KeyNote Speakers
Philip S. Yu
UIC Distinguished Professor and Wexler Chair in Information
Technology, Department of Computer Science, University of Illinois at Chicago
Title: Broad Learning for Recommendations via Fusion of Heterogeneous Information
Abstract:
In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a new type of learning task, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective fusion of relevant knowledge across different data sources, which depend upon not only the relatedness of these data sources, but also the target application problem. In this talk we examine how to fuse heterogeneous information to improve effectiveness on recommendation systems.
In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a new type of learning task, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective fusion of relevant knowledge across different data sources, which depend upon not only the relatedness of these data sources, but also the target application problem. In this talk we examine how to fuse heterogeneous information to improve effectiveness on recommendation systems.
Speaker Biography:
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,100 referred conference and journal papers cited more than 104,000 times with an H-index of 152. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,100 referred conference and journal papers cited more than 104,000 times with an H-index of 152. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).
Guoliang Chen
Academician of Chinese Academy of Sciences
Professor of Nanjing University of Posts and Telecommunications
Tile: Foundations of Computation Theory for Big Data
Abstract:
In computational science, the content of computation theory mainly includes computability, computational complexity, and algorithm design and analysis. This report only discusses the former two issues, and focuses on the computational complexity theory with big data: it mainly includes computation models and computation theories; the computation of P problem and parallel NC problem; the computation of NP problem and its interactive IP problem. Finally, in the conclusion, we present the inclusion relations of various complex problems and the research countermeasures for P and NP problems in the case of big data.
In computational science, the content of computation theory mainly includes computability, computational complexity, and algorithm design and analysis. This report only discusses the former two issues, and focuses on the computational complexity theory with big data: it mainly includes computation models and computation theories; the computation of P problem and parallel NC problem; the computation of NP problem and its interactive IP problem. Finally, in the conclusion, we present the inclusion relations of various complex problems and the research countermeasures for P and NP problems in the case of big data.
Speaker Biography
Guoliang Chen is Academician of Chinese Academy of Sciences and is Professor of Nanjing University of Posts and Telecommunications. He is a PhD supervisor and Honorary Dean of School of Computer Science and Technology, Nanjing University of Posts and Telecommunications. Professor Chen is also the Director of Institute of High Performance Computing and Big Data Processing, Nanjing University of Posts and Telecommunications, the Director of Academic Committee of Nanjing University of Posts and Telecommunications, the Deputy Director of the Academic Committee of the Wireless Sensor Network of Jiangsu Provincial High-tech Key Lab. He is the First National Teaching Teacher of Higher Education and enjoys national government special allowance. He received a Ph.D. degree from Xi'an Jiaotong University in 1961. At the same time, Professor He serves as part-time position of Dean of the School of Software Science and Technology, University of Science and Technology of China, Dean of School of Computer Science, Shenzhen University, Director of National High-Performance Computing Center, Director of Instructional Committee of Computer Basic Course of Higher Education Ministry, Director of International High-Performance Computing (Asia), China Computer Society Director and director of the High Performance Computing Professional Committee, etc. And Professor Chen also serves as Director of the Academic Committee of the National Key Laboratory about computer science.
His research interests mainly include parallel algorithms and high-performance computing and its applications. Professor Chen has undertaken more than 20 scientific research projects including the National 863 Plan, the National “Climbing” Plan, the National 973 Plan, and the National Natural Science Foundation of China. A number of research achievements have been widely quoted at home and abroad and reached the international advanced level. He has published more than 200 papers and published more than 10 academic works and textbooks. He won the Second Prize of National Science and Technology Progress Award, the First Prize of Science and Technology Progress Award and the Second Prize of the Ministry of Education, the First Prize of Science and Technology Progress Award of the Chinese Academy of Sciences, the Second Prize of the National Teaching Achievement, the First Prize of the Ministry of Water Resources, and the Second Progress of Anhui Province Science and Technology Progress Awards, 2009 Anhui Provincial Major Science and Technology Achievement Awards, etc. Professor Chen won the 15th anniversary of the advanced personal important contribution award of National 863 Plan, Baosteel Education Fund outstanding teacher’s special award, and the glorious title of the model worker in Anhui Province.
For years, Professor Chen has developed a complete set of parallel algorithm disciplines for “algorithmic theory-algorithm design-algorithm implementation-algorithm application” around the teaching and research of parallel algorithms. He proposed the parallel computing research method of "parallel machine architecture-parallel algorithm-parallel programming", established China's first national high-performance computing center, built a parallel research and teaching base for China's parallel algorithms, and trained more than 200 Postdoctoral, doctoral and postgraduate students. Professor Chen is the academic leader in non-numerical parallel algorithm research in China and has a certain influence and status in academic circles and education circles at home and abroad. Academician Chen first established China’s first national high-performance computing center in 1995, and successfully developed China’s first domestic high-performance general-purpose processor chip Godson single-core, four-core and eight-core, KD-50, KD-60 and KD-90 in 2007, 2009, 2012 and 2014 respectively, which provide infrastructure for cloud computing, big data processing and universal high performance computing in China.
Guoliang Chen is Academician of Chinese Academy of Sciences and is Professor of Nanjing University of Posts and Telecommunications. He is a PhD supervisor and Honorary Dean of School of Computer Science and Technology, Nanjing University of Posts and Telecommunications. Professor Chen is also the Director of Institute of High Performance Computing and Big Data Processing, Nanjing University of Posts and Telecommunications, the Director of Academic Committee of Nanjing University of Posts and Telecommunications, the Deputy Director of the Academic Committee of the Wireless Sensor Network of Jiangsu Provincial High-tech Key Lab. He is the First National Teaching Teacher of Higher Education and enjoys national government special allowance. He received a Ph.D. degree from Xi'an Jiaotong University in 1961. At the same time, Professor He serves as part-time position of Dean of the School of Software Science and Technology, University of Science and Technology of China, Dean of School of Computer Science, Shenzhen University, Director of National High-Performance Computing Center, Director of Instructional Committee of Computer Basic Course of Higher Education Ministry, Director of International High-Performance Computing (Asia), China Computer Society Director and director of the High Performance Computing Professional Committee, etc. And Professor Chen also serves as Director of the Academic Committee of the National Key Laboratory about computer science.
His research interests mainly include parallel algorithms and high-performance computing and its applications. Professor Chen has undertaken more than 20 scientific research projects including the National 863 Plan, the National “Climbing” Plan, the National 973 Plan, and the National Natural Science Foundation of China. A number of research achievements have been widely quoted at home and abroad and reached the international advanced level. He has published more than 200 papers and published more than 10 academic works and textbooks. He won the Second Prize of National Science and Technology Progress Award, the First Prize of Science and Technology Progress Award and the Second Prize of the Ministry of Education, the First Prize of Science and Technology Progress Award of the Chinese Academy of Sciences, the Second Prize of the National Teaching Achievement, the First Prize of the Ministry of Water Resources, and the Second Progress of Anhui Province Science and Technology Progress Awards, 2009 Anhui Provincial Major Science and Technology Achievement Awards, etc. Professor Chen won the 15th anniversary of the advanced personal important contribution award of National 863 Plan, Baosteel Education Fund outstanding teacher’s special award, and the glorious title of the model worker in Anhui Province.
For years, Professor Chen has developed a complete set of parallel algorithm disciplines for “algorithmic theory-algorithm design-algorithm implementation-algorithm application” around the teaching and research of parallel algorithms. He proposed the parallel computing research method of "parallel machine architecture-parallel algorithm-parallel programming", established China's first national high-performance computing center, built a parallel research and teaching base for China's parallel algorithms, and trained more than 200 Postdoctoral, doctoral and postgraduate students. Professor Chen is the academic leader in non-numerical parallel algorithm research in China and has a certain influence and status in academic circles and education circles at home and abroad. Academician Chen first established China’s first national high-performance computing center in 1995, and successfully developed China’s first domestic high-performance general-purpose processor chip Godson single-core, four-core and eight-core, KD-50, KD-60 and KD-90 in 2007, 2009, 2012 and 2014 respectively, which provide infrastructure for cloud computing, big data processing and universal high performance computing in China.
