[{"user_id":"102979","publisher":"Association for Computing Machinery","_id":"48893","page":"600–608","status":"public","place":"New York, NY, USA","citation":{"ieee":"A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 600–608, doi: <a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>.","mla":"Nikfarjam, Adel, et al. “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 600–608, doi:<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>.","apa":"Nikfarjam, A., Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 600–608. <a href=\"https://doi.org/10.1145/3449639.3459384\">https://doi.org/10.1145/3449639.3459384</a>","bibtex":"@inproceedings{Nikfarjam_Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, series={GECCO’21}, title={Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={600–608}, collection={GECCO’21} }","ama":"Nikfarjam A, Bossek J, Neumann A, Neumann F. Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’21. Association for Computing Machinery; 2021:600–608. doi:<a href=\"https://doi.org/10.1145/3449639.3459384\">10.1145/3449639.3459384</a>","short":"A. Nikfarjam, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 600–608.","chicago":"Nikfarjam, Adel, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 600–608. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459384\">https://doi.org/10.1145/3449639.3459384</a>."},"doi":"10.1145/3449639.3459384","language":[{"iso":"eng"}],"series_title":"GECCO’21","date_updated":"2023-12-13T10:50:06Z","publication_identifier":{"isbn":["978-1-4503-8350-9"]},"author":[{"last_name":"Nikfarjam","first_name":"Adel","full_name":"Nikfarjam, Adel"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"title":"Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem","year":"2021","department":[{"_id":"819"}],"keyword":["evolutionary algorithms","evolutionary diversity optimisation","high-order entropy","traveling salesperson problem"],"type":"conference","date_created":"2023-11-14T15:59:00Z","extern":"1","abstract":[{"lang":"eng","text":"Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference"},{"language":[{"iso":"eng"}],"series_title":"GECCO’21","doi":"10.1145/3449639.3459385","year":"2021","title":"Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions","publication_identifier":{"isbn":["978-1-4503-8350-9"]},"author":[{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"date_updated":"2023-12-13T10:49:25Z","date_created":"2023-11-14T15:58:59Z","keyword":["evolutionary algorithms","evolutionary diversity optimisation","sub-modular functions"],"type":"conference","department":[{"_id":"819"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems with uniform and knapsack constraints. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation (EDO) approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark problems and analyse trade-offs in terms of solution quality and diversity of the resulting solution sets.","lang":"eng"}],"extern":"1","page":"261–269","_id":"48891","publisher":"Association for Computing Machinery","user_id":"102979","status":"public","place":"New York, NY, USA","citation":{"short":"A. Neumann, J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 261–269.","chicago":"Neumann, Aneta, Jakob Bossek, and Frank Neumann. “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 261–269. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459385\">https://doi.org/10.1145/3449639.3459385</a>.","apa":"Neumann, A., Bossek, J., &#38; Neumann, F. (2021). Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 261–269. <a href=\"https://doi.org/10.1145/3449639.3459385\">https://doi.org/10.1145/3449639.3459385</a>","ieee":"A. Neumann, J. Bossek, and F. Neumann, “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 261–269, doi: <a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>.","ama":"Neumann A, Bossek J, Neumann F. Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’21. Association for Computing Machinery; 2021:261–269. doi:<a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>","bibtex":"@inproceedings{Neumann_Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO’21}, title={Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Neumann, Aneta and Bossek, Jakob and Neumann, Frank}, year={2021}, pages={261–269}, collection={GECCO’21} }","mla":"Neumann, Aneta, et al. “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 261–269, doi:<a href=\"https://doi.org/10.1145/3449639.3459385\">10.1145/3449639.3459385</a>."}},{"status":"public","title":"Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation","year":"2021","author":[{"full_name":"Nikfarjam, Adel","first_name":"Adel","last_name":"Nikfarjam"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Neumann, Aneta","first_name":"Aneta","last_name":"Neumann"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"publication_identifier":{"isbn":["978-1-4503-8352-3"]},"date_updated":"2023-12-13T10:49:59Z","page":"1–11","_id":"48892","language":[{"iso":"eng"}],"publisher":"Association for Computing Machinery","user_id":"102979","publication":"Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms","citation":{"bibtex":"@inbook{Nikfarjam_Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, title={Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation}, booktitle={Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={1–11} }","ama":"Nikfarjam A, Bossek J, Neumann A, Neumann F. Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation. In: <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–11.","mla":"Nikfarjam, Adel, et al. “Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.” <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–11.","chicago":"Nikfarjam, Adel, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation.” In <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–11. New York, NY, USA: Association for Computing Machinery, 2021.","short":"A. Nikfarjam, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–11.","ieee":"A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation,” in <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.","apa":"Nikfarjam, A., Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation. In <i>Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–11). Association for Computing Machinery."},"extern":"1","abstract":[{"lang":"eng","text":"Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Often, it is desirable to compute not just a single solution for a given problem, but a diverse set of high quality solutions from which a decision maker can choose one for implementation. Currently, there are only a few approaches for computing a diverse solution set for the TSP. Furthermore, almost all of them assume that the optimal solution is known. In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown. We show how to adopt EAX to not only find a high-quality solution but also to maximise the diversity of the population. The resulting EAX-based EDO approach, termed EAX-EDO is capable of obtaining diverse high-quality tours when the optimal solution for the TSP is known or unknown. A comparison to existing approaches shows that they are clearly outperformed by EAX-EDO."