Calton's research interests are in the areas of service computing, distributed and cloud computing, dynamic analytics on changing big data. His current projects include cloud computing (WISE/Elba project) and dynamic analytics (GRAIT-DM project) research. Using experimental data from realistic benchmarks, the WISE project studies millibottlenecks, very short bottleneck phenomena that have large impact on n-tier application latency. The GRAIT-DM project continuously collect fake news on COVID-19 and other disasters to distinguish real information from fake news. The sponsors for Calton's research include both government funding agencies such as NSF, and companies from industry such as Fujitsu. He is the director of Center for Experimental Research in Computer Systems (CERCS) at Georgia Tech. He is also the director of RCN on Big Data for Smart Cities, managed as part of the GRAIT-DM project.
Tilte: Evolution of Knowledge and Software in an Evolving World
Abstract: The physical world evolves, often
unexpectedly such as the COVID-19 pandemic. Following
the physical world, the cyber world also evolves and
grows with big data, software tools, and applications.
These constant changes are causing an increasingly rapid
pace in software updates and AI model updates. The
evolving nature of big data and their knowledge appears
to be fundamentally incompatible with the inherent
assumptions of Complete and Timeless Knowledge in
classic AI/ML models. In an evolving world, static
training data suffer from knowledge obsolescence due to
truly novel timely information from both physical and
cyber worlds. Knowledge obsolescence introduces a
widening distance between static ML models and the
evolving world, called cyber-physical gap. The widening
cyber-physical gap has significant impact on software
applications that interact with the physical world,
including e-business. We will discuss the implications
of cyber-physical gap on AI models and software
applications such as e-businesses.
Keywords: knowledge evolution, cyber-physical
gap, knowledge obsolescence
Prof. Minho Jo, Korea University, South Korea
Minho Jo (Senior Member, IEEE) received the B.A. degree from the Department of Industrial Engineering, Chosun University, Gwangju, South Korea, in 1984, and the Ph.D. degree from the Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA, in 1994.He is the Full Professor with the Department of Computer Convergence Software, Korea University, Sejong, South Korea, where he is the Director of the IoT & AI Lab. Prof. Jo is the Director of Brain Korea 21 “IoT Data Science” supported by the Korean government. His current research interests include IoT, blockchain, LLM/ChatGPT, artificial intelligence and optimization theory, big data, network security, cloud/edge computing, wireless energy harvesting, and autonomous vehicles. The average number of citations per publication authored by Prof. Minho Jo (from 2013 through 2022) is 41.7 and Average Field-Weighted Citation Impact (FWCI) of Prof. Minho Jo (from 2013 through 2022) is 4.42 (based on SCOPUS SciVal. Exactly FWCI = 1 means that the output performs just as expected for the global average. FWCI = 4.42 means 342% more cited than the global average.)
Prof. Jo is a recipient of the 2018 IET Best Paper Premium Award by the United Kingdom’s Royal Institute of Engineering and Technology. He was awarded with 2011 Headong Outstanding Scholar Prize. He is one of the founders of the Samsung Electronics LCD Division. He is the Founder and the Editor-in-Chief of KSII Transactions on Internet and Information Systems (SCIE/JCR and SCOPUS indexed. https://itiis.org). He was the South Korea’s Presidential Commission on Policy Planning. He served as an Associate Editor of IEEE SYSTEMS JOURNAL, IEEE ACCESS, IEEE INTERNET OF THINGS JOURNAL, Editor of IEEE WIRELESS COMMUNICATIONS, and Editor of NETWORK, respectively. Prof. Minho Jo is the Chair of the 5th IEEE International Conference on Advanced Information and Communication Technologies (AICT 2023), and the General Co-Chair of VTC2021-Fall (2021 IEEE 94th Vehicular Technology Conference), respectively.
Title: ChatGPT and Large Language Model for e-Biz
Abstract: ChatGPT has become powerful in practical tasks in many areas such as e-business, economics, finance, science, engineering, software development, customer services, human resources management, marketing, public relations, entertainment, military, and many more. ChatGPT is one of large language models (LLMs). In this keynote speaking, Prof. Jo will introduce how ChatGPT came from the transformer and its architecture, and present practical task examples in e-Biz analytics and applications.
Prof. Dr. Lili Yang, a professor in Department of Statistics and Data Science at Southern University of Science and Technology. She was working at Loughborough University in the UK. She has been a tenure assistant professor, associate professor and then professor in the School of Business and Economics at Loughborough University, and a senate member of the Loughborough University. She has also served as Associate Director of the Service Management Research Center and the head of the Emergency Management Research Group. She was selected as a Fellow of the British Computer Society in 2008 (BCS Fellow). Recently she joined the Southern University of Science and Technology. Professor Yang has long been engaged in the research of theories and methods of public safety emergency management to preside over and complete more than 20 important projects funded by the European Commission (European Commission FP7), the UK National Engineering and Natural Sciences Fund (EPSRC), or the British Ministry of Defence (MoD), etc. Her many scientific research results have been successfully applied into real life practices. She has published three books and more than 100 papers. She has been acting as an EPSRC Associate Peer Review Colleague for a long time and playing the role as an expert reviewer for different scientific funding bodies in the UK, China, Portugal and Luxembourg etc. At present in Southern University of Science and Technology, she is leading a project of the National Natural Science Foundation of China, and a sub-project leader for the project of National Key Research and Development Program of the Ministry of Science and Technology.
Tilte: Adaptive Public Health Strategies in the Pandemic: Insights from the UK and China’s Policy Transitions
Abstract: The COVID-19 pandemic elicited diverse
strategic responses worldwide, notably in the UK and
China. These nations grappled with the intersecting
challenges of public health management, urban mobility,
and socio-economic impacts during the crisis. In the UK,
the shift from strict national lockdowns to the Plan B
work-from-home (WFH) policy prompted an analysis of its
effects on urban mobility and travel patterns. Using
Google Mobility data in Leeds, our study assessed how
lockdown adherence and perceived risks influenced travel
behaviours. Furthermore, a comprehensive examination
across London, Leeds, and Newcastle, employing mobile
Track-and-Trace data and machine-learning causal
inference, revealed regional and socio-demographic
differences in response to WFH directives. These
findings underline the importance of socially equitable
and customized policy interventions.
Concurrently, China’s approach with its dynamic
zero-COVID policy, while effectively controlling the
spread of the virus, had to navigate the challenges of
reopening and long COVID symptoms management. A study in
Shenzhen using a fine-grained agent-based model revealed
that transitioning strategies from this policy had
varying degrees of effectiveness. A gradual transition
maintaining some restrictions could mitigate infection
outbreaks, but the social cost and duration of epidemics
also depended heavily on the strictness of measures.
The UK and China's experiences highlight the complex
nature of public health strategies and their societal
implications. This research stresses the need for
flexible, context-aware approaches that harmonize health
objectives with socio-economic factors. The findings
offer crucial lessons for shaping balanced pandemic
policies that equally prioritize health and
socio-economic stability.