@book{34922,
  author       = {{Radtke, Sabine and Freier, M. Pia}},
  isbn         = {{ 978-3-86884-552-5}},
  keywords     = {{Para Sport}},
  publisher    = {{Sportverlag Strauß}},
  title        = {{{Das Stützpunktsystem im paralympischen Leistungssport. Eine empirische Studie unter Berücksichtigung der Perspektive von Para-Athletinnen und -Athleten sowie des Stützpunktpersonals. }}},
  year         = {{2022}},
}

@book{48098,
  author       = {{Radtke, Sabine and Freier, M. Pia}},
  publisher    = {{Sportverlag Strauß}},
  title        = {{{Das Stützpunktsystem im paralympischen Leistungssport. Eine empirische Studie unter Berücksichtigung der Perspektive von Para-Athletinnen und -Athleten sowie des Stützpunktpersonals. }}},
  year         = {{2022}},
}

@inbook{48106,
  author       = {{Podworny, Susanne and Fleischer, Yannik}},
  booktitle    = {{Proceedings of the 15th international conference on technology in mathematics teaching (ICTMT 15)}},
  editor       = {{Jankvist, U.T. and Elicer, R. and Clark-Wilson, A and Weigand, Hans-Georg and Thomson, M}},
  pages        = {{308--315}},
  publisher    = {{Danish School of Education}},
  title        = {{{ An approach to teaching data science in middle school}}},
  year         = {{2022}},
}

@inbook{48103,
  author       = {{Fleischer, Yannik and Podworny, Susanne}},
  booktitle    = {{Proceedings of the 15th international conference on technology in mathematics teaching (ICTMT 15)}},
  editor       = {{Jankvist, U.T. and Elicer, R. and Clark-Wilson, A and Weigand, Hans-Georg and Thomson, M}},
  pages        = {{280--281}},
  publisher    = {{Danish School of Education}},
  title        = {{{ Teaching machine learning with decision trees in middle school using CODAP}}},
  year         = {{2022}},
}

@article{48108,
  abstract     = {{<jats:p>Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.</jats:p>}},
  author       = {{PODWORNY, SUSANNE and HÜSING, SVEN and SCHULTE, CARSTEN}},
  issn         = {{1570-1824}},
  journal      = {{STATISTICS EDUCATION RESEARCH JOURNAL}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}}},
  doi          = {{10.52041/serj.v21i2.46}},
  volume       = {{21}},
  year         = {{2022}},
}

@inproceedings{48105,
  abstract     = {{<jats:p>Decision-making processes are often based on data and data-driven machine learning methods in different areas such as recommender systems, medicine, criminalistics, etc. Well-informed citizens need at least a minimal understanding and critical reflection of corresponding data-driven machine learning methods. Decision trees are a method that can foster a preformal understanding of machine learning. We developed an exploratory teaching unit introducing decision trees in grade 6 along the question “How can Artificial Intelligence help us decide whether food is rather recommendable or not?” Students’ performances in an assessment task and self-assessment show that young learners can use a decision tree to classify new items and that they found the corresponding teaching unit informative.</jats:p>}},
  author       = {{Podworny, Susanne and Fleischer, Yannik and Hüsing, Sven}},
  booktitle    = {{Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Grade 6 Students’ Perception and Use of Data-Based Decision Trees}}},
  doi          = {{10.52041/iase.icots11.t2h3}},
  year         = {{2022}},
}

@inproceedings{48102,
  abstract     = {{<jats:p>We report on our work with students in our data science courses, focusing on the analysis of students’ results. This study represents an in-depth analysis of students’ creation and documentation of machine learning models. The students were supported by educationally designed Jupyter Notebooks, which are used as worked examples. Using the worked example, students document their results in a so-called computational essay. We examine which aspects of creating computational essays are difficult for students to find out how worked examples should be designed to support students without being too prescriptive. We analyze the computational essays produced by students and draw consequences for redesigning our worked example.</jats:p>}},
  author       = {{Fleischer, Yannik and Hüsing, Sven and Biehler, Rolf and Podworny, Susanne and Schulte, Carsten}},
  booktitle    = {{Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Jupyter Notebooks for Teaching, Learning, and Doing Data Science}}},
  doi          = {{10.52041/iase.icots11.t10e3}},
  year         = {{2022}},
}

@inproceedings{48107,
  author       = {{Podworny, Susanne and Fleischer, Yannik and Stroop, Dietlinde and Biehler, Rolf}},
  location     = {{Bozen-Bolzano, Italy}},
  title        = {{{An example of rich, real and multivariate survey data for use in school}}},
  year         = {{2022}},
}

@article{48104,
  author       = {{Podworny, Susanne}},
  journal      = {{mathematik lehren}},
  pages        = {{36--40}},
  title        = {{{Vokabeln lernen im Schlaf? Statistische Testprozeduren verstehen}}},
  volume       = {{232}},
  year         = {{2022}},
}