}],"date_created":"2023-11-14T15:59:00Z","place":"New York, NY, USA","keyword":["edge assembly crossover (EAX)","evolutionary algorithms","evolutionary diversity optimisation (EDO)","traveling salesperson problem (TSP)"],"type":"book_chapter","department":[{"_id":"819"}]},{"keyword":["Dynamic optimization","Evolutionary algorithms","Running time analysis"],"type":"journal_article","department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:54Z","abstract":[{"lang":"eng","text":"We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient."}],"issue":"10","publication":"Algorithmica","doi":"10.1007/s00453-021-00838-3","language":[{"iso":"eng"}],"date_updated":"2023-12-13T10:51:34Z","intvolume":"        83","year":"2021","title":"Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem","publication_identifier":{"issn":["0178-4617"]},"author":[{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"},{"first_name":"Pan","last_name":"Peng","full_name":"Peng, Pan"},{"last_name":"Sudholt","first_name":"Dirk","full_name":"Sudholt, Dirk"}],"citation":{"ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” <i>Algorithmica</i>, vol. 83, no. 10, pp. 3148–3179, 2021, doi: <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>.","apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. <i>Algorithmica</i>, <i>83</i>(10), 3148–3179. <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">https://doi.org/10.1007/s00453-021-00838-3</a>","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.” <i>Algorithmica</i> 83, no. 10 (2021): 3148–3179. <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">https://doi.org/10.1007/s00453-021-00838-3</a>.","short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, Algorithmica 83 (2021) 3148–3179.","mla":"Bossek, Jakob, et al. “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.” <i>Algorithmica</i>, vol. 83, no. 10, 2021, pp. 3148–3179, doi:<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>.","bibtex":"@article{Bossek_Neumann_Peng_Sudholt_2021, title={Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}, volume={83}, DOI={<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>}, number={10}, journal={Algorithmica}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2021}, pages={3148–3179} }","ama":"Bossek J, Neumann F, Peng P, Sudholt D. Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. <i>Algorithmica</i>. 2021;83(10):3148–3179. doi:<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>"},"user_id":"102979","volume":83,"page":"3148–3179","_id":"48854","status":"public"},{"abstract":[{"text":"We propose a novel method for automated algorithm selection in the domain of single-objective continuous black-box optimization. In contrast to existing methods, we use convolutional neural networks as the selection apparatus which bases its decision on a so-called ‘fitness map’. This fitness map is a 2D representation of a two dimensional search space where different gray scales indicate the quality of found solutions in certain areas. Our devised approach uses a modular CMA-ES framework which offers the option to create the conventional CMA-ES, CMA-ES with the alternate step-size adaptation and many other variants proposed over the years. In total, 4 608 different configurations are possible where most configurations are of complementary nature. In this proof-of-concept work, we consider a subset of 32 possible configurations. The developed method is evaluated against an excerpt of BBOB functions and its performance is compared against baselines that are commonly used in automated algorithm selection - the best standalone algorithm (configuration) and the best obtainable sequence of configurations. While the results indicate that the use of the fitness map is not superior on every benchmark problem, it indubitably shows its merit on more hard-to-solve problems. This offers a promising perspective for generalizing to other types of optimization problems and problem domains.","lang":"eng"}],"publication":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","citation":{"mla":"Prager, Raphael Patrick, et al. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2021, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>.","ama":"Prager RP, Seiler M, Trautmann H, Kerschke P. Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In: <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2021:1-8. doi:<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>","bibtex":"@inproceedings{Prager_Seiler_Trautmann_Kerschke_2021, title={Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization}, DOI={<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>}, booktitle={2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2021}, pages={1–8} }","apa":"Prager, R. P., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 1–8. <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">https://doi.org/10.1109/SSCI50451.2021.9660174</a>","ieee":"R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization,” in <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2021, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>.","short":"R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8.","chicago":"Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” In <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 1–8, 2021. <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">https://doi.org/10.1109/SSCI50451.2021.9660174</a>."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:25:08Z","date_updated":"2024-06-07T07:12:28Z","status":"public","year":"2021","title":"Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization","author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"id":"105520","last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"}],"user_id":"15504","doi":"10.1109/SSCI50451.2021.9660174","page":"1-8","_id":"46315","language":[{"iso":"eng"}]},{"citation":{"mla":"Assenmacher, Dennis, et al. “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 2021, pp. 1–14.","bibtex":"@inproceedings{Assenmacher_Niemann_Müller_Seiler_Riehle_Trautmann_2021, place={Virtual Event}, title={RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)}, author={Assenmacher, Dennis and Niemann, Marco and Müller, Kilian and Seiler, Moritz and Riehle, Dennis M. and Trautmann, Heike}, year={2021}, pages={1–14} }","ama":"Assenmacher D, Niemann M, Müller K, Seiler M, Riehle DM, Trautmann H. RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In: <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>. ; 2021:1–14.","ieee":"D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann, “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets,” in <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 2021, pp. 1–14.","apa":"Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., &#38; Trautmann, H. (2021). RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 1–14.","short":"D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D.M. Riehle, H. Trautmann, in: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), Virtual Event, 2021, pp. 1–14.","chicago":"Assenmacher, Dennis, Marco Niemann, Kilian Müller, Moritz Seiler, Dennis M. Riehle, and Heike Trautmann. “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” In <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 1–14. Virtual Event, 2021."