--- _id: '33734' abstract: - lang: eng text: 'Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'''' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'''' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis' author: - first_name: N'Dah Jean full_name: KOUAGOU, N'Dah Jean id: '87189' last_name: KOUAGOU - first_name: Stefan full_name: Heindorf, Stefan id: '11871' last_name: Heindorf orcid: 0000-0002-4525-6865 - first_name: Caglar full_name: Demir, Caglar id: '43817' last_name: Demir - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Neural Class Expression Synthesis. In: Pesquita C, Jimenez-Ruiz E, McCusker J, et al., eds. The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023). Vol 13870. Springer International Publishing; 2023:209-226. doi:https://doi.org/10.1007/978-3-031-33455-9_13' apa: KOUAGOU, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2023). Neural Class Expression Synthesis. In C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, & S. Hertling (Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023) (Vol. 13870, pp. 209–226). Springer International Publishing. https://doi.org/10.1007/978-3-031-33455-9_13 bibtex: '@inproceedings{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2023, title={Neural Class Expression Synthesis}, volume={13870}, DOI={https://doi.org/10.1007/978-3-031-33455-9_13}, booktitle={The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}, publisher={Springer International Publishing}, author={KOUAGOU, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, editor={Pesquita, Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}, year={2023}, pages={209–226} }' chicago: KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “Neural Class Expression Synthesis.” In The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), edited by Catia Pesquita, Ernesto Jimenez-Ruiz, Jamie McCusker, Daniel Faria, Mauro Dragoni, Anastasia Dimou, Raphael Troncy, and Sven Hertling, 13870:209–26. Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-33455-9_13. ieee: 'N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class Expression Synthesis,” in The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), Hersonissos, Crete, Greece, 2023, vol. 13870, pp. 209–226, doi: https://doi.org/10.1007/978-3-031-33455-9_13.' mla: KOUAGOU, N’Dah Jean, et al. “Neural Class Expression Synthesis.” The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), edited by Catia Pesquita et al., vol. 13870, Springer International Publishing, 2023, pp. 209–26, doi:https://doi.org/10.1007/978-3-031-33455-9_13. short: 'N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, S. Hertling (Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), Springer International Publishing, 2023, pp. 209–226.' conference: end_date: 2023-06-01 location: Hersonissos, Crete, Greece name: 20th Extended Semantic Web Conference start_date: 2023-05-28 date_created: 2022-10-15T19:20:11Z date_updated: 2023-07-02T18:10:02Z department: - _id: '574' - _id: '760' doi: https://doi.org/10.1007/978-3-031-33455-9_13 editor: - first_name: Catia full_name: Pesquita, Catia last_name: Pesquita - first_name: Ernesto full_name: Jimenez-Ruiz, Ernesto last_name: Jimenez-Ruiz - first_name: Jamie full_name: McCusker, Jamie last_name: McCusker - first_name: Daniel full_name: Faria, Daniel last_name: Faria - first_name: Mauro full_name: Dragoni, Mauro last_name: Dragoni - first_name: Anastasia full_name: Dimou, Anastasia last_name: Dimou - first_name: Raphael full_name: Troncy, Raphael last_name: Troncy - first_name: Sven full_name: Hertling, Sven last_name: Hertling external_id: unknown: - https://link.springer.com/chapter/10.1007/978-3-031-33455-9_13 intvolume: ' 13870' keyword: - Neural network - Concept learning - Description logics language: - iso: eng main_file_link: - open_access: '1' url: https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Kouagou_2023_Neural.pdf oa: '1' page: 209 - 226 project: - _id: '410' name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale' - _id: '407' grant_number: '101070305' name: 'ENEXA: Efficient Explainable Learning on Knowledge Graphs' - _id: '285' grant_number: NW21-059D name: 'SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems' publication: The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023) publication_identifier: unknown: - 978-3-031-33455-9 publication_status: published publisher: Springer International Publishing status: public title: Neural Class Expression Synthesis type: conference user_id: '11871' volume: 13870 year: '2023' ... --- _id: '34140' abstract: - lang: eng text: In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits. author: - first_name: Jad full_name: Maalouly, Jad last_name: Maalouly - first_name: Dennis full_name: Hemker, Dennis last_name: Hemker - first_name: Christian full_name: Hedayat, Christian last_name: Hedayat - first_name: Christian full_name: Rückert, Christian last_name: Rückert - first_name: Ivan full_name: Kaufmann, Ivan last_name: Kaufmann - first_name: Marcel full_name: Olbrich, Marcel last_name: Olbrich - first_name: Sven full_name: Lange, Sven id: '38240' last_name: Lange - first_name: Harald full_name: Mathis, Harald last_name: Mathis citation: ama: 'Maalouly J, Hemker D, Hedayat C, et al. AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development. In: 2022 Kleinheubach Conference. IEEE; 2022.' apa: Maalouly, J., Hemker, D., Hedayat, C., Rückert, C., Kaufmann, I., Olbrich, M., Lange, S., & Mathis, H. (2022). AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development. 