Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway

Jerry Chun-Wei Lin received his Ph.D. from the Department of Computer Science and Information Engineering, National Cheng Kung University in 2010. He is currently a full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has published more than 450 research articles in refereed journals (IEEE TKDE, IEEE TCYB, IEEE TII, IEEE TITS, IEEE TFS, IEEE TNNLS, IEEE TNSE, IEEE TETCI, IEEE SysJ, IEEE SensJ, IEEE IOTJ, ACM TKDD, ACM TDS, ACM TMIS, ACM TOIT, ACM TIST) and international conferences (IEEE ICDE, IEEE ICDM, PKDD, PAKDD), 11 edited books, as well as 33 patents (held and filed, 3 US patents). His research interests include data mining, soft computing, artificial intelligence/machine learning, and privacy preserving and security technologies. He is the Editor-in-Chief of the International Journal of Data Science and Pattern Recognition, the Guest Editor/Associate Editor for several IEEE/ACM journals such as IEEE TFS, IEEE TITS, IEEE TII, ACM TMIS, ACM TOIT, and IEEE Access. He has recognized as the most cited Chinese Researcher respectively in 2018, 2019, and 2020 by Scopus/Elsevier. He is the Fellow of IET (FIET), ACM Distinguished Member and IEEE Senior Member.


Title: Utility-Oriented Mining: Techniques and Modeling

Abstract: As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns about customer buying behavior that can then be used for decision making. In recent years, the demand for utility-oriented pattern mining and analytics has increased because it can discover more useful and interesting information than basic binary-based pattern mining approaches, which has been used in many domains and applications, e.g., cross-marketing, e-commerce, finance, medical and biomedical applications. In this talk, I will first highlight the benefits by using the utility-oriented pattern mining and analytics compared to the past studies (e.g., association rule/frequent itemset mining). I will then provide a general overview of the state of the art in utility-oriented pattern mining and analytic techniques according to three main categories (i.e., data level, constraint level, and application level). Several techniques and modeling on different aspects (levels) of utility-oriented pattern mining will be presented and reviewed.

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.
Some intelligent methods are used to extract information in our work, 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.
Digital ink is a new media compared with digital image and digital video. It is captured from handwriting and freehand drawing using digital pen. Various digital pens are used with pads, smart phones, papers. 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 can capture more information in handwriting with less data. Digital ink Chinese text are stroke sets, have neither text line, nor Chinese characters.
Histograms of stroke gaps are used to extract them. Chinese characters are recognized using stroke and structure information. Strokes are recognized using Hidden Markov Model and Hidden Conditional Random Field.
Some writing strokes in digital ink Chinese characters do not corresponding to Chinese characters’ strokes. Writing movement features are used to identified digital ink Chinese characters with wrong writing. There are many writing strokes’ errors in these characters, such as wrong strokes’ orders or directions, more or less strokes, incomplete or extra, broken or joined. These errors should be extracted for students’ learning and teachers’ teaching. Writing strokes in digital ink Chinese characters are matched to Chinese characters’ strokes using Genetic Algorithm. Writing strokes’ errors are extracted based on the matching.
Some digital ink Chinese characters are not normal due to unnormal strokes or components. The whole characters are evaluated for stability with barycenter, symmetry with barycenter and convex hulls, the slant membership with their ellipse. Knowledge databases of reference Chinese characters are constructed.
Some digital ink Chinese characters’ components are not normal due to unnormal strokes and their relations. The components are evaluated for spacing with their boundary distances, alignment with their barycenter. Six categories of Chinese character structures are covered. Knowledge databases of reference Chinese characters’ components are constructed.
In summary, digital ink Chinese texts written by junior international students contain many information including errors and unnormal issues. It is difficult to recognize them. Intelligent methods can be used to address them. We have done some of them. With developing new intelligent methods and collecting more data, more valued information can be extracted.

Edwin P. Christmann, Slippery Rock University, US

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: Higher Education’s History: An Expectation of Progressive Development

Abstract: Since the University of Bologna’s founding in 1088 AD, higher education has evolved from religious institutions of higher learning into schools that are now reaching a broad audience of learners. Today, students attend school via online learning platforms and have access to a variety of majors. This presentation will explore the history of higher education and the events over time that have changed colleges and universities into institutions that are now able to accommodate a kaleidoscope of students of varying ability levels studying across a diversity of academic majors in institutions worldwide. Subsequently, the financing of education, the intellectual and academic ability of students, and the demands of industry will be discussed as factors that will drive the future of higher education along with possible future developments.


Kehong Zhang, Lanzhou University of Finance and Economics, China

My name is Kehong Zhang, Doctor of Computer Science, Associate Professor of Lanzhou University of Finance and Economics, Dean of the computer and science department. I am ACM member, IEEE member, CCF professional member, Executive Committee of Lanzhou Branch of China Computer Federation and Academic member of CCF YOCSEF Lanzhou branch. I am now engaged in the analysis and processing of graph data, knowledge graph and attribute network. As a basic research, the research content has a wide range of applications, such as intelligent logistics, library and information analysis, social network, path planning and commodity correlation analysis. In my specific work and study, I also studied theories and methods combining economy and computer technology, such as logistics planning of different commodities, construction of e-commerce platform, recommendation of commodities and other fields. At the same time, I also studied big data analysis. In addition, I have been studying computer education for a long time, paying particular attention to the cultivation schemes and cultivation modes of Computer Science and Technology and Intelligent Science and Technology in financial and economic university. I also combined with the characteristics of the university, formulate and implement relevant programs and plans.


Title: Research on The Integration of Business Intelligence and Innovation and Entrepreneurship Education for Computer Science

Abstract: The cultivation of innovation and entrepreneurship ability of students majoring in computer science in Finance and economics university is a key issue for the development of this major. In view of the characteristics of artificial intelligence technology and finance and economics colleges, this paper puts forward the new engineering teaching reform idea of integrating business intelligence with innovation and entrepreneurship, and analyzes the objective combined with the actual situation of finance and economics colleges, and puts forward specific measures. This is helpful to the reform of new engineering teaching and the cultivation of innovative business intelligence technical talents.

Peter Tong, Concordia International School Shanghai, China

Peter pioneered the Big Data Analytics program for K-12 education 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. 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. He is a member of the program committee for International Big Data and Analytics Educational Conference and is on the Advisory Board to True North School Hanoi, Vietnam. He has taught in Australia, Canada, Indonesia, Malaysia, Singapore and is currently teaching in China.

Title: The Development and the Need for a K-12 Big Data Analytics Online Course

Abstract: Concordia International School Shanghai created a Big Data Analytics course from ground zero in 2014 to meet the need of middle and high school students. Since then, our high school students have been presenting their real-life practical analytics projects at international big data and education conferences for the past eight consecutive years. The outbreak of COVID-19 in early 2020 has affected all of us in one form or another and has disrupted all age groups and sectors of education significantly. This pandemic catalyzed the necessity to adapt the face-to-face delivery mode of courses and prompted us to develop more suitable teaching material and delivery methods. With the knowledge and skills acquired since its inception, the pioneering teacher along with his dream team of students developed an online Big Data Analytics course to further serve many more students both locally and globally. This presentation will present an overview layout of this online version of the Big Data Analytics course and explore ways to enhance it as a hybrid or blended learning course.



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