},"publication":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)","abstract":[{"text":"Abuse and hate are penetrating social media and many comment sections of news media companies. These platform providers invest considerable efforts to mod- erate user-generated contributions to prevent losing readers who get appalled by inappropriate texts. This is further enforced by legislative actions, which make non-clearance of these comments a punishable action. While (semi-)automated solutions using Natural Language Processing and advanced Machine Learning techniques are getting increasingly sophisticated, the domain of abusive language detection still struggles as large non-English and well-curated datasets are scarce or not publicly available. With this work, we publish and analyse the largest annotated German abusive language comment datasets to date. In contrast to existing datasets, we achieve a high labelling standard by conducting a thorough crowd-based an- notation study that complements professional moderators’ decisions, which are also included in the dataset. We compare and cross-evaluate the performance of baseline algorithms and state-of-the-art transformer-based language models, which are fine-tuned on our datasets and an existing alternative, showing the usefulness for the community.","lang":"eng"}],"date_created":"2023-08-04T07:22:59Z","place":"Virtual Event","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","author":[{"full_name":"Assenmacher, Dennis","last_name":"Assenmacher","first_name":"Dennis"},{"full_name":"Niemann, Marco","first_name":"Marco","last_name":"Niemann"},{"full_name":"Müller, Kilian","last_name":"Müller","first_name":"Kilian"},{"id":"105520","last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"full_name":"Riehle, Dennis M.","last_name":"Riehle","first_name":"Dennis M."},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"}],"year":"2021","title":"RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets","status":"public","date_updated":"2024-06-07T07:13:04Z","_id":"46312","language":[{"iso":"eng"}],"page":"1–14","user_id":"15504"},{"doi":"10.1145/3450218.3477308","user_id":"15504","editor":[{"full_name":"Computing Machinery Association, for","first_name":"for","last_name":"Computing Machinery Association"}],"page":"1–15","_id":"46313","publisher":"Association for Computing Machinery","language":[{"iso":"eng"}],"date_updated":"2024-06-10T11:57:04Z","status":"public","year":"2021","title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","author":[{"full_name":"Heins, Jonathan","first_name":"Jonathan","last_name":"Heins"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Pohl, Janina","last_name":"Pohl","first_name":"Janina"},{"full_name":"Seiler, Moritz","first_name":"Moritz","last_name":"Seiler","id":"105520"},{"id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}],"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Dornbirn, Austria","date_created":"2023-08-04T07:23:57Z","abstract":[{"text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.","lang":"eng"}],"publication":"Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)","citation":{"chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” In <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>, edited by for Computing Machinery Association, 1–15. Dornbirn, Austria: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3450218.3477308\">https://doi.org/10.1145/3450218.3477308</a>.","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in:  for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI), Association for Computing Machinery, Dornbirn, Austria, 2021, pp. 1–15.","apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In  for Computing Machinery Association (Ed.), <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)</i> (pp. 1–15). Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3450218.3477308\">https://doi.org/10.1145/3450218.3477308</a>","ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)</i>, 2021, pp. 1–15, doi: <a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>.","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: Computing Machinery Association  for, ed. <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>. Association for Computing Machinery; 2021:1–15. doi:<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>","bibtex":"@inproceedings{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={Dornbirn, Austria}, title={On the Potential of Normalized TSP Features for Automated Algorithm Selection}, DOI={<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>}, booktitle={Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)}, publisher={Association for Computing Machinery}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, editor={Computing Machinery Association, for}, year={2021}, pages={1–15} }","mla":"Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>, edited by for Computing Machinery Association, Association for Computing Machinery, 2021, pp. 1–15, doi:<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>."}},{"date_updated":"2023-10-16T12:59:10Z","title":"Towards Real-Time and Unsupervised Campaign Detection in Social Media","status":"public","year":"2020","author":[{"full_name":"Assenmacher, D","last_name":"Assenmacher","first_name":"D"},{"last_name":"Adam","first_name":"L","full_name":"Adam, L"},{"id":"100740","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"},{"full_name":"Grimme, C","first_name":"C","last_name":"Grimme"}],"user_id":"15504","_id":"46319","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than an- alyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and com- putational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learn- ing techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real- time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordi- nated campaigns."}],"publication":"Proceedings of the Florida Artificial Intelligence Research Society Conference","citation":{"ieee":"D. Assenmacher, L. Adam, H. Trautmann, and C. Grimme, “Towards Real-Time and Unsupervised Campaign Detection in Social Media,” 2020.","apa":"Assenmacher, D., Adam, L., Trautmann, H., &#38; Grimme, C. (2020). Towards Real-Time and Unsupervised Campaign Detection in Social Media. <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>.","chicago":"Assenmacher, D, L Adam, Heike Trautmann, and C Grimme. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” In <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>. Florida, USA, 2020.","short":"D. Assenmacher, L. Adam, H. Trautmann, C. Grimme, in: Proceedings of the Florida Artificial Intelligence Research Society Conference, Florida, USA, 2020.","mla":"Assenmacher, D., et al. