[{"publication_status":"published","publication_identifier":{"isbn":["9781728157306"]},"year":"2021","place":"Valencia, Spain ","citation":{"ieee":"T. Sander <i>et al.</i>, “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction,” in <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, Valencia, Spain , 2021.","chicago":"Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneis, Christian Hedayat, Harald Kuhn, and Franz-Barthold Gockel. “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction.” In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE, 2021. <a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">https://doi.org/10.1109/icit46573.2021.9453646</a>.","ama":"Sander T, Lange S, Hilleringmann U, et al. Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction. In: <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>","apa":"Sander, T., Lange, S., Hilleringmann, U., Geneis, V., Hedayat, C., Kuhn, H., &#38; Gockel, F.-B. (2021). Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction. In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE. <a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">https://doi.org/10.1109/icit46573.2021.9453646</a>","bibtex":"@inproceedings{Sander_Lange_Hilleringmann_Geneis_Hedayat_Kuhn_Gockel_2021, place={Valencia, Spain }, title={Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction}, DOI={<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>}, booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)}, publisher={IEEE}, author={Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold}, year={2021} }","mla":"Sander, Tom, et al. “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction.” <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>.","short":"T. Sander, S. Lange, U. Hilleringmann, V. Geneis, C. Hedayat, H. Kuhn, F.-B. Gockel, in: 22nd IEEE International Conference on Industrial Technology (ICIT), IEEE, Valencia, Spain , 2021."},"publisher":"IEEE","date_updated":"2022-01-06T06:55:33Z","date_created":"2021-06-20T23:32:11Z","author":[{"last_name":"Sander","full_name":"Sander, Tom","first_name":"Tom"},{"first_name":"Sven","id":"38240","full_name":"Lange, Sven","last_name":"Lange"},{"first_name":"Ulrich","full_name":"Hilleringmann, Ulrich","last_name":"Hilleringmann"},{"first_name":"Volker","last_name":"Geneis","full_name":"Geneis, Volker"},{"first_name":"Christian","full_name":"Hedayat, Christian","last_name":"Hedayat"},{"full_name":"Kuhn, Harald","last_name":"Kuhn","first_name":"Harald"},{"last_name":"Gockel","full_name":"Gockel, Franz-Barthold","first_name":"Franz-Barthold"}],"title":"Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9453646"}],"conference":{"end_date":"2021-03-12","location":"Valencia, Spain ","name":"22nd IEEE International Conference on Industrial Technology (ICIT)","start_date":"2021-03-10"},"doi":"10.1109/icit46573.2021.9453646","type":"conference","publication":"22nd IEEE International Conference on Industrial Technology (ICIT)","abstract":[{"lang":"eng","text":"During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters."}],"status":"public","_id":"22481","user_id":"38240","department":[{"_id":"59"},{"_id":"485"}],"keyword":["Image Processing","Defect Detection","wooden surfaces","Machine Learning","Neural Networks"],"language":[{"iso":"eng"}]},{"status":"public","file":[{"date_updated":"2023-01-10T15:07:03Z","date_created":"2023-01-10T15:07:03Z","creator":"stschn","file_size":133340,"file_name":"main.pdf","file_id":"35890","access_level":"open_access","content_type":"application/pdf","relation":"main_file"}],"abstract":[{"text":"Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.","lang":"eng"}],"type":"working_paper","file_date_updated":"2023-01-10T15:07:03Z","language":[{"iso":"eng"}],"keyword":["nfv","coordination","machine learning","reinforcement learning","phd","digest"],"ddc":["004"],"department":[{"_id":"75"}],"user_id":"35343","_id":"35889","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"citation":{"short":"S.B. Schneider, Conventional and Machine Learning Approaches for Network and Service Coordination, 2021.","bibtex":"@book{Schneider_2021, title={Conventional and Machine Learning Approaches for Network and Service Coordination}, author={Schneider, Stefan Balthasar}, year={2021} }","mla":"Schneider, Stefan Balthasar. <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>. 