Analysis of two-grid methods for miscible displacement problem by mixed finite element methods
2017-01-16 15:00 - 16:00 (Asia/Macau) | E11-2027
Instructors/Speakers
Prof. Yanping CHEN
Professor of School of Mathematical Sciences
South China Normal University
Abstract
The miscible displacement of one incompressible fluid by another in a porous medium is governed by a system of two equations. One is elliptic form equation for the pressure and the other is parabolic form equation for the concentration of one of the fluids. Since only the velocity and not the pressure appears explicitly in the concentration equation, we use a mixed finite element method for the approximation of the pressure equation. In order to find a stable finite element discretization method, we use different discretization method for the concentration equation, such as finite element method with characteristic; mixed finite element method with characteristic; expanded mixed finite element method with characteristic etc. To linearize the discretized equations, we use one (two) Newton iterations on the fine grid in our methods. Firstly, we solve an original non-linear coupling problem. Then, solve a linear system on the fine grid and while in second method we make a correction on the coarse grid between one (two) Newton iterations on the fine grid. We obtain the error estimates of two-grid method, it is shown that coarse space can be extremely coarse and we achieve asymptotically optimal approximation. Finally, numerical experiment indicates that two-grid algorithm is very effective.
Biography
Prof. Chen Yanping currently is a professor from School of Mathematical Sciences at South China Normal University. She is also a Guangdong Provincial “Zhujiang Scholar”. Prof. Chen obtained her PhD degree at Shandong University, and continued her postdoc research at Nanjing University. After that, Prof. Chen worked at Xiangtan University, and served as associate director of Hunan Key Laboratory for Computation and Simulation in Science and Engineering from 2002 to 2008. Since 2008, Prof. Chen moved to South China Normal University.

Selected Research Highlights in Computing and AI
2017-01-23 15:00 - 16:00 | E11-1041 (University of Macau)
Instructors/Speakers
Prof. James Kwok
Hong Kong University of Science and Technology
Abstract
We will present our work in software engineering, AI and mobile computing. In software engineering, it is common to find user reviews in Android play store complaining the performance and energy issues in various Android applications. This talk introduces some recent studies made by the CASTLE research group at HKUST on these issues. We will discuss several interesting empirical findings made from the bug reports and code fixes of popular Android applications. Challenges and opportunities are identified. We will discuss some preliminary approaches and their results.
In AI, big data is everywhere. Besides the huge data scale, big data problems are also characterized by their high complexities. Often, there are a lots of input features and involve a lot of learning tasks related in some complicated manner. In this talk, I will describe several recent approaches in tackling these problems. These algorithms are flexible, computationally efficient, and have better empirical performance than existing approaches.
In mobile computing, Mobile Augmented Reality (MAR) is widely regarded as one of the most promising technologies in the next ten years. With MAR, we are able to blend information from our senses and mobile devices in myriad ways that were not possible before. The way to supplement the real world other than to replace real world with an artificial environment makes it especially preferable for applications such as tourism, navigation, entertainment, advertisement, and education. In this talk, I will introduce some latest MAR research activities in our HKUST-DT Systems and Media Lab.
Biography
Prof. Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. Prof. Kwok served/is serving as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems and the Neurocomputing journal, and as Program Chair for a number of international conferences. He is an IEEE Fellow.

Intelligent Service Robot: Opportunity and Challenge
2017-02-21 14:00 - 15:00 | E11-4045
Instructors/Speakers
Prof. Zhi LIU
School of Automation
Guangdong University of Technology
China
Abstract
With the development of the crossed-research realm which involves the information science, mechanical engineering and material science, robotic systems have promising application potential and strong market demand in assisting or even replacing human labors. Therefore, researches on robotic systems have not only a bright application prospect, but also a significant academic value. This report provides the recent progress on service robots, which contain high degrees of freedom and require strongly coupled motion/force analyses. Researches are launched to tackle uncertainties including dynamical nonlinearities and motion constraints in robotic systems and to improve the control performances.
Biography
Dr. Liu received his PhD degree in Electrical Engineering from Tsinghua University in January 2004 He has been with the School of Automation at Guangdong University of Technology since 2005 and was Professor in 2009. His research interests include computational intelligence, intelligent control, robot systems and technology. Dr. Liu was the recipient of the First Prize of Progress Award issued by Ministry of Education Science and Technology in 2008. He was supported by NSFC three times, Ministry of Education Fok Ying Tung Education Foundation in 2010, Guangzhou Zhujiang New Star in 2011, Science Fund for Distinguished Young Scholars of Guangdong Province in 2012, New Century Excellent Talent issued by Ministry of Education in 2012. Dr. Liu has published over 80 papers in the international journals and conferences, includes 32 IEEE Trans papers, 9 granted patents for invention.

