@inproceedings{6972,
  abstract     = {{In recent years, a variety of different approaches
have been proposed to tackle the problem of scaling and placing
network services, consisting of interconnected virtual network
functions (VNFs). This paper presents a placement abstraction
layer (PAL) that provides a clear and simple northbound interface
for using such algorithms while hiding their internal
functionality and implementation. Through its southbound interface,
PAL can connect to different back ends that evaluate
the calculated placements, e.g., using simulations, emulations, or
testbed approaches. As an example for such evaluation back ends,
we introduce a novel placement emulation framework (PEF)
that allows executing calculated placements using real, containerbased
VNFs on real-world network topologies. In a case study,
we show how PAL and PEF facilitate reusing and evaluating
placement algorithms as well as validating their underlying
models and performance claims.}},
  author       = {{Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}},
  booktitle    = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018)}},
  location     = {{Verona, Italy}},
  publisher    = {{IEEE}},
  title        = {{{A Generic Emulation Framework for Reusing and Evaluating VNF Placement Algorithms}}},
  doi          = {{10.1109/NFV-SDN.2018.8725795}},
  year         = {{2018}},
}

@inproceedings{6974,
  abstract     = {{A key challenge of network function virtualization
(NFV) is the complexity of developing and deploying new
network services. Currently, development requires many manual
steps that are time-consuming and error-prone (e.g., for creating
service descriptors). Furthermore, existing management and
orchestration (MANO) platforms only offer limited support of
standardized descriptor models or package formats, limiting the
re-usability of network services.

To this end, we introduce a fully integrated, open-source
NFV service development kit (SDK) with multi-MANO platform
support. Our SDK simplifies many NFV service development
steps by offering initial generation of descriptors, advanced
project management, as well as fully automated packaging and
submission for on-boarding. To achieve multi-platform support,
we present a package format that extends ETSI’s VNF package
format. In this demonstration, we present the end-to-end workflow
to develop an NFV service that is then packaged for multiple
platforms, i.e., 5GTANGO and OSM.}},
  author       = {{Schneider, Stefan Balthasar and Peuster, Manuel and Tavernier, Wouter and Karl, Holger}},
  booktitle    = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018)}},
  location     = {{Verona, Italy}},
  publisher    = {{IEEE}},
  title        = {{{A Fully Integrated Multi-Platform NFV SDK}}},
  doi          = {{10.1109/NFV-SDN.2018.8725794}},
  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{1181,
  abstract     = {{The main idea in On-The-Fly Computing is to automatically compose existing software services according to the wishes of end-users. However, since user requirements are often ambiguous, vague and incomplete, the selection and composition of suitable software services is a challanging task. In this paper, we present our current approach to improve requirement descriptions before they are used for software composition. This procedure is fully automated, but also has limitations, for example, if necessary information is missing. In addition, and in response to the limitations, we provide insights into our above-mentioned current work that combines the existing optimization approach with a
chatbot solution.}},
  author       = {{Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018)}},
  editor       = {{Schmid, Klaus and Spoletini, Paola and Ben Charrada, Eya and Chisik, Yoram and Dalpiaz, Fabiano and Ferrari, Alessio and Forbrig, Peter and Franch, Xavier and Kirikova, Marite and Madhavji, Nazim and Palomares, Cristina and Ralyté, Jolita and Sabetzadeh, Mehrdad and Sawyer, Pete and van der Linden, Dirk and Zamansky, Anna}},
  issn         = {{1613-0073}},
  location     = {{Utrecht, The Netherlands}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{How to Deal with Inaccurate Service Requirements? Insights in Our Current Approach and New Ideas}}},
  volume       = {{2075}},
  year         = {{2018}},
}

