--- _id: '6512' abstract: - lang: eng text: Scheduling problems are essential for decision making in many academic disciplines, including operations management, computer science, and information systems. Since many scheduling problems are NP-hard in the strong sense, there is only limited research on exact algorithms and how their efficiency scales when implemented on parallel computing architectures. We address this gap by (1) adapting an exact branch-and-price algorithm to a parallel machine scheduling problem on unrelated machines with sequence- and machine-dependent setup times, (2) parallelizing the adapted algorithm by implementing a distributed-memory parallelization with a master/worker approach, and (3) conducting extensive computational experiments using up to 960 MPI processes on a modern high performance computing cluster. With our experiments, we show that the efficiency of our parallelization approach can lead to superlinear speedup but can vary substantially between instances. We further show that the wall time of serial execution can be substantially reduced through our parallelization, in some cases from 94 hours to less than six minutes when our algorithm is executed on 960 processes. author: - first_name: Gerhard full_name: Rauchecker, Gerhard last_name: Rauchecker - first_name: Guido full_name: Schryen, Guido id: '72850' last_name: Schryen citation: ama: 'Rauchecker G, Schryen G. Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm. Computers & Operations Research. 2019;(104):338-357.' apa: 'Rauchecker, G., & Schryen, G. (2019). Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm. Computers & Operations Research, (104), 338–357.' bibtex: '@article{Rauchecker_Schryen_2019, title={Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm}, number={104}, journal={Computers & Operations Research}, publisher={Elsevier}, author={Rauchecker, Gerhard and Schryen, Guido}, year={2019}, pages={338–357} }' chicago: 'Rauchecker, Gerhard, and Guido Schryen. “Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm.” Computers & Operations Research, no. 104 (2019): 338–57.' ieee: 'G. Rauchecker and G. Schryen, “Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm,” Computers & Operations Research, no. 104, pp. 338–357, 2019.' mla: 'Rauchecker, Gerhard, and Guido Schryen. “Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm.” Computers & Operations Research, no. 104, Elsevier, 2019, pp. 338–57.' short: G. Rauchecker, G. Schryen, Computers & Operations Research (2019) 338–357. date_created: 2019-01-08T13:50:44Z date_updated: 2022-01-06T07:03:08Z ddc: - '000' department: - _id: '277' file: - access_level: open_access content_type: application/pdf creator: hsiemes date_created: 2019-01-08T14:03:53Z date_updated: 2019-01-08T14:03:53Z file_id: '6513' file_name: cor-parallel-bp-for-upmsp.pdf file_size: 4153528 relation: main_file file_date_updated: 2019-01-08T14:03:53Z has_accepted_license: '1' issue: '104' keyword: - parallel machine scheduling with setup times - parallel branch-and-price algorithm - high performance computing - master/worker parallelization language: - iso: eng oa: '1' page: 338-357 publication: Computers & Operations Research publisher: Elsevier status: public title: 'Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm' type: journal_article user_id: '61579' year: '2019' ... --- _id: '6514' abstract: - lang: eng text: Recommender Agents (RAs) facilitate consumers’ online purchase decisions for complex, multi-attribute products. As not all combinations of attribute levels can be obtained, users are forced into trade-offs. The exposure of trade-offs in a RA has been found to affect consumers’ perceptions. However, little is known about how different preference elicitation methods in RAs affect consumers by varying degrees of trade-off exposure. We propose a research model that investigates how different levels of trade-off exposure cognitively and affectively influence consumers’ satisfaction with RAs. We operationalize these levels in three different RA types and test our hypotheses in a laboratory experiment with 116 participants. Our results indicate that with increasing tradeoff exposure, perceived enjoyment and perceived control follow an inverted Ushaped relationship. Hence, RAs using preference elicitation methods with medium trade-off exposure yield highest consumer satisfaction. This contributes to the understanding of trade-offs in RAs and provides valuable implications to e-commerce practitioners. author: - first_name: Veronika full_name: Schuhbeck, Veronika last_name: Schuhbeck - first_name: Nils full_name: Siegfried, Nils last_name: Siegfried - first_name: Verena full_name: Dorner, Verena last_name: Dorner - first_name: Alexander full_name: Benlian, Alexander last_name: Benlian - first_name: Michael full_name: Scholz, Michael last_name: Scholz - first_name: Guido full_name: Schryen, Guido id: '72850' last_name: Schryen citation: ama: 'Schuhbeck V, Siegfried N, Dorner V, Benlian A, Scholz M, Schryen G. Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents. In: Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik. Siegen, Germany; 2019:55-64.' apa: 'Schuhbeck, V., Siegfried, N., Dorner, V., Benlian, A., Scholz, M., & Schryen, G. (2019). Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents. In Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik (pp. 55–64). Siegen, Germany.' bibtex: '@inproceedings{Schuhbeck_Siegfried_Dorner_Benlian_Scholz_Schryen_2019, place={Siegen, Germany}, title={Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents}, booktitle={Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik}, author={Schuhbeck, Veronika and Siegfried, Nils and Dorner, Verena and Benlian, Alexander and Scholz, Michael and Schryen, Guido}, year={2019}, pages={55–64} }' chicago: 'Schuhbeck, Veronika, Nils Siegfried, Verena Dorner, Alexander Benlian, Michael Scholz, and Guido Schryen. “Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents.” In Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik, 55–64. Siegen, Germany, 2019.' ieee: 'V. Schuhbeck, N. Siegfried, V. Dorner, A. Benlian, M. Scholz, and G. Schryen, “Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents,” in Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik, Siegen, Germany, 2019, pp. 55–64.' mla: 'Schuhbeck, Veronika, et al. “Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents.” Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik, 2019, pp. 55–64.' short: 'V. Schuhbeck, N. Siegfried, V. Dorner, A. Benlian, M. Scholz, G. Schryen, in: Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik, Siegen, Germany, 2019, pp. 55–64.' conference: end_date: 2019-02-27 location: Siegen, Germany name: 14th International Conference on Wirtschaftsinformatik start_date: 2019-02-24 date_created: 2019-01-08T14:06:32Z date_updated: 2022-01-06T07:03:09Z ddc: - '000' department: - _id: '277' file: - access_level: closed content_type: application/pdf creator: hsiemes date_created: 2019-01-08T14:07:17Z date_updated: 2021-08-13T13:26:11Z file_id: '6515' file_name: WALKING THE MIDDLE PATH.pdf file_size: 371490 relation: main_file - access_level: open_access content_type: application/pdf creator: hsiemes date_created: 2021-08-13T13:25:53Z date_updated: 2021-08-13T13:25:53Z file_id: '23393' file_name: Walking the Middle Path_ How Medium Trade-Off Exposure Leads to H.pdf file_size: 331001 relation: main_file file_date_updated: 2021-08-13T13:26:11Z has_accepted_license: '1' keyword: - Recommender Agents - Preference Elicitation Method - Trade-off Exposure - Customer Satisfaction language: - iso: eng oa: '1' page: 55-64 place: Siegen, Germany publication: Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik status: public title: 'Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents' type: conference user_id: '61579' year: '2019' ... --- _id: '6860' author: - first_name: Haitham full_name: Afifi, Haitham id: '65718' last_name: Afifi - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Afifi H, Karl H. Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding. In: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019). Las Vegas: IEEE.' apa: 'Afifi, H., & Karl, H. (n.d.). Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding. In 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019). Las Vegas: IEEE.' bibtex: '@inproceedings{Afifi_Karl, place={Las Vegas}, title={Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding}, booktitle={2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019)}, publisher={IEEE}, author={Afifi, Haitham and Karl, Holger} }' chicago: 'Afifi, Haitham, and Holger Karl. “Power Allocation with a Wireless Multi-Cast Aware Routing for Virtual Network Embedding.” In 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019). Las Vegas: IEEE, n.d.' ieee: H. Afifi and H. Karl, “Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding,” in 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019). mla: Afifi, Haitham, and Holger Karl. “Power Allocation with a Wireless Multi-Cast Aware Routing for Virtual Network Embedding.” 