DSCI 2021 - Keynotes
Home Organisation Committee Program Committee Call for Papers Camera-Ready Paper Conference Programme Keynote Speeches Journal Special Issues Conference Venue IUCC-2021 CIT-2021 SmartCNS-2021 Contacts The conference will be held virtually on 20-22 December 2021.
Keynote Speakers

Prof Wen-Hua Chen

Loughborough University, UK

Are we safe to move into a highly automated society?


ABSTRACT: Driverless cars, unmanned aircraft, fully automated mining in deep ground/sea, and healthcare robots looking after elder and disabled people — these are hot words we are seeing daily in the media, and discussed and debated at home and pubs. There is a huge aspiration about future highly automated society driven by advances in computer and information technology, particularly mobile communication, data science and artificial intelligence. But are we ready for that? Are they safe? This talk will discuss some key enabled technologies involved in these highly automated systems, the progress and the remaining challenges. It argues that a significant progress has been made in individual functions such as perception and decision making, but more is required in understanding their interactions and their influence on the overall performance and safety at a system level. It is essential to develop tools for efficient design and analysis, and safeguarding the behaviour of these highly automated systems. Examples including agent planning and autonomous source search are discussed to illustrate interaction between perception, optimisation, system dynamics and operation environment.

BIO: Dr Wen-Hua Chen holds Professor in Autonomous Vehicles in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK. Prof. Chen has a considerable experience in control, signal processing and artificial intelligence and their applications in aerospace, automotive and agriculture systems. In the last 15 years, he has been working on the development and application of unmanned aircraft system and intelligent vehicle technologies, spanning autopilots, situational awareness, decision making, verification, and remote sensing for precision agriculture and environment monitoring. His unmanned vehicles related research is widely supported by the UK government and industry. He is a Chartered Engineer, and a Fellow of IEEE, the Institution of Mechanical Engineers, and the Institution of Engineering and Technology, UK. Recently Prof Chen was awarded the prestigious EPSRC (Engineering and Physical Science Research Council) Established Career Fellowship to develop advanced analysis and design tools for safe operation of robotics and autonomous systems.

Prof (Kit) Kai-Kit Wong

University College London, UK

Bruce Lee inspired Fluid Antenna Systems for 6G


ABSTRACT: “Be formless … shapeless, like water!”, which were the words used by Bruce Lee, as he was revealing the philosophy of Jeet Kune Do, the martial arts system Lee founded in 1967. Many parallels can be drawn in wireless communications technologies where engineers have been seeking greater flexibility in using the spectral and energy resources for improving network performance. In this talk, I will speak on a novel antenna technology, referred to as fluid antenna, that adopts a software-controlled, position-flexible antenna to operate on the best signal envelope within a given space. This talk presents some preliminary results on fluid antenna systems, which shows great promises on massive connectivity for 6G.

BIO: (Kit) Kai-Kit Wong received the BEng, the MPhil, and the PhD degrees, all in Electrical and Electronic Engineering, from the Hong Kong University of Science and Technology, Hong Kong, in 1996, 1998, and 2001, respectively. He is Chair Professor of Wireless Communications at the Department of Electronic and Electrical Engineering, University College London, UK. His current research centers around 5G and beyond mobile communications. He is a co-recipient of the 2020 Premium Award for Best Paper in IET Electronics Letters, the 2013 IEEE Signal Processing Letters Best Paper Award, the 2000 IEEE VTS Japan Chapter Award at the IEEE Vehicular Technology Conference in Japan in 2000, and a few other international best paper awards. He is Fellow of IEEE and IET. He is the Editor-in-Chief for IEEE Wireless Communications Letters since 2020, and also the Subject Editor-in-Chief for Wireless Communications of IET Electronics Letters since June 2020.

