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(2021). <i>Replacing the Ex-Def Baseline in AutoML by Naive AutoML</i>. 8th ICML Workshop on Automated Machine Learning, Virtual.","mla":"Mohr, Felix, and Marcel Dominik Wever. <i>Replacing the Ex-Def Baseline in AutoML by Naive AutoML</i>. 2021.","bibtex":"@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021} }","short":"F. Mohr, M.D. Wever, in: 2021.","chicago":"Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021.","ieee":"F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual, 2021.","ama":"Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. In: ; 2021."}},{"language":[{"iso":"eng"}],"_id":"22927","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B4","_id":"12"}],"department":[{"_id":"7"}],"series_title":"Leibniz International Proceedings in Informatics","user_id":"477","status":"public","publication":"Proceedings of the 35th International Symposium on Distributed Computing (DISC)","type":"conference","title":"On Strong Observational Refinement and Forward Simulation","publisher":"Schloß Dagstuhl","date_updated":"2022-01-06T06:55:43Z","author":[{"full_name":"Derrick, John","last_name":"Derrick","first_name":"John"},{"last_name":"Doherty","full_name":"Doherty, Simon","first_name":"Simon"},{"last_name":"Dongol","full_name":"Dongol, Brijesh","first_name":"Brijesh"},{"first_name":"Gerhard","full_name":"Schellhorn, Gerhard","last_name":"Schellhorn"},{"full_name":"Wehrheim, Heike","id":"573","last_name":"Wehrheim","first_name":"Heike"}],"date_created":"2021-08-03T07:38:56Z","year":"2021","citation":{"mla":"Derrick, John, et al. “On Strong Observational Refinement and Forward Simulation.” <i>Proceedings of the 35th International Symposium on Distributed Computing (DISC)</i>, Schloß Dagstuhl.","bibtex":"@inproceedings{Derrick_Doherty_Dongol_Schellhorn_Wehrheim, series={Leibniz International Proceedings in Informatics}, title={On Strong Observational Refinement and Forward Simulation}, booktitle={Proceedings of the 35th International Symposium on Distributed Computing (DISC)}, publisher={Schloß Dagstuhl}, author={Derrick, John and Doherty, Simon and Dongol, Brijesh and Schellhorn, Gerhard and Wehrheim, Heike}, collection={Leibniz International Proceedings in Informatics} }","short":"J. Derrick, S. Doherty, B. Dongol, G. Schellhorn, H. Wehrheim, in: Proceedings of the 35th International Symposium on Distributed Computing (DISC), Schloß Dagstuhl, n.d.","apa":"Derrick, J., Doherty, S., Dongol, B., Schellhorn, G., &#38; Wehrheim, H. (n.d.). On Strong Observational Refinement and Forward Simulation. In <i>Proceedings of the 35th International Symposium on Distributed Computing (DISC)</i>. Schloß Dagstuhl.","ama":"Derrick J, Doherty S, Dongol B, Schellhorn G, Wehrheim H. On Strong Observational Refinement and Forward Simulation. In: <i>Proceedings of the 35th International Symposium on Distributed Computing (DISC)</i>. Leibniz International Proceedings in Informatics. Schloß Dagstuhl.","chicago":"Derrick, John, Simon Doherty, Brijesh Dongol, Gerhard Schellhorn, and Heike Wehrheim. “On Strong Observational Refinement and Forward Simulation.” In <i>Proceedings of the 35th International Symposium on Distributed Computing (DISC)</i>. Leibniz International Proceedings in Informatics. Schloß Dagstuhl, n.d.","ieee":"J. Derrick, S. Doherty, B. Dongol, G. Schellhorn, and H. Wehrheim, “On Strong Observational Refinement and Forward Simulation,” in <i>Proceedings of the 35th International Symposium on Distributed Computing (DISC)</i>."},"publication_status":"accepted"},{"language":[{"iso":"eng"}],"user_id":"53103","department":[{"_id":"28"},{"_id":"66"}],"_id":"22959","status":"public","type":"conference","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","conference":{"name":"GECCO '21: Genetic and Evolutionary Computation Conference","start_date":"2021-07-10","end_date":"2021-07-14","location":"Lille, France"},"doi":"10.1145/3449639.3459283","title":"Concurrent model synchronisation with multiple objectives","author":[{"first_name":"Nils","id":"53103","full_name":"Weidmann, Nils","last_name":"Weidmann"},{"first_name":"Gregor","last_name":"Engels","id":"107","full_name":"Engels, Gregor"}],"date_created":"2021-08-07T15:18:48Z","date_updated":"2022-01-06T06:55:44Z","citation":{"bibtex":"@inproceedings{Weidmann_Engels_2021, title={Concurrent model synchronisation with multiple objectives}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459283\">10.1145/3449639.3459283</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Weidmann, Nils and Engels, Gregor}, year={2021} }","short":"N. Weidmann, G. Engels, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","mla":"Weidmann, Nils, and Gregor Engels. “Concurrent Model Synchronisation with Multiple Objectives.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, doi:<a href=\"https://doi.org/10.1145/3449639.3459283\">10.1145/3449639.3459283</a>.","apa":"Weidmann, N., &#38; Engels, G. (2021). Concurrent model synchronisation with multiple objectives. In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. Lille, France. <a href=\"https://doi.org/10.1145/3449639.3459283\">https://doi.org/10.1145/3449639.3459283</a>","ieee":"N. Weidmann and G. Engels, “Concurrent model synchronisation with multiple objectives,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Lille, France, 2021.","chicago":"Weidmann, Nils, and Gregor Engels. “Concurrent Model Synchronisation with Multiple Objectives.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021. <a href=\"https://doi.org/10.1145/3449639.3459283\">https://doi.org/10.1145/3449639.3459283</a>.","ama":"Weidmann N, Engels G. Concurrent model synchronisation with multiple objectives. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. ; 2021. doi:<a href=\"https://doi.org/10.1145/3449639.3459283\">10.1145/3449639.3459283</a>"},"year":"2021","publication_status":"published"},{"status":"public","publication":"2021 International Conference on Code Quality (ICCQ)","type":"conference","language":[{"iso":"eng"}],"_id":"23374","department":[{"_id":"241"},{"_id":"662"},{"_id":"76"}],"user_id":"5786","year":"2021","citation":{"apa":"Kummita, S., Piskachev, G., Spath, J., &#38; Bodden, E. (2021). Qualitative and Quantitative Analysis of Callgraph Algorithms for Python. <i>2021 International Conference on Code Quality (ICCQ)</i>. <a href=\"https://doi.org/10.1109/iccq51190.2021.9392986\">https://doi.org/10.1109/iccq51190.2021.9392986</a>","short":"S. Kummita, G. Piskachev, J. Spath, E. Bodden, in: 2021 International Conference on Code Quality (ICCQ), 2021.","mla":"Kummita, Sriteja, et al. “Qualitative and Quantitative Analysis of Callgraph Algorithms for Python.” <i>2021 International Conference on Code Quality (ICCQ)</i>, 2021, doi:<a href=\"https://doi.org/10.1109/iccq51190.2021.9392986\">10.1109/iccq51190.2021.9392986</a>.","bibtex":"@inproceedings{Kummita_Piskachev_Spath_Bodden_2021, title={Qualitative and Quantitative Analysis of Callgraph Algorithms for Python}, DOI={<a href=\"https://doi.org/10.1109/iccq51190.2021.9392986\">10.1109/iccq51190.2021.9392986</a>}, booktitle={2021 International Conference on Code Quality (ICCQ)}, author={Kummita, Sriteja and Piskachev, Goran and Spath, Johannes and Bodden, Eric}, year={2021} }","chicago":"Kummita, Sriteja, Goran Piskachev, Johannes Spath, and Eric Bodden. “Qualitative and Quantitative Analysis of Callgraph Algorithms for Python.” In <i>2021 International Conference on Code Quality (ICCQ)</i>, 2021. <a href=\"https://doi.org/10.1109/iccq51190.2021.9392986\">https://doi.org/10.1109/iccq51190.2021.9392986</a>.","ieee":"S. Kummita, G. Piskachev, J. Spath, and E. Bodden, “Qualitative and Quantitative Analysis of Callgraph Algorithms for Python,” 2021, doi: <a href=\"https://doi.org/10.1109/iccq51190.2021.9392986\">10.1109/iccq51190.2021.9392986</a>.","ama":"Kummita S, Piskachev G, Spath J, Bodden E. Qualitative and Quantitative Analysis of Callgraph Algorithms for Python. In: <i>2021 International Conference on Code Quality (ICCQ)</i>. ; 2021. doi:<a href=\"https://doi.org/10.1109/iccq51190.2021.9392986\">10.1109/iccq51190.2021.9392986</a>"},"publication_status":"published","title":"Qualitative and Quantitative Analysis of Callgraph Algorithms for Python","doi":"10.1109/iccq51190.2021.9392986","date_updated":"2022-01-06T06:55:50Z","author":[{"last_name":"Kummita","full_name":"Kummita, Sriteja","first_name":"Sriteja"},{"full_name":"Piskachev, Goran","last_name":"Piskachev","first_name":"Goran"},{"first_name":"Johannes","last_name":"Spath","full_name":"Spath, Johannes"},{"full_name":"Bodden, Eric","last_name":"Bodden","first_name":"Eric"}],"date_created":"2021-08-09T12:01:11Z"},{"title":"Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems ","conference":{"name":"29th European Conference on Information Systems (ECIS)","location":"Virtual Conference/Workshop"},"date_updated":"2022-01-06T06:55:12Z","publisher":"AIS","author":[{"first_name":"Christian","last_name":"Vorbohle","full_name":"Vorbohle, Christian","id":"29951"},{"first_name":"Sebastian","last_name":"Gottschalk","full_name":"Gottschalk, Sebastian","id":"47208"}],"date_created":"2021-04-23T13:38:54Z","year":"2021","citation":{"ieee":"C. Vorbohle and S. Gottschalk, “Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems ,” in <i>Proceedings of the 29th European Conference on Information Systems (ECIS)</i>, Virtual Conference/Workshop.","chicago":"Vorbohle, Christian, and Sebastian Gottschalk. “Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems .” In <i>Proceedings of the 29th European Conference on Information Systems (ECIS)</i>. AIS, n.d.","ama":"Vorbohle C, Gottschalk S. Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems . In: <i>Proceedings of the 29th European Conference on Information Systems (ECIS)</i>. AIS.","apa":"Vorbohle, C., &#38; Gottschalk, S. (n.d.). Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems . In <i>Proceedings of the 29th European Conference on Information Systems (ECIS)</i>. Virtual Conference/Workshop: AIS.","short":"C. Vorbohle, S. Gottschalk, in: Proceedings of the 29th European Conference on Information Systems (ECIS), AIS, n.d.","bibtex":"@inproceedings{Vorbohle_Gottschalk, title={Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems }, booktitle={Proceedings of the 29th European Conference on Information Systems (ECIS)}, publisher={AIS}, author={Vorbohle, Christian and Gottschalk, Sebastian} }","mla":"Vorbohle, Christian, and Sebastian Gottschalk. “Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems .” <i>Proceedings of the 29th European Conference on Information Systems (ECIS)</i>, AIS."},"publication_status":"accepted","keyword":["Platform Ecosystems","Platform Ecosystem Modeling Language","Platform Ecosystem Development Tool","Business Models","Design Science"],"language":[{"iso":"eng"}],"_id":"21727","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C5","_id":"17"}],"department":[{"_id":"66"},{"_id":"534"},{"_id":"276"}],"user_id":"47208","abstract":[{"text":"Platform-based business models underlie the success of many of today’s largest, fastest-growing, and most disruptive companies. Despite the success of prominent examples, such as Uber and Airbnb, creating a profitable platform ecosystem presents a key challenge for many companies across all industries. Although research provides knowledge about platforms’ different value drivers (e.g., network effects), companies that seek to transform their current business model into a platform-based one lack an artifact to reduce knowledge boundaries, collaborate effectively, and cope with the complexities and dynamics of platform ecosystems. We address this challenge by developing two artifacts and combining research from variability modeling, business model dependencies, and system dynamics. This paper presents a design science research approach to develop the platform ecosystem modeling language and the platform ecosystem development tool that support researcher and practitioner by visualizing and simulating platform ecosystems. ","lang":"eng"}],"status":"public","publication":"Proceedings of the 29th European Conference on Information Systems (ECIS)","type":"conference"},{"doi":"10.1109/TNSM.2021.3076503","title":"Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning","date_created":"2021-04-27T08:04:16Z","author":[{"first_name":"Stefan Balthasar","last_name":"Schneider","orcid":"0000-0001-8210-4011","id":"35343","full_name":"Schneider, Stefan