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019), IEEE. short: 'H. Afifi, H. Karl, in: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019), IEEE, Las Vegas, n.d.' date_created: 2019-01-17T15:51:34Z date_updated: 2022-01-06T07:03:22Z ddc: - '000' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: hafifi date_created: 2019-01-17T15:49:37Z date_updated: 2019-01-17T15:49:37Z file_id: '6861' file_name: globecom.pdf file_size: 320283 relation: main_file file_date_updated: 2019-01-17T15:49:37Z has_accepted_license: '1' language: - iso: eng oa: '1' place: Las Vegas project: - _id: '27' name: 'Akustische Sensornetzwerke - Teilprojekt ' - _id: '27' name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke publication: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019) publication_status: accepted publisher: IEEE status: public title: Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding type: conference user_id: '65718' year: '2019' ... --- _id: '16847' abstract: - lang: eng text: In this work we describe our results achieved in the ProtestNews Lab at CLEF 2019. To tackle the problems of event sentence detection and event extraction we decided to use contextualized string embeddings. The models were trained on a data corpus collected from Indian news sources, but evaluated on data obtained from news sources from other countries as well, such as China. Our models have obtained competitive results and have scored 3rd in the event sentence detection task and 1st in the event extraction task based on average F1-scores for different test datasets. author: - first_name: Gabriella full_name: Skitalinskaya, Gabriella last_name: Skitalinskaya - first_name: Jonas full_name: Klaff, Jonas last_name: Klaff - first_name: Maximilian full_name: Spliethöver, Maximilian id: '84035' last_name: Spliethöver orcid: 0000-0003-4364-1409 citation: ama: 'Skitalinskaya G, Klaff J, Spliethöver M. CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction. Vol 2380. Lugano, Switzerland; 2019.' apa: 'Skitalinskaya, G., Klaff, J., & Spliethöver, M. (2019). CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction (Vol. 2380). Lugano, Switzerland.' bibtex: '@book{Skitalinskaya_Klaff_Spliethöver_2019, place={Lugano, Switzerland}, series={CEUR Workshop Proceedings}, title={CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction}, volume={2380}, author={Skitalinskaya, Gabriella and Klaff, Jonas and Spliethöver, Maximilian}, year={2019}, collection={CEUR Workshop Proceedings} }' chicago: 'Skitalinskaya, Gabriella, Jonas Klaff, and Maximilian Spliethöver. CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction. Vol. 2380. CEUR Workshop Proceedings. Lugano, Switzerland, 2019.' ieee: 'G. Skitalinskaya, J. Klaff, and M. Spliethöver, CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction, vol. 2380. Lugano, Switzerland, 2019.' mla: 'Skitalinskaya, Gabriella, et al. CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction. Vol. 2380, 2019.' short: 'G. Skitalinskaya, J. Klaff, M. Spliethöver, CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction, Lugano, Switzerland, 2019.' date_created: 2020-04-23T15:18:40Z date_updated: 2022-01-06T06:52:57Z extern: '1' intvolume: ' 2380' language: - iso: eng main_file_link: - open_access: '1' url: http://ceur-ws.org/Vol-2380/paper_118.pdf oa: '1' page: '7' place: Lugano, Switzerland report_number: '118' series_title: CEUR Workshop Proceedings status: public title: 'CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction' type: report user_id: '84035' volume: 2380 year: '2019' ... --- _id: '11965' abstract: - lang: eng text: 'We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood criterion derived from a spatial mixture model of the observations. It is trained from scratch without requiring any parallel data consisting of degraded input and clean training targets. Thus, training can be carried out on real recordings of noisy speech rather than simulated ones. In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely. We demonstrate the effectiveness of our approach by speech recognition experiments on two different datasets: one mainly deteriorated by noise (CHiME 4) and one by reverberation (REVERB). The results show that the performance of the proposed system is on par with a supervised system using oracle target masks for training and with a system trained using a model-based teacher.' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Heymann J, Haeb-Umbach R. Unsupervised training of neural mask-based beamforming. In: INTERSPEECH 2019, Graz, Austria. ; 2019.' apa: Drude, L., Heymann, J., & Haeb-Umbach, R. (2019). Unsupervised training of neural mask-based beamforming. In INTERSPEECH 2019, Graz, Austria. bibtex: '@inproceedings{Drude_Heymann_Haeb-Umbach_2019, title={Unsupervised training of neural mask-based beamforming}, booktitle={INTERSPEECH 2019, Graz, Austria}, author={Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2019} }' chicago: Drude, Lukas, Jahn Heymann, and Reinhold Haeb-Umbach. “Unsupervised Training of Neural Mask-Based Beamforming.” In INTERSPEECH 2019, Graz, Austria, 2019. ieee: L. Drude, J. Heymann, and R. Haeb-Umbach, “Unsupervised training of neural mask-based beamforming,” in INTERSPEECH 2019, Graz, Austria, 2019. mla: Drude, Lukas, et al. “Unsupervised Training of Neural Mask-Based Beamforming.” INTERSPEECH 2019, Graz, Austria, 2019. short: 'L. Drude, J. Heymann, R. Haeb-Umbach, in: INTERSPEECH 2019, Graz, Austria, 