@inproceedings{24024, abstract = {{Recently it has been demonstrated that an optoelectronic phase-locked loop (OEPLL) using a mode-locked laser as a reference oscillator achieves significantly lower phase noise than conventional electronic frequency synthesizers. In this paper a concept for an OEPLL-based frequency synthesizer is presented and it is investigated how it can be used as a local oscillator (LO) for THz transceivers in order to improve the signal quality in THz wireless communications. The concept of the OEPLL is presented and it's measured phase noise is compared to the phase noise of a laboratory-grade electronic frequency synthesizer. The measured phase noise spectra of both synthesizers at 10 GHz are then used to model LO phase noise at 320 GHz. Based on models of generic zero-IF transmit and receive frontends, THz signals with different modulation formats and Baud rates are simulated at system level using the modeled LO phase noise for the two LO approaches. Finally, the results are compared.}}, author = {{Scheytt, Christoph and Wrana, Dominik and Bahmanian, Meysam and Kallfass, Ingmar}}, booktitle = {{2020 Third International Workshop on Mobile Terahertz Systems (IWMTS)}}, location = {{Essen, Germany }}, title = {{{Ultra-Low Phase Noise Frequency Synthesis for THz Communications Using Optoelectronic PLLs}}}, doi = {{10.1109/IWMTS49292.2020.9166347}}, year = {{2020}}, } @inproceedings{13226, abstract = {{The canonical problem for the class Quantum Merlin-Arthur (QMA) is that of estimating ground state energies of local Hamiltonians. Perhaps surprisingly, [Ambainis, CCC 2014] showed that the related, but arguably more natural, problem of simulating local measurements on ground states of local Hamiltonians (APX-SIM) is likely harder than QMA. Indeed, [Ambainis, CCC 2014] showed that APX-SIM is P^QMA[log]-complete, for P^QMA[log] the class of languages decidable by a P machine making a logarithmic number of adaptive queries to a QMA oracle. In this work, we show that APX-SIM is P^QMA[log]-complete even when restricted to more physical Hamiltonians, obtaining as intermediate steps a variety of related complexity-theoretic results. We first give a sequence of results which together yield P^QMA[log]-hardness for APX-SIM on well-motivated Hamiltonians: (1) We show that for NP, StoqMA, and QMA oracles, a logarithmic number of adaptive queries is equivalent to polynomially many parallel queries. These equalities simplify the proofs of our subsequent results. (2) Next, we show that the hardness of APX-SIM is preserved under Hamiltonian simulations (a la [Cubitt, Montanaro, Piddock, 2017]). As a byproduct, we obtain a full complexity classification of APX-SIM, showing it is complete for P, P^||NP, P^||StoqMA, or P^||QMA depending on the Hamiltonians employed. (3) Leveraging the above, we show that APX-SIM is P^QMA[log]-complete for any family of Hamiltonians which can efficiently simulate spatially sparse Hamiltonians, including physically motivated models such as the 2D Heisenberg model. Our second focus considers 1D systems: We show that APX-SIM remains P^QMA[log]-complete even for local Hamiltonians on a 1D line of 8-dimensional qudits. This uses a number of ideas from above, along with replacing the "query Hamiltonian" of [Ambainis, CCC 2014] with a new "sifter" construction.}}, author = {{Gharibian, Sevag and Piddock, Stephen and Yirka, Justin}}, booktitle = {{Proceedings of the 37th Symposium on Theoretical Aspects of Computer Science (STACS 2020)}}, pages = {{38}}, title = {{{Oracle complexity classes and local measurements on physical Hamiltonians}}}, year = {{2020}}, } @inproceedings{46319, abstract = {{The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than an- alyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and com- putational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learn- ing techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real- time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordi- nated campaigns.}}, author = {{Assenmacher, D and Adam, L and Trautmann, Heike and Grimme, C}}, booktitle = {{Proceedings of the Florida Artificial Intelligence Research Society Conference}}, title = {{{Towards Real-Time and Unsupervised Campaign Detection in Social Media}}}, year = {{2020}}, } @inproceedings{20505, abstract = {{Speech activity detection (SAD), which often rests on the fact that the noise is "more'' stationary than speech, is particularly challenging in non-stationary environments, because the time variance of the acoustic scene makes it difficult to discriminate speech from noise. We propose two approaches to SAD, where one is based on statistical signal processing, while the other utilizes neural networks. The former employs sophisticated signal processing to track the noise and speech energies and is meant to support the case for a resource efficient, unsupervised signal processing approach. The latter introduces a recurrent network layer that operates on short segments of the input speech to do temporal smoothing in the presence of non-stationary noise. The systems are tested on the Fearless Steps challenge database, which consists of the transmission data from the Apollo-11 space mission. The statistical SAD achieves comparable detection performance to earlier proposed neural network based SADs, while the neural network based approach leads to a decision cost function of 1.07% on the evaluation set of the 2020 Fearless Steps Challenge, which sets a new state of the art.