2021.","apa":"Schneider, S. B. (2021). <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>.","ieee":"S. B. Schneider, <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>. 2021.","chicago":"Schneider, Stefan Balthasar. <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>, 2021.","ama":"Schneider SB. <i>Conventional and Machine Learning Approaches for Network and Service Coordination</i>.; 2021."},"year":"2021","has_accepted_license":"1","title":"Conventional and Machine Learning Approaches for Network and Service Coordination","author":[{"id":"35343","full_name":"Schneider, Stefan Balthasar","orcid":"0000-0001-8210-4011","last_name":"Schneider","first_name":"Stefan Balthasar"}],"date_created":"2023-01-10T15:08:50Z","date_updated":"2023-01-10T15:09:05Z","oa":"1"},{"publication":"21st Koli Calling International Conference on Computing Education Research","type":"conference","status":"public","abstract":[{"lang":"eng","text":" Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts."}],"department":[{"_id":"67"}],"series_title":"Koli Calling '21","user_id":"58041","_id":"27491","language":[{"iso":"eng"}],"keyword":["data awareness","machine learning","data science education","data-driven digital artifacts","artificial intelligence"],"publication_identifier":{"isbn":["9781450384889"]},"quality_controlled":"1","citation":{"ama":"Höper L. Developing and Evaluating the Concept Data Awareness for K12 Computing Education. In: <i>21st Koli Calling International Conference on Computing Education Research</i>. Koli Calling ’21. Association for Computing Machinery; 2021. doi:<a href=\"https://doi.org/10.1145/3488042.3490509\">10.1145/3488042.3490509</a>","ieee":"L. Höper, “Developing and Evaluating the Concept Data Awareness for K12 Computing Education,” 2021, doi: <a href=\"https://doi.org/10.1145/3488042.3490509\">10.1145/3488042.3490509</a>.","chicago":"Höper, Lukas. “Developing and Evaluating the Concept Data Awareness for K12 Computing Education.” In <i>21st Koli Calling International Conference on Computing Education Research</i>. Koli Calling ’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3488042.3490509\">https://doi.org/10.1145/3488042.3490509</a>.","mla":"Höper, Lukas. “Developing and Evaluating the Concept Data Awareness for K12 Computing Education.” <i>21st Koli Calling International Conference on Computing Education Research</i>, Association for Computing Machinery, 2021, doi:<a href=\"https://doi.org/10.1145/3488042.3490509\">10.1145/3488042.3490509</a>.","short":"L. Höper, in: 21st Koli Calling International Conference on Computing Education Research, Association for Computing Machinery, New York, NY, USA, 2021.","bibtex":"@inproceedings{Höper_2021, place={New York, NY, USA}, series={Koli Calling ’21}, title={Developing and Evaluating the Concept Data Awareness for K12 Computing Education}, DOI={<a href=\"https://doi.org/10.1145/3488042.3490509\">10.1145/3488042.3490509</a>}, booktitle={21st Koli Calling International Conference on Computing Education Research}, publisher={Association for Computing Machinery}, author={Höper, Lukas}, year={2021}, collection={Koli Calling ’21} }","apa":"Höper, L. (2021). Developing and Evaluating the Concept Data Awareness for K12 Computing Education. <i>21st Koli Calling International Conference on Computing Education Research</i>. <a href=\"https://doi.org/10.1145/3488042.3490509\">https://doi.org/10.1145/3488042.3490509</a>"},"place":"New York, NY, USA","year":"2021","author":[{"first_name":"Lukas","last_name":"Höper","id":"58041","full_name":"Höper, Lukas"}],"date_created":"2021-11-16T07:59:49Z","date_updated":"2024-09-16T08:32:39Z","publisher":"Association for Computing Machinery","doi":"10.1145/3488042.3490509","title":"Developing and Evaluating the Concept Data Awareness for K12 Computing Education"},{"abstract":[{"text":"Artificial intelligence (AI) has the potential for far-reaching – in our opinion – irreversible changes.