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Keynote Speaker I

Prof. Jaap van den Herik
Leiden University, the Netherlands
Fellow of the European AI community (EurAI)

Speech: Disruptive Developments in the Court Room
(The lecture will inform the audience on the recent developments in intelligent legal-based systems.)
Courts aim to judge almost perfectly,thereby relying on the best support available. Currently I see to possible futures. Future 1:Judges need the most advanced systems (computers and intelligent programs) for a proper assessment of cases (Comfort Zone) Future 2: A totally different and disruptive approach. A series of ‘capable systems’ replace traditional judges by autonomously executing their work. Lawyers and judges prefer to be supported by advanced computer programs so that they can handle as perfect judges. I will discuss the core values as required to be present at judges as well as other core values to be present at lawyers. The key question is whether intelligent systems can mimic these features and achieve the same level of values.
The two most challenging obstacles  of research in this respect are
(1) prediction of the verdict by the judges when taken into account the culture and the legacy, and
(1) e-discovery for confidential information as compared to privileged information ( for lawyers)
Finally, a timeline of expected occurrences will be given.

Biography: Jaap van den Herik studied mathematics (with honours) at the Vrije Universiteit Amsterdam and received his PhD degree at Delft University of Technology in 1983. In 1984 he was visiting professor at the McGill School of Computer Science in Montreal. Thereafter, he was subsequently affiliated with Maastricht University (1987-2008), Tilburg University (2008-2016) as a full professor of Computer Science and with Leiden University as part time professor of Computer Science and Law (since 1988).

He is the founding director of IKAT (Institute of Knowledge and Agent Technology) and TiCC (Tilburg center for Cognition and Communication) and was supervisor of 82 PhD researchers.

At Leiden University, Van den Herik was affiliated with the department of Computer Science (now LIACS) between 1984 and 1988. He became professor of Computer Science and Law in 1988, at the Center for Law in the Information Society (eLaw). Furthermore, he was Professor at the Leiden Institute of Advanced Computer Science (LIACS) from 2014 to 2019. In 2014 he founded the Leiden Centre of Data Science (LCDS) together with Joost Kok and Jacqueline Meulman. From January 2019 he continues his work at the Mathematical Institute of the Leiden University.

Van den Herik’s research interests include artificial intelligence, intelligent legal systems, big data and social innovation. In 2012, he received an ERC Advanced Grant together with Jos Vermaseren (PI, Nikhef) and Aske Plaat, for the research proposal “Solving High Energy Physics Equations using Monte Carlo Gaming Techniques.” Van den Herik received a Humies Award in 2014, for his work on chess programming together with Omid E. David, Moshe Koppel, and Nathan S. Netanyahu.

Van den Herik has been active in many organizations and advisory boards, such as the Belgian Netherlands Association of AI (honorary member), JURIX (honorary chair), the CSVN (honorary member), the ICGA, ToKeN, Catch, the consortium BiG Grid, and Legal Delta. Furthermore, he is a fellow of the European AI community (EurAI), and member of the Royal Holland Society of Sciences and Humanities. Currently he employs with the LCDS team mates initiatives in The Hague to start an Executive Master on Legal Technologies.

Keynote Speaker II

Prof. Matthias Rätsch
Head of the ‘Vision Systems for intelligent Robots’ (ViSiR)
Reutlingen University, Germany

Speech: “Humanoid Robots and Artificial Super Intelligence - The Terminating End or the Last Hope for Humans?”
Recent research in humanoid robotics and artificial intelligence, termed as fourth industrial or robot revolution, show that artificial intelligence and robots will play a major role in our future lives. When will the Technological Singularity take place and what happens when Transhumanism starts? The humanoid robots are gaining super intelligence based on machine learning and interaction with humans. Another field to use artificial intelligence is autonomous driving. Driving cars is the biggest group of workers today with 70 Mill employees. All big automotive companies work on autonomous driving cars.
In this talk we will define what artificial super intelligence means, what is possible current and in future in the field of autonomous driving, robotics and human machine interaction. Why is reinforcement and transfer learning a new generation of deep learning and why mid-level fusion of RGB and depth-information is improving scene labeling for autonomous driving?
The use of AI for Human-Robot-Interaction is illustrated on robots of the RT-Lions team taking part on World Championships in RoboCup. Practical examples are shown from collaborations with strong industrial partners, like BMW, Mercedes Benz Daimler, BOSCH or Kuka.

