@misc{34187, abstract = {{BloKK-Beitrag für das ZeKK, 03.12.2022}}, author = {{Lebock, Sarah}}, title = {{{Blogpost "Von der Grundstimmung als philosophischer Ausgangspunkt"}}}, year = {{2022}}, } @article{33669, abstract = {{Far-field multi-speaker automatic speech recognition (ASR) has drawn increasing attention in recent years. Most existing methods feature a signal processing frontend and an ASR backend. In realistic scenarios, these modules are usually trained separately or progressively, which suffers from either inter-module mismatch or a complicated training process. In this paper, we propose an end-to-end multi-channel model that jointly optimizes the speech enhancement (including speech dereverberation, denoising, and separation) frontend and the ASR backend as a single system. To the best of our knowledge, this is the first work that proposes to optimize dereverberation, beamforming, and multi-speaker ASR in a fully end-to-end manner. The frontend module consists of a weighted prediction error (WPE) based submodule for dereverberation and a neural beamformer for denoising and speech separation. For the backend, we adopt a widely used end-to-end (E2E) ASR architecture. It is worth noting that the entire model is differentiable and can be optimized in a fully end-to-end manner using only the ASR criterion, without the need of parallel signal-level labels. We evaluate the proposed model on several multi-speaker benchmark datasets, and experimental results show that the fully E2E ASR model can achieve competitive performance on both noisy and reverberant conditions, with over 30% relative word error rate (WER) reduction over the single-channel baseline systems.}}, author = {{Zhang, Wangyou and Chang, Xuankai and Boeddeker, Christoph and Nakatani, Tomohiro and Watanabe, Shinji and Qian, Yanmin}}, issn = {{Print ISSN: 2329-9290 Electronic ISSN: 2329-9304}}, journal = {{IEEE/ACM Transactions on Audio, Speech, and Language Processing}}, title = {{{End-to-End Dereverberation, Beamforming, and Speech Recognition in A Cocktail Party}}}, doi = {{10.1109/TASLP.2022.3209942}}, year = {{2022}}, } @article{34197, abstract = {{AbstractComprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.}}, author = {{Panzner, Melina and von Enzberg, Sebastian and Meyer, Maurice and Dumitrescu, Roman}}, issn = {{1868-7865}}, journal = {{Journal of the Knowledge Economy}}, keywords = {{Economics and Econometrics}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Characterization of Usage Data with the Help of Data Classifications}}}, doi = {{10.1007/s13132-022-01081-z}}, year = {{2022}}, } @article{34196, abstract = {{Mounting sensors in disk stack separators is often a major challenge due to the operating conditions. However, a process cannot be optimally monitored without sensors. Virtual sensors can be a solution to calculate the sought parameters from measurable values. We measured the vibrations of disk stack separators and applied machine learning (ML) to detect whether the separator contains only water or whether particles are also present. We combined seven ML classification algorithms with three feature engineering strategies and evaluated our model successfully on vibration data of an experimental disk stack separator. Our experimental results demonstrate that random forest in combination with manual feature engineering using domain specific knowledge about suitable features outperforms all other models with an accuracy of 91.27 %.}}, author = {{Merkelbach, Silke and Afroze, Lameya and Janssen, Nils and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2345-0533}}, journal = {{Vibroengineering PROCEDIA}}, keywords = {{General Medicine}}, pages = {{21--26}}, publisher = {{JVE International Ltd.}}, title = {{{Using vibration data to classify conditions in disk stack separators}}}, doi = {{10.21595/vp.2022.23000}}, volume = {{46}}, year = {{2022}}, } @inbook{34195, author = {{Hobscheidt, Daniela and Menzefricke, Jörn Steffen and Gabriel, Stefan and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Praxishandbuch Robotic Process Automation (RPA)}}, isbn = {{9783658383787}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Soziotechnische Herausforderungen bei der Einführung von RPA managen}}}, doi = {{10.1007/978-3-658-38379-4_8}}, year = {{2022}}, } @inbook{34193, author = {{Bansmann, Michael and Dumitrescu, Roman and Fechtelpeter, Christian}}, booktitle = {{Gestaltung digitalisierter Arbeitswelten}}, isbn = {{9783662580134}}, issn = {{2523-3637}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Transfer von Arbeit 4.0-Anwendungsszenarien}}}, doi = {{10.1007/978-3-662-58014-1_3}}, year = {{2022}}, } @inbook{34194, author = {{Brock, Jonathan and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Praxishandbuch Robotic Process Automation (RPA)}}, isbn = {{9783658383787}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Nutzung von Process Mining in RPA-Projekten}}}, doi = {{10.1007/978-3-658-38379-4_5}}, year = {{2022}}, } @article{34200, abstract = {{Praxeologische Kompetenzansätze verstehen Kompetenz als sozial erlernt und folglich als relativ zum sozialen Kontext. Damit einher geht die Frage, wie solche praxeologisch gerahmten Kompetenzen eigentlich unabhängig von der sie hervorbringenden Praxis evaluiert werden können – und eben dadurch erst für einen breiteren Kompetenzdiskurs fruchtbar sind. Die Dokumentarische Evaluationsforschung bietet hierzu erste Anhaltspunkte, offenbart aber auch Grenzen, die mit dem Evaluationsverständnis zusammenhängen, sich jedoch in der Forschungspraxis so nicht finden lassen. Aus der Differenz zwischen Methode und Praxis dokumentarischer Evaluation lässt sich formulieren, wie eine praxeologische Evaluation gestaltet werden könnte. Dabei spielt die Formulierung von Referenzrahmen eine zentrale Rolle, welche einerseits der zu evaluierenden Praktik external sein, andererseits praktisch formuliert werden müssen, damit sie soziale Praktiken jenseits ihrer eigenen Sinnhaftigkeit evaluativ (er-)fassen können.}}, author = {{Bloh, Thiemo}}, issn = {{1619-5515}}, journal = {{Zeitschrift für Evaluation}}, keywords = {{Strategy and Management, Applied Psychology, Social Sciences (miscellaneous), Education, Communication, Statistics and Probability}}, number = {{02}}, pages = {{193--215}}, publisher = {{Waxmann}}, title = {{{Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung}}}, doi = {{10.31244/zfe.2022.02.02}}, volume = {{2022}}, year = {{2022}}, } @article{34198, author = {{Bloh, Thiemo and Caruso, Carina}}, journal = {{die hochschullehre}}, title = {{{Ein kritisch-multiperspektivischer Blick auf Forschendes Lernen in der Lehrkräftebildung. Fragen, Erwägungen und Rekonstruktionen.}}}, doi = {{10.3278/HSL2221W}}, volume = {{8}}, year = {{2022}}, } @article{34199, author = {{Bloh, Thiemo}}, journal = {{Zeitschrift für Pädagogik}}, number = {{6}}, pages = {{749--762}}, title = {{{Grundlagentheoretische Differenzen in der Lehrkräftekooperationsforschung}}}, doi = {{10.3262/ZP2206749}}, volume = {{68}}, year = {{2022}}, }