Yong Shi
Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences,China
Title: Optimization based Data Mining and Bio-Medicine Science
Abstract:
With the development of data science, the technique of optimization has become a significant method in data mining and decision making. This paper reports how to apply the theoretical results of Multiple-Criteria Optimization based Data Mining to solve the problems in bio-medicine science and big healthcare data analyses with the significant findings as well as strong social influences. Firstly, it will illustrate the theoretical framework of Multiple-Criteria Multiple-Constraint (MC2) Programming for solving the problems of diagnosis decision under uncertainty, such as treatments of AIDS and dementia. Secondly, the paper will introduce the segmentation technique to deal with different data in health care, by using the optimization data mining algorithms, so as to provide technical assurance for accurate analysis. These achievements have been applied in the study of structural and functional evolution of antibody molecules, which becomes a new method to discover the key features of hot spots prediction in protein interactions. Finally, a big data intelligent knowledge system promoted by the “Internet + health care” schema will be presented. This system has been successfully implemented by well-known Chunyu Medical Doctor Co. providing efficient and high-quality services for 125 million registered users and more 500,000 registered doctors.
With the development of data science, the technique of optimization has become a significant method in data mining and decision making. This paper reports how to apply the theoretical results of Multiple-Criteria Optimization based Data Mining to solve the problems in bio-medicine science and big healthcare data analyses with the significant findings as well as strong social influences. Firstly, it will illustrate the theoretical framework of Multiple-Criteria Multiple-Constraint (MC2) Programming for solving the problems of diagnosis decision under uncertainty, such as treatments of AIDS and dementia. Secondly, the paper will introduce the segmentation technique to deal with different data in health care, by using the optimization data mining algorithms, so as to provide technical assurance for accurate analysis. These achievements have been applied in the study of structural and functional evolution of antibody molecules, which becomes a new method to discover the key features of hot spots prediction in protein interactions. Finally, a big data intelligent knowledge system promoted by the “Internet + health care” schema will be presented. This system has been successfully implemented by well-known Chunyu Medical Doctor Co. providing efficient and high-quality services for 125 million registered users and more 500,000 registered doctors.
Speaker Biography:
Yong Shi, serves as the Director, Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science and the Director of the Key Lab of Big Data Mining and Knowledge Management, Chinese Academy of Sciences. He has been the Director of Tianfu International Institute of Big Data Strategy and Technology, Chengdu since 2018. He is the elected member of the International Eurasian Academy of Science (2017), the counselor of the State Council of PRC (2016), and the elected fellow of the World Academy of Sciences for Advancement of Science in Developing Countries (2015). His research interests include business intelligence, data mining, and multiple criteria decision making. He has published more than 29 books, over 350 papers in various journals and numerous conferences/proceedings papers. He is the Editor-in-Chief of International Journal of Information Technology and Decision Making (SCI), Editor-in-Chief of Annals of Data Science (Springer) and a member of Editorial Board for some academic journals. Dr. Shi has received many distinguished awards including WIC Outstanding Research Contribution Award, 2018; Application Contribution Award of Chinese Society on System Science and System Engineering, 2016; the Georg Cantor Award of the International Society on Multiple Criteria Decision Making (MCDM), 2009; Fudan Prize of Distinguished Contribution in Management, Fudan Premium Fund of Management, China, 2009; Outstanding Young Scientist Award, National Natural Science Foundation of China, 2001; and Speaker of Distinguished Visitors Program (DVP) for 1997-2000, IEEE Computer Society. He has also consulted or worked on business projects for a number of international companies in data mining and knowledge management.
Yong Shi, serves as the Director, Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science and the Director of the Key Lab of Big Data Mining and Knowledge Management, Chinese Academy of Sciences. He has been the Director of Tianfu International Institute of Big Data Strategy and Technology, Chengdu since 2018. He is the elected member of the International Eurasian Academy of Science (2017), the counselor of the State Council of PRC (2016), and the elected fellow of the World Academy of Sciences for Advancement of Science in Developing Countries (2015). His research interests include business intelligence, data mining, and multiple criteria decision making. He has published more than 29 books, over 350 papers in various journals and numerous conferences/proceedings papers. He is the Editor-in-Chief of International Journal of Information Technology and Decision Making (SCI), Editor-in-Chief of Annals of Data Science (Springer) and a member of Editorial Board for some academic journals. Dr. Shi has received many distinguished awards including WIC Outstanding Research Contribution Award, 2018; Application Contribution Award of Chinese Society on System Science and System Engineering, 2016; the Georg Cantor Award of the International Society on Multiple Criteria Decision Making (MCDM), 2009; Fudan Prize of Distinguished Contribution in Management, Fudan Premium Fund of Management, China, 2009; Outstanding Young Scientist Award, National Natural Science Foundation of China, 2001; and Speaker of Distinguished Visitors Program (DVP) for 1997-2000, IEEE Computer Society. He has also consulted or worked on business projects for a number of international companies in data mining and knowledge management.
Peizhuang Wang
Advisor of CAS Research Center on Fictitious Economy & Data Science
Title: Scene labelling in factor space
Abstract:
Big data is the servant we use, not our master。The satellite turns every day, magnum data asks to have a big cloud to calculate a center to wait on it, make the power consumption that computation expend is small to be able to ignore, but the computational capacity of big data hits probably however breathtaking. Pollution caused by big data is also gradually put on the agenda. For big data, the strategy is: save, don't calculate. It must be calculated for a definite purpose, and it must be worthy to do it.
To make the stored big data easy to query and use, it is necessary to have a title or label for each frame of data, that is, to indicate whether it is a figure, a landscape or a machine structure for each frame of the picture. Even several people and several cars. This is essentially scenario analysis, but in big data it's tagging the data.
We first analyze entropy from four factors, and find that it has overall expressiveness, uniformity, nesting and disorder, the four properties respectively. Among them, the fourth property brings convenience to entropy calculation, but also brings great defect: Entropy cannot be used to label the ordered structure of a scene. We get rid of the fourth constraint, and the entropy we get is called the pan-entropy.
For an example, this paper further gives a simple uniformity degree, which can be used to distinguish the texture of different objects in the scene, and to complete the task of scene label simply and quickly.
This article wants to show that factor space is the mathematical foundation of data science. Can lead the trend of big data.
Thank professor Y. Li, who was the co-conspirator of this idea, and professor S. C. Guo who gave the author a lot of inspiration。
Pei-ZhuangWang received the BS degree in Mathematics from Beijing Normal University, China in 1957. He was a close friend of L. A. Zadeh, the father of the fuzzy set theory. He was a main academic leader of fuzzy mathematics and its application in China, puts forward Falling Shadow Measure, Truth Value Flow Reasoning and Factors Space theories, such as a former vice-chairman of International Fuzzy Systems Association, Currently, he is the head of College of Intelligence Engineering and Math, Liaoning Technical University, His research interests mainly include cognition mathematics applied in data science and artificial intelligence.
Big data is the servant we use, not our master。The satellite turns every day, magnum data asks to have a big cloud to calculate a center to wait on it, make the power consumption that computation expend is small to be able to ignore, but the computational capacity of big data hits probably however breathtaking. Pollution caused by big data is also gradually put on the agenda. For big data, the strategy is: save, don't calculate. It must be calculated for a definite purpose, and it must be worthy to do it.
To make the stored big data easy to query and use, it is necessary to have a title or label for each frame of data, that is, to indicate whether it is a figure, a landscape or a machine structure for each frame of the picture. Even several people and several cars. This is essentially scenario analysis, but in big data it's tagging the data.
We first analyze entropy from four factors, and find that it has overall expressiveness, uniformity, nesting and disorder, the four properties respectively. Among them, the fourth property brings convenience to entropy calculation, but also brings great defect: Entropy cannot be used to label the ordered structure of a scene. We get rid of the fourth constraint, and the entropy we get is called the pan-entropy.
For an example, this paper further gives a simple uniformity degree, which can be used to distinguish the texture of different objects in the scene, and to complete the task of scene label simply and quickly.
This article wants to show that factor space is the mathematical foundation of data science. Can lead the trend of big data.
Thank professor Y. Li, who was the co-conspirator of this idea, and professor S. C. Guo who gave the author a lot of inspiration。
Pei-ZhuangWang received the BS degree in Mathematics from Beijing Normal University, China in 1957. He was a close friend of L. A. Zadeh, the father of the fuzzy set theory. He was a main academic leader of fuzzy mathematics and its application in China, puts forward Falling Shadow Measure, Truth Value Flow Reasoning and Factors Space theories, such as a former vice-chairman of International Fuzzy Systems Association, Currently, he is the head of College of Intelligence Engineering and Math, Liaoning Technical University, His research interests mainly include cognition mathematics applied in data science and artificial intelligence.