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>, 2020.","bibtex":"@inproceedings{Assenmacher_Adam_Trautmann_Grimme_2020, place={Florida, USA}, title={Towards Real-Time and Unsupervised Campaign Detection in Social Media}, booktitle={Proceedings of the Florida Artificial Intelligence Research Society Conference}, author={Assenmacher, D and Adam, L and Trautmann, Heike and Grimme, C}, year={2020} }","ama":"Assenmacher D, Adam L, Trautmann H, Grimme C. Towards Real-Time and Unsupervised Campaign Detection in Social Media. In: <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>. ; 2020."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Florida, USA","date_created":"2023-08-04T07:29:36Z"},{"date_updated":"2023-10-16T13:04:15Z","year":"2020","title":"Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis","status":"public","author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"full_name":"Wang, Hao","last_name":"Wang","first_name":"Hao"},{"full_name":"Bäck, Thomas H. W.","first_name":"Thomas H. W.","last_name":"Bäck"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"}],"doi":"10.1109/SSCI47803.2020.9308510","user_id":"15504","page":"996–1003","_id":"46328","language":[{"iso":"eng"}],"abstract":[{"text":"In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.","lang":"eng"}],"publication":"Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)","citation":{"bibtex":"@inproceedings{Prager_Trautmann_Wang_Bäck_Kerschke_2020, place={Canberra, Australia}, title={Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis}, DOI={<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>}, booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Prager, Raphael Patrick and Trautmann, Heike and Wang, Hao and Bäck, Thomas H. W. and Kerschke, Pascal}, year={2020}, pages={996–1003} }","ama":"Prager RP, Trautmann H, Wang H, Bäck THW, Kerschke P. Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. In: <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2020:996–1003. doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>","mla":"Prager, Raphael Patrick, et al. “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 996–1003, doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>.","chicago":"Prager, Raphael Patrick, Heike Trautmann, Hao Wang, Thomas H. W. Bäck, and Pascal Kerschke. “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” In <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 996–1003. Canberra, Australia, 2020. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">https://doi.org/10.1109/SSCI47803.2020.9308510</a>.","short":"R.P. Prager, H. Trautmann, H. Wang, T.H.W. Bäck, P. Kerschke, in: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 996–1003.","ieee":"R. P. Prager, H. Trautmann, H. Wang, T. H. W. Bäck, and P. Kerschke, “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis,” in <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 996–1003, doi: <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>.","apa":"Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., &#38; Kerschke, P. (2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 996–1003. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">https://doi.org/10.1109/SSCI47803.2020.9308510</a>"},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Canberra, Australia","date_created":"2023-08-04T07:37:30Z"},{"publication_identifier":{"isbn":["978-3-030-49570-1"]},"author":[{"full_name":"Assenmacher, D","last_name":"Assenmacher","first_name":"D"},{"full_name":"Clever, L","first_name":"L","last_name":"Clever"},{"last_name":"Pohl","first_name":"JS","full_name":"Pohl, JS"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","id":"100740"},{"last_name":"Grimme","first_name":"C","full_name":"Grimme, C"}],"title":"A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media","year":"2020","date_updated":"2023-10-16T12:59:28Z","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-49570-1_14","publication":"Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis","abstract":[{"lang":"eng","text":"The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020."}],"date_created":"2023-08-04T07:30:29Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","status":"public","publisher":"Springer International Publishing","_id":"46320","page":"201–214","editor":[{"first_name":"G","last_name":"Meiselwitz","full_name":"Meiselwitz, G"}],"user_id":"15504","citation":{"short":"D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, C. Grimme, in: G. Meiselwitz (Ed.), Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis, Springer International Publishing, Cham, 2020, pp. 201–214.","chicago":"Assenmacher, D, L Clever, JS Pohl, Heike Trautmann, and C Grimme. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” In <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, edited by G Meiselwitz, 201–214. Cham: Springer International Publishing, 2020. <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">https://doi.org/10.1007/978-3-030-49570-1_14</a>.","apa":"Assenmacher, D., Clever, L., Pohl, J., Trautmann, H., &#38; Grimme, C. (2020). A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In G. Meiselwitz (Ed.), <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i> (pp. 201–214). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">https://doi.org/10.1007/978-3-030-49570-1_14</a>","ieee":"D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, and C. Grimme, “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media,” in <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, 2020, pp. 201–214, doi: <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>.","ama":"Assenmacher D, Clever L, Pohl J, Trautmann H, Grimme C. A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In: Meiselwitz G, ed. <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>. Springer International Publishing; 2020:201–214. doi:<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>","bibtex":"@inproceedings{Assenmacher_Clever_Pohl_Trautmann_Grimme_2020, place={Cham}, title={A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>}, booktitle={Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis}, publisher={Springer International Publishing}, author={Assenmacher, D and Clever, L and Pohl, JS and Trautmann, Heike and Grimme, C}, editor={Meiselwitz, G}, year={2020}, pages={201–214} }","mla":"Assenmacher, D., et al. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, edited by G Meiselwitz, Springer International Publishing, 2020, pp. 201–214, doi:<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>."