@inproceedings{11829,
  abstract     = {{This contribution investigates Direction of Arrival (DoA) estimation using linearly arranged microphone arrays. We are going to develop a model for the DoA estimation error in a reverberant scenario and show the existence of a bias, that is a consequence of the linear arrangement and limited field of view (FoV) bias: First, the limited FoV leading to a clipping of the measurements, and, second, the angular distribution of the signal energy of the reflections being non-uniform. Since both issues are a consequence of the linear arrangement of the sensors, the bias arises largely independent of the kind of DoA estimator. The experimental evaluation demonstrates the existence of the bias for a selected number of DoA estimation methods and proves that the prediction from the developed theoretical model matches the simulation results.}},
  author       = {{Jacob, Florian and Haeb-Umbach, Reinhold}},
  booktitle    = {{12. ITG Fachtagung Sprachkommunikation (ITG 2016)}},
  title        = {{{On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays}}},
  year         = {{2016}},
}

@inproceedings{11834,
  abstract     = {{We present a system for the 4th CHiME challenge which significantly increases the performance for all three tracks with respect to the provided baseline system. The front-end uses a bi-directional Long Short-Term Memory (BLSTM)-based neural network to estimate signal statistics. These then steer a Generalized Eigenvalue beamformer. The back-end consists of a 22 layer deep Wide Residual Network and two extra BLSTM layers. Working on a whole utterance instead of frames allows us to refine Batch-Normalization. We also train our own BLSTM-based language model. Adding a discriminative speaker adaptation leads to further gains. The final system achieves a word error rate on the six channel real test data of 3.48%. For the two channel track we achieve 5.96% and for the one channel track 9.34%. This is the best reported performance on the challenge achieved by a single system, i.e., a configuration, which does not combine multiple systems. At the same time, our system is independent of the microphone configuration. We can thus use the same components for all three tracks.}},
  author       = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}},
  booktitle    = {{Computer Speech and Language}},
  title        = {{{Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition}}},
  year         = {{2016}},
}

@article{11840,
  author       = {{Kinoshita, Keisuke and Delcroix, Marc and Gannot, Sharon and Habets, Emanuel A. P. and Haeb-Umbach, Reinhold and Kellermann, Walter and Leutnant, Volker and Maas, Roland and Nakatani, Tomohiro and Raj, Bhiksha and Sehr, Armin and Yoshioka, Takuya}},
  journal      = {{EURASIP Journal on Advances in Signal Processing}},
  title        = {{{A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research}}},
  year         = {{2016}},
}

@inproceedings{11908,
  abstract     = {{This paper describes automatic speech recognition (ASR) systems developed jointly by RWTH, UPB and FORTH for the 1ch, 2ch and 6ch track of the 4th CHiME Challenge. In the 2ch and 6ch tracks the final system output is obtained by a Confusion Network Combination (CNC) of multiple systems. The Acoustic Model (AM) is a deep neural network based on Bidirectional Long Short-Term Memory (BLSTM) units. The systems differ by front ends and training sets used for the acoustic training. The model for the 1ch track is trained without any preprocessing. For each front end we trained and evaluated individual acoustic models. We compare the ASR performance of different beamforming approaches: a conventional superdirective beamformer [1] and an MVDR beamformer as in [2], where the steering vector is estimated based on [3]. Furthermore we evaluated a BLSTM supported Generalized Eigenvalue beamformer using NN-GEV [4]. The back end is implemented using RWTH?s open-source toolkits RASR [5], RETURNN [6] and rwthlm [7]. We rescore lattices with a Long Short-Term Memory (LSTM) based language model. The overall best results are obtained by a system combination that includes the lattices from the system of UPB?s submission [8]. Our final submission scored second in each of the three tracks of the 4th CHiME Challenge.}},
  author       = {{Menne, Tobias and Heymann, Jahn and Alexandridis, Anastasios and Irie, Kazuki and Zeyer, Albert and Kitza, Markus and Golik, Pavel and Kulikov, Ilia and Drude, Lukas and Schlüter, Ralf and Ney, Hermann and Haeb-Umbach, Reinhold and Mouchtaris, Athanasios}},
  booktitle    = {{Computer Speech and Language}},
  title        = {{{The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation}}},
  year         = {{2016}},
}

@inproceedings{11920,
  abstract     = {{In this paper we demonstrate an algorithm to learn words from speech using non-parametric Bayesian hierarchical models in an unsupervised setting. We exploit the assumption of a hierarchical structure of speech, namely the formation of spoken words as a sequence of phonemes. We employ the Nested Hierarchical Pitman-Yor Language Model, which allows an a priori unknown and possibly unlimited number of words. We assume the n-gram probabilities of words, the m-gram probabilities of phoneme sequences in words and the phoneme sequences of the words themselves as latent variables to be learned. We evaluate the algorithm on a cross language task using an existing speech recognizer trained on English speech to decode speech in the Xitsonga language supplied for the 2015 ZeroSpeech challenge. We apply the learning algorithm on the resulting phoneme graphs and achieve the highest token precision and F score compared to present systems.}},
  author       = {{Walter, Oliver and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th German Conference on Pattern Recognition (GCPR 2016)}},
  title        = {{{Unsupervised Word Discovery from Speech using Bayesian Hierarchical Models}}},
  year         = {{2016}},
}

