Title
Tentative title: "Deep Learning for Recommender Systems"

Abstract
TBA

Biodata
Yannis Manolopoulos holds a 5-years Diploma degree in Electrical Engineering (1981) and a Ph.D. degree in Computer Engineering (1986), both from the Aristotle University of Thessaloniki. He is Professor and Vice-Rector of the Open University of Cyprus as well as 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 President of the Board of the University of Western Macedonia in Greece and Vice-President of the Greek Computer Society. His research interest focuses in Data Management. He has co-authored 6 monographs and 10 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=56). He has also received 5 best paper awards from SIGMOD, ECML/PKDD, MEDES (2) and ISSPIT conferences. He delivered keynote talks at 22 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 20 doctoral examination committees at: Brazil, Denmark, France, Italy, Poland and Spain. Currently, he serves in the Editorial Boards of the following journals (among others): Digital (EiC), 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.

Title
Toward New Computing Model for Decision Support Systems Dealing with Complex Phenomena: Interactive Granular Computing

Abstract
TBA

Biodata
Andrzej Skowron, ECCAI and IRSS Fellow, Member EU Academy of Sciences, received the Ph. D. and D. Sci. (habilitation) from the University of Warsaw in Poland. In 1991 he received the Scientific Title of Professor. He is Full Professor in the Systems Research Institute, Polish Academy of Sciences as well as in Digital Research Center of Cardinal Stefan Wyszyński University in Warsaw. He is Emeritus Professor in Faculty of Mathematics, Computer Science and Mechanics at the University of Warsaw. Andrzej Skowron is the (co)author of more than 400 scientific publications and editor of many books and volumes of conference proceedings. His areas of expertise include reasoning with incomplete information, approximate reasoning, soft computing methods and applications, rough sets, rough mereology, granular computing, intelligent systems, knowledge discovery and data mining, decision support systems, adaptive and autonomous systems, perception based computing, and interactive computational systems. He was the supervisor of more than 20 PhD Theses. In the period 1995-2009 he was the Editor-in-Chief of Fundamenta Informaticae journal. He is on Editorial Boards of many others international journals. Andrzej Skowron was the President of the International Rough Set Society from 1996 to 2000. He has delivered numerous invited talks at international conferences. He was serving as (co-)program chair or PC member of more than 200 international conferences. He was involved in numerous research and commercial projects including dialog-based search engine (Nutech), fraud detection for Bank of America (Nutech), logistic project for General Motors (Nutech), algorithmic trading (Adgam), control of UAV (Linköping University), and medical decision support (e.g., in Polish-American Pediatric Clinic in Cracow). Andrzej Skowron was on the ICI Thomson Reuters/ Clarivate Analytics lists of the most cited researchers in Computer Science (globally) in 2012, 2016, 2017.

Title
Coordination and cooperation in robot soccer

Abstract
TBA

Biodata
Daniele Nardi is full Professor (since 2000) at Sapienza Univ. Roma, Faculty of Ingegneria dell'Informazione, Informatica, Statistica, Dept. Computer, Control and Management Engineering "A. Ruberti", teaching AI in the Master in AI and Robotics. He is the leader of the research lab "Cognitive Robot Teams", researching on: Cognitive Robotics, Localization, Navigation, Perception, Multi-robot Cooperation, Human Robot Interaction; and in: Robots for elderly, Disaster Response robots, Cultural Heritage, Precision Agriculture, RoboCup Soccer Player robots. Daniele Nardi has a rich publication record in AI and Robotics (H-index > 50). He is EurAI Fellow, and former President of RoboCup Federation (2011-2014).