},"place":"Cham"},{"date_updated":"2023-10-16T13:03:15Z","publication_identifier":{"isbn":["978-3-030-43823-4"]},"author":[{"last_name":"Carnein","first_name":"Matthias","full_name":"Carnein, Matthias"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"},{"last_name":"Bifet","first_name":"Albert","full_name":"Bifet, Albert"},{"full_name":"Pfahringer, Bernhard","first_name":"Bernhard","last_name":"Pfahringer"}],"status":"public","year":"2020","title":"Towards Automated Configuration of Stream Clustering Algorithms","user_id":"15504","doi":"10.1007/978-3-030-43823-4_12","_id":"46325","language":[{"iso":"eng"}],"page":"137–143","abstract":[{"text":"Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.","lang":"eng"}],"citation":{"ama":"Carnein M, Trautmann H, Bifet A, Pfahringer B. Towards Automated Configuration of Stream Clustering Algorithms. In: <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>. ; 2020:137–143. doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>","bibtex":"@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg, Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>}, booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020}, pages={137–143} }","mla":"Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering Algorithms.” <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>.","chicago":"Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “Towards Automated Configuration of Stream Clustering Algorithms.” In <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. Würzburg, Germany, 2020. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>.","short":"M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), Würzburg, Germany, 2020, pp. 137–143.","apa":"Carnein, M., Trautmann, H., Bifet, A., &#38; Pfahringer, B. (2020). Towards Automated Configuration of Stream Clustering Algorithms. <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>","ieee":"M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated Configuration of Stream Clustering Algorithms,” in <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi: <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>."},"publication":"Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:35:24Z","place":"Würzburg, Germany"},{"date_updated":"2023-10-16T13:00:15Z","author":[{"first_name":"Dennis","last_name":"Assenmacher","full_name":"Assenmacher, Dennis"},{"last_name":"Frischlich ","first_name":"Lena","full_name":"Frischlich , Lena"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"full_name":"Adam, Lena","last_name":"Adam","first_name":"Lena"}],"title":"Inside the tool set of automation: Free social bot code revisited","status":"public","year":"2020","editor":[{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"full_name":"Preuß, Mike","first_name":"Mike","last_name":"Preuß"},{"full_name":"Takes, Frank","last_name":"Takes","first_name":"Frank"},{"first_name":"Annie","last_name":"Waldherr","full_name":"Waldherr, Annie"}],"user_id":"15504","_id":"46321","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"publisher":"Springer","page":"101–114","abstract":[{"lang":"eng","text":"Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such automated programs, it is still unclear how technically sophisticated those bots are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo of social bot development as well as first insights into the overall skills of publicly available bot code."}],"citation":{"ieee":"D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, and L. Adam, “Inside the tool set of automation: Free social bot code revisited,” in <i>Disinformation in open online media</i>, 2020, pp. 101–114.","apa":"Assenmacher, D., Frischlich , L., Trautmann, H., Grimme, C., &#38; Adam, L. (2020). Inside the tool set of automation: Free social bot code revisited. In C. Grimme, M. Preuß, F. Takes, &#38; A. Waldherr (Eds.), <i>Disinformation in open online media</i> (pp. 101–114). Springer.","chicago":"Assenmacher, Dennis, Lena Frischlich , Heike Trautmann, Christian Grimme, and Lena Adam. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” In <i>Disinformation in Open Online Media</i>, edited by Christian Grimme, Mike Preuß, Frank Takes, and Annie Waldherr, 101–114. Lecture Notes in Computer Science. Wiesbaden: Springer, 2020.","short":"D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, L. Adam, in: C. Grimme, M. Preuß, F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Springer, Wiesbaden, 2020, pp. 101–114.","mla":"Assenmacher, Dennis, et al. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” <i>Disinformation in Open Online Media</i>, edited by Christian Grimme et al., Springer, 2020, pp. 101–114.","bibtex":"@inproceedings{Assenmacher_Frischlich _Trautmann_Grimme_Adam_2020, place={Wiesbaden}, series={Lecture Notes in Computer Science}, title={Inside the tool set of automation: Free social bot code revisited}, booktitle={Disinformation in open online media}, publisher={Springer}, author={Assenmacher, Dennis and Frischlich , Lena and Trautmann, Heike and Grimme, Christian and Adam, Lena}, editor={Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={101–114}, collection={Lecture Notes in Computer Science} }","ama":"Assenmacher D, Frischlich  L, Trautmann H, Grimme C, Adam L. Inside the tool set of automation: Free social bot code revisited. In: Grimme C, Preuß M, Takes F, Waldherr A, eds. <i>Disinformation in Open Online Media</i>. Lecture Notes in Computer Science. Springer; 2020:101–114."},"publication":"Disinformation in open online media","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Wiesbaden","date_created":"2023-08-04T07:31:13Z"},{"date_updated":"2023-10-16T13:03:36Z","status":"public","year":"2020","title":"confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms","author":[{"full_name":"Carnein, Matthias","first_name":"Matthias","last_name":"Carnein"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"full_name":"Bifet, Albert","last_name":"Bifet","first_name":"Albert"},{"full_name":"Pfahringer, Bernhard","first_name":"Bernhard","last_name":"Pfahringer"}],"doi":"10.1007/978-3-030-53552-0_10","user_id":"15504","page":"80–95","language":[{"iso":"eng"}],"_id":"46326","abstract":[{"text":"Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.","lang":"eng"}],"publication":"Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)","citation":{"mla":"Carnein, Matthias, et al. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 2020, pp. 80–95, doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>.","bibtex":"@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Athens, Greece}, title={confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>}, booktitle={Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020}, pages={80–95} }","ama":"Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. In: <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>. ; 2020:80–95. doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>","ieee":"M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms,” in <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 2020, pp. 80–95, doi: <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>.","apa":"Carnein, M., Trautmann, H., Bifet, A., &#38; Pfahringer, B. (2020). confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 80–95. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">https://doi.org/10.1007/978-3-030-53552-0_10</a>","short":"M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020), Athens, Greece, 2020, pp. 80–95.","chicago":"Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” In <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 80–95. Athens, Greece, 2020. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">https://doi.org/10.1007/978-3-030-53552-0_10</a>."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Athens, Greece","date_created":"2023-08-04T07:36:03Z"},{"publication":"Disinformation in open online media","citation":{"apa":"Lena, C., Frischlich, L., Trautmann, H., &#38; Grimme, C. (2020). Automated detection of nostalgic text in the context of societal pessimism. In C. Grimme, M. Preuß, F. Takes, &#38; A. Waldherr (Eds.), <i>Disinformation in open online media</i> (pp. 48–58).","ieee":"C. Lena, L. Frischlich, H. Trautmann, and C. Grimme, “Automated detection of nostalgic text in the context of societal pessimism,” in <i>Disinformation in open online media</i>, 2020, pp. 48–58.","chicago":"Lena, Clever, Lena Frischlich, Heike Trautmann, and Christian Grimme. “Automated Detection of Nostalgic Text in the Context of Societal Pessimism.” In <i>Disinformation in Open Online Media</i>, edited by Christian Grimme, Mike Preuß, Frank Takes, and Annie Waldherr, 48–58. Hamburg, Deutschland, 2020.","short":"C. Lena, L. Frischlich, H. Trautmann, C. Grimme, in: C. Grimme, M. Preuß, F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Hamburg, Deutschland, 2020, pp. 48–58.","mla":"Lena, Clever, et al. “Automated Detection of Nostalgic Text in the Context of Societal Pessimism.” <i>Disinformation in Open Online Media</i>, edited by Christian Grimme et al., 2020, pp. 48–58.","ama":"Lena C, Frischlich L, Trautmann H, Grimme C. Automated detection of nostalgic text in the context of societal pessimism. In: Grimme C, Preuß M, Takes F, Waldherr A, eds. <i>Disinformation in Open Online Media</i>. ; 2020:48–58.","bibtex":"@inproceedings{Lena_Frischlich_Trautmann_Grimme_2020, place={Hamburg, Deutschland}, title={Automated detection of nostalgic text in the context of societal pessimism}, booktitle={Disinformation in open online media}, author={Lena, Clever and Frischlich, Lena and Trautmann, Heike and Grimme, Christian}, editor={Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={48–58} }"},"abstract":[{"lang":"eng","text":"In online media environments, nostalgia can be used as important ingredient of propaganda strategies, specifically, by creating societal pessimism. This work addresses the automated detection of nostalgic text as a first step towards automatically identifying nostalgia-based manipulation strategies. We compare the performance of standard machine learning approaches on this challenge and demonstrate the successful transfer of the best performing approach to real-world nostalgia detection in a case study."}],"date_created":"2023-08-04T07:36:43Z","place":"Hamburg, Deutschland","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"year":"2020","title":"Automated detection of nostalgic text in the context of societal pessimism","status":"public","author":[{"full_name":"Lena, Clever","last_name":"Lena","first_name":"Clever"},{"last_name":"Frischlich","first_name":"Lena","full_name":"Frischlich, Lena"},{"id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"}],"date_updated":"2023-10-16T13:03:56Z","page":"48–58","language":[{"iso":"eng"}],"_id":"46327","user_id":"15504","editor":[{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"full_name":"Preuß, Mike","last_name":"Preuß","first_name":"Mike"},{"full_name":"Takes, Frank","last_name":"Takes","first_name":"Frank"},{"first_name":"Annie","last_name":"Waldherr","full_name":"Waldherr, Annie"}]},{"abstract":[{"lang":"eng","text":"The past decade has been characterized by a strong increase in the use of social media and a continuous growth of public online discussion. With the failure of purely manual moderation, platform operators started searching for semi-automated solutions, where the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques is promising. However, this requires huge financial investments for algorithmic implementations, data collection, and model training, which only big players can afford. To support smaller or medium-sized media enterprises (SME), we developed an integrated comment moderation system as an IT platform. This platform acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating such a platform, however, requires a robust technology stack, integrated workflows and well-defined interfaces between all parties. In this paper, we develop and discuss a suitable IT architecture and present a prototypical implementation."}],"citation":{"mla":"Riehle, Dennis M., et al. “Building an Integrated Comment Moderation System – Towards a Semi-Automatic Moderation Tool.” <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>, edited by Gabriele Meiselwitz, Springer International Publishing, 2020, pp. 71–86.","bibtex":"@inproceedings{Riehle_Niemann_Brunk_Assenmacher_Trautmann_Becker_2020, place={Cham}, title={Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool}, booktitle={Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing}, publisher={Springer International Publishing}, author={Riehle, Dennis M. and Niemann, Marco and Brunk, Jens and Assenmacher, Dennis and Trautmann, Heike and Becker, Jörg}, editor={Meiselwitz, Gabriele}, year={2020}, pages={71–86} }","ama":"Riehle DM, Niemann M, Brunk J, Assenmacher D, Trautmann H, Becker J. Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool. In: Meiselwitz G, ed. <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>. Springer International Publishing; 2020:71–86.","ieee":"D. M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, and J. Becker, “Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool,” in <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>, 2020, pp. 71–86.","apa":"Riehle, D. M., Niemann, M., Brunk, J., Assenmacher, D., Trautmann, H., &#38; Becker, J. (2020). Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool. In G. Meiselwitz (Ed.), <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i> (pp. 71–86). Springer International Publishing.","short":"D.M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, J. Becker, in: G. Meiselwitz (Ed.), Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing, Springer International Publishing, Cham, 2020, pp. 71–86.","chicago":"Riehle, Dennis M., Marco Niemann, Jens Brunk, Dennis Assenmacher, Heike Trautmann, and Jörg Becker. “Building an Integrated Comment Moderation System – Towards a Semi-Automatic Moderation Tool.” In <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>, edited by Gabriele Meiselwitz, 71–86. Cham: Springer International Publishing, 2020."},"publication":"Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Cham","date_created":"2023-08-04T07:38:42Z","date_updated":"2023-10-16T13:04:36Z","publication_identifier":{"isbn":["978-3-030-49576-3"]},"author":[{"last_name":"Riehle","first_name":"Dennis M.","full_name":"Riehle, Dennis M."