Balthasar"},{"first_name":"Ramin","full_name":"Khalili, Ramin","last_name":"Khalili"},{"first_name":"Adnan","last_name":"Manzoor","full_name":"Manzoor, Adnan"},{"first_name":"Haydar","full_name":"Qarawlus, Haydar","last_name":"Qarawlus"},{"first_name":"Rafael","full_name":"Schellenberg, Rafael","last_name":"Schellenberg"},{"first_name":"Holger","full_name":"Karl, Holger","id":"126","last_name":"Karl"},{"first_name":"Artur","last_name":"Hecker","full_name":"Hecker, Artur"}],"date_updated":"2022-01-06T06:55:15Z","publisher":"IEEE","oa":"1","citation":{"apa":"Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., &#38; Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>","mla":"Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>.","bibtex":"@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021, title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}, DOI={<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>}, journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }","short":"S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021).","ieee":"S. B. Schneider <i>et al.</i>, “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning,” <i>Transactions on Network and Service Management</i>, 2021.","chicago":"Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, 2021. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>.","ama":"Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. 2021. doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>"},"year":"2021","has_accepted_license":"1","file_date_updated":"2021-04-27T08:01:26Z","language":[{"iso":"eng"}],"keyword":["network management","service management","coordination","reinforcement learning","self-learning","self-adaptation","multi-objective"],"ddc":["000"],"article_type":"original","department":[{"_id":"75"}],"user_id":"35343","_id":"21808","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"status":"public","file":[{"content_type":"application/pdf","relation":"main_file","date_created":"2021-04-27T08:01:26Z","creator":"stschn","date_updated":"2021-04-27T08:01:26Z","file_id":"21809","file_name":"ris-accepted-version.pdf","access_level":"open_access","file_size":4172270,"description":"Author version of the accepted paper"}],"abstract":[{"text":"Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge).\r\n\r\nWe propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.","lang":"eng"}],"publication":"Transactions on Network and Service Management","type":"journal_article"},{"user_id":"36113","department":[{"_id":"558"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"13","name":"SFB 901 - Subproject C1"}],"_id":"22057","file_date_updated":"2021-05-10T16:02:02Z","type":"book_chapter","status":"public","author":[{"first_name":"Tibor","last_name":"Jager","full_name":"Jager, Tibor"},{"first_name":"Rafael","full_name":"Kurek, Rafael","last_name":"Kurek"},{"first_name":"David","id":"36113","full_name":"Niehues, David","last_name":"Niehues"}],"date_updated":"2022-01-06T06:55:23Z","doi":"10.1007/978-3-030-75245-3_22","publication_status":"published","publication_identifier":{"isbn":["9783030752446","9783030752453"],"issn":["0302-9743","1611-3349"]},"has_accepted_license":"1","citation":{"ama":"Jager T, Kurek R, Niehues D. Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance. In: <i>Public-Key Cryptography – PKC 2021</i>. Cham; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-75245-3_22\">10.1007/978-3-030-75245-3_22</a>","ieee":"T. Jager, R. Kurek, and D. Niehues, “Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance,” in <i>Public-Key Cryptography – PKC 2021</i>, Cham, 2021.","chicago":"Jager, Tibor, Rafael Kurek, and David Niehues. “Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance.” In <i>Public-Key Cryptography – PKC 2021</i>. Cham, 2021. <a href=\"https://doi.org/10.1007/978-3-030-75245-3_22\">https://doi.org/10.1007/978-3-030-75245-3_22</a>.","mla":"Jager, Tibor, et al. “Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance.” <i>Public-Key Cryptography – PKC 2021</i>, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-75245-3_22\">10.1007/978-3-030-75245-3_22</a>.","short":"T. Jager, R. Kurek, D. Niehues, in: Public-Key Cryptography – PKC 2021, Cham, 2021.","bibtex":"@inbook{Jager_Kurek_Niehues_2021, place={Cham}, title={Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-75245-3_22\">10.1007/978-3-030-75245-3_22</a>}, booktitle={Public-Key Cryptography – PKC 2021}, author={Jager, Tibor and Kurek, Rafael and Niehues, David}, year={2021} }","apa":"Jager, T., Kurek, R., &#38; Niehues, D. (2021). Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance. In <i>Public-Key Cryptography – PKC 2021</i>. Cham. <a href=\"https://doi.org/10.1007/978-3-030-75245-3_22\">https://doi.org/10.1007/978-3-030-75245-3_22</a>"},"place":"Cham","language":[{"iso":"eng"}],"ddc":["000"],"publication":"Public-Key Cryptography – PKC 2021","file":[{"file_size":701068,"file_id":"22058","access_level":"closed","file_name":"Jager et al. - 2021 - Efficient Adaptively-Secure IB-KEMs and VRFs via N.pdf","date_updated":"2021-05-10T16:02:02Z","creator":"davnie","date_created":"2021-05-10T16:02:02Z","relation":"main_file","content_type":"application/pdf"}],"abstract":[{"text":"We construct more efficient cryptosystems with provable\r\nsecurity against adaptive attacks, based on simple and natural hardness\r\nassumptions in the standard model. Concretely, we describe:\r\n– An adaptively-secure variant of the efficient, selectively-secure LWE-\r\nbased identity-based encryption (IBE) scheme of Agrawal, Boneh,\r\nand Boyen (EUROCRYPT 2010). In comparison to the previously\r\nmost efficient such scheme by Yamada (CRYPTO 2017) we achieve\r\nsmaller lattice parameters and shorter public keys of size O(log λ),\r\nwhere λ is the security parameter.\r\n– Adaptively-secure variants of two efficient selectively-secure pairing-\r\nbased IBEs of Boneh and Boyen (EUROCRYPT 2004). One is based\r\non the DBDH assumption, has the same ciphertext size as the cor-\r\nresponding BB04 scheme, and achieves full adaptive security with\r\npublic parameters of size only O(log λ). The other is based on a q-\r\ntype assumption and has public key size O(λ), but a ciphertext is\r\nonly a single group element and the security reduction is quadrat-\r\nically tighter than the corresponding scheme by Jager and Kurek\r\n(ASIACRYPT 2018).