@misc{32400, author = {{Anonymous, Anonymous}}, title = {{{Performance Analysis of FHE Libraries}}}, year = {{2021}}, } @techreport{35889, abstract = {{Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.}}, author = {{Schneider, Stefan Balthasar}}, keywords = {{nfv, coordination, machine learning, reinforcement learning, phd, digest}}, title = {{{Conventional and Machine Learning Approaches for Network and Service Coordination}}}, year = {{2021}}, } @inbook{35752, author = {{Frischemeier, Daniel and Podworny, Susanne and Biehler, Rolf}}, booktitle = {{Konzepte und Studien Lehrinnovationen in der Hochschulmathematik . Konzepte und Studien zur Hochschuldidaktik und Lehrerbildung Mathematikzur Hochschuldidaktik und Lehrerbildung Mathematik}}, editor = {{Biehler, Rolf and Eichler, Andreas and Hochmuth, Reinhard and Rach, Stefanie and Schaper, Niclas}}, isbn = {{9783662628539}}, issn = {{2197-8751}}, pages = {{227--249}}, publisher = {{Springer Spektrum}}, title = {{{Integration fachwissenschaftlicher und fachdidaktischer Komponenten in der Lehramtsausbildung Mathematik Grundschule am Beispiel einer Veranstaltung zur Leitidee „Daten, Häufigkeit und Wahrscheinlichkeit“}}}, doi = {{10.1007/978-3-662-62854-6_11}}, year = {{2021}}, } @inproceedings{47605, abstract = {{In diesem Beitrag werden die Elemente „Begleitseminar“ und „Begleitforschungsseminar“ des Praxissemesters für Lehramtsstudierende der beruflichen Fachrichtungen Maschinenbautechnik und Elektrotechnik an der Universität Paderborn im Hinblick auf die Veränderungen durch die Covid-19-Pandemie erläutert.}}, author = {{Jonas-Ahrend, Gabriela and Vernholz, Mats and Temmen, Katrin}}, location = {{Osnabrück}}, title = {{{Digitale Begleitseminare im Praxissemester der gewerblich-technischen Fachrichtungen}}}, year = {{2021}}, } @article{46318, abstract = {{Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.}}, author = {{Grimme, Christian and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André H. and Wang, Hao and Emmerich, Michael}}, issn = {{0305-0548}}, journal = {{Computers & Operations Research}}, keywords = {{Multimodal optimization, Multi-objective continuous optimization, Landscape analysis, Visualization, Benchmarking, Theory, Algorithms}}, pages = {{105489}}, title = {{{Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization}}}, doi = {{https://doi.org/10.1016/j.cor.2021.105489}}, volume = {{136}}, year = {{2021}}, } @inproceedings{46311, abstract = {{In this work we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems by first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective space, and subsequently exploiting local structures and ridges of the resulting landscapes. Our study particularly focusses on the sensitivity of this multiobjectivization approach w.r.t. (i) the parametrization of the artificial second objective, as well as (ii) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient and Nelder-Mead local search (as optimizers in the respective module) and compare the performance of the resulting hybrid local search to their original single-objective counterparts. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Combined with more sophisticated local search and metaheuristics this may help in solving highly multimodal optimization problems in future.}}, author = {{Aspar, Pelin and Kerschke, Pascal and Steinhoff, Vera and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings}}, editor = {{et al. Ishibuchi, H.}}, pages = {{311–322}}, publisher = {{Springer}}, title = {{{Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization}}}, doi = {{10.1007/978-3-030-72062-9_25}}, year = {{2021}}, } @article{46317, abstract = {{One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity themes: Crime & conflict, Jobs, Attention, and Wellbeing. The outcomes of the workshop discussions identify three attributes of humanity that need preservation: a critique of the design and application of AI, and the intelligent machines it can create; human involvement in the loop of intelligent machine decision-making processes; and the ability to interpret and explain intelligent machine decision-making processes. The article provides an agenda for future AI and humanity research.