},{"first_name":"Marco","last_name":"Niemann","full_name":"Niemann, Marco"},{"first_name":"Jens","last_name":"Brunk","full_name":"Brunk, Jens"},{"first_name":"Dennis","last_name":"Assenmacher","full_name":"Assenmacher, Dennis"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Becker, Jörg","first_name":"Jörg","last_name":"Becker"}],"title":"Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool","year":"2020","status":"public","editor":[{"full_name":"Meiselwitz, Gabriele","last_name":"Meiselwitz","first_name":"Gabriele"}],"user_id":"15504","language":[{"iso":"eng"}],"_id":"46329","publisher":"Springer International Publishing","page":"71–86"},{"date_created":"2023-08-04T07:41:37Z","type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"publication":"Social Media + Society","issue":"3","citation":{"ama":"Assenmacher D, Clever L, Frischlich L, Quandt T, Trautmann H, Grimme C. Demystifying Social Bots: On the Intelligence of Automated Social Media Actors. <i>Social Media + Society</i>. 2020;6(3):2056305120939264. doi:<a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>","bibtex":"@article{Assenmacher_Clever_Frischlich_Quandt_Trautmann_Grimme_2020, title={Demystifying Social Bots: On the Intelligence of Automated Social Media Actors}, volume={6}, DOI={<a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>}, number={3}, journal={Social Media + Society}, author={Assenmacher, Dennis and Clever, Lena and Frischlich, Lena and Quandt, Thorsten and Trautmann, Heike and Grimme, Christian}, year={2020}, pages={2056305120939264} }","mla":"Assenmacher, Dennis, et al. “Demystifying Social Bots: On the Intelligence of Automated Social Media Actors.” <i>Social Media + Society</i>, vol. 6, no. 3, 2020, p. 2056305120939264, doi:<a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>.","short":"D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, C. Grimme, Social Media + Society 6 (2020) 2056305120939264.","chicago":"Assenmacher, Dennis, Lena Clever, Lena Frischlich, Thorsten Quandt, Heike Trautmann, and Christian Grimme. “Demystifying Social Bots: On the Intelligence of Automated Social Media Actors.” <i>Social Media + Society</i> 6, no. 3 (2020): 2056305120939264. <a href=\"https://doi.org/10.1177/2056305120939264\">https://doi.org/10.1177/2056305120939264</a>.","apa":"Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., &#38; Grimme, C. (2020). Demystifying Social Bots: On the Intelligence of Automated Social Media Actors. <i>Social Media + Society</i>, <i>6</i>(3), 2056305120939264. <a href=\"https://doi.org/10.1177/2056305120939264\">https://doi.org/10.1177/2056305120939264</a>","ieee":"D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, and C. Grimme, “Demystifying Social Bots: On the Intelligence of Automated Social Media Actors,” <i>Social Media + Society</i>, vol. 6, no. 3, p. 2056305120939264, 2020, doi: <a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>."},"abstract":[{"lang":"eng","text":" Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots. "}],"page":"2056305120939264","_id":"46333","language":[{"iso":"eng"}],"doi":"10.1177/2056305120939264","user_id":"15504","volume":6,"year":"2020","title":"Demystifying Social Bots: On the Intelligence of Automated Social Media Actors","status":"public","author":[{"last_name":"Assenmacher","first_name":"Dennis","full_name":"Assenmacher, Dennis"},{"full_name":"Clever, Lena","last_name":"Clever","first_name":"Lena"},{"first_name":"Lena","last_name":"Frischlich","full_name":"Frischlich, Lena"},{"full_name":"Quandt, Thorsten","last_name":"Quandt","first_name":"Thorsten"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"}],"date_updated":"2023-10-16T13:06:34Z","intvolume":"         6"},{"department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:40:33Z","place":"Canberra, Australia","abstract":[{"text":"Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.","lang":"eng"}],"citation":{"ieee":"V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, and C. Grimme, “Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent,” in <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 2445–2452, doi: <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>.","apa":"Steinhoff, V., Kerschke, P., Aspar, P., Trautmann, H., &#38; Grimme, C. (2020). Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2445–2452. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">https://doi.org/10.1109/SSCI47803.2020.9308259</a>","short":"V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, C. Grimme, in: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 2445–2452.","chicago":"Steinhoff, Vera, Pascal Kerschke, Pelin Aspar, Heike Trautmann, and Christian Grimme. “Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent.” In <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2445–2452. Canberra, Australia, 2020. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">https://doi.org/10.1109/SSCI47803.2020.9308259</a>.","mla":"Steinhoff, Vera, et al. “Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent.” <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 2445–2452, doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>.","bibtex":"@inproceedings{Steinhoff_Kerschke_Aspar_Trautmann_Grimme_2020, place={Canberra, Australia}, title={Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent}, DOI={<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>}, booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Steinhoff, Vera and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Grimme, Christian}, year={2020}, pages={2445–2452} }","ama":"Steinhoff V, Kerschke P, Aspar P, Trautmann H, Grimme C. Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. In: <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2020:2445–2452. doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>"},"publication":"Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)","user_id":"15504","doi":"10.1109/SSCI47803.2020.9308259","language":[{"iso":"eng"}],"_id":"46332","page":"2445–2452","date_updated":"2023-10-16T13:05:49Z","author":[{"first_name":"Vera","last_name":"Steinhoff","full_name":"Steinhoff, Vera"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Aspar, Pelin","last_name":"Aspar","first_name":"Pelin"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"}],"title":"Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent","year":"2020","status":"public"},{"doi":"10.1145/3377930.3390174","language":[{"iso":"eng"}],"series_title":"GECCO ’20","date_updated":"2023-12-13T10:43:41Z","publication_status":"published","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"},{"last_name":"Peng","first_name":"Pan","full_name":"Peng, Pan"},{"first_name":"Dirk","last_name":"Sudholt","full_name":"Sudholt, Dirk"}],"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"title":"More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization","year":"2020","department":[{"_id":"819"}],"type":"conference","keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"date_created":"2023-11-14T15:58:53Z","abstract":[{"lang":"eng","text":"Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\\lambda$}) RLS) lead to an exponential speedup in {$\\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands."}],"extern":"1","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","user_id":"102979","publisher":"Association for Computing Machinery","_id":"48847","page":"1277–1285","status":"public","place":"New York, NY, USA","citation":{"short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1277–1285.","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390174\">https://doi.org/10.1145/3377930.3390174</a>.","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 1277–1285, doi: <a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>.","apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2020). More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1277–1285. <a href=\"https://doi.org/10.1145/3377930.3390174\">https://doi.org/10.1145/3377930.3390174</a>","bibtex":"@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA}, series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2020}, pages={1277–1285}, collection={GECCO ’20} }","ama":"Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:1277–1285. doi:<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>","mla":"Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 1277–1285, doi:<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>."}},{"user_id":"102979","page":"111–124","_id":"48849","publisher":"Springer-Verlag","status":"public","place":"Berlin, Heidelberg","citation":{"mla":"Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 111–124, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>.","bibtex":"@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin, Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag}, author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={111–124} }","ama":"Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies for One-Shot Optimization Tasks. In: <i>Parallel Problem Solving from Nature (PPSN XVI)</i>. Springer-Verlag; 2020:111–124. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>","ieee":"J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling Strategies for One-Shot Optimization Tasks,” in <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 111–124, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>.","apa":"Bossek, J., Doerr, C., Kerschke, P., Neumann, A., &#38; Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 111–124. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">https://doi.org/10.1007/978-3-030-58112-1_8</a>","short":"J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 111–124.","chicago":"Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 111–124. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">https://doi.org/10.1007/978-3-030-58112-1_8</a>."},"doi":"10.1007/978-3-030-58112-1_8","language":[{"iso":"eng"}],"date_updated":"2023-12-13T10:43:53Z","publication_status":"published","title":"Evolving Sampling Strategies for One-Shot Optimization Tasks","year":"2020","author":[{"id":"102979","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"full_name":"Doerr, Carola","last_name":"Doerr","first_name":"Carola"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Neumann","first_name":"Aneta","full_name":"Neumann, Aneta"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"publication_identifier":{"isbn":["978-3-030-58111-4"]},"type":"conference","keyword":["Continuous optimization","Fully parallel search","One-shot optimization","Regression","Surrogate-assisted optimization"],"department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:53Z","abstract":[{"text":"One-shot optimization tasks require to determine the set of solution candidates prior to their evaluation, i.e., without possibility for adaptive sampling. We consider two variants, classic one-shot optimization (where our aim is to find at least one solution of high quality) and one-shot regression (where the goal is to fit a model that resembles the true problem as well as possible). For both tasks it seems intuitive that well-distributed samples should perform better than uniform or grid-based samples, since they show a better coverage of the decision space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy point sets are indeed very commonly used designs for one-shot optimization tasks. We study in this work how well low star discrepancy correlates with performance in one-shot optimization. Our results confirm an advantage of low-discrepancy designs, but also indicate the correlation between discrepancy values and overall performance is rather weak. We then demonstrate that commonly used designs may be far from optimal. More precisely, we evolve 24 very specific designs that each achieve good performance on one of our benchmark problems. Interestingly, we find that these specifically designed samples yield surprisingly good performance across the whole benchmark set. Our results therefore give strong indication that significant performance gains over state-of-the-art one-shot sampling techniques are possible, and that evolutionary algorithms can be an efficient means to evolve these.","lang":"eng"}],"extern":"1","publication":"Parallel Problem Solving from Nature (PPSN XVI)"},{"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","extern":"1","abstract":[{"lang":"eng","text":"Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour."}],"date_created":"2023-11-14T15:58:53Z","keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"type":"conference","department":[{"_id":"819"}],"title":"The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics","year":"2020","author":[{"id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob"},{"full_name":"Casel, Katrin","last_name":"Casel","first_name":"Katrin"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"publication_status":"published","date_updated":"2023-12-13T10:43:33Z","series_title":"GECCO ’20","language":[{"iso":"eng"}],"doi":"10.1145/3377930.3390243","citation":{"short":"J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1286–1294.","chicago":"Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1286–1294. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390243\">https://doi.org/10.1145/3377930.3390243</a>.","ieee":"J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 1286–1294, doi: <a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>.","apa":"Bossek, J., Casel, K., Kerschke, P., &#38; Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1286–1294. <a href=\"https://doi.org/10.1145/3377930.3390243\">https://doi.org/10.1145/3377930.3390243</a>","bibtex":"@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY, USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel, Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294}, collection={GECCO ’20} }","ama":"Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>","mla":"Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 1286–1294, doi:<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>."},"place":"New York, NY, USA","status":"public","page":"1286–1294","_id":"48851","publisher":"Association for Computing Machinery","user_id":"102979"}]