@inproceedings{11754, abstract = {{Recent advances in discriminatively trained mask estimation networks to extract a single source utilizing beamforming techniques demonstrate, that the integration of statistical models and deep neural networks (DNNs) are a promising approach for robust automatic speech recognition (ASR) applications. In this contribution we demonstrate how discriminatively trained embeddings on spectral features can be tightly integrated into statistical model-based source separation to separate and transcribe overlapping speech. Good generalization to unseen spatial configurations is achieved by estimating a statistical model at test time, while still leveraging discriminative training of deep clustering embeddings on a separate training set. We formulate an expectation maximization (EM) algorithm which jointly estimates a model for deep clustering embeddings and complex-valued spatial observations in the short time Fourier transform (STFT) domain at test time. Extensive simulations confirm, that the integrated model outperforms (a) a deep clustering model with a subsequent beamforming step and (b) an EM-based model with a beamforming step alone in terms of signal to distortion ratio (SDR) and perceptually motivated metric (PESQ) gains. ASR results on a reverberated dataset further show, that the aforementioned gains translate to reduced word error rates (WERs) even in reverberant environments.}}, author = {{Drude, Lukas and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2017, Stockholm, Schweden}}, title = {{{Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings}}}, year = {{2017}}, } @inproceedings{11770, abstract = {{In this contribution we show how to exploit text data to support word discovery from audio input in an underresourced target language. Given audio, of which a certain amount is transcribed at the word level, and additional unrelated text data, the approach is able to learn a probabilistic mapping from acoustic units to characters and utilize it to segment the audio data into words without the need of a pronunciation dictionary. This is achieved by three components: an unsupervised acoustic unit discovery system, a supervisedly trained acoustic unit-to-grapheme converter, and a word discovery system, which is initialized with a language model trained on the text data. Experiments for multiple setups show that the initialization of the language model with text data improves the word segementation performance by a large margin.}}, author = {{Glarner, Thomas and Boenninghoff, Benedikt and Walter, Oliver and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2017, Stockholm, Schweden}}, title = {{{Leveraging Text Data for Word Segmentation for Underresourced Languages}}}, year = {{2017}}, } @inproceedings{1180, abstract = {{These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @inproceedings{11809, abstract = {{This paper presents an end-to-end training approach for a beamformer-supported multi-channel ASR system. A neural network which estimates masks for a statistically optimum beamformer is jointly trained with a network for acoustic modeling. To update its parameters, we propagate the gradients from the acoustic model all the way through feature extraction and the complex valued beamforming operation. Besides avoiding a mismatch between the front-end and the back-end, this approach also eliminates the need for stereo data, i.e., the parallel availability of clean and noisy versions of the signals. Instead, it can be trained with real noisy multichannel data only. Also, relying on the signal statistics for beamforming, the approach makes no assumptions on the configuration of the microphone array. We further observe a performance gain through joint training in terms of word error rate in an evaluation of the system on the CHiME 4 dataset.}}, author = {{Heymann, Jahn and Drude, Lukas and Boeddeker, Christoph and Hanebrink, Patrick and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System}}}, year = {{2017}}, } @article{11811, abstract = {{Acoustic beamforming can greatly improve the performance of Automatic Speech Recognition (ASR) and speech enhancement systems when multiple channels are available. We recently proposed a way to support the model-based Generalized Eigenvalue beamforming operation with a powerful neural network for spectral mask estimation. The enhancement system has a number of desirable properties. In particular, neither assumptions need to be made about the nature of the acoustic transfer function (e.g., being anechonic), nor does the array configuration need to be known. While the system has been originally developed to enhance speech in noisy environments, we show in this article that it is also effective in suppressing reverberation, thus leading to a generic trainable multi-channel speech enhancement system for robust speech processing. To support this claim, we consider two distinct datasets: The CHiME 3 challenge, which features challenging real-world noise distortions, and the Reverb challenge, which focuses on distortions caused by reverberation. We evaluate the system both with respect to a speech enhancement and a recognition task. For the first task we propose a new way to cope with the distortions introduced by the Generalized Eigenvalue beamformer by renormalizing the target energy for each frequency bin, and measure its effectiveness in terms of the PESQ score. For the latter we feed the enhanced signal to a strong DNN back-end and achieve state-of-the-art ASR results on both datasets. We further experiment with different network architectures for spectral mask estimation: One small feed-forward network with only one hidden layer, one Convolutional Neural Network and one bi-directional Long Short-Term Memory network, showing that even a small network is capable of delivering significant performance improvements.