}}, author = {{Coombs, Crispin and Stacey, Patrick and Kawalek, Peter and Simeonova, Boyka and Becker, Jörg and Bergener, Katrin and Carvalho, João Álvaro and Fantinato, Marcelo and Garmann-Johnsen, Niels F. and Grimme, Christian and Stein, Armin and Trautmann, Heike}}, journal = {{International Journal of Information Management}}, title = {{{What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective}}}, doi = {{10.1016/j.ijinfomgt.2021.102311}}, volume = {{58}}, year = {{2021}}, } @inproceedings{24000, author = {{Heitkaemper, Jens and Schmalenstroeer, Joerg and Ion, Valentin and Haeb-Umbach, Reinhold}}, booktitle = {{Speech Communication; 14th ITG-Symposium}}, pages = {{1--5}}, title = {{{A Database for Research on Detection and Enhancement of Speech Transmitted over HF links}}}, year = {{2021}}, } @article{45381, author = {{Dröse, Jennifer and Prediger, S. and Neugebauer, P. and Danhier, R. D. and Mertins, B.}}, journal = {{International Electronic Journal of Mathematics Education, 16(1), em0625}}, title = {{{Investigating students' processes of noticing and interpreting syntactic language features in word problem solving through eye-tracking}}}, doi = {{doi.org/10.29333/iejme/9674n }}, year = {{2021}}, } @article{45380, author = {{Dröse, Jennifer and Prediger, S.}}, journal = {{Studies in Educational Evaluation, 68 (100953)}}, pages = {{1--15}}, title = {{{Identifying obstacles is not enough for everybody – Differential efficacy of an intervention fostering fifth graders’ comprehension for word problems}}}, doi = {{doi.org/10.1016/j.stueduc.2020.100953}}, year = {{2021}}, } @inproceedings{44843, abstract = {{Unsupervised blind source separation methods do not require a training phase and thus cannot suffer from a train-test mismatch, which is a common concern in neural network based source separation. The unsupervised techniques can be categorized in two classes, those building upon the sparsity of speech in the Short-Time Fourier transform domain and those exploiting non-Gaussianity or non-stationarity of the source signals. In this contribution, spatial mixture models which fall in the first category and independent vector analysis (IVA) as a representative of the second category are compared w.r.t. their separation performance and the performance of a downstream speech recognizer on a reverberant dataset of reasonable size. Furthermore, we introduce a serial concatenation of the two, where the result of the mixture model serves as initialization of IVA, which achieves significantly better WER performance than each algorithm individually and even approaches the performance of a much more complex neural network based technique.}}, author = {{Boeddeker, Christoph and Rautenberg, Frederik and Haeb-Umbach, Reinhold}}, booktitle = {{ITG Conference on Speech Communication}}, location = {{Kiel}}, title = {{{A Comparison and Combination of Unsupervised Blind Source Separation Techniques}}}, year = {{2021}}, } @inproceedings{28259, author = {{Boeddeker, Christoph and Zhang, Wangyou and Nakatani, Tomohiro and Kinoshita, Keisuke and Ochiai, Tsubasa and Delcroix, Marc and Kamo, Naoyuki and Qian, Yanmin and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{Convolutive Transfer Function Invariant SDR Training Criteria for Multi-Channel Reverberant Speech Separation}}}, doi = {{10.1109/icassp39728.2021.9414661}}, year = {{2021}}, } @inproceedings{23998, author = {{Schmalenstroeer, Joerg and Heitkaemper, Jens and Ullmann, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{29th European Signal Processing Conference (EUSIPCO)}}, pages = {{1--5}}, title = {{{Open Range Pitch Tracking for Carrier Frequency Difference Estimation from HF Transmitted Speech}}}, year = {{2021}}, } @article{22528, abstract = {{Due to the ad hoc nature of wireless acoustic sensor networks, the position of the sensor nodes is typically unknown. This contribution proposes a technique to estimate the position and orientation of the sensor nodes from the recorded speech signals. The method assumes that a node comprises a microphone array with synchronously sampled microphones rather than a single microphone, but does not require the sampling clocks of the nodes to be synchronized. From the