2022 Kleinheubach Conference. 2022 Kleinheubach Conference, Miltenberg, Germany. bibtex: '@inproceedings{Maalouly_Hemker_Hedayat_Rückert_Kaufmann_Olbrich_Lange_Mathis_2022, place={Miltenberg, Germany}, title={AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}, booktitle={2022 Kleinheubach Conference}, publisher={IEEE}, author={Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}, year={2022} }' chicago: 'Maalouly, Jad, Dennis Hemker, Christian Hedayat, Christian Rückert, Ivan Kaufmann, Marcel Olbrich, Sven Lange, and Harald Mathis. “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development.” In 2022 Kleinheubach Conference. Miltenberg, Germany: IEEE, 2022.' ieee: J. Maalouly et al., “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development,” presented at the 2022 Kleinheubach Conference, Miltenberg, Germany, 2022. mla: Maalouly, Jad, et al. “AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development.” 2022 Kleinheubach Conference, IEEE, 2022. short: 'J. Maalouly, D. Hemker, C. Hedayat, C. Rückert, I. Kaufmann, M. Olbrich, S. Lange, H. Mathis, in: 2022 Kleinheubach Conference, IEEE, Miltenberg, Germany, 2022.' conference: end_date: 2022-09-29 location: Miltenberg, Germany name: 2022 Kleinheubach Conference start_date: 2022-09-27 date_created: 2022-11-24T14:21:17Z date_updated: 2022-11-24T14:21:34Z department: - _id: '59' - _id: '485' keyword: - emc - pcb - electronic system development - machine learning - neural network language: - iso: eng main_file_link: - url: https://ieeexplore.ieee.org/document/9954484 place: Miltenberg, Germany project: - _id: '52' name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing' publication: 2022 Kleinheubach Conference publication_identifier: eisbn: - 978-3-948571-07-8 publication_status: published publisher: IEEE status: public title: AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development type: conference user_id: '38240' year: '2022' ... --- _id: '35620' abstract: - lang: eng text: Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density. article_type: original author: - first_name: Jan-Peter full_name: Kucklick, Jan-Peter id: '77066' last_name: Kucklick - first_name: Oliver full_name: Müller, Oliver id: '72849' last_name: Müller citation: ama: 'Kucklick J-P, Müller O. Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. ACM Transactions on Management Information Systems. Published online 2022. doi:10.1145/3567430' apa: 'Kucklick, J.-P., & Müller, O. (2022). Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. ACM Transactions on Management Information Systems. https://doi.org/10.1145/3567430' bibtex: '@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal}, DOI={10.1145/3567430}, journal={ACM Transactions on Management Information Systems}, publisher={Association for Computing Machinery (ACM)}, author={Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }' chicago: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” ACM Transactions on Management Information Systems, 2022. https://doi.org/10.1145/3567430.' ieee: 'J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,” ACM Transactions on Management Information Systems, 2022, doi: 10.1145/3567430.' mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” ACM Transactions on Management Information Systems, Association for Computing Machinery (ACM), 2022, doi:10.1145/3567430.' short: J.-P. Kucklick, O. Müller, ACM Transactions on Management Information Systems (2022). date_created: 2023-01-10T05:16:02Z date_updated: 2023-01-10T05:20:18Z department: - _id: '195' - _id: '196' doi: 10.1145/3567430 keyword: - Interpretability - Convolutional Neural Network - Accuracy-Interpretability Trade-Of - Real Estate Appraisal - Hedonic Pricing - Grad-Ram language: - iso: eng main_file_link: - url: https://dl.acm.org/doi/pdf/10.1145/3567430 publication: ACM Transactions on Management Information Systems publication_identifier: issn: - 2158-656X - 2158-6578 publication_status: published publisher: Association for Computing Machinery (ACM) status: public title: 'Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal' type: journal_article user_id: '77066' year: '2022' ... --- _id: '20504' abstract: - lang: eng text: 'In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradually replacing components of an utterance-level permutation invariant training (u-PIT) based separation system in the frequency domain until the TasNet system is reached, thus blending components of frequency domain approaches with those of time domain approaches. Some of the intermediate variants achieve comparable signal-to-distortion ratio (SDR) gains to TasNet, but retain the advantage of frequency domain processing: compatibility with classic signal processing tools such as frequency-domain beamforming and the human interpretability of the masks. Furthermore, we show that the scale invariant signal-to-distortion ratio (si-SDR) criterion used as loss function in TasNet is related to a logarithmic mean square error criterion and that it is this criterion which contributes most reliable to the performance advantage of TasNet. Finally, we critically assess which gains in a noise-free single channel environment generalize to more realistic reverberant conditions.' author: - first_name: Jens full_name: Heitkaemper, Jens id: '27643' last_name: Heitkaemper - first_name: Darius full_name: Jakobeit, Darius last_name: Jakobeit - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Lukas