Keynote speakers – MECO 2021

 

Konstantin Novoselov, Nobel Laureate in Physics in 2010 for the “Grounfbreaking experiments regarding the two-dimensional material graphene”. National University of Singapore. CV (Wikipedia)

WELCOME ADDRESS with KEYNOTE WISDOM for MECO ANNIVERSARY

Many greetings to the MECO Anniversary!

“Supercomputers have become a ubiquitous instrument in many areas of science and technology. It is very hard to imagine modern physics, biology, or chemistry, without the use of this versatile tool. The breakthroughs in the development of supercomputers expand the range of problems we can tackle. Supercomputers as well as specialized computers will undoubtedly contribute significantly to the overall landscape of discoveries in many different disciplines in the future.”

[modified from: “Methodologies and Applications of Supercomputing,” Milutinovic, V., Kotlar, M., eds, IGI Global, Hershey PA, USA, 2021, with Contributions of Milutinovic, and Stojanovic, R.]

 

Danilo P. Mandic is a Professor in signal processing with Imperial College London, UK, and has been working in the areas of adaptive signal processing and bioengineering. He is a Fellow of the IEEE and member of the Board of Governors of International Neural Networks Society (INNS). He has received five best paper awards in Brain Computer Interface, runs the Smart Environments Lab at Imperial, and has more than 300 publications in journals and conferences. Prof Mandic has received the 2019 Dennis Gabor Award by the International Neural Networks Society (for outstanding achievements in neural engineering), and the President Award for Excellence in Postgraduate Supervision at Imperial. His work on Hearables appeared in IEEE Spectrum, MIT Technology Review and has led to several granted patents in this area.

He will be speaking on the topic of Hearables: From in-ear recording of vital signs and neural function to doctorless hospitals

Abstract: Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities – the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios. This framework opens up the avenues for a subsequent use of a number of machine learning paradigms, from lifelong learning to Big Data, to be used in a real world application of utmost importance – new generation health systems. =

 

Yannis Manolopoulos,  professor and Vice-rector of the Open University of Cyprus, professor Emeritus of the Aristotle University of Thessaloniki.He has been with the University of Toronto, the University of Maryland at College Park, the University of Cyprus and the Hellenic Open University. He has also served as Rector of the University of Western Macedonia in Greece and Vice-Chair of the Greek Computer Society. His research interest focuses in Data Management. He has co-authored 5 monographs and 8 textbooks in Greek, as well as >350 journal and conference papers. He has received >15000 citations from >2300 distinct academic institutions from >100 countries (h-index=55). He has also received 5 best paper awards from SIGMOD, ECML/PKDD, MEDES (2) and ISSPIT conferences. He has served as main co-organizer of the following major conferences (ranked in A and B categories of CORE Portal): SOFSEM’2020, WI’2019, IDEAS’2019, ICANN’2018, DASFAA’2018, TPDL’2017, CAiSE’2014, ADBIS’2014, WISE’2014, WISE’2013, ICANN’2010, ADBIS’2009, ADBIS’2006, SSDBM’2004, SSTD’2003, ADBIS’2002. He delivered keynote talks at 20 conferences at: Albania, Algeria, Austria, Bulgaria, Cyprus, Czechia, France, Greece, Italy, Kosovo, Lebanon, Luxembourg, Morocco, Poland, Romania and Russia. He served as external member of doctoral examination committees in:  Brazil, Denmark, France, Italy, Poland and Spain. He has also acted as evaluator for funding agencies and universities in Austria, Canada, Cyprus, Czechia, Denmark, Estonia, EU, Georgia, Greece, Hong-Kong, Israel, Italy, Jordan, Lithuania, Poland, Russia and Turkey.

Currently, he serves in the Editorial Boards of the following journals (among others): Information Systems, World Wide Web, Computer Journal, Data Science and Analytics, as well as in the Board of the Research and Innovation Foundation of Cyprus.

He will be speaking on the topic of: “Recommending POIs in LBSNs with Deep Learning”

Abstract: In recent years, the representation of real-life problems into k-partite graphs introduced a new era in Machine Learning. The combination of virtual and physical layers through Location Based Social Networks (LBSNs) offered a different meaning into the constructed graphs. To this point, multiple diverse models have been introduced in the literature that aim to support users with personalized recommendations. These approaches represent the mathematical models that aim to understand users’ behavior by detecting patterns in users’ check-ins, reviews, ratings and friendships. In this talk, we discuss about state-of-the-art methods for POI recommendations based on deep learning techniques. First, we categorize these methods based on data factors or features they use, the data representation, the methodologies applied and the recommendation types they support. By briefly representing recent key approaches, we highlight the limitations and trends. The future of the area is illustrated.

