Keynote I: AI: Technical and Ethical Challenges

Prof. Ke Gong, Nankai University, Tianjin, China

BIOGRAPHY: Prof. Gong Ke, Ph.D., is currently the President-elect of the World Federation of Engineering Organizations (WFEO), the Executive Director of the Chinese Institute of the New Generation Artificial Intelligence Development Strategies, and the Chairman of the Academic Committee of Nankai University. In Jan. 1982, he obtained his bachelor's degree in Electronic Engineering from Beijing Institute of Technology and then studied further in Technical University of Graz, Austria, with State scholarship, and obtained PhD in Technical Science in Nov.1986. He returned to China and joined Tsinghua University in 1987. During his service at Tsinghua University, he had been the Dean of the Electronic Engineering Department, Director of Tsinghua Aerospace Research Center, Deputy Dean of the Graduate School, Head of the Science & Technology Office, Director of Chinese State Key Laboratory on Microwave and Digital Communications, and Director of Chinese National Research Center for Information Science and Technology (formerly known as the National Lab) at Tsinghua. From 1999-2006 he was vice president of Tsinghua University. From 2006-2011 he was President of Tianjin University, then the President of Nankai University from 2011 to 2018.

Prof. GONG has led the initiative on developing China’s wireless transmission standard for digital television as well as the research on experimental microsatellite, through which he has received a number of awards such as the State Technological Invention Award. He is foreign member of the Russian Academy of Cosmonautics, and served as member the Aerospace Experts Committee for the National High-tech R&D Program (2001-2010), etc.



Keynote II: Memory-augmented sequence2sequence learning

Prof. Fei Wu, Zhejiang University, Zhejiang, China

ABSTRACT: Neural networks with a memory capacity provide a promising approach to media understanding (e.g., Q-A and visual classification). In this talk, I will present how to utilize the information in external memory to boost media understanding. In general, the relevant information (e.g., knowledge instance and exemplar data) w.r.t the input data is sparked from external memory in the manner of memory-augmented learning. Memory-augmented learning is an appropriate method to integrate data-driven learning, knowledge-guided inference and experience exploration.


BIOGRAPHY: Fei Wu is a professor at the college of computer science, Zhejiang University. From October, 2009 to August 2010, Fei Wu was a visiting scholar at Prof. Bin Yu's group, University of California, Berkeley. Currently, he is the vice-dean of college of Computer Science, and the director of Institute of Artificial Intelligence of Zhejiang University. His research interests mainly include Artificial Intelligence, cross-media computing, and multimedia retrieval. He has won various honors such as the Award of National Science Fund for Distinguished Young Scholars of China (2016).



Keynote III: Brain-Inspired Stigmergy Learning

Prof. Honggang Zhang, Zhejiang University, Zhejiang, China


ABSTRACT: Originating from entomology, stigmergy has provided an effective framework for swarm collaboration and collective intelligence. Based on the recent discoveries on astrocytes in regulating synaptic transmission in brain’s neural networks, this speech will address the mapping of stigmergy mechanism into the neuron interaction and investigate its characteristics and advantages.

In particular, we will explain the short-range cooperative interaction between various synapses that may not be directly connected by the neighboring neurons and propose a novel stigmergic learning model. Within this model, the state change of an agent (e.g. neuron) will expand its influence to affect the states of others. The strength of the interaction is determined by the level of neural activity as well as the distance between the agents (e.g. neurons). Inspired by these schemes, we further put forward a collective intelligence model to help solve the task assignment and coordination problems. The numerical simulation results have verified the effectiveness and performance of the stigmergic learning model.