\r\n– A very efficient adaptively-secure verifiable random function where\r\nproofs, public keys, and secret keys have size O(log λ).\r\nAs a technical contribution we introduce blockwise partitioning, which\r\nleverages the assumption that a cryptographic hash function is weak\r\nnear-collision resistant to prove full adaptive security of cryptosystems.","lang":"eng"}],"date_created":"2021-05-10T15:56:24Z","title":"Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance","year":"2021"},{"publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030752477","9783030752484"]},"has_accepted_license":"1","year":"2021","place":"Cham","citation":{"short":"D. Niehues, in: Public-Key Cryptography – PKC 2021, Cham, 2021.","bibtex":"@inbook{Niehues_2021, place={Cham}, title={Verifiable Random Functions with Optimal Tightness}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-75248-4_3\">10.1007/978-3-030-75248-4_3</a>}, booktitle={Public-Key Cryptography – PKC 2021}, author={Niehues, David}, year={2021} }","mla":"Niehues, David. “Verifiable Random Functions with Optimal Tightness.” <i>Public-Key Cryptography – PKC 2021</i>, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-75248-4_3\">10.1007/978-3-030-75248-4_3</a>.","apa":"Niehues, D. (2021). Verifiable Random Functions with Optimal Tightness. In <i>Public-Key Cryptography – PKC 2021</i>. Cham. <a href=\"https://doi.org/10.1007/978-3-030-75248-4_3\">https://doi.org/10.1007/978-3-030-75248-4_3</a>","ama":"Niehues D. Verifiable Random Functions with Optimal Tightness. In: <i>Public-Key Cryptography – PKC 2021</i>. Cham; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-75248-4_3\">10.1007/978-3-030-75248-4_3</a>","chicago":"Niehues, David. “Verifiable Random Functions with Optimal Tightness.” In <i>Public-Key Cryptography – PKC 2021</i>. Cham, 2021. <a href=\"https://doi.org/10.1007/978-3-030-75248-4_3\">https://doi.org/10.1007/978-3-030-75248-4_3</a>.","ieee":"D. Niehues, “Verifiable Random Functions with Optimal Tightness,” in <i>Public-Key Cryptography – PKC 2021</i>, Cham, 2021."},"date_updated":"2022-01-06T06:55:24Z","date_created":"2021-05-10T16:07:50Z","author":[{"last_name":"Niehues","full_name":"Niehues, David","id":"36113","first_name":"David"}],"title":"Verifiable Random Functions with Optimal Tightness","doi":"10.1007/978-3-030-75248-4_3","type":"book_chapter","publication":"Public-Key Cryptography – PKC 2021","abstract":[{"text":"Verifiable random functions (VRFs), introduced by Micali,\r\nRabin and Vadhan (FOCS’99), are the public-key equivalent of pseudo-\r\nrandom functions. A public verification key and proofs accompanying the\r\noutput enable all parties to verify the correctness of the output. How-\r\never, all known standard model VRFs have a reduction loss that is much\r\nworse than what one would expect from known optimal constructions of\r\nclosely related primitives like unique signatures. We show that:\r\n1. Every security proof for a VRF that relies on a non-interactive\r\nassumption has to lose a factor of Q, where Q is the number of adver-\r\nsarial queries. To that end, we extend the meta-reduction technique\r\nof Bader et al. (EUROCRYPT’16) to also cover VRFs.\r\n2. This raises the question: Is this bound optimal? We answer this ques-\r\ntion in the affirmative by presenting the first VRF with a reduction\r\nfrom the non-interactive qDBDHI assumption to the security of VRF\r\nthat achieves this optimal loss.\r\nWe thus paint a complete picture of the achievability of tight verifiable\r\nrandom functions: We show that a security loss of Q is unavoidable and\r\npresent the first construction that achieves this bound.","lang":"eng"}],"file":[{"content_type":"application/pdf","relation":"main_file","date_updated":"2021-05-10T16:09:17Z","creator":"davnie","date_created":"2021-05-10T16:09:17Z","file_size":697361,"access_level":"closed","file_name":"Niehues - 2021 - Verifiable Random Functions with Optimal Tightness.pdf","file_id":"22060"}],"status":"public","project":[{"_id":"1","name":"SFB 901"},{"_id":"4","name":"SFB 901 - Project Area C"},{"name":"SFB 901 - Subproject C1","_id":"13"}],"_id":"22059","user_id":"36113","department":[{"_id":"558"}],"ddc":["000"],"file_date_updated":"2021-05-10T16:09:17Z","language":[{"iso":"eng"}]},{"publication_status":"accepted","citation":{"mla":"Witschen, Linus Matthias, et al. “Timing Optimization for Virtual FPGA Configurations.” <i>Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21)</i>, edited by Frank Hannig et al., Springer Lecture Notes in Computer Science, doi:<a href=\"https://doi.org/10.1007/978-3-030-79025-7_4\">10.1007/978-3-030-79025-7_4</a>.","short":"L.M. Witschen, T. Wiersema, M. Raeisi Nafchi, A. Bockhorn, M. Platzner, in: F. Hannig, S. Derrien, P. Diniz, D. Chillet (Eds.), Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21), Springer Lecture Notes in Computer Science, n.d.","bibtex":"@inproceedings{Witschen_Wiersema_Raeisi Nafchi_Bockhorn_Platzner, series={Reconfigurable Computing: Architectures, Tools, and Applications}, title={Timing Optimization for Virtual FPGA Configurations}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-79025-7_4\">10.1007/978-3-030-79025-7_4</a>}, booktitle={Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21)}, publisher={Springer Lecture Notes in Computer Science}, author={Witschen, Linus Matthias and Wiersema, Tobias and Raeisi Nafchi, Masood and Bockhorn, Arne and Platzner, Marco}, editor={Hannig, Frank and Derrien, Steven and Diniz, Pedro and Chillet, Daniel}, collection={Reconfigurable Computing: Architectures, Tools, and Applications} }","apa":"Witschen, L. M., Wiersema, T., Raeisi Nafchi, M., Bockhorn, A., &#38; Platzner, M. (n.d.). Timing Optimization for Virtual FPGA Configurations. In F. Hannig, S. Derrien, P. Diniz, &#38; D. Chillet (Eds.), <i>Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21)</i>. Springer Lecture Notes in Computer Science. <a href=\"https://doi.org/10.1007/978-3-030-79025-7_4\">https://doi.org/10.1007/978-3-030-79025-7_4</a>","chicago":"Witschen, Linus Matthias, Tobias Wiersema, Masood Raeisi Nafchi, Arne Bockhorn, and