@inbook{16938,
  author       = {{Engels, Gregor and Maier, Günter W. and Ötting, Sonja K. and Steffen, Eckhard and Teetz, Alexander}},
  booktitle    = {{Zukunft der Arbeit – Eine praxisnahe Betrachtung}},
  editor       = {{Wischmann, Steffen and Hartmann, Ernst Andreas}},
  isbn         = {{9783662492659}},
  pages        = {{221--231}},
  publisher    = {{Springer Verlag}},
  title        = {{{Gerechtigkeit in flexiblen Arbeits- und Managementprozessen}}},
  doi          = {{10.1007/978-3-662-49266-6_16}},
  year         = {{2018}},
}

@article{1043,
  abstract     = {{Approximate computing (AC) is an emerging paradigm for energy-efficient computation. The basic idea of AC is to sacrifice high precision for low energy by allowing hardware to carry out “approximately correct” calculations. This provides a major challenge for software quality assurance: programs successfully verified to be correct might be erroneous on approximate hardware. In this letter, we present a novel approach for determining under what conditions a software verification result is valid for approximate hardware. To this end, we compute the allowed tolerances for AC hardware from successful verification runs. More precisely, we derive a set of constraints which—when met by the AC hardware—guarantees the verification result to carry over to AC. On the practical side, we furthermore: 1) show how to extract tolerances from verification runs employing predicate abstraction as verification technology and 2) show how to check such constraints on hardware designs. We have implemented all techniques, and exemplify them on example C programs and a number of recently proposed approximate adders.}},
  author       = {{Isenberg, Tobias and Jakobs, Marie-Christine and Pauck, Felix and Wehrheim, Heike}},
  issn         = {{1943-0663}},
  journal      = {{IEEE Embedded Systems Letters}},
  pages        = {{22--25}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Validity of Software Verification Results on Approximate Hardware}}},
  doi          = {{10.1109/LES.2017.2758200}},
  year         = {{2018}},
}

@misc{1044,
  author       = {{Leer, Richard}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Measuring Performance of a Static Analysis Framework with an application to Immutability Analysis}}},
  year         = {{2018}},
}

@misc{1045,
  author       = {{Strüwer, Jan Niclas}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Interactive Data Visualization for Exploded Supergraphs}}},
  year         = {{2018}},
}

@proceedings{10591,
  editor       = {{Abiteboul, S. and Arenas, M. and Barceló, P. and Bienvenu, M. and Calvanese, D. and David, C. and Hull, R. and Hüllermeier, Eyke and Kimelfeld, B. and Libkin, L. and Martens, W. and Milo, T. and Murlak, F. and Neven, F. and Ortiz, M. and Schwentick, T. and Stoyanovich, J. and Su, J. and Suciu, D. and Vianu, V. and Yi, K.}},
  number       = {{1}},
  pages        = {{1--29}},
  title        = {{{Research Directions for Principles of Data Management}}},
  volume       = {{7}},
  year         = {{2018}},
}

@inproceedings{10598,
  abstract     = {{Approximate computing has become a very popular design
strategy that exploits error resilient computations to achieve higher
performance and energy efficiency. Automated synthesis of approximate
circuits is performed via functional approximation, in which various
parts of the target circuit are extensively examined with a library
of approximate components/transformations to trade off the functional
accuracy and computational budget (i.e., power). However, as the number
of possible approximate transformations increases, traditional search
techniques suffer from a combinatorial explosion due to the large
branching factor. In this work, we present a comprehensive framework
for automated synthesis of approximate circuits from either structural
or behavioral descriptions. We adapt the Monte Carlo Tree Search
(MCTS), as a stochastic search technique, to deal with the large design
space exploration, which enables a broader range of potential possible
approximations through lightweight random simulations. The proposed
framework is able to recognize the design Pareto set even with low
computational budgets. Experimental results highlight the capabilities of
the proposed synthesis framework by resulting in up to 61.69% energy
saving while maintaining the predefined quality constraints.}},
  author       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}},
  keywords     = {{Approximate computing, High-level synthesis, Accuracy, Monte-Carlo tree search, Circuit simulation}},
  pages        = {{219--224}},
  title        = {{{An MCTS-based Framework for Synthesis of Approximate Circuits}}},
  doi          = {{10.1109/VLSI-SoC.2018.8645026}},
  year         = {{2018}},
}

