Keynote Speaker Ⅰ
Prof. Hamid Reza Karimi
Member Academia Europa, Distinguished Fellow IIAV, Fellow ISCM
Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Speech Title: Intelligent Fault Diagnosis of Rotary Machinery Systems
Abstract: Industry 4.0 has enabled the automation of process improvements and decision making based on the collection of large amounts of plant data. Due to the economic advantages of maintenance optimization, there is significant interest from both academia and industry on the topic of fault detection and prognostics for complex systems. The objective of this speech is to address some challenges and recent results on fault diagnosis of rotary machinery systems, with a focus on advanced artificial intelligence algorithms developments. The talk will be concluded with some concluding remarks on both technical and practical aspects of intelligent fault diagnosis for rotary machinery systems.
Biography: Hamid Reza Karimi is currently Professor of Applied Mechanics with the Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy. His original research and development achievements span a broad spectrum within the topic of control systems, mechatronics, intelligent systems, and fault diagnosis with applications such as vehicles, robotics and manufacturing systems. Prof. Karimi is a Member of Academia Europa, Distinguished Fellow of the International Institute of Acoustics and Vibration (IIAV), Fellow of The International Society for Condition Monitoring (ISCM), Member of Agder Academy of Science and Letters and also a member of the IFAC Technical Committee on Mechatronic Systems, the IFAC Technical Committee on Robust Control, and the IFAC Technical Committee on Automotive Control. He is serving as Chief Editor, Subject Editor, Technical Editor or Associate Editor for some International Journals and Book Series Editor for Springer, CRC Press and Elsevier. Prof. Karimi has been awarded as the 2016-2021 Web of Science Highly Cited Researcher in Engineering, the 2020 IEEE Transactions on Circuits and Systems Guillemin-Cauer Best Paper Award, August-Wilhelm-Scheer Visiting Professorship Award, JSPS (Japan Society for the Promotion of Science) Research Award, and Alexander-von-Humboldt-Stiftung research Award, for instance. He has also participated as General Chair, keynote/plenary speaker, distinguished speaker or program chair for several international conferences in the areas of Control Systems, Robotics and Mechatronics.
Keynote Speaker Ⅱ
Prof. Subhas Chandra Mukhopadhyay
Fellow of IET (UK), Fellow of IETE (India), Topical Editor of IEEE Sensors journal,
IEEE Review of Biomedical Engineering, the Founding chair of IEEE Sensors Council NSW chapter
School of Engineering, Macquarie University, North Ryde, NSW 2109, Australia
Keynote Speaker Ⅲ
Prof. Mitsuo Gen
2015 IEEE T-SM Best Paper Award by IEEE Transactions on Semiconductor Manufacturing
World's Top Computer Science Scientists: H-Index Computer Science Ranking in Japan
Fuzzy Logic Systems Institute and Research Institute of Science & Technology, Tokyo Univ. of Science
Speech Title: Recent Advances in Hybrid Metaheuristics for Semiconductor Manufacturing Scheduling
Abstract: In the real-world of scheduling problems, there are many combinatorial optimization problems (COPs) imposing on more complex structure, nonlinear constraints, and multiple objectives. The COPs make the problem intractable to the traditional approaches because of NP-hard ones. Flexible Jobshop Scheduling Problem (FJSP) is a generalization of the jobshop and parallel machine environment, which provides a closer real manufacturing systems. In order to develop an efficient algorithm whose reasonable computational time for NP-hard COPs, we have to consider 1) quality of solution, 2) computational time and 3) effectiveness of the nondominated solutions for multiobjective COP. As a subset of metaheuristics, Evolutionary Algorithm (EA) is a generic population-based metaheuristic such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Estimation of Distribution Algorithm (EDA) and Teaching-Learning Based Optimization (TLBO). EA is a very powerful and broadly applicable stochastic search and optimization technique which is effective for solving various NP hard problems. The objective of this keynote talk is to address recent metaheuristic algorithms for variant FJSP models such as Mo-FJSP, FJSP with sequence dependent & setup time (FJSP-SDST), distributed FJSP (D-FJSP) and fuzzy FJSP (F-FJSP) models. We will secondly survey the practical semiconductor manufacturing scheduling models for the hard disc device (HDD) and the thin-film transistor-liquid crystal display (TFT-LCD) manufacturing systems based on FJSP, respectively by hybrid multiobjective GA (H-MoGA) with PSO and Cauchy distribution.
