@inproceedings{32590,
  author       = {{Richter, Cedric and Wehrheim, Heike}},
  booktitle    = {{2022 IEEE Conference on Software Testing, Verification and Validation (ICST)}},
  pages        = {{162--173}},
  title        = {{{Learning Realistic Mutations: Bug Creation for Neural Bug Detectors}}},
  doi          = {{10.1109/ICST53961.2022.00027}},
  year         = {{2022}},
}

@inproceedings{32591,
  author       = {{Richter, Cedric and Wehrheim, Heike}},
  booktitle    = {{2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR)}},
  pages        = {{418--422}},
  title        = {{{TSSB-3M: Mining single statement bugs at massive scale}}},
  doi          = {{10.1145/3524842.3528505}},
  year         = {{2022}},
}

@misc{32398,
  author       = {{Siek, Hanna}},
  title        = {{{Bringing Structure to Structure-Preserving Signatures: Overview, Implementation and Comparison of Selected SPS Schemes}}},
  year         = {{2022}},
}

@misc{31485,
  author       = {{Kramer, Paul}},
  title        = {{{On Transforming Lattice-Based Cryptography to the Ring Setting}}},
  year         = {{2022}},
}

@inproceedings{31806,
  abstract     = {{The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds}},
  author       = {{Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}},
  keywords     = {{2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba}},
  location     = {{Barcelona (Spain)}},
  title        = {{{ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}}},
  doi          = {{10.1145/3511095.3531287}},
  year         = {{2022}},
}

@article{32854,
  author       = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}},
  journal      = {{IFAC-PapersOnLine}},
  number       = {{13}},
  pages        = {{133–138}},
  publisher    = {{Elsevier}},
  title        = {{{Practical Network Conditions for the Convergence of Distributed Optimization}}},
  volume       = {{55}},
  year         = {{2022}},
}

@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}},
}

@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}},
}

@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}},
}

@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}},
}

@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}},
}

@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}},
}

@inbook{34292,
  author       = {{Wolters, Dennis and Engels, Gregor}},
  booktitle    = {{Product-Focused Software Process Improvement}},
  editor       = {{Taibi, Davide and Kuhrmann, Marco and Mikkonen, Tommi and Klünder, Jil and Abrahamsson, Pekka}},
  isbn         = {{9783031213878}},
  issn         = {{0302-9743}},
  pages        = {{235--242}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Towards Situational Process Management for Professional Education Programmes}}},
  doi          = {{10.1007/978-3-031-21388-5_16}},
  volume       = {{13709}},
  year         = {{2022}},
}