2019.' date_created: 2019-07-18T09:11:39Z date_updated: 2022-01-06T06:51:14Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-13T06:36:44Z date_updated: 2019-08-13T06:41:35Z file_id: '12914' file_name: INTERSPEECH_2019_Drude_Paper.pdf file_size: 223413 relation: main_file file_date_updated: 2019-08-13T06:41:35Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: INTERSPEECH 2019, Graz, Austria status: public title: Unsupervised training of neural mask-based beamforming type: conference user_id: '59789' year: '2019' ... --- _id: '12874' abstract: - lang: eng text: We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26% over the unsupervised teacher. author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Daniel full_name: Hasenklever, Daniel last_name: Hasenklever - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Drude L, Hasenklever D, Haeb-Umbach R. Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation. In: ICASSP 2019, Brighton, UK. ; 2019.' apa: Drude, L., Hasenklever, D., & Haeb-Umbach, R. (2019). Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation. In ICASSP 2019, Brighton, UK. bibtex: '@inproceedings{Drude_Hasenklever_Haeb-Umbach_2019, title={Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation}, booktitle={ICASSP 2019, Brighton, UK}, author={Drude, Lukas and Hasenklever, Daniel and Haeb-Umbach, Reinhold}, year={2019} }' chicago: Drude, Lukas, Daniel Hasenklever, and Reinhold Haeb-Umbach. “Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation.” In ICASSP 2019, Brighton, UK, 2019. ieee: L. Drude, D. Hasenklever, and R. Haeb-Umbach, “Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation,” in ICASSP 2019, Brighton, UK, 2019. mla: Drude, Lukas, et al. “Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation.” ICASSP 2019, Brighton, UK, 2019. short: 'L. Drude, D. Hasenklever, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK, 2019.' date_created: 2019-07-23T07:37:54Z date_updated: 2022-01-06T06:51:21Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-14T07:19:13Z date_updated: 2019-08-14T07:19:13Z file_id: '12925' file_name: ICASSP_2019_Drude_Paper.pdf file_size: 368225 relation: main_file file_date_updated: 2019-08-14T07:19:13Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: ICASSP 2019, Brighton, UK status: public title: Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation type: conference user_id: '59789' year: '2019' ... --- _id: '12875' abstract: - lang: eng text: 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. First proposed as an iterative algorithm, follow-up works have reformulated it as a recursive least squares algorithm and therefore enabled its use in online applications. For this algorithm, the estimation of the power spectral density (PSD) of the anechoic signal plays an important role and strongly influences its performance. Recently, we showed that using a neural network PSD estimator leads to improved performance for online automatic speech recognition. This, however, comes at a price. To train the network, we require parallel data, i.e., utterances simultaneously available in clean and reverberated form. Here we propose to overcome this limitation by training the network jointly with the acoustic model of the speech recognizer. To be specific, the gradients computed from the cross-entropy loss between the target senone sequence and the acoustic model network output is backpropagated through the complex-valued dereverberation filter estimation to the neural network for PSD estimation. Evaluation on two databases demonstrates improved performance for on-line processing scenarios while imposing fewer requirements on the available training data and thus widening the range of applications. author: - first_name: Jahn full_name: Heymann, Jahn id: '9168' last_name: Heymann - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Keisuke full_name: Kinoshita, Keisuke last_name: Kinoshita - first_name: Tomohiro full_name: Nakatani, Tomohiro last_name: Nakatani citation: ama: 'Heymann J, Drude L, Haeb-Umbach R, Kinoshita K, Nakatani T. Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR. In: ICASSP 2019, Brighton, UK. ; 2019.' apa: Heymann, J., Drude, L., Haeb-Umbach, R., Kinoshita, K., & Nakatani, T. (2019). Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR. In ICASSP 2019, Brighton, UK. bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_Kinoshita_Nakatani_2019, title={Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR}, booktitle={ICASSP 2019, Brighton, UK}, author={Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani, Tomohiro}, year={2019} }' chicago: Heymann, Jahn, Lukas Drude, Reinhold Haeb-Umbach, Keisuke Kinoshita, and Tomohiro Nakatani. “Joint Optimization of Neural Network-Based WPE Dereverberation and Acoustic Model for Robust Online ASR.” In ICASSP 2019, Brighton, UK, 2019. ieee: J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR,” in ICASSP 2019, Brighton, UK, 2019. mla: Heymann, Jahn, et al. “Joint Optimization of Neural Network-Based WPE Dereverberation and Acoustic Model for Robust Online ASR.” ICASSP 2019, Brighton, UK, 2019. short: 'J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: ICASSP 2019, Brighton, UK, 2019.' date_created: 2019-07-23T07:42:26Z date_updated: 2022-01-06T06:51:22Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-12-17T07:28:06Z date_updated: 2019-12-17T07:28:06Z file_id: '15334' file_name: ICASSP_2019_Heymann_Paper.pdf file_size: 199109 relation: main_file file_date_updated: 2019-12-17T07:28:06Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: ICASSP 2019, Brighton, UK status: public title: Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR type: conference user_id: '59789' year: '2019' ... --- _id: '12876' abstract: - lang: eng text: In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available. Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds. The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend. author: - first_name: Gerhard full_name: Kurz, Gerhard last_name: Kurz - first_name: Igor full_name: Gilitschenski, Igor last_name: Gilitschenski - first_name: Florian full_name: Pfaff, Florian last_name: Pfaff - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Uwe D. full_name: Hanebeck, Uwe D. last_name: Hanebeck - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach - first_name: Roland Y. full_name: Siegwart, Roland Y. last_name: Siegwart citation: ama: 'Kurz G, Gilitschenski I, Pfaff F, et al. Directional Statistics and Filtering Using libDirectional. In: Journal of Statistical Software 89(4). ; 2019.' apa: Kurz, G., Gilitschenski, I., Pfaff, F., Drude, L., Hanebeck, U. D., Haeb-Umbach, R., & Siegwart, R. Y. (2019). Directional Statistics and Filtering Using libDirectional. In Journal of Statistical Software 89(4). bibtex: '@inproceedings{Kurz_Gilitschenski_Pfaff_Drude_Hanebeck_Haeb-Umbach_Siegwart_2019, title={Directional Statistics and Filtering Using libDirectional}, booktitle={Journal of Statistical Software 89(4)}, author={Kurz, Gerhard and Gilitschenski, Igor and Pfaff, Florian and Drude, Lukas and Hanebeck, Uwe D. and Haeb-Umbach, Reinhold and Siegwart, Roland Y.}, year={2019} }' chicago: Kurz, Gerhard, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck, Reinhold Haeb-Umbach, and Roland Y. Siegwart. “Directional Statistics and Filtering Using LibDirectional.” In Journal of Statistical Software 89(4), 2019. ieee: G. Kurz et al., “Directional Statistics and Filtering Using libDirectional,” in Journal of Statistical Software 89(4), 2019. mla: Kurz, Gerhard, et al. “Directional Statistics and Filtering Using LibDirectional.” Journal of Statistical Software 89(4), 2019. short: 'G. Kurz, I. Gilitschenski, F. Pfaff, L. Drude, U.D. Hanebeck, R. Haeb-Umbach, R.Y. Siegwart, in: Journal of Statistical Software 89(4), 2019.' date_created: 2019-07-23T07:44:59Z date_updated: 2022-01-06T06:51:22Z ddc: - '000' department: - _id: '54' file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-14T07:16:05Z date_updated: 2019-08-14T07:16:05Z file_id: '12923' file_name: JournalofStatisticalSoftware_2019_Drude_Paper.pdf file_size: 1522964 relation: main_file file_date_updated: 2019-08-14T07:16:05Z has_accepted_license: '1' language: - iso: eng oa: '1' publication: Journal of Statistical Software 89(4) status: public title: Directional Statistics and Filtering Using libDirectional type: conference user_id: '59789' year: '2019' ... --- _id: '12882' abstract: - lang: eng text: One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework. author: - first_name: Haitham full_name: Afifi, Haitham id: '65718' last_name: Afifi - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Sebastian full_name: Eikenberg, Sebastian last_name: Eikenberg - first_name: Arnold full_name: Mueller, Arnold last_name: Mueller - first_name: Lars full_name: Gansel, Lars last_name: Gansel - first_name: Alexander full_name: Makejkin, Alexander last_name: Makejkin - first_name: Kai full_name: Hannemann, Kai last_name: Hannemann - first_name: Rafael full_name: Schellenberg, Rafael last_name: Schellenberg citation: ama: 'Afifi H, Karl H, Eikenberg S, et al. A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo). Marrakech, Morocco; 2019.' apa: Afifi, H., Karl, H., Eikenberg, S., Mueller, A., Gansel, L., Makejkin, A., … Schellenberg, R. (2019). A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO. In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo). Marrakech, Morocco. bibtex: '@inproceedings{Afifi_Karl_Eikenberg_Mueller_Gansel_Makejkin_Hannemann_Schellenberg_2019, place={Marrakech, Morocco}, title={A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO}, booktitle={2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo)}, author={Afifi, Haitham