INVITED SPEAKERS
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.