2019 8th International Conference on Knowledge Discovery
November 17-19, 2019, Phuket, Thailand
Prof. Vincent Ribiere IKI-SEA, Bangkok University, Thailand
Dr. Vincent Ribiere is an enthusiastic and creative International consultant and professor with a passion for helping organizations solving their organizational knowledge and innovation management challenges. He is the Founder and Managing Director of the Institute for Knowledge and Innovation Southeast Asia (IKI-SEA), a center of Excellence at Bangkok University as well as the Program Director of the Ph.D. in KM and Innovation Management. He has also co-founded various Internationals events in the fields of Business Creativity and Innovation (Creative Bangkok, ASCIM, CreativeMornings Bangkok, G-LINK) and he co-founded various communities of KM and Innovation academics and practitioners nationally (iKlub, Thai KM Network) and Internationally (KM Global Network).Vincent has a strong entrepreneurial spirit and he enjoys sharing his knowledge and experiences. He delivers keynote speeches and workshops at various International Professional and Academic conferences and he is the Author of more than 80 publications.
Abstract: Various studies predict that in few decades many procedural jobs will be replaced by computers and robots, but can computers also take over jobs involving some levels of creativity? Until recently, most researchers will have answered “No” to this question, but recent advances in the field AI might make them think twice. During his talk Dr. Vincent will present how AI can practically contribute to the various aspects and processes of Creativity.
Prof. Eric Tsui Hong Kong Polytechnic University, Hong Kong
Eric Tsui had spent 16 years in industry with Computer Sciences Corporation (CSC) in Australia taking on various capacities including Chief Research Officer and Innovation Manager. During this period, he has made significant contributions to the company's expert systems products, applied research and innovation programmes.
He joined PolyU in 2005. His speciality areas are Knowledge technologies including Search Engines, Portals, Personal Knowledge Management, Personal Learning Environments, and Knowledge Cloud services.
Professor Tsui is also an honorary advisor of KM and Community of Practice to three Hong Kong government departments. In the past decade, he has supervised or involved in more than 200 KM projects in Hong Kong, Asia and Australia.
In 2014 and 2018, he twice received the Global Knowledge Management Leadership Award, among many other awards on his use of technologies to support Teaching and Learning. He was listed an outstanding and exemplary academic in PolyU's last 2 annual reports.
Since August 2015, he has designed and launched two MOOCs (Massive Open Online Course) -“Knowledge Management and Big Data in Business” and “Industry 4.0: How to revolutionalise your business?” on the MIT edX platform. Together, they have attracted more than 77,000 enrolments and one of them is rated among the Top 7 Business MOOCs worldwide (Source: Canadian Business, April, 2018)
Abstract: With the rapid development and usage of information and communication technology (ICT), massive open online courses (MOOCs) are increasing its awareness and popularity in the educational field, especially in Higher Education (HE). Globally, various HE institutions are offering different kinds of courses in a variety of categories, in order to attract and attain the massiveness and openness of MOOCs. In the meanwhile, they are also facing challenges to put much effort in order to increase their competitiveness in terms of higher completion rate and higher motivation of learner’ engagement, etc. Various researchers and practitioners are paying attention to implementation of learning analytics for better understanding and support of learning and teaching in MOOCs. Learning analytics tools and methods are proposed so as to improve and enhance the learning environment and practice in order to support learners and instructors in the learning processes of MOOCs. In this presentation, the speaker attempts to provide an overview of the challenges in the MOOCs’ learning environment, current landscape and different applications of learning analytics. Some real-life applications are also included to demonstrate how the learning analytics method can help learners to improve learning practices. Implications for further development of the learning analytics method are also discussed.
