Prof. Massimo Marchiori (UNIPD), Technical Director (EISMD)
University of Padua, Italy; European Center for Science, Media and Democracy, Belgium
Smart World meets Society and Business: innovative uses of technology and data science
Massimo Marchiori is currently Professor at the University of Padua (Italy) and Technical Director of the European Institute for Science, Media and Democracy (Belgium). Working at MIT (USA) he led the development of several world standards, like P3P (web privacy), XQuery (semi-structured information) and OWL (web reasoning).
Among others, he created Hypersearch (Google's forerunner), Volunia (the next-generation social search engine), Negapedia (the negative version of Wikipedia).
He works in many multidisciplinary fields, also in cooperation with several companies, focusing on new technologies that can impact our society.
He won a variety of awards, including the IBM research award, the Lifetime Membership Award of the Oxford Society, the Microsoft Data Science Award, the MIT Technology Review TR35 award given to the world best innovators.
In this keynote we will explore some recent trends of smart technologies, combined with data science solution, in applied societal settings. We will show via suitable practical examples how smart world solutions can have a real impact in society, combining the public (people and local authorities) but also the private (companies and businesses). These two sides (public and private) can in fact be combined using special strategies, so having smart world solutions that can be appealing in both scenarios. We will present real example and success stories, taking also into account financial constraints that are crucially important for deployment in the real world, and expose key lessons learnt along the way
Prof. Plamen Angelov, Ph.D., DSc, FIEEE, FIET
Professor in Intelligent Systems
Director of Research
School of Computing and Communications
Towards Anthropomorphic Machine Learning and AI: Fast, Accurate and Explainable Deep Learning
Plamen P. Angelov is a computer scientist. He is a professor and chair in Intelligent Systems at the School of Computing and Communications of Lancaster University, Lancaster, United Kingdom. He is also a Vice President of the International Neural Networks Society and a founder of the Intelligent Systems Research group and the Data Science group at the School of Computing and Communications. Angelov is a founding co-Editor-in-Chief of Evolving Systems and is an associate editor of the IEEE Transactions on Cybernetics, the IEEE Transactions on Fuzzy Systems, and Fuzzy Sets and Systems, Soft Computing.
Despite the astonishing success of the machine learning (ML) and AI recently, 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 and can learn from a single or handful of examples. We learn by example, using similarities to previously acquired prototypes, not by using parametric analytical models. We can explain and pass aggregated knowledge to other humans. 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. 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 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.
Prof.Muhammad Ali Imran
FIET, SMIEEE, DIC, SFHEA
Professor of Communication Systems, Dean University of Glasgow, UESTC
Director Communications Sensing and Imaging Group
School of Engineering
Role of Communications in remote healthcare and overcoming digital divide
Muhammad Ali Imran (M'03, SM'12) Fellow IET, Senior Member IEEE, Senior Fellow HEA is Dean University of Glasgow UESTC and a Professor of Wireless Communication Systems with research interests in self organised networks, wireless networked control systems and the wireless sensor systems. He heads the Communications, Sensing and Imaging CSI research group at University of Glasgow and is the Director of Glasgow-UESTC Centre for Educational Development and Innovation. He is an Affiliate Professor at the University of Oklahoma, USA and a visiting Professor at 5G Innovation Centre, University of Surrey, UK. He has over 20 years of combined academic and industry experience with several leading roles in multi-million pounds funded projects. He has filed 15 patents; has authored/co-authored over 400 journal and conference publications; has edited 7 books and authored more than 30 book chapters; has successfully supervised over 40 postgraduate students at Doctoral level. He has been a consultant to international projects and local companies in the area of self-organised networks.
The high-performance digital connectivity has long been a luxury only enjoyed by urban centres. The rural communities struggle to get access to digital connectivity and hence the services which rely on this - including telemedicine. Our research is paving the way to make a step change by introducing low-cost digital connectivity solutions for the rural communities. We are also working on low cost sensing solutions and futuristic advances in remote healthcare and monitoring to reach out to the disadvantaged population groups for improving their healthcare. This talk will provide an overview of research underpinning this ambition.
The talk will focus on telemedicine enablers both in urban and rural areas: the communication infrastructure and sensing technologies. For communication infrastructure we will review specific techniques that meet the constraints of different telemedicine applications (latency, jitter, throughput and reliability). We will also review the medical sensing paradigms including wearables based sensors, remote sensing (exploiting RADAR principles and wireless channel quality variations) and the emerging field of nano-scale sensing.
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