@article{45484, abstract = {{AbstractGraffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (Ingrid) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present IngridKG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update IngridKG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of IngridKG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.}}, author = {{Sherif, Mohamed Ahmed and da Silva, Ana Alexandra Morim and Pestryakova, Svetlana and Ahmed, Abdullah Fathi and Niemann, Sven and Ngomo, Axel-Cyrille Ngonga}}, issn = {{2052-4463}}, journal = {{Scientific Data}}, keywords = {{Library and Information Sciences, Statistics, Probability and Uncertainty, Computer Science Applications, Education, Information Systems, Statistics and Probability}}, number = {{1}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{IngridKG: A FAIR Knowledge Graph of Graffiti}}}, doi = {{10.1038/s41597-023-02199-8}}, volume = {{10}}, year = {{2023}}, } @article{45485, author = {{Kruse, Stephan and Serino, Laura and Folge, Patrick Fabian and Echeverria Oviedo, Dana and Bhattacharjee, Abhinandan and Stefszky, Michael and Scheytt, J. Christoph and Brecht, Benjamin and Silberhorn, Christine}}, issn = {{1041-1135}}, journal = {{IEEE Photonics Technology Letters}}, keywords = {{Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials}}, number = {{14}}, pages = {{769--772}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{A Pulsed Lidar System With Ultimate Quantum Range Accuracy}}}, doi = {{10.1109/lpt.2023.3277515}}, volume = {{35}}, year = {{2023}}, } @unpublished{45498, abstract = {{We present a novel method for high-order phase reduction in networks of weakly coupled oscillators and, more generally, perturbations of reducible normally hyperbolic (quasi-)periodic tori. Our method works by computing an asymptotic expansion for an embedding of the perturbed invariant torus, as well as for the reduced phase dynamics in local coordinates. Both can be determined to arbitrary degrees of accuracy, and we show that the phase dynamics may directly be obtained in normal form. We apply the method to predict remote synchronisation in a chain of coupled Stuart-Landau oscillators.}}, author = {{von der Gracht, Sören and Nijholt, Eddie and Rink, Bob}}, booktitle = {{arXiv:2306.03320}}, pages = {{29}}, title = {{{A parametrisation method for high-order phase reduction in coupled oscillator networks}}}, year = {{2023}}, } @phdthesis{45580, author = {{Castenow, Jannik}}, title = {{{Local Protocols for Contracting and Expanding Robot Formation Problems}}}, doi = {{10.17619/UNIPB/1-1750}}, year = {{2023}}, } @phdthesis{45579, author = {{Knollmann, Till}}, title = {{{Online Algorithms for Allocating Heterogeneous Resources}}}, doi = {{10.17619/UNIPB/1-1751}}, year = {{2023}}, } @article{45596, abstract = {{Dielectric metasurfaces provide a unique platform for efficient harmonic generation and optical wavefront manipulation at the nanoscale. Tailoring phase and amplitude of a nonlinearly generated wave with a high emission efficiency using resonance-based metasurfaces is a challenging task that often requires state-of-the-art numerical methods. Here, we propose a simple yet effective approach combining a sampling method with a Monte Carlo approach to design the third-harmonic wavefront generated by all-dielectric metasurfaces composed of elliptical silicon nanodisks. Using this approach, we theoretically demonstrate the full nonlinear 2π phase control with a uniform and highest possible amplitude in the considered parameter space, allowing us to design metasurfaces operating as third harmonic beam deflectors capable of steering light into a desired direction with high emission efficiency. The TH beam deflection with a record calculated average conversion efficiency of 1.2 × 10–1 W–2 is achieved. We anticipate that the proposed approach will be widely applied as alternative to commonly used optimization algorithms with higher complexity and implementation effort for the design of metasurfaces with other holographic functionalities.}}, author = {{Hähnel, David and Förstner, Jens and Myroshnychenko, Viktor}}, issn = {{2330-4022}}, journal = {{ACS Photonics}}, keywords = {{tet_topic_meta}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Efficient Modeling and Tailoring of Nonlinear Wavefronts in Dielectric Metasurfaces}}}, doi = {{10.1021/acsphotonics.2c01967}}, year = {{2023}}, } @inproceedings{45695, author = {{Hotegni, Sedjro Salomon and Mahabadi, Sepideh and Vakilian, Ali}}, booktitle = {{Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.