@inbook{45373,
  author       = {{Dröse, Jennifer and Neugebauer, P. and Delucchi Danhier, R. and Mertins, B.}},
  booktitle    = {{Eye-Tracking in der Mathematik- und Naturwissenschaftsdidaktik. Forschung und Praxis}},
  editor       = {{Kleine, P. and Graulich, N. and Kuhn, J. and Schindler, M.}},
  pages        = {{209--225}},
  title        = {{{Eye-Tracking Studie zu Textaufgaben in Klasse 5: Bemerken und Interpretieren syntaktischer Strukturen}}},
  doi          = {{https://doi.org/10.1007/978-3-662-63214-7}},
  year         = {{2022}},
}

@inproceedings{33847,
  abstract     = {{The scope of speech enhancement has changed from a monolithic view of single,
independent tasks, to a joint processing of complex conversational speech
recordings. Training and evaluation of these single tasks requires synthetic
data with access to intermediate signals that is as close as possible to the
evaluation scenario. As such data often is not available, many works instead
use specialized databases for the training of each system component, e.g
WSJ0-mix for source separation. We present a Multi-purpose Multi-Speaker
Mixture Signal Generator (MMS-MSG) for generating a variety of speech mixture
signals based on any speech corpus, ranging from classical anechoic mixtures
(e.g., WSJ0-mix) over reverberant mixtures (e.g., SMS-WSJ) to meeting-style
data. Its highly modular and flexible structure allows for the simulation of
diverse environments and dynamic mixing, while simultaneously enabling an easy
extension and modification to generate new scenarios and mixture types. These
meetings can be used for prototyping, evaluation, or training purposes. We
provide example evaluation data and baseline results for meetings based on the
WSJ corpus. Further, we demonstrate the usefulness for realistic scenarios by
using MMS-MSG to provide training data for the LibriCSS database.}},
  author       = {{Cord-Landwehr, Tobias and von Neumann, Thilo and Boeddeker, Christoph and Haeb-Umbach, Reinhold}},
  booktitle    = {{2022 International Workshop on Acoustic Signal Enhancement (IWAENC)}},
  location     = {{Bamberg}},
  title        = {{{MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator}}},
  year         = {{2022}},
}

@inproceedings{33807,
  author       = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  publisher    = {{IEEE}},
  title        = {{{On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-Varying Sampling Rate Offsets and Speaker Changes}}},
  doi          = {{10.1109/icassp43922.2022.9746284}},
  year         = {{2022}},
}

@article{33451,
  abstract     = {{We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural network for object recognition from radar data. The automatic labeling approach rests on the simultaneous recording of camera and lidar data in addition to the radar spectrum. By warping radar spectra into the camera image, state-of-the-art object recognition algorithms can be applied to label relevant objects, such as cars, in the camera image. The warping operation is designed to be fully differentiable, which allows backpropagating the gradient computed on the camera image through the warping operation to the neural network operating on the radar data. As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors. The
proposed scene flow estimation approach is compared against a state-of-the-art scene flow algorithm, and it outperforms it by approximately 30% w.r.t. mean average error. The feasibility of the overall framework for automatic label generation for
RD spectra is verified by evaluating the performance of neural networks trained with the proposed framework for Direction-of-Arrival estimation.}},
  author       = {{Grimm, Christopher and Fei, Tai and Warsitz, Ernst and Farhoud, Ridha and Breddermann, Tobias and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Vehicular Technology}},
  number       = {{9}},
  pages        = {{9435--9449}},
  title        = {{{Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications}}},
  doi          = {{10.1109/TVT.2022.3182411}},
  volume       = {{71}},
  year         = {{2022}},
}

@inproceedings{33696,
  author       = {{Wiechmann, Jana and Glarner, Thomas and Rautenberg, Frederik and Wagner, Petra and Haeb-Umbach, Reinhold}},
  booktitle    = {{18. Phonetik und Phonologie im deutschsprachigen Raum (P&P)}},
  location     = {{Bielefeld}},
  title        = {{{Technically enabled explaining of voice characteristics}}},
  year         = {{2022}},
}

@inproceedings{33957,
  abstract     = {{Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.}},
  author       = {{Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}},
  keywords     = {{Assistance system, Knowledge graph, Information retrieval, Neural networks, AR}},
  location     = {{Stuttgart}},
  title        = {{{AI-Based Assistance System for Manufacturing}}},
  doi          = {{10.1109/ETFA52439.2022.9921520}},
  year         = {{2022}},
}