}}, author = {{Heitkaemper, Jens and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2020 Virtual Shanghai China}}, keywords = {{voice activity detection, speech activity detection, neural network, statistical speech processing}}, title = {{{Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments}}}, year = {{2020}}, } @article{45383, author = {{Dröse, Jennifer and Prediger, Susanne}}, journal = {{Journal für Mathematik-Didaktik, 41(2)}}, pages = {{399--422}}, title = {{{Enhancing Fifth Graders’ Awareness of Syntactic Features in Mathematical Word Problems: A Design Research Study on the Variation Principle}}}, doi = {{doi.org/10.1007/s13138-019-00153-z}}, year = {{2020}}, } @inbook{29413, author = {{Flaßkamp, K. and Ober-Blöbaum, Sina and Peitz, S. }}, booktitle = {{Advances in Dynamics, Optimization and Computation}}, editor = {{Junge, Oliver and Schütze, Oliver and Froyland, Gary and Ober-Blöbaum, Sina and Padberg-Gehle, Kathrin}}, pages = {{209--237}}, publisher = {{Springer International Publishing}}, title = {{{Symmetry in optimal control: A multiobjective model predictive control approach}}}, year = {{2020}}, } @inproceedings{20762, abstract = {{The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multispeaker speech recognition. However, up until now, state-of-theart neural network–based time domain source separation has not yet been combined with E2E speech recognition. We here demonstrate how to combine a separation module based on a Convolutional Time domain Audio Separation Network (Conv-TasNet) with an E2E speech recognizer and how to train such a model jointly by distributing it over multiple GPUs or by approximating truncated back-propagation for the convolutional front-end. To put this work into perspective and illustrate the complexity of the design space, we provide a compact overview of single-channel multi-speaker recognition systems. Our experiments show a word error rate of 11.0% on WSJ0-2mix and indicate that our joint time domain model can yield substantial improvements over cascade DNN-HMM and monolithic E2E frequency domain systems proposed so far.}}, author = {{von Neumann, Thilo and Kinoshita, Keisuke and Drude, Lukas and Boeddeker, Christoph and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, pages = {{7004--7008}}, title = {{{End-to-End Training of Time Domain Audio Separation and Recognition}}}, doi = {{10.1109/ICASSP40776.2020.9053461}}, year = {{2020}}, } @inproceedings{20764, abstract = {{Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown. To cope with this, we extend an iterative speech extraction system with mechanisms to count the number of sources and combine it with a single-talker speech recognizer to form the first end-to-end multi-talker automatic speech recognition system for an unknown number of active speakers. Our experiments show very promising performance in counting accuracy, source separation and speech recognition on simulated clean mixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a new state-of-the-art word error rate on the WSJ0-2mix database. Furthermore, our system generalizes well to a larger number of speakers than it ever saw during training, as shown in experiments with the WSJ0-4mix database. }}, author = {{von Neumann, Thilo and Boeddeker, Christoph and Drude, Lukas and Kinoshita, Keisuke and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. Interspeech 2020}}, pages = {{3097--3101}}, title = {{{Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source Counting, Separation and ASR}}}, doi = {{10.21437/Interspeech.2020-2519}}, year = {{2020}}, } @inproceedings{18651, abstract = {{We present an approach to deep neural network based (DNN-based) distance estimation in reverberant rooms for supporting geometry calibration tasks in wireless acoustic sensor networks. Signal diffuseness information from acoustic signals is aggregated via the coherent-to-diffuse power ratio to obtain a distance-related feature, which is mapped to a source-to-microphone distance estimate by means of a DNN. This information is then combined with direction-of-arrival estimates from compact microphone arrays to infer the geometry of the sensor network. Unlike many other approaches to geometry calibration, the proposed scheme does only require that the sampling clocks of the sensor nodes are roughly synchronized. In simulations we show that the proposed DNN-based distance estimator generalizes to unseen acoustic environments and that precise estimates of the sensor node positions are obtained. }}, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Brendel, Andreas and Kellermann, Walter and Haeb-Umbach, Reinhold}}, booktitle = {{European Signal Processing Conference (EUSIPCO)}}, title = {{{Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Network}}}, year = {{2020}}, } @article{31553, author = {{Seitz, Susanne and Häsel-Weide, Uta and Wilke, Yannik and Wallner, Melina}}, journal = {{K:ON Kölner Online-Journal für Lehrer*innenbildung}}, number = {{2}}, title = {{{Expertise von Lehrpersonen für inklusiven Mathematikunterricht der Sekundarstufe - Ausgangspunkte zur Professionalisierungsforschung.}}}, doi = {{10.18716/ojs/kON/2020.2.03}}, year = {{2020}}, } @inproceedings{46328, abstract = {{In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike and Wang, Hao and Bäck, Thomas H. W. and Kerschke, Pascal}}, booktitle = {{Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{996–1003}}, title = {{{Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis}}}, doi = {{10.1109/SSCI47803.2020.9308510}}, year = {{2020}}, } @inproceedings{46320, abstract = {{The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020.