IIST, Macau) as a research fellow and then senior research fellow. Then he worked from 2013 to 2015 at Birmingham City University as Professor of Software Engineering before he returned back China to take his current position. Zhiming Liu is a member of CCF, a Fellow of British Computer Society, and a senior member of ACM. He also a founder of the international conferences ICTAC, SEFM, FACS and SETTA.

3D reconstruction with Quadcopters Summer Camp 20172017四軸飛行器三維重建夏令營
2017-07-27 - 2017-07-28

Robotics and Artificial Intelligence Summer Camp 20172017 機器人與人工智能夏令營
2017-07-27 - 2017-07-28

Research of key technology of primary mirror hydraulic supporting system
2017-07-31 10:00 - 11:00 | E11-1009
Instructors/Speakers
Prof. Xiongbin PENG
Assistant Professor
Department of Mechatronics Engineering
Shantou University
China
Abstract
Large scale reflecting telescope is a technology-intensive equipment in many fields such as national security, space detection, universe exploration. They are usually equipped with large diameter primary mirrors, which guarantee them with stronger light gathering power and high resolution, to explore instant galaxies from hunderds of millions of light year away. In this context, this talk will firstly introduce the difficulties when designing a hydraulic support system for large scale primary mirrors. Secondly, the supporting stiffness of a hydraulic support system is analyzed. Thirdly, the methodology for the precise position control of a support system shall be discussed. Finally, the future directions with critical research areas in field of hydraulic support system for a primary mirror shall be highlighted.
Biography
Dr. Xiongbin Peng is currently working as an Assistant Professor in Department of Mechatronics Engineering at Shantou University. He received his PhD degree in Mechatronic Engineering from Zhejiang University, Hangzhou, China in 2016. His main research interests include Thermal Management Design for Energy Storage Systems, Position Control of Primary Mirror Hydraulic Support System. He has worked jointly with Changchun Institute of optics, fine mechanics and physics, Chinese Academy of Sciences for 2 years on high stiffness design and precise position control of primary mirror hydraulic support system. He is the co-founder of Product Design Research Groups (PDRG, www.pdrg-group.org) involving collaborators from 12 countries. Through his research group, he has experience of supervising 3 undergraduate and 2 post graduate students.

Recent Trends and Challenges in Energy Storage Systems
2017-07-31 11:00 - 12:00 | E11-1009
Instructors/Speakers
Prof. Akhil GARG
Associate Professor
Department of Mechatronics Engineering
Shantou University
China
Abstract
In the recent years, Energy storage systems such as battery packs/fuel cells used in Electric vehicles (EVs) has been extensively promoted in developing economies where the environmental burden due to extensive production exists. In this context, this talk will firstly introduce the four main research problems undertaken. The development of evolutionary algorithms with applications in fuel cells is illustrated. Secondly, the experimental methodology of using mechanical parameter such as the stack stress to estimate the state of health of battery shall be discussed. The methodology for solving the design optimization problem of battery pack enclosure shall be discussed.
Biography
Dr. Akhil Garg is currently working as an Associate Professor in Department of Mechatronics Engineering at Shantou University. He received his PhD degree in Mechanical and Aerospace Engineering from Nanyang Technological University (NTU), Singapore in 2015. His main research interests include Design for Energy Storage Systems, Applications of AI in Sustainable manufacturing, Robust Optimization, Evolutionary algorithms. He has worked jointly with Aerospace Industry Rolls-Royce Singapore for 2 years on Design optimization of finishing process. He has published over 25 SCI Journals in field of sustainable manufacturing, cleaner energy systems and artificial intelligence.

Physically-based Realistic Hair Dynamics Simulation
2017-08-22 10:30 - 11:30 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Bin SHENG
Department of Computer Science and Engineering
Shanghai Jiao Tong University
Shanghai
China
Abstract
Realistic hair simulation is essential in many fields such as Computer Graphics and Virtual Reality. Currently, many models have successfully made progress in high-quality hair simulation. However, the computation cost of these models is very high and is not suite for realtime simulation and rendering. In order to simulate realistic hair in a very short time, we introduced a data-driven reduced hair model solution. Our approach captured a small set of guide hair and the corresponding interpolation relationship using a list of precomputed full simulation hair movement sequences. In simulation step, we only simulated the guide hair with a mass-spring model and interpolated the rest hair strands using the interpolation relationship we extracted from the full simulating data. Furthermore, we used a signed distance field to resolve the hair-body collision and introduced an improved hair-hair correction model based on “Position Based Dynamic” framework. Our model successfully simulate 40K hair strands in real-time with very high quality with complex head movement, which can also be applied to different hairstyle and hair geometry.
Biography
SHENG Bin received his BA degree in English and BE degree in computer science from Huazhong University of Science and Technology in 2004, and MS degree in software engineering from University of Macau in 2007, and PhD Degree in computer science from The Chinese University of Hong Kong in 2011. He is currently an associate professor in Department of Computer Science and Engineering at Shanghai Jiao Tong University. He serves on the editorial board of IET Image Processing. His research interests include virtual reality, computer graphics and image based techniques.

