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X-WR-CALNAME:Faculty of Science and Technology | University of Macau
X-ORIGINAL-URL:https://www.fst.um.edu.mo
X-WR-CALDESC:Events for Faculty of Science and Technology | University of Macau
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TZID:Asia/Macau
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:CST
DTSTART:20180101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20180910T110000
DTEND;TZID=Asia/Macau:20180910T120000
DTSTAMP:20260610T055127
CREATED:20180910T030037Z
LAST-MODIFIED:20220927T043443Z
UID:5998-1536577200-1536580800@www.fst.um.edu.mo
SUMMARY:Topic Classification using RNN: A Combined Approach towards Topic Discovery
DESCRIPTION:Instructors/Speakers\nProf. Yungcheol BYUN\nJeju National University\nJeju\, Korea \nAbstract\nDr. Yungcheol Byun is a full professor at the Computer Engineering Department (CE) at Jeju National University (http://www.jejunu.ac.kr). His research interests include the areas of Pattern Recognition & Image Processing\, Artificial Intelligence & Machine Learning\, Pattern-based Security\, Home Network and Ubiquitous Computing\, u-Healthcare\, and RFID & IoT Middleware System. He directs the Machine Laboratory at the CE department. Recently\, he studied at University of Florida as a visiting professor from 2012 to 2014. He is currently serving as a director of Information Science Technology Institute\, and other academic societies. Outside of his research activities\, Dr. Byun has been hosting international conferences including CNSI (Computer\, Network\, Systems\, and Industrial Engineering)\, ICESI (Electric Vehicle\, Smart Grid\, and Information Technology)\, and serving as a conference and workshop chair\, program chair\, and session chair in various kinds of international conferences and workshops. Dr. Byun was born in Jeju\, Korea\, and received his Ph.D. and MS from Yonsei University (http://www.yonsei.ac.kr) in 1995 and 2001 respectively\, and BS from Jeju National University in 1993. Before joining Jeju National University\, he worked as a special lecturer in SAMSUNG Electronics (http://www.samsung.com) in 2000 and 2001. From 2001 to 2003\, he was a senior researcher of Electronics and Telecommunications Research Institute (ETRI\, https://etri.re.kr/eng/main/main.etri). He was promoted to join Jeju National University as an assistant professor in 2003. \nBiography\nIn natural language processing (NLP)\, language model is doubtlessly an intrinsic element\, as it plays a fundamental role in many conventional NLP tasks\, e.g.\, speech recognition to image captioning etc. Therefore\, learning an exceptional language model usually enhance the hidden aspects or metrics; forging its pivotal role in NLP. Language models are gaining popularity as of the abundance of online texts\, comments and reviews. Due to the advancement of e-commerce\, people do write their reviews about the products they have received. In crowdfunding sites\, comments are so critical that negative reviews can damage the reputation of the product’s creator or can affect the buying of others. Life is too fast these days that people find it difficult to go through abundant of text data to take a decision. Therefore\, topic discovery is quite valuable in various aspects as of saving time of the user\, providing the summary of text in form of discussion topics\, and providing contextual information etc. Topic models are being studied for decades and are of fundamental importance as these models act as a tool in order to infer the latent topics and extracting semantic structure of a document. In this speech\, we have used the Latent Topic Model (LDA) in order to generate topics for crowdfunding comments. Our proposed model is recurrent neural network (RNN) based language model\, which uses the latent topics generated by LDA\, is constructed to extract the comprehensive semantic meaning related words in comments. Moreover\, this combined approach is of better capability on creating topic clusters then traditional ones\, which signifies that blending the information from deep learning and topic modeling is a substantial way to generate an improved understanding of crowdfunding comments. \n 
URL:https://www.fst.um.edu.mo/event/topic-classification-using-rnn-a-combined-approach-towards-topic-discovery/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20180929T150000
DTEND;TZID=Asia/Macau:20180929T160000
DTSTAMP:20260610T055127
CREATED:20180929T070047Z
LAST-MODIFIED:20220927T043442Z
UID:5996-1538233200-1538236800@www.fst.um.edu.mo
SUMMARY:Penalized Nonparametric Likelihood-based Inference for Current Status Data Model
DESCRIPTION:Instructors/Speakers\nProf. Xingqiu ZHAOAssociate ProfessorDepartment of Applied MathematicsThe Hong Kong Polytechnic UniversityHong Kong \nAbstract\nIn this paper\, we develop a penalized nonparametric likelihood method to estimate an unknown cumulative hazard function with current status data. Deriving the limiting distribution of such nonparametric estimator is a very challenging theoretical problem. For the problem\, we construct the Sobolev space equipped with a special inner product and deduce a functional Bahadur representation in the space. Using this key tool\, we establish the pointwise asymptotic normality of the proposed estimator. \nFurthermore\, we study the penalized likelihood ratio tests for local and global ypotheses and obtain their limiting distributions\, and also show the optimality of the test. A simulation study is presented for comparing the performance of the proposed penalized likelihood ratio test and the classical likelihood ratio test. \nBiography\nProf. Xingqiu Zhao received her PhD at McMaster University and is currently an associate professor at Hong Kong Polytech. Her main research interests are panel count data\, Longitudinal data analysis and large deviation with applications in survival analysis. She has published more than 50 papers on international journals such as Annals of Statistics\, JASA\, Bernoulli and so on. \n 
URL:https://www.fst.um.edu.mo/event/penalized-nonparametric-likelihood-based-inference-for-current-status-data-model/
LOCATION:E11-1036
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181003T103000
DTEND;TZID=Asia/Macau:20181003T113000
DTSTAMP:20260610T055127
CREATED:20181003T023033Z
LAST-MODIFIED:20220927T043404Z
UID:5990-1538562600-1538566200@www.fst.um.edu.mo
SUMMARY:Sign-based methods for solving complementarity problems
DESCRIPTION:Instructors/Speakers\nProf. Hua ZHENG\nAssociate Professor\nSchool of Mathematics and Statistics\nShaoguan University\nChina \nAbstract\nIn this talk\, using the sign patterns of the solution of the equivalent modulus equation\, the resolution of some complementarity problems shrinks to find the zero of a differentiable function. \nThen\, some sign-based methods can be established. Numerical examples show the effectiveness of the new methods. \nBiography\nProf. Zheng is currently an associate professor in the Shaoguan University\, China. He got his Ph.D. from the South China Normal University in 2015. His recent research mainly focuses on the study of different kinds of complementarity problems with particular interest on algorithms for solving these problems. \n 
URL:https://www.fst.um.edu.mo/event/sign-based-methods-for-solving-complementarity-problems/
LOCATION:E11-2027
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181018T150000
DTEND;TZID=Asia/Macau:20181018T160000
DTSTAMP:20260610T055127
CREATED:20181018T070012Z
