Invited Speech

Prof. Ran He
Institute of Automation of Chinese Academy of Sciences, China

Ran He is a Professor at the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, Beijing, China. He received the B.E. degree in Computer Science from Dalian University of Technology, the M.S. degree in Computer Science from Dalian University of Technology, and Ph.D. degree in Pattern Recognition and Intelligent Systems from Institute of Automation, Chinese Academy of Sciences in 2001, 2004 and 2009, respectively. His research interests focus on information theoretic learning, pattern recognition, and computer vision. He has published over 140 journal and conference papers in these fields, and has widely published at highly ranked international journals, such as IEEE TPAMI, TIP, TIFS, IJCV, PR, and leading international conferences, such as ICCV, CVPR, NIPS, IJCAI, AAAI. He is currently serving as an associate editor of Elsevier Neurocomputing and IET Image Processing, and served as area chair and senior program member of several conferences. His research was supported by NSFC for Excellent Young Scientist Programme, and Beijing Natural Science Funds for Distinguished Young Scholars.

Speech Title: "Variational Image Analysis under Limited Computational Resource"

Image data tend to be high-dimensional and large-scale. When given infinite computational resource, machine learning algorithms can generate exact results (prohibitively expensive). Variational approximation methods arise from the use of a finite amount of processor time. These methods are often built on top of standard function approximators. In this talk, we introduce a group of variational inference and learning algorithms that scale to high-dimensional and large-scale image datasets. First, we address the linear approximation to learn robust and compact local features of image data, named ordinal measures. Second, we address the quadratic approximation of a family of loss functions that widely used in image analysis. Accordingly, a half-quadratic optimization framework is proposed for modeling sparsity, low-rank recovery and noise. Third, we introduce an Introspective Variational Autoencoders to approximate the posterior distribution, then we can generate high-resolution images from the learnt distribution, paving a way for analysis via synthesis.


Assoc. Prof. Mohd Nazri Ismail
National Defence University of Malaysia, Malaysia

Born in Penang, Malaysia in 1971, Assoc. Prof. Dr. Mohd Nazri Ismail had graduated in Computer Science (Bachelor Degree) and Engineering Science (Master Degree) at Universiti Kebangsaan Malaysia (UKM) and Multimedia University (MMU). He pursued his doctoral programme in the same institution (UKM) and obtained his Ph. D in 2010. Starting as a Network Engineer in 1995, he became Lecturer In 2001, at University of Kuala Lumpur (UniKL) and National Defence University of Malaysia. He had a deep involvement in computer network research and was awarded the prestigious “Educator Award 2009 – R&D/Education category” by MARA (Malaysia Agency). He has supervised Ph.D. and Master Students and teaching at undergraduate and post graduate level. He has published more than 100 papers in national and international journals (index by ISI, SCOPUS, IET etc) and IEEE conferences. He has attended many international conferences throughout the world and has chaired many technical sessions. He has appointed as Technical Program Committee and organized more than 60 national and international conferences. He has appointed as Editorial Board member more than 90 international journals and 40 international reviewer panels (journal/proceeding). Awards and laurels won by Assoc. Prof. Dr. Mohd Nazri Ismail run into volumes and he has received 28 awards in R&D/Education. He is a member of IAENG, IEEE Cloud Computing Community, SDIWC, IAEST, UACEE, IAOE and IACSIT. He has also published 10 books on Computer Network Security, Wireless Technology and Internet of Things (IoT).

Speech Title: "Development of Wireless Sensor Network (WSN) and Mobile Ad-Hoc Network (MANET) Communication for Military Operation and SAR (Search and Rescue) Operation"

The study investigates and develops components for implementing an effective military and SAR (Search & Rescue) acknowledge/information/communication in closed network architecture. Since military and SAR personnel are always on the move, the dissemination of knowledge/information/communication needs a mobile platform to accommodate mobility of people. The mobile and wireless network platform should be able to sustain the remoteness and seclusion of military operation areas. Communication is one of key problems of a military operation especially due to environmental constraints. This study proposes on establishing a future soldier and SAR communication device with mobile Wireless Sensor Network (WSN) and Mobile Ah-Hoc Network (MANET) to suit the infantry operations in the urban and rural areas. The operational areas are considered to restricted and challenging locations. Wireless sensor network (WSN) and Mobile Ah-Hoc Network (MANET) will become inexpensive and common over the next decade Thus, a thorough study is vital to develop the most suitable smart equipment and network requirements for Malaysia’s military and SAR eco-system. Finally, this study has successfully developed new low cost device prototype using WSN and MANET approach for Military and SAR operation. This approach is able to transmit death and location status, movement location status, health monitoring status to the base station.