\r\nThey range from effects on the individual and society to new societal and social issues. The question arises\r\nas to how students can learn the basic functioning of AI systems, what areas of life and society are affected\r\nby these and – most important – how their own lives are affected by these changes. Therefore, we are developing and evaluating school materials for the German ”Science Year AI”. It can be used for students of all\r\nschool types from the seventh grade upwards and will be distributed to about 2000 schools in autumn with\r\nthe support of the Federal Ministry of Education and Research. The material deals with the following aspects\r\nof AI: Discussing everyday experiences with AI, how does machine learning work, historical development\r\nof AI concepts, difference between man and machine, future distribution of roles between man and machine,\r\nin which AI world do we want to live and how much AI would we like to have in our lives. Through an\r\naccompanying evaluation, high quality of the technical content and didactic preparation is achieved in order\r\nto guarantee the long-term applicability in the teaching context in the different age groups and school types.\r\nIn this paper, we describe the current state of the material development, the challenges arising, and the results\r\nof tests with different classes to date. We also present first ideas for evaluating the results.","lang":"eng"}],"publication":"ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings","keyword":["Artificial Intelligence","Machine Learning","Teaching Material","Societal Aspects","Ethics. Social Aspects","Science Year","Simulation Game"],"language":[{"iso":"eng"}],"year":"2019","quality_controlled":"1","title":"Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material","date_created":"2019-12-16T17:50:08Z","editor":[{"last_name":"Jasutė","full_name":"Jasutė, Eglė","first_name":"Eglė"},{"first_name":"Sergei","last_name":"Pozdniakov","full_name":"Pozdniakov, Sergei"}],"status":"public","type":"conference","_id":"15332","department":[{"_id":"67"}],"user_id":"32312","page":"65 - 73","intvolume":"        12","citation":{"ama":"Schlichtig M, Opel SA, Budde L, Schulte C. Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material. In: Jasutė E, Pozdniakov S, eds. <i>ISSEP 2019 - 12th International Conference on Informatics in Schools: Situation, Evaluation and Perspectives, Local Proceedings</i>. Vol 12. ; 2019:65-73.","chicago":"Schlichtig, Michael, Simone Anna Opel, Lea Budde, and Carsten Schulte. “Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material.” In <i>ISSEP 2019 - 12th International Conference on Informatics in Schools: Situation, Evaluation and Perspectives, Local Proceedings</i>, edited by Eglė Jasutė and Sergei Pozdniakov, 12:65–73, 2019.","ieee":"M. Schlichtig, S. A. Opel, L. Budde, and C. Schulte, “Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material,” in <i>ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings</i>, Lanarca, 2019, vol. 12, pp. 65–73.","mla":"Schlichtig, Michael, et al. “Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material.” <i>ISSEP 2019 - 12th International Conference on Informatics in Schools: Situation, Evaluation and Perspectives, Local Proceedings</i>, edited by Eglė Jasutė and Sergei Pozdniakov, vol. 12, 2019, pp. 65–73.","bibtex":"@inproceedings{Schlichtig_Opel_Budde_Schulte_2019, title={Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material}, volume={12}, booktitle={ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings}, author={Schlichtig, Michael and Opel, Simone Anna and Budde, Lea and Schulte, Carsten}, editor={Jasutė, Eglė and Pozdniakov, Sergei}, year={2019}, pages={65–73} }","short":"M. Schlichtig, S.A. Opel, L. Budde, C. Schulte, in: E. Jasutė, S. Pozdniakov (Eds.), ISSEP 2019 - 12th International Conference on Informatics in Schools: Situation, Evaluation and Perspectives, Local Proceedings, 2019, pp. 65–73.","apa":"Schlichtig, M., Opel, S. A., Budde, L., &#38; Schulte, C. (2019). Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material. In E. Jasutė &#38; S. Pozdniakov (Eds.), <i>ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings</i> (Vol. 12, pp. 65–73)."},"publication_identifier":{"isbn":["978-9925-553-27-3"]},"publication_status":"published","conference":{"start_date":"2019-11-18","name":"ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives","location":"Lanarca","end_date":"2019-11-20"},"main_file_link":[{"url":"http://cyprusconferences.org/issep2019/wp-content/uploads/2019/10/LocalISSEP-v5.pdf"}],"date_updated":"2022-07-26T11:41:41Z","volume":12,"author":[{"first_name":"Michael","last_name":"Schlichtig","orcid":"0000-0001-6600-6171","id":"32312","full_name":"Schlichtig, Michael"},{"last_name":"Opel","full_name":"Opel, Simone Anna","id":"72932","first_name":"Simone