Jifa Gu
Academy of Mathematics and Systems sience, CAS
Title: Wisdom ,DIKW, Big data, DS and AI
Abstract:
This talk will introduce the relationship within Wisdom, DIKW, Big Data, Data science (DS) and AI in China. Big Data and AI has attracted a lot of attention from the scholars in the fields of Information and computer science, especially from press. But with time we may find as a theoretical discipline the big data has given way to Data science. Since data science may cover more contents for research. From the data itself it should cover data, big data, small data, experimental data and experiential data, artificial data etc.. The DS and AI are interdisciplinary sciences, they not only relate to computer science, statistics, but also should connect with the information science, knowledge science and systems science. The cycle of DIKW has described the development from the Data to wisdom. AI ,as matter of fact is intelligence, not wisdom, and it has intelligence quotient, but not emotional quotient ,it has capability of computation, but not the ability of scheme. Both DS and AI should pay attention the morality and social value.
This talk will introduce the relationship within Wisdom, DIKW, Big Data, Data science (DS) and AI in China. Big Data and AI has attracted a lot of attention from the scholars in the fields of Information and computer science, especially from press. But with time we may find as a theoretical discipline the big data has given way to Data science. Since data science may cover more contents for research. From the data itself it should cover data, big data, small data, experimental data and experiential data, artificial data etc.. The DS and AI are interdisciplinary sciences, they not only relate to computer science, statistics, but also should connect with the information science, knowledge science and systems science. The cycle of DIKW has described the development from the Data to wisdom. AI ,as matter of fact is intelligence, not wisdom, and it has intelligence quotient, but not emotional quotient ,it has capability of computation, but not the ability of scheme. Both DS and AI should pay attention the morality and social value.
Biography:
Jifa GU, Professor, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, PhD, Institute of Mathematics, USSR Academy of Sciences. His main fields are Operations Research and Systems Engineering. He published more than 30 books and 300 journal papers. He participated in practices on missile, energy, environment, water resource, regional strategy and various projects on evaluation. In 1995 with Dr. Zhu ZC proposed Wuli-Shili-Renli system approach and got applications in many practical cases. In recent ten years he has engaged in the study and application of Meta-synthesis system approach and Knowledge Science. He has participated in several national research programs related to social issues, digging experiences from TCM veteran doctors and study on the collective behaviors in Shanghai World Expo. He had been President of Systems Engineering Society of China, President of International Federation of System Research. Now he is academician and vice president of International Academy of System and Cybernetics Sciences, academician of International Eurasian Academy of Sciences.
Jifa GU, Professor, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, PhD, Institute of Mathematics, USSR Academy of Sciences. His main fields are Operations Research and Systems Engineering. He published more than 30 books and 300 journal papers. He participated in practices on missile, energy, environment, water resource, regional strategy and various projects on evaluation. In 1995 with Dr. Zhu ZC proposed Wuli-Shili-Renli system approach and got applications in many practical cases. In recent ten years he has engaged in the study and application of Meta-synthesis system approach and Knowledge Science. He has participated in several national research programs related to social issues, digging experiences from TCM veteran doctors and study on the collective behaviors in Shanghai World Expo. He had been President of Systems Engineering Society of China, President of International Federation of System Research. Now he is academician and vice president of International Academy of System and Cybernetics Sciences, academician of International Eurasian Academy of Sciences.
Yanchun Zhang
Victoria University, Australia
Fudan University, China
Title: Smart Health： Sleep structure analysis and computer aided diagnosis of sleep disorders
Abstract:
A normal person spends about a third of his life in sleep. Healthy sleep is vital to people's normal lives. Sleep analysis can be used to diagnose certain physiological and neurological diseases such as apnea, insomnia and narcolepsy. This talk will introduce the sleep stage analysis and the corresponding electroencephalogram (EEG) characteristics at each stage. We also introduce some data mining or AI techniques to aid the diagnosis of sleep disorder.
A normal person spends about a third of his life in sleep. Healthy sleep is vital to people's normal lives. Sleep analysis can be used to diagnose certain physiological and neurological diseases such as apnea, insomnia and narcolepsy. This talk will introduce the sleep stage analysis and the corresponding electroencephalogram (EEG) characteristics at each stage. We also introduce some data mining or AI techniques to aid the diagnosis of sleep disorder.
Speaker Biography:
Yanchun Zhang is a Professor and Director of Centre for Applied Informatics at Victoria University since 2004. Dr Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases, data mining, web services and e-health. He has published over 300 research papers in international journals and conference proceedings including ACM Transactions on Computer and Human Interaction (TOCHI), IEEE Transactions on Knowledge and Data Engineering (TKDE), VLDBJ, SIGMOD and ICDE conferences, and a dozen of books and journal special issues in the related areas. Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer), and also the founding editor of Web Information Systems Engineering Book Series and Health Information Science Book Series. He is Chairman of International Web information Systems Engineering Society (WISE). He was a member of Australian Research Council's College of Experts (2008-2010), and also serves as expert panel member at various international funding agencies such as National Natural Science Fund of China (NSFC), “National 1000 Talents Program” of China, the Royal Society of New Zealand Marsden Fund and National Natural Science Fund of China (NSFC). He is one of the National "Thousand Talents Program" Experts in China since 2010 (currently with Fudan University).
Yanchun Zhang is a Professor and Director of Centre for Applied Informatics at Victoria University since 2004. Dr Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases, data mining, web services and e-health. He has published over 300 research papers in international journals and conference proceedings including ACM Transactions on Computer and Human Interaction (TOCHI), IEEE Transactions on Knowledge and Data Engineering (TKDE), VLDBJ, SIGMOD and ICDE conferences, and a dozen of books and journal special issues in the related areas. Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer), and also the founding editor of Web Information Systems Engineering Book Series and Health Information Science Book Series. He is Chairman of International Web information Systems Engineering Society (WISE). He was a member of Australian Research Council's College of Experts (2008-2010), and also serves as expert panel member at various international funding agencies such as National Natural Science Fund of China (NSFC), “National 1000 Talents Program” of China, the Royal Society of New Zealand Marsden Fund and National Natural Science Fund of China (NSFC). He is one of the National "Thousand Talents Program" Experts in China since 2010 (currently with Fudan University).
Jing He
Nanjing University of Finance and Economic
Swinburne University of Technology
Title: Is NP=P？ A Polynomial-time solution for finite graph isomorphism
Abstract:
This talk will introduce a polynomial-time solution for finite graph isomorphism. It targets to provide a solution for one of the seven-millennium problems: NP versus P. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we proposed a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem and Fundamental Theorem of Arithmetic. For each of two n-ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.
This talk will introduce a polynomial-time solution for finite graph isomorphism. It targets to provide a solution for one of the seven-millennium problems: NP versus P. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we proposed a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem and Fundamental Theorem of Arithmetic. For each of two n-ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.
Speaker Biography:
Dr. Jing He is a professor in school of software and electrical engineering, Swinburne University of Technology. She was awarded a PhD degree from the Academy of Mathematics and System Science, Chinese Academy of Sciences in 2006. Prior to joining Victoria University, she worked in the University of Chinese Academy of Sciences, China during 2006-2008. She has been active in areas of Algorithm and Chips, Artificial Intelligence, Data Mining, Web service/Web search, Spatial and Temporal Database, Multiple Criteria Decision Making, Intelligent Systems, Scientific Workflow and some industry fields such as E-Health, Petroleum Exploration and Development, Water recourse Management and e-Research. She has published over 160 research papers in refereed international journals and conference proceedings, including ACM Transaction on Internet Technology (TOIT), IEEE Transaction on Knowledge and Data Engineering (TKDE), Information Systems, the Computer journal, Computers and Mathematics with Applications, Concurrency and Computation: Practice and Experience, International Journal of Information Technology & Decision Making, Applied Soft Computing, and Water Resource Management. She has received over 3.5 million Australian dollar research funding from the Australian Research Council (ARC) with ARC Early Career Researcher Award (DECRA), ARC Discovery Project, ARC Linkage Project and National Natural Science Foundation of China (NSFC) since 2008.
Dr. Jing He is a professor in school of software and electrical engineering, Swinburne University of Technology. She was awarded a PhD degree from the Academy of Mathematics and System Science, Chinese Academy of Sciences in 2006. Prior to joining Victoria University, she worked in the University of Chinese Academy of Sciences, China during 2006-2008. She has been active in areas of Algorithm and Chips, Artificial Intelligence, Data Mining, Web service/Web search, Spatial and Temporal Database, Multiple Criteria Decision Making, Intelligent Systems, Scientific Workflow and some industry fields such as E-Health, Petroleum Exploration and Development, Water recourse Management and e-Research. She has published over 160 research papers in refereed international journals and conference proceedings, including ACM Transaction on Internet Technology (TOIT), IEEE Transaction on Knowledge and Data Engineering (TKDE), Information Systems, the Computer journal, Computers and Mathematics with Applications, Concurrency and Computation: Practice and Experience, International Journal of Information Technology & Decision Making, Applied Soft Computing, and Water Resource Management. She has received over 3.5 million Australian dollar research funding from the Australian Research Council (ARC) with ARC Early Career Researcher Award (DECRA), ARC Discovery Project, ARC Linkage Project and National Natural Science Foundation of China (NSFC) since 2008.
