@inproceedings{33253,
  author       = {{Hansmeier, Tim and Brede, Mathis and Platzner, Marco}},
  booktitle    = {{GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  location     = {{Boston, MA, USA}},
  pages        = {{2071--2079}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{XCS on Embedded Systems: An Analysis of Execution Profiles and Accelerated Classifier Deletion}}},
  doi          = {{10.1145/3520304.3533977}},
  year         = {{2022}},
}

@inproceedings{33274,
  author       = {{Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)}},
  pages        = {{51 -- 61}},
  title        = {{{Analyzing Culture-Specific Argument Structures in Learner Essays}}},
  year         = {{2022}},
}

@inproceedings{33491,
  author       = {{Maack, Marten and Pukrop, Simon and Rasmussen, Anna Rodriguez}},
  booktitle    = {{30th Annual European Symposium on Algorithms, ESA 2022, September 5-9, 2022, Berlin/Potsdam, Germany}},
  editor       = {{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz}},
  pages        = {{77:1–77:13}},
  publisher    = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}},
  title        = {{{(In-)Approximability Results for Interval, Resource Restricted, and Low Rank Scheduling}}},
  doi          = {{10.4230/LIPIcs.ESA.2022.77}},
  volume       = {{244}},
  year         = {{2022}},
}

@techreport{32106,
  abstract     = {{We study the consequences of modeling asymmetric bargaining power in two-person bargaining problems. Comparing application of an asymmetric version of a bargaining solution to an upfront modification of the disagreement point, the resulting distortion crucially depends on the bargaining solution concept. While for the Kalai-Smorodinsky solution weaker players benefit from modifying the disagreement point, the situation is reversed for the Nash bargaining solution. There, weaker players are better off in the asymmetric bargaining solution. When comparing application of the asymmetric versions of the Nash and the Kalai-Smorodinsky solutions, we demonstrate that there is an upper bound for the weight of a player, so that she is better off with the Nash bargaining solution. This threshold is ultimately determined by the relative utilitarian bargaining solution. From a mechanism design perspective, our results provide valuable information for a social planner, when implementing a bargaining solution for unequally powerful players.}},
  author       = {{Haake, Claus-Jochen and Streck, Thomas}},
  keywords     = {{Asymmetric bargaining power, Nash bargaining solution, Kalai-Smorodinsky bargaining solution}},
  pages        = {{17}},
  title        = {{{Distortion through modeling asymmetric bargaining power}}},
  volume       = {{148}},
  year         = {{2022}},
}

@article{34132,
  abstract     = {{<jats:p>How can Knowledge In/Equity be addressed in qualitative research by taking the idea of Open Science into account? Two projects from the Open Science Fellows Programme by Wikimedia Deutschland will be used to illustrate how Open Science practices can succeed in qualitative research, thereby reducing In/Equity. In this context, In/Equity is considered as a fair and equal representation of people, their knowledge and insights and comprehends questions about how epistemic, structural, institutional and personal biases generate and shape knowledge as guidance. Three questions guide this approach: firstly, what do we understand by In/Equity in the context of knowledge production in these projects? Secondly, who will be involved in knowledge generation and to what extent will they be valued or unvalued? Thirdly, how can data be made accessible for re-use to enable true participation and sharing?</jats:p>}},
  author       = {{Steinhardt, Isabel and Kruschick, Felicitas}},
  issn         = {{2367-7163}},
  journal      = {{Research Ideas and Outcomes}},
  keywords     = {{Open Science, Knowledge Equity, Qualitative Methods}},
  publisher    = {{Pensoft Publishers}},
  title        = {{{Knowledge Equity and Open Science in qualitative research – Practical research considerations}}},
  doi          = {{10.3897/rio.8.e86387}},
  volume       = {{8}},
  year         = {{2022}},
}

@inproceedings{34140,
  abstract     = {{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       = {{Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}},
  booktitle    = {{2022 Kleinheubach Conference}},
  keywords     = {{emc, pcb, electronic system development, machine learning, neural network}},
  location     = {{Miltenberg, Germany}},
  publisher    = {{IEEE}},
  title        = {{{AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}}},
  year         = {{2022}},
}

