Speakers

Keynote Speaker 

Professor Tao Zhang

JiMei University,  China     

Tao Zhang, professor, doctoral tutor, "Distinguished Expert" of overseas high-level talents introduced by the Central Organization Department, Chief Scientist of Aerospace Changfeng of Aerospace Science and Industry Group, Vice President of Beijing European and American Alumni Association, President of Electronic Branch of Beijing Expert Association, Distinguished Expert of Beijing Overseas High-level Talents "Haiju Project", "High-end Leading Talent" of National Zhongguancun Science and Technology Demonstration and Entrepreneurship Base, Beijing International "High-end Business Talent" Award, and Chief Technical Expert of National Golden Shield Project. Expert leader of key R&D projects of informatization construction of National Development and Reform Commission, expert of national informatization projects, member of National Science and Technology Award Expert Committee, special expert of key R&D of Ministry of Science and Technology, and expert of National Natural Science Foundation of China. He used to be the chief architect of the German Volkswagen Computer Expert System, the senior consultant of the banking business of IBM Corporation of America, and the vice president of the technology and operation department of Bank of America. Engaged in the research and application of big data core algorithms, data integration and data governance, the design and construction of powerful intelligent engine middle and back-office architecture, providing 360-dimensional support for applications in various fields of foreground application scenarios, and leading the design, development and implementation of 28 intelligent application systems: 1. Smart financial risk prevention and control, 2. Smart city, 3. Safe city, 4. Intelligent transportation, 5. Smart medical care, 6. Wisdom.

Report Title: Internet of things full scene smart community comprehensive services and demonstration applications

Abstract:  "The community is the grass-roots foundation. Only with a solid foundation can the national building be stable.""Strengthen the construction of community service capacity, better provide accurate and refined services for the masses, create a community governance pattern of co-construction, co-governance and sharing, and effectively enhance the residents' sense of gain, happiness and security“____Xi JingPing.
The smart community has a large number of smart devices and terminals, and generates massive data and diversified service scenarios, providing a platform for the integrated development of various elements of the "new infrastructure".The smart community integrates the big data of underwear, food, housing and transportation in the shared area, optimizes the resource allocation of "new infrastructure", improves the government's ability to make quick decisions and personalized services, and provides a comprehensive carrier for the development of "new infrastructure". For the whole scenario of community public safety, livelihood services and comprehensive governance, the technical system based on Risk Intelligent Research and judgment, intelligent enjoyment of livelihood services, and intelligent and accurate governance urgently needs to be improved. The integrated community service platform based on 5g, edge computing, artificial intelligence, block-chain and other new technologies has not yet formed. The scientific, sophisticated and intelligen.

Keynote Speaker 

Professor Bing Shi

Wuhan University of Technology,  China     

Bing Shi, Professor, Hubei Chutian student, vice president of computer college. He graduated from the Department of computer science and technology of Nanjing University with a bachelor's degree and a master's degree. He graduated from the school of electronics and computer of the University of Southampton with a doctor's degree. He also engaged in postdoctoral research in the University of Southampton. Dr. Shi Bing is a member of IEEE, ACM and CCF. He is mainly engaged in the research of artificial intelligence and multi-agent systems. He has published more than 20 papers in CCF recommended conferences and journals. He is an authoritative multi-agent system conference, AAMAS 2017, AAMAS 2018, AAMAS 2019, He is a member of AAMAS 2020 procedure Committee, and is responsible for reviewing manuscripts of several journals at the same time. 

Report Title: Task Matching, Dispatching and Pricing in Spatial Crowdsourcing

Abstract: Nowadays, spatial crowdsourcing (e.g. bike-sharing and ride-sharing), have attracted a lot of attentions. In such spatial crowdsourcing tasks, task matching, worker dispatching and pricing are key challenges. In this talk, we will discuss these challenges, and proposal some reinforcement learning and game theory based approaches to solve these issues. We also run extensive experiments to show the effectiveness of the proposed approaches.

Keynote Speaker 

Professor Xiaofang Yuan

Hunan  University，China

Dr. Xiaofang Yuan is a Professor in the Department of Electrical and Information Engineering at Hunan University. He has been a member of Chinese society of Mechanical Engineering, China Society of automation, and Chinese society of artificial intelligence. His research area includes intelligent control theory and application, electric vehicle control, robot drive control. His research has been funded by NNSF of China, Key R & D plan of the Ministry of science and technology, etc.  He received the national science and technology progress award, and 6 provincial science and technology awards. He has published over 60 papers. 

Report Title: Research  on 3D Path Planning Methods for Intelligent Driving Vehicles          

Abstract: In the traditional field of vehicle path planning, the distance traveled is usually considered to achieve a two-dimensional map path planning method. This report proposes a path planning solution for intelligent driving vehicles from the perspective of 3D maps. For vehicles traveling on three-dimensional terrain, the energy consumption of uphill driving is much greater than that of flat and downhill driving. Therefore, a cost function for 3D map path planning is designed by comprehensively considering the two objectives of driving distance and energy consumption. For several path planning scenarios, some path planning schemes based on intelligent optimization algorithms and deep learning algorithms were explored, and simulation validation studies were conducted on electric vehicles and mining vehicles.