Anna"},{"last_name":"Budde","full_name":"Budde, Lea","id":"32443","first_name":"Lea"},{"full_name":"Schulte, Carsten","id":"60311","last_name":"Schulte","first_name":"Carsten"}]},{"title":"OpenML: An R Package to Connect to the Machine Learning Platform OpenML","doi":"10.1007/s00180-017-0742-2","date_updated":"2023-12-13T10:51:17Z","volume":34,"date_created":"2023-11-14T15:58:57Z","author":[{"last_name":"Casalicchio","full_name":"Casalicchio, Giuseppe","first_name":"Giuseppe"},{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"first_name":"Michel","last_name":"Lang","full_name":"Lang, Michel"},{"last_name":"Kirchhoff","full_name":"Kirchhoff, Dominik","first_name":"Dominik"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Benjamin","full_name":"Hofner, Benjamin","last_name":"Hofner"},{"last_name":"Seibold","full_name":"Seibold, Heidi","first_name":"Heidi"},{"first_name":"Joaquin","last_name":"Vanschoren","full_name":"Vanschoren, Joaquin"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"}],"year":"2019","intvolume":"        34","page":"977–991","citation":{"ieee":"G. Casalicchio <i>et al.</i>, “OpenML: An R Package to Connect to the Machine Learning Platform OpenML,” <i>Computational Statistics</i>, vol. 34, no. 3, pp. 977–991, 2019, doi: <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","chicago":"Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i> 34, no. 3 (2019): 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>.","ama":"Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>. 2019;34(3):977–991. doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>","short":"G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner, H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.","mla":"Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i>, vol. 34, no. 3, 2019, pp. 977–991, doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","bibtex":"@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019, title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML}, volume={34}, DOI={<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>}, number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd}, year={2019}, pages={977–991} }","apa":"Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., &#38; Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>, <i>34</i>(3), 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>"},"publication_identifier":{"issn":["0943-4062"]},"issue":"3","keyword":["Databases","Machine learning","R","Reproducible research"],"language":[{"iso":"eng"}],"_id":"48877","department":[{"_id":"819"}],"user_id":"102979","abstract":[{"lang":"eng","text":"OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online."}],"status":"public","publication":"Computational Statistics","type":"journal_article"},{"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"_id":"3852","user_id":"49109","department":[{"_id":"355"}],"file_date_updated":"2018-08-09T06:14:43Z","type":"conference","urn":"38527","status":"public","oa":"1","date_updated":"2022-01-06T06:59:46Z","author":[{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"conference":{"start_date":"2018-07-10","name":"ICML 2018 AutoML Workshop","location":"Stockholm, Sweden","end_date":"2018-07-15"},"has_accepted_license":"1","citation":{"apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In <i>ICML 2018 AutoML Workshop</i>. Stockholm, Sweden.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.","mla":"Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” <i>ICML 2018 AutoML Workshop</i>, 2018.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in <i>ICML 2018 AutoML Workshop</i>, Stockholm, Sweden, 2018.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In <i>ICML 2018 AutoML Workshop</i>, 2018.","ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: <i>ICML 2018 AutoML Workshop</i>. ; 2018."},"ddc":["006"],"keyword":["automated machine learning","complex pipelines","hierarchical planning"],"language":[{"iso":"eng"}],"publication":"ICML 2018 AutoML Workshop","abstract":[{"text":"In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.\r\nVarious AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.\r\nMore specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.\r\nIn this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.