@inproceedings{30733,
  abstract     = {{Hamilton-Jacobi reachability methods for safety-critical control have been well studied, but the safety guarantees derived rely on the accuracy of the numerical computation. Thus, it is crucial to understand and account for any inaccuracies that occur due to uncertainty in the underlying dynamics and environment as well as the induced numerical errors. To this end, we propose a framework for modeling the error of the value function inherent in Hamilton-Jacobi reachability using a Gaussian process. The derived safety controller can be used in conjuncture with arbitrary controllers to provide a safe hybrid control law. The marginal likelihood of the Gaussian process then provides a confidence metric used to determine switches between a least restrictive controller and a safety controller. We test both the prediction as well as the correction capabilities of the presented method in a classical pursuit-evasion example.}},
  author       = {{Vertovec, Nikolaus and Ober-Blöbaum, Sina and Margellos, Kostas}},
  location     = {{London}},
  pages        = {{1870--1875}},
  title        = {{{Verification of safety critical control policies using kernel methods}}},
  year         = {{2022}},
}

@inproceedings{46306,
  abstract     = {{Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.}},
  author       = {{Schneider, Lennart and Schäpermeier, Lennart and Prager, Raphael Patrick and Bischl, Bernd and Trautmann, Heike and Kerschke, Pascal}},
  booktitle    = {{Parallel Problem Solving from Nature — PPSN XVII}},
  editor       = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}},
  isbn         = {{978-3-031-14714-2}},
  pages        = {{575–589}},
  publisher    = {{Springer International Publishing}},
  title        = {{{HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis}}},
  doi          = {{10.1007/978-3-031-14714-2_40}},
  year         = {{2022}},
}

@article{46308,
  abstract     = {{Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems based on first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective domain and subsequently exploiting local structures of the resulting landscapes. Our study particularly focuses on the sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization of the artificial second objective, as well as (2) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Nelder–Mead local search as optimizer in the respective module and compare the performance of the resulting hybrid local search to its original single-objective counterpart. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Hence, combined with more sophisticated local search and metaheuristics, this may help solve highly multimodal optimization problems in the future.}},
  author       = {{Aspar, Pelin and Steinhoff, Vera and Schäpermeier, Lennart and Kerschke, Pascal and Trautmann, Heike and Grimme, Christian}},
  journal      = {{Natural Computing}},
  pages        = {{1–15}},
  title        = {{{The objective that freed me: a multi-objective local search approach for continuous single-objective optimization}}},
  doi          = {{10.1007/s11047-022-09919-w}},
  volume       = {{1}},
  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{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}},
}

@inproceedings{33857,
  author       = {{Kuhlmann, Michael and Seebauer, Fritz and Ebbers, Janek and Wagner, Petra and Haeb-Umbach, Reinhold}},
  booktitle    = {{Interspeech 2022}},
  publisher    = {{ISCA}},
  title        = {{{Investigation into Target Speaking Rate Adaptation for Voice Conversion}}},
  doi          = {{10.21437/interspeech.2022-10740}},
  year         = {{2022}},
}

@inproceedings{45378,
  author       = {{Dröse, Jennifer and Wessel, Lena}},
  booktitle    = {{Proceedings of the 45th Conference of the International Group for the Psychology of Mathematics Education. PME}},
  editor       = {{Fernandez, C. and Llinares, S. and Gutiérrez, A. and Planas, N.}},
  title        = {{{Prospective Teachers‘ Competence of Fostering Students’ Understanding in Script Writing Task}}},
  year         = {{2022}},
}

@inproceedings{33808,
  author       = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Heitkaemper, Jens and Haeb-Umbach, Reinhold}},
  booktitle    = {{2022 International Workshop on Acoustic Signal Enhancement (IWAENC)}},
  location     = {{ Bamberg, Germany }},
  publisher    = {{IEEE}},
  title        = {{{Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription}}},
  doi          = {{10.1109/IWAENC53105.2022.9914772}},
  year         = {{2022}},
}

@inproceedings{34072,
  abstract     = {{Performing an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research. The recently proposed polyphonic sound detection (PSD)-receiver operating characteristic (ROC) and PSD score (PSDS) make an important step into the direction of an evaluation of SED systems which is independent from a certain decision threshold. This allows to obtain a more complete picture of the overall system behavior which is less biased by threshold tuning. Yet, the PSD-ROC is currently only approximated using a finite set of thresholds. The choice of
the thresholds used in approximation, however, can have a severe impact on the resulting PSDS. In this paper we propose a method which allows for computing system performance on an evaluation set for all possible thresholds jointly, enabling accurate computation not only of the PSD-ROC and PSDS but also of other collar-based
and intersection-based performance curves. It further allows to select the threshold which best fulfills the requirements of a given application. Source code is publicly available in our SED evaluation package sed_scores_eval.}},
  author       = {{Ebbers, Janek and Haeb-Umbach, Reinhold and Serizel, Romain}},
  booktitle    = {{Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  title        = {{{Threshold Independent Evaluation of Sound Event Detection Scores}}},
  year         = {{2022}},
}