LAST-MODIFIED:20220927T043404Z
UID:5985-1539874800-1539878400@www.fst.um.edu.mo
SUMMARY:軟土工程及複合地基理論的新發展
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/%e8%bb%9f%e5%9c%9f%e5%b7%a5%e7%a8%8b%e5%8f%8a%e8%a4%87%e5%90%88%e5%9c%b0%e5%9f%ba%e7%90%86%e8%ab%96%e7%9a%84%e6%96%b0%e7%99%bc%e5%b1%95/
LOCATION:E11-3033
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181114T110000
DTEND;TZID=Asia/Macau:20181114T120000
DTSTAMP:20260610T055127
CREATED:20181114T030054Z
LAST-MODIFIED:20220927T043403Z
UID:5983-1542193200-1542196800@www.fst.um.edu.mo
SUMMARY:A Peridynamic View of Classical Continuum Mechanics
DESCRIPTION:Instructors/Speakers\nProf. Xiaowei HE\nInstitute of Software\nChinese Academy of Sciences\nChina \nAbstract\nPeridynamics is a formulation of the classical elastic theory that was originally targeted at simulating deformable objects with discontinuities\, especially fractures. In this talk\, I will introduce how to reformulate classical continuum mechanics with the peridynamic theory. To get an intuitive model that can be easily controlled\, we formulate the strain energy density function as a function parameterized by the dilatation and bond stretches\, which can be decomposed into multiple one-dimensional functions independently. To account for nonlinear material behaviors\, we also propose a set of nonlinear basis functions to help design a nonlinear strain energy function more easily. For an anisotropic material\, we additionally introduce an anisotropic kernel to control the elastic behavior for each bond independently. Experiments show that our model is flexible enough to approximately regenerate various hyperelastic materials in classical elastic theory\, including St.Venant-Kirchhoff and Neo-Hookean materials. \nBiography\nProf. Xiaowei He is currently an associate professor at the Institute of Software\, Chinese Academy of Sciences. He received both his BS and MS degrees from Peking University\, and his Ph.D. from Institute of Software\, Chinese Academy of Sciences. His research interests are mainly focused on computer graphics\, computational physics\, smoothed particle hydrodynamics\, peridynamics and nolocal theory. In recent years\, he has published several papers in international journals/conferences including SIGGRAPH\, TVCG\, SCA\, etc. Among them\, he proposed an efficient phase-field-based fluid solver to simulate arbitrarily complex multi-phase flows\, which was adopted by Adobe to realize a real-time three-dimensional oil painting system. Recently\, he has been doing research on how to apply machine learning to help improve both the performance and accuracy over traditional numerical solvers. He received two grants as PI from the Natural Science Foundation of China (NSFC) in 2014 and 2018\, respectively. \n 
URL:https://www.fst.um.edu.mo/event/a-peridynamic-view-of-classical-continuum-mechanics/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181115T110000
DTEND;TZID=Asia/Macau:20181115T120000
DTSTAMP:20260610T055128
CREATED:20181115T030046Z
LAST-MODIFIED:20220927T043403Z
UID:5981-1542279600-1542283200@www.fst.um.edu.mo
SUMMARY:Dendritic Neuron Model-Based Learning Algorithms and Applications
DESCRIPTION:Instructors/Speakers\nProf. Mengchu ZHOU\nThe Helen and John C. Hartmann Department of Electrical and Computer Engineering\nNew Jersey Institute of Technology\nNewark\, NJ 07102\, USA \nAbstract\nAn artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved great success in many fields\, e.g.\, classification\, prediction and control. However\, traditional ANNs suffer from many problems\, such as the hard understanding problem\, the slow and difficult training problem and the difficulty to scale them up. These drawbacks motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses\, not only for a better understanding of a biological neuronal system\, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems\, six learning algorithms including biogeography-based optimization\, particle swarm optimization\, genetic algorithm\, ant colony optimization\, evolutionary strategy and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi’s experimental design method. The experiments on fourteen different problems involving classification\, approximation and prediction are conducted by using a multi-layer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification\, approximation and prediction problems. \nBiography\nMengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology\, Nanjing\, China in 1983\, M.S. degree in Automatic Control from Beijing Institute of Technology\, Beijing\, China in 1986\, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute\, Troy\, NY in 1990. He joined New Jersey Institute of Technology (NJIT)\, Newark\, NJ in 1990\, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets\, intelligent automation\, Internet of Things\, big data\, web services\, and intelligent transportation. He has over 800 publications including 12 books\, 460+ journal papers (360+ in IEEE transactions)\, 12 patents and 28 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation\, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems\, Man and Cybernetics Society. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is VP for Conferences and Meetings\, IEEE Systems\, Man and Cybernetics Society. He is a Fellow of The Institute of Electrical and Electronics Engineers (IEEE)\, International Federation of Automatic Control (IFAC)\, American Association for the Advancement of Science (AAAS) and Chinese Association of Automation (CAA). \n 
URL:https://www.fst.um.edu.mo/event/dendritic-neuron-model-based-learning-algorithms-and-applications/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181120T150000
DTEND;TZID=Asia/Macau:20181120T160000
DTSTAMP:20260610T055128
CREATED:20181120T070001Z
LAST-MODIFIED:20220927T043403Z
UID:5979-1542726000-1542729600@www.fst.um.edu.mo
SUMMARY:Formal design of embedded real-time systems
DESCRIPTION:Instructors/Speakers\nProf. Naijun ZHAN\nState Key Lab of Computer Science\nInstitute of Software\nChinese Academy of Sciences\nBeijing \nAbstract\nRecently we propose an approach to designing embedded real-time systems formally. Using our approach\, one can first build a graphical model of a system to be developed with Simulink/Stateflow (S/S)\, and then conduct extensive simulation. In order to verify the graphical model formally\, we translate S/S diagrams into HCSP automatically. HCSP is a formal modeling language for hybrid systems\, an extension of CSP by introducing differential equations to model continuous evolution and several kinds of interrupts to model the interaction between continuous evolution and discrete jumps. Using Hybrid Hoare Logic and its theorem prover\, the translated HCSP model can be verified. For justifying the correctness of the translation\, we give an inverse translation from HCSP to Simulink\, so that the consistency can be checked by co-simulation. By providing a set of refinement rules\, an HCSP process can be generated into a piece of SystemC code\, approximate bisimilar to the original HCSP process. All work can be supported by a developed tool MARS. Several real-world case studies have been investigated to check the feasibility of the approach. \nBiography\nProf. Naijun Zhan is a distinguished research professor of State Key Lab. of Computer Science\, Institute of Software\, the Chinese Academy of Sciences. He got his bachelor degree and master degree both from Nanjing University\, and his PhD from Institute of Software Chinese Academy of Sciences. Prior to join Institute of Software\, Chinese Academy of Sciences\, he worked at the Faculty of Mathematics and Informatics\, Mannheim University\, Germany as a research fellow. He is the winner of Outstanding Youth Fund of Natural Science Foundation of China of 2016. His research interests cover formal design of real-time\, embedded and hybrid systems\, program verification\, concurrent computation models\, modal and temporal logics\, and so on. Now\, he serves the editorial boards of Formal Aspects of Computing\, Journal of Logical and Algebraic Methods in Programming\, Journal of Software\, Journal of Computer Research and Development. \n 