Prof. Liangxiu Han

Manchester Metropolitan University, UK

Scalable Deep Learning from Big Data


ABSTRACT: In recent years, deep learning has attracted much attention due to its’ nature in discovering correlation structure in data in an unsupervised fashion and has been applied into various domains such as in speech recognition and image classification, nature language processing and computer vision. In typical neural networks, it requires large-scale data to learn parameters (often reach to millions), which is a computationally intensive process and takes a lot of time to train a model. Scalable deep learning is therefore much needed, which can train complex models over a vast amount of data, allowing for optimal training performance in terms of computing time and accuracy. This talk will focus on the latest developments and real-world applications of scalable deep learning from big data.

BIO: Prof. Liangxiu Han has a PhD in Computer Science from Fudan University, Shanghai, P.R. China (2002). Prof. Han is currently a Professor of Computer Science at the Department of Computing and Mathematics, Manchester Metropolitan University. She is a co-Director of Centre for Advanced Computational Science and Deputy Director of ManMet Crime and Well-Being Big Data Centre. Han’s research areas mainly lie in the development of novel big data analytics/Machine Learning/AI, and development of novel intelligent architectures that facilitates big data analytics (e.g., parallel and distributed computing, Cloud/Service-oriented computing/data intensive computing) as well as applications in different domains (e.g. Precision Agriculture, Health, Smart Cities, Cyber Security, Energy, etc.) using various large scale datasets such as images, sensor data, network traffic, web/texts and geo-spatial data. As a Principal Investigator (PI) or Co-PI, Prof. Han has been conducting research in relation to big data/Machine Learning/AI, cloud computing/parallel and distributed computing (funded by EPSRC, BBSRC, Innovate UK, Horizon 2020, British Council, Royal Society, Industry, Charity, respectively, etc.).

Prof. Han has served as an associate editor/a guest editor for a number of reputable international journals and a chair (or Co-Chair) for organisation of a number of international conferences/workshops in the field. She has been invited to give a number of keynotes and talks on different occasions (including international conferences, national and international institutions/organisations).

Prof. Han is a member of EPSRC Peer Review College, an independent expert for Horizon 2020 proposal evaluation/mid-term project review, and British Council Peer Review Panel.

Dr. Shiqiang Wang

IBM T.J. Watson Research Center, US

Efficient Federated Learning: Current Solutions and Open Challenges


ABSTRACT: Federated learning (FL) is an emerging technique for model training from decentralized data. Compared to learning from data in a central storage, FL has benefits of privacy preservation and communication bandwidth reduction. A challenge in FL is that data and model characteristics can vary largely across different tasks, and an FL task with improper configuration could waste a lot of computation/communication resources and may cause the trained model to diverge from the optimal result. In this talk, I will first give an overview of FL. Then, I will present a few adaptive FL methods that learn near-optimal configurations (e.g., synchronization interval, compressed model size) over time during the FL process, to reach the best model accuracy with the smallest amount of training time. In the end, I will discuss some open problems and challenges in FL and also the broad area of edge AI.

BIO: Shiqiang Wang is a Research Staff Member at IBM T. J. Watson Research Center, NY, USA. He received his Ph.D. from Imperial College London, United Kingdom, in 2015. His current research focuses on the intersection of distributed computing, machine learning, networking, and optimization. He has made foundational contributions to edge computing and federated learning that generated both academic and industrial impact. Dr. Wang serves as an associate editor of the IEEE Transactions on Mobile Computing. He has also been actively organizing workshops at the intersection of edge computing and machine learning, and regularly participates in technical program committees (TPCs) of prominent conferences and review panels of research grants. He received the IEEE Communications Society Leonard G. Abraham Prize in 2021, IBM Outstanding Technical Achievement Award (OTAA) in 2019 and 2021, multiple Invention Achievement Awards from IBM since 2016, Best Paper Finalist of the IEEE International Conference on Image Processing (ICIP) 2019, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015. For more details, please visit: https://shiqiang.wang/

