--- _id: '46307' abstract: - lang: eng text: Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection. author: - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Seiler M, Prager RP, Kerschke P, Trautmann H. A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery; 2022:657–665. doi:10.1145/3512290.3528834' apa: Seiler, M., Prager, R. P., Kerschke, P., & Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. Proceedings of the Genetic and Evolutionary Computation Conference, 657–665. https://doi.org/10.1145/3512290.3528834 bibtex: '@inproceedings{Seiler_Prager_Kerschke_Trautmann_2022, place={New York, NY, USA}, title={A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes}, DOI={10.1145/3512290.3528834}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Seiler, Moritz and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={657–665} }' chicago: 'Seiler, Moritz, Raphael Patrick Prager, Pascal Kerschke, and Heike Trautmann. “A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes.” In Proceedings of the Genetic and Evolutionary Computation Conference, 657–665. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528834.' ieee: 'M. Seiler, R. P. Prager, P. Kerschke, and H. Trautmann, “A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2022, pp. 657–665, doi: 10.1145/3512290.3528834.' mla: Seiler, Moritz, et al. “A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2022, pp. 657–665, doi:10.1145/3512290.3528834. short: 'M. Seiler, R.P. Prager, P. Kerschke, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 657–665.' date_created: 2023-08-04T07:15:59Z date_updated: 2024-06-07T07:13:23Z department: - _id: '34' - _id: '819' doi: 10.1145/3512290.3528834 language: - iso: eng page: 657–665 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - '9781450392372' publisher: Association for Computing Machinery status: public title: A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes type: conference user_id: '15504' year: '2022' ... --- _id: '46304' abstract: - lang: eng text: In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models. author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke citation: ama: 'Prager RP, Seiler M, Trautmann H, Kerschke P. Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:3–17. doi:10.1007/978-3-031-14714-2_1' apa: 'Prager, R. P., Seiler, M., Trautmann, H., & Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_1' bibtex: '@inproceedings{Prager_Seiler_Trautmann_Kerschke_2022, place={Cham}, title={Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods}, DOI={10.1007/978-3-031-14714-2_1}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}, year={2022}, pages={3–17} }' chicago: 'Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” In Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 3–17. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_1.' ieee: 'R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 3–17, doi: 10.1007/978-3-031-14714-2_1.' mla: 'Prager, Raphael Patrick, et al. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 3–17, doi:10.1007/978-3-031-14714-2_1.' short: 'R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 3–17.' date_created: 2023-08-04T07:12:33Z date_updated: 2024-06-07T07:13:47Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-031-14714-2_1 editor: - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Anna V. full_name: Kononova, Anna V. last_name: Kononova - first_name: Hernán full_name: Aguirre, Hernán last_name: Aguirre - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Gabriela full_name: Ochoa, Gabriela last_name: Ochoa - first_name: Tea full_name: Tušar, Tea last_name: Tušar language: - iso: eng page: 3–17 place: Cham publication: Parallel Problem Solving from Nature — PPSN XVII publication_identifier: isbn: - 978-3-031-14714-2 publisher: Springer International Publishing status: public title: 'Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods' type: conference user_id: '15504' year: '2022' ... --- _id: '46303' abstract: - lang: eng text: Social media platforms are essential for information sharing and, thus, prone to coordinated dis- and misinformation campaigns. Nevertheless, research in this area is hampered by strict data sharing regulations imposed by the platforms, resulting in a lack of benchmark data. Previous work focused on circumventing these rules by either pseudonymizing the data or sharing fragments. In this work, we will address the benchmarking crisis by presenting a methodology that can be used to create artificial campaigns out of original campaign building blocks. We conduct a proof-of-concept study using the freely available generative language model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly be adapted to an underlying social media stream and evade state-of-the-art campaign detection approaches based on stream clustering. Thus, we not only provide a framework for artificial benchmark generation but also demonstrate the possible adversarial nature of such benchmarks for challenging and advancing current campaign detection methods. author: - first_name: Janina Susanne full_name: Pohl, Janina Susanne last_name: Pohl - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: ama: 'Pohl JS, Assenmacher D, Seiler M, Trautmann H, Grimme C. Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In: the Advancement of Artificial Intelligence (AAAI) Association for, ed. Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM). AAAI Press; 2022:1–10. doi:10.36190/2022.91' apa: Pohl, J. S., Assenmacher, D., Seiler, M., Trautmann, H., & Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM) (pp. 1–10). AAAI Press. https://doi.org/10.36190/2022.91 bibtex: '@inproceedings{Pohl_Assenmacher_Seiler_Trautmann_Grimme_2022, place={Palo Alto, CA, USA}, title={Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches}, DOI={10.36190/2022.91}, booktitle={Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)}, publisher={AAAI Press}, author={Pohl, Janina Susanne and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian}, editor={the Advancement of Artificial Intelligence (AAAI) Association, for}, year={2022}, pages={1–10} }' chicago: 'Pohl, Janina Susanne, Dennis Assenmacher, Moritz Seiler, Heike Trautmann, and Christian Grimme. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” In Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), edited by for the Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA, USA: AAAI Press, 2022. https://doi.org/10.36190/2022.91.' ieee: 'J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,” in Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), 2022, pp. 1–10, doi: 10.36190/2022.91.' mla: Pohl, Janina Susanne, et al. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), edited by for the Advancement of Artificial Intelligence (AAAI) Association, AAAI Press, 2022, pp. 1–10, doi:10.36190/2022.91. short: 'J.S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, C. Grimme, in: for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), AAAI Press, Palo Alto, CA, USA, 2022, pp. 1–10.' date_created: 2023-08-04T07:11:34Z date_updated: 2024-06-07T07:13:35Z department: - _id: '34' - _id: '819' doi: 10.36190/2022.91 editor: - first_name: for full_name: the Advancement of Artificial Intelligence (AAAI) Association, for last_name: the Advancement of Artificial Intelligence (AAAI) Association language: - iso: eng page: 1–10 place: Palo Alto, CA, USA publication: Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM) publisher: AAAI Press status: public title: Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches type: conference user_id: '15504' year: '2022' ... --- _id: '46309' abstract: - lang: eng text: Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field. author: - first_name: Lena full_name: Clever, Lena last_name: Clever - first_name: Janina Susanne full_name: Pohl, Janina Susanne last_name: Pohl - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(8):1–44. doi:10.3390/app12189094' apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(8), 1–44. https://doi.org/10.3390/app12189094' bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094}, number={8}, journal={Applied Sciences}, author={Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={1–44} }' chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature Review.” Applied Sciences 12, no. 8 (2022): 1–44. https://doi.org/10.3390/app12189094.' ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,” Applied Sciences, vol. 12, no. 8, pp. 1–44, 2022, doi: 10.3390/app12189094.' mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” Applied Sciences, vol. 12, no. 8, 2022, pp. 1–44, doi:10.3390/app12189094.' short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 1–44. date_created: 2023-08-04T07:17:23Z date_updated: 2024-06-10T12:02:17Z department: - _id: '34' - _id: '819' doi: 10.3390/app12189094 intvolume: ' 12' issue: '8' language: - iso: eng page: 1–44 publication: Applied Sciences status: public title: 'Process-Oriented Stream Classification Pipeline: A Literature Review' type: journal_article user_id: '15504' volume: 12 year: '2022' ... --- _id: '46302' author: - first_name: J full_name: Heins, J last_name: Heins - first_name: J full_name: Rook, J last_name: Rook - first_name: L full_name: Schäpermeier, L last_name: Schäpermeier - first_name: P full_name: Kerschke, P last_name: Kerschke - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Heins J, Rook J, Schäpermeier L, Kerschke P, Bossek J, Trautmann H. BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In: Rudolph G, Kononova A, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:192–206.' apa: 'Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Springer International Publishing.' bibtex: '@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022, place={Cham}, title={BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Heins, J and Rook, J and Schäpermeier, L and Kerschke, P and Bossek, Jakob and Trautmann, Heike}, editor={Rudolph, G and Kononova, AV and Aguirre, H and Kerschke, P and Ochoa, G and Tušar, T}, year={2022}, pages={192–206} }' chicago: 'Heins, J, J Rook, L Schäpermeier, P Kerschke, Jakob Bossek, and Heike Trautmann. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” In Parallel Problem Solving from Nature — PPSN XVII, edited by G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa, and T Tušar, 192–206. Cham: Springer International Publishing, 2022.' ieee: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann, “BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 192–206.' mla: 'Heins, J., et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” Parallel Problem Solving from Nature — PPSN XVII, edited by G Rudolph et al., Springer International Publishing, 2022, pp. 192–206.' short: 'J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 192–206.' date_created: 2023-08-04T07:10:52Z date_updated: 2024-06-10T12:02:35Z department: - _id: '34' - _id: '819' editor: - first_name: G full_name: Rudolph, G last_name: Rudolph - first_name: AV full_name: Kononova, AV last_name: Kononova - first_name: H full_name: Aguirre, H last_name: Aguirre - first_name: P full_name: Kerschke, P last_name: Kerschke - first_name: G full_name: Ochoa, G last_name: Ochoa - first_name: T full_name: Tušar, T last_name: Tušar language: - iso: eng page: 192–206 place: Cham publication: Parallel Problem Solving from Nature — PPSN XVII publication_identifier: isbn: - 978-3-031-14714-2 publisher: Springer International Publishing status: public title: 'BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems' type: conference user_id: '15504' year: '2022' ... --- _id: '46305' abstract: - lang: eng text: Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space. author: - first_name: J full_name: Rook, J last_name: Rook - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: C full_name: Grimme, C last_name: Grimme citation: ama: 'Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Fieldsend J, Wagner M, eds. Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO ’22. Association for Computing Machinery; 2022:356–359. doi:10.1145/3520304.3528998' apa: Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In J. Fieldsend & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 356–359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998 bibtex: '@inproceedings{Rook_Trautmann_Bossek_Grimme_2022, place={New York, NY, USA}, series={GECCO ’22}, title={On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}, DOI={10.1145/3520304.3528998}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Rook, J and Trautmann, Heike and Bossek, Jakob and Grimme, C}, editor={Fieldsend, J and Wagner, M.}, year={2022}, pages={356–359}, collection={GECCO ’22} }' chicago: 'Rook, J, Heike Trautmann, Jakob Bossek, and C Grimme. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” In Proceedings of the Genetic and Evolutionary Computation Conference Companion, edited by J Fieldsend and M. Wagner, 356–359. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3520304.3528998.' ieee: 'J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 356–359, doi: 10.1145/3520304.3528998.' mla: Rook, J., et al. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, edited by J Fieldsend and M. Wagner, Association for Computing Machinery, 2022, pp. 356–359, doi:10.1145/3520304.3528998. short: 'J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: J. Fieldsend, M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.' date_created: 2023-08-04T07:14:24Z date_updated: 2026-02-19T15:12:35Z department: - _id: '34' - _id: '819' doi: 10.1145/3520304.3528998 editor: - first_name: J full_name: Fieldsend, J last_name: Fieldsend - first_name: M. full_name: Wagner, M. last_name: Wagner language: - iso: eng page: 356–359 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion publication_identifier: isbn: - '9781450392686' publisher: Association for Computing Machinery series_title: GECCO ’22 status: public title: On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems type: conference user_id: '14972' year: '2022' ... --- _id: '46318' abstract: - lang: eng text: 'Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.' author: - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Pelin full_name: Aspar, Pelin last_name: Aspar - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Mike full_name: Preuss, Mike last_name: Preuss - first_name: André H. full_name: Deutz, André H. last_name: Deutz - first_name: Hao full_name: Wang, Hao last_name: Wang - first_name: Michael full_name: Emmerich, Michael last_name: Emmerich citation: ama: 'Grimme C, Kerschke P, Aspar P, et al. Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Computers & Operations Research. 2021;136:105489. doi:https://doi.org/10.1016/j.cor.2021.105489' apa: 'Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A. H., Wang, H., & Emmerich, M. (2021). Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Computers & Operations Research, 136, 105489. https://doi.org/10.1016/j.cor.2021.105489' bibtex: '@article{Grimme_Kerschke_Aspar_Trautmann_Preuss_Deutz_Wang_Emmerich_2021, title={Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization}, volume={136}, DOI={https://doi.org/10.1016/j.cor.2021.105489}, journal={Computers & Operations Research}, author={Grimme, Christian and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André H. and Wang, Hao and Emmerich, Michael}, year={2021}, pages={105489} }' chicago: 'Grimme, Christian, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Mike Preuss, André H. Deutz, Hao Wang, and Michael Emmerich. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.” Computers & Operations Research 136 (2021): 105489. https://doi.org/10.1016/j.cor.2021.105489.' ieee: 'C. Grimme et al., “Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization,” Computers & Operations Research, vol. 136, p. 105489, 2021, doi: https://doi.org/10.1016/j.cor.2021.105489.' mla: 'Grimme, Christian, et al. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.” Computers & Operations Research, vol. 136, 2021, p. 105489, doi:https://doi.org/10.1016/j.cor.2021.105489.' short: C. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A.H. Deutz, H. Wang, M. Emmerich, Computers & Operations Research 136 (2021) 105489. date_created: 2023-08-04T07:28:34Z date_updated: 2023-10-16T12:58:42Z department: - _id: '34' - _id: '819' doi: https://doi.org/10.1016/j.cor.2021.105489 intvolume: ' 136' keyword: - Multimodal optimization - Multi-objective continuous optimization - Landscape analysis - Visualization - Benchmarking - Theory - Algorithms language: - iso: eng page: '105489' publication: Computers & Operations Research publication_identifier: issn: - 0305-0548 status: public title: 'Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization' type: journal_article user_id: '15504' volume: 136 year: '2021' ... --- _id: '46311' abstract: - lang: eng text: "In this work we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems by first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective space, and subsequently exploiting local structures and ridges of the resulting landscapes. Our study particularly focusses on the sensitivity of this multiobjectivization approach w.r.t. (i) the parametrization of the artificial second objective, as well as (ii) the position of the initial starting points in the search space.