Control for Intelligent Manufacturing – a multi-scale challenge
2017-02-21 16:30 - 17:30 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. Han-Xion LI
Department of Systems Engineering and Engineering Management
College of Science and Engineering
City University of Hong Kong
Hong Kong
Abstract
China Industry 2025 will require full automation in all sectors from customer up to the production. This will bring great challenge to all sectors in manufacturing systems. All the device and systems in the future manufacturing should have capabilities of sensing and basic intelligence for control and adaptation. This will require the control action to be distributed, and to be integrated with different approaches including system design and intelligence-based method.
The presentation will discuss several fundamental issues related to the intelligent manufacturing.

For the multi-time scale system, the dynamic design should be conducted to make the system robust to disturbance without control at the fast scale, while at the slow scale, the statistics-based control will be designed to maintain the consistent performance. Integrated design and control has been a challenge.
For the spatially distributed dynamic system, the uniform temporal performance is required over the entire spatial domain. This strong space/time coupled dynamics makes the modeling and control extremely difficult, particularly under the limited sensing and actuating. The spatio-temporal modeling and control can be effectively developed using space/time separation based intelligent approach.
Intelligent manufacturing will need the decision system for the high-level supervision and management. In difference to the low-level control action, the high-level decision is required to handle human knowledge under uncertainties. The probabilistic-fuzzy system would be a useful platform as it possesses the capability to produce linguistic rules under both deterministic uncertainty and stochastic variation.

Control action will be different at different scales. More design is required at the fast time scale, and more control is needed at the slow time scale. More quantitative action is required at the low-level operation, while more qualitative action is needed at the high-level supervision. The systematic work in this area should be built in a bottom-up approach, step by step from dynamic design, process control, intelligent supervision, up to plant-wide management control etc.. This is a multi-scale challenge.
Biography
Han-Xiong LI (李涵雄) received his B.E. degree in aerospace engineering from the National University of Defence Technology, China, M.E. degree in electrical engineering from Delft University of Technology, Delft, The Netherlands, and Ph.D. degree in electrical engineering from the University of Auckland, Auckland, New Zealand. Currently, he is a full professor in the Department of Systems Engineering and Engineering Management, the City University of Hong Kong. Over the last thirty years, he has had opportunities to work in different fields, including military service, investment, industry, and academia. His current research is in the field of intelligent manufacturing, with special interest on system intelligence and control, distributed parameter systems, intelligent learning and decision informatics. He authored 2 books and published over 180 SCI journal papers with h-index 34 (ISI web of science). He was rated as highly cited Chinese scholar by Elsevier in 2014 & 2015.Dr. Li serves as Associate Editor for IEEE Transactions on Systems, Man & Cybernetics: Systems (2016 – ), IEEE Transactions on Cybernetics (2002 – 2016), and IEEE Transactions on Industrial Electronics (2009-2015). He was awarded the Distinguished Young Scholar (overseas) by the China National Science Foundation in 2004, a Chang Jiang professor by the Ministry of Education, China in 2006, and a national professorship in China Thousand Talents Program in 2010. He serves as the distinguished expert for Hunan Government and China Federation of Returned Overseas. He is a fellow of the IEEE.

