Invited Speakers

Prof. Ji Zhang (IET Fellow, RSA Fellow, BCS Fellow, IEEE Senior Member)

University of Southern Queensland, Australia

Prof. Ji Zhang is currently a full professor in Computer Science (equivalent to a distinguished/endowed professor in North American universities) in School of Sciences, Faculty of Health, Engineering and Sciences at the University of Southern Queensland (USQ), Australia. He is an IET Fellow, RSA Fellow, BCS Fellow, IEEE Senior Member, ACM Member, Australian Endeavour Fellow, Queensland International Fellow and Izaak Walton Killam Scholar (Canada). He is also an academic expert of Australian Academy of Sciences. He was the Principal Advisor for Research for the Division of ICT Services at USQ (2010-2013).
Prof. Zhang's research interests include big data analytics, data science, data mining, machine learning and computational intelligence. He has published over 240 papers, many appearing in top-tier international journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Cybernetics, IEEE Transactions on Dependable and Secure Computing (TDSC), ACM Transactions on Knowledge Discovery from Data (TKDD), ACM Transactions on Intelligent Systems and Technology (TIST), ACM Transactions on Management Information Systems (TMIS), ACM Transactions on Spatial Algorithms and Systems, Information Sciences, Knowledge-based Systems, Neurocomputing, WWW Journal, Journal of Intelligent Information Systems (JIIS), Bioinformatics, Knowledge and Information Systems (KAIS), and top international conferences such as AAAI, IJCAI, VLDB, SIGKDD, ICDE, ICDM, WWW, CIKM, CVPR, COLING, PAKDD and DASFAA. He has also authored one monograph and 10 book chapters. He has three(3) papers as the highest cited papers in image mining and one paper as the highly cited paper in pattern mining. He received three(3) best paper awards respectively in WWW workshop 2021, DIKW 2021 and WISE 2019, and the student travel award of ICDM 2006.

Title: Improving Friend Recommendations for Online Learning with Fine-grained Evolving Interests

Abstract:Friend recommendation plays an important role in promoting user experience in online social networks (OSNs). However, existing studies often overlook users' fine-grained interests and the evolving features of those interests, which can lead to unsuitable recommendations. In particular, some OSNs, such as online learning communities, have little work on friend recommendation. To address this gap, we propose a framework for improving friend recommendation with fine-grained, evolving interests. We focus on the online learning community as an application scenario, which is a special type of OSN for people to learn courses online. Our framework, called Learning Partner Recommendation Framework with Evolution of Fine-grained Interest (LPRF-E), extracts a sequence of learning interest tags that change over time. We then explore the time feature to predict evolving learning interests. We also recommend learning partners based on fine-grained interest similarity and refine the framework with users' social influence. Extensive experiments on two real datasets validate that our proposed models achieve high accuracy and can recommend learning partners with high quality.

pROF. Edwin P. Christmann
Slippery Rock University, USa

Edwin P. Christmann, professor and chair of the secondary education department and graduate coordinator of Slippery Rock University’s mathematics and science teaching program and earned his Ph.D. at Old Dominion University. He served as a contributing editor to the National Science Teachers Association’s middle schools journal, Science Scope, serves on the editorial review boards of several other research journals, and has authored the books Technology-Based Inquiry for Middle School and Beyond the Numbers: Making Sense of Statistics; and he has coauthored Interpreting Assessment Data: Statistical Techniques You Can Use, Designing Elementary Instruction and Assessment: Using the Cognitive Domain, Designing and Assessing IEP Instruction for Students with Mild Disabilities: Using the Cognitive Domain, and Designing Middle and High School Instruction and Assessment: Using the Cognitive Domain. In addition, he has written over 100 articles and is a frequent speaker at international conferences. He currently teaches graduate-level courses in measurement and assessments, science education, and statistics, which are built on the foundation of his math and science experiences.

