Keynote Lectures

Keynote Lecturer: Associate Professor Irena Vodenska, Ph.D.
Institution: Department of Administrative Sciences; MET, Boston University, US

Title: Innovative Modeling of Cascading Failures in Global Financial Networks
Summary: Stability of the banking system and macro-prudential regulation are essential for healthy economic growth. Bank networks are not isolated by national boundaries, but are rather global either through ownership or operations. Interconnectivity among banks contributes to increased vulnerability of the entire financial institution network. It is importance to understand the financial system susceptibility to distress, because of high level of leverage, interconnectivity of system's entities, similar risk exposure of financial institutions, and possibility of systemic crisis propagation through the network. Current bank stress tests, conducted by central banks, do not take in consideration the connectivity of the banks and the potential for a one bank’s financial risk spilling over to the rest of the system. To address this, we create a bipartite network model for cascading bank failures with bank nodes on one side and asset nodes on the other with weighted links between the two layers. We propose a model for systemic risk propagation based on common bank exposures to specific asset classes. After stressing the bank network, we find that while the system is able to withstand shocks for a wide range of parameters, we identify a critical threshold for asset risk beyond which the system transitions from stable to unstable. We also identify asset classes that increase bank vulnerabilities under given stress scenarios.

Keynote Lecturer: Professor Matjaz Gams, Ph.D.
Institution: Jozef Stefan Institute, Department of Intelligent Systems, Slovenia

Title: Superintelligence and future of human civilization
Summary: The lecture will analyze the prospects of superintelligence and the transformation of the human civilization along the way. The study and predictions are based on basic information laws such as Moore's law that will likely pertain their properties for decades. Besides the positive aspects also some potential dangers are highlighted in order to enable avoiding them.
Keynote Lecturer: Professor Gábor Vattay, Ph.D.
Institution: Eotvos University, Hungary

Title: Video Pandemics: Worldwide Viral Spreading of Psy’s Gangnam Style Video
Summary: Viral videos can reach global penetration traveling through international channels of communication similarly to real diseases starting from a well-localized source. In past centuries, disease fronts propagated in a concentric spatial fashion from the the source of the outbreak via the short range human contact network. The emergence of long-distance air-travel changed these ancient patterns. However, recently, Brockmann and Helbing have shown that concentric propagation waves can be reinstated if propagation time and distance is measured in the flight-time and travel volume weighted underlying air-travel network. Here, we adopt this method for the analysis of viral meme propagation in Twitter messages, and define a similar weighted network distance in the communication network connecting countries and states of the World. We recover a wave-like behavior on average and assess the randomizing effect of non-locality of spreading. We show that similar result can be recovered from Google Trends data as well.
Keynote Lecturer: Associate Professor Lubomir Chitkushev,, Ph.D.
Institution: Associate Professor of Computer Science and Associate Director, Center for Reliable Systems and Cyber Security, Boston University, USA

Title: Internet of Things (IoT): Protocol Commonality based on RINA Principles
Summary:Internet of Things (IoT) is a global infrastructure that enables advanced services by providing connections of physical and virtual entities based on existing and developing communication technologies. The scope of IoT is neither easy to define nor predict as the applications seem endless - from industry and healthcare, transportation and shipping to smart homes and cities. Along with the explosion of the number of IoT devices and application there has been lately a significant proliferation of IoT protocols to control or retrieve information. Because IoT applications have limited functionality and they operate under very tight constraints of cost, bandwidth, power, tight resources on computing and memory, there is a tendency to include only the functionality absolutely necessary. Consequently, IoT protocols try to be as minimal as possible and cut every possible corner. To try to understand them all and why they should be used in particular situations is a daunting task. The question arises whether all of different IoT protocols are necessary and could IoT be simpler. We apply the fundamental networking principles embodied in the Recursive InterNet Architecture (RINA) to IoT protocols and demonstrate the possibilities and advantages of commonality in this emerging area. Not only that customers are able to more easily mix and match devices from different vendors in their network, but commonality also reduces the cost of operations, it creates economies of scale and reduces product costs faster. Moreover, the key to effective network management has always been commonality. In summary, IoT protocols designed in line with RINA principles are built to work together, they complement each other and are more efficient and make management simpler and more effective.
Keynote Lecturer: Prof. Dr. Elisabeth André, Ph.D.
Institution: Chair of Human-Centered Multimedia, Faculty of Applied Informatics, Augsburg University

Title: Empowering Multimodal Behavior Analysis by Interactive Machine Learning
Summary:Well described corpora that are rich of human multimodal behavior are needed in a number of disciplines, such as Health Monitoring or Behavioral Psychology. However, populating captured user data with adequate descriptions can be an extremely exhausting and time-consuming task. In my talk, I will present an approach that facilitates the acquisition of annotated data sets by involving end users directly in the machine learning process. I will demonstrate how the combination of active learning and cooperative learning helps speed up annotation of human behavioral signals in large multi-modal databases collected in various European projects. I will also discuss ideas of how to adapt the approach in such a way that it enables end users to collect and label behavioral data in the wild, for example, to keep track of factors that influence their fitness and wellbeing.