}}, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}}, journal = {{Computer Speech and Language}}, title = {{{A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing}}}, year = {{2017}}, } @misc{119, author = {{Wever, Marcel Dominik}}, publisher = {{Universität Paderborn}}, title = {{{Active Learning of User Requirement Specifications in Dynamic Software Service Markets}}}, year = {{2017}}, } @inproceedings{15912, author = {{Grothe, Martin and Niemann, Tobias and Somorovsky, Juraj and Schwenk, Jörg}}, booktitle = {{11th {USENIX} Workshop on Offensive Technologies ({WOOT} 17)}}, publisher = {{{USENIX} Association}}, title = {{{Breaking and Fixing Gridcoin}}}, year = {{2017}}, } @inproceedings{10206, author = {{Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke}}, booktitle = {{Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017)}}, pages = {{193--206}}, title = {{{Planning with Independent Task Networks}}}, doi = {{10.1007/978-3-319-67190-1_15}}, year = {{2017}}, } @article{30148, author = {{Ficara, Elena}}, journal = {{Fatum Magazin}}, pages = {{9}}, title = {{{Was ist Synthese?}}}, volume = {{6}}, year = {{2017}}, } @inproceedings{29930, author = {{Ott, Markus and Beckmann, Alexander and Böcker, Joachim}}, booktitle = {{European Battery, Hybrid and Fuel Cell Electric Vehicle Congress Geneva, 14th-16th March 2017}}, location = {{Geneva, Switzerland}}, title = {{{A Compensation Method for Production Tolerances in Electric Drive Systems Using an Extended Open-Loop Torque Control}}}, year = {{2017}}, } @inbook{24363, author = {{Horwath, Ilona}}, booktitle = {{Scotts Institutionenbegriff Als Heuristik Zur Analyse von Geschlechterverhältnissen in Organisationen: Inspirationsquelle „Geschlechterwissen,}}, title = {{{Scotts Institutionenbegriff als Heuristik zur Analyse von Geschlechterverhältnissen in Organisationen: Inspirationsquelle „Geschlechterwissen}}}, year = {{2017}}, } @phdthesis{9986, abstract = {{Das Ultraschall-Drahtbonden mit Aluminiumdraht ist aktuell ein Standardverfahren zur Kontaktierung von Leistungshalbleitermodulen. Jedoch soll die Einführung von Kupferdraht als Bondmaterial in der Zukunft zu zahlreichen Verbesserungen der Module führen. Dies ist bedingt durch die wesentlich besseren elektrischen und thermischen Eigenschaften von Kupfer gegenüber Aluminium, die dazu führen, dass z. B. eine weitere Miniaturisierung elektrischer Komponenten möglich wird. Darüber hinaus könnte insbesondere die Verlässlichkeit der Module signifikant verbessert werden. Doch bisher wurde Kupferdraht trotz seiner überlegenen physikalischen Eigenschaften im Wesentlichen nur vereinzelt eingesetzt, da der Prozess deutlich empfindlicher auf Störgrößen reagiert. Um dennoch zuverlässige Kupferbondverbindungen in Leistungshalbleitermodulen herstellen zu können, wird in dieser Arbeit ein Verfahren vorgestellt, das die Herstellung von Ultraschall-Bondverbindungen mittels modellbasierter Mehrzieloptimierung realisiert. Dazu ist umfangreiches Prozesswissen in Form eines geeigneten Modells für den Bondprozess notwendig. Das vorgestellte Modell ist in der Lage, alle notwendigen Effekte und den Einfluss wesentlicher Prozessparameter detailliert abzubilden, sodass eine anschließende Mehrzieloptimierung zur Bestimmung der pareto-optimalen Betriebspunkte und zugehörigen Prozessparameter ermöglicht wird. Die dafür notwendigen Teilmodelle und Mehrzieloptimierungen werden dargestellt und validiert. Überdies wird ein Prototyp in Form einer modifizierten Bondmaschine inklusive einer Möglichkeit zur Verhaltensanpassung aufgebaut und getestet.}}, author = {{Unger, Andreas}}, keywords = {{Drahtbondverbindungen}}, publisher = {{Universität Paderborn}}, title = {{{Modellbasierte Mehrzieloptimierung zur Herstellung von Ultraschall-Drahtbondverbindungen in Leistungshalbleitermodulen}}}, year = {{2017}}, } @misc{13110, author = {{Hagengruber, Ruth Edith}}, title = {{{Bergès, Sandrine; Coffee, Alan (Eds.). The Social and Political Philosophy of Mary Wollstonecraft. Oxford University Press, 2017.}}}, year = {{2017}}, } @inproceedings{35065, author = {{Süßmann, Johannes}}, booktitle = {{Neujahrsempfang der Universität Paderborn 15. Januar 2017. Laudationes}}, pages = {{17 f.