and Karl, Holger and Eikenberg, Sebastian and Mueller, Arnold and Gansel, Lars and Makejkin, Alexander and Hannemann, Kai and Schellenberg, Rafael}, year={2019} }' chicago: Afifi, Haitham, Holger Karl, Sebastian Eikenberg, Arnold Mueller, Lars Gansel, Alexander Makejkin, Kai Hannemann, and Rafael Schellenberg. “A Rapid Prototyping for Wireless Virtual Network Embedding Using MARVELO.” In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo). Marrakech, Morocco, 2019. ieee: H. Afifi et al., “A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO,” in 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo), 2019. mla: Afifi, Haitham, et al. “A Rapid Prototyping for Wireless Virtual Network Embedding Using MARVELO.” 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo), 2019. short: 'H. Afifi, H. Karl, S. Eikenberg, A. Mueller, L. Gansel, A. Makejkin, K. Hannemann, R. Schellenberg, in: 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo), Marrakech, Morocco, 2019.' date_created: 2019-07-24T07:28:45Z date_updated: 2022-01-06T06:51:22Z ddc: - '006' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: hafifi date_created: 2021-01-30T12:39:43Z date_updated: 2021-01-30T12:42:31Z file_id: '21113' file_name: demo.pdf file_size: 102976 relation: main_file file_date_updated: 2021-01-30T12:42:31Z has_accepted_license: '1' keyword: - WSN - virtualization - VNE language: - iso: eng oa: '1' place: Marrakech, Morocco project: - _id: '27' name: 'Akustische Sensornetzwerke - Teilprojekt ' publication: 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo) status: public title: A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO type: conference user_id: '65718' year: '2019' ... --- _id: '12890' abstract: - lang: eng text: 'We formulate a generic framework for blind source separation (BSS), which allows integrating data-driven spectro-temporal methods, such as deep clustering and deep attractor networks, with physically motivated probabilistic spatial methods, such as complex angular central Gaussian mixture models. The integrated model exploits the complementary strengths of the two approaches to BSS: the strong modeling power of neural networks, which, however, is based on supervised learning, and the ease of unsupervised learning of the spatial mixture models whose few parameters can be estimated on as little as a single segment of a real mixture of speech. Experiments are carried out on both artificially mixed speech and true recordings of speech mixtures. The experiments verify that the integrated models consistently outperform the individual components. We further extend the models to cope with noisy, reverberant speech and introduce a cross-domain teacher–student training where the mixture model serves as the teacher to provide training targets for the student neural network.' author: - first_name: Lukas full_name: Drude, Lukas id: '11213' last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: Drude L, Haeb-Umbach R. Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation. IEEE Journal of Selected Topics in Signal Processing. 2019. doi:10.1109/JSTSP.2019.2912565 apa: Drude, L., & Haeb-Umbach, R. (2019). Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation. IEEE Journal of Selected Topics in Signal Processing. https://doi.org/10.1109/JSTSP.2019.2912565 bibtex: '@article{Drude_Haeb-Umbach_2019, title={Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation}, DOI={10.1109/JSTSP.2019.2912565}, journal={IEEE Journal of Selected Topics in Signal Processing}, author={Drude, Lukas and Haeb-Umbach, Reinhold}, year={2019} }' chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE Journal of Selected Topics in Signal Processing, 2019. https://doi.org/10.1109/JSTSP.2019.2912565. ieee: L. Drude and R. Haeb-Umbach, “Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation,” IEEE Journal of Selected Topics in Signal Processing, 2019. mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE Journal of Selected Topics in Signal Processing, 2019, doi:10.1109/JSTSP.2019.2912565. short: L. Drude, R. Haeb-Umbach, IEEE Journal of Selected Topics in Signal Processing (2019). date_created: 2019-07-26T08:38:46Z date_updated: 2022-01-06T06:51:23Z ddc: - '050' department: - _id: '54' doi: 10.1109/JSTSP.2019.2912565 file: - access_level: open_access content_type: application/pdf creator: huesera date_created: 2019-08-07T07:12:21Z date_updated: 2019-08-14T07:11:22Z file_id: '12903' file_name: IEEE Jounal_2019_Drude_Paper.pdf file_size: 967424 relation: main_file file_date_updated: 2019-08-14T07:11:22Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: IEEE Journal of Selected Topics in Signal Processing publication_identifier: eissn: - 1941-0484 status: public title: Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation type: journal_article user_id: '11213' year: '2019' ...