@article{29069, author = {{Eickelmann, Birgit}}, journal = {{PlanBD #2. Fachmagazin für Schule in der digitalen Welt}}, title = {{{Re-Definition der digitalisierungsbezogenen Schulleitungsfunktionen in der Pandemie-Zeit und danach}}}, year = {{2021}}, } @article{29070, author = {{Gerick, Julia and Eickelmann, Birgit and Feldmann, Barbara and Rothärmel, Anne}}, journal = {{SchulVerwaltung NRW}}, number = {{6}}, pages = {{176--179}}, title = {{{GuTe DigiSchulen NRW. Zielsetzung und Konzeption des qualitativen Vertiefungsprojekts zur Studie ICILS 2018 NRW zu erfolgreichen digitalisierungsbezogenen Schulentwicklungsprozessen}}}, volume = {{32}}, year = {{2021}}, } @misc{29151, abstract = {{Automation becomes a vital part in the High-Performance computing system in situational dynamics to take the decisions on the fly. Heterogeneous compute nodes consist of computing resources such as CPU, GPU and FPGA and are the important components of the high-performance computing system that can adapt the automation to achieve the given goal. While implanting automation in the computing resources, management of the resources is one of the essential aspects that need to be taken care of. Tasks are continuously executed on the resources using its unique characteristics. Effective scheduling is essential to make the best use of the characteristics provided by each resource. Scheduling enables the execution of each task by allocating resources so that they take advantage of all the characteristics of the compute resources. Various scheduling heuristics can be used to create effective scheduling, which might require the execution time to schedule the task efficiently. Providing actual execution time is not possible in many cases; hence we can provide the estimations for the actual execution time . The purpose of this master's thesis is to design a predictive model or system that estimates the execution time required to execute tasks using historical execution time data on the heterogeneous compute nodes. In this thesis, regression techniques(SGD Regressor, Passive-Aggressive Regressor, MLP Regressor, and XCSF Regressor) are compared in terms of their prediction accuracy in order to determine which technique produces reliable predictions for the execution time. These estimations must be generated in an online learning environment in which data points arrive in any sequence, one by one, and the regression model must learn from them. After evaluating the regression algorithms, it is seen that the XCSF regressor provides the highest overall prediction accuracy for the supplied data sets. The regression technique's parameters also play a significant role in achieving an acceptable prediction accuracy. As a remark, when using online learning in regression analysis, the accuracy depends upon both the order of sequential data points that are coming to train the model and the parameter configuration for each regression technique.}}, author = {{Kashikar, Chinmay}}, publisher = {{Paderborn University}}, title = {{{A Comparison of Machine Learning Techniques for the On-line Characterization of Tasks Executed on Heterogeneous Compute Nodes}}}, year = {{2021}}, } @inproceedings{27050, author = {{J. Daymude, Joshua and W. Richa, Andrea and Scheideler, Christian}}, booktitle = {{35th International Symposium on Distributed Computing, DISC 2021, October 4-8, 2021, Freiburg, Germany (Virtual Conference)}}, editor = {{Gilbert, Seth}}, pages = {{20:1--20:19}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{The Canonical Amoebot Model: Algorithms and Concurrency Control}}}, doi = {{10.4230/LIPIcs.DISC.2021.20}}, volume = {{209}}, year = {{2021}}, } @phdthesis{27503, author = {{Hasnain, Asif}}, title = {{{Automating Network Resource Allocation for Coflows with Deadlines}}}, doi = {{10.17619/UNIPB/1-1241 }}, year = {{2021}}, } @article{27870, author = {{Eickelmann, Birgit and Drossel, Kerstin}}, journal = {{SchulVerwaltung BW}}, pages = {{174--188}}, title = {{{Gelingensbedingungen digitaler Optimalschulen}}}, volume = {{6}}, year = {{2021}}, } @article{27871, author = {{Tondeur, Jo and Petko, Dominik and Christensen, Rhonda and Drossel, Kerstin and Starkey, Louise and Knezek, Gerald and Schmidt-Crawford, Denise A.