Jiangbo Qian
Ningbo University, China
Title: Multi-granularity Locality-sensitive Bloom Filter
Abstract:
In many applications, such as homeland security, image processing, social network, and bioinformatics, it is often required to support an Approximate Membership Query (AMQ) to answer a question like “is an (query) object near to at least one of the objects in the given data set?” However, existing techniques for processing AMQs require a key parameter, i.e., the distance value, to be defined in advance for the query processing. In this talk, we introduce two novel filters, called MLBF and H-MLBF, which can process AMQs with multiple distance granularities for high-dimensional data and binary code, respectively. Experiments using synthetic and real data show that the new technique can handle AMQs with low false positive and negative rates for multiple distance granularities.
In many applications, such as homeland security, image processing, social network, and bioinformatics, it is often required to support an Approximate Membership Query (AMQ) to answer a question like “is an (query) object near to at least one of the objects in the given data set?” However, existing techniques for processing AMQs require a key parameter, i.e., the distance value, to be defined in advance for the query processing. In this talk, we introduce two novel filters, called MLBF and H-MLBF, which can process AMQs with multiple distance granularities for high-dimensional data and binary code, respectively. Experiments using synthetic and real data show that the new technique can handle AMQs with low false positive and negative rates for multiple distance granularities.
Speaker Biography:
Jiangbo Qian is currently a Professor and Chair of Department of Computer Science and Technology at Ningbo University. He obtained a PhD degree in Computer Science from Southeast University (China) in 2006. He was a visiting scholar in the Department of Computer and Information Science at The University of Michigan – Dearborn, USA. His research interests include machine learning, database management, streaming data processing, multidimensional indexing, Bloom filter, k-nearest neighbor search in complex data spaces, and hardware/software co-design. His research has been funded by highly competitive sources, including Natural Science Foundation of China. He has published over 100 research papers in high quanlity journals and conference proceedings including IEEE Trans. on Computers, IEEE/ACM Trans. on Networking, IEEE Trans. on Parallel and Distributed Systems.
Jiangbo Qian is currently a Professor and Chair of Department of Computer Science and Technology at Ningbo University. He obtained a PhD degree in Computer Science from Southeast University (China) in 2006. He was a visiting scholar in the Department of Computer and Information Science at The University of Michigan – Dearborn, USA. His research interests include machine learning, database management, streaming data processing, multidimensional indexing, Bloom filter, k-nearest neighbor search in complex data spaces, and hardware/software co-design. His research has been funded by highly competitive sources, including Natural Science Foundation of China. He has published over 100 research papers in high quanlity journals and conference proceedings including IEEE Trans. on Computers, IEEE/ACM Trans. on Networking, IEEE Trans. on Parallel and Distributed Systems.
Yun Yang
Swinburne University of Technology, Australia
Title: Trade-off between the cost and performance for smart cities in the cloud/edge
Abstract:
In the era of cloud computing and more recently edge computing, to support smart cities, it is essential to look after all sorts of data. In this talk, some examples for supporting smart cities will be presented to illustrate the trade-off between the cost and performance with the cloud/edge.
In the era of cloud computing and more recently edge computing, to support smart cities, it is essential to look after all sorts of data. In this talk, some examples for supporting smart cities will be presented to illustrate the trade-off between the cost and performance with the cloud/edge.
Speaker Biography:
Yun Yang is a full professor at School of Software and Electrical Engineering, Swinburne University of Technology. He received his PhD degree in computer science from the University of Queensland in 1992. He then worked at CRC for Distributed Systems Technology (DSTC) and Deakin University. His current research interests include cloud and edge computing, service-oriented computing, software development technologies, and workflow systems. He is on the editorial boards of IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Cloud Computing.
Yun Yang is a full professor at School of Software and Electrical Engineering, Swinburne University of Technology. He received his PhD degree in computer science from the University of Queensland in 1992. He then worked at CRC for Distributed Systems Technology (DSTC) and Deakin University. His current research interests include cloud and edge computing, service-oriented computing, software development technologies, and workflow systems. He is on the editorial boards of IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Cloud Computing.
Hans-Arno Jacobsen
IEEE Fellow
Technische Universität München
Technische Universität München
Title: Deconstructing Blockchains: Concepts, Applications, and Systems
Abstract:
Popularly known for powering cryptocurrencies such as Bitcoin and Ethereum, blockchains are seen as a disruptive technology capable of impacting a wide variety of domains, ranging from finance to governance, by offering superior security, reliability, and transparency in a decentralized manner. In this talk, we take a comprehensive look at all aspects related to blockchains by deconstructing blockchain systems into six layers: Application, Modeling, Contract, System, Data, and Network. We review potential applications which can benefit from blockchains, and we describe the associated research challenges. Finally, we conclude by reviewing potential research directions.
Popularly known for powering cryptocurrencies such as Bitcoin and Ethereum, blockchains are seen as a disruptive technology capable of impacting a wide variety of domains, ranging from finance to governance, by offering superior security, reliability, and transparency in a decentralized manner. In this talk, we take a comprehensive look at all aspects related to blockchains by deconstructing blockchain systems into six layers: Application, Modeling, Contract, System, Data, and Network. We review potential applications which can benefit from blockchains, and we describe the associated research challenges. Finally, we conclude by reviewing potential research directions.
Speaker Biography:
Hans-Arno Jacobsen, IEEE Fellow, is a professor in Computer Engineering and Computer Sciences. His pioneering research lies at the interface between computer science, computer engineering, and information systems. He holds numerous patents and was involved in important industrial developments with partners such as Bell Canada, Computer Associates, IBM, Yahoo, and Sun Microsystems. His principal areas of research include the design and the development of middleware and distributed systems, event processing, service computing, and applications in enterprise data processing. He has held endowed faculty positions, such as the Bell Canada Chair in Software Systems at the University of Toronto and the Alexander von Humboldt Professorship at the Technical University of Munich.
Hans-Arno Jacobsen, IEEE Fellow, is a professor in Computer Engineering and Computer Sciences. His pioneering research lies at the interface between computer science, computer engineering, and information systems. He holds numerous patents and was involved in important industrial developments with partners such as Bell Canada, Computer Associates, IBM, Yahoo, and Sun Microsystems. His principal areas of research include the design and the development of middleware and distributed systems, event processing, service computing, and applications in enterprise data processing. He has held endowed faculty positions, such as the Bell Canada Chair in Software Systems at the University of Toronto and the Alexander von Humboldt Professorship at the Technical University of Munich.
Shui Yu
University of Technology Sydney, Australia
Title: Big Data Privacy: from Networking and Artificial Intelligence Perspectives
Abstract:
Big data is revolution for our society. However, it also introduces a significant threat to our privacy. In this talk, we firstly present the essential issues of privacy preserving in the big data setting, then we review the current work of the field from two perspectives: networking and artificial intelligence. Then we discuss the challenges in the domain and possible promising directions. We humbly hope this talk will shed light for forthcoming researchers to explore the uncharted part of this promising land.
Big data is revolution for our society. However, it also introduces a significant threat to our privacy. In this talk, we firstly present the essential issues of privacy preserving in the big data setting, then we review the current work of the field from two perspectives: networking and artificial intelligence. Then we discuss the challenges in the domain and possible promising directions. We humbly hope this talk will shed light for forthcoming researchers to explore the uncharted part of this promising land.
Speaker Biography:
Shui Yu is a Professor of School of Software, University of Technology Sydney, Australia. Dr Yu’s research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 35. Dr Yu actively serves his research communities in various roles. He is currently serving a number of prestigious editorial boards, including IEEE Communications Surveys and Tutorials (Area Editor), IEEE Communications Magazine (Series Editor). He has served many international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, and general chair for ACSW 2017. He is a Senior Member of IEEE, a member of AAAS and ACM, and a Distinguished Lecturer of IEEE Communication Society.
Shui Yu is a Professor of School of Software, University of Technology Sydney, Australia. Dr Yu’s research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 35. Dr Yu actively serves his research communities in various roles. He is currently serving a number of prestigious editorial boards, including IEEE Communications Surveys and Tutorials (Area Editor), IEEE Communications Magazine (Series Editor). He has served many international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, and general chair for ACSW 2017. He is a Senior Member of IEEE, a member of AAAS and ACM, and a Distinguished Lecturer of IEEE Communication Society.
