Speakers

Keynote Speakers of 2024

Prof. Jinwen Ma


Peking University, China

 

   

Speech Title: The Improved Large Language Model for Mathematical Computing and Reasoning

Abstract: After the release of chatGPT and GPT-4, the transformer-based large language models have shown tremendous performance improvements and achieved great success in natural language processing and artificial intelligence. However, the key bottleneck problems of the large language model on its further developments and applications are the low ability of mathematical computing and reasoning. In this speech, we investigate how to improve the large language model to get the high performance of mathematical computing and reasoning by adding the continual learning mechanisms and inserting a system of inference rules. We can also improve the mathematical performance of the large language model by adopting and fusing a knowledge database in some way. As long as the large language model has the real ability of mathematical computing and reasoning, it can become powerful and applicable in the real scenarios and make the great-leap-forward development in artificial intelligence.

Biography: Jinwen Ma received the Ph.D. degree in probability theory and statistics from Nankai University, Tianjin, China, in 1992. Then, he joined the Department or Institute of Mathematics of Shantou University, Guangdong Province, China, and become a full professor in 1999. Since September 2001, he has joined the Department of Information Science at the School of Mathematical Sciences, Peking University, where he has served as the chair and full professor as well as a Ph. D. tutor in applied mathematics and now he is a full professor and PhD tutor in the Department of Information and Computational Sciences at the School of Mathematical Sciences, Peking University. During 1995 and 2004, he visited several times to the Department of Computer Science & Engineering, the Chinese University of Hong Kong as a Research Associate or Fellow. From September 2005 to August 2006, he was a Research Scientist with the Amari Research Unit, RIKEN Brain Science Institute, Japan. From September 2011 to February 2012, he visited as a Scientist to the Department of Systems Medicine and Bioengineering, The Methodist Hospital Research Institute, Houston, USA.
His main research interests include neural computation, machine learning, independent component analysis (ICA), computer vision, big data and large language models. He is the author or coauthor of more than 200 academic papers among which more than 60 papers were indexed by the Science Citation Index (SCI)－Expended. In fact, these papers have been cited over 4000 times. He has served as the Principal or Major Investigator for eleven national and three provincial or ministerial and two other scientific research grants as well as over 10 cross-sectional research projects. At present, he is the vice-director member of the Signal Processing Society in the Chinese Institute of Electronics (CIE) and a member on the editorial board of “Signal Processing (in Chinese)”. Moreover, he is the director of the Education Informationization Special Committee of China Chapter of International Information Study Society. He has served as a program committee member of several major international conferences such as ISNN, ICIC, ICONIP, ICSP. He was a co-chair of the program committee of 1999 Chinese Conference on Neural Networks and Signal Processing and the chair of the organization committee of the Third International Conference of Intelligence Science (ICIS 2018). He was selected in the 2017 AI Impact Scholars released by Ascemap and scholar.chinaso.com and the World’s Top 2% Scientists 2020 (Career Scientific Impact) released by Stanford University.

 

Assoc. Prof. Hoshang Kolivand


Liverpool John Moores University, UK

 

   

Speech Title: How Machine Learning is Reshaping Mixed Reality

Abstract: In this talk, we delve into the profound impact of ML on Mixed Reality, uncovering the latest advancements and groundbreaking innovations that are reshaping our digital experiences. From sophisticated real-time simulations to personalized virtual environments, explore how AI's integration with Mixed Reality is driving unprecedented immersion and transforming the way we perceive and interact with the virtual world. Join us as we unravel the limitless possibilities and implications of this transformative fusion.