@misc{10782,
  author       = {{Clausing, Lennart}},
  publisher    = {{Ruhr-University Bochum}},
  title        = {{{Development of a Hardware / Software Codesign for sonification of LIDAR-based sensor data}}},
  year         = {{2018}},
}

@inbook{10783,
  author       = {{Couso, Ines and Hüllermeier, Eyke}},
  booktitle    = {{Frontiers in Computational Intelligence}},
  editor       = {{Mostaghim, Sanaz and Nürnberger, Andreas and Borgelt, Christian}},
  pages        = {{31--46}},
  publisher    = {{Springer}},
  title        = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}},
  year         = {{2018}},
}

@inproceedings{1096,
  abstract     = {{to appear}},
  author       = {{Beyer, Dirk and Jakobs, Marie-Christine and Lemberger, Thomas and Wehrheim, Heike}},
  booktitle    = {{Proceedings of the 40th International Conference on Software Engineering (ICSE)}},
  location     = {{Gothenburg, Sweden}},
  pages        = {{1182----1193}},
  publisher    = {{ACM}},
  title        = {{{Reducer-Based Construction of Conditional Verifiers}}},
  year         = {{2018}},
}

@misc{1097,
  author       = {{Jentzsch, Felix Paul}},
  keywords     = {{Approximate Computing, Proof-Carrying Hardware, Formal Verification}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Enforcing IP Core Connection Properties with Verifiable Security Monitors}}},
  year         = {{2018}},
}

@inproceedings{11711,
  author       = {{Ajjour, Yamen and Wachsmuth, Henning and Kiesel, Dora and Riehmann, Patrick and Fan, Fan and Castiglia, Giuliano and Adejoh, Rosemary and Fröhlich, Bernd and Stein, Benno}},
  booktitle    = {{Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}},
  pages        = {{60--65}},
  title        = {{{Visualization of the Topic Space of Argument Search Results in args. me}}},
  year         = {{2018}},
}

@inproceedings{11712,
  author       = {{El Baff, Roxanne and Wachsmuth, Henning and Al Khatib, Khalid and Stein, Benno}},
  booktitle    = {{Proceedings of the 22nd Conference on Computational Natural Language Learning}},
  pages        = {{454--464}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus}}},
  year         = {{2018}},
}

@inproceedings{11760,
  abstract     = {{Acoustic event detection, i.e., the task of assigning a human interpretable label to a segment of audio, has only recently attracted increased interest in the research community. Driven by the DCASE challenges and the availability of large-scale audio datasets, the state-of-the-art has progressed rapidly with deep-learning-based classi- fiers dominating the field. Because several potential use cases favor a realization on distributed sensor nodes, e.g. ambient assisted living applications, habitat monitoring or surveillance, we are concerned with two issues here. Firstly the classification performance of such systems and secondly the computing resources required to achieve a certain performance considering node level feature extraction. In this contribution we look at the balance between the two criteria by employing traditional techniques and different deep learning architectures, including convolutional and recurrent models in the context of real life everyday audio recordings in realistic, however challenging, multisource conditions.}},
  author       = {{Ebbers, Janek and Nelus, Alexandru and Martin, Rainer and Haeb-Umbach, Reinhold}},
  booktitle    = {{DAGA 2018, München}},
  title        = {{{Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection}}},
  year         = {{2018}},
}

@inproceedings{11835,
  abstract     = {{Signal dereverberation using the weighted prediction error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. But in its original formulation, WPE requires multiple iterations over a sufficiently long utterance, rendering it unsuitable for online low-latency applications. Recently, two methods have been proposed to overcome this limitation. One utilizes a neural network to estimate the power spectral density (PSD) of the target signal and works in a block-online fashion. The other method relies on a rather simple PSD estimation which smoothes the observed PSD and utilizes a recursive formulation which enables it to work on a frame-by-frame basis. In this paper, we integrate a deep neural network (DNN) based estimator into the recursive frame-online formulation. We evaluate the performance of the recursive system with different PSD estimators in comparison to the block-online and offline variant on two distinct corpora. The REVERB challenge data, where the signal is mainly deteriorated by reverberation, and a database which combines WSJ and VoiceHome to also consider (directed) noise sources. The results show that although smoothing works surprisingly well, the more sophisticated DNN based estimator shows promising improvements and shortens the performance gap between online and offline processing.}},
  author       = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani, Tomohiro}},
  booktitle    = {{IWAENC 2018, Tokio, Japan}},
  title        = {{{Frame-Online DNN-WPE Dereverberation}}},
  year         = {{2018}},
}