Prof. Luigi Atzori

University of Cagliari, Italy

Monitoring of people mobility by looking at our devices’ WiFi probe frames


ABSTRACT: The knowledge on how people move in urban areas is of key importance for the implementation of major city management tasks, such as: planning public transport services; safety management during major city events; analyzing the influence of public areas on city quality of life. With reference to this context, this talk illustrates how the WiFi management frames sent by our devices can be observed to develop a passive solution to collect meaningful data. Herein, the major challenge is to de-randomize the MAC addresses which are frequently changed by our devices for privacy reasons. It consists in clustering frame messages which are probably sent by the same physical device. Key mobility indicators can be extracted from this method: crowd density per area of interest, people flows, recurrent mobility patterns, and mobility heat maps. A dataset for further improving the algorithm performance is also presented.

BIO: Luigi Atzori (PhD, 2000) is professor of Telecommunications at the University of Cagliari, where he leads the activities of the Net4U laboratory (Networks for Humans) with around 20 affiliated researchers. Since 2018, he has been the coordinator of the master degree course in Internet Technology Engineering at the University of Cagliari. His research interests fall in the area of Internet of Things (IoT), with particular reference to the design of effective algorithms for the realization of social networks among connected devices to create the Social IoT paradigm. His interests also falls in the area of Quality of Experience (QoE), with particular application to the management of services and resources in new generation networks for multimedia communications. Lately, he also applies the study of QoE to IoT services. He is the founding partner of the GreenShare spinoff where he currently serves as CIO; the company provides IoT services in the sustainable mobility sector. He is a regular reviewer for the EU and for Irish, Spanish and Swedish research programs. He has been the coordinator of European projects in the areas of QoE and IoT (QoE-Net, Demanes and Netergit). He serves regularly in the conference organizing committee of the sector and as associate and guest editor of several international journals (such as IEEE IoT journal, Ad Hoc Networks, IEEE IEEE Open Journal of the Communications Society, IEEE Communications Magazine).

Prof Lu Liu

University of Leicester, UK

Provision of Data-Centric Services for Artificial Intelligence of Things


ABSTRACT: Given the recent proliferation in the number of smart devices connected to the Internet, the era of Artificial Intelligence of Things (AIoT) is challenged with massive amounts of data generation and data-centric service provision. Edge Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains. However, efficient provision and management of data-centric services are one of the prevailing challenges in the era of AIoT with Smart Edge Computing. To address this challenge, Professor Liu will introduce his recent research work on data-centric service model design with the process of how to adaptively index services, how to efficiently discover services, how to securely request services and finally dependably integrate services in a dynamic environment. Professor Liu will further present his work on data-centric service application development for engineering data analytics, social data analytics, workload data analytics and commercial data analytics.

BIO: Professor Lu Liu is the Head of School of Computing and Mathematical Sciences at the University of Leicester, UK. Professor Liu received his PhD degree from Surrey Space Centre at the University of Surrey, UK. Professor Liu's research is in the areas of data science, service computing, cloud computing and the Internet of Things and he has over 200 scientific publications in reputable journals, academic books and international conferences. Professor Liu has secured many research projects which are supported by research councils, BIS, Innovate UK, British Council and leading industries. He has been the recipient of seven Best Paper Awards from international conferences and was invited to deliver seven keynote speeches at international conferences. Professor Liu is a Fellow of BCS (British Computer Society). He currently serves as an Editorial Board member of 6 international journals and has chaired over 20 international conferences.