@inbook{34108,
  author       = {{Hagengruber, Ruth Edith}},
  booktitle    = {{Sitzungsberichte der Leibniz-Sozietät der Wissenschaften 150/151, Jahrgang 2022: „Cyberscience – Wissenschaftsforschung und Informatik. Digitale Medien und die Zukunft der Kultur wissenschaftlicher Tätigkeit. Arbeitskreis „Emergente Systeme/Informatik und Gesellschaft“ der Leibniz-Sozietät der Wissenschaften zu Berlin in Kooperation mit der Gesellschaft für Wissenschaftsforschung“}},
  editor       = {{Banse, Gerhard and Fuchs-Kittowski, Klaus}},
  pages        = {{253–256}},
  title        = {{{Die „dritte Wissensdimension“. Eine Epistemologie für eine neue Wissenswelt}}},
  year         = {{2022}},
}

@inbook{32179,
  abstract     = {{This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.}},
  author       = {{Kersting, Joschka and Ahmed, Mobeen and Geierhos, Michaela}},
  booktitle    = {{HCI International 2022 Posters}},
  editor       = {{Stephanidis, Constantine and Antona, Margherita and Ntoa, Stavroula}},
  isbn         = {{9783031064166}},
  issn         = {{1865-0929}},
  keywords     = {{On-The-Fly Computing, Chatbot, Knowledge Base}},
  location     = {{Virtual}},
  pages        = {{419----426}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Chatbot-Enhanced Requirements Resolution for Automated Service Compositions}}},
  doi          = {{10.1007/978-3-031-06417-3_56}},
  volume       = {{1580}},
  year         = {{2022}},
}

@inproceedings{34152,
  author       = {{Otroshi, Mortaza and Meschut, Gerson}},
  location     = {{Rostock}},
  publisher    = {{Europäische Forschungsgesellschaft für Blechverarbeitung e.V. }},
  title        = {{{Methodenentwicklung zur Verbesserung der Schädigungsmodellierung in der numerischen 3D-Belastungssimulation mechanischer Fügeverfahren unter Berücksichtigung der fügeinduzierten Vorbeanspruchung}}},
  year         = {{2022}},
}

@inproceedings{34155,
  author       = {{Krauter, Stefan and Bendfeld, Jörg}},
  booktitle    = {{Proceedings of the 8th World Conference on Photovoltaik Energy Conversion}},
  location     = {{Milano / Italy}},
  title        = {{{Microinverter PV Systems: New Efficiency Rankings and Formula for Energy Yield Assessment for any PV Panel Size at different Microinverter types}}},
  year         = {{2022}},
}

@inproceedings{34156,
  author       = {{Kakande, Josephine Nakato and Philipo, Godiana Hagile and Krauter, Stefan}},
  booktitle    = {{Proceedings of the 8th World Conference on Photovoltaik Energy Conversion}},
  location     = {{Milano / Italy}},
  title        = {{{Optimal Design of a Semi Grid-Connected PV System for a Site in Lwak, Kenya Using HOMER}}},
  year         = {{2022}},
}

@inproceedings{34153,
  author       = {{Otroshi, Mortaza and Meschut, Gerson}},
  publisher    = {{Europäische Forschungsgesellschaft für Blechverarbeitung e.V.}},
  title        = {{{Schädigungsmodellierung von Hilfsfügeelementen beim mechanischen Fügen von Stahlwerkstoffen}}},
  year         = {{2022}},
}

