Angelov of Lancaster University, UK

We are witnessing an explosion of data (streams) being generated and growing exponentially. Nowadays we carry in our pockets Gigabytes of data in the form of USB flash memory sticks, smartphones, smartwatches etc. Extracting useful and human-intelligible/understandable information and knowledge from these big data streams is of immense importance for the society, economy and science. The mainstream Deep Learning quickly became a synonymous of a powerful method to enable items and processes with elements of AI in the sense that it makes possible human like performance in recognising images and speech. However, the currently used methods for deep learning which are based on neural networks (recurrent, belief, etc.) is opaque (not transparent), requires huge amount of training data and computing power (hours of training using GPUs), is offline and its online versions based on reinforcement learning has no proven convergence, does not guarantee same result for the same input (lacks repeatability) and, more importantly, it does not provide insight, transparency (is a “black-box” type).
In this talk a new, recently introduced approach will be presented which offers highly efficient classifiers, predictive models, etc. but is fully interpretable, transparent and human-intelligible. Moreover, the local optimality as well as the convergence (and respectively, stability) of the proposed systems was theoretically proven and illustrated with examples. The proposed method is prototype-based and non-iterative. It is based on the density and thus it is computationally very efficient (learning on a large amount of images takes few seconds and does not require GPUs or other accelerators as the mainstream deep learning does. However, the performance of the proposed method is on par or better than the competitive alternatives.
The major advantages of this new paradigm is the liberation from the restrictive and often unrealistic assumptions and requirements concerning the nature of the data (random, deterministic, fuzzy), the need to formulate and assume a priori the type of distribution models, membership functions, the independence of the individual data observations, their large (theoretically infinite) number, etc. From a pragmatic point of view, this direct approach from data (streams) to complex, layered model representation is automated fully and leads to very efficient model structures. In addition, the proposed new concept learns in a way similar to the way people learn – it can start from a single example. Thus, the proposed approach is in its nature anthropomorphic.

Biography:

Prof. Plamen P. Angelov (MEng 1989, PhD 1993, DSc 2015) is a Fellow of the IEEE and of the IET. He is Vice President of the International Neural Networks Society (INNS) and IEEE Distinguished Lecturer. He has 30 years of professional experience in high level research and holds a Personal Chair in Intelligent Systems at Lancaster University, UK and the Director of the LIRA (Lancaster Intelligent, Robotic and Autonomous systems) Research Centre, www.lancaster.ac.uk/lira which is cross-Faculty and has over 30 academics and large number of postdocs and postgraduate students. Previously, he formed and led two research groups (Intelligent Systems, 2010-2013 and Data Science, 2014-2017) at the School of Computing and Communications with over 20 academics, researchers and PhD students each. He has authored or co- authored over 300 peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 6 patents, 3 research monographs (by Wiley, 2012 and Springer, 2002 and 2018) cited over 7700 times with an h-index of 42 and i10-index of 132. 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 algorithms for knowledge extraction in the form of human-intelligible fuzzy rule-based systems. 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 Cybernetics. He gave over 20 key note talks at high profile conferences. Prof. Angelov was General co-Chair of a number of high profile conferences and a series of annual IEEE Symposia on Evolving and Adaptive Intelligent Systems and more recently on Deep Learning. Dr Angelov is the founding Chair of the Technical Committee on Evolving Intelligent Systems, SMC Society of the IEEE and was previously chairing the Standards Committee of the Computational Intelligent Society of the IEEE (2010-2012). He was also a member of International Program Committee of over 100 international conferences (primarily IEEE). More details can be found at www.lancs.ac.uk/staff/angelov 

Cantoni of University of Pavia

The technological revolution which has completely transformed social relations and which has enabled communication and sharing of multimedia formats is also rapidly transforming the field of art and cultural heritage management. This contribution focuses on the innovative use of interactive digital technologies in digital humanities practices. Multimodal interaction modalities have been developed for the exhibition “1525-2015. Pavia, the Battle, the Future. Nothing was the same again”. Visitors could observe and analyze seven ancient tapestries through 3D reconstructions, virtual simulations, eye interaction and gesture navigation, along with transpositions of the tapestries into tactile images that enable the exploration by partially sighted and blind people. Fired by this successful exhibition, a more ambitious project, the 3D reconstruction of Renaissance Pavia, was undertaken. Advanced techniques and innovative applications have led to a resource that has promoted the history of the city and its architectural richness through videos with virtual tours of Renaissance Pavia. In conclusion, an international challenge, namely “Digital Anastylosis of Frescoes (DAF)”, is proposed: the goal is to Develop learning tools that enable digital reconstruction of destroyed frescoes, supporting their restoration, through the solution of ‘puzzles’ formed by their original fragments, often mixed with spurious elements.

Biography:

Virginio Cantoni is a Full Professor in Computer Engineering at the University of Pavia. In the period 2008-2011 he has been seconded to the Centro Linceo ‘Beniamino Segre’' of the Italian Academy of Lincei. He has been the founder and first Director of the University of Pavia’s European School of Advanced Studies in Media Science and Technology and Director of the Interdepartmental Centre for Cognitive Science. His research activity is concerned with pattern recognition, computer vision and multimedia. He is author or co-author of more than 300 journal, conference papers and book chapters as well as editor or co-editor of more than 30 books and co-author of five books. He has organized many International Conferences, Seminars and Workshops including a NATO Advanced Research Workshop on pyramidal systems for computer vision. An Expert and Project Reviewer for the EU Commission, he became a Fellow of the IAPR in 1994 and Fellow of the IEEE in 1997. 

Address:Pavia, Italy