@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}}, } @article{11916, abstract = {{We present an experimental comparison of seven state-of-the-art machine learning algorithms for the task of semantic analysis of spoken input, with a special emphasis on applications for dysarthric speech. Dysarthria is a motor speech disorder, which is characterized by poor articulation of phonemes. In order to cater for these noncanonical phoneme realizations, we employed an unsupervised learning approach to estimate the acoustic models for speech recognition, which does not require a literal transcription of the training data. Even for the subsequent task of semantic analysis, only weak supervision is employed, whereby the training utterance is accompanied by a semantic label only, rather than a literal transcription. Results on two databases, one of them containing dysarthric speech, are presented showing that Markov logic networks and conditional random fields substantially outperform other machine learning approaches. Markov logic networks have proved to be especially robust to recognition errors, which are caused by imprecise articulation in dysarthric speech.}}, author = {{Despotovic, Vladimir and Walter, Oliver and Haeb-Umbach, Reinhold}}, journal = {{Speech Communication 99 (2018) 242-251 (Elsevier B.V.)}}, title = {{{Machine learning techniques for semantic analysis of dysarthric speech: An experimental study}}}, year = {{2018}}, } @inproceedings{11983, author = {{Bloessl, Bastian and Klingler, Florian and Missbrenner, Fabian and Sommer, Christoph}}, booktitle = {{2017 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781538609866}}, title = {{{A systematic study on the impact of noise and OFDM interference on IEEE 802.11p}}}, doi = {{10.1109/vnc.2017.8275633}}, year = {{2018}}, } @inproceedings{11986, author = {{Buse, Dominik S. and Schettler, Max and Kothe, Nils and Reinold, Peter and Sommer, Christoph and Dressler, Falko}}, booktitle = {{2018 14th Annual Conference on Wireless On-demand Network Systems and Services (WONS)}}, isbn = {{9783903176010}}, title = {{{Bridging worlds: Integrating hardware-in-the-loop testing with large-scale VANET simulation}}}, doi = {{10.23919/wons.2018.8311659}}, year = {{2018}}, } @inproceedings{11987, author = {{Buse, Dominik S. and Sommer, Christoph and Dressler, Falko}}, booktitle = {{IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}}, isbn = {{9781538659793}}, title = {{{Demo abstract: Integrating a driving simulator with city-scale VANET simulation for the development of next generation ADAS systems}}}, doi = {{10.1109/infcomw.2018.8406997}}, year = {{2018}}, } @article{11990, author = {{Djahel, Soufiene and Sommer, Christoph and Marconi, Annapaola}}, issn = {{1524-9050}}, journal = {{IEEE Transactions on Intelligent Transportation Systems}}, pages = {{2152--2155}}, title = {{{Guest Editorial: Introduction to the Special Issue on Advances in Smart and Green Transportation for Smart Cities}}}, doi = {{10.1109/tits.2018.2848018}}, year = {{2018}}, } @article{12007, author = {{Eckhoff, David and Sommer, Christoph}}, issn = {{0140-3664}}, journal = {{Computer Communications}}, pages = {{118--128}}, title = {{{Readjusting the privacy goals in Vehicular Ad-Hoc Networks: A safety-preserving solution using non-overlapping time-slotted pseudonym pools}}}, doi = {{10.1016/j.comcom.2018.03.006}}, year = {{2018}}, } @article{12009, author = {{Gläser, Stefan and Sommer, Christoph and Gehlen, Guido and Sories, Sabine}}, issn = {{2192-9092}}, journal = {{ATZelektronik worldwide}}, pages = {{14--17}}, title = {{{Cooperative vehicle applications with cellular communication}}}, doi = {{10.1007/bf03242188}}, year = {{2018}}, }