The Skew diffusion processes and their applications in finance
2017-08-23 10:30 - 11:30 | E11-1012
Instructors/Speakers
Prof. Yongjin WANG
Professor
School of Business
Nankai University
China
Abstract
In this talk, we begin with the Ito & McKean[1965]’s construction of Skew B.M. (via the Brownian excursions), and introduce a class of the so-called Skew diffusion processes. Specifically, our considerations are limited on the Skew O-U processes and the Skew Feller-branching processes (the latter are also called Skew CIR processes). For those two processes we first give the explicit expressions on the transition densities, in term of classical Special Functions. Next we study the hitting times of the processes up (or down) crossing some given levels, and we obtained the Laplace Transforms expressions of those random stopping times. These results are fundamental for the quantitative analysis of the processes. On the other hand, some observations from the FX market data show that, the special structures of Skew O-U processes can capture the important “sticky” phenomena, which frequently appeared in the market while the FX prices go up (down) to some specific level. Whereas the usual Geometric BM or Geometric O-U processes fails to do. So with the good tractable characters the Skew O-U procesess can be significantly introduced to model some FX and other assets price dynamics, alternatively we can proceed to the derivative securities pricing with such new models.
Biography
Prof. Yongjin Wang was awarded his PhD at Nankai University in 1992 and is currently a full professor at Nankai University. He has published more than 50 high quality papers about probability and mathematical finance.

Granular micromechanics: a paradigm for micromorphic continuum mechanics
2017-08-30 10:00 - 11:00 | E11-1042
Instructors/Speakers
Prof. Anil Misra
Professor
Department of Civil, Environmental and Architectural Engineering
The University of Kansas, Lawrence, Kansas, U.S.A.
Abstract
Granular materials take a variety of forms, spanning the spectrum from highly consolidated dense solids formed of particulate precursors to confined packing of non-cohesive particles. In all their forms, these materials are characterized by complex structures and compositions. More importantly, they share the common trait that their behavior at the macro-scale, that contains large numbers of grains (>106), is profoundly affected by the grain-scale interactions. For many important problems in engineering and science, continuum description of their mechanical behavior is desired. Discrete atomic or coarse-grained models often pose insurmountable challenges. Granular micromechanics method is a practical approach for continuum modeling these materials. In this approach, the material representative volume element (RVE) is modeled as a collection of grains which are interacting with each other through different inter-granular mechanisms. The resultant models offer the versatility of investigating the influence of both the macro-scale parameters and the grain-scale parameters on the overall stress-strain response by incorporating the effect of nearest neighbor grain interactions through the inter-granular force-displacement relationship and orientation vector. We have been developing the granular micromechanics approaches since 1980s. In their earlier formats, the models based upon granular micromechanics were successful in describing the small strain behavior. In recent years, these models have undergone further refinement and have been successfully applied to model a number of phenomena exhibited by granular geomaterials. In particular, the models have been shown to successfully describe the damage and softening in cementitious materials [1]. The approach also leads to a 2nd gradient continuum theory involving strain gradient and its conjugated double stresses useful for modeling shear bands [2]. The method has also been extended to include rate effects [3], incorporate thermomechanical consistency [3,4] and develop efficient numerical scheme [5] that can be implemented into finite element formulation. The model predictions have shown both quantitative and qualitative consistency with the observed behavior for asphalts and cementitious materials [3-5, 9]. Recently, the method has been extended to include non-classical terms which lead to micromorphic models [6,10]. The derived model has been applied to study the wave dispersion relationships and topology optimization [8]. Method for identifying elastic constants for the derived model has been demonstrated for regular and irregular grain assemblies by performing discrete simulations [7]. In the proposed presentation, we will describe some of the recent developments in granular micromechanics.
Biography
Anil Misra received his bachelor’s degree in civil engineering from the Indian Institute of Technology, Kanpur, India, and his M.S. and Ph.D. degrees from the University of Massachusetts at Amherst. He is currently a Professor in the Civil, Environmental and Architectural Engineering Department of the University of Kansas, Lawrence. He also serves as Associate Director of the University of Kansas Bioengineering Research Center (KU-BERC). Dr. Misra has a broad research interest that spans topics covering both basic and applied aspects of mechanics of geomaterials, interfaces and biomaterials, including analytical, computational and experimental granular micromechanics, particle and atomistic methods, multi-scale modeling, constitutive behavior, micro-macro correlations, and multi-modal material characterization using high resolution techniques. He has co-edited three books; guest edited three journal special issues; and authored more than 200 papers in journals, edited books and conference proceedings. He has made more than 100 presentations of his research results at national and international fora. His research has been funded by a variety of sources, including the United States National Science Foundation, National Institute of Health, and private industry. He is active in various professional societies and serves as reviewer and editorial board member of a number of journals. (webpage: http://people.ku.edu/~amisra/)