@inbook{12935,
  author       = {{Komprecht, Anna Maria and Röwenstrunk, Daniel}},
  booktitle    = {{„Ei, dem alten Herrn zoll’ ich Achtung gern“}},
  publisher    = {{Allitera Verlag, München}},
  title        = {{{Projektmanagement in digitalen Forschungsprojekten}}},
  doi          = {{10.25366/2018.33}},
  year         = {{2016}},
}

@inproceedings{15907,
  author       = {{Aviram, Nimrod and Schinzel, Sebastian and Somorovsky, Juraj and Heninger, Nadia and Dankel, Maik and Steube, Jens and Valenta, Luke and Adrian, David and Halderman, J. Alex and Dukhovni, Viktor and Käsper, Emilia and Cohney, Shaanan and Engels, Susanne and Paar, Christof and Shavitt, Yuval}},
  booktitle    = {{25th {USENIX} Security Symposium ({USENIX} Security 16)}},
  isbn         = {{978-1-931971-32-4}},
  pages        = {{689--706}},
  publisher    = {{{USENIX} Association}},
  title        = {{{DROWN: Breaking TLS Using SSLv2}}},
  year         = {{2016}},
}

@inproceedings{15913,
  author       = {{Böck, Hanno and Zauner, Aaron and Devlin, Sean and Somorovsky, Juraj and Jovanovic, Philipp}},
  booktitle    = {{10th {USENIX} Workshop on Offensive Technologies ({WOOT} 16)}},
  publisher    = {{{USENIX} Association}},
  title        = {{{Nonce-Disrespecting Adversaries: Practical Forgery Attacks on GCM in TLS}}},
  year         = {{2016}},
}

@article{32164,
  author       = {{Lohmeier, Yvonne and Karsten, Andrea}},
  journal      = {{JoSch – Journal der Schreibberatung}},
  keywords     = {{Schreibberatung, Schreibdidaktik, Schreibprozess, Sprache}},
  number       = {{11}},
  pages        = {{13 -- 23}},
  publisher    = {{wbv}},
  title        = {{{Schreiben eine Stimme geben. Über die Beziehung von lautem Sprechen und Schreiben im Schreibprozess}}},
  doi          = {{10.3278/JOS1601W013}},
  year         = {{2016}},
}

@article{29250,
  author       = {{Steinhardt, Isabel}},
  journal      = {{Zeitschrift für Hochschulentwicklung}},
  number       = {{1}},
  pages        = {{225--237}},
  title        = {{{Habitussensibilisierung durch Videoanalysen von Lehramtsstudierenden}}},
  doi          = {{10.3217/zfhe-11-01/13}},
  volume       = {{11}},
  year         = {{2016}},
}

@misc{29556,
  author       = {{Schönhärl, Korinna}},
  booktitle    = {{H-Soz Kult}},
  title        = {{{Review: Schwarz, Steffen L.: Despoten – Barbaren – Wirtschaftspartner. Die Allgemeine Zeitung und der Diskurs über das Osmanische Reich 1821-1840, Köln Weimar Wien 2016}}},
  year         = {{2016}},
}

@article{6071,
  abstract     = {{Particular differences between an object and its surrounding cause salience, guide attention, and improve performance in various tasks. While much research has been dedicated to identifying which feature dimensions contribute to salience, much less regard has been paid to the quantitative strength of the salience caused by feature differences. Only a few studies systematically related salience effects to a common salience measure, and they are partly outdated in the light of new findings on the time course of salience effects. We propose Bundesen’s Theory of Visual Attention (TV A) as a theoretical basis for measuring salience and introduce an empirical and modeling approach to link this theory to data retrieved from temporal-order judgments. With this procedure, TV A becomes applicable to a broad range of salience-related stimulus material. Three experiments with orientation pop-out displays demonstrate the feasibility of the method. A 4th experiment substantiates its applicability t}},
  author       = {{Krüger, Alexander and Tünnermann, Jan and Scharlau, Ingrid}},
  issn         = {{1895-1171}},
  journal      = {{Advances in Cognitive Psychology}},
  keywords     = {{salience, visual attention, Bayesian inference, theory of visual attention, computational modeling, Visual Attention, Computational Modeling, Inference, Judgment, Statistical Probability}},
  number       = {{1}},
  pages        = {{20 -- 38}},
  title        = {{{Fast and conspicuous? Quantifying salience with the theory of visual attention.}}},
  doi          = {{10.5709/acp-0184-1}},
  volume       = {{12}},
  year         = {{2016}},
}