URL:https://www.fst.um.edu.mo/event/formal-design-of-embedded-real-time-systems/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181121T100000
DTEND;TZID=Asia/Macau:20181121T110000
DTSTAMP:20260610T055128
CREATED:20181121T020037Z
LAST-MODIFIED:20220927T043402Z
UID:5977-1542794400-1542798000@www.fst.um.edu.mo
SUMMARY:Principal metabolic flux mode analysis
DESCRIPTION:Instructors/Speakers\nDr. Sahely BHADRA\nComputer Science and Engineering\nIndian Institute of Technology (IIT)\nPalakkad \nAbstract\nMotivation:\nIn the analysis of metabolism\, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data\, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods\, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis\, on the other hand\, are able to capture the metabolic flux modes\, however\, they are primarily designed for the analysis of single samples at a time\, and not best suited for exploratory analysis on a large sets of samples. \nResults:\nWe propose a new methodology for the analysis of metabolism\, called Principal Metabolic Flux Mode Analysis (PMFA)\, which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short\, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer\, which penalizes projections that are far from any flux modes of the network. For interpretability\, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition\, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches. \nBiography\nDr. Sahely Bhadra is assistant Professor in Indian Institute of Technology\, Palakkad since July\, 2017. She has received her PhD from Computer Science and Automation department of Indian Institute of Science in 2012. Before joining IIT Palakkad she did postdoctoral research in Max Planck Institute for Informatics (2012-2014) \, Helsinki Institute for Information Technology (2014-2016) and Northeastern University (2017). Her research interest is Machine Learning and Optimization for multi view \, structured and  noisy data. She is interested in learning models to solve problem in biology. \n 
URL:https://www.fst.um.edu.mo/event/principal-metabolic-flux-mode-analysis/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181124T085000
DTEND;TZID=Asia/Macau:20181124T123000
DTSTAMP:20260610T055128
CREATED:20181124T101352Z
LAST-MODIFIED:20220927T043402Z
UID:5843-1543049400-1543062600@www.fst.um.edu.mo
SUMMARY:Symposium on Basic Soil Properties and Engineering Applications
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/symposium-on-basic-soil-properties-and-engineering-applications/
LOCATION:E11-G015\, Taipa\, Macau
CATEGORIES:cee_events,conferences,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181126T091500
DTEND;TZID=Asia/Macau:20181126T091500
DTSTAMP:20260610T055128
CREATED:20181126T011503Z
LAST-MODIFIED:20220927T043402Z
UID:5972-1543223700-1543223700@www.fst.um.edu.mo
SUMMARY:Sustainable Technology for Smart City Seminar
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/sustainable-technology-for-smart-city-seminar/
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181127T110000
DTEND;TZID=Asia/Macau:20181127T120000
DTSTAMP:20260610T055128
CREATED:20181127T030029Z
LAST-MODIFIED:20220927T043401Z
UID:5968-1543316400-1543320000@www.fst.um.edu.mo
SUMMARY:Sensing and Analytics for Human Machine Systems
DESCRIPTION:Instructors/Speakers\nProf. Honghai LIU\nUniversity of Portsmouth\nUK \nAbstract\nIt requires innovative technologies and theoretical foundation of sensing and analytics as increasing complexity of modern systems with humans involved actively. The state of the art in human machine interface is largely dominant by solutions that are ad-hoc and application dependent. This talk attempts to summarize challenges for sensing and analytics from the perspective of human-machine systems\, and presents a computational framework aiming at anchoring behaviors to hardcoded features. Two projects will be introduced to showcase the framework: human hand skill transfer and interaction with children with autism spectrum disorders. The talk will conclude with comments on open issues and challenges in human-machine systems. \nBiography\nHonghai Liu received his Ph.D from King’s College\, University London\, UK. He is a Chair in Human Machine Systems at the University of Portsmouth\, UK. He previously held research appointments at the Universities of London\, University of Aberdeen\, and project leader appointments in large-scale industrial control and system integration industry. He is interested in intelligent sensing\, biomechatronics\, pattern recognition\, intelligent video analytics\, intelligent robotics and their practical applications with an emphasis on approaches that could make contribution to the intelligent connection of perception to action using contextual information. His research has been funded by UK research councils\, EU FP7\, the Leverhulme Trust\, the Royal Society and industry partners. He has authored/co-authored more than 200 per-reviewed journals and conference papers. He is an IET Fellow and JSPS Fellow. \nHonghai is an energetic contributor to our research community. He was the chair for IEEE Systems\, Man and Cybernetics Society’s Technical Committees\, also a Member of the IEEE Society’s Board of Governors\, leading the research theme on human machine systems. He is a Co-Editor-in-Chief for the Springer Journal of Intelligent Robotics and Applications and Associate Editor for IEEE Transactions on Human Machine Systems\, IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics. \n 
URL:https://www.fst.um.edu.mo/event/sensing-and-analytics-for-human-machine-systems/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181128T173000
DTEND;TZID=Asia/Macau:20181128T183000
DTSTAMP:20260610T055128
CREATED:20181128T042938Z
LAST-MODIFIED:20181128T042938Z
UID:6531-1543426200-1543429800@www.fst.um.edu.mo
SUMMARY:Energy Transformation and Smart Grid