}}, keywords = {{Fair range clustering}}, location = {{Honolulu, Hawaii, USA}}, title = {{{Approximation Algorithms for Fair Range Clustering}}}, year = {{2023}}, } @inproceedings{43060, author = {{Hebrok, Sven Niclas and Nachtigall, Simon and Maehren, Marcel and Erinola, Nurullah and Merget, Robert and Somorovsky, Juraj and Schwenk, Jörg}}, booktitle = {{32nd USENIX Security Symposium}}, title = {{{We Really Need to Talk About Session Tickets: A Large-Scale Analysis of Cryptographic Dangers with TLS Session Tickets}}}, year = {{2023}}, } @inbook{45552, abstract = {{The field of teaching technologies is in constant interplay between educational and industrial advances. Since the beginning of the twenty-first century, digitalization and automatization have become increasingly important. In industrial and social life, we see similar fast-moving developments. These factors challenge education, specifically vocational education, greatly, and raise two very different, yet very much connected questions: how to prepare students for their vocational lives and how to prepare teachers to communicate the necessary competencies to their students? This chapter provides an overview of advances, challenges, and possible solutions, focusing on the three key fields of vocational education in Germany: Industry 4.0, Education 4.0, and innovative teacher education. Most importantly, however, the text examines the continuous interplay between and among these fields. The beginning of the chapter is dedicated to vocational teacher education, in accordance with industrial and educational advances. Specifying this, characteristics of Industry 4.0, as well as students' and teachers' perceptions of Industry 4.0, are discussed. This is followed by an introduction to the concept of so-called learning factories as a possible way of integrating aspects of Industry 4.0 in German vocational schools. The end of the chapter is dedicated to the required changes in educational settings today and in the future. Though Industry 4.0, Education 4.0, and innovative teacher education are each widely discussed in the current literature, the interplay of all three fields reveals a research gap. This chapter tries to close this gap and provide an important contribution to the research field.}}, author = {{Jonas-Ahrend, Gabriela and Vernholz, Mats and Temmen, Katrin}}, booktitle = {{Teacher Education in the Wake of Covid-19 }}, editor = {{Craig, Cheryl J. and Mena, Juanjo and Kane, Ruth G.}}, isbn = {{9781804554630}}, issn = {{1479-3687}}, pages = {{175--191}}, publisher = {{Emerald Publishing Limited}}, title = {{{Teaching Technologies: Continuous Interplay Between Educational and Industrial Advances}}}, doi = {{10.1108/s1479-368720230000041019}}, volume = {{41}}, year = {{2023}}, } @article{45712, author = {{Häsel-Weide, Uta}}, journal = {{Die Grundschulzeitschrift}}, number = {{339}}, pages = {{6--11}}, publisher = {{Friedrich Verlag}}, title = {{{ Inklusiver Mathematikunterricht. Mathematiklernen in Vielfalt von Kompetenzen, Wegen und Lernsituationen}}}, year = {{2023}}, } @article{45713, author = {{Graf, Lara Marie and Wienhues, Inga and Häsel-Weide, Uta}}, journal = {{Die Grundschulzeitschrift}}, number = {{339}}, pages = {{20--23}}, publisher = {{Friedrich Verlag}}, title = {{{Addition und Subtraktion verstehen}}}, year = {{2023}}, } @misc{45762, author = {{Simon-Mertens, Florian}}, title = {{{Effizienzanalyse leichtgewichtiger Neuronaler Netze für FPGA-basierte Modulationsklassifikation}}}, year = {{2023}}, } @inproceedings{45778, abstract = {{RISC-V has received worldwide acceptance in the industry and by the academic community. As of today, multiple RISC-V applications and variants are under investigation for embedded IoT systems, from resource-limited single-core processors up to multi-core systems for High-Performance Computing (HPC). Recently, the Grid of Processing Cells (GPC) platform has been proposed as a scalable parallel grid-oriented network of processor cores with local memories. This paper describes a prototype design of the GPC platform for hardware implementation at Register-Transfer Level (RTL) based on modified RISC-V Rocket processors with scratchpad memories. It introduces a scalable Chisel-based implementation of the modified Rocket cores with RTL generation and a functional test using Verilator simulation. This work also includes the adaptation of the Chipyard software toolchain to extend the compiler to multi-core grids with different local address spaces.}}, author = {{Luchterhandt, Lars and Nellius, Tom and Beck, Robert and Dömer, Rainer and Kneuper, Pascal and Müller, Wolfgang and Sadiye, Babak}}, booktitle = {{MBMV 2023 - 26. Workshop "Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen“}}, location = {{Germany, Freiburg}}, publisher = {{VDE Verlag}}, title = {{{Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen}}}, year = {{2023}}, } @inproceedings{45776, author = {{Ecker, Wolfgang and Krstic, Milos and Ulbricht, Markus and Mauderer, Andreas and Jentzsch, Eyck and Koch, Andreas and Koppelmann, Bastian and Müller, Wolfgang and Sadiye, Babak and Bruns, Niklas and Drechsler, Rolf and Müller-Gritschneder, Daniel and Schlamelcher, Jan and Grüttner, Kim and Bormann, Jörg and Kunz, Wolfgang and Heckmann, Reinhold and Angst, Gerhard and Wimmer, Ralf and Becker, Bernd and Faller, Tobias and Palomero Bernardo, Paul and Brinkmann, Oliver and Partzsch, Johannes and Mayr, Christian}}, booktitle = {{Scale4Edge – Scaling RISC-V for Edge Applications}}, location = {{ Barcelona, Spain,}}, title = {{{Scale4Edge – Scaling RISC-V for Edge Applications}}}, year = {{2023}}, } @inproceedings{45793, abstract = {{The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.