@phdthesis{29769,
  abstract     = {{Wettstreit zwischen der Entwicklung neuer Hardwaretrojaner und entsprechender Gegenmaßnahmen beschreiten Widersacher immer raffiniertere Wege um Schaltungsentwürfe zu infizieren und dabei selbst fortgeschrittene Test- und Verifikationsmethoden zu überlisten. Abgesehen von den konventionellen Methoden um einen Trojaner in eine Schaltung für ein Field-programmable Gate Array (FPGA) einzuschleusen, können auch die Entwurfswerkzeuge heimlich kompromittiert werden um einen Angreifer dabei zu unterstützen einen erfolgreichen Angriff durchzuführen, der zum Beispiel Fehlfunktionen oder ungewollte Informationsabflüsse bewirken kann. Diese Dissertation beschäftigt sich hauptsächlich mit den beiden Blickwinkeln auf Hardwaretrojaner in rekonfigurierbaren Systemen, einerseits der Perspektive des Verteidigers mit einer Methode zur Erkennung von Trojanern auf der Bitstromebene, und andererseits derjenigen des Angreifers mit einer neuartigen Angriffsmethode für FPGA Trojaner. Für die Verteidigung gegen den Trojaner ``Heimtückische LUT'' stellen wir die allererste erfolgreiche Gegenmaßnahme vor, die durch Verifikation mittels Proof-carrying Hardware (PCH) auf der Bitstromebene direkt vor der Konfiguration der Hardware angewendet werden kann, und präsentieren ein vollständiges Schema für den Entwurf und die Verifikation von Schaltungen für iCE40 FPGAs. Für die Gegenseite führen wir einen neuen Angriff ein, welcher bösartiges Routing im eingefügten Trojaner ausnutzt um selbst im fertigen Bitstrom in einem inaktiven Zustand zu verbleiben: Hierdurch kann dieser neuartige Angriff zur Zeit weder von herkömmlichen Test- und Verifikationsmethoden, noch von unserer vorher vorgestellten Verifikation auf der Bitstromebene entdeckt werden.}},
  author       = {{Ahmed, Qazi Arbab}},
  keywords     = {{FPGA Security, Hardware Trojans, Bitstream-level Trojans, Bitstream Verification}},
  publisher    = {{ Paderborn University, Paderborn, Germany}},
  title        = {{{Hardware Trojans in Reconfigurable Computing}}},
  doi          = {{10.17619/UNIPB/1-1271}},
  year         = {{2022}},
}

@inproceedings{34166,
  abstract     = {{Within innovation management, choosing the best fitting product idea is the most important decision point. The future existence and success of the organization is depending on selected product ideas. To find the best fitting idea for an organization, it needs evaluation criteria representing the organizations mission Therefore, this study focuses on a holistic overview about evaluation criteria and on supporting organizations in the process to select its individual evaluation criteria. Based on a literature study, existing approaches regarding evaluation criteria in product idea selection are identified and a list of evaluation criteria is reworked. Using the list of evaluation criteria, a prioritization method is created. Within an interview with experts in innovation management, the method is discussed regarding usability and the level of assistance in product idea selection. The developed criteria can be used directly by innovation manager and industrial practitioners to evaluate potential product ideas.}},
  author       = {{Gräßler, Iris and Koch, Anna-Sophie}},
  booktitle    = {{XXXIII Proceedings of the ISPIM Innovation Conference}},
  isbn         = {{978-952-335-694-8}},
  keywords     = {{innovation management, evaluation criteria, idea selection, idea evaluation, meta-study}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{Evaluation Criteria in Product Idea Selection Decisions}}},
  year         = {{2022}},
}

@inproceedings{31054,
  abstract     = {{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.}},
  author       = {{Kersting, Joschka and Bäumer, Frederik Simon}},
  booktitle    = {{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}},
  editor       = {{Kersting, Joschka}},
  keywords     = {{Sentiment analysis, Natural language processing, Aspect phrase extraction}},
  location     = {{Barcelona, Spain}},
  pages        = {{5--9}},
  publisher    = {{IARIA}},
  title        = {{{Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis}}},
  year         = {{2022}},
}

@article{31881,
  author       = {{Hoyer, Britta and De Jaegher, Kris}},
  journal      = {{International Journal of Game Theory}},
  publisher    = {{Springer}},
  title        = {{{Network Disruption and the Common-Enemy Effect}}},
  doi          = {{10.1007/s00182-022-00812-5}},
  year         = {{2022}},
}

@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     = {{<jats:title>Abstract</jats:title><jats:p>Comprehensive 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.</jats:p>}},
  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     = {{<jats:p>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 %.</jats:p>}},
  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}},
}