observed audio signals, the distances between the acoustic sources and arrays, as well as the directions of arrival, are estimated. They serve as input to a non-linear least squares problem, from which both the sensor nodes’ positions and orientations, as well as the source positions, are alternatingly estimated in an iterative process. Given one set of unknowns, i.e., either the source positions or the sensor nodes’ geometry, the other set of unknowns can be computed in closed-form. The proposed approach is computationally efficient and the first one, which employs both distance and directional information for geometry calibration in a common cost function. Since both distance and direction of arrival measurements suffer from outliers, e.g., caused by strong reflections of the sound waves on the surfaces of the room, we introduce measures to deemphasize or remove unreliable measurements. Additionally, we discuss modifications of our previously proposed deep neural network-based acoustic distance estimator, to account not only for omnidirectional sources but also for directional sources. Simulation results show good positioning accuracy and compare very favorably with alternative approaches from the literature.}}, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, issn = {{1687-4722}}, journal = {{EURASIP Journal on Audio, Speech, and Music Processing}}, title = {{{Geometry calibration in wireless acoustic sensor networks utilizing DoA and distance information}}}, doi = {{10.1186/s13636-021-00210-x}}, year = {{2021}}, } @inproceedings{23994, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks}}}, doi = {{10.1109/icassp39728.2021.9413831}}, year = {{2021}}, } @inproceedings{23999, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{Speech Communication; 14th ITG-Symposium}}, pages = {{1--5}}, title = {{{On Source-Microphone Distance Estimation Using Convolutional Recurrent Neural Networks}}}, year = {{2021}}, } @inproceedings{23997, author = {{Chinaev, Aleksej and Enzner, Gerald and Gburrek, Tobias and Schmalenstroeer, Joerg}}, booktitle = {{29th European Signal Processing Conference (EUSIPCO)}}, pages = {{1--5}}, title = {{{Online Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks with Packet Loss}}}, year = {{2021}}, } @inproceedings{29304, abstract = {{In this work we address disentanglement of style and content in speech signals. We propose a fully convolutional variational autoencoder employing two encoders: a content encoder and a style encoder. To foster disentanglement, we propose adversarial contrastive predictive coding. This new disentanglement method does neither need parallel data nor any supervision. We show that the proposed technique is capable of separating speaker and content traits into the two different representations and show competitive speaker-content disentanglement performance compared to other unsupervised approaches. We further demonstrate an increased robustness of the content representation against a train-test mismatch compared to spectral features, when used for phone recognition.}}, author = {{Ebbers, Janek and Kuhlmann, Michael and Cord-Landwehr, Tobias and Haeb-Umbach, Reinhold}}, booktitle = {{Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, pages = {{3860–3864}}, title = {{{Contrastive Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised Learning of Disentangled Speech Representations}}}, year = {{2021}}, } @article{31578, author = {{Häsel-Weide, Uta and Seitz, Simone and Wallner, Melina and Wilke, Yannik and Heckmann, Lara}}, journal = {{QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte}}, number = {{1}}, title = {{{Mit Aufgaben im inklusiven Mathematikunterricht professionell umgehen - Erkenntnisse einer Interviewstudie mit Lehrpersonen der Sekundarstufe}}}, doi = {{10.21248/qfi.57}}, volume = {{3}}, year = {{2021}}, } @inproceedings{31583, author = {{Hattermann, Mathias and Häsel-Weide, Uta and Wallner, Melina}}, booktitle = {{Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education }}, editor = {{Inprasitha, M. and Changsri, N. and Boonsena, N.}}, pages = {{9--15}}, title = {{{Conceptualiziation processes of 6th graders for rotational symmetry}}}, volume = {{3}}, year = {{2021}}, }