@inproceedings{1182,
  abstract     = {{Natural language requirement descriptions are often unstructured, contradictory and incomplete and are therefore challenging for automatic processing. Although many of these deficits can be compensated by means of Natural Language Processing, there still remain cases where interaction with end-users is necessary for clarification. In this paper, we present our idea of using chatbot technology to establish end-user communication in order to support the automatic compensation of some deficits in natural language requirement descriptions.}},
  author       = {{Friesen, Edwin and Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018)}},
  editor       = {{Schmid, Klaus  and Spoletini, Paola  and Ben Charrada, Eya  and Chisik, Yoram  and Dalpiaz, Fabiano  and Ferrari, Alessio  and Forbrig, Peter  and Franch, Xavier  and Kirikova, Marite  and Madhavji, Nazim  and Palomares, Cristina  and Ralyté, Jolita  and Sabetzadeh, Mehrdad  and Sawyer, Pete  and van der Linden, Dirk  and Zamansky, Anna }},
  issn         = {{1613-0073}},
  location     = {{Utrecht, The Netherlands}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{CORDULA: Software Requirements Extraction Utilizing Chatbot as Communication Interface}}},
  volume       = {{2075}},
  year         = {{2018}},
}

@inproceedings{1183,
  abstract     = {{As our world grows in complexity, companies and employees alike need, more than ever before, solutions tailored to their exact needs. Since such tools cannot always be purchased off-the-shelf and need to be designed from the ground up, developers rely on software requirements. In this paper, we present our vision of a syntactic rule-based extraction
tool for software requirements specification documents. In contrast to other methods, our tool will allow stakeholders to express their needs and wishes in unfiltered natural language, which we believe is essential for non-expert users.}},
  author       = {{Caron, Matthew and Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018)}},
  editor       = {{Schmid, Klaus and Spoletini, Paola and Ben Charrada, Eya and Chisik, Yoram and Dalpiaz, Fabiano and Ferrari, Alessio and Forbrig, Peter and Franch, Xavier and Kirikova, Marite and Madhavji, Nazim and Palomares, Cristina and Ralyté, Jolita and Sabetzadeh, Mehrdad and Sawyer, Pete and van der Linden, Dirk and Zamansky, Anna}},
  issn         = {{1613-0073}},
  location     = {{Utrecht, The Netherlands}},
  publisher    = {{CEUR-WS.org}},
  title        = {{{Back to Basics: Extracting Software Requirements with a Syntactic Approach}}},
  volume       = {{2075}},
  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}},
}

@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}},
}

@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{12898,
  abstract     = {{Deep clustering (DC) and deep attractor networks (DANs) are a data-driven way to monaural blind source separation. Both approaches provide astonishing single channel performance but have not yet been generalized to block-online processing. When separating speech in a continuous stream with a block-online algorithm, it needs to be determined in each block which of the output streams belongs to whom. In this contribution we solve this block permutation problem by introducing an additional speaker identification embedding to the DAN model structure. We motivate this model decision by analyzing the embedding topology of DC and DANs and show, that DC and DANs themselves are not sufficient for speaker identification. This model structure (a) improves the signal to distortion ratio (SDR) over a DAN baseline and (b) provides up to 61% and up to 34% relative reduction in permutation error rate and re-identification error rate compared to an i-vector baseline, respectively.}},
  author       = {{Drude, Lukas and von Neumann, Thilo and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2018, Calgary, Canada}},
  title        = {{{Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation}}},
  year         = {{2018}},
}

@inproceedings{12900,
  abstract     = {{Deep attractor networks (DANs) are a recently introduced method to blindly separate sources from spectral features of a monaural recording using bidirectional long short-term memory networks (BLSTMs). Due to the nature of BLSTMs, this is inherently not online-ready and resorting to operating on blocks yields a block permutation problem in that the index of each speaker may change between blocks. We here propose the joint modeling of spatial and spectral features to solve the block permutation problem and generalize DANs to multi-channel meeting recordings: The DAN acts as a spectral feature extractor for a subsequent model-based clustering approach. We first analyze different joint models in batch-processing scenarios and finally propose a block-online blind source separation algorithm. The efficacy of the proposed models is demonstrated on reverberant mixtures corrupted by real recordings of multi-channel background noise. We demonstrate that both the proposed batch-processing and the proposed block-online system outperform (a) a spatial-only model with a state-of-the-art frequency permutation solver and (b) a spectral-only model with an oracle block permutation solver in terms of signal to distortion ratio (SDR) gains.}},
  author       = {{Drude, Lukas and Higuchi,,  Takuya  and Kinoshita, Keisuke  and Nakatani, Tomohiro  and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2018, Calgary, Canada}},
  title        = {{{Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation}}},
  year         = {{2018}},
}

