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 for 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.