Inst. Softwaretechnik und Theoretische Informatik

283 Items

Recent Submissions
Efficient learning machines

Alber, Maximilian (2019)

Science is in a constant state of evolution. There is a permanent quest for advancing knowledge in the light of changing capabilities and matters. The field of Machine Learning itself is shaped by the ever-increasing amount of data and computing power, creating new challenges as well as paving the way for new opportunities. This thesis is on adapting learning-based machines to these emerging pr...

Konzeption eines MOOC der TU9 zum Thema Communication Acoustics

Möller, Sebastian ; Ahrens, Jens ; Altinsoy, Ercan ; Fels, Janina ; Müller, Gerhard ; Reimers, Gabriel ; Seeber, Bernhard ; Vorländer, Michael ; Weinzierl, Stefan (2016)

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On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Choi, Soo-In ; Han, Chang-Hee ; Choi, Ga-Young ; Shin, Jaeyoung ; Song, Kwang Soup ; Im, Chang-Hwan ; Hwang, Han-Jeong (2018-08-29)

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main e...

Co-Clustering under the Maximum Norm

Bulteau, Laurent ; Froese, Vincent ; Hartung, Sepp ; Niedermeier, Rolf (2016-02-25)

Co-clustering, that is partitioning a numerical matrix into “homogeneous” submatrices, has many applications ranging from bioinformatics to election analysis. Many interesting variants of co-clustering are NP-hard. We focus on the basic variant of co-clustering where the homogeneity of a submatrix is defined in terms of minimizing the maximum distance between two entries. In this context, we sp...

Finding Supported Paths in Heterogeneous Networks

Fertin, Guillaume ; Komusiewicz, Christian ; Mohamed-Babou, Hafedh ; Rusu, Irena (2015-10-09)

Subnetwork mining is an essential issue in the analysis of biological, social and communication networks. Recent applications require the simultaneous mining of several networks on the same or a similar vertex set. That is, one searches for subnetworks fulfilling different properties in each input network. We study the case that the input consists of a directed graph D and an undirected graph G...

Towards exact molecular dynamics simulations with invariant machine-learned models

Chmiela, Stefan (2019)

Molecular dynamics (MD) simulations constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, one of the widely recognized and increasingly pressing issues in MD simulations is the lack of accuracy of underlying classical interatomic potentials, which hinders truly predictive modeling of dynamics and function of (bio)molecular systems. ...

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

Donner, Christian ; Opper, Manfred (2018)

We present an approximate Bayesian inference approach for estimating the intensity of a inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian process (GP) prior via a sigmoid link function. Augmenting the model using a latent marked Poisson process and Polya--Gamma random variables we obtain a representation of the likelihood which is conjugate to the GP prior...

Advanced EEG signal processing with applications in brain-computer interfaces

Nicolae, Irina-Emilia (2019)

Advances in signal processing push forward the Neurotechnology domain along with the Brain-Computer Interface (BCI) research which deals with the analysis of brain activity. Heading for a future that will most probably happen, where either healthy persons or people with disabilities communicate and control external devices without muscle control, a symbiotic relationship between humans and mach...

Bayesian inference of inhomogeneous point process models

Donner, Christian (2019)

Arrival times of airplanes, positions of car accidents or astronomical objects in space, locations of ecological crisis, spike times of neurons, etc. are all data that surround us and can be viewed as realisations of point processes. Nowadays, the modelling of these data becomes increasingly more important, when we attempt to draw meaningful conclusions from this ever expanding amount of data. ...

Compiling Modelica

Höger, Christoph (2019)

The equation-based object-oriented modeling language Modelica is an openly accessible standard with many implementations and applications. Most, if not all, tools that execute a Modelica simulation follow a common scheme: Models are loaded, composed, analyzed, and transformed into a system of equations. This system is then further simplified, translated into efficient code, and simulated....

Optimizing the depth and the direction of prospective planning using information values

Sezener, Can Eren ; Dezfouli, Amir ; Keramati, Mehdi (2019-03-12)

Evaluating the future consequences of actions is achievable by simulating a mental search tree into the future. Expanding deep trees, however, is computationally taxing. Therefore, machines and humans use a plan-until-habit scheme that simulates the environment up to a limited depth and then exploits habitual values as proxies for consequences that may arise in the future. Two outstanding quest...