DESCRIPTION:Instructors/Speakers\nProf. Xiaoli HUANG\nDeputy Chief Engineer of the Electric Power Planning and Engineering Institute (National Electric Power Planning and Research Center)\nDirector of the Smart Grid Department\, and Professor-level Chartered Engineer \nAbstract\nEnergy is essential for human activities and is one of the main driving forces for the development of the world economy. Every major advancement in human civilization is accompanied by reforms in energy. Nowadays\, the development of energy industry faces new challenges. The large-scale consumption of fossil resources has brought serious threats to global climate environment. Environmental pollution problems caused by improper development and use of energy are becoming more and more severe. The increasing energy consumption makes the contradiction between energy shortage and social development more prominent. There is an urgent need for energy transformation and development. Smart grids play an important role in energy transformation. The smart grid has been widely recognized worldwide since it was proposed. After more than ten years of practice\, the concept\, characteristics\, value and application of smart grid have been continuously enriched and developed. In general\, the smart grid enhances the flexibility of the power network. It improves the interaction between the elements of the network\, and therefore improves the flexibility and adaptability of the overall system\, meeting the needs of future energy development. Smart grid is the key to energy structure adjustments and to the future energy system. \nBiography\nProf. Xiaoli HUANG\, Deputy Chief Engineer of the Electric Power Planning and Engineering Institute (National Electric Power Planning and Research Center)\, Director of the Smart Grid Department\, and Professor-level Chartered Engineer. She has long been engaged in power system planning\, design\, power engineering consulting works. She was the deputy director of the Power Planning and Design Standards Committee and a member of the National Smart Grid Promotion Working Group. She presided over the national “13th Five-Year” power planning topic “Smart Power Grid Planning Research”\, the National Energy Administration’s major research topic “New Model of Energy Development\, New Business and Internet + Smart Energy Development Pathway”\, US Energy Foundation “Adaptation Research on new power planning methods for renewable energy development”\, World Bank “Research on distribution network technical specifications and trading mechanisms for high-proportion distributed renewable energy access”\, and other research projects\, hosted in 2022 Beijing Winter Olympics “Low Carbon Olympic Green Energy Planning”\, “Zhangjiakou Green Energy System Planning”\, “Distribution Network Planning and Design Regulations”\, “Southern Power Grid Smart Grid Planning Research” and “Inner Mongolia Grid’s 13th Five-Year Information Planning”. She is a co-editor of the book “Energy Transformation and Smart Grid”. \n 
URL:https://www.fst.um.edu.mo/event/energy-transformation-and-smart-grid/
LOCATION:E11-G015
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20181203
DTEND;VALUE=DATE:20181204
DTSTAMP:20260610T055128
CREATED:20181203T100409Z
LAST-MODIFIED:20220927T043401Z
UID:5839-1543795200-1543881599@www.fst.um.edu.mo
SUMMARY:Conference on “The Establishment of a Resilient City: Infrastructure\, Urban Planning and Law”韌性城市的建立：基建、城市與法律學術研討會
DESCRIPTION:
URL:/wp-content/uploads/2020/05/conf20181203_01.pdf#new_tab
CATEGORIES:cee_events,conferences,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181206T110000
DTEND;TZID=Asia/Macau:20181206T120000
DTSTAMP:20260610T055128
CREATED:20181206T024425Z
LAST-MODIFIED:20220927T043400Z
UID:6490-1544094000-1544097600@www.fst.um.edu.mo
SUMMARY:Probability Distribution Modelling of Data and Its Applications in Structural Health Monitoring
DESCRIPTION:Instructors/Speakers\nProf. He-Qing MU\nAssociate Professor\nSchool of Civil Engineering and Transportation\nSouth China University of Technology\nGuangzhou\nChina \nAbstract\nProbability distribution modelling of data is an important task in science and engineering. This research attempts to conduct probability distribution modelling of univariate and multivariate data. The first part introduces the theories of the traditional Bayesian inference and the Bayesian Network for probability distribution modelling. The second part introduced the structural health monitoring system of the Xinguang Bridge\, which is a three-span half-through arch bridge with the mid span of 428 m\, two side spans of 177 m each\, and width of 37.62 m\, over the Pearl River of Guangzhou City of China. The third part is devoted to the probability distribution modelling of the traffic load effect data and modal frequency–multiple environmental factor data of the Xinguang Bridge. \nBiography\nProf. He-Qing MU is an Associate Professor of Civil Engineering Department of South China University of Technology. He serves as the corresponding member of the Engineering Practice of Risk Assessment and Management Committee of the International Society of Soil Mechanics and Geotechnical Engineering\, the Member of Youth Committee of Structural Control & Health Monitoring\, Youth Committee of Random Vibration\, of Chinese Society for Vibration Engineering. He received his Ph.D. in civil engineering from University of Macau. He was a visiting scholar of California Institute of Technology (Caltech) and University of California\, Berkeley (UC Berkeley). He has obtained funding for several research projects\, including NSFC\, Natural Science Foundation of Guangdong Province; Foundation of the State Key Laboratory of Subtropical Building Science\, etc. In 2018\, he was elected as the recipient of the Pearl River S&T Nova Program of Guangzhou (珠江科技新星). His research interests include Bayesian Inference\, Earthquake Engineering\, Robust Data Analysis\, Structural Health Monitoring. \n 
URL:https://www.fst.um.edu.mo/event/probability-distribution-modelling-of-data-and-its-applications-in-structural-health-monitoring/
LOCATION:N6-2031
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181207T110000
DTEND;TZID=Asia/Macau:20181207T120000
DTSTAMP:20260610T055128
CREATED:20181207T030005Z
LAST-MODIFIED:20220927T043400Z
UID:5958-1544180400-1544184000@www.fst.um.edu.mo
SUMMARY:Can Deep Learning Learn to Count? on cognitive deficit of the current state of deep learning
DESCRIPTION:Instructors/Speakers\nProf. Xiaolin WU\nIEEE Fellow\nMcMaster University\nCanada \nAbstract\nSubitizing\, or the sense of small natural numbers\, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills\, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense\, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly\, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number\, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the connectionist CNN machinery itself. A recurrent neural network capable of subitizing does exist\, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also\, we investigate\, using subitizing as a test bed\, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting\, pointing to both cognitive deficit of pure DL\, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful as visual numerosity represents a minimum level of human intelligence. \nBiography\nXiaolin Wu\, Ph.D. in computer science\, University of Calgary\, Canada\, 1988. Dr. Wu started his academic career in 1988\, and has since been on the faculty of University of Western Ontario\, New York Polytechnic University (NYU Poly)\, and currently McMaster University\, where he is a McMaster Distinguished Engineering Professor at the Department of Electrical & Computer Engineering and holds the NSERC senior industrial research chair in Digital Cinema. His research interests include image processing\, multimedia signal coding and communication\, joint source-channel coding\, multiple description coding\, and network-aware visual communication. He has published over three hundred research papers and holds five patents in these fields. Dr. Wu is an IEEE fellow\, an associated editor of IEEE Transactions on Image Processing\, and served on the technical committees of many IEEE international conferences/workshops. Dr. Wu received numerous international awards and honors. \n 
URL:https://www.fst.um.edu.mo/event/can-deep-learning-learn-to-count-on-cognitive-deficit-of-the-current-state-of-deep-learning/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181211T110000
