Connecting Trust – decentralization of the internet

Assoc.-Prof. Dr. Antorweep Chakravorty | November 25, 2019 | 16:00 | S.2.42

Abstract:

Blockchain is an innovation for creating distributed trust between users facilitating the exchange of value over a network. It can be seen as a decentralized read-only database operated collectively by participants in the network. Participants in the network can be different organizations that provide computing infrastructure to maintain a single version of a decentralized ledger. Each participant locally maintain the same version of this ledger in their own environment and agree upon any updates or changes to its state by employing some consensus algorithms. This enables the trust to be distributed throughout the network, without the need for a central intermediary. The decentralization of trust allows the blockchain technology to be transparent, secure, auditable, redundant and immutable. Since each participant maintains the same version of the truth, it removes the potential of conflict. Additionally, it also enhances the trust of end-users using applications provided by organizations driven by blockchains as they are able to get confirmation about operations on their data from multiple distinct entities rather than a single centralized party. These features of the blockchain has lead to its adoption not only in financial sectors but also in health, energy, IoT, supply chain and smart cities.

 

CV:

Dr. Antorweep Chakravorty is an Associate Professor at the University of Stavanger. His current research and development work is in the field of applied Blockchains, Big Data, Large Scale Machine Learning and Data Privacy. He has an interest in real-world problems, especially development of privacy enabled data-driven services in smart energy, healthcare and smart city domains. Antorweep completed his PhD. in 2015 with a thesis on Privacy Preserving Big Data Analytics at the University of Stavanger, Norway. Along with having a background in applied research in data-driven solutions, he is also involved in mentoring, teaching and supervision. He spent 6 months on a research exchange program at IBM Thomas J. Watson Research Center, New York, USA.

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Enhancing Context Knowledge Repositories with Justifiable Exceptions

Prof. Dr. Thomas Eiter | October 25, 2019 | 14:00 | S.2.69

Abstract:

The Contextualized Knowledge Repository (CKR) framework was conceived as a logic-based approach for representing context dependent knowledge, which is a well-known area of study in AI, based on description logics. The framework has a two-layer structure with a global context that contains context-independent knowledge and meta-information about the contexts, and a set of local contexts with specific knowledge bases.  In many practical cases, it is desirable that inherited global knowledge can be „overridden“ at the local level. In order to address this need, an extension of CKR with global defeasible axioms was developed: these axioms locally apply to individuals unless an exception for overriding exists; such an exception, however, requires a justification that is provable from the knowledge base.

The formalization of this intuition has some desirable semantic properties, and furthermore allows for a translation of reasoning tasks on extended CKRs to datalog programs under the answer set (i.e., stable) semantics. This work complements other work on nonmonotonic extensions of description logics with an expressive formalism for exception handling by overriding, and adds to the body of results on using deductive database technology in these areas.

This is joint work with Loris Bozzato and Luciano Serafini (Fondazione Bruno Kessler, Trento).

CV:

Thomas Eiter is a full professor in the Faculty of Informatics at Vienna University of Technology (TU Wien), Austria, and Head of the Institute of Information Systems, where he also leads the Knowledge Based Systems Group. From 1996-1998, he was an associate professor of Computer Science at the University of Giessen, Germany.

Prof. Eiter’s current research interests include knowledge representation and reasoning, computational logic, foundations of information systems, and complexity in AI.  He has contributed to the DLV system and some of its extensions, e.g. the DLVHEX system. He has been involved in various national and international research and training projects, and he has been serving on a number of professional committees and boards. Prof. Eiter’s work has been honored with some best paper awards; he is a Fellow of the  European Association for Artificial Intelligence (EurAI), a Member of the Academia Europea, and a Corresponding Member of the Austrian Academy of Sciences.

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Extracting extreme aspects from time series with applications

Prof. Milan Stehlík | October 18, 2019 | 15:00 | V.1.27

Abstract:

Extracting chaotical and stochastic parts of information from time series needs very specific techniques. Motivated by two applications, image processing for cancer discrimination and methane emissions modelling we will explain the necessary techniques for statistical learning on chaotical and stochastic parts from data. In particular, Tsallis Entropy will be introduced and its role in information theory for dynamical system explained. Iterated function systems will be used as an example for chaos re-simulation. Construction of stochastic fractals will be discussed. We will show the importance of decomposition of data to stochastic, deterministic and chaotic part.

CV:

Professor Milan Stehlík  obtained his PhD in 2003 at Comenius University, Bratislava,  Slovakia,  and he habilitated in Statistics in 2011 at Johannes Kepler University in Linz, Austria. During 1.3.2014-1.10.2015 he was Associate Professor at Universidad Técnica Federico Santa María, Chile. In 2015 he received Full Professorship at University of Valparaiso, Valparaiso, Chile.

Currently he is Visiting professor at the Department of Statistics & Actuarial Science, The University of Iowa. In 2018 he was visiting Full Professor at School of Mathematics & Statistical Sciences Arizona State University, AZ, USA. He was involved in several international projects and collaborations in Austria, Spain, Russia, Canada, Germany, USA among others.

He does research in Extremes, Optimal design of experiments, Statistical Modelling, Neural Computing, Cancer discrimination. He servers as Associate Editor for Europe of Neural Computing and Applications, Associate Editor of Journal of Applied Statistics and Revstat.  He has been Principal Investigator of Innovative project LIT-2016-1-SEE-023 Title: Modeling complex dependencies: how to make strategic multicriterial decisions?  at Linz Institute of Technology, Austria and Chilean FONDECYT Regular. He published more than 180 papers and gave more than 190 talks.

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