\r\n\r\nAs SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient and Nelder-Mead local search (as optimizers in the respective module) and compare the performance of the resulting hybrid local search to their original single-objective counterparts. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Combined with more sophisticated local search and metaheuristics this may help in solving highly multimodal optimization problems in future." author: - first_name: Pelin full_name: Aspar, Pelin last_name: Aspar - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Vera full_name: Steinhoff, Vera last_name: Steinhoff - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: ama: 'Aspar P, Kerschke P, Steinhoff V, Trautmann H, Grimme C. Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In: et al. Ishibuchi H, ed. Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. Springer; 2021:311–322. doi:10.1007/978-3-030-72062-9_25' apa: 'Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., & Grimme, C. (2021). Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In H. et al. Ishibuchi (Ed.), Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 311–322). Springer. https://doi.org/10.1007/978-3-030-72062-9_25' bibtex: '@inproceedings{Aspar_Kerschke_Steinhoff_Trautmann_Grimme_2021, place={Heidelberg, Berlin}, title={Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization}, DOI={10.1007/978-3-030-72062-9_25}, booktitle={Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings}, publisher={Springer}, author={Aspar, Pelin and Kerschke, Pascal and Steinhoff, Vera and Trautmann, Heike and Grimme, Christian}, editor={et al. Ishibuchi, H.}, year={2021}, pages={311–322} }' chicago: 'Aspar, Pelin, Pascal Kerschke, Vera Steinhoff, Heike Trautmann, and Christian Grimme. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.” In Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, edited by H. et al. Ishibuchi, 311–322. Heidelberg, Berlin: Springer, 2021. https://doi.org/10.1007/978-3-030-72062-9_25.' ieee: 'P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, and C. Grimme, “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization,” in Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, 2021, pp. 311–322, doi: 10.1007/978-3-030-72062-9_25.' mla: 'Aspar, Pelin, et al. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.” Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, edited by H. et al. Ishibuchi, Springer, 2021, pp. 311–322, doi:10.1007/978-3-030-72062-9_25.' short: 'P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, C. Grimme, in: H. et al. Ishibuchi (Ed.), Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, Springer, Heidelberg, Berlin, 2021, pp. 311–322.' date_created: 2023-08-04T07:21:17Z date_updated: 2023-10-16T12:54:29Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-72062-9_25 editor: - first_name: H. full_name: et al. Ishibuchi, H. last_name: et al. Ishibuchi language: - iso: eng page: 311–322 place: Heidelberg, Berlin publication: 'Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings' publisher: Springer status: public title: 'Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization' type: conference user_id: '15504' year: '2021' ... --- _id: '46317' abstract: - lang: eng text: 'One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity themes: Crime & conflict, Jobs, Attention, and Wellbeing. The outcomes of the workshop discussions identify three attributes of humanity that need preservation: a critique of the design and application of AI, and the intelligent machines it can create; human involvement in the loop of intelligent machine decision-making processes; and the ability to interpret and explain intelligent machine decision-making processes. The article provides an agenda for future AI and humanity research.' author: - first_name: Crispin full_name: Coombs, Crispin last_name: Coombs - first_name: Patrick full_name: Stacey, Patrick last_name: Stacey - first_name: Peter full_name: Kawalek, Peter last_name: Kawalek - first_name: Boyka full_name: Simeonova, Boyka last_name: Simeonova - first_name: Jörg full_name: Becker, Jörg last_name: Becker - first_name: Katrin full_name: Bergener, Katrin last_name: Bergener - first_name: João Álvaro full_name: Carvalho, João Álvaro last_name: Carvalho - first_name: Marcelo full_name: Fantinato, Marcelo last_name: Fantinato - first_name: Niels F. full_name: Garmann-Johnsen, Niels F. last_name: Garmann-Johnsen - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Armin full_name: Stein, Armin last_name: Stein - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: Coombs C, Stacey P, Kawalek P, et al. What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. International Journal of Information Management. 2021;58. doi:10.1016/j.ijinfomgt.2021.102311 apa: Coombs, C., Stacey, P., Kawalek, P., Simeonova, B., Becker, J., Bergener, K., Carvalho, J. Á., Fantinato, M., Garmann-Johnsen, N. F., Grimme, C., Stein, A., & Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. International Journal of Information Management, 58. https://doi.org/10.1016/j.ijinfomgt.2021.102311 bibtex: '@article{Coombs_Stacey_Kawalek_Simeonova_Becker_Bergener_Carvalho_Fantinato_Garmann-Johnsen_Grimme_et al._2021, title={What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective}, volume={58}, DOI={10.1016/j.ijinfomgt.2021.102311}, journal={International Journal of Information Management}, author={Coombs, Crispin and Stacey, Patrick and Kawalek, Peter and Simeonova, Boyka and Becker, Jörg and Bergener, Katrin and Carvalho, João Álvaro and Fantinato, Marcelo and Garmann-Johnsen, Niels F. and Grimme, Christian and et al.