Title
From Hyper-parametric towards Prototype-based Deep Learning

Abstract
Machine Learning (ML) and AI justifiably attract the attention and interest not only of the wider scientific community and industry, but also society and policy makers. However, even the most powerful (in terms of accuracy) algorithms such as deep learning (DL) can give a wrong output, which may be fatal. Due to the hyper-parametric, cumbersome and opaque model used by DL, some authors started to talk about a dystopian “black box” society. Despite the success in this area, the way computers learn is still principally different from the way people acquire new knowledge, recognise objects and make decisions. People do not need a huge amount of annotated data. They learn by example, using similarities to previously acquired prototypes, not by using parametric analytical models. Current ML approaches are focused primarily on accuracy and overlook explainability, the semantic meaning of the internal model representation, reasoning and its link with the problem domain. They also overlook the efforts to collect and label training data and rely on assumptions about the data distribution that are often not satisfied. The ability to detect the unseen and unexpected and start learning this new class/es in real time with no or very little supervision is critically important and is something that no currently existing classifier can offer. The challenge is to fill this gap between high level of accuracy and the semantically meaningful solutions. The most efficient algorithms that have fuelled interest towards ML and AI recently are also computationally very hungry – they require specific hardware accelerators such as GPU, huge amounts of labeled data and time. They produce parameterised models with hundreds of millions of coefficients, which are also impossible to interpret or be manipulated by a human. Once trained, such models are inflexible to new knowledge. They cannot dynamically evolve their internal structure to start recognising new classes. They are good only for what they were originally trained for. They also lack robustness, formal guarantees about their behaviour and explanatory and normative transparency. This makes problematic use of such algorithms in high stake complex problems such as aviation, health, bailing from jail, etc. where the clear rationale for a particular decision is very important and the errors are very costly. All these challenges and identified gaps require a dramatic paradigm shift and a radical new approach. In this talk the speaker will present such a new approach towards the next generation of computationally lean ML and AI algorithms that can learn in real-time using normal CPUs on computers, laptops, smartphones or even be implemented on chip that will change dramatically the way these new technologies are being applied. It is explainable-by-design. It focuses on addressing the open research challenge of developing highly efficient, accurate ML algorithms and AI models that are transparent, interpretable, explainable and fair by design. Such systems are able to self-learn lifelong, and continuously improve without the need for complete re-training, can start learning from few training data samples, explore the data space, detect and learn from unseen data patterns, collaborate with humans or other such algorithms seamlessly.
References:
[1] E. A. Soares, P. Angelov, Towards Explainable Deep Neural Networks (xDNN), Neural Networks, 130: 185-194, October 2020.
[2] E. A. Soares, P. Angelov, Detecting and Learning from Unknown by Extremely Weak Supervision: eXploratory Classifier (xClass), Neural Computing and Applications, 2021, also in https://arxiv.org/pdf/1911.00616.pdf
[3] P. P. Angelov, X. Gu, Toward anthropomorphic machine learning, IEEE Computer, 51(9):18–27, 2018.
[4] P. Angelov. X. Gu, Empirical Approach to Machine Learning, Springer, 2019, ISBN 978-3-030-02383-6.
[5] P. Angelov, X. Gu, J. Principe, A generalized methodology for data analysis, IEEE Transactions on Cybernetics, 48(10): 2981-2993, Oct 2018.

Biodata
Prof. Angelov (MEng 1989, PhD 1993, DSc 2015) is a Fellow of the IEEE, of the IET and of the HEA. He is Governor of the International Neural Networks Society (INNS) being his Vice President for two terms till end of 2020. He holds a Personal Chair in Intelligent Systems at Lancaster University, UK. He has authored or co-authored 350+ peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 6 patents, three research monographs (by Wiley, 2012 and Springer, 2002 and 2019) cited over 11000+ times with an h-index of 55. He is the founding Director of LIRA (Lancaster Intelligent, Robotic and Autonomous systems) Research Centre (www.lancaster.ac.uk/lira) which includes over 50 academics across 15 Departments from all Faculties of the University. He has an active research portfolio in the area of computational intelligence and machine learning and internationally recognised results into online and evolving learning and explainable AI. Prof. Angelov leads numerous projects (including several multimillion ones) funded by UK research councils, EU, industry, UK MoD. His research was recognised by ‘The Engineer Innovation and Technology 2008 Special Award’ and ‘For outstanding Services’ (2013) by IEEE and INNS. He is also the founding co-Editor-in-Chief of Springer’s journal on Evolving Systems and Associate Editor of several leading international scientific journals, including IEEE Transactions on Fuzzy Systems, of the IEEE Transactions on Cybernetics (the IEEE Transactions with the highest impact factor, 11.47), IEEE Transactions on AI as well as of several other journals such as Fuzzy Sets and Systems, Soft Computing, etc. He gave over a dozen plenary and key note talks at high profile conferences. Prof. Angelov was General co-Chair of a number of high profile conferences. He was also a member of International Program Committee of 100+ international conferences (primarily IEEE). More details can be found at www.lancs.ac.uk/staff/angelov