Biography:Mitsuo Gen received his PhD in Engineering from Kogakuin University, PhD degree in Informatics from Kyoto University, Japan. He is Senior Research Scientist at FLSI (Fuzzy Logic Systems Institute) and Visiting Prof. at RIST (Research Institute of Science and Technology), Tokyo University of Science (TUS). He was faculties at Ashikaga Institute of Technology for 1974-2003, at Waseda University for 2003-2010. He was visiting faculties at University of California at Berkeley for 1999.08-2000.03, Texas A&M University for 2000.01-03 & 2000.08-09, Ulsan National Institute of Science & Technology for 2010.09-12, Hanyang University in S. Korea for 2010.12-2012.06 and National Tsing Hua University in Taiwan for 2012-2014. His research field is Evolutionary Computation, Manufacturing Scheduling and Logistics Systems. He received the 2015 IEEE T-SM Best Paper Award by IEEE Transactions on Semiconductor Manufacturing in 2016. Recently he ranked #10 in Computer Science & Electronics in Japan and #1426 in the world by Research.com. (World's Top Computer Science Scientists: H-Index Computer Science Ranking in Japan | Research.com). He is coauthor of the following books: Genetic Algorithms and Engineering Design, John Wiley & Sons, New York, 1997; Genetic Algorithms and Engineering Optimization, John Wiley & Sons, New York, 2000; Network Models and Optimization: Multiobjective Genetic Algorithm Approach, Springer, London, 2008; Introduction to Evolutionary Algorithms, Springer, London, 2010.
Keynote Speaker Ⅳ
Prof. M.Grazia Speranza
Member of the Academy of Sciences of Bologna, IFORS Fellow
Department of Economics and Management, University of Brescia, Italy
Speech Title: Trends in transportation and logistics and the role of optimization
Abstract: Technological changes have been dramatic in the last decades. The Internet of Things (IoT) makes also objects and places capable of receiving, storing and transmitting information. Coordination opportunities are enormous in all fields. The technological developments are changing the way people and freight move. Analytics, and optimization in particular, are aimed at extracting value from the so called ‘big data’. A systemic approach to problems and advanced analytical methods are even more vital than in the past. In this talk the main trends in transportation and logistics will be presented and some research directions will be discussed with examples of integrated and collaborative problems in transportation and logistics.
Biography:M. Grazia Speranza is full professor of Operations Research at the University of Brescia, where she served as Dean of the Faculty of Economics and Business and Deputy Rector. She is currently President of IFORS (International Federation of the Operational Research Societies) and a former President of EURO (association of European Operational Research Societies) and of TSL (Transportation Science and Logistics society of INFORMS). As EURO President she founded the EURO Journal on Transportation and Logistics, the EURO Journal on Computational Optimization and the EURO Journal on Decision Processes. Her research focuses on mixed integer programming and combinatorial optimization with applications to transportation, supply chain management, scheduling and portfolio selection. Recent research is oriented towards the study of routing problems enabled by technological developments. Grazia is author of about 200 papers that appeared in international journals and volumes. She has been plenary speaker at several international conferences and member of the scientific committee of the major international conferences in the field. She was visiting professor at the London School of Economics and at Brunel University during her sabbatical and has given talks and seminars at many universities around the world. She has been guest editor of special issues of journals, editor of several international journals and is co-editor-in-chief of the series of books ‘EURO Advanced Tutorials in Operational Research’. Grazie has been a member of many evaluation committees, including the European Research Council (ERC) mathematics panel. She is included in https://100esperte.it/ and in the book ‘100 donne contro gli stereotipi per la scienza', Egea, 2017 as one of the best 100 Italian women in the STEM area. In 2019 she was awarded with the Laurea honoris causa by the University of Freiburg, Switzerland. She is a member of the Academy of Sciences of the University of Bologna.
Keynote Speaker Ⅴ
Prof. Craig Knoblock
AAAI Fellow, ACM Fellow, IJCAI past President and Trustee
University of Southern California, the USA
Speech Title: Building and Using Knowledge Graphs to Turn Data into Knowledge
Abstract: Creating knowledge graphs from data provides a way of combining sources of information in ways that can then be exploiting to solve various real-world problems. However, the challenge in building knowledge graphs is getting the data into a usable form. In this talk I will highlight some of the techniques we have developed for ingesting data into a knowledge graph, including automatic techniques for finding errors in tables and methods for understanding the content of a given data source. I will also describe some of the applications we have developed using knowledge graphs and how we were able to transform challenging tasks into ones that could be addressed, including combating human trafficking, identifying illegal arms sales, and predicting cyber attacks.