Marco Platzner. “Timing Optimization for Virtual FPGA Configurations.” In <i>Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21)</i>, edited by Frank Hannig, Steven Derrien, Pedro Diniz, and Daniel Chillet. Reconfigurable Computing: Architectures, Tools, and Applications. Springer Lecture Notes in Computer Science, n.d. <a href=\"https://doi.org/10.1007/978-3-030-79025-7_4\">https://doi.org/10.1007/978-3-030-79025-7_4</a>.","ieee":"L. M. Witschen, T. Wiersema, M. Raeisi Nafchi, A. Bockhorn, and M. Platzner, “Timing Optimization for Virtual FPGA Configurations,” in <i>Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21)</i>, Virtual conference, doi: <a href=\"https://doi.org/10.1007/978-3-030-79025-7_4\">10.1007/978-3-030-79025-7_4</a>.","ama":"Witschen LM, Wiersema T, Raeisi Nafchi M, Bockhorn A, Platzner M. Timing Optimization for Virtual FPGA Configurations. In: Hannig F, Derrien S, Diniz P, Chillet D, eds. <i>Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21)</i>. Reconfigurable Computing: Architectures, Tools, and Applications. Springer Lecture Notes in Computer Science. doi:<a href=\"https://doi.org/10.1007/978-3-030-79025-7_4\">10.1007/978-3-030-79025-7_4</a>"},"year":"2021","date_created":"2021-05-04T14:18:46Z","author":[{"id":"49051","full_name":"Witschen, Linus Matthias","last_name":"Witschen","first_name":"Linus Matthias"},{"first_name":"Tobias","full_name":"Wiersema, Tobias","id":"3118","last_name":"Wiersema"},{"last_name":"Raeisi Nafchi","full_name":"Raeisi Nafchi, Masood","first_name":"Masood"},{"first_name":"Arne","full_name":"Bockhorn, Arne","last_name":"Bockhorn"},{"first_name":"Marco","last_name":"Platzner","full_name":"Platzner, Marco","id":"398"}],"publisher":"Springer Lecture Notes in Computer Science","date_updated":"2022-02-14T11:03:09Z","doi":"10.1007/978-3-030-79025-7_4","conference":{"end_date":"2021-07-01","location":"Virtual conference","name":"International Symposium on Applied Reconfigurable Computing","start_date":"2021-06-29"},"title":"Timing Optimization for Virtual FPGA Configurations","publication":"Proceedings of International Symposium on Applied Reconfigurable Computing (ARC'21)","type":"conference","status":"public","editor":[{"last_name":"Hannig","full_name":"Hannig, Frank","first_name":"Frank"},{"last_name":"Derrien","full_name":"Derrien, Steven","first_name":"Steven"},{"first_name":"Pedro","full_name":"Diniz, Pedro","last_name":"Diniz"},{"full_name":"Chillet, Daniel","last_name":"Chillet","first_name":"Daniel"}],"department":[{"_id":"78"}],"user_id":"3118","series_title":"Reconfigurable Computing: Architectures, Tools, and Applications","_id":"21953","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"12","name":"SFB 901 - Subproject B4"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"language":[{"iso":"eng"}]},{"citation":{"ama":"Gottschalk S, Aziz MS, Yigitbas E, Engels G. Design Principles for a Crowd-Based Prototype Validation Platform. In: Wang X, Martini A, Nguyen-Duc A, Stray V, eds. <i>Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings</i>. Vol 434. Lecture Notes in Business Information Processing. Springer; 2021:205–220. doi:<a href=\"https://doi.org/10.1007/978-3-030-91983-2_16\">10.1007/978-3-030-91983-2_16</a>","ieee":"S. Gottschalk, M. S. Aziz, E. Yigitbas, and G. Engels, “Design Principles for a Crowd-Based Prototype Validation Platform,” in <i>Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings</i>, 2021, vol. 434, pp. 205–220, doi: <a href=\"https://doi.org/10.1007/978-3-030-91983-2_16\">10.1007/978-3-030-91983-2_16</a>.","chicago":"Gottschalk, Sebastian, Muhammad Suffyan Aziz, Enes Yigitbas, and Gregor Engels. “Design Principles for a Crowd-Based Prototype Validation Platform.” In <i>Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings</i>, edited by Xiaofeng Wang, Antonio Martini, Anh Nguyen-Duc, and Viktoria Stray, 434:205–220. Lecture Notes in Business Information Processing. Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-91983-2_16\">https://doi.org/10.1007/978-3-030-91983-2_16</a>.","apa":"Gottschalk, S., Aziz, M. S., Yigitbas, E., &#38; Engels, G. (2021). Design Principles for a Crowd-Based Prototype Validation Platform. In X. Wang, A. Martini, A. Nguyen-Duc, &#38; V. Stray (Eds.), <i>Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings</i> (Vol. 434, pp. 205–220). Springer. <a href=\"https://doi.org/10.1007/978-3-030-91983-2_16\">https://doi.org/10.1007/978-3-030-91983-2_16</a>","mla":"Gottschalk, Sebastian, et al. “Design Principles for a Crowd-Based Prototype Validation Platform.” <i>Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings</i>, edited by Xiaofeng Wang et al., vol. 434, Springer, 2021, pp. 205–220, doi:<a href=\"https://doi.org/10.1007/978-3-030-91983-2_16\">10.1007/978-3-030-91983-2_16</a>.","bibtex":"@inproceedings{Gottschalk_Aziz_Yigitbas_Engels_2021, series={Lecture Notes in Business Information Processing}, title={Design Principles for a Crowd-Based Prototype Validation Platform}, volume={434}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-91983-2_16\">10.1007/978-3-030-91983-2_16</a>}, booktitle={Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings}, publisher={Springer}, author={Gottschalk, Sebastian and Aziz, Muhammad Suffyan and Yigitbas, Enes and Engels, Gregor}, editor={Wang, Xiaofeng and Martini, Antonio and Nguyen-Duc, Anh and Stray, Viktoria}, year={2021}, pages={205–220}, collection={Lecture Notes in Business Information Processing} }","short":"S. Gottschalk, M.S. Aziz, E. Yigitbas, G. Engels, in: X. Wang, A. Martini, A. Nguyen-Duc, V. Stray (Eds.), Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings, Springer, 2021, pp. 205–220."