CECC 2015: 15th Central European Conference on Cryptology

The 15th Central European Conference on Cryptology, CECC 2015, is organised by the Institute of Mathematics and the System Security Group of Alpen-Adria-Universität Klagenfurt.

The conference will be held in Klagenfurt am Wörthersee, Austria, on July 8-10, 2015, at the university campus.

The aim of the conference is to gather researchers interested in discussing recent advances in all aspects of cryptology, including but not limited to:

  • cryptanalysis
  • cryptographic applications in information security
  • design of cryptographic systems
  • encryption schemes
  • general cryptographic protocols
  • post-quantum cryptography
  • pseudorandomness
  • signature schemes
  • steganography

All participants are encouraged to present a contributed talk. Registration and abstract submission will be opened on December 15, 2014.

Further information at https://www.math.aau.at/cecc2015/

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Österreichische Computer Gesellschaft (OCG): Förderpreis

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Ausschreibung 2015: Die Ausschreibung für den OCG Förderpreis 2015 endet am 9. Dezember 2014

Motivation

Die OCG verleiht zur Förderung der Informatik und Wirtschaftsinformatik jährlich den OCG-Förderpreis.

Ziel

Die Förderung von hervorragenden Diplom- bzw. Magisterarbeiten

Zielgruppe

Alle Studentinnen und Studenten mit Diplomarbeiten bzw. Magisterarbeiten auf dem Gebiet der Informatik, Wirtschaftsinformatik und ihren Anwendungen

Konzept

Zur Bewerbung ist ein Online-Formular auszufüllen, die zu prüfende Arbeit, eine (max. zehnseitige) Kurzfassung sowie ein ausführliches Literaturverzeichnis sind auf der OCG Website innerhalb der Einreichfrist hochzuladen.
Eine Erstreckung der Einreichfrist ist nicht möglich.

Voraussetzung

Die eingereichte Arbeit muss innerhalb der letzten zwei Jahre an einer österreichischen Universität approbiert worden sein. Die eingereichten Unterlagen verbleiben bei der Österreichischen Computer Gesellschaft.

Preise

Der OCG-Förderpreis wird mit Euro 2.000,- dotiert.

Jury

  • Prof. Dr. Günter Haring (Vorsitz)
  • Prof. Dr. Martin Hitz
  • Prof. Dr. Gerti Kappel
  • Prof. Dr. Gabriele Anderst-Kotsis
  • Prof. Dr. Gustaf Neumann
  • Prof. Dr. Franz Wotawa
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Efficient retrieval using models based on feature signatures

Abstract: Recent trends in large-scale image retrieval consider image representations based on the Bag of Visual Words model, because such representation enables efficient filtering using inverted files, while for more effective retrieval the representation can be enhanced by VLAD or Hemming embedding. In order to improve the effectiveness of the retrieval even more, some models try to relax from Bag of Visual Words model (i.e., from a shared feature space dictionary) and model content of multimedia objects by means of so-called feature signatures. Whereas the effectiveness of models based on feature signatures seems to be promising for several retrieval tasks, the efficiency of the models still represents a serious performance bottleneck, because the models employ expensive adaptive distance measures to compare two feature signatures. In this talk, we present several recent approaches that significantly improve the efficiency of the retrieval when using models based on feature signatures, making the models applicable also for large-scale image retrieval.

CV: Jakub Lokoc received the doctoral degree in software systems from the Charles University in Prague, Czech Republic. He is an assistant professor in the Department of Software Engineering at the Charles University in Prague, Faculty of Mathematics and Physics, Czech Republic. He is a member of siret research group and his research interests include metric access methods, multimedia retrieval and exploration, and similarity modeling.

Lokoc

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Rückblick: Quality of Experience – Why Bother? [Slides][Video]

Der Rückblick zum TEWI-Kolloquium von Martin Varela am 27.10.2014 beinhaltet die Videoaufzeichnung sowie die Folien:

Video

[iframe height=“350″ src=“http://video.aau.at/video.php?video=ftf_valera.mp4″]

Slides

Abstract: In this talk I’ll discuss some ideas about the reasons, both technical and business-related why we care about Quality of Experience (QoE). I’ll present some results related to QoE-driven network and application-level management, and first steps into economically exploiting QoE models, in particular concerning Service Level Agreements, as well as some enablers for doing so.

Bio: Martín Varela received his PhD and MSc from the University of Rennes 1 (Rennes,France), in 2005 and 2002 respectively. He has been an ERCIM fellow, and spent time at SICS and VTT, where he is currently a Principal Scientist. His research interests lie in the QoE domain, with a particular focus on real-time QoE estimates for generic services and applications thereof. He is currently leading VTT’s work on QoE, and is a Finnish management committee member for COST Action IC1003 Qualinet. He has also served as Scientific Coordinator for the Celtic Plus QuEEN project. He is currently co-chair for the IEEE MMTC QoE Interest Group.

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CONTINUOUS TIME BAYESIAN NETWORKS FOR MINING STREAMING DATA

Abstract: Streaming data are relevant in finance, computer science, and engineering while they are becoming increasingly important in medicine and biology. In particular, classification, clustering and structural learning of streaming data are receiving increasing attention. These tasks require algorithms and models capable to represent dynamic, sequence and time. Dynamic Bayesian networks and hidden Markov models are used to analyze streaming data. However, these models are concerned with equally spaced time data and thus suffer from several limitations because it is not clear how to discretize timestamps. The talk introduces continuous time Bayesian networks and continuous time Bayesian networks classifiers. Algorithms for parametric and structural learning of continuous time Bayesian network models to solve classification, clustering and structural learning based on multivariate discrete state continuous time trajectories are described. Stationary and non-stationary continuous time Bayesian networks are presented together with their structural learning based on the marginal likelihood approach. Numerical experiments concerning real world applications in finance, biomedicine, neurology and biology are presented.

CV: Prof Fabio Stella is an associate professor at the Dipartimento di Informatica, Sistemistica e Comunicazione of the Università degli Studi di Milano-Bicocca. His research focuses on models and algorithms for data analysis and decision making under uncertainty in the areas of Business Intelligence, Data and Text Mining and Computational Finance. In the winter term 2014/15 he is giving the course 625.605 – Business Intelligence in Klagenfurt.

image001

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