}, year={2021} }' chicago: Coombs, Crispin, Patrick Stacey, Peter Kawalek, Boyka Simeonova, Jörg Becker, Katrin Bergener, João Álvaro Carvalho, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” International Journal of Information Management 58 (2021). https://doi.org/10.1016/j.ijinfomgt.2021.102311. ieee: 'C. Coombs et al., “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective,” International Journal of Information Management, vol. 58, 2021, doi: 10.1016/j.ijinfomgt.2021.102311.' mla: Coombs, Crispin, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” International Journal of Information Management, vol. 58, 2021, doi:10.1016/j.ijinfomgt.2021.102311. short: C. Coombs, P. Stacey, P. Kawalek, B. Simeonova, J. Becker, K. Bergener, J.Á. Carvalho, M. Fantinato, N.F. Garmann-Johnsen, C. Grimme, A. Stein, H. Trautmann, International Journal of Information Management 58 (2021). date_created: 2023-08-04T07:27:14Z date_updated: 2023-10-16T12:58:02Z department: - _id: '34' - _id: '819' doi: 10.1016/j.ijinfomgt.2021.102311 intvolume: ' 58' language: - iso: eng publication: International Journal of Information Management status: public title: What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective type: journal_article user_id: '15504' volume: 58 year: '2021' ... --- _id: '46315' abstract: - lang: eng 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. author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke citation: ama: 'Prager RP, Seiler M, Trautmann H, Kerschke P. Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). ; 2021:1-8. doi:10.1109/SSCI50451.2021.9660174' apa: Prager, R. P., Seiler, M., Trautmann, H., & Kerschke, P. (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI50451.2021.9660174 bibtex: '@inproceedings{Prager_Seiler_Trautmann_Kerschke_2021, title={Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization}, DOI={10.1109/SSCI50451.2021.9660174}, 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} }' 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 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8, 2021. https://doi.org/10.1109/SSCI50451.2021.9660174. 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 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8, doi: 10.1109/SSCI50451.2021.9660174.' mla: Prager, Raphael Patrick, et al. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8, doi:10.1109/SSCI50451.2021.9660174. short: 'R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8.' date_created: 2023-08-04T07:25:08Z date_updated: 2024-06-07T07:12:28Z department: - _id: '34' - _id: '819' doi: 10.1109/SSCI50451.2021.9660174 language: - iso: eng page: 1-8 publication: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) status: public title: Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization type: conference user_id: '15504' year: '2021' ... --- _id: '46312' abstract: - lang: eng 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. author: - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Marco full_name: Niemann, Marco last_name: Niemann - first_name: Kilian full_name: Müller, Kilian last_name: Müller - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Dennis M. full_name: Riehle, Dennis M. last_name: Riehle - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Assenmacher D, Niemann M, Müller K, Seiler M, Riehle DM, Trautmann H. RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021). ; 2021:1–14.' apa: 'Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., & Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 1–14.' bibtex: '@inproceedings{Assenmacher_Niemann_Müller_Seiler_Riehle_Trautmann_2021, place={Virtual Event}, title={RP-Mod & 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} }' chicago: 'Assenmacher, Dennis, Marco Niemann, Kilian Müller, Moritz Seiler, Dennis M. Riehle, and Heike Trautmann. “RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 1–14. Virtual Event, 2021.' ieee: 'D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann, “RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 2021, pp. 1–14.' mla: 'Assenmacher, Dennis, et al. “RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 2021, pp. 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.' date_created: 2023-08-04T07:22:59Z date_updated: 2024-06-07T07:13:04Z department: - _id: '34' - _id: '819' language: - iso: eng page: 1–14 place: Virtual Event publication: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021) status: public title: 'RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets' type: conference user_id: '15504' year: '2021' ... --- _id: '46313' abstract: - lang: eng 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.' author: - first_name: Jonathan full_name: Heins, Jonathan last_name: Heins - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Janina full_name: Pohl, Janina last_name: Pohl - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke citation: 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. Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI). Association for Computing Machinery; 2021:1–15. doi:10.1145/3450218.3477308' apa: Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3450218.3477308 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={10.1145/3450218.3477308}, 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} }' 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 Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI), edited by for Computing Machinery Association, 1–15. Dornbirn, Austria: Association for Computing Machinery, 2021. https://doi.org/10.1145/3450218.3477308.' 