\r\nWe evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.","lang":"eng"}],"file":[{"content_type":"application/pdf","relation":"main_file","date_updated":"2018-08-09T06:14:43Z","creator":"wever","date_created":"2018-08-09T06:14:43Z","file_size":297811,"file_name":"38.pdf","file_id":"3853","access_level":"open_access"}],"date_created":"2018-08-09T06:14:54Z","title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","quality_controlled":"1","year":"2018"},{"_id":"2331","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B1","_id":"9"}],"department":[{"_id":"36"},{"_id":"1"},{"_id":"579"}],"user_id":"477","article_type":"original","file_date_updated":"2018-11-02T15:16:29Z","type":"journal_article","status":"public","date_updated":"2022-01-06T06:55:49Z","volume":112,"author":[{"last_name":"Kim","full_name":"Kim, Yeongsu ","first_name":"Yeongsu "},{"full_name":"Lee, Seungwoo","last_name":"Lee","first_name":"Seungwoo"},{"last_name":"Dollmann","full_name":"Dollmann, Markus","id":"27578","first_name":"Markus"},{"first_name":"Michaela","last_name":"Geierhos","orcid":"0000-0002-8180-5606","id":"42496","full_name":"Geierhos, Michaela"}],"doi":"10.14257/ijast.2018.112.12","publication_identifier":{"issn":["2005-4238"],"eissn":["2207-6360"]},"has_accepted_license":"1","publication_status":"published","page":"123-136","intvolume":"       112","citation":{"ieee":"Y. Kim, S. Lee, M. Dollmann, and M. Geierhos, “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure,” <i>International Journal of Advanced Science and Technology</i>, vol. 112, pp. 123–136, 2018.","chicago":"Kim, Yeongsu , Seungwoo Lee, Markus Dollmann, and Michaela Geierhos. “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.” <i>International Journal of Advanced Science and Technology</i> 112 (2018): 123–36. <a href=\"https://doi.org/10.14257/ijast.2018.112.12\">https://doi.org/10.14257/ijast.2018.112.12</a>.","ama":"Kim Y, Lee S, Dollmann M, Geierhos M. Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure. <i>International Journal of Advanced Science and Technology</i>. 2018;112:123-136. doi:<a href=\"https://doi.org/10.14257/ijast.2018.112.12\">10.14257/ijast.2018.112.12</a>","apa":"Kim, Y., Lee, S., Dollmann, M., &#38; Geierhos, M. (2018). Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure. <i>International Journal of Advanced Science and Technology</i>, <i>112</i>, 123–136. <a href=\"https://doi.org/10.14257/ijast.2018.112.12\">https://doi.org/10.14257/ijast.2018.112.12</a>","mla":"Kim, Yeongsu, et al. “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.” <i>International Journal of Advanced Science and Technology</i>, vol. 112, SERSC Australia, 2018, pp. 123–36, doi:<a href=\"https://doi.org/10.14257/ijast.2018.112.12\">10.14257/ijast.2018.112.12</a>.","bibtex":"@article{Kim_Lee_Dollmann_Geierhos_2018, title={Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure}, volume={112}, DOI={<a href=\"https://doi.org/10.14257/ijast.2018.112.12\">10.14257/ijast.2018.112.12</a>}, journal={International Journal of Advanced Science and Technology}, publisher={SERSC Australia}, author={Kim, Yeongsu  and Lee, Seungwoo and Dollmann, Markus and Geierhos, Michaela}, year={2018}, pages={123–136} }","short":"Y. Kim, S. Lee, M. Dollmann, M. Geierhos, International Journal of Advanced Science and Technology 112 (2018) 123–136."},"keyword":["Software Engineering","Natural Language Processing","Semantic Annotation","Machine Learning","Feature Engineering","Syntactic Structure"],"ddc":["000"],"language":[{"iso":"eng"}],"publication":"International Journal of Advanced Science and Technology","abstract":[{"lang":"eng","text":"A user generally writes software requirements in ambiguous and incomplete form by using natural language; therefore, a software developer may have difficulty in clearly understanding what the meanings are. To solve this problem with automation, we propose a classifier for semantic annotation with manually pre-defined semantic categories. To improve our classifier, we carefully designed syntactic features extracted by constituency and dependency parsers. Even with a small dataset and a large number of classes, our proposed classifier records an accuracy of 0.75, which outperforms the previous model, REaCT."}],"file":[{"relation":"main_file","success":1,"content_type":"application/pdf","access_level":"closed","file_name":"12.pdf","file_id":"5297","file_size":586968,"date_created":"2018-11-02T15:16:29Z","creator":"ups","date_updated":"2018-11-02T15:16:29Z"}],"publisher":"SERSC Australia","date_created":"2018-04-13T09:19:22Z","title":"Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure","quality_controlled":"1","year":"2018"},{"citation":{"ama":"Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver Complementarity through Machine Learning. <i>Evolutionary Computation</i>. 