Prof. Yoshifumi Manabe Faculty of Informatics, Kogakuin University, Tokyo, Japan
Yoshifumi Manabe was born in 1960. He received his B.E., M.E., and Dr.E. degrees from Osaka University, Osaka, Japan, in 1983, 1985, and 1993, respectively. From 1985 to 2013, he worked for Nippon Telegraph and Telephone Corporation. From 2001 to 2013, he was a guest associate professor of Graduate School of Informatics, Kyoto University. Since 2013, he has been a professor of the Faculty of Informatics, Kogakuin University, Tokyo, Japan. His research interests include distributed algorithms, cryptography, game theory, and graph theory. Dr. Manabe is a member of ACM, IEEE, IEICE, IPSJ, and JSIAM.
Abstract: Computers are commonly used for encryption and decryption in cryptography. In 1990, a new kind of cryptographic protocol has been proposed in which physical cards are used instead of computers to securely calculate values. They are useful when computes cannot be used. Also, people who do not know about cryptography can execute and understand the protocols. den Boer first showed a five-card protocol to securely calculate logical AND of two inputs. Since then, many protocols have been proposed to calculate logical functions and specific computations such as millionaires' problem, voting, random permutation, grouping and so on. This talk shows several protocols and recent results using private operations. The number of cards used in the protocol is the most important criteria to evaluate card-based cryptographic protocols. Using private operations, the theoretically minimum number of cards is achieved in many problems.
Asst. Prof. Adela Sau Mui Lau, Madonna University, USA
Prof. Lau is an Assistant
Professor of Data Analytics and Business Research and
director of center for business development in the School of Business at
Madonna University in USA. Her research specialties include
risk management and big data analytics; e-learning and
knowledge management; and e-business strategies, informatics
and applications in finance/healthcare/marketing/enterprise.
She is currently the editorial board member of the
International Journal of Knowledge Engineering and Data
Mining, and the International Journal of E-Healthcare, and
is the reviewer of several top journals such as Journal of
Medical Internet Research, Industrial Management & Data
Systems, Expert Systems with Applications, etc. Prof. Lau is
the Advisory Board Member of Asia Financial Risk Think Tank
to research potential financial risks across USA, Europe and
Asia using big data analytics with the international
Prof. Lau published over 40 journal and conference papers and funded over 30 research and industrial collaboration and consultancy projects in the area of machine learning, business intelligence, social media analytics, big data analytics, intelligence applications, risk management, information system adoption, ontology/taxonomy building, enterprise business process re-engineering, portal design, knowledge management, e-learning, public/community health studies, healthcare systems and nursing clinical quality control & assessment. She gained several research and service awards including NANDA Foundation Research Grant Award and Faculty Merit Award in Services and was the co-director of the Center for Integrative Digital Health, and the committee member of Knowledge Management Research Center and Data Science Center in her prior institutions. She supervised the IT team on innovative healthcare product development, initiated and developed industrial applied-research consultancy projects.
Abstract: Big data analytics collects numerical, texture, audio and video data to perform data analysis. Knowledge management supports big data analytics processes by organizing and relating big data to create ontology repositories and processes ontology reasoning, integration and analysis for big data analysis. The traditional tools and techniques used in knowledge management include ontology engineering, data mining, text mining, key performance indicators, workflow automation, monitoring and alerting. Organizations used these knowledge management tools and techniques to organize and filter the company information and to retrieve relevant information for managerial decision making. However, these knowledge management tools and techniques cannot provide optimal decision opinion to managers. So, wisdom management is the next generation of knowledge management. Wisdom management contains all processes of knowledge management. In addition, it adopted prescriptive analytics other than descriptive and predictive techniques used in knowledge management to perform what-if analysis for creating optimal solutions for managers. However, the research question in wisdom management is how to evaluate and improve the optimal solutions. Currently, no research has been conducted for this topic. Therefore, this presentation is aimed to discuss how machine learning being applied to access the what-if analysis in prescriptive analytics and provide a feedback loop for improving what-if analysis. Scenario-based learning was also introduced to be used in machine learning for improving what-of analysis and optimal solutions generation in wisdom management process.