@inproceedings{48299,
  abstract     = {{Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However, sensitive personal information, such as documents with people{’}s profiles or relationships as edges, are prone to privacy leaks, as the trained model might reveal the original input. Although differential privacy (DP) offers a well-founded privacy-preserving framework, GCNs pose theoretical and practical challenges due to their training specifics. We address these challenges by adapting differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages. We propose a simple yet efficient method based on random graph splits that not only improves the baseline privacy bounds by a factor of 2.7 while retaining competitive F1 scores, but also provides strong privacy guarantees of epsilon = 1.0. We show that, under certain modeling choices, privacy-preserving GCNs perform up to 90{%} of their non-private variants, while formally guaranteeing strong privacy measures.}},
  author       = {{Igamberdiev, Timour and Habernal, Ivan}},
  booktitle    = {{Proceedings of the Thirteenth Language Resources and Evaluation Conference}},
  pages        = {{338–350}},
  publisher    = {{European Language Resources Association}},
  title        = {{{Privacy-Preserving Graph Convolutional Networks for Text Classification}}},
  year         = {{2022}},
}

@inproceedings{48300,
  abstract     = {{Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent.}},
  author       = {{Igamberdiev, Timour and Arnold, Thomas and Habernal, Ivan}},
  booktitle    = {{Proceedings of the 29th International Conference on Computational Linguistics}},
  pages        = {{2927–2933}},
  publisher    = {{International Committee on Computational Linguistics}},
  title        = {{{DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting}}},
  year         = {{2022}},
}

@inproceedings{48298,
  author       = {{Habernal, Ivan}},
  booktitle    = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{How reparametrization trick broke differentially-private text representation learning}}},
  doi          = {{10.18653/v1/2022.acl-short.87}},
  year         = {{2022}},
}

@inproceedings{48354,
  author       = {{Moritzer, Elmar and Wächter, Julian}},
  location     = {{Fukuoka (Japan)}},
  title        = {{{Qualification of Different Carbon Fiber Reinforced Polyether Ether Ketone Materials for the FFF Process}}},
  year         = {{2022}},
}

@book{48353,
  author       = {{Gevers, Karina and Schraa, L and Uhlig, K and Töws, P and Stommel, M}},
  isbn         = {{978-3-88355-430-3 }},
  pages        = {{169--175}},
  title        = {{{Bewertung von IR-Schweißverbindungen an kurzfaserverstärkten Thermoplasten mittels digitaler Bildkorrelation}}},
  volume       = {{Tagung Werkstoffprüfung 2022 - Werkstoffe und Bauteile auf dem Prüfstand}},
  year         = {{2022}},
}

@book{48356,
  author       = {{Schöppner, Volker and Vogtschmidt, Sascha}},
  isbn         = {{978-3-96144-190-7 }},
  pages        = {{534--540}},
  title        = {{{Schweißnahtkennwerte für die lebensdaueroptimierte Bauteilauslegung von hochtemperaturbeständigen Thermoplasten}}},
  year         = {{2022}},
}

@book{48358,
  author       = {{Schöppner, Volker and Dörner, M. and Frank, Maximilian and Schall, Christoph}},
  title        = {{{On the Wave to Successful Mixing}}},
  year         = {{2022}},
}

@inproceedings{35126,
  author       = {{Förster, Nikolas and Hölscher, Jonas and Piepenbrock, Till and Rehlaender, Philipp and Wallscheid, Oliver and Schafmeister, Frank and Böcker, Joachim}},
  booktitle    = {{2022 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe)}},
  pages        = {{P.1--P.9}},
  title        = {{{An Open-Source FEM Magnetic Toolbox for Calculating Electric and Thermal Behavior of Power Electronic Magnetic Components}}},
  year         = {{2022}},
}

@article{30863,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>In this paper a measurement procedure to identify viscoelastic material parameters of plate-like samples using broadband ultrasonic waves is presented. Ultrasonic Lamb waves are excited via the thermoelastic effect using laser radiation and detected by a piezoelectric transducer. The resulting measurement data is transformed to yield information about multiple propagating Lamb waves as well as their attenuation. These results are compared to simulation results in an inverse procedure to identify the parameters of an elastic and a viscoelastic material model.</jats:p>}},
  author       = {{Johannesmann, Sarah and Claes, Leander and Feldmann, Nadine and Zeipert, Henning and Henning, Bernd}},
  issn         = {{2196-7113}},
  journal      = {{tm - Technisches Messen}},
  keywords     = {{Electrical and Electronic Engineering, Instrumentation}},
  number       = {{7 - 8}},
  pages        = {{493 -- 506}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Lamb wave based approach to the determination of acoustic material parameters}}},
  doi          = {{10.1515/teme-2021-0134}},
  volume       = {{89}},
  year         = {{2022}},
}

@inproceedings{6588,
  author       = {{Johannesmann, Sarah and Claes, Leander and Henning, Bernd}},
  booktitle    = {{Fortschritte der Akustik - DAGA 2022}},
  location     = {{Stuttgart}},
  pages        = {{1401--1404}},
  title        = {{{Estimation of viscoelastic material parameters of polymers using Lamb waves}}},
  year         = {{2022}},
}

@misc{6560,
  author       = {{Johannesmann, Sarah}},
  title        = {{{Inverses Verfahren zur Bestimmung viskoelastischer Materialparameter}}},
  year         = {{2022}},
}