}}, issn = {{1042-1629}}, journal = {{Educational Technology Research and Development}}, pages = {{2187--2208}}, title = {{{Quality criteria for conceptual technology integration models in education: bridging research and practice}}}, doi = {{10.1007/s11423-020-09911-0}}, volume = {{69}}, year = {{2021}}, } @book{27872, author = {{Vennemann, Mario and Eickelmann, Birgit and Labusch, Amelie and Drossel, Kerstin}}, publisher = {{Waxmann}}, title = {{{ICILS 2018 #Deutschland. Dokumentation der Erhebungsinstrumente der zweiten Computer and Information Literacy Study}}}, year = {{2021}}, } @inbook{27877, author = {{Eickelmann, Birgit and Drossel, Kerstin and Heldt, Melanie}}, booktitle = {{Quality in Teacher Education and Professional Development}}, editor = {{Chi-Kin Lee, John and Ehmke, Timo}}, pages = {{107--124}}, publisher = {{Routledge}}, title = {{{ICT in teacher education and ICT-related teacher professional development in Germany}}}, year = {{2021}}, } @book{27878, author = {{Heldt, Melanie and Drossel, Kerstin}}, publisher = {{Empirische Pädagogik}}, title = {{{Typen unterrichtsbezogener Lehrerkooperation und ihr Zusammenhang mit Einstellungen und der Nutzung digitaler Medien}}}, year = {{2021}}, } @inproceedings{21005, abstract = {{Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}}, keywords = {{Coflow scheduling, Reinforcement learning, Deadlines}}, location = {{Vancouver BC Canada}}, publisher = {{IEEE Communications Society}}, title = {{{Learning Coflow Admissions}}}, doi = {{10.1109/INFOCOMWKSHPS51825.2021.9484599}}, year = {{2021}}, } @misc{21197, author = {{Mengshi, Ma}}, title = {{{Self-stabilizing Arrow Protocol on Spanning Trees with a Low Diameter}}}, year = {{2021}}, } @article{21242, author = {{Lüttenberg, Hedda and Beverungen, Daniel and Poniatowski, Martin and Kundisch, Dennis and Wünderlich, Nancy}}, journal = {{Wirtschaftsinformatik & Management}}, number = {{2}}, pages = {{120--131}}, title = {{{Drei Strategien zur Etablierung digitaler Plattformen in der Industrie}}}, volume = {{13}}, year = {{2021}}, } @article{21264, abstract = {{Abstract Background Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training. Methods In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback. Results The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7). Conclusion The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development. }}, author = {{Boschmann, Alexander and Neuhaus, Dorothee and Vogt, Sarah and Kaltschmidt, Christian and Platzner, Marco and Dosen, Strahinja}}, issn = {{1743-0003}}, journal = {{Journal of NeuroEngineering and Rehabilitation}}, title = {{{Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis}}}, doi = {{10.1186/s12984-021-00822-6}}, year = {{2021}}, } @inproceedings{21280, author = {{Masendorf, Lukas and Wächter, Michael and Esderts, Alfons and Otroshi, Mortaza and Meschut, Gerson}}, title = {{{Simulationsbasierte Lebensdauerabschätzung einer stanzgenieteten Fügeverbindung unter zyklischer Belastung}}}, year = {{2021}}, } @inproceedings{21543, abstract = {{Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).}}, author = {{Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}}, booktitle = {{IEEE International Conference on Distributed Computing Systems (ICDCS)}}, keywords = {{network management, service management, coordination, reinforcement learning, distributed}}, location = {{Washington, DC, USA}}, publisher = {{IEEE}}, title = {{{Distributed Online Service Coordination Using Deep Reinforcement Learning}}}, year = {{2021}}, } @techreport{21569, abstract = {{Die kontinuierliche Weiterentwicklung des eigenen Geschäftsmodells ist für eine Organisation von entscheidender Bedeutung, um wettbewerbsfähig und somit nachhaltig erfolgreich zu bleiben. Während für die Entwicklung neuer Geschäftsmodelle häufig Workshops und einfache Software-Tools zur Visualisierung genutzt werden, wurden in der Forschung bereits erste Ansätze von datengetriebener Geschäftsmodellentwicklung (GME) vorgestellt. Diese Ansätze nutzen dabei Daten, Informationen oder auch Wissen aus internen und externen Unternehmensquellen, um den GME-Prozess zu unterstützen. Innerhalb dieses Beitrags zeigen wir einige Ansätze aus der aktuellen Literatur und analysieren wie ihre Datennutzung den GME-Prozess unterstützt. Weiterhin stellen wir mit dem BMDL Feature Modeler ein Tool vor, welches den GME-Prozess mit Expertenwissen unterstützt.