@article{48052,
  author       = {{Schäfer, Louis and Günther, Matthias and Martin, Alex and Lüpfert, Mariella and Mandel, Constantin and Rapp, Simon and Lanza, Gisela and Anacker, Harald and Albers, Albert and Köchling, Daniel}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  keywords     = {{General Medicine}},
  pages        = {{104--109}},
  publisher    = {{Elsevier BV}},
  title        = {{{Systematics for an Integrative Modelling of Product and Production System}}},
  doi          = {{10.1016/j.procir.2023.06.019}},
  volume       = {{118}},
  year         = {{2023}},
}

@article{47817,
  author       = {{Dyck, Florian and Anacker, Harald and Dumitrescu, Roman}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  keywords     = {{General Medicine}},
  pages        = {{912--917}},
  publisher    = {{Elsevier BV}},
  title        = {{{Virtual Assembly for Engineering – A Systematic Literature Review}}},
  doi          = {{10.1016/j.procir.2023.06.157}},
  volume       = {{118}},
  year         = {{2023}},
}

@article{48294,
  abstract     = {{<jats:p>Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especially for the user. We demonstrate our arguments on two existing suicide risk assessment datasets of Twitter and Reddit posts. We present the first analysis juxtaposing user history length and differential privacy budgets and elaborate how modeling additional user context enables utility preservation while maintaining acceptable user privacy guarantees.</jats:p>}},
  author       = {{Sawhney, Ramit and Neerkaje, Atula and Habernal, Ivan and Flek, Lucie}},
  issn         = {{2334-0770}},
  journal      = {{Proceedings of the International AAAI Conference on Web and Social Media}},
  pages        = {{766--776}},
  publisher    = {{Association for the Advancement of Artificial Intelligence (AAAI)}},
  title        = {{{How Much User Context Do We Need? Privacy by Design in Mental Health NLP Applications}}},
  doi          = {{10.1609/icwsm.v17i1.22186}},
  volume       = {{17}},
  year         = {{2023}},
}

@inproceedings{48297,
  author       = {{Senge, Manuel and Igamberdiev, Timour and Habernal, Ivan}},
  booktitle    = {{Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks}}},
  doi          = {{10.18653/v1/2022.emnlp-main.496}},
  year         = {{2023}},
}

@inproceedings{48292,
  author       = {{Igamberdiev, Timour and Habernal, Ivan}},
  booktitle    = {{Findings of the Association for Computational Linguistics: ACL 2023}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{DP-BART for Privatized Text Rewriting under Local Differential Privacy}}},
  doi          = {{10.18653/v1/2023.findings-acl.874}},
  year         = {{2023}},
}

@inproceedings{48295,
  author       = {{Bongard, Leonard and Held, Lena and Habernal, Ivan}},
  booktitle    = {{Proceedings of the Natural Legal Language Processing Workshop 2022}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{The Legal Argument Reasoning Task in Civil Procedure}}},
  doi          = {{10.18653/v1/2022.nllp-1.17}},
  year         = {{2023}},
}

@article{48290,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/trusthlt/mining-legal-arguments">https://github.com/trusthlt/mining-legal-arguments</jats:ext-link>.</jats:p>}},
  author       = {{Habernal, Ivan and Faber, Daniel and Recchia, Nicola and Bretthauer, Sebastian and Gurevych, Iryna and Spiecker genannt Döhmann, Indra and Burchard, Christoph}},
  issn         = {{0924-8463}},
  journal      = {{Artificial Intelligence and Law}},
  keywords     = {{Law, Artificial Intelligence}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Mining legal arguments in court decisions}}},
  doi          = {{10.1007/s10506-023-09361-y}},
  year         = {{2023}},
}

@inproceedings{48422,
  author       = {{Humpert, Lynn and Tihlarik, Amelie and Wäschle, Moritz and Anacker, Harald and Dumitrescu, Roman and Albers, Albert and Röbenack, Silke and Pfeifer, Sabine}},
  booktitle    = {{IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD)}},
  location     = {{Rabat, Marokko}},
  title        = {{{Investigating the potential of artificial intelligence for the employee from the perspective of AI-experts}}},
  year         = {{2023}},
}

@inproceedings{48421,
  author       = {{Humpert, Lynn and Zagatta, Kristin and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD)}},
  location     = {{Rabat, Marokko}},
  title        = {{{Identification of fields of action for validation in Systems Engineering}}},
  year         = {{2023}},
}

@inproceedings{48427,
  author       = {{Gabriel, Stefan and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Procedia CIRP}},
  location     = {{Dublin, Ireland}},
  title        = {{{Strategic planning of the collaboration between humans and artificial intelligence in production}}},
  year         = {{2023}},
}

@inproceedings{48425,
  author       = {{Mundt, Enrik and Wilke, Daria and Anacker, Harald and Dumitrescu, Roman}},
  location     = {{Maui, Hawaii}},
  title        = {{{Principles for the effective application of Systems Engineering: A  systematic literature review and application use case}}},
  year         = {{2023}},
}