@article{6080,
  abstract     = {{Peripheral visual cues lead to large shifts in psychometric distributions of temporal-order judgments. In one view, such shifts are attributed to attention speeding up processing of the cued stimulus, so-called prior entry. However, sometimes these shifts are so large that it is unlikely that they are caused by attention alone. Here we tested the prevalent alternative explanation that the cue is sometimes confused with the target on a perceptual level, bolstering the shift of the psychometric function. We applied a novel model of cued temporal-order judgments, derived from Bundesen’s Theory of Visual Attention.We found that cue–target confusions indeed contribute to shifting psychometric functions. However, cue-induced changes in the processing rates of the target stimuli play an important role, too. At smaller cueing intervals, the cue increased the processing speed of the target. At larger intervals, inhibition of return was predominant. Earlier studies of cued TOJs were insensitive}},
  author       = {{Tünnermann, Jan and Scharlau, Ingrid}},
  issn         = {{1664-1078}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{cueing, temporal-order judgements, theory of visual attention (TVA), peripheral cue, processing speed, stimulus encoding, prior entry, Attention, Cues, Face Perception, Judgment}},
  title        = {{{Peripheral visual cues: Their fate in processing and effects on attention and temporal-order perception.}}},
  doi          = {{10.3389/fpsyg.2016.01442}},
  volume       = {{7}},
  year         = {{2016}},
}

@article{28977,
  abstract     = {{The paper presents a hermeneutic approach to teaching and learning mathematics in which the hermeneutic circle is translated into methods of reading mathematical texts with the help of the three decisive steps 1) making prior knowledge explicit, 2) interpreting texts, and 3) fusing horizons of prior understanding and text content.}},
  author       = {{Schnieder, Jörn and Scharlau, Ingrid}},
  journal      = {{Special Issue: The Philosophy of Mathematics Education at ICME 13}},
  title        = {{{Reading mathematical texts with philosophical methods}}},
  volume       = {{31}},
  year         = {{2016}},
}

@inbook{31488,
  author       = {{Scharlau, Ingrid and Karsten, Andrea and Nettingsmeier, Pia and Golombek, Christiane and Schäfer, Stefanie}},
  booktitle    = {{Akademisches Schreiben. Halbband 1: Vom Qualitätspakt Lehre geförderte Schreibzentren und Schreibwerkstätten}},
  editor       = {{Knorr, Dagmar}},
  pages        = {{163 -- 166}},
  title        = {{{Zwei Fenster mit verschiedener Sicht}}},
  volume       = {{13}},
  year         = {{2016}},
}

@inproceedings{137,
  abstract     = {{Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity.Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata.We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. It significantly outperforms the state of the art represented by the rule-based Wikidata Abuse Filter (0.865 ROC-AUC) and a prototypical vandalism detector recently introduced by Wikimedia within the Objective Revision Evaluation Service (0.859 ROC-AUC).}},
  author       = {{Heindorf, Stefan and Potthast, Matthias and Stein, Benno and Engels, Gregor}},
  booktitle    = {{Proceedings of the 25th International Conference on Information and Knowledge Management (CIKM 2016)}},
  pages        = {{327----336}},
  title        = {{{Vandalism Detection in Wikidata}}},
  doi          = {{10.1145/2983323.2983740}},
  year         = {{2016}},
}

@techreport{35989,
  author       = {{Schlegel-Matthies, Kirsten and Gigerenzer, Gerd and Wagner, Gert G.}},
  issn         = {{2365-919X}},
  pages        = {{51}},
  title        = {{{Digitale Welt und Gesundheit. eHealth und mHealth – Chancen und Risiken der Digitalisierung im Gesundheitsbereich}}},
  year         = {{2016}},
}

@inbook{36285,
  author       = {{Weber, Jutta and Suchman, Lucy}},
  booktitle    = {{Autonomous Weapon Systems. Law, Ethics, Policy}},
  editor       = {{Buta, Nehal and Kress, Claus and Beck, Susanne and Geiss, Robin and Liu, Hin-Yan}},
  pages        = {{75--102}},
  publisher    = {{Cambridge University Press}},
  title        = {{{Human-Machine Autonomies}}},
  year         = {{2016}},
}

@article{36289,
  author       = {{Weber, Jutta}},
  journal      = {{Environment and Planning D. Society and Space. Special Issue on ‚The Politics of the List: Law, Security, Technology‘ (Hg.: Marieke de Goede/Anna Leander/Gavin Sullivan)}},
  pages        = {{107--125}},
  title        = {{{Keep Adding. Kill Lists, Drone Warfare and the Politics of Databases}}},
  volume       = {{Vol. 34, No.1, February 2016}},
  year         = {{2016}},
}

@misc{48789,
  author       = {{Hartung, Olaf}},
  booktitle    = {{sehepunkte}},
  issn         = {{1618-6168}},
  publisher    = {{sehepunkte 16 (2016), Nr. 1}},
  title        = {{{ Rezension von: Jutta Berger / Christian Schmidtmann (Hgg.): Referendariat Geschichte. Kompaktwissen für Berufseinstieg und Examensvorbereitung, Berlin: Cornelsen 2014}}},
  volume       = {{16, 1}},
  year         = {{2016}},
}