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes}}, publisher = {{Gesellschaft für Informatik}}, title = {{{Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung}}}, volume = {{1}}, year = {{2021}}, } @inproceedings{21639, abstract = {{The development of effective business models is an essential task in highly competitive markets like mobile ecosystems. Existing development methods for these business models do not specifically focus that the development process profoundly depends on the situation (e.g., market size, regulations) of the mobile app developer. Here, a mismatch between method and situation can lead to poor resource management and longer development cycles. In software engineering, situational method engineering is used for software projects to configure a development method out of a method repository based on the project situation. Analogously, we support creating situation-specific business model development methods with a method base and new user roles. Here, the method engineer obtains the knowledge of the domain expert and stores it in the method base as elements, building blocks, and patterns. The expert knowledge is derived from a grey literature review on mobile development processes. After this, the method engineer constructs the development method based on the described situation of the business developer. We provide an open-source tool and evaluate it by constructing a local event platform's business model development method. }}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Enterprise, Business-Process and Information Systems Modeling}}, keywords = {{Business Model Development, Situational Method Engineering, Mobile App, Business Model Development Tools}}, publisher = {{Springer}}, title = {{{Situation-specific Business Model Development Methods for Mobile App Developers}}}, doi = {{10.1007/978-3-030-79186-5_17}}, year = {{2021}}, } @article{23675, abstract = {{AbstractThe application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.}}, author = {{Mirbabaie, Milad and Hofeditz, Lennart and Frick, Nicholas R. J. and Stieglitz, Stefan}}, issn = {{0951-5666}}, journal = {{AI & SOCIETY}}, title = {{{Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research}}}, doi = {{10.1007/s00146-021-01239-4}}, year = {{2021}}, } @inproceedings{20693, abstract = {{In practical, large-scale networks, services are requested by users across the globe, e.g., for video streaming. Services consist of multiple interconnected components such as microservices in a service mesh. Coordinating these services requires scaling them according to continuously changing user demand, deploying instances at the edge close to their users, and routing traffic efficiently between users and connected instances. Network and service coordination is commonly addressed through centralized approaches, where a single coordinator knows everything and coordinates the entire network globally. While such centralized approaches can reach global optima, they do not scale to large, realistic networks. In contrast, distributed approaches scale well, but sacrifice solution quality due to their limited scope of knowledge and coordination decisions. To this end, we propose a hierarchical coordination approach that combines the good solution quality of centralized approaches with the scalability of distributed approaches. In doing so, we divide the network into multiple hierarchical domains and optimize coordination in a top-down manner. We compare our hierarchical with a centralized approach in an extensive evaluation on a real-world network topology. Our results indicate that hierarchical coordination can find close-to-optimal solutions in a fraction of the runtime of centralized approaches.}}, author = {{Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}}, booktitle = {{IFIP/IEEE International Symposium on Integrated Network Management (IM)}}, keywords = {{network management, service management, coordination, hierarchical, scalability, nfv}}, location = {{Bordeaux, France}}, publisher = {{IFIP/IEEE}}, title = {{{Divide and Conquer: Hierarchical Network and Service Coordination}}}, year = {{2021}}, }