Kaizhu Huang
Xi’an Jiaotong-Liverpool University, China
Title: Harnessing Explainable Adversarial Examples for Robust Machine Learning
Abstract:
Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the data mining and machine learning community. Being difficult to distinguish from real examples, such adversarial examples could change the prediction of many of the best machine learning models including the state-of-the-art deep learning models. Recent attempts have been made to build robust models that take into account adversarial examples. However, these methods can either lead to performance drops, or are ad-hoc in nature and lack mathematic motivations. In this talk, we propose a unified framework to explain, and harness adversarial examples. More specifically, using the unified framework, we develop a family of gradient regularization methods that effectively penalize the gradient of loss function w.r.t. inputs. Importantly, such gradient regularization terms are shown highly robust to perturbations both theoretically and empirically. We also extend the theory of adversarial example in semi-supervised learning and manifold geometric space. By applying this technique to deep learning networks, we conduct a series of experiments and achieve encouraging results in real applications.
Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the data mining and machine learning community. Being difficult to distinguish from real examples, such adversarial examples could change the prediction of many of the best machine learning models including the state-of-the-art deep learning models. Recent attempts have been made to build robust models that take into account adversarial examples. However, these methods can either lead to performance drops, or are ad-hoc in nature and lack mathematic motivations. In this talk, we propose a unified framework to explain, and harness adversarial examples. More specifically, using the unified framework, we develop a family of gradient regularization methods that effectively penalize the gradient of loss function w.r.t. inputs. Importantly, such gradient regularization terms are shown highly robust to perturbations both theoretically and empirically. We also extend the theory of adversarial example in semi-supervised learning and manifold geometric space. By applying this technique to deep learning networks, we conduct a series of experiments and achieve encouraging results in real applications.
Speaker Biography:
Kaizhu Huang is currently a Professor and Head, Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, China. He is also the founding director of Suzhou Municipal Key Laboratory of Cognitive Computation and Applied Technology. Prof. Huang has been working in machine learning, neural information processing, and pattern recognition. He was the recipient of 2011 Asia Pacific Neural Network Society (APNNS) Younger Researcher Award. He also received Best Book Award in National Three 100 Competition 2009. He has published 8 books in Springer and over 140 international research papers (including 60+ international journals) e.g., in journals (JMLR, Neural Computation, IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-Cybernetics) and conferences (NIPS, IJCAI, SIGIR, UAI, CIKM, ICDM, ICML, ECML, CVPR). He serves as associated editors in three international journals and board member in three international book series. He has been sitting in the grant evaluation panels in Hong Kong RGC, Singapore AI programmes, and NSFC, China. He served as chairs in many international conferences and workshops such as BICS, ICONIP, AAAI, ACML, ICDAR, ACPR, SDA, and AICS. His homepage can be seen at http://www.premilab.com/KaizhuHUANG.ashx.
Kaizhu Huang is currently a Professor and Head, Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, China. He is also the founding director of Suzhou Municipal Key Laboratory of Cognitive Computation and Applied Technology. Prof. Huang has been working in machine learning, neural information processing, and pattern recognition. He was the recipient of 2011 Asia Pacific Neural Network Society (APNNS) Younger Researcher Award. He also received Best Book Award in National Three 100 Competition 2009. He has published 8 books in Springer and over 140 international research papers (including 60+ international journals) e.g., in journals (JMLR, Neural Computation, IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-Cybernetics) and conferences (NIPS, IJCAI, SIGIR, UAI, CIKM, ICDM, ICML, ECML, CVPR). He serves as associated editors in three international journals and board member in three international book series. He has been sitting in the grant evaluation panels in Hong Kong RGC, Singapore AI programmes, and NSFC, China. He served as chairs in many international conferences and workshops such as BICS, ICONIP, AAAI, ACML, ICDAR, ACPR, SDA, and AICS. His homepage can be seen at http://www.premilab.com/KaizhuHUANG.ashx.
Wanlei Zhou
University of Technology Sydney, Australia.
Title: Enhancing Location Privacy in the Digital Age: Selected Application Cases
Abstract:
In this talk we first systematically present the current research status of the location privacy issue in the digital age, including the location privacy definition, the attacks and adversaries, the location privacy preserving mechanisms, the location privacy metrics, and the current status of location based applications. Then we present two application cases in some details. The first application case is to enhance privacy of location-based services (LBS) in wireless vehicular networks, where we develop an LBS privacy-enhancing scheme that is dedicated to the vehicular environment by exploring the unique features of queries from in-vehicle users. The second application case is to deal with the trajectory privacy preserving in mobile crowd sensing (MCS), where we develop a location privacy preserving framework based on economic models for MCS applications. After that, we briefly discuss three application cases related to location privacy: Location Preservation for crowd sensing, Private data release via differential privacy, and Private Point-of-Interest (POI) Queries.
In this talk we first systematically present the current research status of the location privacy issue in the digital age, including the location privacy definition, the attacks and adversaries, the location privacy preserving mechanisms, the location privacy metrics, and the current status of location based applications. Then we present two application cases in some details. The first application case is to enhance privacy of location-based services (LBS) in wireless vehicular networks, where we develop an LBS privacy-enhancing scheme that is dedicated to the vehicular environment by exploring the unique features of queries from in-vehicle users. The second application case is to deal with the trajectory privacy preserving in mobile crowd sensing (MCS), where we develop a location privacy preserving framework based on economic models for MCS applications. After that, we briefly discuss three application cases related to location privacy: Location Preservation for crowd sensing, Private data release via differential privacy, and Private Point-of-Interest (POI) Queries.
Speaker Biography:
Professor Wanlei Zhou is currently the Head of School of Computer Science in University of Technology Sydney (UTS), Australia. He received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991, all in Computer Science and Engineering. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. Before joining UTS, Professor Zhou held the positions of Alfred Deakin Professor, Chair of Information Technology, and Associate Dean (International Research Engagement) of Faculty of Science, Engineering and Built Environment, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology in Deakin University (May 2006-Dec 2008). Professor Zhou also served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His research interests include security and privacy, bioinformatics, and e-learning. Professor Zhou has published more than 400 papers in refereed international journals and refereed international conferences proceedings, including many articles in IEEE transactions and journals.
Professor Wanlei Zhou is currently the Head of School of Computer Science in University of Technology Sydney (UTS), Australia. He received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991, all in Computer Science and Engineering. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. Before joining UTS, Professor Zhou held the positions of Alfred Deakin Professor, Chair of Information Technology, and Associate Dean (International Research Engagement) of Faculty of Science, Engineering and Built Environment, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology in Deakin University (May 2006-Dec 2008). Professor Zhou also served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His research interests include security and privacy, bioinformatics, and e-learning. Professor Zhou has published more than 400 papers in refereed international journals and refereed international conferences proceedings, including many articles in IEEE transactions and journals.
Hui Wang
Ulster University, UK
Title: AI in food authentication
Abstract:
AI has recently been on the spotlight in media, and has been identified as a priority area of development in national and international research and industrial strategies. AI has already been applied in numerous industries, including security, finance, healthcare, education, transportation, production and more. AI is also increasingly applied in the food industry for tasks from food sorting, quality control, supply chain monitoring, new product design, food identification food authentication. This talk will provide an overview of AI applications in the food industry. It will focus on a particular application, food authentication, covering the rationale, the techniques, the current challenges and opportunities. It will also present latest work by the speaker’s team in this area.
AI has recently been on the spotlight in media, and has been identified as a priority area of development in national and international research and industrial strategies. AI has already been applied in numerous industries, including security, finance, healthcare, education, transportation, production and more. AI is also increasingly applied in the food industry for tasks from food sorting, quality control, supply chain monitoring, new product design, food identification food authentication. This talk will provide an overview of AI applications in the food industry. It will focus on a particular application, food authentication, covering the rationale, the techniques, the current challenges and opportunities. It will also present latest work by the speaker’s team in this area.
Speaker Biography:
Dr Hui Wang is Professor of Computer Science in Ulster University, UK. Prof Wang obtained his BSc in Computer Science and MSc in Artificial Intelligence from Jilin University, and his PhD in Artificial Intelligence from University of Ulster. His research interests are learning, reasoning and combinatorial data analytics, as well as applications in decision support systems and text/image/video/time series/spectral data understanding. He has over 250 publications in these areas. He is an associate editor of IEEE Transactions on Cybernetics, and an associate editor of International Journal of Machine Learning and Cybernetics. He is the Chair of IEEE SMCS Ireland Chapter, and a member of IEEE SMCS Board of Governors (2011-2013). He is principal investigator of a number of regional, national and international projects in the areas of image/video analytics (Horizon 2020 funded ASGARD 2016-20, Horizon 2020 funded DESIREE 2016-19, FP7 funded SAVASA 2011-14, Royal Society funded VIAD 2014-16), text analytics (INI funded DEEPFLOW 2010-14, Royal Society funded BEACON 2009-11), and intelligent content management (FP5 funded ICONS 2002-05); and is co-investigator of several other funded projects.