Craig Knoblock is the Keston Executive Director of the Information Sciences Institute, Research Professor of both Computer Science and Spatial Sciences, and Vice Dean of Engineering at the University of Southern California. He received his Bachelor of Science degree from Syracuse University and his Master’s and Ph.D. from Carnegie Mellon University in computer science. His research focuses on techniques for describing, acquiring, and exploiting the semantics of data. He has worked extensively on source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the web, and combining these techniques to build knowledge graphs. He has published more than 400 journal articles, book chapters, and conference and workshop papers on these topics and has received 7 best paper awards on this work. He also co-authored a recent book titled Knowledge Graphs Fundamentals, Techniques, and Applications, which was published in 2021 by MIT Press. Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association of Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE). He is also past President of the International Joint Conference on Artificial Intelligence (IJCAI) and winner of the Robert S. Engelmore Award.
Keynote Speaker Ⅵ
Prof. Geoffrey Webb
Program Committee Chair of ACM SIGKDD and IEEE ICDM，IEEE Fellow，
Technical Advisor to BigML Inc, Monash University
Speech Title: Recent Advances in Assessing Time Series Similarity Through Dynamic Time Warping
Time series are a ubiquitous data type that capture information as it evolves over time. Dynamic Time Warping is the classic technique for assessing degrees of similarity between time series. This talk outlines our impactful program of research that has transformed the state of the art in practical application of Dynamic Time Warping to big data tasks, including fast and effective lower bounds, fast dynamic programming methods for calculating Dynamic Time Warping, and an intuitive and effective variant of Dynamic Time Warping that moderates its sometimes-excessive flexibilty.
Professor Geoff Webb is Research Director of the Monash University Data Futures Institute. An eminent and highly-cited data scientist, he was editor in chief of the Data Mining and Knowledge Discovery journal, from 2005 to 2014. He has been Program Committee Chair of both ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM and member of the ACM SIGKDD Executive. He is a Technical Advisor to machine learning as a service startup BigML Inc and to recommender systems startup FROOMLE. He developed many of the key mechanisms of support-confidence association discovery in the 1980s. His OPUS search algorithm remains the state-of-the-art in rule search. He pioneered multiple research areas as diverse as black-box user modelling, interactive data analytics and statistically-sound pattern discovery. He has developed many useful machine learning algorithms that are widely deployed. His many awards include IEEE Fellow and the inaugural Eureka Prize for Excellence in Data Science (2017).
Keynote Speaker Ⅶ
Prof. Zhongfei (Mark) Zhang
Fellow of IEEE, IAPR, and AAIA
Binghamton University, State University of New York
Speech Title: Small Can Be Powerful
Learning with knowledge distillation is a hot topic in todays machine learning literature, with the motivation of equipping a student, typically much lighter-weight network with the knowledge from a teacher, typically much larger network. This research has a wide spectrum of real-world applications of developing powerful but light-weight and resource-limited front end devices. In the existing literature, typically the student always underperforms the teacher after knowledge distillation and this is attributed as a result of the typical huge capacity gap between the teacher and the student. In this talk, I will examine the problem of the performance gap between the teacher and the student from a different perspective --- distillation data, and I will show through theoretic analysis and empirical evaluations that even with the existence of the huge capacity gap, as long as the student is large enough, with appropriate distillation data, the student can be made to exhibit the same performance as that of the teacher, and can even outperform the teacher.
Zhongfei (Mark) Zhang is a professor at Computer Science Department, Binghamton University, State University of New York (SUNY), USA. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst, USA. His research interests are in the broad areas of machine learning, data mining, computer vision, and pattern recognition, and specifically focus on multimedia/multimodal data understanding and mining. He was on the faculty of Computer Science and Engineering at SUNY Buffalo, before he joined the faculty of Computer Science at SUNY Binghamton. He is the author or co-author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering. He has published over 200 papers in the premier venues in his areas. He holds more than twenty inventions, has served as members of organization committees of several premier international conferences in his areas including general co-chair and lead program chair, and as editorial board members for several international journals. He served as a French CNRS Chair Professor of Computer Science at the University of Lille 1 in France, a JSPS Fellow in Chuo University, Japan, a QiuShi Chair Professor in Zhejiang University, China, as well as visiting professorships from many universities and research labs in the world when he was on leave from Binghamton University years ago. He received many honors including SUNY Chancellors Award for Scholarship and Creative Activities, SUNY Chancellors Promising Inventor Award, and best paper awards from several premier conferences in his areas. He is a Fellow of IEEE, IAPR, and AAIA.