full_name: Drude, Lukas last_name: Drude - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Heitkaemper J, Jakobeit D, Boeddeker C, Drude L, Haeb-Umbach R. Demystifying TasNet: A Dissecting Approach. In: ICASSP 2020 Virtual Barcelona Spain. ; 2020.' apa: 'Heitkaemper, J., Jakobeit, D., Boeddeker, C., Drude, L., & Haeb-Umbach, R. (2020). Demystifying TasNet: A Dissecting Approach. ICASSP 2020 Virtual Barcelona Spain.' bibtex: '@inproceedings{Heitkaemper_Jakobeit_Boeddeker_Drude_Haeb-Umbach_2020, title={Demystifying TasNet: A Dissecting Approach}, booktitle={ICASSP 2020 Virtual Barcelona Spain}, author={Heitkaemper, Jens and Jakobeit, Darius and Boeddeker, Christoph and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2020} }' chicago: 'Heitkaemper, Jens, Darius Jakobeit, Christoph Boeddeker, Lukas Drude, and Reinhold Haeb-Umbach. “Demystifying TasNet: A Dissecting Approach.” In ICASSP 2020 Virtual Barcelona Spain, 2020.' ieee: 'J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, and R. Haeb-Umbach, “Demystifying TasNet: A Dissecting Approach,” 2020.' mla: 'Heitkaemper, Jens, et al. “Demystifying TasNet: A Dissecting Approach.” ICASSP 2020 Virtual Barcelona Spain, 2020.' short: 'J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, R. Haeb-Umbach, in: ICASSP 2020 Virtual Barcelona Spain, 2020.' date_created: 2020-11-25T14:56:53Z date_updated: 2022-01-13T08:47:32Z ddc: - '000' department: - _id: '54' file: - access_level: closed content_type: application/pdf creator: jensheit date_created: 2020-12-11T12:36:37Z date_updated: 2020-12-11T12:36:37Z file_id: '20699' file_name: ms.pdf file_size: 3871374 relation: main_file success: 1 file_date_updated: 2020-12-11T12:36:37Z has_accepted_license: '1' keyword: - voice activity detection - speech activity detection - neural network - statistical speech processing language: - iso: eng license: https://creativecommons.org/publicdomain/zero/1.0/ project: - _id: '52' name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing' publication: ICASSP 2020 Virtual Barcelona Spain quality_controlled: '1' status: public title: 'Demystifying TasNet: A Dissecting Approach' type: conference user_id: '40767' year: '2020' ... --- _id: '20505' abstract: - lang: eng text: "Speech activity detection (SAD), which often rests on the fact that the noise is \"more'' stationary than speech, is particularly challenging in non-stationary environments, because the time variance of the acoustic scene makes it difficult to discriminate speech from noise. We propose two approaches to SAD, where one is based on statistical signal processing, while the other utilizes neural networks. The former employs sophisticated signal processing to track the noise and speech energies and is meant to support the case for a resource efficient, unsupervised signal processing approach.\r\nThe latter introduces a recurrent network layer that operates on short segments of the input speech to do temporal smoothing in the presence of non-stationary noise. The systems are tested on the Fearless Steps challenge database, which consists of the transmission data from the Apollo-11 space mission.\r\nThe statistical SAD achieves comparable detection performance to earlier proposed neural network based SADs, while the neural network based approach leads to a decision cost function of 1.07% on the evaluation set of the 2020 Fearless Steps Challenge, which sets a new state of the art." author: - first_name: Jens full_name: Heitkaemper, Jens id: '27643' last_name: Heitkaemper - first_name: Joerg full_name: Schmalenstroeer, Joerg id: '460' last_name: Schmalenstroeer - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'Heitkaemper J, Schmalenstroeer J, Haeb-Umbach R. Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments. In: INTERSPEECH 2020 Virtual Shanghai China. ; 2020.' apa: Heitkaemper, J., Schmalenstroeer, J., & Haeb-Umbach, R. (2020). Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments. INTERSPEECH 2020 Virtual Shanghai China. bibtex: '@inproceedings{Heitkaemper_Schmalenstroeer_Haeb-Umbach_2020, title={Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments}, booktitle={INTERSPEECH 2020 Virtual Shanghai China}, author={Heitkaemper, Jens and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2020} }' chicago: Heitkaemper, Jens, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments.” In INTERSPEECH 2020 Virtual Shanghai China, 2020. ieee: J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments,” 2020. mla: Heitkaemper, Jens, et al. “Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments.” INTERSPEECH 2020 Virtual Shanghai China, 2020. short: 'J. Heitkaemper, J. Schmalenstroeer, R. Haeb-Umbach, in: INTERSPEECH 2020 Virtual Shanghai China, 2020.' date_created: 2020-11-25T15:03:19Z date_updated: 2023-10-26T08:28:49Z ddc: - '000' department: - _id: '54' file: - access_level: closed content_type: application/pdf creator: jensheit date_created: 2020-12-11T12:33:04Z date_updated: 2020-12-11T12:33:04Z file_id: '20697' file_name: ms.pdf file_size: 998706 relation: main_file success: 1 file_date_updated: 2020-12-11T12:33:04Z has_accepted_license: '1' keyword: - voice activity detection - speech activity detection - neural network - statistical speech processing language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: INTERSPEECH 2020 Virtual Shanghai China status: public title: Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments type: conference user_id: '460' year: '2020' ...