@misc{11713, author = {{Wachsmuth, Henning}}, booktitle = {{Computational Linguistics}}, number = {{3}}, pages = {{603 -- 606}}, publisher = {{ACL}}, title = {{{Book Review: Argumentation Mining}}}, volume = {{45}}, year = {{2019}}, } @inproceedings{11714, author = {{Ajjour, Yamen and Wachsmuth, Henning and Kiesel, Johannes and Potthast, Martin and Hagen, Matthias and Stein, Benno}}, booktitle = {{Proceedings of the 42nd Edition of the German Conference on Artificial Intelligence}}, pages = {{48--59}}, title = {{{Data Acquisition for Argument Search: The args.me Corpus}}}, year = {{2019}}, } @article{11950, abstract = {{Advances in electromyographic (EMG) sensor technology and machine learning algorithms have led to an increased research effort into high density EMG-based pattern recognition methods for prosthesis control. With the goal set on an autonomous multi-movement prosthesis capable of performing training and classification of an amputee’s EMG signals, the focus of this paper lies in the acceleration of the embedded signal processing chain. We present two Xilinx Zynq-based architectures for accelerating two inherently different high density EMG-based control algorithms. The first hardware accelerated design achieves speed-ups of up to 4.8 over the software-only solution, allowing for a processing delay lower than the sample period of 1 ms. The second system achieved a speed-up of 5.5 over the software-only version and operates at a still satisfactory low processing delay of up to 15 ms while providing a higher reliability and robustness against electrode shift and noisy channels.}}, author = {{Boschmann, Alexander and Agne, Andreas and Thombansen, Georg and Witschen, Linus Matthias and Kraus, Florian and Platzner, Marco}}, issn = {{0743-7315}}, journal = {{Journal of Parallel and Distributed Computing}}, keywords = {{High density electromyography, FPGA acceleration, Medical signal processing, Pattern recognition, Prosthetics}}, pages = {{77--89}}, publisher = {{Elsevier}}, title = {{{Zynq-based acceleration of robust high density myoelectric signal processing}}}, doi = {{10.1016/j.jpdc.2018.07.004}}, volume = {{123}}, year = {{2019}}, } @inbook{11952, author = {{Senft, Björn and Rittmeier, Florian and Fischer, Holger Gerhard and Oberthür, Simon}}, booktitle = {{Design, User Experience, and Usability. Practice and Case Studies}}, isbn = {{9783030235345}}, issn = {{0302-9743}}, location = {{Orlando, FL, USA}}, title = {{{A Value-Centered Approach for Unique and Novel Software Applications}}}, doi = {{10.1007/978-3-030-23535-2_27}}, year = {{2019}}, } @inproceedings{11985, author = {{Bronner, Fabian and Sommer, Christoph}}, booktitle = {{2018 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781538694282}}, title = {{{Efficient Multi-Channel Simulation of Wireless Communications}}}, doi = {{10.1109/vnc.2018.8628350}}, year = {{2019}}, } @inbook{12043, author = {{Reinold, Peter and Meyer, Norbert and Buse, Dominik and Klingler, Florian and Sommer, Christoph and Dressler, Falko and Eisenbarth, Markus and Andert, Jakob}}, booktitle = {{Proceedings}}, isbn = {{9783658252939}}, issn = {{2198-7432}}, title = {{{Verkehrssimulation im Hardware-in-the-Loop-Steuergerätetest}}}, doi = {{10.1007/978-3-658-25294-6_15}}, year = {{2019}}, } @inbook{12072, author = {{Sommer, Christoph and Eckhoff, David and Brummer, Alexander and Buse, Dominik S. and Hagenauer, Florian and Joerer, Stefan and Segata, Michele}}, booktitle = {{Recent Advances in Network Simulation}}, isbn = {{9783030128418}}, issn = {{2522-8595}}, title = {{{Veins: The Open Source Vehicular Network Simulation Framework}}}, doi = {{10.1007/978-3-030-12842-5_6}}, year = {{2019}}, } @inproceedings{12076, author = {{Yigitbas, Enes and Heindörfer, Joshua and Engels, Gregor}}, booktitle = {{Proceedings of the Mensch und Computer 2019 (MuC ’19)}}, pages = {{885----888}}, publisher = {{ACM}}, title = {{{A Context-aware Virtual Reality First Aid Training Application}}}, year = {{2019}}, } @inproceedings{12870, author = {{Feldkord, Björn and Knollmann, Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 17th Workshop on Approximation and Online Algorithms (WAOA)}}, pages = {{120 -- 137}}, publisher = {{Springer}}, title = {{{Managing Multiple Mobile Resources}}}, doi = {{10.1007/978-3-030-39479-0_9}}, year = {{2019}}, } @inproceedings{12880, abstract = {{By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CONvolutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations.}}, author = {{Guenther, Michael and Afifi, Haitham and Brendel, Andreas and Karl, Holger and Kellermann, Walter}}, booktitle = {{2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019)}}, title = {{{Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks}}}, year = {{2019}}, } @inproceedings{12881, abstract = {{Internet of Things (IoT) applications witness an exceptional evolution of traffic demands, while existing protocols, as seen in wireless sensor networks (WSNs), struggle to cope with these demands. Traditional protocols rely on finding a routing path between sensors generating data and sinks acting as gateway or databases. Meanwhile, the network will suffer from high collisions in case of high data rates. In this context, in-network processing solutions are used to leverage the wireless nodes' computations, by distributing processing tasks on the nodes along the routing path. Although in-network processing solutions are very popular in wired networks (e.g., data centers and wide area networks), there are many challenges to adopt these solutions in wireless networks, due to the interference problem. In this paper, we solve the problem of routing and task distribution jointly using a greedy Virtual Network Embedding (VNE) algorithm, and consider power control as well. Through simulations, we compare the proposed algorithm to optimal solutions and show that it achieves good results in terms of delay. Moreover, we discuss its sub-optimality by driving tight lower bounds and loose upper bounds. We also compare our solution with another wireless VNE solution to show the trade-off between delay and symbol error rate.