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 Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI), 2021, pp. 1–15, doi: 10.1145/3450218.3477308.' mla: Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI), edited by for Computing Machinery Association, Association for Computing Machinery, 2021, pp. 1–15, doi:10.1145/3450218.3477308. 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.' date_created: 2023-08-04T07:23:57Z date_updated: 2024-06-10T11:57:04Z department: - _id: '34' - _id: '819' doi: 10.1145/3450218.3477308 editor: - first_name: for full_name: Computing Machinery Association, for last_name: Computing Machinery Association language: - iso: eng page: 1–15 place: Dornbirn, Austria publication: Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) publisher: Association for Computing Machinery status: public title: On the Potential of Normalized TSP Features for Automated Algorithm Selection type: conference user_id: '15504' year: '2021' ... --- _id: '46319' 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. author: - first_name: D full_name: Assenmacher, D last_name: Assenmacher - first_name: L full_name: Adam, L last_name: Adam - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: C full_name: Grimme, C last_name: Grimme citation: ama: 'Assenmacher D, Adam L, Trautmann H, Grimme C. Towards Real-Time and Unsupervised Campaign Detection in Social Media. In: Proceedings of the Florida Artificial Intelligence Research Society Conference. ; 2020.' apa: Assenmacher, D., Adam, L., Trautmann, H., & Grimme, C. (2020). Towards Real-Time and Unsupervised Campaign Detection in Social Media. Proceedings of the Florida Artificial Intelligence Research Society Conference. 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} }' chicago: Assenmacher, D, L Adam, Heike Trautmann, and C Grimme. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” In Proceedings of the Florida Artificial Intelligence Research Society Conference. Florida, USA, 2020. ieee: D. Assenmacher, L. Adam, H. Trautmann, and C. Grimme, “Towards Real-Time and Unsupervised Campaign Detection in Social Media,” 2020. mla: Assenmacher, D., et al. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” Proceedings of the Florida Artificial Intelligence Research Society Conference, 2020. short: 'D. Assenmacher, L. Adam, H. Trautmann, C. Grimme, in: Proceedings of the Florida Artificial Intelligence Research Society Conference, Florida, USA, 2020.' date_created: 2023-08-04T07:29:36Z date_updated: 2023-10-16T12:59:10Z department: - _id: '34' - _id: '819' language: - iso: eng place: Florida, USA publication: Proceedings of the Florida Artificial Intelligence Research Society Conference status: public title: Towards Real-Time and Unsupervised Campaign Detection in Social Media type: conference user_id: '15504' year: '2020' ... --- _id: '46328' abstract: - lang: eng 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. author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Hao full_name: Wang, Hao last_name: Wang - first_name: Thomas H. W. full_name: Bäck, Thomas H. W. last_name: Bäck - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke citation: 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: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI). ; 2020:996–1003. doi:10.1109/SSCI47803.2020.9308510' apa: Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., & Kerschke, P. (2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), 996–1003. https://doi.org/10.1109/SSCI47803.2020.9308510 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={10.1109/SSCI47803.2020.9308510}, 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} }' 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 Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), 996–1003. Canberra, Australia, 2020. https://doi.org/10.1109/SSCI47803.2020.9308510. 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 Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp. 996–1003, doi: 10.1109/SSCI47803.2020.9308510.' mla: Prager, Raphael Patrick, et al. “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp. 996–1003, doi:10.1109/SSCI47803.2020.9308510. 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.' date_created: 2023-08-04T07:37:30Z date_updated: 2023-10-16T13:04:15Z department: - _id: '34' - _id: '819' doi: 10.1109/SSCI47803.2020.9308510 language: - iso: eng page: 996–1003 place: Canberra, Australia publication: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI) status: public title: Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis type: conference user_id: '15504' year: '2020' ... --- _id: '46320' 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. author: - first_name: D full_name: Assenmacher, D last_name: Assenmacher - first_name: L full_name: Clever, L last_name: Clever - first_name: JS full_name: Pohl, JS last_name: Pohl - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: C full_name: Grimme, C last_name: Grimme citation: 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. 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; 2020:201–214. doi:10.1007/978-3-030-49570-1_14' apa: 'Assenmacher, D., Clever, L., Pohl, J., Trautmann, H., & Grimme, C. (2020). A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. 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 (pp. 201–214). Springer International Publishing. https://doi.org/10.1007/978-3-030-49570-1_14' bibtex: '@inproceedings{Assenmacher_Clever_Pohl_Trautmann_Grimme_2020, place={Cham}, title={A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media}, DOI={10.1007/978-3-030-49570-1_14}, 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} }' chicago: 'Assenmacher, D, L Clever, JS Pohl, Heike Trautmann, and C Grimme. