Speaker Profile

Prof. Vanessa Evers

University of Twente


Vanessa Evers is a full professor of Computer Science at the University of Twente’s Human Media Interaction group and Director of the DesignLab for multidisciplinary research. She received a M.SC. in Information Systems from the University of Amsterdam, and a Ph. D. from the Open University, UK. During her Master studies she spent two years at the Institute of Management Information Studies of the University of New South Wales, Sydney. After her Ph.D. she has worked for the Boston Consulting Group, London and later became an assistant professor at the University of Amsterdam’s Institute of Informatics. She was a visiting researcher at Stanford University (2005-2007). Her research interests focus on on interaction with intelligent and autonomous systems such as robots or machine learning systems as well as cultural aspects of Human Computer Interaction. She has published over 80 peer reviewed publications, many of which in high quality journals and conferences in human computer interaction and human robot interaction. She serves on Program Committees of ACM/IEEE HRI, ACM SIGCHI, HSI, ACM CSCW and ACM Multimedia. Vanessa is frequently interviewed about her work on national public tv, newspapers or magazines. She won the best thesis prize awarded by the Dutch National Society of Registered Information Specialists, was co-author of the James Chen best paper award of the journal on User Modeling and User Adapted Interaction together with then her Ph.D. student Henriette Cramer. She holds the 2014 Opzij talent award. Vanessa is an editor for the International Journal of Social Robotics, she is co-chair of the ACM International Human Robot Interaction Steering Committee and Associate Editor of the Human Robot Interaction Journal.

Keynote Abstract

The current expectation is that artificially intelligent systems such as robots or personal voice agents will be integrated into every aspect of our lives be it home-life, work, leisure, care or education. To ensure that this process happens in a responsible and seamless way I pose the theory that robots must be able to learn socially from people. I will argue that social norms, embedded in people and the context of use must be taken into account when designing artificially intelligent technology and must be interpreted automatically. Specifically, I will address the following questions:

- How do people learn socially?

- Can AI achieve social intelligence?

- How can the design of robots and their social behaviour impact acceptance and optimize collaboration?

By discussing my groups' previous research which involved practical deployments of robots in the real world, I will explore the fundamentally social relationship people have with autonomous robots and offer essential rules for effective human-robot collaboration

Mochamad Hariadi

Institut Teknologi Sepuluh Nopember


Mochamad Hariadi received the B.Eng. degree in Electrical Engineering Department of Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia, in 1995. He received both M.Sc. and Ph. D. degrees in Graduate School of Information Science Tohoku University Japan, in 2003 and 2006 respectively. Currently, he is a staff of Computer Engineering Department of Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. He is the project leader in joint research with PREDICT JICA project Japan and WINDS project Japan. His research interest is in Video and Image Processing, Data Mining and Intelligent System. He is a member of IEEE, IEICE, and IAENG.

Keynote Abstract

The emerging Edge Computing technology distributes the computational intelligence activities processed by Big Data to the boundaries. Since Fog and edge architectures provide a link between centralized clouds and the world of IoT and sensors, it constructs an orchestra of devices of different sizes that coordinate the communication with sensors and cloud services. This Edge Computing orchestra allows the Big Data processing from or for the IoT devices and sensors between clouds locally.

Obviously, these edge architectures cross organizational boundaries, which causes trust concerns. Thus, the combination of Big Data platform and IoT devices encounters security issues. The origin of data from the IoT devices such as sensors or actuators identities require identification. Additionally, data needs to be stored securely. Orchestration activities across boundaries are subject to a contractual perspective. In this case, the block chain technology is employed as a platform to enable trust.

By employing Big Data, IoT and Blockchain in a orchestration of Edge Computing platform, a distributed intelligent platform with secure and trusted data are provided. An open access hyper ledger within distributed clouds enabled real time processing of distributed data.