DTEND;TZID=Asia/Macau:20181211T120000
DTSTAMP:20260610T055128
CREATED:20181211T030050Z
LAST-MODIFIED:20220927T043400Z
UID:5954-1544526000-1544529600@www.fst.um.edu.mo
SUMMARY:Is a Picture Worth a Thousand Words? A Deep Learning Approach for Assessing the Impact of Investors’ Emotions Mined from Multimodal Social Postings on Corporate Credit Ratings
DESCRIPTION:Instructors/Speakers\nProf. Raymond Yiu Keung LAU\nDepartment of Information Systems\nCity University of Hong Kong \nAbstract\nWith the rise of the Social Web\, it is increasingly more popular for investors to express their feelings about firms and products on online social media. Though previous studies have examined the relationship between investors’ sentiments captured in texts (e.g.\, online news articles) and corporate credit ratings\, there is a research gap in terms of studying the relationship between investors’ emotions captured in multimodal social postings and corporate credit ratings. By drawing upon the appraisal theory and the notion of affect-as-information\, this study aims to fill the current research gap by examining the influence of investors’ emotions (e.g.\, trust\, joy\, and anger) captured in multimodal social postings on corporate credit ratings. In particular\, we apply state-of-the-art machine learning methods (e.g.\, topic modeling and deep convolutional neural networks) to extract investors’ emotions toward corporations from both texts and images posted to online social media. Our empirical results show that investors’ emotions such as “trust” and “joy” captured in textual social postings are positively associated with corporate credit rating\, while investors’ emotion such as “anger” captured in the images of the social postings is negatively associated with corporate credit rating. Moreover\, the negative influence of the “anger” emotion extracted from images is larger than the positive influence of “trust” and “joy” extracted from texts. The managerial implication of our study is that corporations should pay attention to both texts and images posted to online social media by investors as these postings affect corporate credit ratings\, and subsequently the costs of corporate borrowings. Our findings also offer new insights for corporations\, investors\, and ratings agencies with respect to the predictive power of investors’ multimodal social posts toward corporate credit ratings. \nBiography\nRAYMOND Y.K. LAU is an Associate Professor in the Department of Information Systems at City University of Hong Kong. He has worked at the academia and the ICT industry for over twenty years. He is the author of more than 200 refereed international journals and conference papers. His research work is published in renowned journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)\, IEEE Transactions on Knowledge and Data Engineering\, IEEE Intelligent Systems\, IEEE Internet Computing\, INFORMS Journal on Computing\, MIS Quarterly\, ACM Transactions on Information Systems\, etc. His research interests include Financial Technology (FinTech)\, Social Media Analytics\, Big Data Analytics\, and Artificial Intelligence (AI) for Business. He is a senior member of the IEEE and the ACM\, respectively. \n 
URL:https://www.fst.um.edu.mo/event/is-a-picture-worth-a-thousand-words-a-deep-learning-approach-for-assessing-the-impact-of-investors-emotions-mined-from-multimodal-social-postings-on-corporate-credit-ratings/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181213T150000
DTEND;TZID=Asia/Macau:20181213T160000
DTSTAMP:20260610T055128
CREATED:20181213T042747Z
LAST-MODIFIED:20181213T042747Z
UID:6529-1544713200-1544716800@www.fst.um.edu.mo
SUMMARY:Improving Information Security in Ranking Systems and Control Systems
DESCRIPTION:Instructors/Speakers\nDr. Guilherme RAMOS\nPh.D. (2018)\nInformation Security at Instituto Superior Técnico\nLisbon\, Portugal \nAbstract\nIn this work\, we propose to advance in the field of information security in the areas of ranking systems (RS) and control systems\, using ideas from the area of information theory. First\, we propose a RS that groups users based on their preferences\, introducing similarity measures. The system presents possibly distinct rankings for the same product in different user groups. Besides presenting more personalized rankings to users\, it is a system more resistant to attacks than the state-of-the-art. We then explore the effect of bribing in reputation-based RS\, in the usual scenario (a ranking for each product) and in the scenario we proposed. We find the optimal bribing strategies\, and we evaluate our methodology with real data\, being the devised ranking system more robust to bribery. Finally\, in control systems\, we present methods to find the placement of the minimum number of inputs in LTI systems and switched LTI systems\, in the eventual scenario where a set of controllers may fail\, e.g.\, due to a cyberattack. In the first case\, we prove that the problem is NP-complete. We design algorithms to solve the problems explicitly\, and also to approximate the solution in polynomial time. \nKeywords: Ranking Systems\, Control Systems\, Information Security\, Bribing. \nBiography\nDr. Guilherme RAMOS has a Ph.D. (2018) in Information Security at Instituto Superior Técnico\, Lisbon\, Portugal\, through the doctoral programme in Physics and Mathematics of Information. He received the B.Sc. (2011) and M.Sc. (2013) degrees in Applied Mathematics from the Instituto Superior Técnico\, Lisbon\, Portugal. His areas of interest are: Structural Systems; Control Theory; Cryptography; Information Theory; Ranking Systems; Recommender Systems; Consensus Algorithms. \n 
URL:https://www.fst.um.edu.mo/event/improving-information-security-in-ranking-systems-and-control-systems/
LOCATION:E11-1006
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181214T110000
DTEND;TZID=Asia/Macau:20181214T120000
DTSTAMP:20260610T055128
CREATED:20181214T030052Z
LAST-MODIFIED:20220927T043400Z
UID:5951-1544785200-1544788800@www.fst.um.edu.mo
SUMMARY:On Text Analysis and Its Applications
DESCRIPTION:Instructors/Speakers\nProf. Raymond WONG\nSchool of Computer Science & Engineering\nUniversity of New South Wales\, Sydney\nAustralia \nAbstract\nDue to the increasing computation power and availability of big text data\, text analysis research\, such as text mining\, natural language processing\, and sentiment analysis\, has drawn significant attention from both research and industry communities. This seminar presents and summarizes our recent research findings in the area of text analysis\, and shares our experience in several related applications. \nBiography\nRaymond Wong is an Associate Professor at the School of Computer Science & Engineering\, University of New South Wales\, Sydney\, Australia. From 2005-2011\, he founded and led the Data Management Program at NICTA (the largest ICT organization in Australia)\, and was a Visiting Professor at Tsinghua University\, Beijing in 2011-13. Raymond co-founded several companies including Cohesive Data Inc that have successfully exited. He has also been involved in numerous technology fund raising\, M&A\, and patent valuation activities. His technical expertise lies in database systems\, big data\, machine learning and NLP. He has published more than 200 research publications and 2 US patents in these areas. He has also supervised 20 PhD students to completion. He received his BSc from Australian National University; MPhil and PhD from Hong Kong University of Science & Technology; and did his Postdoc at UCLA and Stanford University. \n 
