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Sascha Hauke

Other affiliations: University of Münster
Bio: 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.

Papers
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Journal ArticleDOI
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.
Abstract: Cloud computing offers massively scalable, elastic resources (e.g., data, computing power, and services) over the internet from remote data centres to the consumers. The growing market penetration, with an evermore diverse provider and service landscape, turns Cloud computing marketplaces a highly competitive one. In this highly competitive and distributed service environment, the assurances are insufficient for the consumers to identify the dependable and trustworthy Cloud providers. This paper provides a landscape and discusses incentives and hindrances to adopt Cloud computing from Cloud consumers’ perspective. Due to these hindrances, potential consumers are not sure whether they can trust the Cloud providers in offering dependable services. Trust-aided unified evaluation framework by leveraging trust and reputation systems can be used to assess trustworthiness (or dependability) of Cloud providers. Hence, cloud-related specific parameters (QoS + ) are required for the trust and reputation systems in Cloud environments. We identify the essential properties and corresponding research challenges to integrate the QoS + parameters into trust and reputation systems. Finally, we survey and analyse the existing trust and reputation systems in various application domains, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) why trust establishment is important in the Cloud computing landscape, 2) how trust can act as a facilitator in this context and 3) what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.

159 citations

Journal ArticleDOI
25 Sep 2009-PLOS ONE
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.
Abstract: Background The default-mode network (DMN) is a functional network with increasing relevance for psychiatric research, characterized by increased activation at rest and decreased activation during task performance. The degree of DMN deactivation during a cognitively demanding task depends on its difficulty. However, the relation of hemodynamic responses in the resting phase after a preceding cognitive challenge remains relatively unexplored. We test the hypothesis that the degree of activation of the DMN following cognitive challenge is influenced by the cognitive load of a preceding working-memory task. Methodology/Principal Findings Twenty-five healthy subjects were investigated with functional MRI at 3 Tesla while performing a working-memory task with embedded short resting phases. Data were decomposed into statistically independent spatio-temporal components using Tensor Independent Component Analysis (TICA). The DMN was selected using a template-matching procedure. The spatial map contained rest-related activations in the medial frontal cortex, ventral anterior and posterior cingulate cortex. The time course of the DMN revealed increased activation at rest after 1-back and 2-back blocks compared to the activation after a 0-back block. Conclusion/Significance We present evidence that a cognitively challenging working-memory task is followed by greater activation of the DMN than a simple letter-matching task. This 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. This finding is highly relevant for neuroimaging studies which include resting phases in cognitive tasks as stable baseline conditions. Further studies investigating the DMN should take possible interactions of tasks and subsequent resting phases into account.

115 citations

Proceedings ArticleDOI
01 Dec 2016
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.
Abstract: Defending key network infrastructure, such as Internet backbone links or the communication channels of critical infrastructure, is paramount, yet challenging. The inherently complex nature and quantity of network data impedes detecting attacks in real world settings. In this paper, we 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. This combination allows us to apply anomaly-based intrusion detection on arbitrarily large amounts of data and, consequently, large networks. Our approach is unsupervised and requires no labeled data. It also accurately detects network-wide anomalies without presuming that the training data is completely free of attacks. The evaluation of our intrusion detection method, on top of real network data, indicates that it can accurately detect resource exhaustion attacks and network profiling techniques of varying intensities. The developed method is efficient because a normality model can be learned by training an RNN within a few seconds only.

45 citations

Journal ArticleDOI
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.
Abstract: Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs. We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies. Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.

24 citations

Book ChapterDOI
07 Apr 2010
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.
Abstract: Solutions calculated by Evolutionary Algorithms have come to surpass exact methods for solving various problems. The Rubik’s Cube multiobjective optimization problem is one such area. In this work we present an evolutionary approach to solve the Rubik’s Cube with a low number of moves by building upon the classic Thistlethwaite’s approach. We provide a group theoretic analysis of the subproblem complexity induced by Thistlethwaite’s group transitions and design an Evolutionary Algorithm from the ground up including detailed derivation of our custom fitness functions. The implementation resulting from these observations is thoroughly tested for integrity and random scrambles, revealing performance that is competitive with exact methods without the need for pre-calculated lookup-tables.

21 citations


Cited by
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Journal Article
TL;DR: AspectJ as mentioned in this paper is a simple and practical aspect-oriented extension to Java with just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns.
Abstract: Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

2,947 citations

Journal ArticleDOI
TL;DR: Functional magnetic resonance imaging studies have revealed that the DMN in the healthy brain is associated with stimulus-independent thought and self-reflection and that greater suppression of theDMN isassociated with better performance on attention-demanding tasks.
Abstract: Neuropsychiatric disorders are associated with abnormal function of the default mode network (DMN), a distributed network of brain regions more active during rest than during performance of many attention-demanding tasks and characterized by a high degree of functional connectivity (i.e., temporal correlations between brain regions). Functional magnetic resonance imaging studies have revealed that the DMN in the healthy brain is associated with stimulus-independent thought and self-reflection and that greater suppression of the DMN is associated with better performance on attention-demanding tasks. In schizophrenia and depression, the DMN is often found to be hyperactivated and hyperconnected. In schizophrenia this may relate to overly intensive self-reference and impairments in attention and working memory. In depression, DMN hyperactivity may be related to negative rumination. These findings are considered in terms of what is known about psychological functions supported by the DMN, and alteration of the DMN in other neuropsychiatric disorders.

1,137 citations

Journal ArticleDOI
23 Jan 2018
TL;DR: This paper presents a novel deep learning technique for intrusion detection, which addresses concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks and details the proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning.
Abstract: Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup ’99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

979 citations

Journal ArticleDOI
TL;DR: It is concluded that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.
Abstract: The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state FMRI signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

931 citations

Journal ArticleDOI
TL;DR: Results provide evidence that the posterior cingulate cortex is involved in supporting internally directed thought, as the region is more highly integrated with the DMN at low task demands.
Abstract: The posterior cingulate cortex (PCC) is a central part of the default mode network (DMN) and part of the structural core of the brain. Although the PCC often shows consistent deactivation when attention is focused on external events, anatomical studies show that the region is not homogeneous, and electrophysiological recordings in nonhuman primates suggest that it is directly involved in some forms of attention. We report a functional magnetic resonance imaging study of an attentionally demanding task (either a zero- or two-back working memory task). Standard subtraction analysis within the PCC shows a relative deactivation as task difficulty increases. In contrast, a dual-regression functional connectivity analysis reveals a clear dissociation between ventral and dorsal parts of the PCC. As task difficulty increases, the ventral PCC shows reduced integration within the DMN and less anticorrelation with the cognitive control network (CCN) activated by the task. The dorsal PCC shows an opposite pattern, with increased DMN integration and more anticorrelation. At rest, the dorsal PCC also shows functional connectivity with both the DMN and attentional networks. As expected, these results provide evidence that the PCC is involved in supporting internally directed thought, as the region is more highly integrated with the DMN at low task demands. In contrast, the task-dependent increases in connectivity between the dorsal PCC and the CCN are consistent with a role for this region in modulating the dynamic interaction between these two networks controlling the efficient allocation of attention.

681 citations