}}, title = {{{Religion und Politik im Röhrentruper Rezeß von 1617. [Laudatio auf Johannes Stüer.] }}}, year = {{2017}}, } @article{29248, author = {{Steinhardt, Isabel and Pohlenz, Philipp and Merkt, Marianne}}, journal = {{Zeitschrift für Hochschulentwicklung}}, number = {{3}}, title = {{{Editorial: Reclaiming Quality Development: Forschung über Lehre und Studium als Teil der Qualitätsentwicklung }}}, doi = {{10.3217/zfhe-12-03/01}}, volume = {{12}}, year = {{2017}}, } @inbook{29735, abstract = {{The present volume has aimed to cover a broad range of approaches to agency at work, exploring its relationship with professional learning and development. Thus, the chapters included in this book have discussed the role of agency in learning and development, considering a variety of working life contexts and applying both conceptual and empirical perspectives. This final chapter provides an overview of both the conceptual approaches and the empirical implementations. We see the perspectives as complementary. From the content of the book, we discern the phenomena as falling on two main dimensions, clustering at opposite ends of these dimensions. Thus, the following contrasts are evidenced: (a) agency understood as a personal capacity, vs. agency as behaviour, and (b) agency as an individual phenomenon, vs. agency as a collective phenomenon. All the chapters emphasise that agency is needed for learning and development. However, they differ in how they view the relationships between the concepts. They also exhibit differences in the empirical decisions taken and the research strategies chosen. In this concluding chapter, we discuss the main similarities and differences emerging from the chapters. We also highlight avenues for future research on agency and its relationship with professional learning.}}, author = {{Paloniemi, Susanna and Goller, Michael}}, booktitle = {{Agency at work: An agentic perspective on professional learning and development}}, editor = {{Goller, Michael and Paloniemi, Susanna}}, isbn = {{9783319609423}}, issn = {{2210-5549}}, keywords = {{Agency, Workplace learning, Professional development}}, pages = {{465--478}}, publisher = {{Springer International Publishing}}, title = {{{The Multifaceted Nature of Agency and Professional Learning}}}, doi = {{10.1007/978-3-319-60943-0_23}}, year = {{2017}}, } @inproceedings{31370, author = {{Hoffmann, Max and Biehler, Rolf}}, booktitle = {{Beiträge zum Mathematikunterricht 2017}}, editor = {{Kortenkamp, Ulrich and Kuzle, Ana}}, pages = {{441--444}}, publisher = {{WTM-Verlag}}, title = {{{Schnittstellenaufgaben für die Analysis I – Konzept, Beispiele und Evaluationsergebnisse}}}, doi = {{10.17877/DE290R-18534}}, year = {{2017}}, } @inbook{32545, abstract = {{Deutliche Unterschiede im Bildungserfolg von Kindern abhängig vom familialen Hintergrund zeigen, dass, ungeachtet der schulischen Bemühungen um die kindliche Bildung, die Familie weiterhin ein wesentlicher Einflussfaktor ist. Vor diesem Hintergrund werden im Folgenden die Wege erörtert, über die sich diese Einflüsse realisieren. Nach einem Überblick zu einschlägigen Prozessen werden in diesem Beitrag familiale Wirkungszusammenhänge am Beispiel der Lesesozialisation genauer beleuchtet. Vor diesem Hintergrund werden abschließend Programme zur Förderung der familialen Unterstützung diskutiert.}}, author = {{Buhl, Heike M. and Hilkenmeier, Johanna}}, booktitle = {{Handbuch Entwicklungs- und Erziehungspsychologie}}, editor = {{Kracke, Bärbel and Noack, Peter}}, isbn = {{9783642540615}}, keywords = {{Lesesozialisation, Familiale Erziehung, Familienklima, Kindliche Bildung, Bildungserwartungen, Häusliche Anregung, Home literacy environment, Vorlesen, Literacy}}, pages = {{163--179}}, publisher = {{Springer}}, title = {{{Bildung und Lesesozialisation im Elternhaus}}}, doi = {{10.1007/978-3-642-54061-5_10-1}}, year = {{2017}}, } @misc{33074, author = {{Ulrich, Tanja}}, title = {{{Grammatikerwerb und grammatische Störungen im Kindesalter. Ergebnisse des Forschungsprojekts GED 4-9 und ihre Implikationen für sprachdiagnostische und - therapeutische Methoden}}}, year = {{2017}}, } @inproceedings{5829, abstract = {{Websites increasingly embed semantic data for search engine optimization. The most common ontology for semantic data, schema.org, is supported by all major search engines and describes over 500 data types, including calendar events, recipes, products, and TV shows. As of today, users wishing to pass this data to their favorite applications, e.g., their calendars, cookbooks, price comparison applications or even smart devices such as TV receivers, rely on cumbersome and error-prone workarounds such as reentering the data or a series of copy and paste operations. In this paper, we present Semantic Data Mediator (SDM), an approach that allows the easy transfer of semantic data to a multitude of services, ranging from web services to applications installed on different devices. SDM extracts semantic data from the currently displayed web page on the client-side, offers suitable services to the user, and by the press of a button, forwards this data to the desired service while doing all the necessary data conversion and service interface adaptation in between. To realize this, we built a reusable repository of service descriptions, data converters, and service adapters, which can be extended by the crowd. Our approach for linking services to websites relies solely on semantic data and does not require any additional support by either website or service developers. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.}}, author = {{Wolters, Dennis and Heindorf, Stefan and Kirchhoff, Jonas and Engels, Gregor}}, booktitle = {{2017 IEEE International Conference on Web Services (ICWS)}}, editor = {{Altintas, Ilkay and Chen, Shiping}}, isbn = {{9781538607527}}, keywords = {{Services, Websites, Semantic Data, schema.org, Data Conversion, Interface Adaptation, Mediation}}, publisher = {{IEEE}}, title = {{{Linking Services to Websites by Leveraging Semantic Data}}}, doi = {{10.1109/icws.2017.80}}, year = {{2017}}, }