S
Sascha Hauke
Researcher at Technische Universität Darmstadt
Publications - 36
Citations - 512
Sascha Hauke is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Reputation & Computational trust. The author has an hindex of 10, co-authored 36 publications receiving 477 citations. Previous affiliations of Sascha Hauke include University of Münster.
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Journal ArticleDOI
Trust as a facilitator in cloud computing: a survey
TL;DR: This work contributes to understanding why trust establishment is important in the Cloud computing landscape, how trust can act as a facilitator in this context and what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.
Journal ArticleDOI
Impact of Working Memory Load on fMRI Resting State Pattern in Subsequent Resting Phases
Martin Pyka,Christian F. Beckmann,Christian F. Beckmann,Sonja Schöning,Sascha Hauke,Dominik Heider,Harald Kugel,Volker Arolt,Carsten Konrad,Carsten Konrad +9 more
TL;DR: Evidence is presented that a cognitively challenging working-memory task is followed by greater activation of the DMN than a simple letter-matching task, which might be interpreted as a functional correlate of self-evaluation and reflection of the preceding task or as relocation of cerebral resources representing recovery from high cognitive demands.
Proceedings ArticleDOI
Analyzing flow-based anomaly intrusion detection using Replicator Neural Networks
TL;DR: In this article, the authors utilize features of network flows, characterized by their entropy, together with an extended version of the original Replicator Neural Network (RNN) and deep learning techniques to learn models of normality.
Journal ArticleDOI
Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers
J. Nikolaj Dybowski,Mona Riemenschneider,Sascha Hauke,Martin Pyka,Jens Verheyen,Daniel Hoffmann,Dominik Heider +6 more
TL;DR: This analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat by combining structural and sequence-based information in classifier ensembles.
Book ChapterDOI
An evolutionary approach for solving the rubik's cube incorporating exact methods
TL;DR: This work presents an evolutionary approach to solve the Rubik's Cube with a low number of moves by building upon the classic Thistlethwaite's approach and designs an Evolutionary Algorithm from the ground up including detailed derivation of the authors' custom fitness functions.