Biography: Hoshang Kolivand is an Assoc. Prof in AI and Mixed Reality at Liverpool John Moores University (LJMU). With an MS degree in Applied Mathematics and Computer Science and a PhD and a Postdoc in Augmented Reality, he is a leading expert in these fields. As the Head of the Applied Computing Research Group at LJMU, Dr. Kolivand leads a team of over 35 researchers, focusing on AI and Augmented Reality. He has published extensively with over 170 papers in international journals and has presented at numerous conferences. Dr. Kolivand is a Senior Member of the IEEE and has served as a keynote speaker at more than 55 international conferences. He has organized over 30 conferences in AR, VR, AI, and HCI. In addition to his academic contributions, Dr. Kolivand has authored book chapters and several products which received over 14 awards for his work in Virtual Reality and Augmented Reality. As a dedicated researcher and educator, Dr. Hoshang Kolivand plays a significant role in advancing AI and Mixed Reality technologies, making valuable contributions to the field through his expertise and leadership.

 

Dr. Aminu Bello Usman
Head of the School of Computer Science

University of Sunderland, UK

 

   

Speech Title: Securing Tomorrow: Enhancing Biometric Image Privacy and Security through IoT and LPWAN Innovations

Abstract: "Securing Tomorrow: Enhancing Biometric Image Privacy and Security through IoT and LPWAN Innovations" the presentation explores advancements in securing biometric data within Internet of Things (IoT) ecosystems using Low Power Wide Area Networks (LPWAN). It focuses on addressing privacy and security challenges in biometric image transmission, storage, and processing. The presentation delves into how IoT and LPWAN technologies can be leveraged to create robust, scalable solutions for safeguarding sensitive biometric data, emphasising the need for innovative encryption techniques, secure protocols, and privacy-preserving mechanisms to protect against cyber threats and data breaches in an increasingly connected world.

Biography: Dr. Aminu Bello Usman served as the head of the School of Computer Science at the University of Sunderland. His research focuses on the Internet of Things (IoT), biometric security, applied AI, data privacy, trust, and user privacy. Dr. Usman is particularly passionate about IoT communication protocols, and his most recent works are on developing models, and frameworks that enhance user privacy and trust, addressing real-world security challenges of IoT and Edge computing.

 

Session Keynote Lecturer

Assoc. Prof. Yi Gu


Middle Tennessee State University, USA

 

   

Speech Title: Multi‑objective Optimization for Large‑Scale Workflow Scheduling and Execution in Clouds

Abstract: Cloud computing has become the most popular distributed paradigm with massive computing resources and a large data storage capacity to run large-scale scientific workflow applications without the need to own any infrastructure. Scheduling workflows in a distributed system is a well-known NP-complete problem, which has become even more challenging with a dynamic and heterogeneous pool of resources in a cloud computing platform. The aim of this work is to design efficient and effective scheduling algorithms for multi-objective optimization of large-scale scientific workflows in cloud environments. We propose two novel genetic algorithm (GA)-based scheduling algorithms to assign workflow tasks to different cloud resources in order to simultaneously optimize makespan, monetary cost, and energy consumption. One is multi-objective optimization for makespan, cost and energy (MOMCE), which combines the strengths of two widely adopted solutions, genetic algorithm and particle swarm optimization, for multi-objective optimization problems. The other is pareto dominance for makespan, cost and energy (PDMCE), which is based on genetic algorithm and non-dominated solutions to achieve a better convergence and a uniform distribution of the approximate Pareto front. The proposed solutions are evaluated by an extensive set of different workflow applications and cloud environments, and compared with other existing methods in the literature to show the performance stability and superiority.

Biography: Prof. Yi Gu is an associate professor in the Department of Computer Science at Middle Tennessee State University (MTSU). She received her M.S. and Ph.D. degrees in Computer Science from University of Memphis in 2008 and 2011, respectively. She had worked as an assistant professor of Computer Science at University of Tennessee Martin from 2011 to 2013 before she joined MTSU as an assistant professor in 2013. Her research interests include workflow scheduling and optimization, parallel and distributed computing, Cloud computing, wireless sensor networks, and cybersecurity. She has published 45+ technical papers in prestigious journals, international conferences, and book chapters. She has received university, state, and federal fundings, such as Tennessee Board of Regents (TBRs), National Science Foundation (NSF), etc.