}}, author = {{Grigoryan, Khoren and Fichtler, Timm and Schreiner, Nick and Rabe, Martin and Panzner, Melina and Kühn, Arno and Dumitrescu, Roman and Koldewey, Christian}}, booktitle = {{Procedia CIRP 33}}, keywords = {{Product Management, Data Analytics, Data-Driven Design, Product-related data, Lifecycle Data, Tool-support}}, location = {{Sydney}}, title = {{{Data-Driven Product Management: A Practitioner-Driven Research Agenda}}}, year = {{2023}}, } @inproceedings{45812, author = {{Özcan, Leon and Fichtler, Timm and Kasten, Benjamin and Koldewey, Christian and Dumitrescu, Roman}}, keywords = {{Digital Platform, Platform Strategy, Strategic Management, Platform Life Cycle, Interview Study, Business Model, Business-to-Business, Two-sided Market, Multi-sided Market}}, location = {{Ljubljana}}, title = {{{Interview Study on Strategy Options for Platform Operation in B2B Markets}}}, year = {{2023}}, } @inproceedings{33734, abstract = {{Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis}}, author = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}}, editor = {{Pesquita, Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}}, keywords = {{Neural network, Concept learning, Description logics}}, location = {{Hersonissos, Crete, Greece}}, pages = {{209 -- 226}}, publisher = {{Springer International Publishing}}, title = {{{Neural Class Expression Synthesis}}}, doi = {{https://doi.org/10.1007/978-3-031-33455-9_13}}, volume = {{13870}}, year = {{2023}}, } @unpublished{37937, abstract = {{Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations.}}, author = {{Sieger, Leonie Nora and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{arXiv:2301.05109}}, title = {{{Counterfactual Explanations for Concepts in ELH}}}, year = {{2023}}, } @phdthesis{44323, abstract = {{Reading between the lines has so far been reserved for humans. The present dissertation addresses this research gap using machine learning methods. Implicit expressions are not comprehensible by computers and cannot be localized in the text. However, many texts arise on interpersonal topics that, unlike commercial evaluation texts, often imply information only by means of longer phrases. Examples are the kindness and the attentiveness of a doctor, which are only paraphrased (“he didn’t even look me in the eye”). The analysis of such data, especially the identification and localization of implicit statements, is a research gap (1). This work uses so-called Aspect-based Sentiment Analysis as a method for this purpose. It remains open how the aspect categories to be extracted can be discovered and thematically delineated based on the data (2). Furthermore, it is not yet explored how a collection of tools should look like, with which implicit phrases can be identified and thus made explicit (3). Last, it is an open question how to correlate the identified phrases from the text data with other data, including the investigation of the relationship between quantitative scores (e.g., school grades) and the thematically related text (4). Based on these research gaps, the research question is posed as follows: Using text mining methods, how can implicit rating content be properly interpreted and thus made explicit before it is automatically categorized and quantified? The uniqueness of this dissertation is based on the automated recognition of implicit linguistic statements alongside explicit statements. These are identified in unstructured text data so that features expressed only in the text can later be compared across data sources, even though they were not included in rating categories such as stars or school grades. German-language physician ratings from websites in three countries serve as the sample domain. The solution approach consists of data creation, a pipeline for text processing and analyses based on this. In the data creation, aspect classes are identified and delineated across platforms and marked in text data. This results in six datasets with over 70,000 annotated sentences and detailed guidelines. The models that were created based on the training data extract and categorize the aspects. In addition, the sentiment polarity and the evaluation weight, i. e., the importance of each phrase, are determined. The models, which are combined in a pipeline, are used in a prototype in the form of a web application. The analyses built on the pipeline quantify the rating contents by linking the obtained information with further data, thus allowing new insights. As a result, a toolbox is provided to identify quantifiable rating content and categories using text mining for a sample domain. This is used to evaluate the approach, which in principle can also be adapted to any other domain.}}, author = {{Kersting, Joschka}}, pages = {{208}}, publisher = {{Universität der Bundeswehr München }}, title = {{{Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining}}}, year = {{2023}}, } @inbook{45875, author = {{Götte, Thorsten and Knollmann, Till and Meyer auf der Heide, Friedhelm and Scheideler, Christian and Werthmann, Julian}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{1----20}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Capabilities and Limitations of Local Strategies in Dynamic Networks}}}, doi = {{10.5281/zenodo.8060372}}, volume = {{412}}, year = {{2023}}, }