},"page":"205–220","intvolume":"       434","year":"2021","author":[{"id":"47208","full_name":"Gottschalk, Sebastian","last_name":"Gottschalk","first_name":"Sebastian"},{"last_name":"Aziz","full_name":"Aziz, Muhammad Suffyan","first_name":"Muhammad Suffyan"},{"first_name":"Enes","last_name":"Yigitbas","orcid":"0000-0002-5967-833X","id":"8447","full_name":"Yigitbas, Enes"},{"first_name":"Gregor","id":"107","full_name":"Engels, Gregor","last_name":"Engels"}],"date_created":"2022-01-11T12:43:22Z","volume":434,"publisher":"Springer","date_updated":"2022-02-15T07:32:52Z","doi":"10.1007/978-3-030-91983-2_16","title":"Design Principles for a Crowd-Based Prototype Validation Platform","type":"conference","publication":"Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings","status":"public","editor":[{"first_name":"Xiaofeng","full_name":"Wang, Xiaofeng","last_name":"Wang"},{"first_name":"Antonio","full_name":"Martini, Antonio","last_name":"Martini"},{"first_name":"Anh","full_name":"Nguyen-Duc, Anh","last_name":"Nguyen-Duc"},{"last_name":"Stray","full_name":"Stray, Viktoria","first_name":"Viktoria"}],"user_id":"47208","series_title":"Lecture Notes in Business Information Processing","department":[{"_id":"66"},{"_id":"534"}],"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"4","name":"SFB 901 - C: SFB 901 - Project Area C"},{"name":"SFB 901 - C5: SFB 901 - Subproject C5","_id":"17"}],"_id":"29235","language":[{"iso":"eng"}]},{"title":"Situation- and  Domain-specific Composition and Enactment of Business Model Development Methods","conference":{"location":"Turin","end_date":"2021-11-26","start_date":"2021-11-25","name":"22nd International Conference on Product-Focused Software Process Improvement"},"date_updated":"2022-02-15T08:36:09Z","publisher":"Springer","date_created":"2021-10-05T20:11:09Z","author":[{"first_name":"Sebastian","last_name":"Gottschalk","id":"47208","full_name":"Gottschalk, Sebastian"},{"first_name":"Enes","id":"8447","full_name":"Yigitbas, Enes","last_name":"Yigitbas","orcid":"0000-0002-5967-833X"},{"full_name":"Nowosad, Alexander","last_name":"Nowosad","first_name":"Alexander"},{"id":"107","full_name":"Engels, Gregor","last_name":"Engels","first_name":"Gregor"}],"year":"2021","citation":{"apa":"Gottschalk, S., Yigitbas, E., Nowosad, A., &#38; Engels, G. (2021). Situation- and  Domain-specific Composition and Enactment of Business Model Development Methods. In <i>Product-focused Software Process Improvement</i>. 22nd International Conference on Product-Focused Software Process Improvement, Turin. Springer.","short":"S. Gottschalk, E. Yigitbas, A. Nowosad, G. Engels, in: Product-Focused Software Process Improvement, Springer, 2021.","bibtex":"@inbook{Gottschalk_Yigitbas_Nowosad_Engels_2021, title={Situation- and  Domain-specific Composition and Enactment of Business Model Development Methods}, booktitle={Product-focused Software Process Improvement}, publisher={Springer}, author={Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}, year={2021} }","mla":"Gottschalk, Sebastian, et al. “Situation- and  Domain-Specific Composition and Enactment of Business Model Development Methods.” <i>Product-Focused Software Process Improvement</i>, Springer, 2021.","ieee":"S. Gottschalk, E. Yigitbas, A. Nowosad, and G. Engels, “Situation- and  Domain-specific Composition and Enactment of Business Model Development Methods,” in <i>Product-focused Software Process Improvement</i>, Springer, 2021.","chicago":"Gottschalk, Sebastian, Enes Yigitbas, Alexander Nowosad, and Gregor Engels. “Situation- and  Domain-Specific Composition and Enactment of Business Model Development Methods.” In <i>Product-Focused Software Process Improvement</i>. Springer, 2021.","ama":"Gottschalk S, Yigitbas E, Nowosad A, Engels G. Situation- and  Domain-specific Composition and Enactment of Business Model Development Methods. In: <i>Product-Focused Software Process Improvement</i>. Springer; 2021."},"keyword":["Business Model Development","Situational Method Engineering","Lean Development","Kanban Boards","Canvas Models"],"language":[{"iso":"eng"}],"_id":"25528","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C5","_id":"17"}],"department":[{"_id":"66"}],"user_id":"47208","abstract":[{"lang":"eng","text":"Developing effective business models is a complex process for a company where several tasks (e.g., conduct customer interviews) need to be accomplished, and decisions (e.g., advertisement as a revenue stream) must be made. Here, domain experts can guide the choices of tasks and decisions with their knowledge. Nevertheless, this knowledge needs to match the situation of the company (e.g., financial resources) and the application domain of the product/service (e.g., mobile app) to reduce the risk of developing ineffective business models with low market penetration. This is not covered by one-size-fits-all development methods without tailoring before the enaction.\r\nTherefore, we conduct a design science study to create a situation-specific development approach for business models. Based on situational method engineering and our previous work in storing knowledge of methods and models in distinct repositories, this paper shows the situation-specific composition and enaction of business model development methods. First, the method engineer composes the development method out of both repositories based on the situational context. Second, the business developer enacts the method and develops the business model.  We implement the approach in a tool and evaluate it with a industrial case study on mobile apps."}],"status":"public","publication":"Product-focused Software Process Improvement","type":"book_chapter"},{"publication_status":"published","citation":{"mla":"Karakaya, Kadiray, and Eric Bodden. “SootFX: A Static Code Feature Extraction Tool for Java and Android.” <i>2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/scam52516.2021.00030\">10.1109/scam52516.2021.00030</a>.","short":"K. Karakaya, E. Bodden, in: 2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM), IEEE, 2021.","bibtex":"@inproceedings{Karakaya_Bodden_2021, title={SootFX: A Static Code Feature Extraction Tool for Java and Android}, DOI={<a href=\"https://doi.org/10.1109/scam52516.2021.00030\">10.1109/scam52516.2021.00030</a>}, booktitle={2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)}, publisher={IEEE}, author={Karakaya, Kadiray and Bodden, Eric}, year={2021} }","apa":"Karakaya, K., &#38; Bodden, E. (2021). SootFX: A Static Code Feature Extraction Tool for Java and Android. <i>2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)</i>. <a href=\"https://doi.org/10.1109/scam52516.2021.00030\">https://doi.org/10.1109/scam52516.2021.00030</a>","ama":"Karakaya K, Bodden E. SootFX: A Static Code Feature Extraction Tool for Java and Android. In: <i>2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)</i>. IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/scam52516.2021.00030\">10.1109/scam52516.2021.00030</a>","chicago":"Karakaya, Kadiray, and Eric Bodden. “SootFX: A Static Code Feature Extraction Tool for Java and Android.” In <i>2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)</i>. IEEE, 2021. <a href=\"https://doi.org/10.1109/scam52516.2021.00030\">https://doi.org/10.1109/scam52516.2021.00030</a>.","ieee":"K. Karakaya and E. Bodden, “SootFX: A Static Code Feature Extraction Tool for Java and Android,” 2021, doi: <a href=\"https://doi.org/10.1109/scam52516.2021.00030\">10.1109/scam52516.2021.00030</a>."