}}, author = {{Assenmacher, D and Clever, L and Pohl, JS and Trautmann, Heike and Grimme, C}}, booktitle = {{Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis}}, editor = {{Meiselwitz, G}}, isbn = {{978-3-030-49570-1}}, pages = {{201–214}}, publisher = {{Springer International Publishing}}, title = {{{A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media}}}, doi = {{10.1007/978-3-030-49570-1_14}}, year = {{2020}}, } @inproceedings{46325, abstract = {{Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.}}, author = {{Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}}, booktitle = {{Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}}, isbn = {{978-3-030-43823-4}}, pages = {{137–143}}, title = {{{Towards Automated Configuration of Stream Clustering Algorithms}}}, doi = {{10.1007/978-3-030-43823-4_12}}, year = {{2020}}, } @inproceedings{46321, abstract = {{Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such automated programs, it is still unclear how technically sophisticated those bots are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo of social bot development as well as first insights into the overall skills of publicly available bot code.}}, author = {{Assenmacher, Dennis and Frischlich , Lena and Trautmann, Heike and Grimme, Christian and Adam, Lena}}, booktitle = {{Disinformation in open online media}}, editor = {{Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}}, pages = {{101–114}}, publisher = {{Springer}}, title = {{{Inside the tool set of automation: Free social bot code revisited}}}, year = {{2020}}, } @inproceedings{46326, abstract = {{Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.}}, author = {{Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}}, booktitle = {{Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)}}, pages = {{80–95}}, title = {{{confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms}}}, doi = {{10.1007/978-3-030-53552-0_10}}, year = {{2020}}, } @inproceedings{46327, abstract = {{In online media environments, nostalgia can be used as important ingredient of propaganda strategies, specifically, by creating societal pessimism. This work addresses the automated detection of nostalgic text as a first step towards automatically identifying nostalgia-based manipulation strategies. We compare the performance of standard machine learning approaches on this challenge and demonstrate the successful transfer of the best performing approach to real-world nostalgia detection in a case study.}}, author = {{Lena, Clever and Frischlich, Lena and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Disinformation in open online media}}, editor = {{Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}}, pages = {{48–58}}, title = {{{Automated detection of nostalgic text in the context of societal pessimism}}}, year = {{2020}}, } @inproceedings{46329, abstract = {{The past decade has been characterized by a strong increase in the use of social media and a continuous growth of public online discussion. With the failure of purely manual moderation, platform operators started searching for semi-automated solutions, where the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques is promising. However, this requires huge financial investments for algorithmic implementations, data collection, and model training, which only big players can afford. To support smaller or medium-sized media enterprises (SME), we developed an integrated comment moderation system as an IT platform. This platform acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating such a platform, however, requires a robust technology stack, integrated workflows and well-defined interfaces between all parties. In this paper, we develop and discuss a suitable IT architecture and present a prototypical implementation.}}, author = {{Riehle, Dennis M. and Niemann, Marco and Brunk, Jens and Assenmacher, Dennis and Trautmann, Heike and Becker, Jörg}}, booktitle = {{Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing}}, editor = {{Meiselwitz, Gabriele}}, isbn = {{978-3-030-49576-3}}, pages = {{71–86}}, publisher = {{Springer International Publishing}}, title = {{{Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool}}}, year = {{2020}}, } @article{46333, abstract = {{ Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots. }}, author = {{Assenmacher, Dennis and Clever, Lena and Frischlich, Lena and Quandt, Thorsten and Trautmann, Heike and Grimme, Christian}}, journal = {{Social Media + Society}}, number = {{3}}, pages = {{2056305120939264}}, title = {{{Demystifying Social Bots: On the Intelligence of Automated Social Media Actors}}}, doi = {{10.1177/2056305120939264}}, volume = {{6}}, year = {{2020}}, } @inproceedings{46332, abstract = {{Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.}}, author = {{Steinhoff, Vera and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{2445–2452}}, title = {{{Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent}}}, doi = {{10.1109/SSCI47803.2020.9308259}}, year = {{2020}}, } @inproceedings{48301, author = {{Glockner, Max and Habernal, Ivan and Gurevych, Iryna}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2020}}, publisher = {{Association for Computational Linguistics}}, title = {{{Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision}}}, doi = {{10.18653/v1/2020.findings-emnlp.97}}, year = {{2020}}, }