@inproceedings{48426,
  author       = {{Tekaat, Julian and Wilke, Daria and Anacker, Harald and Dumitrescu, Roman}},
  location     = {{Würzburg}},
  title        = {{{Integration von Design Thinking in Systems Engineering mit Hilfe des Systemdenkens}}},
  year         = {{2023}},
}

@unpublished{48502,
  abstract     = {{The prediction of photon echoes is an important technique for gaining an understanding of optical quantum systems. However, this requires a large number of simulations with varying parameters and/or input pulses, which renders numerical studies expensive. This article investigates how we can use data-driven surrogate models based on the Koopman operator to accelerate this process. In order to be successful, we require a model that is accurate over a large number of time steps. To this end, we employ a bilinear Koopman model using extended dynamic mode decomposition and simulate the optical Bloch equations for an ensemble of inhomogeneously broadened two-level systems. Such systems are well suited to describe the excitation of excitonic resonances in semiconductor nanostructures, for example, ensembles of semiconductor quantum dots. We perform a detailed study on the required number of system simulations such that the resulting data-driven Koopman model is sufficiently accurate for a wide range of parameter settings. We analyze the L2 error and the relative error of the photon echo peak and investigate how the control positions relate to the stabilization. After proper training, the dynamics of the quantum ensemble can be predicted accurately and numerically very efficiently by our methods.}},
  author       = {{Peitz, Sebastian and Hunstig, Anna and Rose, Hendrik and Meier, Torsten}},
  title        = {{{Accelerating the analysis of optical quantum systems using the Koopman operator}}},
  year         = {{2023}},
}

@inbook{46460,
  author       = {{Ngonga Ngomo, Axel-Cyrille and Demir, Caglar and Kouagou, N'Dah Jean and Heindorf, Stefan and Karalis, Nikoloas and Bigerl, Alexander}},
  booktitle    = {{Compendium of Neurosymbolic Artificial Intelligence}},
  pages        = {{272–286}},
  publisher    = {{IOS Press}},
  title        = {{{Class Expression Learning with Multiple Representations}}},
  year         = {{2023}},
}

@article{47854,
  author       = {{Scholtysik, Michel and Koldewey, Christian and Rohde, Malte and Dumitrescu, Roman}},
  issn         = {{2212-8271}},
  journal      = {{Procedia CIRP}},
  keywords     = {{General Medicine}},
  pages        = {{841--846}},
  publisher    = {{Elsevier BV}},
  title        = {{{Integrative conceptualization of products and business models for the circular economy: A systematic literature review}}},
  doi          = {{10.1016/j.procir.2023.03.129}},
  volume       = {{119}},
  year         = {{2023}},
}

@techreport{47855,
  author       = {{Scholtysik, Michel and Koldewey, Christian and Dumitrescu, Roman and Rasor, Anja and Ködding, Patrick and Wegel, Arthur and Fischer, Lena}},
  title        = {{{Die Transformation zum Smart Service-Anbieter}}},
  year         = {{2023}},
}

@inproceedings{49349,
  author       = {{Scholtysik, Michel and Koldewey, Christian and Dumitrescu, Roman and Pierenkemper, Christoph and Hensen, Christian}},
  title        = {{{Einstieg in die Kreislaufwirtschaft: Integrative Planung von Produkten und Geschäftsmodellen}}},
  year         = {{2023}},
}

@inproceedings{49322,
  author       = {{Ködding, Patrick and Tissen, Denis and Koldewey, Christian}},
  booktitle    = {{Proceedings of the 56th CIRP Conference on Manufacturing Systems, CIRP CMS ‘23}},
  location     = {{Capetown, South Africa}},
  publisher    = {{Elsevier Ltd.}},
  title        = {{{A Data Map for Product Creation: Tasks, Data Flows, and IT-Systems from the Initial Idea to the Start of Production}}},
  year         = {{2023}},
}

@inproceedings{49365,
  author       = {{Brock, Jonathan and Rempe, Niclas and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{5th Conference on Production Systems and Logistics }},
  location     = {{STELLENBOSCH,  SOUTH AFRICA}},
  title        = {{{A Framework For The Domain-Driven Utilization Of Manufacturing Sensor Data In Process Mining: An Action Design Approach}}},
  year         = {{2023}},
}

@inproceedings{49362,
  author       = {{Weller, Julian and Migenda, Nico and Wegel, Arthur and Kohlhase, Martin and Schenk, Wolfram and Dumitrescu, Roman}},
  booktitle    = {{IEEE ADACIS 2023}},
  location     = {{Marrakesh, Marokko}},
  title        = {{{Conceptual Framework for Prescriptive Analytics based on decision Theory}}},
  year         = {{2023}},
}