Prof. Hui Yu

University of Portsmouth, UK

Wearable Emotion Sensing for Human-Machine Interaction


ABSTRACT: With the increasing demand of machine intelligence across a wide range of application scenarios, human-machine interaction (HMI) emerges as another essential communication, whereby facial-expression-aware is one of the principal features for natural interaction. The principal branch of my research was driven by these thoughts: combining knowledge of creative technologies with multiple disciplines, such as psychology, cognition, visual computing, computer graphics and machine learning. Particularly, biometric data precisely record the facial muscle activity or brain activity closely related to facial movements and the internal emotional states. These multiple sensing channels would help provide an insight into the emotion and perception of facial expression, to develop widely accessible HMI solutions able to track facial motions, and recognise affective states in a highly efficient and precise manner. This talk will discuss the development of visual facial data and electromyogram (EMG) processing for emotion detection with the application focusing on VR/AR.

BIO: Hui Yu is a Professor with the University of Portsmouth, UK. He is the Head of the Visual Computing Group at the university. His main research interest lies in visual computing, particularly in understanding and sensing emotion and the visual information of human related issues with semantic interpretation. It involves and develops knowledge and technologies in vision, machine learning, virtual reality, brain-computer interaction and robotics. Professor Yu’s research work has led to several awards and successful collaboration with worldwide institutions and industries. Prof. Yu is Scientific Advisor for some high-tech companies in the UK. Prof. Yu is the PI on grants from a diverse range of funding sources including the EPSRC, EU FP7, RAEng, Royal Society, Innovate UK and Industry. He has been awarded Industrial Fellowship by the Royal Academy of Engineering. He serves as Associated Editor of IEEE Transactions on Human-Machine Systems, IEEE Transactions on Computational Social Systems and Neurocomputing journal.

Prof. Shiyan Hu

University of Southampton, UK

Data Analytics for Smart Energy Cyber-Physical System Security


ABSTRACT: The massive deployment of advanced metering infrastructures has mandated a transformative shift of the classical power grid towards a more reliable smart grid. However, such a cyber-physical power system is vulnerable to various cyberattacks. In this talk, I will describe a systematic machine learning and data analytics framework, which is based on partially observable Markov decision process, orthogonal matching pursuit, and empirical mode decomposition, to detect anomaly energy usage behavior through analyzing the massive smart meter data. I will also discuss how this framework can be used to detect smart grid cyberattacks such as energy theft. I will conclude the talk with some of the ongoing research in this direction.

BIO: Prof. Shiyan Hu received his Ph.D. in Computer Engineering from Texas A&M University in 2008. He is the Professor and Chair of Cyber-Physical System Security at University of Southampton, where he is Director of Cyber Security Academy. His research interests include Cyber-Physical Systems (CPS) and CPS Security, where he has published more than 150 refereed papers, including 60+ in IEEE Transactions. Prof. Hu is an ACM Distinguished Speaker, an IEEE Systems Council Distinguished Lecturer, a recipient of U.S. National Science Foundation CAREER Award, and a recipient of IEEE Computer Society TCSC Middle Career Researcher Award. His publications have received a few distinctions such as the 2018 IEEE Systems Journal Best Paper Award, the 2017 Keynote Paper in IEEE Transactions on Computer-Aided Design, the March 2014 Front Cover Paper in IEEE Transactions on Nanobioscience, multiple Thomson Reuters Highly Cited Papers, etc. He is the Chair for IEEE Technical Committee on Cyber-Physical Systems and the Editor-In-Chief of IET Cyber-Physical Systems: Theory & Applications. He has served as the 2020 Editor-in-Chief Search Committee Chair for ACM Transactions on Design Automation of Electronic Systems. He is an Associate Editor for IEEE Transactions on Computer-Aided Design, IEEE Transactions on Industrial Informatics, IEEE Transactions on Circuits and Systems II, ACM Transactions on Design Automation of Electronic Systems, ACM Transactions on Cyber-Physical Systems, and a Guest Editor for 8 IEEE/ACM journals including Proceedings of the IEEE (PIEEE) and IEEE Transactions on Computers. He has held chair positions in various IEEE conferences. He is a Member of European Academy of Sciences and Arts, a Fellow of IET, and a Fellow of British Computer Society. More information about him is at http://personal.southampton.ac.uk/sh2e19/.



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