URL:https://www.fst.um.edu.mo/event/on-text-analysis-and-its-applications/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181218T143000
DTEND;TZID=Asia/Macau:20181218T143000
DTSTAMP:20260610T055128
CREATED:20181218T063043Z
LAST-MODIFIED:20220927T043359Z
UID:5946-1545143400-1545143400@www.fst.um.edu.mo
SUMMARY:Smart City and Sustainability Forum
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/smart-city-and-sustainability-forum/
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190103T143000
DTEND;TZID=Asia/Macau:20190103T143000
DTSTAMP:20260610T055128
CREATED:20190103T042431Z
LAST-MODIFIED:20190103T042431Z
UID:6526-1546525800-1546525800@www.fst.um.edu.mo
SUMMARY:Future Electricity Supply for Smart City
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/future-electricity-supply-for-smart-city/
LOCATION:N1-1005
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190104T110000
DTEND;TZID=Asia/Macau:20190104T120000
DTSTAMP:20260610T055128
CREATED:20190104T030035Z
LAST-MODIFIED:20220927T043359Z
UID:5944-1546599600-1546603200@www.fst.um.edu.mo
SUMMARY:Recent Progress on Deep Learning Based Face Recognition\, Analysis and Translation
DESCRIPTION:Instructors/Speakers\nProf. Linlin SHEN\nShenzhen University\nShenzhen\nChina \nAbstract\nIn this talk\, I will mainly introduce deep learning and its applications in face recognition\, analysis and translation. The history of face recognition using hand-crafted features and deep neural networks will be firstly briefed. Publicly available face datasets for both training and testing will be introduced. The most recent progress about the performance of facial recognition algorithms and their real applications will be followed. The methodologies to address face spoofing and classification of facial attributes including age\, gender and expressions will be explored. Finally\, the progress about face translation using GAN\, pix2pix\, starGAN and our proposed GAN will be presented. \nBiography\nProf. Linlin Shen is currently a professor at Computer Science and Software Engineering\, Shenzhen University\, Shenzhen\, China. He is also an Honorary professor at School of Computer Science\, University of Nottingham\, UK. He serves as the director of Computer Vision Institute and China-UK joint research lab for visual information processing. He received the BSc and MEng degrees from Shanghai Jiaotong University\, Shanghai\, China\, and the Ph.D. degree from the University of Nottingham\, Nottingham\, U.K. He was a Research Fellow with the University of Nottingham\, working on MRI brain image processing. His research interests include deep learning\, facial recognition\, analysis/synthesis and medical image processing. Prof. Shen is listed as the Most Cited Chinese Researcher by Elsevier. He received the Most Cited Paper Award from the journal of Image and Vision Computing. His cell classification algorithms were the winners of the International Contest on Pattern Recognition Techniques for Indirect Immunofluorescence Images held by ICIP 2013 and ICPR 2016. \n 
URL:https://www.fst.um.edu.mo/event/recent-progress-on-deep-learning-based-face-recognition-analysis-and-translation/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190124T100000
DTEND;TZID=Asia/Macau:20190124T110000
DTSTAMP:20260610T055128
CREATED:20190124T020021Z
LAST-MODIFIED:20220927T043358Z
UID:5941-1548324000-1548327600@www.fst.um.edu.mo
SUMMARY:Infiltration and excess pore water pressure in front of a tunnel boring machine (TBM): Experiments\, Mechanisms and Computational models
DESCRIPTION:Instructors/Speakers\nDr. Tao XU\nPost-Doctoral Fellow\nFaculty of Engineering and Architecture\nGhent University\nBelgium \nAbstract\nThe Tunnel Boring Machine (TBM) tunneling technique has been developed to construct tunnels that require strict settlement control\, for example\, in urban areas with a large amount of buildings\, historic areas etc. When tunneling with a TBM in saturated sand\, groundwater flow into the excavation face needs to be impeded. Both stability of the tunnel face and limitation of the groundwater flow are achieved by pressurizing the drilling fluid at the tunnel face. Due to the pressure difference between the excavation chamber of the TBM and the ground\, drilling fluid (slurry or foam) will infiltrate into the ground and thus there will be a flow in front of the tunnel face. In such a situation\, part of support pressure applied through the drilling fluid at the tunnel face will be transferred into excess pore water pressure in the ground. As a result\, the effective support pressure at the tunnel face and thus the stability of tunnel face will be reduced. The reduction of the effective support pressure depends on the infiltration distance\, the infiltration velocity and the drilling speed. However\, the infiltration distance of the foam or the slurry\, influence of this infiltration on the permeability of the infiltrated ground\, and consequently on the excess pore water pressure\, are still only partly understood. Finding answers to these questions\, therefore\, is significantly important for the safety of tunnel. \nThe purpose of this study is to improve our understanding of the mechanism of drilling fluid infiltration in front of the tunnel face through laboratory experiments\, and develop computational models considering such an infiltration process to predict the excess pore water pressures induced by TBM tunneling. The research concerns both slurry and Earth Pressure Balance shields (EPB shields). With the results of the experiments\, a sounder theoretical basis for the description of infiltration of drilling fluid (slurry or foam)\, and the computation of excess pore water pressure in front of tunnel face have been established. \nBiography\nCurrently\, Dr. Tao Xu is a Post-Doctoral Fellow at Ghent University. In 2018 he gained his PhD from Ghent University. His research has been published in top geotechnical journals Géotechnique and Tunnelling and Underground Space Technology. Prior to his doctoral research\, he carried out research on hydro-mechanical behavior of unsaturated soils and stability of unsaturated soil nailed slopes subjected to rainfall at Harbin Institute of Technology from 2012 to 2014. In 2014\, he started his PhD at Ghent University. His doctoral research focused on slurry and foam infiltration in front of a TBM and excess pore water pressures caused by TBM drilling. In the meantime\, as an invited guest researcher\, he participated in German Collaborative Project SFB 837 ‘Interaction modelling in mechanized tunneling’. He will join University of Macau on March 2019 as a research fellow responsible for assisting on establishing TBM tunneling research. \n 
URL:https://www.fst.um.edu.mo/event/infiltration-and-excess-pore-water-pressure-in-front-of-a-tunnel-boring-machine-tbm-experiments-mechanisms-and-computational-models/
LOCATION:E11-3033
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190213T143000
DTEND;TZID=Asia/Macau:20190213T153000
DTSTAMP:20260610T055128
CREATED:20190213T063031Z
LAST-MODIFIED:20220927T042640Z
UID:5939-1550068200-1550071800@www.fst.um.edu.mo
SUMMARY:AI in Bloomberg