Keynote Speaker 

Professor Xinguo YU

National Engineering Research Center for E-Learning, Central China Normal University, China     

Xinguo YU is the dean and Professor of CCNU Wollongong Joint Institute and Professor of National Engineering Research Center for E-Learning at Central China Normal University, Wuhan, China, senior member of both IEEE and ACM, and an adjunct professor of University of Wollongong, Australia. He is a member of steering board of PSIVT conference and a member of steering board of Smart Educational Technology Branch Society under Automation Society, China. He received Ph.D. degree in Computer Science from National University of Singapore. His current research mainly focuses on intelligent educational technology, educational robotics, multimedia analysis, computer vision, artificial intelligence, and virtual reality. He has published over 100 research papers. He is an Associate Editor of International Journal of Digital Crime and Forensics，was Guest Editor of Multimedia Systems and International Journal of Pattern Recognition and Artificial Intelligence. He is general chair of International Conference on Internet Multimedia Computing and Service 2012 and Pacific-Rim Symposium on Image and Video Technology 2017, and program chair of International Conference of Educational Innovation through Technology 2015, Pacific-Rim Symposium on Image and Video Technology 2015.

Title: New Advances in Solving Algebra Problems    

Abstract: Solving algebra problems continues to attract significant research interest as evidenced by the large number of algorithms and theories proposed over the past decade. Hence, many new advances have made from 2014. Two seminar papers from MIT, published in 2014, started the approach that uses the new methods and techniques in NLP to develop the solving algorithms. Deep neural networks became the main steps of solving algorithms. The relation-centric approach is another new approach of the solving algorithms since 2015. Besides these two approaches, the theoretical justification to the algorithms has started to be an active research topic. Leveraged on these advances, some further research directions can be discussed.  

Keynote Speaker 

ProfessorPhilippe Fournier-Viger

Shenzhen University, China          

Philippe Fournier-Viger (Ph.D) is a Canadian researcher, distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China in 2015 and became full professor after receiving a talent title from the National Science Foundation of China. He has published more than 375 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 11,000 citations. He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate edito-in-chief of the Applied Intelligence journal and has been keynote speaker for over 15 international conferences and co-edited four books for Springer. He is a co-founder of the UDML, PMDB and MLiSE series of workshops held at the ICDM, PKDD, DASFAA and KDD conferences. 

Title：Advances and challenges for the automatic discovery of interesting patterns in data

Abstract: Intelligent systems and tools can play an important role in various domains such as for factory automation, e-business, and manufacturing. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex data and can yield large amounts of data such usage logs, images, videos, and data collected from industrial sensors. Managing the data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems and other applications. The talk will first briefly review early study on designing algorithms for identifying frequent patterns. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques for intelligent systems will be discussed.

ProfessorSyed Abdul Rehman Khan

Dr Syed Abdul Rehman Khan is an expert of Supply Chain and Logistics Management. Dr Khan achieved his CSCP—Certified Supply Chain Professional certificate from the U.S.A. and completed his PhD in China. Since 2018, Dr Khan is a teacher of  Logistics and Supply Chain Management. He has more than twelve years’ core experience of supply chain and logistics at industry and academic levels. He has attended several international conferences and also has been invited as a keynote speaker in different countries. He has published more than 100+ scientific research papers in different well-renowned international peer-reviewed journals (SSCI/SCI and ABS listed) and conferences, including couple of research paper indexed in Essential Science Indicators (ESI). Dr Khan is the authored of 7 books related to the sustainability in supply chain and business operations. He is a regular contributor to conferences and workshops around the world. During the last three years, Dr Khan has won 5 different national/provincial-level research projects. Besides, Dr Khan has achieved scientific innovation awards three times consecutively by the Education Department of Shaanxi Provincial Government, China. Further, Dr. Khan is the member of project evaluation board of  APCIS – Supply Chain and Logistics, US; National Science Foundation and Ministry of Education, Oman; Science Foundation of Kazakistan; and Business Research and Service Institute, US. Also, Dr Khan holds memberships in the following well-renowned institutions and supply chain bodies/associations: APCIS-U.S.; Production and Operation Management Society, India; Council of Supply Chain Management of Professionals U.S.; Supply Chain Association of Pakistan; and Global Supply Chain Council China.

Title:Modern Logistics Systems and Supply Chain: Leading Towards Low-carbon Economy

Abstract：The objective of this study is to examine the green supply chain management (GSCM) practices, economic growth, and socio-environmental indicators on a macro-level in a panel of European Union (EU) member states. The study employed different advanced statistical techniques, including generalized method of moments and fully generalized least square for testing the hypotheses. At the same time, the study also adopted Dumitrescu Hurlin Panel Causality test to the cause-effect relationship between the endogenous and exogenous variables. The findings revealed that green supply chain management practices are much useful to improve environmental sustainability through a reduction in carbon emissions and PM2.5, which further correlated with a reduction in the pollution-related mortality rates. Also, the results indicate that green supply chain practices spur economic growth in terms of providing trade opportunities around the globe particularly in pro-environmentalist countries without any non-tariff barriers/penalties and attract foreign investors due to green policies. In our best of knowledge, in the literature review, none of the research conducted on GSCM practices and Macro-level indicators, this study will assist both policymakers and practitioners to understand the holistic role of GSCM practices to greening the economy and provide a framework to develop game-changing environmental policies in the context of cross-border supply chain and trade.