Title: The Achievement Levels of Graduate Students Enrolled in Online and Face-To-Face Statistics

Abstract:This research compared the achievement of male and female students who were enrolled in an online univariate statistics course to students enrolled in a traditional face-to-face univariate statistics course. The subjects, 47 graduate students enrolled in univariate statistics classes at a public, comprehensive university, were randomly assigned to groups that used either online instruction or traditional face-to-face instruction. The effects of the independent variables of online univariate statistics instruction versus traditional face-to-face instruction on the dependent variable of statistics achievement were analyzed with a two-way analysis of variance. There was a significant difference between the achievement of students who used online univariate statistics instruction and those who used traditional face-to-face instruction (p = .001). The traditional face-to-face group scored higher with an effect size of 0.979, indicating that, on the average, those who were enrolled in a traditional face-to-face univariate statistics class outperformed 83.4% of those enrolled in the online statistics course. Moreover, females using online instruction outperformed males using online instruction and males enrolled in a traditional face-to-face course scored higher than females, with an effect size of 0.651, indicating that, on the average, those males outperformed 74.22% of the females enrolled in a traditional face-to-face statistics course.

Prof. Hai-Ning Liang

Xi’an Jiaotong-Liverpool University, China

Hai-Ning Liang is a Professor with the Department of Computing at Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, China. He is the Founding Head of the Department of Computing. He is also the Founding Deputy Director of the Suzhou Key Lab for Virtual Reality Technologies, Suzhou Key Lab for Intelligent Virtual Engineering, and the XJTLU Virtual Engineering Center. He completed his PhD in computer science from Western University, Canada. Before joining XJTLU, he worked at the University of Queensland in Australia and the University of Manitoba in Canada. His main research interest falls in the area of human-computer interaction, focusing on virtual/augmented reality and gaming and learning technologies. He has published widely in top-rated journals and conferences in these areas, such as ACM ToG, ToCHI, IMWUT, UIST, CHI, and IEEE TVCG, VR, ISMAR. He is actively involved in the organization of leading international conferences and editing special issues and has given numerous invited talks at conferences.

Title: Data exploration via visual representations across display platforms: Case studies involving single and multiple users in leaning tasks

Abstract:We are in the era of big data. Data visualization is a fast-growing area that uses computer-supported, interactive, and visual representation of abstract data to enhance people's exploration and understanding of the data. Human processing efficiency of graphics, images, and visual representations tends to be much higher than with numbers and texts. Visualizations of data enable people to effectively observe, browse, manipulate, study, explore, discover, understand, and interact with them, so as to discover hidden relationships and patterns in the data and help users obtain new insights and make effective decisions. In recent years, display technologies have developed quickly. This process has led to the creation of new interactive systems and platforms with unique affordances. For example, virtual reality (VR) provides highly immersive experiences and richer interactive possibilities and is now increasingly being used in data visualization and exploration. In this talk, I will share our research on the exploration of user performance, behavior, and feedback when exploring visualized data/information when completing visual analytic learning activities. We conducted a series of user studies with single and multiple users across several platforms (i.e., desktop, large displays, mobile tablets, and VR) to explore users’ performance, behaviors, and subjective preferences when using these devices for both single users and multiple users. Our research contributes to a better understanding of the potential effects and benefits that different display and interactive platforms could have on analytical reasoning, learning, and exploratory experiences. The aim of this research is to provide insights and guidelines for the future design and use of data/information visualization technologies for single and multiple users across different platforms.