Dr Hui Wang is Professor of Computer Science in Ulster University, UK. Prof Wang obtained his BSc in Computer Science and MSc in Artificial Intelligence from Jilin University, and his PhD in Artificial Intelligence from University of Ulster. His research interests are learning, reasoning and combinatorial data analytics, as well as applications in decision support systems and text/image/video/time series/spectral data understanding. He has over 250 publications in these areas. He is an associate editor of IEEE Transactions on Cybernetics, and an associate editor of International Journal of Machine Learning and Cybernetics. He is the Chair of IEEE SMCS Ireland Chapter, and a member of IEEE SMCS Board of Governors (2011-2013). He is principal investigator of a number of regional, national and international projects in the areas of image/video analytics (Horizon 2020 funded ASGARD 2016-20, Horizon 2020 funded DESIREE 2016-19, FP7 funded SAVASA 2011-14, Royal Society funded VIAD 2014-16), text analytics (INI funded DEEPFLOW 2010-14, Royal Society funded BEACON 2009-11), and intelligent content management (FP5 funded ICONS 2002-05); and is co-investigator of several other funded projects.
Chengqi Zhang
University of Technology Sydney, Australia
Title: Digital future for quality life
Abstract:
Digitalisation is everywhere. Data is powerful resource, and data science is building a smart brain for our society and cities and for a better future life. Applying AI for the quality of life is highly depending on the data collected from different facets in our life, e.g. water, transport, healthcare, education, communication, energy and finance. Fueled by enormous data, we can providing smart prediction, risk evaluation, trend analysis, performance optimization, and decision support for our future quality life.
Digitalisation is everywhere. Data is powerful resource, and data science is building a smart brain for our society and cities and for a better future life. Applying AI for the quality of life is highly depending on the data collected from different facets in our life, e.g. water, transport, healthcare, education, communication, energy and finance. Fueled by enormous data, we can providing smart prediction, risk evaluation, trend analysis, performance optimization, and decision support for our future quality life.
Speaker Biography:
Professor Chengqi ZHANG is currently Associate Vice President of the University of Technology Sydney, Distinguished Professor of Information Technology. He is also the chairman of the ACS National Committee for Artificial Intelligence. He received his bachelor's degree from Fudan University in March 1982, his master's degree from Jilin University in March 1985, his Ph.D. degree from the University of Queensland in Australia in October 1991, and his Doctor of Science (higher Doctorate) from Deakin University in October 2002. The above degrees are in the field of computer science (Artificial Intelligence Major). The focus of his research is data mining and its applications. So far, a total of 318 scientific papers have been published. Many papers are published in the top journals. He was invited to give 18 keynote speeches at international conferences. Since 2004, he has received 13 ARC grants with a total research funding of 5.8 million Australian dollars. Thirty students were supervised to complete their doctoral studies, and eight of them are now full professors. In 2011, he received the New South Wales Science and Engineering (Engineering and ICT) award and in 2011 he received the award for Vice Chancellor of the University of Technology Sydney for Excellence in Research (Leadership).
Professor Chengqi ZHANG is currently Associate Vice President of the University of Technology Sydney, Distinguished Professor of Information Technology. He is also the chairman of the ACS National Committee for Artificial Intelligence. He received his bachelor's degree from Fudan University in March 1982, his master's degree from Jilin University in March 1985, his Ph.D. degree from the University of Queensland in Australia in October 1991, and his Doctor of Science (higher Doctorate) from Deakin University in October 2002. The above degrees are in the field of computer science (Artificial Intelligence Major). The focus of his research is data mining and its applications. So far, a total of 318 scientific papers have been published. Many papers are published in the top journals. He was invited to give 18 keynote speeches at international conferences. Since 2004, he has received 13 ARC grants with a total research funding of 5.8 million Australian dollars. Thirty students were supervised to complete their doctoral studies, and eight of them are now full professors. In 2011, he received the New South Wales Science and Engineering (Engineering and ICT) award and in 2011 he received the award for Vice Chancellor of the University of Technology Sydney for Excellence in Research (Leadership).
Tianrui Li
Southwest Jiaotong University, China
Title: Data-Driven Intelligence: Challengues and our Solutions
Abstract:
Data-Driven Intelligence has become a hot research topic in the area of information science. This talk aims to outline the challengues on Data-Driven Intelligence. Then our solutions for Data-Driven Intelligence are provided, which cover the following aspects. 1) A hierarchical entropy-based approach is demonstrated to evaluate the effectiveness of data collection, the first step of Data-Driven Intelligence. 2) A multi-view-based method is illustrated for filling missing data, the preprocessing step for Data-Driven Intelligence. 3) A unified framework is outlined for Parallel Large-scale Feature Selection to manage Big Data with high dimension. 4) A MapReduce-based parallel method together with three parallel strategies are presented to compute rough set approximations for classification, which is a fundamental part in rough set-based data analysis similar to frequent pattern mining in association rules. 5) Incremental learning-based approaches are shown for updating approximations and knowledge in dynamic data environments, e.g., the variation of objects, attributes or attribute values, which improve the computational efficiency by using previously acquired learning results to facilitate knowledge maintenance without re-implementing the original data mining algorithm. 6) A deep-learning-based model to deal with multiple different sources of data is developed.
Data-Driven Intelligence has become a hot research topic in the area of information science. This talk aims to outline the challengues on Data-Driven Intelligence. Then our solutions for Data-Driven Intelligence are provided, which cover the following aspects. 1) A hierarchical entropy-based approach is demonstrated to evaluate the effectiveness of data collection, the first step of Data-Driven Intelligence. 2) A multi-view-based method is illustrated for filling missing data, the preprocessing step for Data-Driven Intelligence. 3) A unified framework is outlined for Parallel Large-scale Feature Selection to manage Big Data with high dimension. 4) A MapReduce-based parallel method together with three parallel strategies are presented to compute rough set approximations for classification, which is a fundamental part in rough set-based data analysis similar to frequent pattern mining in association rules. 5) Incremental learning-based approaches are shown for updating approximations and knowledge in dynamic data environments, e.g., the variation of objects, attributes or attribute values, which improve the computational efficiency by using previously acquired learning results to facilitate knowledge maintenance without re-implementing the original data mining algorithm. 6) A deep-learning-based model to deal with multiple different sources of data is developed.
Speaker Biography:
Tianrui Li received his B.S. degree, M.S. degree and Ph.D. degree from the Southwest Jiaotong University, China in 1992, 1995 and 2002 respectively. He was a Post-Doctoral Researcher at Belgian Nuclear Research Centre (SCK • CEN), Belgium from 2005-2006, a visiting professor at Hasselt University, Belgium in 2008, the University of Technology, Sydney, Australia in 2009 and the University of Regina, Canada in 2014. And, he is presently a Professor and the Director of the Key Lab of Cloud Computing and Intelligent Technique of Sichuan Province, Southwest Jiaotong University, China. Since 2000, he has co-edited 6 books, 10 special issues of international journals, 15 proceedings, received 5 Chinese invention patents and published over 300 research papers (e.g., AI, IEEE TKDE, IEEE TEC, IEEE TFS, IEEE TIFS, IEEE ASLP, IEEE TIE, IEEE TC, IEEE TVT) in refereed journals and conferences (e.g., KDD, IJCAI, UbiComp, WWW). 3 papers were ESI Hot Papers and 12 papers was ESI Highly Cited Papers. His Google H-index is 37. He serves as the area editor of International Journal of Computational Intelligence Systems (SCI), editor of Knowledge-based Systems (SCI) and Information Fusion (SCI), etc. He is an IRSS fellow and Steering Committee Chair (2019-2020), IEEE CIS Emergent Technologies Technical Committee (ETTC) member (2019-2020), IEEE CIS Senior Members Committee member (2018-2020), a distinguished member of CCF, a senior member of ACM, IEEE, CAAI, ACM SIGKDD member, Chair of IEEE CIS Chengdu Chapter (2013-2018), Treasurer of ACM SIGKDD China Chapter and CCF YOCSEF Chengdu Chair (2013-2014). Over sixty graduate students (including 8 Post-Docs, 15 Doctors) have been trained. Their employment units include Microsoft Research Asia, Sichuan University, Huawei, JD, Baidu, Alibaba, and Tencent. They have received 2 "Si Shi Yang Hua" Medals, Best Papers/Dissertation Awards 16 times, Champion of Sina Weibo Interaction-prediction at Tianchi Big Data Competition (Bonus 200,000 RMB), Second Place of Social Influence Analysis Contest of IJCAI-2016 Competitions and Second Place of Weather forecast Contest of AI Challenger 2018.
