Invited Speakers
| Prof. Por Lip Yee, IEEE Senior Member University of Malaya, Malaysia Dr. Lip Yee, an esteemed Professor at the Department of System and Computer Technology within the Faculty of Computer Science and Information Technology at the Universiti Malaya, Malaysia, earned his Ph.D. from the Universiti Malaya under the mentorship of Prof. Abdullah bin Gani in 2012. He is also a senior member of the IEEE. Since then, Dr. Por has made significant contributions to academia, research, and industry. Dr. Por's research endeavors have been at the forefront of technological advancement, particularly in the domains of security and quality assurance of information (NEC 2020: 0611) and machine learning (NEC 2020: 0613). His research interests include machine learning, support vector machines, deep learning, long-short-term memory, computer vision, AIoT, IoT, blockchain, authentication, graphic passwords, PIN-entry, cryptography, data hiding, steganography, and watermarking. His pioneering work has garnered recognition both nationally and internationally, with over 100 academic papers published in reputable journals. Notably, he was among the first few pioneers to publish in the top 1% of ISI journals, with commendable citations across platforms such as Web of Science, Scopus, and Google Scholar. Web of Science (H-index: 18, 1343 citations), Scopus (H-index: 22, 1748 citations), and Google Scholar (H-index: 29, 2777 citations) databases (last update: July 13, 2024).https://www.webofscience.com/wos/author/record/B-5309-2010
Abstract: This study introduces a graphical authentication algorithm designed to address vulnerabilities arising from shoulder-surfing attacks while maintaining a focus on usability. Authentication systems must balance robust security with operational efficiency and user accessibility. Traditional alphanumeric passwords are prone to brute-force and dictionary attacks, with increasing complexity often leading to usability challenges. Graphical passwords, which leverage human cognitive strengths in visual recognition and recall, offer an alternative approach for secure and user-friendly authentication. This study investigates the susceptibility of graphical passwords to shoulder-surfing, a threat model in which attackers intercept credentials through direct visual observation. The proposed algorithm employs dynamic image segmentation, stochastic pattern overlays, and adaptive cognitive load balancing to mitigate the risks associated with observational attacks while ensuring ease of use. Experimental results from simulations and user testing demonstrate a mean reduction in authentication time by 18% compared to conventional graphical password schemes, with significant improvements in both security and usability. These findings underscore the algorithm’s potential for deployment in secure, performance-critical environments requiring high efficiency, strong security, and user-centered design. |
Assoc. Prof.Ata Jahangir Moshayedi Jiangxi University of Science and Technology, China Dr. Ata Jahangir Moshayedi?an Associate Professor at Jiangxi University of Science and Technology in China, holds a PhD in Electronic Science from Savitribai Phule Pune University in India. He is a distinguished member of IEEE(Senior Member) and ACM, as well as a Life Member of the Instrument Society of India and a Lifetime Member of the Speed Society of India. Additionally, he contributes to the academic community as a valued member of various editorial teams for international conferences and journals. Dr. Moshayedi's academic achievements are, marked by a portfolio of over 90 papers published,2 patent and 12 copyright , across esteemed national and international journals and conferences along with 3 books on robotics (VR and mobile olfaction) and embedded systems. In addition to his scholarly publications, he has authored three books and is credited with two patents and nine copyrights, emblematic of his pioneering contributions to the field. His research interest includes Robotics and Automation/ Sensor modeling/Bio-inspired robot, Mobile Robot Olfaction/Plume Tracking, Embedded Systems / Machine vision-based Systems/Virtual reality, and Machine vision/Artificial Intelligence. Currently, Dr. Moshayedi is actively engaged in pioneering work at Jiangxi University, where he is developing a model for Automated Guided Vehicles (AGVs) and advancing the realm of Food Delivery Service Robots. Title : Food Delivery Robot as the Service Robot Evolution, Efficiency, and Performance Enhancement Abstract: Service robots enhance efficiency and safety by automating tasks, offering 24/7 availability, and adapting to various environments. These robots complement human workers, reduce operational costs, and improve customer experiences, while also addressing critical societal needs such as accessibility and healthcare support. This keynote explores the evolution and advancements in service robots, with a focus on food delivery robots. The presentation will first introduce the role of service robots, followed by a discussion of their components and types. The keynote will then delve into a specific example, the FOODIEBOT, designed for food delivery tasks. The discussion will cover the modeling and design process, along with the implementation of optimization techniques such as BAS, PSO, POA, and EO to enhance the robot’s performance. The research examines the operational efficiency of the FOODIEBOT through various optimization methods, analyzing their effectiveness across different delivery routes. This talk offers a comprehensive understanding of the FOODIEBOT’s design, demonstrating the significant impact of optimization strategies on its performance and highlighting their practical implications for the future of automated service robots. |
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| Prof. Shi Zhenghao Xi'an University of Technology, China Shi Zhenghao, professor, doctoral supervisor, member of the Academic Committee of Xi'an University of Technology, IEEE Senior member, outstanding member of CCF, "500 Elite Talents" in Taizhou City, Zhejiang Province, executive director of Shaanxi Computer Society, director of the "Computer Vision Technology Professional Committee" of Shaanxi Computer Society, deputy director of the "Biomedical Intelligent Computing Professional Committee" of Shaanxi Computer Society, head of the research team for "Intelligent Image Processing and Applications" of Xi'an University of Technology, and editorial board member of the "Chinese Journal of Image Graphics". My main research areas are machine vision, medical image processing, and machine learning. I have published and accepted 60 academic papers as the first author or corresponding author, and have won 2 second prizes of Shaanxi Provincial Science and Technology Progress Award (ranked first), 1 second prize of Xi'an Science and Technology Progress Award (ranked first), 3 second prizes of Shaanxi Western Higher Education Science and Technology Award (ranked first), 2 second prize of Shaanxi Provincial Computer Society Science and Technology Progress Award (ranked first), and won the 2022 Wiley Weili China Open Science High Contribution Author Award. Title: Sleep Apnea Syndrome Detection based on Transformer Abstract: Obstructive sleep apnea (OSA) is a common sleep disorder that is an important factor leading to cardiovascular diseases such as hypertension, heart failure, and stroke. Timely detection and treatment of OSA are of great significance in ensuring people's life and health. This report will introduce the research progress of this issue based on our practical experience in this field in the past two years, with a focus on reporting our work and achievements in using the Transformer method for OSA detection. |