Enhancing Video Coding by Data-driven Techniques and Advanced Models
2017-09-11 15:00 - 16:00 | E11-1041 (University of Macau)
Instructors/Speakers
Prof. KWONG Tak Wu Sam
Head and Professor of the Department of Computer Science
City University of Hong Kong
Hong Kong
China
Abstract
In June 6th 2016, Cisco released the White paper[1], VNI Forecast and Methodology 2015-2020, reported that 82 percent of Internet traffic will come from video applications such as video surveillance, content delivery network, so on by 2020. It also reported that Internet video surveillance traffic nearly doubled, Virtual reality traffic quadrupled, TV grew 50 percent and similar increases for other applications in 2015. The annual global traffic will first time exceed the zettabyte(ZB;1000 exabytes[EB]) threshold in 2016, and will reach 2.3 ZB by 2020. It implies that 1.886ZB belongs to video data. Thus, in order to relieve the burden on video storage, streaming and other video services, researchers from the video community have developed a series of video coding standards. Among them, the most up-to-date is the High Efficiency Video Coding(HEVC) or H.265 standard, which has successfully halved the coding bits of its predecessor, H.264/AVC, without significant increase in perceived distortion. With the rapid growth of network transmission capacity, enjoying high definition video applications anytime and anywhere with mobile display terminals will be a desirable feature in the near future. Due to the lack of hardware computing power and limited bandwidth, lower complexity and higher compression efficiency video coding scheme are still desired. For higher video compression performance, the key optimization problems, mainly decision making and resource allocation problem, shall be solved. In this talk, I will present the most recent research results on machine learning and game theory based video coding. This is very different from the traditional approaches in video coding. We hope applying these intelligent techniques to vide coding could allow us to go further and have more choices in trading off between cost and resources.
Biography
Sam Kwong received the B.Sc. degree from the State University of New York at Buffalo, Buffalo, NY, in 1983, the M.A.Sc. degree in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 1985, and the Ph.D. degree from the Fernuniversität Hagen, Hagen, Germany, in 1996. From 1985 to 1987, he was a Diagnostic Engineer with Control Data Canada, where he designed the diagnostic software to detect the manufacture faults of the VLSI chips in the Cyber 430 machine. He is the associate editor of the IEEE transactions on Evolutionary Computation, the Associate Editor for the IEEE Transactions on Industrial Informatics, the IEEE Transactions on Industrial Electronics, and the Journal of Information Science. Currently, he is the Head and Professor of the department of Computer Science, City In terms of professional services, he was General Chair of IEEE SMC 2015 and gained the right to host the prestigious IEEE International Conference on Systems, Man, and Cybernetics at CityU on October 9-12, 2015, with nearly 600 scholars from around the world participating. He is also the Vice President for IEEE Systems, Man and Cybernetics for conferences and meetings from 2014 till present. Prof. Kwong was elevated to IEEE fellow for his contributions on Optimization Techniques for Cybernetics and Video coding in 2014. He is also appointed as IEEE Distinguished Lecturer for IEEE SMC society from March 2017. In his more recent work, Prof Kwong has also proposed a number of optimization techniques for video coding, including developing efficient algorithms to address the high computational complexity problems for mode decision and this enables adaptive thresholds to be derived to make intelligent decisions for mode decision and motion estimation, thus significantly reducing the computational complexity in H.264 encoding in a clever way. In addition, this enables the practical implementation of video coder to industry. His innovations have already inspired the publication of a number of top-ranked journal papers (IEEE Transactions on Image Processing, IEEE Transactions on Circuit and Systems for Video Technology, IEEE Transactions on Broadcasting, IEEE Transactions on Multimedia, IEEE Transactions on Industrial Electronics). His discoveries have also been patented in the US from 2011 to 2017.