 

Title: Privacy Protection, Ethics, Robustness and Regulatory Issues in Autonomous Systems

Abstract: One of the most important challenges of the present decade in Autonomous Systems  and CPS is the accommodation of ethics, trustworthiness, reliability and robustness  issues related to embedded intelligence. This lecture is divided in 3 sections. First, we will overview the typical regulatory, data security and privacy protection issues and restrictions that should be considered when designing modern CPS, e.g., autonomous cars or drones. Second, we will describe private data de-identification algorithms (e.g., face de-identification), discussing the differences between traditional, GAN-based and adversarial-attack-based privacy protection methodologies. Finally, the presentation will focus on Autonomous Systems  and CPS AI robustness. Slight imperceptible changes to sensorial data (e.g., images captured by a CPS camera) that may be crafted by adversaries or even be produced by environmental noise, lead to a dramatic decrease of performance, especially in deep learning-based trained classification models. We will present modern AI neural network training schemes that alleviate this threat, by focusing on enhancing the robustness of CPS classification systems against adversarial treats.

Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities.

His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human-centred computing, affective computing, 3D imaging and biomedical imaging. He has published over 1000 papers, contributed in 47 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 9 international journals and General or Technical Chair of 4 international conferences. He participated in 71 R&D projects, primarily funded by the European Union and is/was principal investigator in 42 such projects. Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: He is AUTH principal investigator in H2020 R&D projects Aerial Core and AI4Media. He is chair of the Autonomous Systems Initiative He is head of the EC funded AI doctoral school of Horizon2020 EU funded R&D project AI4Media (1 of the 4 in Europe). He has 32200+ citations to his work and h-index 85+ (Google Scholar).

 

Benoît Dupont de Dinechin

Title: “Engineering a Manycore Processor for Edge Computing”

Abstract: Edge computing applications such as autonomous driving systems (ADS) and 5G radio access network (RAN) require significant computing capabilities and predictable response times, while being constrained by size, weight and power (SWaP). Such applications significantly benefit from computing platforms based on manycore processors.  We first expose the differences between multi-core architectures and many-core architectures, currently mainly represented by GPGPU processors. Then, by using the MPPA3 processor from Kalray as an illustration, we present some of the challenges and the choices involved by engineering an edge processing computing platform based on a manycore architecture. On the local architecture, energy efficiency and time predictability can be leveraged from a Fisher-style VLIW architecture. Accelerating deep learning inference is achieved by tightly coupling a tensor coprocessor. On the global architecture, the cache coherence domains are preferably localized to the compute units. These compute units are connected by a network-on-chip capable of multi-casting, where deadlock-free routing requires some care. The computing platform is completed by providing standard and open programming environments. Among these, OpenCL, OpenVX and OpenMP appear as the most relevant for compute-intensive edge applications, once these environments are enabled to efficiently exploit the compute unit local memories of the manycore architecture.

Benoît Dupont de Dinechin is the Chief Technology Officer of Kalray. He is the main architect of the Kalray VLIW core including its deep learning coprocessor, and the co-architect of the Kalray Multi-Purpose Processing Array (MPPA) family of processors. Benoît also defined the Kalray software roadmap and still contributes to its production compilers. Before joining Kalray, Benoît was managing Research and Development of the STMicroelectronics Software, Tools, Services division, and was promoted to STMicroelectronics Fellow in 2008. Prior to STMicroelectronics, Benoît worked at the Cray Research park (Minnesota, USA), where he designed and developed the software pipeliner of the Cray T3E production compilers.

Benoît earned an engineering degree in Radar and Telecommunications from the Ecole Nationale Supérieure de l’Aéronautique et de l’Espace (Toulouse, France), and a doctoral degree in computer systems from the University Pierre et Marie Curie (Paris) under the direction of Prof. P. Feautrier. He completed his post-doctoral studies at the McGill University (Montreal, Canada) at the ACAPS laboratory led by Prof. G.R. Gao. Benoît authored 16 patents in the area of computer architecture, and published over 60 conference papers, journal articles and book chapters in the areas of parallel computing, compiler design and operations research