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 and became full professor at the Harbin Institute of Technology (Shenzhen), after obtaining a title of national talent from the National Science Foundation of China. He has published more than 350 research papers related to data mining, intelligent systems and applications, which have received more than 10,000 citations (H-Index 51). He is editor-in-chief of Data Science and Pattern Recognition and former associate editor-in-chief of the Applied Intelligence journal (SCI, Q1). He is the founder of the popular SPMF data mining library, offering more than 230 algorithms, cited in more than 1,000 research papers. He is a co-founder of the UDML, PMDB and MLiSE series of workshops held at the ICDM, PKDD and KDD conferences. Website: http://www.philippe-fournier-viger.com
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 software engineering. 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 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 tools.
The talk will first briefly review early study on designing algorithms for identifying frequent patterns and how can be used for instance to analyze customer behavior and detect malwares. 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.
Prof. Dr Lili Yang is working in Southern University of
Science and Technology in Shenzhen, China. In the past
years, she has been carrying out 14 projects funded by
NSFC, MOST in China, as well as provincial and Shenzhen
municipal funding bodies. The total budget has reached
to over 200 millions RMB. Before back to China, Prof
Yang was working in Loughborough University, UK. She
conducted a significant amount of research both
independently and working in team. As the principal
investigator she led 18 projects and carried out 6
projects as co-investigator in the UK. She was invited
by the UK Cabinet Office and gave a presentation to
their staff in London, and BBC radio for discussing her
research projects. She has published over 120 journal
and conference papers. Her publications appear in the
top journals such as Applied Energy, Environmental
Research Letters, Climate Changes, Information Systems
Research, European Journal of Operational Research, to
be named. Her research has generated impact to the
research community in the whole world.
Title: CovFSA: A fine-grained stochastic agent-based model of COVID-19 dynamics and non-pharmaceuticals
Abstract: COVID-19 has brought a series of challenges to people’s lives worldwide. It is of particular interest to simulate COVID-19 dynamics and predict its trend with different interventions for decision-makers. Here, a fine-grained stochastic agent-based model of COVID-19 (CovFSA) using demographic data is proposed to investigate the possible outcomes of adopting various policy interventions, including the strict Dynamic COVID-Zero policy applied in China and possible reopen strategies. In CovFSA, the fine-grained agents, synthesised with personal geodemographic attributes, compose the residential unit (or residential community) and the complex contact networks. Then, the stochastic contact behaviours of agents are simulated based on the multiplex social network structures dependent on demographic information. To balance the computational speed and granularity, CovFSA implements the novel scale extension algorithm to enlarge the simulation size from 30,000 residents to a megacity with 20 million residents. Furthermore, CovFSA incorporates the realistic Dynamic Zero policy in China for reflecting and evaluating its effects and efficiency in various scenarios for the first time. Finally, the model uses the Shenzhen outbreak in March 2022 as a case study to validate its usefulness and feasibility. CovFSA could fit the happened pandemic well, even in stringent interventions and provide a flexible platform to discuss the complex interactions between dynamic policy interventions and COVID-19 trends for decision-makers.
Helen S. Du is the “100-talent program” distinguished professor at the Guangdong University of Technology (GDUT). She received her Ph.D from the City University of Hong Kong, department of Informatin Systems. She is currently the Head of the Electronic Commerce Department and Director of the Center for International Exchange at School of Management, GDUT. Her recent research interests include consumer behavior in the digital era, human-computer interaction and gamification design, pro-environment behavior and green innovation management. As the principle investigator, she has taken charge of two National Natural Science Foundation of China projects and two Basic and Applied Basic Research Foundation of Guangdong Province projects. She has published over 60 articles in leading journals and international conferences, such as International Journal of Information Management, Journal of the Association for Information Science and Technology, Journal of Cleaner Production, International Journal of Human-Computer Studies, Decision Support Systems, Internet Research, among others. She is serving on the editorial board of Online Information Review (SSCI indexed) and Journal of Computer Information Systems (SCI indexed), and is the AE of Journal of Information & Knowledge Management (EI indexed).
Title: How Gamification Drives Online Consumers’ Value Co-Creation Behavior
Abstract: Recently, the gamified e-commerce platforms represented by Alipay and Taobao have attracted a substantial number of consumers to engage in the value co-creation activities, such as recommendations, helping others and feedback, by providing highly attractive gamified functions and features, and have achieved great commercial success. This suggests that the academic research on stimulating consumers' behavioral motivation through gamification and thus promoting their value co-creation behavior has important theoretical and practical significance. Drawing on the “Affordances-Psychological Outcomes-Behavioral Outcomes” framework and related literature, this talk presents our recent study regarding the impact of perceived gamification affordances on consumer citizenship behavior (a type of value co-creation behavior). Based on the empirical analysis from the survey of 387 gamified MiniApps consumers in the e-commerce platforms, the research extends our understanding of the direct and indirect effects of perceived gamification affordances on consumer citizenship behavior, and the mediating role of psychological ownership.