2018;26(4):597–620. doi:<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>","ieee":"P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” <i>Evolutionary Computation</i>, vol. 26, no. 4, pp. 597–620, 2018, doi: <a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>.","chicago":"Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” <i>Evolutionary Computation</i> 26, no. 4 (2018): 597–620. <a href=\"https://doi.org/10.1162/evco_a_00215\">https://doi.org/10.1162/evco_a_00215</a>.","apa":"Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., &#38; Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. <i>Evolutionary Computation</i>, <i>26</i>(4), 597–620. <a href=\"https://doi.org/10.1162/evco_a_00215\">https://doi.org/10.1162/evco_a_00215</a>","mla":"Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine Learning.” <i>Evolutionary Computation</i>, vol. 26, no. 4, 2018, pp. 597–620, doi:<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>.","bibtex":"@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>}, number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018}, pages={597–620} }","short":"P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary Computation 26 (2018) 597–620."},"intvolume":"        26","page":"597–620","year":"2018","issue":"4","publication_identifier":{"issn":["1063-6560"]},"doi":"10.1162/evco_a_00215","title":"Leveraging TSP Solver Complementarity through Machine Learning","author":[{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Lars","full_name":"Kotthoff, Lars","last_name":"Kotthoff"},{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"first_name":"Holger H.","full_name":"Hoos, Holger H.","last_name":"Hoos"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"date_created":"2023-11-14T15:58:58Z","volume":26,"date_updated":"2023-12-13T10:51:26Z","status":"public","abstract":[{"lang":"eng","text":"The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers\\textemdash namely, LKH, EAX, restart variants of those, and MAOS\\textemdash on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement."}],"type":"journal_article","publication":"Evolutionary Computation","language":[{"iso":"eng"}],"keyword":["automated algorithm selection","machine learning.","performance modeling","Travelling Salesperson Problem"],"user_id":"102979","department":[{"_id":"819"}],"_id":"48884"},{"type":"journal_article","status":"public","department":[{"_id":"36"},{"_id":"1"},{"_id":"579"}],"user_id":"477","_id":"1098","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B1","_id":"9"}],"file_date_updated":"2018-12-12T15:30:59Z","article_type":"original","has_accepted_license":"1","publication_identifier":{"issn":["2205-8494"]},"publication_status":"published","page":"1-6","intvolume":"         4","citation":{"ieee":"Y.-S. Kim, S.-W. Lee, M. Dollmann, and M. Geierhos, “Semantic Annotation of Software Requirements with Language Frame,” <i>International Journal of Software Engineering for Smart Device</i>, vol. 4, no. 2, pp. 1–6, 2017.","chicago":"Kim, Yeong-Su, Seung-Woo  Lee, Markus Dollmann, and Michaela Geierhos. “Semantic Annotation of Software Requirements with Language Frame.” <i>International Journal of Software Engineering for Smart Device</i> 4, no. 2 (2017): 1–6.","ama":"Kim Y-S, Lee S-W, Dollmann M, Geierhos M. Semantic Annotation of Software Requirements with Language Frame. <i>International Journal of Software Engineering for Smart Device</i>. 2017;4(2):1-6.","apa":"Kim, Y.-S., Lee, S.-W., Dollmann, M., &#38; Geierhos, M. (2017). Semantic Annotation of Software Requirements with Language Frame. <i>International Journal of Software Engineering for Smart Device</i>, <i>4</i>(2), 1–6.","mla":"Kim, Yeong-Su, et al. “Semantic Annotation of Software Requirements with Language Frame.” <i>International Journal of Software Engineering for Smart Device</i>, vol. 4, no. 2, Global Vision School Publication, 2017, pp. 1–6.","bibtex":"@article{Kim_Lee_Dollmann_Geierhos_2017, title={Semantic Annotation of Software Requirements with Language Frame}, volume={4}, number={2}, journal={International Journal of Software Engineering for Smart Device}, publisher={Global Vision School Publication}, author={Kim, Yeong-Su and Lee, Seung-Woo  and Dollmann, Markus and Geierhos, Michaela}, year={2017}, pages={1–6} }","short":"Y.