Prof. Kenneth K. M. Lam
The Hong Kong Polytechnic University, Hong Kong
Biography: Prof. Kin-Man Lam received his Associateship in Electronic Engineering from the Hong Kong Polytechnic University in 1986. He won the S.L. Poa Education Foundation Scholarship for overseas studies and was awarded an M.Sc. degree in communication engineering from the Department of Electrical Engineering, Imperial College of Science, Technology and Medicine, England, in 1987. In August 1993, he undertook a Ph.D. degree program in the Department of Electrical Engineering at the University of Sydney, Australia. He completed his Ph.D. studies in August 1996.
From 1990 to 1993, Prof. Lam was a lecturer at the Department of Electronic Engineering of The Hong Kong Polytechnic University. He joined the Department of Electronic and Information Engineering, The Hong Kong Polytechnic University again as an Assistant Professor in October 1996. He became an Associate Professor in 1999, and is now a Professor. He was actively involved in professional activities. He has been a member of the organizing committee or program committee of many international conferences. In particular, he was a General Chair of the 2012 IEEE International Conference on Signal Processing, Communications, & Computing (ICSPCC 2012), the APSIPA ASC 2015, and the 2017 International Conference on Multimedia and Expo, all which were held in Hong Kong. Prof. Lam was the Chairman of the IEEE Hong Kong Chapter of Signal Processing between 2006 and 2008. He was an Associate Editor of IEEE Trans. on Image Processing from 2009 to 2014. He received an Honorable Mention of the Annual Pattern Recognition Society Award for an outstanding contribution to the Pattern Recognition Journal in 2004. In 2008, he also received the Best Paper Award at the International Conference on Neural Networks and Signal Processing.
Prof. Lam was the Director-Student Services and the Director-Membership Services of the IEEE Signal Processing Society between 2012 and 2014, and between 2015 and 2017, respectively. He was an Associate Editor of IEEE Trans. on Image Processing between 2009 and 2014, and an Area Editor of the IEEE Signal Processing Magazine between 2015 and 2017. Currently, he is the VP-Publications of the Asia-Pacific Signal and Information Processing Association (APSIPA). Prof. Lam serves as an Associate Editor of Digital Signal Processing, APSIPA Trans. on Signal and Information Processing, and EURASIP International Journal on Image and Video Processing. He is also an Editor of HKIE Transactions. His current research interests include human face recognition, image and video processing, and computer vision.
Abstract: A lot of research on face recognition has been conducted over the past two decades or more. Various face recognition methods have been proposed, but investigations are still underway to tackle different problems and challenges for face recognition. The existing algorithms can only solve some of the problems, and their performances degrade in real-world applications. In this speech, we will first discuss the performances of face recognition techniques on face images at different resolutions. Then, we will focus on the issues and methods for low-resolution face recognition. For low-resolution face recognition, we will present different approaches, and focus more on the approach based on feature super-resolution and fusion. Furthermore, the use of deep learning for face recognition will also be presented and discussed.
Jeju National University, Jeju, Korea
Dr. Yungcheol Byun is a full professor at the Computer Engineering Department (CE) at Jeju National University (http://www.jejunu.ac.kr). His research interests include the areas of Pattern Recognition & Image Processing, Artificial Intelligence & Machine Learning, Pattern-based Security, Home Network and Ubiquitous Computing, u-Healthcare, and RFID & IoT Middleware System. He directs the Machine Laboratory at the CE department. Recently, he studied at University of Florida as a visiting professor from 2012 to 2014. He is currently serving as a director of Information Science Technology Institute, and other academic societies. Outside of his research activities, Dr. Byun has been hosting international conferences including CNSI (Computer, Network, Systems, and Industrial Engineering), ICESI (Electric Vehicle, Smart Grid, and Information Technology), and serving as a conference and workshop chair, program chair, and session chair in various kinds of international conferences and workshops. Dr. Byun was born in Jeju, Korea, and received his Ph.D. and MS from Yonsei University (http://www.yonsei.ac.kr) in 1995 and 2001 respectively, and BS from Jeju National University in 1993. Before joining Jeju National University, he worked as a special lecturer in SAMSUNG Electronics (http://www.samsung.com) in 2000 and 2001. From 2001 to 2003, he was a senior researcher of Electronics and Telecommunications Research Institute (ETRI, https://etri.re.kr/eng/main/main.etri). He was promoted to join Jeju National University as an assistant professor in 2003.
Abstract: In natural language processing (NLP), language model is doubtlessly an intrinsic element, as it plays a fundamental role in many conventional NLP tasks, e.g., speech recognition to image captioning etc. Therefore, learning an exceptional language model usually enhance the hidden aspects or metrics; forging its pivotal role in NLP. Language models are gaining popularity as of the abundance of online texts, comments and reviews. Due to the advancement of e-commerce, people do write their reviews about the products they have received. In crowdfunding sites, comments are so critical that negative reviews can damage the reputation of the product’s creator or can affect the buying of others. Life is too fast these days that people find it difficult to go through abundant of text data to take a decision. Therefore, topic discovery is quite valuable in various aspects as of saving time of the user, providing the summary of text in form of discussion topics, and providing contextual information etc. Topic models are being studied for decades and are of fundamental importance as these models act as a tool in order to infer the latent topics and extracting semantic structure of a document. In this speech, we have used the Latent Topic Model (LDA) in order to generate topics for crowdfunding comments. Our proposed model is recurrent neural network (RNN) based language model, which uses the latent topics generated by LDA, is constructed to extract the comprehensive semantic meaning related words in comments. Moreover, this combined approach is of better capability on creating topic clusters then traditional ones, which signifies that blending the information from deep learning and topic modeling is a substantial way to generate an improved understanding of crowdfunding comments.
Macau University of Science and Technology, Macau
Biography: Dong Li received the Ph.D. degree in Electronics and Communication Engineering from Sun Yat-Sen University, Guangzhou, China, in 2010. Since 2010, he has been with the Faculty of Information Technology, Macau University of Science and Technology (MUST), Macau, China, where he is currently an Associate Professor. He held research and visiting positions in Institute for Infocomm Research (I2R), Singapore, and The Chinese University of Hong Kong, Shenzhen, China. His research interests focus on signal processing and machine learning for wireless communications, and Internet of Things (IoT). He was the recipient of the 2011 and 2016 MUST Bank of China (BOC) excellent research award. He served as a Technical Program Committee (TPC) Vice Chair of the IEEE International Conference on Communications Systems (ICCS) in 2014. He has been an executive board member of IEEE Macau Section since 2016.
Abstract: Ambient backscatter (AmBack) has emerged as a promising technology
for various communication and Internet of Things (IoT) applications due to
the recent advances in the hardware implementation. In contrast to
traditional wireless systems, no carrier generation and power supply are
needed for AmBack, and the dedicated reader and round-trip path loss can be
avoided compared to the radio frequency identification (RFID) technology. In
this talk, we begin with the fundamentals of AmBack, and then highlights
several interesting topics in signal transmission schemes including
frequency division multiple access (FDMA)-based interference free
transmission, hybrid active and passive transmission, and opportunistic
AmBack-assisted relay transmission.