@inproceedings{12901,
  abstract     = {{This work examines acoustic beamformers employing neural networks (NNs) for mask prediction as front-end for automatic speech recognition (ASR) systems for practical scenarios like voice-enabled home devices. To test the versatility of the mask predicting network, the system is evaluated with different recording hardware, different microphone array designs, and different acoustic models of the downstream ASR system. Significant gains in recognition accuracy are obtained in all configurations despite the fact that the NN had been trained on mismatched data. Unlike previous work, the NN is trained on a feature level objective, which gives some performance advantage over a mask related criterion. Furthermore, different approaches for realizing online, or adaptive, NN-based beamforming are explored, where the online algorithms still show significant gains compared to the baseline performance.}},
  author       = {{Boeddeker, Christoph and Erdogan, Hakan and Yoshioka, Takuya and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2018, Calgary, Canada}},
  title        = {{{Exploring Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition}}},
  year         = {{2018}},
}

@inproceedings{15905,
  author       = {{Poddebniak, Damian and Dresen, Christian and Müller, Jens and Ising, Fabian and Schinzel, Sebastian and Friedberger, Simon and Somorovsky, Juraj and Schwenk, Jörg}},
  booktitle    = {{27th {USENIX} Security Symposium ({USENIX} Security 18)}},
  isbn         = {{978-1-939133-04-5}},
  pages        = {{549--566}},
  publisher    = {{{USENIX} Association}},
  title        = {{{Efail: Breaking S/MIME and OpenPGP Email Encryption using Exfiltration Channels}}},
  year         = {{2018}},
}

@inproceedings{15906,
  author       = {{Böck, Hanno and Somorovsky, Juraj and Young, Craig}},
  booktitle    = {{27th {USENIX} Security Symposium ({USENIX} Security 18)}},
  isbn         = {{978-1-939133-04-5}},
  pages        = {{817--849}},
  publisher    = {{{USENIX} Association}},
  title        = {{{Return Of Bleichenbacher\textquoterights Oracle Threat (ROBOT)}}},
  year         = {{2018}},
}

@inproceedings{15914,
  author       = {{Engelbertz, Nils and Erinola, Nurullah and Herring, David and Somorovsky, Juraj and Mladenov, Vladislav and Schwenk, Jörg}},
  booktitle    = {{12th {USENIX} Workshop on Offensive Technologies ({WOOT} 18)}},
  publisher    = {{{USENIX} Association}},
  title        = {{{Security Analysis of eIDAS -- The Cross-Country Authentication Scheme in Europe}}},
  year         = {{2018}},
}

@inproceedings{16042,
  author       = {{Triebus, Marcel and Tröster, Thomas and Camberg, Alan Adam and Bienia, S. and Dröder, K.}},
  booktitle    = {{15. Deutsches LS-Dyna Forum}},
  location     = {{Bamberg}},
  title        = {{{Modelling the Interface of Hybrid Metal-FRP Components Joint by Form Closures}}},
  year         = {{2018}},
}

@inproceedings{16043,
  author       = {{Camberg, Alan Adam and Tröster, Thomas and Schneidt , A. and Sotirov , N. and Tölle, J.}},
  booktitle    = {{15. Deutsches LS-Dyna Forum}},
  location     = {{Bamberg}},
  title        = {{{The influence of damage accumulation on failure prediction: a comparative assessment of *MAT_224 and *MAT_024 + GISSMO for the application in non-isothermal sheet metal forming}}},
  year         = {{2018}},
}

@unpublished{16292,
  abstract     = {{In a recent article, we presented a framework to control nonlinear partial
differential equations (PDEs) by means of Koopman operator based reduced models
and concepts from switched systems. The main idea was to transform a control
system into a set of autonomous systems for which the optimal switching
sequence has to be computed. These individual systems can be approximated very
efficiently by reduced order models obtained from data, and one can guarantee
equality of the full and the reduced objective function under certain
assumptions. In this article, we extend these results to continuous control
inputs using convex combinations of multiple Koopman operators corresponding to
constant controls, which results in a bilinear control system. Although
equality of the objectives can be carried over when the PDE depends linearly on
the control, we show that this approach is also valid in other scenarios using
several flow control examples of varying complexity.}},
  author       = {{Peitz, Sebastian}},
  booktitle    = {{arXiv:1801.06419}},
  title        = {{{Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data}}},
  year         = {{2018}},
}