Research on Cognitive Modeling and Collaborative Behaviors Analysis in the Era of Big Data
2017-02-21 17:30 - 18:30 | E11-4045 (University of Macau)
Instructors/Speakers
Prof. En-Hong CHEN
School of Computer Science
University of Science of Technology of China
China
Abstract
The era of big data raises numerous behavior records of social entities, which results in a serious challenge to traditional analytic techniques because of the correlation between big data and social characteristics. Therefore, the way of exploiting domain knowledge for precisely understanding the social entities have become research hotspots in the literature. In this report, we will first demonstrate the multiple-dimensional description of social entities, and then introduce our research studies on two main issues, i.e., the cognitive modeling and the collaborative behaviors analysis of individuals.
Biography
Dr. Enhong Chen is currently a professor and vice Dean of School of Computer Science, the vice Director of the National Engineering Laboratory for Speech and Language Information Processing at University of Science and Technology of China (USTC). He is the Distinguished Young Scholar of the National Natural Science Foundation of China.His research interests include data mining and machine learning, social network analysis and recommender systems. He has published over 100 papers in refereed journals and conferences, including TKDE, TMC, TKDD, TIST, IJCAI, AAAI, KDD, ICDM, CIKM and Nature Communications. He is an associate editor of WWW Journal, IEEE Transactions on Systems, Man, and Cybernetics: Systems,etc. He was on program committees of numerous conferences including IJCAI, AAAI, KDD, ICDM, SDM. He was a PC Chair of China Conference of Data Mining (CCDM) 2014, China National Computer Conference (CNCC) 2015. He received the Best Application Paper Award on KDD’08, the Best Research Paper Award on ICDM’11 and the Best of SDM’15 Award. Also, he is an IEEE senior member since 2007, Fellow of China Computer Federation (CCF).

Enabling Secure Outsourced Middlebox Services
2017-02-27 11:00 - 12:00 | E11-4045 (University of Macau)
Instructors/Speakers
Dr. Cong WANG
City University of Hong Kong
Abstract
Modern enterprise networks heavily rely on the ubiquitous network middleboxes for advanced traffic-processing functions, such as intrusion detection, web application firewalls, and load balancers. Recent advances in software packet processing and virtualization technologies are further pushing forward the paradigm of migrating middleboxes to third-party providers, e.g., clouds and ISPs, as virtualized services, with well-understood benefits on reduced maintenance cost and increased service scalability. Despite promising, this new paradigm of middlebox services also raises fundamental security challenges. This is majorly because the network traffic is now redirected to and processed by service providers, which are not necessarily in the same trust domain as enterprises. In this talk, I will present some of our recent research efforts towards secure outsourced middlebox services. Our first challenge is to ensure that those middleboxes consistently perform network functions as intended. Practical assurance mechanisms have to be designed to enforce both individual middleboxes and middlebox service chains to process packets via designated functions. As redirecting traffic to service providers would further raise privacy concerns on the unwanted exposure of traffic flows, I will also discuss our initial efforts on privacy-preserving deep packet inspection in outsourced middleboxes. Finally, along the line I will talk about some possible future research directions.
Biography
Cong Wang has been an Assistant Professor at the Department of Computer Science, City University of Hong Kong, since the Summer of 2012. He received his PhD in the Electrical and Computer Engineering from Illinois Institute of Technology, USA, in 2012, M.Eng in Communication and Information System in 2007, and B.Eng in Electronic Information Engineering in 2004, both from Wuhan University, China. His current research interests include data and computation outsourcing security in the context of cloud computing, network security in emerging Internet architecture, multimedia security and its applications, and privacy-enhancing technologies in the context of big data and IoT. He has published frequently in peer-reviewed journal and conference papers. His H-index is 23, and his total citation has exceeded 10,000, according to Google Scholar (as of Jan. 2017). He received the President’s Awards, City University of Hong Kong in 2016, the Best Paper Award of IEEE MSN 2015 and CHINACOM 2009. His research has been supported by multiple government research fund agencies, including National Natural Science Foundation of China, Hong Kong Research Grants Council, and Hong Kong Innovation and Technology Commission. He has been serving as the TPC co-chairs for a number of IEEE conferences/workshops. He is a member of IEEE and ACM.

Magical Mathematics - Mathematical Magic
2017-03-01 15:30 - 16:30 | 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
