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9931 Publications


2018 | Journal Article | LibreCat-ID: 1043
T. Isenberg, M.-C. Jakobs, F. Pauck, and H. Wehrheim, “Validity of Software Verification Results on Approximate Hardware,” IEEE Embedded Systems Letters, pp. 22–25, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 1044
R. Leer, Measuring Performance of a Static Analysis Framework with an application to Immutability Analysis. Universität Paderborn, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 1045
J. N. Strüwer, Interactive Data Visualization for Exploded Supergraphs. Universität Paderborn, 2018.
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2018 | Conference (Editor) | LibreCat-ID: 10591
S. Abiteboul et al., Eds., Research Directions for Principles of Data Management, vol. 7, no. 1. 2018, pp. 1–29.
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2018 | Conference Paper | LibreCat-ID: 10598
M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “An MCTS-based Framework for Synthesis of Approximate Circuits,” in 26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), 2018, pp. 219–224.
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2018 | Mastersthesis | LibreCat-ID: 10782
L. Clausing, Development of a Hardware / Software Codesign for sonification of LIDAR-based sensor data. Ruhr-University Bochum, 2018.
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2018 | Book Chapter | LibreCat-ID: 10783
I. Couso and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in Frontiers in Computational Intelligence, S. Mostaghim, A. Nürnberger, and C. Borgelt, Eds. Springer, 2018, pp. 31–46.
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2018 | Conference Paper | LibreCat-ID: 1096
D. Beyer, M.-C. Jakobs, T. Lemberger, and H. Wehrheim, “Reducer-Based Construction of Conditional Verifiers,” in Proceedings of the 40th International Conference on Software Engineering (ICSE), Gothenburg, Sweden, 2018, pp. 1182--1193.
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2018 | Bachelorsthesis | LibreCat-ID: 1097
F. P. Jentzsch, Enforcing IP Core Connection Properties with Verifiable Security Monitors. Universität Paderborn, 2018.
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2018 | Conference Paper | LibreCat-ID: 11711
Y. Ajjour et al., “Visualization of the Topic Space of Argument Search Results in args. me,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2018, pp. 60–65.
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2018 | Conference Paper | LibreCat-ID: 11712
R. El Baff, H. Wachsmuth, K. Al Khatib, and B. Stein, “Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus,” in Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018, pp. 454–464.
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2018 | Conference Paper | LibreCat-ID: 11760 | OA
J. Ebbers, A. Nelus, R. Martin, and R. Haeb-Umbach, “Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection,” in DAGA 2018, München, 2018.
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2018 | Conference Paper | LibreCat-ID: 11835 | OA
J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Frame-Online DNN-WPE Dereverberation,” in IWAENC 2018, Tokio, Japan, 2018.
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2018 | Conference Paper | LibreCat-ID: 11837 | OA
J. Heitkaemper, J. Heymann, and R. Haeb-Umbach, “Smoothing along Frequency in Online Neural Network Supported Acoustic Beamforming,” in ITG 2018, Oldenburg, Germany, 2018.
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2018 | Mastersthesis | LibreCat-ID: 1186
A. Kemper, Pure Nash Equilibria in Robust Congestion Games via Potential Functions. Universität Paderborn, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 1187
M. Nachtigall, Scenario-driven Strategy Analysis in a n-player Composition Game Model. Universität Paderborn, 2018.
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2018 | Conference Paper | LibreCat-ID: 11872 | OA
L. Drude et al., “Integration neural network based beamforming and weighted prediction error dereverberation,” in INTERSPEECH 2018, Hyderabad, India, 2018.
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2018 | Conference Paper | LibreCat-ID: 11873 | OA
L. Drude, J. Heymann, C. Boeddeker, and R. Haeb-Umbach, “NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing,” in ITG 2018, Oldenburg, Germany, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 1188
J. Kempf, Learning deterministic bandit behaviour form compositions. Universität Paderborn, 2018.
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2018 | Journal Article | LibreCat-ID: 11916 | OA
V. Despotovic, O. Walter, and R. Haeb-Umbach, “Machine learning techniques for semantic analysis of dysarthric speech: An experimental study,” Speech Communication 99 (2018) 242-251 (Elsevier B.V.), 2018.
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