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Keynote Lectures

Data Science and Industrial Applications
Abir Hussain, University of Sharjah, United Arab Emirates

Evolving Embodied Intelligence
A. G. Eiben, VU University Amsterdam, Netherlands

Federated Learning: A Hype or a Trend?
Anna Wilbik, Maastricht University, Netherlands

 

Data Science and Industrial Applications

Abir Hussain
University of Sharjah
United Arab Emirates
 

Brief Bio
Abir Hussain is a professor of Machine Learning and a senior member at the Faculty of Engineering and Technology.She completed her PhD study at The University of Manchester (UMIST), UK in 2000 with a thesis title Polynomial Neural Networks for Image and Signal Processing. She works in the research areas of Neural Networks, Signal Prediction, Telecommunication Fraud Detection and Image Compression. Abir has published in a number of high esteemed and high impact journals including IEEE Access, Pattern Recognition Letters, IEEE Internet of Things Magazine, Ad Hoc Networks, IEEE Transactions on Sustainable Computing, Expert Systems with Applications, PloS ONE, Electronic Letters, Neurocomputing, and Neural Networks and Applications.
Abir has successfully supervised over 20 research students and has been an external examiner to various UK and overseas Universities for postgraduate research degrees. She an editor for Elsevier Neural Networks journal and Plos One.


Abstract
Data Science represents a multidisciplinary field that involves using various techniques including pattern recognition, computer programmed algorithms and image analysis. knowledge can be gained through datasets gathered from various resource and with various sizes. Mobile phones, remote sensors, software logs and social media platforms can be used to gather large datasets with heterogeneous properties stored using unstructured format and complex relationships between values. Deep learning approaches can be used to extract knowledge and analysis large and complex datasets. Detection and localization of objects within images is considered an essential task in various computer vision algorithms. Various studies addressed the detection and tracking of facial landmarks including the iris and pupils which has various applications including eye gaze estimation for human machine interfaces. Composite of techniques are utilized in sequential phases while leveraging the deep learning based facial landmark detection to extract eye information within an image/video frame. Existence of background noise and dark patches within the image frame (such as eye-browse) have been identified as a major cause degrading the performance of computer vision-based iris and pupil detection. However, this issue can be resolved by utilising modern deep learning algorithms for reliable face and eye frame extraction from an ordinary quality images/video. The presentation will elaborate on the use of deep learning for image analysis as well as on new applications, trends and chances.



 

 

Evolving Embodied Intelligence

A. G. Eiben
VU University Amsterdam
Netherlands
 

Brief Bio
A.E.(Gusz) Eiben is Full Professor of Computational Intelligence at the Computer Science Department of the VU Amsterdam and Visiting Professor in the Department of Electronics of the University of York. Anecdotic fact: he is the first author of the first paper of the first European conference in the area, the PPSN-1990. Since than he has published several research papers and co-authored the first comprehensive book on Evolutionary Computing. He has been organizing committee member of practically all major international evolutionary conferences and editorial board member of related international journals. A significant part of his work concerns the design and calibration of evolutionary algorithms in an off-line (parameter tuning) and in an on-line fashion (parameter control). Over the last couple of years he became interested in embodied evolutionary processes, hence in evolutionary robotics. This research is driven by the grand vision of the Evolution of Things.


Abstract
Evolutionary robotics is the art of employing evolution to develop the brains (controllers), the bodies (morphologies), or both for autonomous robots. In this talk I will outline the concept of an EvoSphere, a robotic ecosystem that evolves in real space and real time and I argue that constructing systems of self-reproducing machines will lead to an exciting mix of evolutionary computing, artificial intelligence, robotics, and artificial life. I will discuss the Evolution of Things and demonstrate how that can be used for engineering as well as for fundamental research. Finally, I will discuss a long-term research programme with some “grand questions”, possible applications, and future perspectives.



 

 

Federated Learning: A Hype or a Trend?

Anna Wilbik
Maastricht University
Netherlands
 

Brief Bio

Anna Wilbik is professor in Data Fusion and Intelligent Interaction at Department of Data Science and Knowledge Engineering, Maastricht University. She received her PhD in Computer Science from the Systems Research Institute, Polish Academy of Science, Warsaw, Poland, in 2010. In 2011, she was a Post-doctoral Fellow with the Department of Electrical and Computer Engineering, University of Missouri, Columbia, USA. Later she was an Assistant Professor at Eindhoven University of Technology (TU/e). Currently she is also a chair of The Fuzzy Systems Technical Committee (FSTC) of CIS IEEE. Her research interests are focused on data fusion, linguistic summaries and computing with words and federated learning. With her research she tries to bridge the gap between the fuzzy sets theory and industrial applications. She makes this connection in research projects collaborating with industry both on the national and the European level. She has published almost 100 papers in international journals and conferences.


Abstract
Federated learning is a recently encountered term, with growing attention from academia and industry, and many ask whether it is a hype, or this technology will stay. The goal of this keynote is to answer this question from both technology challenge view and application perspective. I will explain what federated learning is, what sets it apart from other technologies. One of the highlights is privacy preservation. Next, I will go into the status quo and the spectrum if the challenges that need to be answered. I will end this talk with my insights into various application domains, where federated learning can make a difference, like a more obvious one – healthcare, and a more demanding one – logistics.



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