Prof.
Xindong Wu
IEEE/AAAS Fellow
Director of the Key Laboratory of Knowledge
Engineering with Big Data (Hefei University
of Technology), Ministry of Education, China
Speech Title: CHACE-KO: A
Connected, Hybrid, Accommodating, Contained,
and Evolving Knowledge-Ocean
Abstract: A knowledge ocean aims for problem
solving with multi-model knowledge at all
times and all over the world, synergizing
knowledge graphs with large language models,
and performing bidirectional reasoning
driven by both data and knowledge. We have
constructed such a large knowledge ocean,
entitled CHACE-KO (a Connected, Hybrid,
Accommodating, Contained, and Evolving
Knowledge-Ocean) that contains the largest
knowledge graph in the world with 490
million entities and 2.257 billion relations
(https://ko.zhonghuapu.com/EN). In this
talk, we will present each of the CHACE
dimensions of the CHACE-KO design, and
illustrate how "big", "dynamic" and
"sparkling" applications are implemented by
these CHACE characteristics and the HAO
intelligence that integrates human
intelligence, artificial intelligence and
organizational intelligence.
Biography:
Xindong Wu is Director and Professor of the
Key Laboratory of Knowledge Engineering with
Big Data (the Ministry of Education of
China), Hefei University of Technology,
China. He is also a Senior Research
Scientist at Zhejiang Lab, China. His
research interests include big data
analytics, data mining and knowledge
engineering. He received his Bachelor's and
Master's degrees in Computer Science from
the Hefei University of Technology, China,
and his Ph.D. degree in Artificial
Intelligence from the University of
Edinburgh, Britain. He is a Foreign Member
of the Russian Academy of Engineering, and a
Fellow of IEEE and the AAAS (American
Association for the Advancement of Science).
Dr. Wu is the Steering Committee Chair of
the IEEE International Conference on Data
Mining (ICDM), and the Editor in-Chief of
Knowledge and Information Systems (KAIS, by
Springer). He was the Editor-in-Chief of the
IEEE Transactions on Knowledge and Data
Engineering (TKDE) between 2005 and 2008 and
Co-Editor-in-Chief of the ACM Transactions
on Knowledge Discovery from Data Engineering
between 2017 and 2020. He served as a
program committee chair/co-chair for ICDM
2003 (the 3rd IEEE International Conference
on Data Mining), KDD 2007 (the 13th ACM
SIGKDD International Conference on Knowledge
Discovery and Data Mining), CIKM 2010 (the
19th ACM Conference on Information and
Knowledge Management), and ICBK 2017 (the
8th IEEE International Conference on Big
Knowledge). One of his completed projects is
Knowledge Engineering With Big Data (BigKE),
which was a 54-month, 45-million RMB,
15-institution national grand project, as
described in detail at
https://ieeexplore.ieee.org/abstract/document/7948800
Prof.
Michele Della Ventura
Professor. Dr., Music Academy 'Studio Musica', Italy
Speech Title: to
be added
Biography:
Michele Della Ventura, professor of Music
Technology, is a learning expert, researcher
and instructional designer. His research
interests include correlation between music
and mathematics (with a particular emphasis
on artificial intelligence research in the
field of computer-aided analysis of tonal
music), intelligent systems and the
effective use of digital technologies for
learning in schools. He continues to
research into technology to support dyslexic
learners, with particular emphasis on the
pedagogy underlying the use of social media
and Web 2.0 technologies.
He is the author of several articles
presented at many conferences and published
in international science magazines and high
school textbooks (also featured at the
International Book Salon of Turin in 2012
and 2018).
He proofreads articles and is a member of
scientific committees in International
Conferences and International Journals.
He was invited as keynote speaker to
International Conferences in Italy, Austria,
Canada, China, Czech Republic, France,
Germany, Hong Kong, Hungary, Ireland, Japan,
Norway, Poland, Portugal, Romania,
Singapore, Spain, UK, USA (Baltimora,
Boston, Las Vegas, New York, Washington).
He teaches Music technologies, Informatics
and Music Informatics in University courses.
Prof. Hui Yu
Professor. Dr., University of Glasgow, the United Kingdom
Speech Title: Facial Sensing for Human-Machine Interaction
Abstract: With the increasing demand of
machine intelligence across a wide range of
application scenarios, human-machine
interaction (HMI) emerges as another
essential communication, whereby
facial-expression-aware is one of the
principal features for natural interaction.
The principal branch of my research has been
driven by the understanding of mechanism of
emotion and facial expression combining
knowledge of creative technologies with
multiple disciplines, such as visual and
cognitive computing, as well as machine
learning. Particularly, biometric data
precisely record the facial muscle activity
or brain activity closely related to facial
movements and the internal emotional states.
These multiple sensing channels would help
provide an insight into the emotion and
perception of facial expression, to develop
widely accessible HMI solutions able to
track facial motions and recognise affective
states in a highly efficient and precise
manner. This talk will discuss the
development of visual capture of facial
expression processing. This talk will also
discuss research about the development and
challenges of image/video clustering as well
as our recent development on this topic.
Biography: Hui Yu is a Professor with the University of Glasgow. He leads the Visual and Cognitive Computing Group at the university. His research interests lie in visual and cognitive computing as well as machine learning with applications to 4D facial expression modelling and analysis, human-machine interaction, intelligent vehicle, and video analysis. Professor Yu’s research work has led to several awards and successful collaboration with worldwide institutions and industries. He is the Associate Vice President of IEEE Systems, Man, and Cybernetics Society and a Scientific Advisor for some high-tech companies in the UK. Prof. Yu is the PI on grants from a diverse range of funding sources including the EPSRC, EU FP7, RAEng, Royal Society, Innovate UK and Industry. He has been awarded Industrial Fellowship by the Royal Academy of Engineering. He serves as an Associated Editor for IEEE Transactions on Human-Machine Systems, IEEE Transactions on Computational Social Systems, IEEE Transactions on Intelligent Vehicles and IEEE/CAA Journal of Automatica Sinica.