Biography: Prof. Matthias Rätsch is a professor at the Reutlingen University for Image Understanding, Artificial Intelligence and Interactive Mobile Robotics. In 2008, he received his Ph.D. degree in the Graphics and Vision Research Group (GraVis) at the University of Basel, Switzerland in 3DMM Face Analysis. His research interests are in the fields of Artificial Intelligence, Deep Learning, Image Understanding, Autonomous Driving, Human-Robot-Interaction, Humanoid Robots and Bionic Grasping.
He is the head of the RoboCup team RT-Lions. The team could win several international competitions (World Champion in Graz 2009, Iran Master 2011, German Master 2009, Vice World Champion in Singapore 2010). After changing to the RoboCup@Home League the team gained the 4th place at the German Open, won the Portuguese Robotics Open and SICK Robot Day. The team was qualified with 35 teams at the World Championship in Nagoya, Japan, 2017, obtaining the 8th place and 2019 in Sydney, Australia, obtaining the 5h place. The team is qualified for 2021 at World Championship in Bordeaux, France.
Prof. Rätsch has been a member of the program committee and a session chair for several international conferences and was invited for several speeches including keynote, seminal and training in Artificial Intelligence, Face Analysis and Robot Vision for academic and industrial sectors.
Prof. Rätsch and his group has published more than 50 international academic research papers and journals, like at the top-rank IEEE Transactions on Image Processing journal or at the SIGGRAPH conference. His publications were recently honored with an award at the IEEE International Conference on Image Processing (ICIP), at the International Conference on Systems, Control and Communications (ICSCC), the Informatics Inside Conference for Human-Centered Computing, and at the IEEE Intelligent Data Acquisition and Advanced Computing Systems Journal. His working group ViSiR could win the Otto-Johansen-Price.
Prof. Rätsch leaded the with 1.1 Mill EUR founded interdisciplinary project “KollRo 4.0” (BMBF, BOSCH) and current two ZIM-projects with 0,4 Mill EUR in the field of Human-Robot-Collaboration and was a member of other funded industrial projects like RTMO (BMBF), GES 3D (BMBF), Face-HMI (SAB, COG), and I-Search (BMBF).

Plenary Speaker I

Prof. Nafiz Arica
Bahcesehir University, Turkey

Speech: Deep Learning Approaches in Face Analysis
Deep learning has shown impressive performances in many problems of artificial intelligence including face analysis which has been a challenging task in computer vision for decades. Face analysis has various application areas such as video surveilence, human-computer interaction and driver fatigue detection. In this talk, I will give a brief overview of deep learning approaches in face analysis. After detecting the face regions in an input image the first step is to preprocess those regions for the subsequent stages. Although the preprocessing operations differ based on the application of analysis I will be focusing on the deep learning methods developed for face alignment, pose estimation, face frontalization and face super-resolution problems. Other subjects in the talk are related to face attribute estimation, face expression and emotion analysis and face recognition. Face attributes include age, gender and other attributes such as glass/no glass and beard/no beard. In facial expression/emotion analysis I will discuss the methods in recognition of prototypical facial expressions, i.e. anger, disgust, fear, happiness, sadness and surprise in addition to facial action units. The face recognition part of the talk covers both the identification and verification problems.

The talk will focus on state-of-the-art deep learning studies in each face analysis problem after grouping the main approaches. Finally I conclude my talk with the challenges and future works on face analysis.

Biography: Nafiz Arica received the BSc degree from the Turkish Naval Academy in 1991. He worked for the Navy as communications and combat officer for four years. In 1995, he joined the Middle East Technical University (METU) where he received both MSc and PhD degrees in computer engineering. His thesis was awarded the thesis of the year in 1998 at METU. He conducted research as a postdoctoral associate both at the Beckman Institute of the University of Illinois, Urbana-Champaign, IL and at the Department of Information Science of Naval Postgraduate School, Monterey, CA. From 2004 to 2013 he worked as a faculty member of Computer Engineering Department at the Turkish Naval Academy, Istanbul. He is now working as Professor in Computer Engineering Department at Bahcesehir University. He also holds the position of dean at the Faculty of Engineering and Natural Sciences.

The general theme of his research is to develop intelligent systems with a focus on “seeing”. In accordance with this ultimate goal, his research interests include various subjects in Computer Vision, Machine Learning and Autonomous Agents. During his research career, he has worked on many problems that have real-world applications in these areas. In particular, the problems, he has addressed covers image/object representation and classification, deep learning, human activity analysis, detecting and tracking moving objects for video surveillance, facial expression analysis. In addition he has studied on simulation and modeling of Unmanned Aerial Vehicle (UAV) flight paths, and path planning algorithms.

For his research, he combines the rigor of basic sciences with the innovative and practical aspects of engineering. His general goal in research is to develop novel techniques based on probabilistic models and engineering approaches. He pursues his research based on three pillars: analysis of real world problems, development of computational models and experimentation on large set of real data.

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