Keynote Speaker Ⅷ
Prof. Dr. Anand Nayyar
ACM Distinguished Speaker, Editor-in-Chief of IGI-Global
and USA Journal titled “International Journal of Smart Vehicles and Smart Transportation (IJSVST)
School of Computer Science, Duy Tan University, Da Nang, Viet Nam
Speech Title: Recent Advances in Assessing Time Series Similarity Through Dynamic Time Warping
In this lecture, complete overview of Software Defined networking, general perspectives, research areas and other research directions will be comprehensively outlines.
Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks and Swarm Intelligence. He is currently working in School of Computer Science-Duy Tan University, Da Nang, Vietnam as Assistant Professor, Scientist, Vice-Chairman (Research) and Director- IoT and Intelligent Systems Lab. A Certified Professional with 80+ Professional certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published more than 125+ Research Papers in various High-Quality ISI-SCI/SCIE/SSCI Impact Factor Journals cum Scopus/ESCI indexed Journals, 50+ Papers in International Conferences indexed with Springer, IEEE Xplore and ACM Digital Library, 40+ Book Chapters in various SCOPUS, WEB OF SCIENCE Indexed Books with Springer, CRC Press, Elsevier and many more with Citations: 5500+, H-Index: 38 and I-Index: 134. Member of more than 50+ Associations as Senior and Life Member including IEEE, ACM. He has authored/co-authored cum Edited 30+ Books of Computer Science. Associated with more than 500+ International Conferences as Programme Committee/Chair/Advisory Board/Review Board member. He has 13 Australian Patents, 7 Indian Design cum Utility Patents, 1 Indian Copyright, 2 Canadian Copyrights and 3 German Patents to his credit in the area of Wireless Communications, Artificial Intelligence, Cloud Computing, IoT and Image Processing. Awarded 36+ Awards for Teaching and Research—Young Scientist, Best Scientist, Young Researcher Award, Outstanding Researcher Award, Excellence in Teaching and many more. He is acting as Associate Editor for Wireless Networks (Springer), Computer Communications (Elsevier), International Journal of Sensor Networks (IJSNET) (Inderscience), Frontiers in Computer Science, PeerJ Computer Science, Human Centric Computing and Information Sciences (HCIS), IET-Quantum Communications, IET Wireless Sensor Systems, IET Networks, IJDST, IJISP, IJCINI, IJGC. He is acting as Editor-in-Chief of IGI-Global, USA Journal titled “International Journal of Smart Vehicles and Smart Transportation (IJSVST)”. He has reviewed more than 1600+ Articles for various Web of Science Indexed Journals He is currently researching in the area of Wireless Sensor Networks, IoT, Swarm Intelligence, Cloud Computing, Artificial Intelligence, Drones, Blockchain, Cyber Security, Network Simulation and Wireless Communications.
Keynote Speaker Ⅸ
Prof. Brian A. BARSKY
Professor of the Graduate School, Professor Emeritus of Computer Science and Vision Science,
Warren and Marjorie Minner Faculty Fellow in Engineering Ethics and Professional/Social Responsibility
Affiliate Professor Emeritus of Optometry, University of California, Berkeley
Speech Title: How Prioritizing Profits over Safety Created the Deadly Boeing 737 MAX and its Ill-Conceived Automated Software
The Boeing 737 MAX airplane crashed twice with no survivors within two years of its first commercial flight. It was grounded worldwide for 19 months in the U.S. and has yet to resume commercial flights in China. Examination of the many factors that led to these disastrous consequences illuminates disquieting ethical issues of corporate behavior and lack of government oversight. There is a complex web of concerns involved. At the heart of the tragedy is an ill-conceived automated computer software approach to a flawed aerodynamic design. Prof. Barsky became involved in this topic when his friend’s granddaughter was killed in the second crash. He met with the head of the Aviation Accident Investigation Sub-Committee of the National Transportation Safety Committee of Indonesia in Jakarta to obtain first-hand the details of the first crash. He was featured prominently in a recent Smithsonian documentary shown in the U.S. and U.K. His full-page op-ed in the Globe and Mail was discussed in the Parliament of Canada. In this talk, Prof. Barsky will elucidate how these tragedies were the consequence of a corporation prioritizing profits over safety as well as of regulatory capture of the government agency which was derelict in its duty to protect the flying public.