Features and machine learning systems for structured and sequential data

Schwenk, Guido (2019)

Modern web and communication technology relies heavily on sequential and structured data for its process execution and communication protocols. Due to its complex properties, a manual analysis and detection of problems on this data is too time-consuming and expensive, and hence not feasible. As a consequence, features and automatic learning systems on this type of data are highly sought after. ...

Exploring Deployment Strategies for the Tor Network

Döpmann, Christoph ; Rust, Sebastian ; Tschorsch, Florian (2018)

In response to upcoming performance and security challenges of anonymity networks like Tor, it will be of crucial importance to be able to develop and deploy performance improvements and state-of-the-art countermeasures. In this paper, we therefore explore different deployment strategies and review their applicability, impact, and risks to the Tor network. In a simulation-based evaluation, whic...

Towards a Concurrent and Distributed Route Selection for Payment Channel Networks

Rohrer, Elias ; Laß, Jann-Frederik ; Tschorsch, Florian (2017)

Payment channel networks use off-chain transactions to provide virtually arbitrary transaction rates. In this paper, we provide a new perspective on payment channels and consider them as a flow network. We propose an extended push-relabel algorithm to find payment flows in a payment channel network. Our algorithm enables a distributed and concurrent execution without violating capacity constrai...

Webchain: Verifiable Citations and References for the World Wide Web

Rohrer, Elias ; Heidel, Steffen ; Tschorsch, Florian (2018)

Readers’ capability to consider and assess sources is imperative. Digital preservation efforts, however, mostly neglected citation provenance, which is a necessity for transparent source verification. We therefore present Webchain, a new system enabling verifiable citations and references on the World Wide Web. Its architecture combines a distributed ledger with secure timestamping to ensure hi...

CircuitStart: A Slow Start For Multi-Hop Anonymity Systems

Döpmann, Christoph ; Tschorsch, Florian (2018)

In order to improve the performance of anonymity networks like Tor, custom transport protocols have been proposed to efficiently deal with the multi-hop nature of such overlay networks. In this work, we tackle the issue of quickly, but safely, ramping up the congestion window during the initial phase of a circuit's lifetime. We propose a tailored startup mechanism called CircuitStart that trans...

P2KMV: A Privacy-preserving Counting Sketch for Efficient and Accurate Set Intersection Cardinality Estimations

Sparka, Hagen ; Tschorsch, Florian ; Scheuermann, Björn (2018-05-01)

In this paper, we propose P2KMV, a novel privacy-preserving counting sketch, based on the k minimum values algorithm. With P2KMV, we offer a versatile privacy-enhanced technology for obtaining statistics, following the principle of data minimization, and aiming for the sweet spot between privacy, accuracy, and computational efficiency. As our main contribution, we develop methods to perform set...

Exploring Deployment Strategies for the Tor Network [Extended Version]

Döpmann, Christoph ; Rust, Sebastian ; Tschorsch, Florian (2018-07-07)

In response to upcoming performance and security challenges of anonymity networks like Tor, it will be of crucial importance to be able to develop and deploy performance improvements and state-of-the-art countermeasures. In this paper, we therefore explore different deployment strategies and review their applicability to the Tor network. In particular, we consider flag day, dual stack, translat...

Representations and optimizations for embedded parallel dataflow languages

Alexandrov, Alexander (2019)

Parallel dataflow engines such as Apache Hadoop, Apache Spark, and Apache Flink have emerged as an alternative to relational databases more suitable for the needs of modern data analysis applications. One of the main characteristics of these systems is their scalable programming model, based on distributed collections and parallel transformations. Notable examples are Flink’s DataSet and Spark’...

One-class classification in the presence of point, collective, and contextual anomalies

Görnitz, Nico (2019)

Anomaly detection has a prominent position in the processing pipeline of any real-world data-driven application. Its central goal is to detect and separate valid data points from malicious-anomalous-ones such that the cleaned data set can be processed further. In many applications, anomalies are even the prime objects of interest and need to be exposed early in order to avoid loss, e.g. in cred...