@article{45345,
  author       = {{Herding, Jana and Büker, P. and Kamin, A.M. and Glawe, K. and Schaper, F.}},
  journal      = {{QfI - Qualifizierung für Inklusion Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte. Themenheft: „Möglichkeiten und Herausforderungen der Digitalisierung im Kontext von Inklusion und Qualifizierung für Inklusion}},
  title        = {{{inklud.nrw – Entwicklung einer OER-fähigen Lehr-/Lernumgebung zum synergetischen Aufbau von inklusions- und digitalisierungsbezogenen Kompetenzen im Lehramtsstudium}}},
  year         = {{2022}},
}

@inbook{45351,
  author       = {{Herding, Jana and Büker, P. and Glawe, K.}},
  booktitle    = {{Professionalisierung von Grundschullehrkräften. Kontext, Bedingungen und Herausforderungen}},
  editor       = {{Mammes, Ingelore and Rotter, Caroline}},
  pages        = {{276--292}},
  publisher    = {{Julius Klinkhardt Verlag}},
  title        = {{{Professionalisierung angehender Grundschullehrkräfte für Inklusion: aktuelle Herausforderungen für die universitäre Lehrer*innenbildung}}},
  year         = {{2022}},
}

@article{45348,
  author       = {{Herding, Jana and Büker, P. and Kamin, A.M. and Glawe, K. and Menke, I. and Schaper, F.}},
  journal      = {{Herausforderung Lehrer*innenbildung – Zeitschrift Zur Konzeption, Gestaltung und Diskussion}},
  number       = {{1}},
  pages        = {{337--355}},
  title        = {{{Inklusions- und digitalisierungsbezogene Kompetenzanforderungen in der Lehrkräftebildung verzahnen: Theoretische und konzeptionelle Grundlagen der Lehr-/Lernumgebung inklud.nrw}}},
  volume       = {{5}},
  year         = {{2022}},
}

@inproceedings{45346,
  author       = {{Herding, Jana and Glawe, K.}},
  booktitle    = {{Tagungsbandbeitrag zur Tagung Inverted Classroom and beyond 2022}},
  title        = {{{Videobasierte Fallarbeit im Format 360° – ein hochschuldidaktisches Konzept zur Reflexionsanregung im Grundschullehramtsstudium}}},
  year         = {{2022}},
}

@inbook{45329,
  author       = {{Mattei, Annalisa and Eremin, Oxana and Krämer, Anike}},
  booktitle    = {{Wissenstransfer als Aufgabe, Herausforderung und Chance kulturwissenschaftlicher Forschung}},
  editor       = {{Harmening, Anda-Lisa and Leinfellner, Stefanie and Meier, Rebecca}},
  pages        = {{323--352}},
  title        = {{{K)Eine Frage des Wissens. Zum Verhältnis der Gender Studies und Wissenstransfer}}},
  year         = {{2022}},
}

@techreport{45325,
  author       = {{Mattei, Annalisa and Chlebos, Laura}},
  pages        = {{175--178}},
  title        = {{{Tagungsbericht. #Metoo in Science. Sexualisierte Diskriminierung und Gewalt an Hochschulen}}},
  volume       = {{39}},
  year         = {{2022}},
}

@inbook{45321,
  author       = {{Schemmer, Susanne Jutta}},
  booktitle    = {{Berufsbildungspolitik. Normalität, Krisen, Perspektiven der Erstausbildung}},
  editor       = {{Eckelt, Marcus and Ketschau, Thilo Joachim and Klassen, Johannes and Schauer, Jennifer and Schmees, Johannes and Steib, Christian}},
  publisher    = {{wbv}},
  title        = {{{Transformation der beruflichen Benachteiligtenförderung durch die Coronakrise – erste Ergebnisse einer qualitativen Studie}}},
  year         = {{2022}},
}

@inbook{45320,
  author       = {{Schemmer, Susanne Jutta and Heisler, Dietmar}},
  booktitle    = {{Psychische Belastungen in der Berufsbiografie: Interdisziplinäre Perspektiven}},
  editor       = {{Stein, Roland and Kranert, Hans-Walter}},
  publisher    = {{wbv}},
  title        = {{{Benachteiligtenförderung – ein Überblick}}},
  year         = {{2022}},
}

@phdthesis{45322,
  author       = {{Zdoupas, Philippos}},
  publisher    = {{Springer VS}},
  title        = {{{Selbstkonzept und Klassenlehrkraftverhalten. Befunde vergleichender Analysen zu Schülerinnen und Schülern mit Förderbedarf in der emotionalen und sozialen Entwicklung}}},
  doi          = {{10.1007/978-3-658-38576-7}},
  year         = {{2022}},
}

@inproceedings{45653,
  author       = {{Vernholz, Mats}},
  location     = {{Stuttgart}},
  title        = {{{Industrie 4.0 in der beruflichen Bildung – Automatisierter Maschinenbaulernbetrieb Paderborn }}},
  doi          = {{https://doi.org/10.48513/joted.v11i2.267 }},
  year         = {{2022}},
}