Brian A. Barsky is Professor of the Graduate School at the University of California, Berkeley where he is a Warren and Marjorie Minner Faculty Fellow in Engineering Ethics and Professional/ Social Responsibility. Prof. Barsky has faculty affiliations in Electrical Engineering and Computer Sciences (EECS), Optometry, Vision Science, Bioengineering, the Berkeley Institute of Design (BID), the Berkeley Center for New Media (BCNM), the Arts Research Center (ARC), and the Berkeley Canadian Studies Program. He attended McGill University in Montréal, where he received a D.C.S. in engineering and a B.Sc. in mathematics and computer science. He studied computer graphics and computer science at Cornell University in Ithaca, where he earned an M.S. degree. His Ph.D. degree is in computer science from the University of Utah in Salt Lake City. His research interests include computational photography, contact lens design, computer methods for optometry and ophthalmology, image synthesis, computer aided geometric design and modeling, CAD/CAM/CIM, interactive and realistic three-dimensional computer graphics, visualization in scientific computing, computer aided cornea modeling and visualization, medical imaging, vision correcting displays, and virtual environments for surgical simulation.
Keynote Speaker Ⅹ
Prof. Dr. Jan Jürjens
Vice-Dean of Institute for Software Technology IST
Director Research Projects at the Fraunhofer Institute for Software and Systems Engineering ISST
Univ. Koblenz & Fraunhofer ISST
Speech Title: Engineering Trustworthy Data-intensive Systems
De facto, algorithms decide what insurance tariffs customers are offered, who is invited to a job interview, who receives which kind of medical treatment or whether a sentenced person should be kept in prison or released. Often times judgement rules have been elucidated with machine learning and data mining from previous observations, i.e. historic data, and they are applied to a person or group of people, i.e. novel case data. This methodology is prone to wrong or misleading judgements (cf. e.g. ), because: - Historic and or novel case data may be wrong or incomplete, e.g. erronous notification about bankruptcy. - Historic data may have insufficient quality. - The algorithms may be wrongly implemented or used, possibly maliciously [2,3]. One of the challenges in this context is that, where data mining is used to discover new knowledge, there is no known "gold standard" against which the result can be verified. Therefore the usual approaches for quality assurance of software do not apply, but a new paradigm is needed which acknowledges the fact that trustworthy use of data analysis software needs dedication and support from cradle to the grave of the process of engineering and using that software as a whole. Another challenge is that data analysis increasingly needs to be able to make use of heterogeneous data sources, such as polymorphic (i.e. relational, graph-oriented, arraybased, etc) data bases, which makes even the semantic interpretation of this data and the joint analysis results (which is a prerequisite for understanding them and for assessing their validity) a challenge on its own. We present an approach for the trustworthy engineering and use of data-intensive software which addresses the above challenges.
Jan Jürjens is a Professor, leading the Institute for Software Technology IST within the Faculty for Computer Science of the University of Koblenz (Germany), where he is also the Vice-Dean for Research. He is also Director Research Projects at the Fraunhofer Institute for Software and Systems Engineering ISST (Dortmund, Germany). Previous positions include a Professorship for Software Engineering at TU Dortmund, a Royal Society Industrial Fellowship at Microsoft Research Cambridge, a non-stipendiary Research Fellowship at Robinson College (Univ. Cambridge), where in 2009 he was appointed as Senior Member, and a Postdoc position at TU München. Jan holds a Doctor of Philosophy in Computing from University of Oxford and is author of "Secure Systems Development with UML" (Springer, 2005; Chinese translation 2009) and other publications mostly on software engineering and IT security. More information: http://jan.jurjens.de.
FCSIT Past Speakers
Prof. Mohd Nazri Bin Kama
Fakulti Teknologi Dan
Informatik Razak Universiti
Prof. Yingxu Wang
University of Calgary, Canada