@inproceedings{11837,
  abstract     = {{We present a block-online multi-channel front end for automatic speech recognition in noisy and reverberated environments. It is an online version of our earlier proposed neural network supported acoustic beamformer, whose coefficients are calculated from noise and speech spatial covariance matrices which are estimated utilizing a neural mask estimator. However, the sparsity of speech in the STFT domain causes problems for the initial beamformer coefficients estimation in some frequency bins due to lack of speech observations. We propose two methods to mitigate this issue. The first is to lower the frequency resolution of the STFT, which comes with the additional advantage of a reduced time window, thus lowering the latency introduced by block processing. The second approach is to smooth beamforming coefficients along the frequency axis, thus exploiting their high interfrequency correlation. With both approaches the gap between offline and block-online beamformer performance, as measured by the word error rate achieved by a downstream speech recognizer, is significantly reduced. Experiments are carried out on two copora, representing noisy (CHiME-4) and noisy reverberant (voiceHome) environments.}},
  author       = {{Heitkaemper, Jens and Heymann, Jahn and Haeb-Umbach, Reinhold}},
  booktitle    = {{ITG 2018, Oldenburg, Germany}},
  title        = {{{Smoothing along Frequency in Online Neural Network Supported Acoustic Beamforming}}},
  year         = {{2018}},
}

@misc{1186,
  author       = {{Kemper, Arne}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Pure Nash Equilibria in Robust Congestion Games via Potential Functions}}},
  year         = {{2018}},
}

@misc{1187,
  author       = {{Nachtigall, Marcel}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Scenario-driven Strategy Analysis in a n-player Composition Game Model}}},
  year         = {{2018}},
}

@inproceedings{11872,
  abstract     = {{The weighted prediction error (WPE) algorithm has proven to be a very successful dereverberation method for the REVERB challenge. Likewise, neural network based mask estimation for beamforming demonstrated very good noise suppression in the CHiME 3 and CHiME 4 challenges. Recently, it has been shown that this estimator can also be trained to perform dereverberation and denoising jointly. However, up to now a comparison of a neural beamformer and WPE is still missing, so is an investigation into a combination of the two. Therefore, we here provide an extensive evaluation of both and consequently propose variants to integrate deep neural network based beamforming with WPE. For these integrated variants we identify a consistent word error rate (WER) reduction on two distinct databases. In particular, our study shows that deep learning based beamforming benefits from a model-based dereverberation technique (i.e. WPE) and vice versa. Our key findings are: (a) Neural beamforming yields the lower WERs in comparison to WPE the more channels and noise are present. (b) Integration of WPE and a neural beamformer consistently outperforms all stand-alone systems.}},
  author       = {{Drude, Lukas and Boeddeker, Christoph and Heymann, Jahn and Kinoshita, Keisuke and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}},
  booktitle    = {{INTERSPEECH 2018, Hyderabad, India}},
  title        = {{{Integration neural network based beamforming and weighted prediction error dereverberation}}},
  year         = {{2018}},
}

@inproceedings{11873,
  abstract     = {{NARA-WPE is a Python software package providing implementations of the weighted prediction error (WPE) dereverberation algorithm. WPE has been shown to be a highly effective tool for speech dereverberation, thus improving the perceptual quality of the signal and improving the recognition performance of downstream automatic speech recognition (ASR). It is suitable both for single-channel and multi-channel applications. The package consist of (1) a Numpy implementation which can easily be integrated into a custom Python toolchain, and (2) a TensorFlow implementation which allows integration into larger computational graphs and enables backpropagation through WPE to train more advanced front-ends. This package comprises of an iterative offline (batch) version, a block-online version, and a frame-online version which can be used in moderately low latency applications, e.g. digital speech assistants.}},
  author       = {{Drude, Lukas and Heymann, Jahn and Boeddeker, Christoph and Haeb-Umbach, Reinhold}},
  booktitle    = {{ITG 2018, Oldenburg, Germany}},
  title        = {{{NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing}}},
  year         = {{2018}},
}

@misc{1188,
  author       = {{Kempf, Jérôme}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Learning deterministic bandit behaviour form compositions}}},
  year         = {{2018}},
}