}}, author = {{Afifi, Haitham and Karl, Holger}}, booktitle = {{2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (WMNC'19)}}, title = {{{An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding}}}, year = {{2019}}, } @inproceedings{12882, abstract = {{One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework.}}, author = {{Afifi, Haitham and Karl, Holger and Eikenberg, Sebastian and Mueller, Arnold and Gansel, Lars and Makejkin, Alexander and Hannemann, Kai and Schellenberg, Rafael}}, booktitle = {{2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo)}}, keywords = {{WSN, virtualization, VNE}}, title = {{{A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO}}}, year = {{2019}}, } @misc{12885, author = {{Haltermann, Jan Frederik}}, title = {{{Analyzing Data Usage in Array Programs}}}, year = {{2019}}, } @inproceedings{12889, author = {{Yigitbas, Enes and Jovanovikj, Ivan and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes (IFIP WG 13.2 & WG 13.5 International Workshop @ INTERACT2019)}}, title = {{{A Model-based Framework for Context-aware Augmented Reality Applications }}}, year = {{2019}}, } @inproceedings{12894, author = {{Augstein, Mirjam and Herder, Eelco and Wörndl, Wolfgang and Yigitbas, Enes}}, booktitle = {{30th ACM Conference on Hypertext and Social Media (HT ’19), September 17–20, 2019, Hof, Germany}}, publisher = {{ACM}}, title = {{{ABIS 2019 – 23rd International Workshop on Personalization and Recommendation on the Web and Beyond}}}, year = {{2019}}, } @inproceedings{12931, author = {{Ajjour, Yamen and Alshomary, Milad and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}}, pages = {{2915 -- 2925}}, title = {{{Modeling Frames in Argumentation}}}, year = {{2019}}, } @article{12967, abstract = {{Modern Boolean satisfiability solvers can emit proofs of unsatisfiability. There is substantial interest in being able to verify such proofs and also in using them for further computations. In this paper, we present an FPGA accelerator for checking resolution proofs, a popular proof format. Our accelerator exploits parallelism at the low level by implementing the basic resolution step in hardware, and at the high level by instantiating a number of parallel modules for proof checking. Since proof checking involves highly irregular memory accesses, we employ Hybrid Memory Cube technology for accelerator memory. The results show that while the accelerator is scalable and achieves speedups for all benchmark proofs, performance improvements are currently limited by the overhead of transitioning the proof into the accelerator memory.}}, author = {{Hansmeier, Tim and Platzner, Marco and Pantho, Md Jubaer Hossain and Andrews, David}}, issn = {{1939-8018}}, journal = {{Journal of Signal Processing Systems}}, number = {{11}}, pages = {{1259 -- 1272}}, title = {{{An Accelerator for Resolution Proof Checking based on FPGA and Hybrid Memory Cube Technology}}}, doi = {{10.1007/s11265-018-1435-y}}, volume = {{91}}, year = {{2019}}, } @phdthesis{15333, author = {{Heindorf, Stefan}}, publisher = {{Universität Paderborn}}, title = {{{Vandalism Detection in Crowdsourced Knowledge Bases}}}, year = {{2019}}, } @inproceedings{15369, author = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Peuster, Manuel and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN)}}, publisher = {{IEEE}}, title = {{{5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing}}}, year = {{2019}}, } @inproceedings{15371, abstract = {{More and more management and orchestration approaches for (software) networks are based on machine learning paradigms and solutions. These approaches depend not only on their program code to operate properly, but also require enough input data to train their internal models. However, such training data is barely available for the software networking domain and most presented solutions rely on their own, sometimes not even published, data sets. This makes it hard, or even infeasible, to reproduce and compare many of the existing solutions. As a result, it ultimately slows down the adoption of machine learning approaches in softwarised networks. To this end, we introduce the "softwarised network data zoo" (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. We present a general methodology to collect, archive, and publish those data sets for use by other researches and, as an example, eight initial data sets, focusing on the performance of virtualised network functions. }}, author = {{Peuster, Manuel and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE/IFIP 15th International Conference on Network and Service Management (CNSM)}}, publisher = {{IEEE/IFIP}}, title = {{{The Softwarised Network Data Zoo}}}, year = {{2019}}, }