pROF. Xi-wen Zhang
Beijing Language and Culture University, China

Xiwen Zhang is currently a Professor of Digital Media Department, School of Information Science, in the Beijing Language and Culture University. He worked as an associated professor from 2002 to 2007 at the Human-computer interaction Laboratory, Institute of Software, Chinese Academy of Sciences. From 2005 to 2006 he was a Postdoctor advised by Prof. Michael R. Lyu in the Department of Computer Science and Engineering, the Chinese University of Hong Kong. From February to April in 2001 he was a Research Assistant by Dr. KeZhang Chen in the Department of Mechanical Engineering, the University of Hong Kong. From 2000 to 2002 he was a Postdoctor advised by Prof. Shijie Cai in the Computer Science and Technology Department, Nanjing University.
Prof. Zhang 's research interests include pattern recognition, computer vision, and human-computer interaction, as well as their applications in digital image, digital video, and digital ink. Prof. Zhang has published over 60 refereed journal and conference papers in his research areas. His SCI paper are published in Pattern Recognition, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, Computer-Aided Design. He has published more than twenty EI papers.
Prof. Zhang received his B.E. in Chemical equipment and machinery from Fushun Petroleum Institute (became Liaoning Shihua University since 2002) in 1995, and his Ph.D. advised by Prof. ZongYing Ou in Mechanical manufacturing and automation from Dalian University of Technology in 2000.

Title:  Intelligently Extracting Information from Digital Ink Chinese Text by Junior International Students

Abstract:Chinese characters have complex structures. Their writing plays an import role in learning Chinese. Junior international students can use digital pen to record their handwriting as digital ink. Various information can be extracted from the digital ink text, such as text line, Chinese characters, stroke errors, shape normalization. Digital ink is a new media compared with digital image and digital video. It is captured from handwriting and freehand drawing using digital pen. Point samples are captured by digital pens, containing positions, time stamp, and pressures. A stroke is a list of sampling points from pen down and movement to pen up. A list of strokes consists of a digital ink. Digital ink Chinese text are stroke sets, have neither text line, nor Chinese characters. Digital ink Chinese texts written by junior international students contain many information including errors and unnormal issues. It is difficult to recognize them. We proposed some intelligent methods to extract information, such as adaptive segmentation based on statistics analysis, classification using machine learning, stroke matching using Genetic Algorithm, evaluating the normalization for entire characters and their components using knowledge bases. With developing new intelligent methods and collecting more data, more valued information can be extracted.

Dr. Peter Tong
Concordia International School Shanghai, China

Peter pioneered the Big Data Analytics program for K-12 in education in 2014 where neither curriculum nor standards exist. His passion in data analytics is evident in his teachings and research work. He shares his data analytics passion with his students by supervising practical data analytics projects in his Big Data Analytics course. Both his and his students' work have been presented regularly at several international big data analytics conferences. In 2020, Peter and his students developed the Concordia International School nnline big data online course, Home | Big Data ( Peter began his career as an aerospace engineer in the preliminary design of a supersonic Mach 2+ Unmanned Aerial Vehicle (UAV) for the Department of National Defence, Canada. He later found his calling to be a teacher. With a background in Electrical Engr. (B.Sc.), Mechanical Engr. (M.Sc.), Aerospace Engr. (Ph.D.) and Dip. Ed. Peter readily integrates practical real life engineering experience into the classroom. He also developed an Aerospace Engineering course for high school and is the teacher advisor to The Concordia Phoenix Squadron, Phoenix Squadron ( He is a member of the program committee for International Big Data and Analytics Educational Conference, on the Advisory Board to True North School Hanoi, Vietnam and STEAMwSeniors, He has taught in Australia, Canada, Indonesia, Malaysia, Singapore and is currently teaching in China.

Title: Leveraging Big Data Analytics in Drone Technology: Unlocking New Frontiers

Abstract:In recent years, the convergence of big data analytics and drone technology has emerged as a compelling research area with vast implications across various industries. This presentation highlights the significance of harnessing the power of big data analytics to advance the capabilities of drones and explore novel applications. The primary focus is to investigate the integration of big data analytics techniques with drone technology. By leveraging the immense amounts of data generated by drones during their flight operations, a wealth of valuable insights can be extracted to enhance operational efficiency, improve decision making, and enable intelligent automation. It will also explore the application of advanced analytics techniques including machine learning, artificial intelligence and predictive modeling to drive innovation in drone autonomy and pave the way for a future where drones and big data analytics become integral components that work closely alongside us for a 22nd century future.

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