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” In Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis, edited by G Meiselwitz, 201–214. Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-49570-1_14.' ieee: 'D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, and C. Grimme, “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media,” in Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis, 2020, pp. 201–214, doi: 10.1007/978-3-030-49570-1_14.' mla: 'Assenmacher, D., et al. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis, edited by G Meiselwitz, Springer International Publishing, 2020, pp. 201–214, doi:10.1007/978-3-030-49570-1_14.' 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.' date_created: 2023-08-04T07:30:29Z date_updated: 2023-10-16T12:59:28Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-49570-1_14 editor: - first_name: G full_name: Meiselwitz, G last_name: Meiselwitz language: - iso: eng page: 201–214 place: Cham 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' publication_identifier: isbn: - 978-3-030-49570-1 publisher: Springer International Publishing status: public title: A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media type: conference user_id: '15504' year: '2020' ... --- _id: '46325' abstract: - lang: eng 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. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Albert full_name: Bifet, Albert last_name: Bifet - first_name: Bernhard full_name: Pfahringer, Bernhard last_name: Pfahringer citation: ama: 'Carnein M, Trautmann H, Bifet A, Pfahringer B. Towards Automated Configuration of Stream Clustering Algorithms. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19). ; 2020:137–143. doi:10.1007/978-3-030-43823-4_12' apa: Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). Towards Automated Configuration of Stream Clustering Algorithms. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), 137–143. https://doi.org/10.1007/978-3-030-43823-4_12 bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg, Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms}, DOI={10.1007/978-3-030-43823-4_12}, 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} }' chicago: Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “Towards Automated Configuration of Stream Clustering Algorithms.” In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), 137–143. Würzburg, Germany, 2020. https://doi.org/10.1007/978-3-030-43823-4_12. ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated Configuration of Stream Clustering Algorithms,” in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), 2020, pp. 137–143, doi: 10.1007/978-3-030-43823-4_12.' mla: Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering Algorithms.” Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), 2020, pp. 137–143, doi:10.1007/978-3-030-43823-4_12. 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.' date_created: 2023-08-04T07:35:24Z date_updated: 2023-10-16T13:03:15Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-43823-4_12 language: - iso: eng page: 137–143 place: Würzburg, Germany publication: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19) publication_identifier: isbn: - 978-3-030-43823-4 status: public title: Towards Automated Configuration of Stream Clustering Algorithms type: conference user_id: '15504' year: '2020' ... --- _id: '46321' 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. author: - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Lena full_name: Frischlich , Lena last_name: 'Frischlich ' - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Lena full_name: Adam, Lena last_name: Adam citation: 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. Disinformation in Open Online Media. Lecture Notes in Computer Science. Springer; 2020:101–114.' apa: 'Assenmacher, D., Frischlich , L., Trautmann, H., Grimme, C., & Adam, L. (2020). Inside the tool set of automation: Free social bot code revisited. In C. Grimme, M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in open online media (pp. 101–114). Springer.' 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} }' chicago: 'Assenmacher, Dennis, Lena Frischlich , Heike Trautmann, Christian Grimme, and Lena Adam. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” In Disinformation in Open Online Media, edited by Christian Grimme, Mike Preuß, Frank Takes, and Annie Waldherr, 101–114. Lecture Notes in Computer Science. Wiesbaden: Springer, 2020.' ieee: 'D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, and L. Adam, “Inside the tool set of automation: Free social bot code revisited,” in Disinformation in open online media, 2020, pp. 101–114.' mla: 'Assenmacher, Dennis, et al. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” Disinformation in Open Online Media, edited by Christian Grimme et al., Springer, 2020, pp. 101–114.' 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.' date_created: 2023-08-04T07:31:13Z date_updated: 2023-10-16T13:00:15Z department: - _id: '34' - _id: '819' editor: - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Mike full_name: Preuß, Mike last_name: Preuß - first_name: Frank full_name: Takes, Frank last_name: Takes - first_name: Annie full_name: Waldherr, Annie last_name: Waldherr language: - iso: eng page: 101–114 place: Wiesbaden publication: Disinformation in open online media publisher: Springer series_title: Lecture Notes in Computer Science status: public title: 'Inside the tool set of automation: Free social bot code revisited' type: conference user_id: '15504' year: '2020' ... --- _id: '46326' abstract: - lang: eng 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. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Albert full_name: Bifet, Albert last_name: Bifet - first_name: Bernhard full_name: Pfahringer, Bernhard last_name: Pfahringer citation: ama: 'Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. In: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020). ; 2020:80–95. doi:10.1007/978-3-030-53552-0_10' apa: 'Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020), 80–95. https://doi.org/10.1007/978-3-030-53552-0_10' bibtex: '@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Athens, Greece}, title={confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms}, DOI={10.1007/978-3-030-53552-0_10}, 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} }' chicago: 'Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” In Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020), 80–95. Athens, Greece, 2020. https://doi.org/10.1007/978-3-030-53552-0_10.' ieee: 'M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms,” in Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020), 2020, pp. 80–95, doi: 10.1007/978-3-030-53552-0_10.' mla: 'Carnein, Matthias, et al. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020), 2020, pp. 80–95, doi:10.1007/978-3-030-53552-0_10.' 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.' date_created: 2023-08-04T07:36:03Z date_updated: 2023-10-16T13:03:36Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-53552-0_10 language: - iso: eng page: 80–95 place: Athens, Greece publication: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020) status: public title: 'confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms' type: conference user_id: '15504' year: '2020' ... --- _id: '46327' 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. author: - first_name: Clever full_name: Lena, Clever last_name: Lena - first_name: Lena full_name: Frischlich, Lena last_name: Frischlich - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: 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. Disinformation in Open Online Media. ; 2020:48–58.' apa: Lena, C., Frischlich, L., Trautmann, H., & Grimme, C. (2020). Automated detection of nostalgic text in the context of societal pessimism. In C. Grimme, M. Preuß, F. Takes, & A. Waldherr (Eds.), Disinformation in open online media (pp. 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} }' chicago: Lena, Clever, Lena Frischlich, Heike Trautmann, and Christian Grimme. “Automated Detection of Nostalgic Text in the Context of Societal Pessimism.” In Disinformation in Open Online Media, edited by Christian Grimme, Mike Preuß, Frank Takes, and Annie Waldherr, 48–58. Hamburg, Deutschland, 2020. ieee: C. Lena, L. Frischlich, H. Trautmann, and C. Grimme, “Automated detection of nostalgic text in the context of societal pessimism,” in Disinformation in open online media, 2020, pp. 48–58. mla: Lena, Clever, et al. “Automated Detection of Nostalgic Text in the Context of Societal Pessimism.” Disinformation in Open Online Media, edited by Christian Grimme et al., 2020, pp. 48–58. 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.' date_created: 2023-08-04T07:36:43Z date_updated: 2023-10-16T13:03:56Z department: - _id: '34' - _id: '819' editor: - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Mike full_name: Preuß, Mike last_name: Preuß - first_name: Frank full_name: Takes, Frank last_name: Takes - first_name: Annie full_name: Waldherr, Annie last_name: Waldherr language: - iso: eng page: 48–58 place: Hamburg, Deutschland publication: Disinformation in open online media status: public title: Automated detection of nostalgic text in the context of societal pessimism type: conference user_id: '15504' year: '2020' ... --- _id: '46329' 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. author: - first_name: Dennis M. full_name: Riehle, Dennis M. last_name: Riehle - first_name: Marco full_name: Niemann, Marco last_name: Niemann - first_name: Jens full_name: Brunk, Jens last_name: Brunk - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Jörg full_name: Becker, Jörg last_name: Becker citation: 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. Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing. Springer International Publishing; 2020:71–86.' apa: Riehle, D. M., Niemann, M., Brunk, J., Assenmacher, D., Trautmann, H., & Becker, J. (2020). Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool. In G. Meiselwitz (Ed.), Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing (pp. 71–86). Springer International Publishing. 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} }' 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 Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing, edited by Gabriele Meiselwitz, 71–86. Cham: Springer International Publishing, 2020.' 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 Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing, 2020, pp. 71–86. mla: Riehle, Dennis M., et al. “Building an Integrated Comment Moderation System – Towards a Semi-Automatic Moderation Tool.” Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing, edited by Gabriele Meiselwitz, Springer International Publishing, 2020, pp. 71–86. 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.' date_created: 2023-08-04T07:38:42Z date_updated: 2023-10-16T13:04:36Z department: - _id: '34' - _id: '819' editor: - first_name: Gabriele full_name: Meiselwitz, Gabriele last_name: Meiselwitz language: - iso: eng page: 71–86 place: Cham publication: Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing publication_identifier: isbn: - 978-3-030-49576-3 publisher: Springer International Publishing status: public title: Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool type: conference user_id: '15504' year: '2020' ...