BIOGRAPHY: Dr. Honggang ZHANG - Full Professor, College of Information Science and Electronic Engineering; Co-Director, York-Zhejiang Lab for Cognitive Radio and Green Communications; Zhejiang University, China; International Chair Professor of Excellence (12/2012 -12/2014), CominLabs Excellence Center (Laboratoire d'Excellence), Université Européenne de Bretagne (UEB, & Supélec, France; Honorary Visiting Professor, the University of York, UK. Dr. Honggang ZHANG received the Ph.D. degree in Electrical Engineering from Kagoshima University, Japan, in March 1999. Prior to that, he received the Bachelor of Engineering and Master of Engineering degrees, both in Electrical Engineering, from Huazhong University of Science & Technology (HUST), China, in 1989, and Lanzhou University of Technology, China, in 1992, respectively. From October 1999 to March 2002, he was with the Shin-Kawasaki Research Center, Telecommunications Advancement Organization (TAO) of Japan, as a TAO Research Fellow. From April 2002 to November 2002, he joined the TOYOTA IT Center, where he performed research and development on software-defined radio (SDR) with applications to Intelligent Transport Systems (ITS). From December 2002 to August 2004, he has been with the UWB Research Consortium, Communications Research Laboratory (CRL) and National Institute of Information and Communications Technology (NICT) of Japan, where his R&D responsibilities were focused on UWB wireless communications, IEEE 802.15.3a & 4a WPAN standardizations, Wireless 1394 and “1394-over-UWB” smart home networks. He was the founding member of UWB Forum and the principle author and contributor for proposing DS-UWB in IEEE 802.15 WPAN standardization task group, for which he initiated the “Soft-Spectrum Adaptation (SSA)” technique and contributed to its worldwide developments. From September 2004 to February 2008, he has been with CREATE-NET (, where he leaded its wireless teams in exploring Cognitive Radio (CR) and its integration with Ultra-Wideband (UWB) technologies for open-spectrum wireless communications and networks evolution (i.e. CR-UWB: Ultra-Wideband Cognitive Radio) while participated a number of European FP6/FP7 projects (EUWB, PULSERS 2).



Keynote IV: Recent Advances in Fog Radio Access Networks for Beyond 5G

Prof. Mugen Peng, Beijing University of Posts and Telecommunications

ABSTRACT: The fifth generation (5G) wireless communication systems are anticipated to provide high spectral and energy efficiency, as well as low latency and massive connections. To achieve these goals, a fog radio access network (F-RAN) architecture has been presented as the advanced wireless access network paradigm, in which edge cloud computing is used to fulfill the distributed cooperative processing and delivering the local content for decreasing the latency and burdens on the fronthaul/backhaul. The state-of-the-art research achievements in aspects of system architecture and key technologies for F-RANs are briefly introduced in this talk. In particular, the system architecture evolution from C-RANs and H-CRANs to F-RANs will be discussed, and the key technologies including the edge cache driven performance analysis and cooperative radio resource allocation will be presented. Some challenges and open issues will be discussed.


BIOGRAPHY: Mugen Peng received the Ph.D. degree in communication and information systems from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2005. Afterward, he joined BUPT, where he has been a Full Professor with the School of Information and Communication Engineering since 2012. During 2014 he was also an academic visiting fellow at Princeton University, USA. He leads a Research Group focusing on wireless transmission and networking technologies in BUPT. He has authored and co-authored over 100 refereed IEEE journal papers and over 200 conference proceeding papers. His main research areas include wireless communication theory, radio signal processing, cooperative communication, self-organization networking, heterogeneous networking, cloud communication, and Internet of Things. Dr. Peng was a recipient of the 2018 Heinrich Hertz Prize Paper Award, the 2014 IEEE ComSoc AP Outstanding Young Researcher Award, and the Best Paper Award in the JCN 2016, IEEE WCNC 2015, IEEE GameNets 2014, IEEE CIT 2014, ICCTA 2011, IC-BNMT 2010, and IET CCWMC 2009. He is currently or have been on the Editorial/Associate Editorial Board of the IEEE Communications Magazine, IEEE ACCESS, IEEE Internet of Things Journal, IET Communications, and China Communications.



