@article{46316, abstract = {{ Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design. }}, author = {{Assenmacher, Dennis and Weber, Derek and Preuss, Mike and Valdez, André Calero and Bradshaw, Alison and Ross, Björn and Cresci, Stefano and Trautmann, Heike and Neumann, Frank and Grimme, Christian}}, journal = {{Social Science Computer Review}}, number = {{6}}, pages = {{1496--1522}}, title = {{{Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem}}}, doi = {{10.1177/08944393211012268}}, volume = {{40}}, year = {{2022}}, } @inproceedings{34176, abstract = {{Cascaded H-bridge Converters (CHBs) are a promising solution in converting power from a three-phase medium voltage of 6.6 kV...30 kV to a lower DC-voltage in the range of 100 V...1 kV to provide pure DC power to applications such as electrolyzers for hydrogen generation, data centers with a DC power distribution and DC microgrids. CHBs can be interpreted as modular multilevel converters with an isolated DC-DC output stage per module, require a large DC-link capacitor for each module to handle the second harmonic voltage ripple caused by the fluctuating input power within a fundamental grid period. Without a zero-sequence voltage injection, star-connected CHBs are operated with approximately sinusoidal arm voltages and currents. The floating star point potential enables to utilize different zero-sequence voltage injection techniques such as a third-harmonic injection with 1/6 of the grid voltage amplitude or a Min-Max voltage injection. Both well-known methods have the advantage to reduce the peak arm voltage and thereby the number of required modules by 13.4 % (to √ 3 2). This paper proves analytically that the third-harmonic injection with 1/6 of the grid voltage amplitude reduces the second harmonic voltage ripple by only 15.1 % compared to no-voltage injection for unity power factor operation and balanced grid voltages. Then it is shown, that the Min-Max injection has the often overlooked advantage of reducing the second harmonic voltage ripple by even 18.8 %. By applying the here proposed zero-sequence voltage injection in saturation modulation, the second harmonic voltage ripple of the DC-link capacitors is reduced by even 24.3 %, while still requiring the same number of modules as the Min-Max injection. For a realistic number of reserve modules, the overall energy ripple in the DC-link capacitors is reduced by 40 %.}}, author = {{Unruh, Roland and Schafmeister, Frank and Böcker, Joachim}}, booktitle = {{24th European Conference on Power Electronics and Applications (EPE'22 ECCE Europe)}}, isbn = {{978-9-0758-1539-9}}, keywords = {{Cascaded H-Bridge, Solid-State Transformer, Zero sequence voltage, Third harmonic injection, Capacitor voltage ripple}}, location = {{Hanover, Germany}}, publisher = {{IEEE}}, title = {{{Zero-Sequence Voltage Reduces DC-Link Capacitor Demand in Cascaded H-Bridge Converters for Large-Scale Electrolyzers by 40%}}}, year = {{2022}}, } @inproceedings{33471, abstract = {{The intelligibility of demodulated audio signals from analog high frequency transmissions, e.g., using single-sideband (SSB) modulation, can be severely degraded by channel distortions and/or a mismatch between modulation and demodulation carrier frequency. In this work a neural network (NN)-based approach for carrier frequency offset (CFO) estimation from demodulated SSB signals is proposed, whereby a task specific architecture is presented. Additionally, a simulation framework for SSB signals is introduced and utilized for training the NNs. The CFO estimator is combined with a speech enhancement network to investigate its influence on the enhancement performance. The NN-based system is compared to a recently proposed pitch tracking based approach on publicly available data from real high frequency transmissions. Experiments show that the NN exhibits good CFO estimation properties and results in significant improvements in speech intelligibility, especially when combined with a noise reduction network.}}, author = {{Heitkämper, Jens and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{Proceedings of the 30th European Signal Processing Conference (EUSIPCO)}}, location = {{Belgrad}}, title = {{{Neural Network Based Carrier Frequency Offset Estimation From Speech Transmitted Over High Frequency Channels}}}, year = {{2022}}, } @inproceedings{33806, author = {{Afifi, Haitham and Karl, Holger and Gburrek, Tobias and Schmalenstroeer, Joerg}}, booktitle = {{2022 International Wireless Communications and Mobile Computing (IWCMC)}}, publisher = {{IEEE}}, title = {{{Data-driven Time Synchronization in Wireless Multimedia Networks}}}, doi = {{10.1109/iwcmc55113.2022.9824980}}, year = {{2022}}, } @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 = {{Abstract 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.}}, 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}}, }