Keynote Speakers of 2021


Chancellor’s Prof. Vinod Kumar, Carleton University, Canada

Dr. Vinod Kumar is a Chancellor’s Professor of Technology and Supply Chain Management of the Sprott School of Business (Director of Sprott School, 1995-2005), Carleton University, Ottawa, Canada. Dr. Kumar has published over 400 articles in refereed journals and proceedings other than publishing dozens of books and monographs; his articles have been cited over 9000 times. He has won several Best Paper Awards in prestigious conferences, Scholarly Achievement Award of Carleton University for the academic years 1985-86 and 1987-88, Research Achievement Award for the year 1993, 2001, 2007 and 2015, and the Graduate Mentoring Award in 2011. Dr. Kumar has widely consulted industry and government. He is co-editor of one, associate-editor of another and on the editorial board of six International Journals. In addition, Dr. Kumar has also served for several years on the Board of Governors and the Senate for Carleton University and on the Board of the Ontario Network of e-Commerce. Dr. Kumar’s research interests are in optimizing performance of operation systems; supply chain sustainability; technology transfer; new product development; technology adoption; e-commerce applications and e-Government. He is on Canadian Who’s Who since last several years. 

How Psychological Resistance and the Trust-building Factors Effect Elders’ Daily Care Technology Adoption Behavior?

The increasing elderly population with low birth rates in western countries has created a shortage of support workers to help them in their daily tasks, and the current health care systems were struggling to keep up with the rising demand for support/care workers. The airborne COVID-19 virus outbreak worsened the situation. The older adults were at the highest risk of death due to the COVID-19 and degradation in their immune systems with their age. The technological advancement in current years (such as Care robots, emotional companion robots, smart home products, and wearable devices) looks like a promising solution to these problems. Question is-- would they be adopted by elderly?
In our previous efforts (2020) we studied to understand the level of trust elderly people must have to accept such technological autonomous systems instead of human support, and how trust and personal characteristics can improve the intent to adopt autonomous systems. This work has been published on-line in “The Journal of Technological Forecasting & Social Change”. In 2021 we continue our research efforts on this theme. Here we study how the psychological resistance factors and trust generating factors affect the technology adoption decisions of elders. The effects of age, gender, income, past employment, and lifestyle on the relationship between the independent variables (the psychological resistance factors and trust generating factors) with the dependent variable (technology adoption decision) are also being studied in this research. The initial results derived from the first set of data obtained so far are presented while the data collection continues.


Chair Prof. Shuanghua Yang, Southern University of Science and Technology, China

Prof. Shuang-Hua Yang, FIET, FInstMC, SMIEEE, is currently serving as the Vice Dean for Academic Affairs of Graduate School and a chair professor in the Department of Computer Science and engineering at SUSTech (Southern University of Science and Technology). Before joined SUSTech Professor Yang spent over 23 years in the UK Higher Education. He was the Head of Department of Computer Science at Loughborough University from 2014 to 2016. He joined Loughborough University in 1997 as a research assistant, and progressing to a research fellow in 1999, a lecturer in 2000, a senior lecturer in 2003, a professor in 2006, and Head of Department of Computer Science in 2014. He was awarded a Doctor of Science degree, a higher doctorate degree, in 2014 from Loughborough University to recognize his scientific achievement in his academic career. He is also an appointed expert at the national level in China. His research interests are mainly focused on Internet of Things and Cyber-Physical Systems. He authored four research monographs and over 200 academic papers. 

Data Desensitization Method and Application

In the big data era information collected often contain some privacy-sensitive elements which refers to information that can cause privacy or security hazards when the information is placed in an incorrect place. Sensitive information could be personal information, network identity information, confidential and credential information or financial information etc. Data desensitization technology is to accurately extract the private information involved in the document, and then delete or hide the sensitive information, so as to achieve the purpose of privacy protection. Data desensitization for any small data set could be achieved by looking at all available data and remove unwanted or sensitive parts. For big data application an efficient and scalable method is required. This talk starts with the motivation of the work and then state of arts. Machine learning based data desensitization methods are the focus in this talk. Various machine learning methods including LSTM, CNN, BERT etc have been used in extracting sensitive information from unstructured data. Finally a real case study used in Shenzhen Public Safety Management Platform is presented.