@misc{45715,
  author       = {{Tcheussi Ngayap, Vanessa Ingrid}},
  title        = {{{FreeRTOS on a MicroBlaze Soft-Core Processor with Hardware Accelerators}}},
  year         = {{2022}},
}

@article{35918,
  author       = {{Berger, Thomas and Dennstädt, Dario and Ilchmann, Achim and Worthmann, Karl}},
  journal      = {{SIAM Journal on Control and Optimization}},
  number       = {{6}},
  pages        = {{3358--3383}},
  title        = {{{Funnel MPC for nonlinear systems with relative degree one}}},
  volume       = {{60}},
  year         = {{2022}},
}

@article{36035,
  author       = {{Berger, Thomas and Ilchmann, A.  and Trenn, S. }},
  journal      = {{IMA Journal of Mathematical Control and Information}},
  number       = {{2}},
  pages        = {{533--563}},
  title        = {{{Quasi feedback forms for differential-algebraic systems}}},
  volume       = {{39}},
  year         = {{2022}},
}

@inbook{44463,
  author       = {{Eckel, Julia and Ehrlich, Nea}},
  booktitle    = {{In: animationstudies 2.0}},
  publisher    = {{In: animationstudies 2.0,  [OPEN ACCESS] }},
  title        = {{{Minds in Motion – Some Basic Thoughts on AI and Animation}}},
  year         = {{2022}},
}

@book{44458,
  editor       = {{Eckel, Julia and Ehrlich, Nea}},
  publisher    = {{Open-Access-Blog of the Society for Animation Studies}},
  title        = {{{animationstudiesblog 2.0 | Theme: AI and Animation}}},
  year         = {{2022}},
}

@inbook{44464,
  author       = {{Eckel, Julia}},
  booktitle    = {{animationstudies 2.0}},
  title        = {{{Intelligence In Between – Documenting AI in Animation}}},
  year         = {{2022}},
}

@inbook{33738,
  author       = {{Zahera, Hamada Mohamed Abdelsamee and Heindorf, Stefan and Balke, Stefan and Haupt, Jonas and Voigt, Martin and Walter, Carolin and Witter, Fabian and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The Semantic Web: ESWC 2022 Satellite Events}},
  isbn         = {{9783031116087}},
  issn         = {{0302-9743}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings}}},
  doi          = {{10.1007/978-3-031-11609-4_9}},
  year         = {{2022}},
}

@inbook{38506,
  author       = {{Zahera, H.M.A and Vollmers, Daniel and Sherif, Mohamed Ahmed and Ngomo, Axel-Cyrille Ngonga}},
  booktitle    = {{The Semantic Web – ISWC 2022}},
  isbn         = {{9783031194320}},
  issn         = {{0302-9743}},
  publisher    = {{Springer International Publishing}},
  title        = {{{MultPAX: Keyphrase Extraction Using Language Models and Knowledge Graphs}}},
  doi          = {{10.1007/978-3-031-19433-7_18}},
  year         = {{2022}},
}

@inproceedings{30373,
  author       = {{Chakraborty, Jaydeep and Zahera, H.M.A and Sherif, Mohamed Ahmed and Bansal, Srividya K.}},
  booktitle    = {{2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}},
  publisher    = {{IEEE}},
  title        = {{{ONTOCONNECT: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling}}},
  doi          = {{10.1109/icmla52953.2021.00155}},
  year         = {{2022}},
}

@inproceedings{31257,
  abstract     = {{Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts, which are used as binary classification models for individuals. However, the number of concepts explored by these approaches can grow to the millions for complex learning problems. This often leads to impractical runtimes. We propose to alleviate this problem by predicting the length of target concepts before the exploration of the solution space. By these means, we can prune the search space during concept learning. To achieve this goal, we compare four neural architectures and evaluate them on four benchmarks. Our evaluation results suggest that recurrent neural network architectures perform best at concept length prediction with a macro F-measure ranging from 38% to 92%. We then extend the CELOE algorithm, which learns ALC concepts, with our concept length predictor. Our extension yields the algorithm CLIP. In our experiments, CLIP is at least 7.5 times faster than other state-of-the-art concept learning algorithms for ALC---including CELOE---and achieves significant improvements in the F-measure of the concepts learned on 3 out of 4 datasets. For reproducibility, we provide our implementation in the public GitHub repository at https://github.com/dice-group/LearnALCLengths}},
  author       = {{Kouagou, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngomo, Ngonga Axel-Cyrille}},
  booktitle    = {{ESWC}},
  keywords     = {{dice knowgraphs raki daikiri kouagou heindorf demir ngonga}},
  location     = {{Hersonissos, Crete, Greece}},
  pages        = {{236 -- 252}},
  publisher    = {{Springer}},
  title        = {{{Learning Concept Lengths Accelerates Concept Learning in ALC}}},
  volume       = {{13261}},
  year         = {{2022}},
}