Tianrui Li received his B.S. degree, M.S. degree and Ph.D. degree from the Southwest Jiaotong University, China in 1992, 1995 and 2002 respectively. He was a Post-Doctoral Researcher at Belgian Nuclear Research Centre (SCK • CEN), Belgium from 2005-2006, a visiting professor at Hasselt University, Belgium in 2008, the University of Technology, Sydney, Australia in 2009 and the University of Regina, Canada in 2014. And, he is presently a Professor and the Director of the Key Lab of Cloud Computing and Intelligent Technique of Sichuan Province, Southwest Jiaotong University, China. Since 2000, he has co-edited 6 books, 10 special issues of international journals, 15 proceedings, received 5 Chinese invention patents and published over 300 research papers (e.g., AI, IEEE TKDE, IEEE TEC, IEEE TFS, IEEE TIFS, IEEE ASLP, IEEE TIE, IEEE TC, IEEE TVT) in refereed journals and conferences (e.g., KDD, IJCAI, UbiComp, WWW). 3 papers were ESI Hot Papers and 12 papers was ESI Highly Cited Papers. His Google H-index is 37. He serves as the area editor of International Journal of Computational Intelligence Systems (SCI), editor of Knowledge-based Systems (SCI) and Information Fusion (SCI), etc. He is an IRSS fellow and Steering Committee Chair (2019-2020), IEEE CIS Emergent Technologies Technical Committee (ETTC) member (2019-2020), IEEE CIS Senior Members Committee member (2018-2020), a distinguished member of CCF, a senior member of ACM, IEEE, CAAI, ACM SIGKDD member, Chair of IEEE CIS Chengdu Chapter (2013-2018), Treasurer of ACM SIGKDD China Chapter and CCF YOCSEF Chengdu Chair (2013-2014). Over sixty graduate students (including 8 Post-Docs, 15 Doctors) have been trained. Their employment units include Microsoft Research Asia, Sichuan University, Huawei, JD, Baidu, Alibaba, and Tencent. They have received 2 "Si Shi Yang Hua" Medals, Best Papers/Dissertation Awards 16 times, Champion of Sina Weibo Interaction-prediction at Tianchi Big Data Competition (Bonus 200,000 RMB), Second Place of Social Influence Analysis Contest of IJCAI-2016 Competitions and Second Place of Weather forecast Contest of AI Challenger 2018.
Yangyong Zhu
Fudan University, China
Title: Defining data assets based on the attributes of data
Abstract:
In the background of big data now, it has been widely recognized that data is the key factor of digital economy. Therefore, it is necessary to understand the meaning behind of the definitions of information assets, digital assets and data assets. A survey of information assets, digital assets, and data assets was given. The physical attributes, existence attributes and information attributes of data and data assets were discussed. Based on these attributes, information assets, digital assets and data assets were merged into data assets. Data assets were defined as valuable, measurable and accessible data resources in cyberspace owned by an accounting subject. According to the definition and attributes of data assets, data assets have the characteristics of both tangible assets and intangible assets, current assets and long-term assets. Therefore, data assets should be considered as a new category of assets.
In the background of big data now, it has been widely recognized that data is the key factor of digital economy. Therefore, it is necessary to understand the meaning behind of the definitions of information assets, digital assets and data assets. A survey of information assets, digital assets, and data assets was given. The physical attributes, existence attributes and information attributes of data and data assets were discussed. Based on these attributes, information assets, digital assets and data assets were merged into data assets. Data assets were defined as valuable, measurable and accessible data resources in cyberspace owned by an accounting subject. According to the definition and attributes of data assets, data assets have the characteristics of both tangible assets and intangible assets, current assets and long-term assets. Therefore, data assets should be considered as a new category of assets.
Speaker Biography:
Zhu, Yangyong, Ph.D., who pioneered the concept of “dataology”, is a Professor of the school of Computer Science at Fudan University , is the Founding Director of Shanghai Key Laboratory of Data Science , is also a cofounder of Fudan's Institute for Data Industry. Dr. Zhu’s work begins in the application of data mining to various fields (e.g., finance, economics, insurance, bioinformatics, sociology) from 1996, and since 2004 has focused on data science, particularly at the theoretical level. After engaging in a broad range of activities at original viewpoints (involving publications), Dr.Zhu is recognized as one of vital global advocates in data science. More specifically, these activities there are: expressing an opinion about “the data resource can be one of the important modern strategic resources for humans” (2008); having published a book of Dataology and a paper of Data Explosion, Data Nature and Dataology (2009); initiating The International Workshop on Dataology and Data Science (2010) and The International Conference on Data Science (2014) with Philip S. YU, Yong SHI, Chengqi ZHANG; serving as executive co chairman for the 462nd Session of Xianshan Science Coference – Theoretical Issues Exploration on Data Science and Big Data (2013); holding a post as editor-in-chief for Big Data Applications and Technologies: Series of Book (2014); having proposed the concept of “Big Data Arena”(2014) and “the Data Finance” (2015).
Zhu, Yangyong, Ph.D., who pioneered the concept of “dataology”, is a Professor of the school of Computer Science at Fudan University , is the Founding Director of Shanghai Key Laboratory of Data Science , is also a cofounder of Fudan's Institute for Data Industry. Dr. Zhu’s work begins in the application of data mining to various fields (e.g., finance, economics, insurance, bioinformatics, sociology) from 1996, and since 2004 has focused on data science, particularly at the theoretical level. After engaging in a broad range of activities at original viewpoints (involving publications), Dr.Zhu is recognized as one of vital global advocates in data science. More specifically, these activities there are: expressing an opinion about “the data resource can be one of the important modern strategic resources for humans” (2008); having published a book of Dataology and a paper of Data Explosion, Data Nature and Dataology (2009); initiating The International Workshop on Dataology and Data Science (2010) and The International Conference on Data Science (2014) with Philip S. YU, Yong SHI, Chengqi ZHANG; serving as executive co chairman for the 462nd Session of Xianshan Science Coference – Theoretical Issues Exploration on Data Science and Big Data (2013); holding a post as editor-in-chief for Big Data Applications and Technologies: Series of Book (2014); having proposed the concept of “Big Data Arena”(2014) and “the Data Finance” (2015).
Jian Cao
Shanghai Jiaotong University, China
Title: Delivering Better Transportation Services for People through Intelligent Data Analytics
Abstract:
Environmental, social and economic sustainability is a must to keep pace with this rapid expansion that is consume our cities’ resources. Smart city technology is paramount to success and meeting these goals. An efficient transportation system is fundamental to the success of any major city. However, there are many challenges to today’s transportation system. Fortunately, big Data is transforming both the plan phase and operations phase of the public transportation. In this talk, some issues about how to apply intelligent data analytics to deliver better transportation services for people are discussed. Especially, we will provide some case studies to show how to take efficiency, social aspects and personalization into considerations when developing analytical models.
Environmental, social and economic sustainability is a must to keep pace with this rapid expansion that is consume our cities’ resources. Smart city technology is paramount to success and meeting these goals. An efficient transportation system is fundamental to the success of any major city. However, there are many challenges to today’s transportation system. Fortunately, big Data is transforming both the plan phase and operations phase of the public transportation. In this talk, some issues about how to apply intelligent data analytics to deliver better transportation services for people are discussed. Especially, we will provide some case studies to show how to take efficiency, social aspects and personalization into considerations when developing analytical models.
Speaker Biography:
Prof. Cao is currently a tenured Professor with Department of Computer Science and Engineering at Shanghai Jiaotong University (SJTU), China. He is the director of the SJTU& Morgan Stanley Joint Research Center on Financial Service Innovation. He is also the leader of the Lab for Cloud and Service Computing. Prof. Cao received his Ph.D. from Nanjing University of Science and Technology in 2000. He was a Post-doctoral Research Fellow of Shanghai Jiaotong University (SJTU) and a visiting scholar of Stanford University. Prof. Cao’s research interests include intelligent data analytics, service computing and network computing. He has published over 200 papers in referred conference and journals such as SIGKDD, IJCAI, AAAI, VLDB, WWW, INFOCOM, TMC, TOIS, TPDS and TKDD. Prof. Cao undertook more than 10 projects funded by National High Technology Research and Development and National Natural Science Foundation of China. Prof. Cao’s research projects are also widely founded by many industry partners including Ctrip, Morgan Stanley, Docomo, Samsung, and Shanghai International Port Group, to name but a few. His research has won 8 provisional Science and Technology Progress Awards.