},"year":"2021","date_created":"2022-02-24T15:44:42Z","author":[{"first_name":"Kadiray","last_name":"Karakaya","full_name":"Karakaya, Kadiray"},{"full_name":"Bodden, Eric","last_name":"Bodden","first_name":"Eric"}],"date_updated":"2022-02-24T15:45:43Z","publisher":"IEEE","doi":"10.1109/scam52516.2021.00030","title":"SootFX: A Static Code Feature Extraction Tool for Java and Android","type":"conference","publication":"2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)","status":"public","user_id":"70410","department":[{"_id":"76"}],"_id":"30084"},{"language":[{"iso":"eng"}],"user_id":"82920","department":[{"_id":"600"}],"_id":"25297","status":"public","type":"conference","publication":"Proceedings of the 8th Workshop on Argument Mining","main_file_link":[{"url":"https://aclanthology.org/2021.argmining-1.19.pdf"}],"title":"Key Point Analysis via Contrastive Learning and Extractive Argument Summarization","author":[{"id":"73059","full_name":"Alshomary, Milad","last_name":"Alshomary","first_name":"Milad"},{"first_name":"Timon","full_name":"Gurcke, Timon","id":"52174","last_name":"Gurcke"},{"last_name":"Syed","full_name":"Syed, Shahbaz","first_name":"Shahbaz"},{"first_name":"Philipp","full_name":"Heinisch, Philipp","last_name":"Heinisch"},{"last_name":"Spliethöver","orcid":"0000-0003-4364-1409","id":"84035","full_name":"Spliethöver, Maximilian","first_name":"Maximilian"},{"last_name":"Cimiano","full_name":"Cimiano, Philipp","first_name":"Philipp"},{"first_name":"Martin","full_name":"Potthast, Martin","last_name":"Potthast"},{"first_name":"Henning","full_name":"Wachsmuth, Henning","id":"3900","last_name":"Wachsmuth"}],"date_created":"2021-10-04T12:40:02Z","date_updated":"2022-03-08T12:47:33Z","citation":{"ieee":"M. Alshomary <i>et al.</i>, “Key Point Analysis via Contrastive Learning and Extractive Argument Summarization,” in <i>Proceedings of the 8th Workshop on Argument Mining</i>, 2021, pp. 184–189.","chicago":"Alshomary, Milad, Timon Gurcke, Shahbaz Syed, Philipp Heinisch, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, and Henning Wachsmuth. “Key Point Analysis via Contrastive Learning and Extractive Argument Summarization.” In <i>Proceedings of the 8th Workshop on Argument Mining</i>, 184–89, 2021.","ama":"Alshomary M, Gurcke T, Syed S, et al. Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In: <i>Proceedings of the 8th Workshop on Argument Mining</i>. ; 2021:184-189.","short":"M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 184–189.","mla":"Alshomary, Milad, et al. “Key Point Analysis via Contrastive Learning and Extractive Argument Summarization.” <i>Proceedings of the 8th Workshop on Argument Mining</i>, 2021, pp. 184–89.","bibtex":"@inproceedings{Alshomary_Gurcke_Syed_Heinisch_Spliethöver_Cimiano_Potthast_Wachsmuth_2021, title={Key Point Analysis via Contrastive Learning and Extractive Argument Summarization}, booktitle={Proceedings of the 8th Workshop on Argument Mining}, author={Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning}, year={2021}, pages={184–189} }","apa":"Alshomary, M., Gurcke, T., Syed, S., Heinisch, P., Spliethöver, M., Cimiano, P., Potthast, M., &#38; Wachsmuth, H. (2021). Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. <i>Proceedings of the 8th Workshop on Argument Mining</i>, 184–189."},"page":"184 - 189","year":"2021"},{"year":"2021","citation":{"ieee":"Z. Nouri, N. Prakash, U. Gadiraju, and H. Wachsmuth, “iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing,” 2021.","chicago":"Nouri, Zahra, Nikhil Prakash, Ujwal Gadiraju, and Henning Wachsmuth. “IClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing.” In <i>Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021</i>, 2021.","ama":"Nouri Z, Prakash N, Gadiraju U, Wachsmuth H. iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing. 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Yigitbas, S. Gorissen, N. Weidmann, G. Engels, in: Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS’21) , ACM/IEEE, 2021.","bibtex":"@inproceedings{Yigitbas_Gorissen_Weidmann_Engels_2021, title={Collaborative Software Modeling in Virtual Reality}, booktitle={Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS’21) }, publisher={ACM/IEEE}, author={Yigitbas, Enes and Gorissen, Simon and Weidmann, Nils and Engels, Gregor}, year={2021} }","apa":"Yigitbas, E., Gorissen, S., Weidmann, N., &#38; Engels, G. (2021). Collaborative Software Modeling in Virtual Reality. <i>Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS’21) </i>.","ieee":"E. Yigitbas, S. Gorissen, N. Weidmann, and G. Engels, “Collaborative Software Modeling in Virtual Reality,” 2021.","chicago":"Yigitbas, Enes, Simon Gorissen, Nils Weidmann, and Gregor Engels. “Collaborative Software Modeling in Virtual Reality.” In <i>Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS’21) </i>. ACM/IEEE, 2021.","ama":"Yigitbas E, Gorissen S, Weidmann N, Engels G. Collaborative Software Modeling in Virtual Reality. In: <i>Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS’21) </i>. ACM/IEEE; 2021."