TY - CONF AB - This paper aims at discussing past limitations set in sentiment analysis research regarding explicit and implicit mentions of opinions. Previous studies have regularly neglected this question in favor of methodical research on standard-datasets. Furthermore, they were limited to linguistically less-diverse domains, such as commercial product reviews. We face this issue by annotating a German-language physician review dataset that contains numerous implicit, long, and complex statements that indicate aspect ratings, such as the physician’s friendliness. We discuss the nature of implicit statements and present various samples to illustrate the challenge described. AU - Kersting, Joschka AU - Bäumer, Frederik Simon ED - Kersting, Joschka ID - 31054 KW - Sentiment analysis KW - Natural language processing KW - Aspect phrase extraction T2 - Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications TI - Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis ER - TY - JOUR AU - Hoyer, Britta AU - De Jaegher, Kris ID - 31881 JF - International Journal of Game Theory TI - Network Disruption and the Common-Enemy Effect ER - TY - GEN AB - BloKK-Beitrag für das ZeKK, 03.12.2022 AU - Lebock, Sarah ID - 34187 TI - Blogpost "Von der Grundstimmung als philosophischer Ausgangspunkt" ER - TY - JOUR AB - 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. AU - Zhang, Wangyou AU - Chang, Xuankai AU - Boeddeker, Christoph AU - Nakatani, Tomohiro AU - Watanabe, Shinji AU - Qian, Yanmin ID - 33669 JF - IEEE/ACM Transactions on Audio, Speech, and Language Processing SN - Print ISSN: 2329-9290 Electronic ISSN: 2329-9304 TI - End-to-End Dereverberation, Beamforming, and Speech Recognition in A Cocktail Party ER - TY - JOUR AB - 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. AU - Panzner, Melina AU - von Enzberg, Sebastian AU - Meyer, Maurice AU - Dumitrescu, Roman ID - 34197 JF - Journal of the Knowledge Economy KW - Economics and Econometrics SN - 1868-7865 TI - Characterization of Usage Data with the Help of Data Classifications ER - TY - JOUR AB - 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 %. AU - Merkelbach, Silke AU - Afroze, Lameya AU - Janssen, Nils AU - von Enzberg, Sebastian AU - Kühn, Arno AU - Dumitrescu, Roman ID - 34196 JF - Vibroengineering PROCEDIA KW - General Medicine SN - 2345-0533 TI - Using vibration data to classify conditions in disk stack separators VL - 46 ER - TY - CHAP AU - Hobscheidt, Daniela AU - Menzefricke, Jörn Steffen AU - Gabriel, Stefan AU - Kühn, Arno AU - Dumitrescu, Roman ID - 34195 SN - 9783658383787 T2 - Praxishandbuch Robotic Process Automation (RPA) TI - Soziotechnische Herausforderungen bei der Einführung von RPA managen ER - TY - CHAP AU - Bansmann, Michael AU - Dumitrescu, Roman AU - Fechtelpeter, Christian ID - 34193 SN - 2523-3637 T2 - Gestaltung digitalisierter Arbeitswelten TI - Transfer von Arbeit 4.0-Anwendungsszenarien ER - TY - CHAP AU - Brock, Jonathan AU - von Enzberg, Sebastian AU - Kühn, Arno AU - Dumitrescu, Roman ID - 34194 SN - 9783658383787 T2 - Praxishandbuch Robotic Process Automation (RPA) TI - Nutzung von Process Mining in RPA-Projekten ER - TY - JOUR AB - 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. AU - Bloh, Thiemo ID - 34200 IS - 02 JF - Zeitschrift für Evaluation KW - Strategy and Management KW - Applied Psychology KW - Social Sciences (miscellaneous) KW - Education KW - Communication KW - Statistics and Probability SN - 1619-5515 TI - Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung VL - 2022 ER -