Prof. Cao is currently a tenured Professor with Department of Computer Science and Engineering at Shanghai Jiaotong University (SJTU), China. He is the director of the SJTU& Morgan Stanley Joint Research Center on Financial Service Innovation. He is also the leader of the Lab for Cloud and Service Computing. Prof. Cao received his Ph.D. from Nanjing University of Science and Technology in 2000. He was a Post-doctoral Research Fellow of Shanghai Jiaotong University (SJTU) and a visiting scholar of Stanford University. Prof. Cao’s research interests include intelligent data analytics, service computing and network computing. He has published over 200 papers in referred conference and journals such as SIGKDD, IJCAI, AAAI, VLDB, WWW, INFOCOM, TMC, TOIS, TPDS and TKDD. Prof. Cao undertook more than 10 projects funded by National High Technology Research and Development and National Natural Science Foundation of China. Prof. Cao’s research projects are also widely founded by many industry partners including Ctrip, Morgan Stanley, Docomo, Samsung, and Shanghai International Port Group, to name but a few. His research has won 8 provisional Science and Technology Progress Awards.
Enhong Chen
University of Science and Technology of China
Title: Enhancing Personalized Learning with Exercise Resources Analytics: Methods and Applications
Abstract:
Recently, with the development of many information technologies, massive educational data (e.g., student exercising records) have been accumulated, providing an access to the data-driven solution for personalized learning. However, existing methods are difficult to accurately analyze the academic level of students without considering the meaningful features of exercise resources, and meanwhile, they usually take less into account about the personalized demands of students during their learning process. Therefore, the analysis results could not be directly applied in some real-world scenarios (e.g., exercise resource searching and exercise recommendation). In this talk, we present a series of research for personalized learning enhanced with the exercise resource analytics, aiming to improve students’ academic levels. Our works have been published on top conferences, such as KDD, IJCAI, AAAI and CIKM, and also applied in “Zhixue.com”, an online learning system by iFLYTEK.
Recently, with the development of many information technologies, massive educational data (e.g., student exercising records) have been accumulated, providing an access to the data-driven solution for personalized learning. However, existing methods are difficult to accurately analyze the academic level of students without considering the meaningful features of exercise resources, and meanwhile, they usually take less into account about the personalized demands of students during their learning process. Therefore, the analysis results could not be directly applied in some real-world scenarios (e.g., exercise resource searching and exercise recommendation). In this talk, we present a series of research for personalized learning enhanced with the exercise resource analytics, aiming to improve students’ academic levels. Our works have been published on top conferences, such as KDD, IJCAI, AAAI and CIKM, and also applied in “Zhixue.com”, an online learning system by iFLYTEK.
Speaker Biography:
Dr. Enhong Chen is a Professor and vice dean of School of Computer Science of University of Science and Technology of China (USTC), CCF Fellow, IEEE Senior Member (Since 2007), winner of the National Science Fund for Distinguished Young Scholars (in 2013), scientific and technological innovation leading talent of “Ten Thousand Talent Program”(in 2017) and member of the Decision Advisory Committee of Shanghai (Since June, 2018). He is also the executive vice-dean of School of Data Science of USTC, the vice director of the National Engineer Laboratory for Speech and Language Information Processing, the director of Anhui Province Key Laboratory of Big Data Analysis and Application, and the chairman of Anhui Province Big Data Industry Alliance. His current research interests are data mining and machine learning, especially social network analysis and recommender systems. He has published more than 200 papers on many journals and conferences, including international journals such as IEEE Trans, ACM Trans, and important data mining conferences, such as KDD, ICDM, NIPS. He won the Best Application Paper Award on KDD2008, Best Student Paper Award on KDD2018 (Research Track) and the Best Research Paper Award on ICDM2011.
Dr. Enhong Chen is a Professor and vice dean of School of Computer Science of University of Science and Technology of China (USTC), CCF Fellow, IEEE Senior Member (Since 2007), winner of the National Science Fund for Distinguished Young Scholars (in 2013), scientific and technological innovation leading talent of “Ten Thousand Talent Program”(in 2017) and member of the Decision Advisory Committee of Shanghai (Since June, 2018). He is also the executive vice-dean of School of Data Science of USTC, the vice director of the National Engineer Laboratory for Speech and Language Information Processing, the director of Anhui Province Key Laboratory of Big Data Analysis and Application, and the chairman of Anhui Province Big Data Industry Alliance. His current research interests are data mining and machine learning, especially social network analysis and recommender systems. He has published more than 200 papers on many journals and conferences, including international journals such as IEEE Trans, ACM Trans, and important data mining conferences, such as KDD, ICDM, NIPS. He won the Best Application Paper Award on KDD2008, Best Student Paper Award on KDD2018 (Research Track) and the Best Research Paper Award on ICDM2011.
Haipeng Peng
Beijing University of Posts and Telecommunications, China
Title: General safety theory
Abstract:
The long-term accumulation of global security has reached a critical period from quantitative change to qualitative change. The global security community needs a "general security theory", and furthermore, excellent talents in the field of general security theory to create a new security theory system. "General Theory of Security" completely refreshes the concept of security, using Xiangnong's "Information Theory", "Cybernetics", "Probability Theory" and "Systems Theory" as tools, it tries to establish a set of basic theories suitable for cyberspace security, known as "General Theory of Security". It is hoped that the establishment and perfection of this theory will help to change the current situation of "craftsmanship thinking" in the field of safety, provide a unified basic theory for "the First-Level Discipline of cyberspace security", and promote the comprehensive upgrading and rapid development of the subject of safety. By revealing the essence of meridian theory and confrontation theory, this report lays the foundation of General Theory of Safety and refreshes the concept of safety. It is hoped that through this report, more colleagues will be attracted to participate in this newly developed "gold mine", and more importantly, the General Theory of Safety could grow up and develop rapidly.
The long-term accumulation of global security has reached a critical period from quantitative change to qualitative change. The global security community needs a "general security theory", and furthermore, excellent talents in the field of general security theory to create a new security theory system. "General Theory of Security" completely refreshes the concept of security, using Xiangnong's "Information Theory", "Cybernetics", "Probability Theory" and "Systems Theory" as tools, it tries to establish a set of basic theories suitable for cyberspace security, known as "General Theory of Security". It is hoped that the establishment and perfection of this theory will help to change the current situation of "craftsmanship thinking" in the field of safety, provide a unified basic theory for "the First-Level Discipline of cyberspace security", and promote the comprehensive upgrading and rapid development of the subject of safety. By revealing the essence of meridian theory and confrontation theory, this report lays the foundation of General Theory of Safety and refreshes the concept of safety. It is hoped that through this report, more colleagues will be attracted to participate in this newly developed "gold mine", and more importantly, the General Theory of Safety could grow up and develop rapidly.
Speaker Biography:
Professor, Ph.D. supervisor and director of the Department of Information Security, School of Cyberspace Security, Beijing University of Posts and Telecommunications. He has been engaged in teaching and conducting scientific research on network and information security for a long time. He has hosted or participated in more than 20 projects at the national, provincial and ministerial levels. As an Applicant, he has employed and published more than 100 papers in famous SCI journals, such as PNAS, IEEE Trans., IEEE Network, Chinese Science, etc., more than 3000 academic citations by Google, 5 highly cited papers by ESI and 10 invention patents. Especially, his core achievement in the field of intelligent security was published in PNAS, the Journal of the American Academy of Sciences. It has been reported or reproduced by more than 200 domestic and foreign media, including Time, Daily Mail and Science and Technology Daily.
Professor, Ph.D. supervisor and director of the Department of Information Security, School of Cyberspace Security, Beijing University of Posts and Telecommunications. He has been engaged in teaching and conducting scientific research on network and information security for a long time. He has hosted or participated in more than 20 projects at the national, provincial and ministerial levels. As an Applicant, he has employed and published more than 100 papers in famous SCI journals, such as PNAS, IEEE Trans., IEEE Network, Chinese Science, etc., more than 3000 academic citations by Google, 5 highly cited papers by ESI and 10 invention patents. Especially, his core achievement in the field of intelligent security was published in PNAS, the Journal of the American Academy of Sciences. It has been reported or reproduced by more than 200 domestic and foreign media, including Time, Daily Mail and Science and Technology Daily.