},"title":"Collaborative Software Modeling in Virtual Reality","date_updated":"2022-03-21T14:37:47Z","publisher":"ACM/IEEE","author":[{"first_name":"Enes","orcid":"0000-0002-5967-833X","last_name":"Yigitbas","full_name":"Yigitbas, Enes","id":"8447"},{"first_name":"Simon","last_name":"Gorissen","full_name":"Gorissen, Simon"},{"first_name":"Nils","last_name":"Weidmann","id":"53103","full_name":"Weidmann, Nils"},{"last_name":"Engels","full_name":"Engels, Gregor","id":"107","first_name":"Gregor"}],"date_created":"2021-07-12T11:42:08Z"},{"abstract":[{"text":"Static analysis is used to automatically detect bugs and security breaches, and aids compileroptimization. Whole-program analysis (WPA) can yield high precision, however causes long analysistimes and thus does not match common software-development workflows, making it often impracticalto use for large, real-world applications.This paper thus presents the design and implementation ofModAlyzer, a novel static-analysisapproach that aims at accelerating whole-program analysis by making the analysis modular andcompositional. It shows how to computelossless, persisted summaries for callgraph, points-to anddata-flow information, and it reports under which circumstances this function-level compositionalanalysis outperforms WPA.We implementedModAlyzeras an extension to LLVM and PhASAR, and applied it to 12 real-world C and C++ applications. At analysis time,ModAlyzermodularly and losslessly summarizesthe analysis effect of the library code those applications share, hence avoiding its repeated re-analysis.The experimental results show that the reuse of these summaries can save, on average, 72% ofanalysis time over WPA. Moreover, because it is lossless, the module-wise analysis fully retainsprecision and recall. Surprisingly, as our results show, it sometimes even yields precision superior toWPA. The initial summary generation, on average, takes about 3.67 times as long as WPA.","lang":"eng"}],"status":"public","publication":"European Conference on Object-Oriented Programming (ECOOP)","type":"conference","language":[{"iso":"eng"}],"_id":"21598","project":[{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"12","name":"SFB 901 - Subproject B4"},{"_id":"1","name":"SFB 901"}],"department":[{"_id":"76"}],"user_id":"60543","year":"2021","citation":{"ama":"Schubert P, Hermann B, Bodden E. Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis. In: <i>European Conference on Object-Oriented Programming (ECOOP)</i>. ; 2021.","apa":"Schubert, P., Hermann, B., &#38; Bodden, E. (2021). Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis. <i>European Conference on Object-Oriented Programming (ECOOP)</i>.","short":"P. Schubert, B. Hermann, E. Bodden, in: European Conference on Object-Oriented Programming (ECOOP), 2021.","bibtex":"@inproceedings{Schubert_Hermann_Bodden_2021, title={Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis}, booktitle={European Conference on Object-Oriented Programming (ECOOP)}, author={Schubert, Philipp and Hermann, Ben and Bodden, Eric}, year={2021} }","mla":"Schubert, Philipp, et al. “Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis.” <i>European Conference on Object-Oriented Programming (ECOOP)</i>, 2021.","chicago":"Schubert, Philipp, Ben Hermann, and Eric Bodden. “Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis.” In <i>European Conference on Object-Oriented Programming (ECOOP)</i>, 2021.","ieee":"P. Schubert, B. Hermann, and E. Bodden, “Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis,” 2021."},"title":"Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis","main_file_link":[{"open_access":"1","url":"https://drops.dagstuhl.de/opus/volltexte/2021/14045/"}],"date_updated":"2022-03-25T07:49:35Z","oa":"1","author":[{"first_name":"Philipp","last_name":"Schubert","orcid":"0000-0002-8674-1859","full_name":"Schubert, Philipp","id":"60543"},{"first_name":"Ben","orcid":"0000-0001-9848-2017","last_name":"Hermann","full_name":"Hermann, Ben","id":"66173"},{"last_name":"Bodden","orcid":"0000-0003-3470-3647","id":"59256","full_name":"Bodden, Eric","first_name":"Eric"}],"date_created":"2021-04-08T11:24:59Z"},{"publication_identifier":{"isbn":["9783030889418","9783030889425"],"issn":["0302-9743","1611-3349"]},"publication_status":"published","intvolume":"     12986","page":"166-180","citation":{"bibtex":"@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>}, booktitle={Proceedings of The 24th International Conference on Discovery Science (DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke}, editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture Notes in Computer Science} }","short":"C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021), Springer, 2021, pp. 166–180.","mla":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, vol. 12986, Springer, 2021, pp. 166–80, doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","apa":"Damke, C., &#38; Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares &#38; L. Torgo (Eds.), <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i> (Vol. 12986, pp. 166–180). Springer. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>","chicago":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” In <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>.","ieee":"C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural Networks,” in <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","ama":"Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>. Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>"},"date_updated":"2022-04-11T22:08:12Z","volume":12986,"author":[{"last_name":"Damke","orcid":"0000-0002-0455-0048","full_name":"Damke, Clemens","id":"48192","first_name":"Clemens"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"doi":"10.1007/978-3-030-88942-5","conference":{"name":"24th International Conference on Discovery Science","start_date":"2021-10-11","end_date":"2021-10-13","location":"Halifax, Canada"},"type":"conference","editor":[{"full_name":"Soares, Carlos","last_name":"Soares","first_name":"Carlos"},{"last_name":"Torgo","full_name":"Torgo, Luis","first_name":"Luis"}],"status":"public","_id":"27381","department":[{"_id":"355"}],"user_id":"48192","series_title":"Lecture Notes in Computer Science","quality_controlled":"1","year":"2021","publisher":"Springer","date_created":"2021-11-11T14:15:18Z","title":"Ranking Structured Objects with Graph Neural Networks","publication":"Proceedings of The 24th International Conference on Discovery Science (DS 2021)","abstract":[{"text":"Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.","lang":"eng"}],"external_id":{"arxiv":["2104.08869"]},"keyword":["Graph-structured data","Graph neural networks","Preference learning","Learning to rank"],"language":[{"iso":"eng"}]}]