Keynote V: Wireless Power Transfer in Cognitive Radio and Multicarrier Communications

Prof. Xin Liu, Dalian University of Technology, Dalian, China

ABSTRACT: Spectrum sensing in cognitive radio (CR) may consume some circuit energy because of AD sampling, which can decrease the stored energy for data transmission. The sensing energy consumption rises with the increase of sampling nodes. Wireless power transfer technology has been proposed, which can collect the radio frequency (RF) energy of the nearby signal resources and then convert the RF energy to the direct current (DC) power, through deploying an energy-harvesting circuit consisting of band-pass filter, rectifying circuit and low-pass filter. The DC power is stored in a rechargeable battery of the communication system instead of a fixed power supply. Wireless power transfer has been used in CR to harvest the RF energy of the PU signal, and the harvested energy can compensate the energy loss of spectrum sensing. A simultaneous wireless information and power transfer (SWIPT) has been investigated for CR to harvest energy and process information simultaneously.


BIOGRAPHY: Xin Liu is currently an Associate Professor in the School of Information and Communication Engineering at Dalian University of Technology, China. He received the B.S. degree in communication engineering in 2006, the M.E. degree in information and communication engineering in 2008, and the Ph.D. degree in information and communication engineering in 2012, from Harbin Institute of Technology, Harbin, China. From Jun. 2012 to Jun. 2013, Xin Liu did postdoctoral research in Nanyang Technological University, Singapore. His recent research interests include Cognitive Radio, Energy Harvesting, and Cloud Computing. He has published more than 50 papers in refereed journals and international conferences. Dr. Liu is a member of the IEEE. He served as a technical program committee (TPC) chair for MLICOM and CHINACOM, he served as a TPC member for many conferences, e.g., Globecom, VTC, WCSP.



Keynote VI: The Research on the Security Risk Points of the Internet of Vehicles (IoV) under the 5G Environments

Prof. Yongjian Wang, National Computer Network and Information Security Management Center

ABSTRACT: With the 5G’s application for the Internet of Vehicles for the future times, many security risk will arise because of the new 5G who more focus on high speed. Firstly, we give the development status of the Iov and its future. Then we analyze the security risk points of the Iov under the 5G environments and give the harm of these points. The need for the surveillance for these risks points will be considered for the national security’s requirement. Some surveillance technology are given and introduced in this report. At last, we give the conclusion for the demand on these risks will occur in the IOV with the 5G’s coming.


BIOGRAPHY: Wang Yongjian, Ph.D., postdoctor, professor of National Computer Network and Information Security Management Center, deputy director of Beijing engineering laboratory of vehicle network security simulation and attack&defense technology, evaluation expert of National Natural Science Foundation, and part-time doctoral tutor of Beijing Institute of Technology. One of the well-known experts in the field of IoT security in China. He has written 4 national standards (to be released), published more than 30 academic papers (mostly searched by SCI/EI), undertook 3 national natural science funds and 5 other funds.


Keynote VII: Research progress in big data stream mining

Prof. Wei Wang, Tianjin Normal University, Tianjin, China

ABSTRACT: With the rapid development of information technology, big data is continuously generated from various aspects such as the Internet system, sensor network system, intelligent cities and so on, which presents the characteristics of multi-source, multiple dimensional, heterogeneous and dynamic and exists in the form of a multi-source heterogeneous big data stream. In this report, I will show the characteristics of big data stream and the research progress in dimension reduction, multi-modal fusion, concept drift detection and few shot learning.


BIOGRAPHY: Wei Wang is a Professor of Electronic and Communication Engineering in Tianjin Normal University.  He obtained his BEng in Measuring and Controlling Technology and Instrument from University of Science and Technology Beijing in 2003, and he obtained his MS and PhD in Measuring Technology and Instrument from Tianjin University in 2006 and 2010 respectively. 

His research interests are data stream mining technology in sensor network, including feature extraction and selection, small samples classification, multi-class identification from sensor network data stream.  He is also focused on RD field especial in optic fibre sensor network.