DESCRIPTION:Instructors/Speakers\nMr. Iat Chong CHAN\nMachine Learning Team\, London\, United Kingdom \nAbstract\nThe Bloomberg Terminal brings together real-time data on every market\, breaking news\, in-depth research\, powerful analytics in one fully integrated solution. In the News product\, we provide\, our award-winning news coverage ensures our clients could get the information they need. While at the same time\, putting a lot of effort into trying to avoid overloading them with excessive information. \nIn this talk\, we will have a general overview of how AI (Artificial intelligence) techniques are being utilised in Bloomberg to allow our clients obtaining the information needed efficiently. Then\, we will focus on a particular application\, which was designed to refine information from a massive amount of news stories. We will also discuss the AI algorithms that underpin it. \nThere is no pre-requisite for most part of this talk. 30% of the talk requires basic understanding of statistics and probabilities. \n  \nBiography\nIat Chong Chan (https://www.linkedin.com/in/iatchongchan) is a research scientist/software developer in Bloomberg Machine Learning Team. His interests mostly lie in the intersection of Computational Linguistics\, Machine Learning\, and High Performance Computing. He has been working on a scalable infrastructure to infer topics of social contents ingested to Bloomberg by statistical models\, and a multi-documents summarisation system to extract the most important information from a text collection. Iat Chong also leads the NLP guild inside Bloomberg\, to advocate the use of ML/NLP techniques for new business problems. Before he joined the company\, he was a MSc student in Dept. of Computer Science at University of Oxford\, supervised by Prof. Stephen Pulman\, and worked on building a better input method on small hand-held devices by a novel Bayesian Network with Variational Inference. \n 
URL:https://www.fst.um.edu.mo/event/ai-in-bloomberg/
LOCATION:E11-G015 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190320T153000
DTEND;TZID=Asia/Macau:20190320T163000
DTSTAMP:20260610T055128
CREATED:20190320T041807Z
LAST-MODIFIED:20190320T041807Z
UID:6522-1553095800-1553099400@www.fst.um.edu.mo
SUMMARY:Drone-assisted Mobile Edge Computing
DESCRIPTION:Instructors/Speakers\nProf. Nirwan ANSARI\nDistinguished Professor\nElectrical and Computer Engineering\, Newark College of Engineering\nNew Jersey Institute of Technology\nNew Jersey\, USA \nAbstract\nIn mobile access networks\, different types of Internet of Things (IoT) devices (e.g.\, sensor nodes and smartphones) will generate vast traffic demands\, thus dramatically increasing the traffic loads of their connected access nodes\, especially in the 5G era. Mobile edge computing enables data collected by IoT devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case\, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users\, drone base station (DBS)\, which can be flexibly deployed over hotspot areas\, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) DBS should be deployed in suitable areas with heavy traffic demands to serve more users; 2) traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore\, we propose a TrAffic Load baLancing (TALL) scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme\, we divide the problem into two sub-problems and design two algorithms to optimize the DBS placement and user association\, respectively. Extensive simulations have been set up to validate the performance of TALL. \nBiography\nNirwan Ansari is Distinguished Professor of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). He has also been a visiting (chair) professor at several universities. Professor Ansari authored Green Mobile Networks: A Networking Perspective (IEEE-Wiley\, 2017) with T. Han\, and co-authored two other books. He has also (co-)authored more than 550 technical publications\, over 250 published in widely cited journals/magazines. He has guest-edited a number of special issues covering various emerging topics in communications and networking. He has been serving on the editorial/advisory board of more than ten journals. His current research focuses on green communications and networking\, cloud computing\, drone-assisted networking\, and various aspects of broadband networks. \nProfessor Ansari was elected to serve in the IEEE Communications Society (ComSoc) Board of Governors as a member-at-large\, has chaired some ComSoc technical and steering committees\, has been serving in many committees such as the IEEE Fellow Committee\, and has been actively organizing numerous IEEE International Conferences/Symposia/Workshops. He has frequently been delivering keynote addresses\, distinguished lectures\, tutorials\, and invited talks. Some of his recognitions include IEEE Fellow\, several Excellence in Teaching Awards\, a few best paper awards\, the NCE Excellence in Research Award\, the ComSoc TC-CSR Distinguished Technical Achievement Award\, the ComSoc AHSN TC Technical Recognition Award\, the IEEE TCGCC Distinguished Technical Achievement Recognition Award\, the NJ Inventors Hall of Fame Inventor of the Year Award\, the Thomas Alva Edison Patent Award\, Purdue University Outstanding Electrical and Computer Engineering Award\, NCE 100 Medal\, and designation as a COMSOC Distinguished Lecturer. He has also been granted 38 U.S. patents. \nHe received a Ph.D. from Purdue University—West Lafayette\, IN\, an MSEE from the University of Michigan—Ann Arbor\, MI\, and a BSEE (summa cum laude with a perfect GPA) from NJIT—Newark\, NJ. \n 
URL:https://www.fst.um.edu.mo/event/drone-assisted-mobile-edge-computing/
LOCATION:E11-1006
CATEGORIES:ece_events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190325T110000
DTEND;TZID=Asia/Macau:20190325T120000
DTSTAMP:20260610T055128
CREATED:20190325T030020Z
LAST-MODIFIED:20220927T042639Z
UID:5937-1553511600-1553515200@www.fst.um.edu.mo
SUMMARY:AI Driven Smart City Development
DESCRIPTION:Instructors/Speakers\nProf. Jane YOU\nDepartment of Computing at the Hong Kong Polytechnic University\nHong Kong \nAbstract\nThe fast advances in AI (artificial intelligence) impose significant impacts on the world on all aspects. Smart sensing and big data analytics are emerging as the driving force to revolutionize our living in the era of information. This seminar presents the study of artificial intelligence for smart city development\, in particular the integration of new portable/wearable devices/sensors with computing technologies for the applications to smart transportation and healthcare. A vision-based approach to anonymous vehicle re-identification (VRI) overcomes the limitations of the existing systems by the fusion of multiple features with learning capacity. The automated truck loading monitoring system is equipped with a portable vehicle-mounted multi-function tool kit to provide a wide range of services for real-time logistic management\, environment protection and road safety control. The smart fetal monitoring belt is the first of its kind which integrates soft sensors\, textile and information technologies to detect fetal movement and uterine contraction for personalized monitoring of fetal wellbeing with quantitative assessment safely\, reliably\, conveniently and cost effectively. \nThese projects are supported by the Hong Kong Government under the schemes of General Research Fund and Innovation Technology Fund together with industry sponsorship. The impact of the work is evidenced by the outputs including research publications\, US patent\, international awards\, technology transfer and entrepreneurship. \nBiography\nJane You is currently a professor in the Department of Computing at the Hong Kong Polytechnic University. She is also the Associate Head of the department. Prof. You obtained her BEng. in Electronic