-S. Kim, S.-W. Lee, M. Dollmann, M. Geierhos, International Journal of Software Engineering for Smart Device 4 (2017) 1–6."},"volume":4,"author":[{"first_name":"Yeong-Su","full_name":"Kim, Yeong-Su","last_name":"Kim"},{"last_name":"Lee","full_name":"Lee, Seung-Woo ","first_name":"Seung-Woo "},{"last_name":"Dollmann","id":"27578","full_name":"Dollmann, Markus","first_name":"Markus"},{"id":"42496","full_name":"Geierhos, Michaela","orcid":"0000-0002-8180-5606","last_name":"Geierhos","first_name":"Michaela"}],"date_updated":"2022-01-06T06:50:55Z","publication":"International Journal of Software Engineering for Smart Device","file":[{"file_id":"6196","access_level":"closed","file_name":"Semantic_Annotation_of_Software_Requirements.pdf","file_size":244655,"date_created":"2018-12-12T15:30:59Z","creator":"ups","date_updated":"2018-12-12T15:30:59Z","relation":"main_file","success":1,"content_type":"application/pdf"}],"abstract":[{"text":"An end user generally writes down software requirements in ambiguous expressions using natural language; hence, a software developer attuned to programming language finds it difficult to understand th meaning of the requirements. To solve this problem we define semantic categories for disambiguation and classify/annotate the requirement into the categories by using machine-learning models. We extensively use a language frame closely related to such categories for designing features to overcome the problem of insufficient training data compare to the large number of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming the previous model, REaCT.","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["Natural Language Processing","Semantic Annotation","Machine Learning"],"ddc":["000"],"issue":"2","quality_controlled":"1","year":"2017","date_created":"2018-01-25T15:23:15Z","publisher":"Global Vision School Publication","title":"Semantic Annotation of Software Requirements with Language Frame"},{"abstract":[{"lang":"eng","text":"The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB’09/10 workshop."}],"status":"public","type":"conference","publication":"Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation","keyword":["machine learning","exploratory landscape analysis","fitness landscape","benchmarking","evolutionary optimization","bbob test set","algorithm selection"],"language":[{"iso":"eng"}],"_id":"46396","series_title":"GECCO ’12","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"place":"New York, NY, USA","year":"2012","citation":{"ama":"Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In: <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>. GECCO ’12. Association for Computing Machinery; 2012:313–320. doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>","chicago":"Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” In <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>.","ieee":"B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 2012, pp. 313–320, doi: <a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>.","apa":"Bischl, B., Mersmann, O., Trautmann, H., &#38; Preuß, M. (2012). Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>","bibtex":"@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY, USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning}, DOI={<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>}, booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320}, collection={GECCO ’12} }","mla":"Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, Association for Computing Machinery, 2012, pp. 313–320, doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>.","short":"B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2012, pp. 313–320."},"page":"313–320","publication_identifier":{"isbn":["9781450311779"]},"title":"Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning","doi":"10.1145/2330163.2330209","publisher":"Association for Computing Machinery","date_updated":"2023-10-16T13:48:48Z","date_created":"2023-08-04T15:51:56Z","author":[{"first_name":"Bernd","full_name":"Bischl, Bernd","last_name":"Bischl"},{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike","first_name":"Heike"},{"full_name":"Preuß, Mike","last_name":"Preuß","first_name":"Mike"}]}]