Assoc. Prof. Feng Liu
Shenzhen University, China
Feng Liu is currently an Associate Professor at School of Computer Science & Software Engineering, Shenzhen University. She obtained her B.Sc. and M.Sc. degrees both from Xidian University, Xi’an, Shaanxi, China. She received her Ph.D. degree in computer science from the Department of Computing at the Hong Kong Polytechnic University in 2014. Her research interests include pattern recognition and image processing, especially focus on their applications to fingerprints. Dr. Liu has published more than 40 papers at some international journals and conferences, including IEEE TIP, TFIS, TCYB, PR, ECCV, AAAI, etc., and participated in many research projects either as principal investigators or as primary researchers. She is a reviewer for many renowned field journals and conferences and a member of the IEEE.
Speech Title: "Research on Optical Coherence Tomography (OCT) –based Fingerprints"
Abstract: Optical coherence tomography (OCT) is a new non-invasive, high-resolution and three-dimensional imaging technology developed in the 1990s. It has been maturely applied in Ophthalmology, dermatology and other fields. However, OCT-based fingerprint research began in 2006. Compared with traditional fingerprints (two-dimensional surface fingerprints, high-resolution fingerprints, and three-dimensional fingerprints), OCT-based fingerprints: 1) can overcome the low-quality image problems caused by the skin status of surface fingerprints (such as dry, wet, stains, etc.), which benefits in improving the recognition accuracy; 2) It can capture sweat glands / pores and other features that can be extracted only on high-resolution fingerprint images, so as to realize alive detection; 3) In essence, it is a kind of three-dimensional fingerprint data containing rich multi-level fingerprint information, which can easily identify false fingerprints without internal fingerprint structure, so as to achieve the purpose of anti-spoofing. Therefore, OCT opens up a new field for fingerprint recognition. This talk will mainly focus on the anti-spoofing technology in OCT-based fingerprints. Firstly, we will review current researches of anti-spoofing technology on OCT-based fingerprint, analyze the existing problems, and report some progress and achievements we have made; Finally, the further direction and expectation of OCT-based fingerprint image is summarized.
Prof. Guangming Lu
Harbin Institute of Technology, China
Guangming Lu received the undergraduate degree in electrical engineering, the master degree in control theory and control engineering, and the Ph.D. degree in computer science from the Harbin Institute of Technology (HIT), Harbin, China, in 1998, 2000, and 2005, respectively. From 2005 to 2007, he was a Postdoctoral Fellow at Tsinghua University. Now, He is a Professor at Harbin Institute of Technology, Shenzhen, China. He has published over 150 papers at some international journals and conferences, including IEEE TIP, TNNLS, TCYB, TCSVT, NeurIPS, CVPR, AAAI, IJCAI, etc. His research interests include computer vision, pattern recognition, and machine learning.
Maejo University, Thailand
Grienggrai Rajchakit was born in 1981. He received the B.S. (Mathematics), Thammasat University, Bangkok, Thailand, in 2003. He received the M.S. (Applied Mathematics), Chiangmai University, Chiangmai, Thailand, in 2005. He was awarded a Ph.D. (Applied Mathematics), King Mongkut’s University of Technology Thonburi, Bangkok, Thailand, in the field of mathematics with specialized area of stability and control of neural networks. Currently, he is working as a lecturer at Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai, Thailand. He was the recipient of Thailand Frontier author Award by Thomson Reuters Web of Science in the year 2016 and TRF-OHEC-Scopus Researcher Awards by The Thailand Research Fund (TRF), Office of the Higher Education Commission (OHEC) and Scopus in the year 2016. His research interests are complex-valued NNs, complex dynamical networks, control theory, stability analysis, sampled-data control, multi-agent systems, T-S fuzzy theory, and cryptography, etc. Dr. Grienggrai Rajchakit serves as a reviewer for various SCI journals. He has authored and co-authored more than 149 research articles in various SCI journals.
Speech Title: "An Application of Neural Networks for Traffic Problems"
Abstract: Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious threat to sustainable urban development. Recently, intelligent traffic systems (ITS) have emerged as an effective tool to mitigate urban congestion. The key to ITS lies in the accurate forecast of traffic flow. However, the existing forecast methods of traffic flow cannot adapt to the stochasticity and sheer length of traffic flow time series. To solve the problem, this paper relies on deep learning (DL) to forecast traffic flow through time series analysis. The authors developed a traffic flow forecast model based on the bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNNs) with delay. The proposed model was compared with two classic forecast models, namely, the autoregressive integrated moving average (ARIMA) model and the Long Short-Term Memory Recurrent Neural Network (LSTM-RNNS) model, through long-term traffic flow forecast experiments, using an actual traffic flow time series from Open ITS. The experimental results show that the proposed BLSTM-RNNs network outperformed the classic models in prediction accuracy. Our research discloses the dynamic evolution law of traffic flow and facilitates the decision-making of traffic management.
Kok Chin Chai
Dr. Kok Chin Chai is a currently a technopreneur who received his PhD degree and completed his postdoctoral research in 2016 and 2017, respectively. He was recognized as professional technologist by Malaysia Board of Technology in 2021. He has developed novel AI models over 8 years, resulting in over 10 scientific publications in collaboration with team members at Deakin University and Universiti Malaysia Sarawak. He managed several industrial-university research projects in Malaysia and Taiwan. By aggregating his research skills and business experiences, he has founded FLINKEN (Automated Manufacturing Factory) and NEUON (AI consultancy and solutions provider). Currently, he is the CEO of NEUON.
Speech Title: "Research To Commercialisation: Challenges and Opportunities"
Abstract: With the background in both AI research and business experiences, Dr Chai founded NEUON in 2018 specialised in AI Computer Vision Solutions. Via the sharing session, he will share his experiences while designing and deploying the AI computer vision technology in industry. The real implementation is always challenging with much more design factors that may not be focused and addressed in academic research. Dr Chai will share his experiences when dealing with their current use cases on road asset management, manufacturing as well as plant science. Furthermore, he will share on their existing initiative and collaboration with research universities and institutes to bring their researches to meet the market needs. It is a great opportunity for all researchers ,academicians, professionals and also entrepreneurs to meet and explore the potentials together.
Dr. Van Thanh Huynh
Deakin University, Australia
Dr. Van Thanh Huynh is an early career researcher and lecturer at Deakin University. He received his PhD degree in robotics and control systems in 2019. Leveraging the nexus of applied mathematics and engineering, he is inspired to develop enabling technologies and engineering methodologies that assist in efficiently and effectively solving robotic, interdisciplinary, and science-driven problems. He has published in prestigious journals, including SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, IEEE Systems Journal, etc. His research is recognised with Deakin University awards. He has successfully attracted competitive funding from both industry and not-for-profit organisations.
Speech Title: "A Hybrid Attention-Based CNN LSTM Model for Traffic Flow Prediction"
Abstract: Intelligent transports systems (ITS) of smart cities have been developed to address the traffic management problem caused by a growing number of urban vehicles and increasing traffic congestion. Giving accurate traffic flow prediction plays a pivotal role in a well-functioning ITS. However, such prediction and associated learning of nonlinear spatial-temporal dynamics are challenging due to existence of nonlinearity and stochasticity and has attracted attention of many researchers. In this talk, a discussion on a hybrid, deep-learning model based on attention CNN-LSTM is presented to solve the traffic flow prediction problem. Comparison of the proposed method with counterparts is also discussed, offering some insights into pros and cons of prevalent deep-learning methods in solving the traffic prediction issue.