Engineering from Xi’an Jiaotong University in 1986 and Ph.D in Computer Science from La Trobe University\, Australia in 1992. She was a lecturer at the University of South Australia and senior lecturer (tenured) at Griffith University from 1993 till 2002. Prof. You was awarded French Foreign Ministry International Postdoctoral Fellowship in 1993 and worked on the project on real-time object recognition and tracking at Universite Paris XI. She also obtained the Academic Certificate issued by French Education Ministry in 1994. \nProf. Jane You has worked extensively in the fields of image processing\, medical imaging\, computer-aided detection\, pattern recognition. So far\, she has more than 280 research papers published. She has been a principal investigator for several ITF (Innovation Technology Fund)\, and GRF (General Research Fund) projects supported by Hong Kong Government. Prof. You is a team member for three successful US patents and three awards including Hong Kong Government Industrial Awards. Her work on retinal imaging led to a US patent (2015)\, a technology transfer agreement (2011) and three international awards – Innovation Award of Excellence (Hong Kong\, 2015)\, a Special Prize and Gold Medal with Jury’s Commendation at the 39th International Exhibition of Inventions of Geneva (April 2011) and the second place of SPIE Medical Imaging’2009 Retinopathy Online Challenge (ROC’2009)). Her recent work on smart fetal monitoring was awarded the special prize and Silver Medal at the 44th International Exhibition of Inventions of Geneva (April 2016) and led to a technology transfer agreement with a company for production (2018). Prof. You is also an associate editor of Pattern Recognition and other journals. \n 
URL:https://www.fst.um.edu.mo/event/ai-driven-smart-city-development/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190402T160000
DTEND;TZID=Asia/Macau:20190402T170000
DTSTAMP:20260610T055128
CREATED:20190402T080059Z
LAST-MODIFIED:20220927T042639Z
UID:5935-1554220800-1554224400@www.fst.um.edu.mo
SUMMARY:Security Challenges in the Internet of Things
DESCRIPTION:Instructors/Speakers\nProf. Kui REN\nSchool of Computer Science and Technology\nZhejiang University\nChina \nAbstract\nThe vision of Internet of things (IoT) is the interconnected physical devices of various forms\, embedded with electronics\, software\, sensors\, actuators\, jointly perform sophisticated tasks ranging from data collection\, exchange\, and aggregation to task scheduling and system operation. IoT is expected to support abundant unprecedented services for the world and referred as “the infrastructure of the information society.” Penetrating into almost every critical aspect of the modern society\, IoT\, however\, also poses critical security challenges. In this talk\, I will discuss the uniqueness of these security challenges. Particularly\, three topics will be covered in depth; that is\, 1) The challenge of the device interfaces; 2) IoT hub security; and 3) Data privacy. \nBiography\nKui Ren is currently a Distinguished Professor in the School of Computer Science and Technology at Zhejiang University\, where he also directs the Institute of Cyber Space Research. His research interests include Cloud and data security\, AI and IoT security\, and Privacy-enhancing technologies. He is a Fellow of IEEE and a Distinguished Member of ACM. \n 
URL:https://www.fst.um.edu.mo/event/security-challenges-in-the-internet-of-things/
LOCATION:E11-1006 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190408
DTEND;VALUE=DATE:20190413
DTSTAMP:20260610T055128
CREATED:20190408T095835Z
LAST-MODIFIED:20220927T042639Z
UID:5837-1554681600-1555113599@www.fst.um.edu.mo
SUMMARY:35th IEEE International Conference on Data Engineering 2019 (ICDE 2019)
DESCRIPTION:
URL:
CATEGORIES:conferences,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190412T110000
DTEND;TZID=Asia/Macau:20190412T120000
DTSTAMP:20260610T055128
CREATED:20190412T030046Z
LAST-MODIFIED:20220927T042638Z
UID:5933-1555066800-1555070400@www.fst.um.edu.mo
SUMMARY:Spatial and Spatio-Temporal Data Analytics and their applications in urban cities
DESCRIPTION:Instructors/Speakers\nProf. Cheng LONG\nThe School of Computer Science and Engineering (SCSE)\nNanyang Technological University (NTU)\nSingapore \nAbstract\nIt is expected that by 2050\, more than 2.5 billion people would reside in cities. While the urbanization has modernized many peoples’ lives\, it causes big challenges such as traffic congestion\, air pollution\, energy consumption\, etc. As part of the effort for solving these problems\, people have been collecting and analyzing data that is being generated in the urban space\, e.g.\, traffic data\, mobility data\, POI data\, etc.\, for finding insights into the problems and/or serving citizens better decision making. Since the majority of the data involves the spatial and/or temporal dimensions\, techniques for spatial and spatio-temporal data analytics are playing critical roles. In this talk\, we will overview this data-driven process\, introduce some of its interesting applications\, and also present some of our recent work on spatial and spatio-temporal data analytics\, including dynamic spatial matching\, co-location pattern mining\, traffic anomaly detection\, and interesting region discovery\, etc. \nBiography\nLONG Cheng is currently an Assistant Professor at the School of Computer Science and Engineering (SCSE)\, Nanyang Technological University (NTU). From 2016 to 2018\, he worked as a lecturer (Asst Professor) at Queen’s University Belfast\, UK. He got the PhD degree from the Department of Computer Science and Engineering\, The Hong Kong University of Science and Technology (HKUST) in 2015. His research interests are broadly in data management and data mining with his vision to achieve scalable spatial computing\, to make sense of urban related data for smarter cities\, and to manage and analyze emerging big data such as IoT data for richer knowledge. His research has been recognized with one “Best Research Award” provided by ACM-Hong Kong\, one “Fulbright-RGC Research Award” provided by Research Grant Council (Hong Kong)\, two “PG Paper Contest Awards” provided by IEEE-HK\, and one “Overseas Research Award” provided by HKUST. He has served as a Program Committee member/referee for several top data management and data mining conferences/journals (TODS\, VLDBJ\, TKDE\, ICDM\, CIKM\, etc.). He is member of ACM and IEEE. \n 
URL:https://www.fst.um.edu.mo/event/spatial-and-spatio-temporal-data-analytics-and-their-applications-in-urban-cities/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190414
DTEND;VALUE=DATE:20190418
DTSTAMP:20260610T055128
CREATED:20190414T095539Z
LAST-MODIFIED:20220927T042638Z
UID:5834-1555200000-1555545599@www.fst.um.edu.mo
SUMMARY:The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019)
DESCRIPTION:
URL:https://pakdd2019.medmeeting.org/Content/100312#new_tab
CATEGORIES:conferences,event_list
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20190416T150000
DTEND;TZID=Asia/Macau:20190416T150000
DTSTAMP:20260610T055128
CREATED:20190416T070028Z
LAST-MODIFIED:20220927T042638Z
UID:5929-1555426800-1555426800@www.fst.um.edu.mo
SUMMARY:Comprehensive strategies for eco-environment remediation on the Chinese Loess Plateau
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/comprehensive-strategies-for-eco-environment-remediation-on-the-chinese-loess-plateau/
LOCATION:E12-G004
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
END:VCALENDAR