{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:10:50Z","timestamp":1743109850199,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031592348"},{"type":"electronic","value":"9783031592355"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-59235-5_12","type":"book-chapter","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T13:04:39Z","timestamp":1723035879000},"page":"127-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Not What I was Trained for \u2013 Out-of-Distribution-Tests for\u00a0Interactive AIs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2918-0765","authenticated-orcid":false,"given":"Benedikt","family":"Severin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3226-1271","authenticated-orcid":false,"given":"Ole","family":"Werger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6826-9212","authenticated-orcid":false,"given":"Marc","family":"Hesenius","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","unstructured":"Amershi, S., et al.: Software engineering for machine learning: a case study. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291\u2013300. IEEE (2019). https:\/\/doi.org\/10.1109\/ICSE-SEIP.2019.00042","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Anthony, L., Brown, Q., Nias, J., Tate, B., Mohan, S.: Interaction and recognition challenges in interpreting children\u2019s touch and gesture input on mobile devices. In: ITS 2012, pp. 225\u2013234. Association for Computing Machinery, New York (2012). https:\/\/doi.org\/10.1145\/2396636.2396671","DOI":"10.1145\/2396636.2396671"},{"key":"12_CR3","unstructured":"Barbu, A., et al.: ObjectNet: a large-scale bias-controlled dataset for pushing the limits of object recognition models. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Beyer, L., H\u00e9naff, O.J., Kolesnikov, A., Zhai, X., Oord, A.V.D.: Are we done with ImageNet? (2020). https:\/\/doi.org\/10.48550\/arXiv.2006.07159. arXiv preprint","DOI":"10.48550\/arXiv.2006.07159"},{"key":"12_CR5","series-title":"Lecture Notes in Logistics","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/978-3-030-58430-6_5","volume-title":"Digital Supply Chains and the Human Factor","author":"I B\u00f6rsting","year":"2021","unstructured":"B\u00f6rsting, I., Hesenius, M.: Towards a systematic approach for chatbot development in digital work environments. In: Klumpp, M., Ruiner, C. (eds.) Digital Supply Chains and the Human Factor. LNL, pp. 79\u201394. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-58430-6_5"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Breck, E., Cai, S., Nielsen, E., Salib, M., Sculley, D.: The ML test score: a rubric for ML production readiness and technical debt reduction. In: 2017 IEEE International Conference on Big Data (BIG DATA), pp. 1123\u20131132 (2017). https:\/\/doi.org\/10.1109\/BigData.2017.8258038","DOI":"10.1109\/BigData.2017.8258038"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Brie, P., Burny, N., Slu\u00ffters, A., Vanderdonckt, J.: Evaluating a large language model on searching for gui layouts. Proc. ACM Hum.-Comput. Interact. 7(EICS) (2023). https:\/\/doi.org\/10.1145\/3593230","DOI":"10.1145\/3593230"},{"issue":"3","key":"12_CR8","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1109\/TR.2019.2892517","volume":"68","author":"VHS Durelli","year":"2019","unstructured":"Durelli, V.H.S., Durelli, R.S., Borges, S.S., Endo, A.T., Eler, M.M., Dias, D.R.C., Guimar\u00e3es, M.P.: Machine learning applied to software testing: a systematic mapping study. IEEE Trans. Reliab. 68(3), 1189\u20131212 (2019). https:\/\/doi.org\/10.1109\/TR.2019.2892517","journal-title":"IEEE Trans. Reliab."},{"key":"12_CR9","unstructured":"Ek, A., Bernardy, J.P., Chatzikyriakidis, S.: How does punctuation affect neural models in natural language inference. In: Proceedings of the Probability and Meaning Conference (PaM 2020), pp. 109\u2013116. Association for Computational Linguistics, Gothenburg (2020). https:\/\/aclanthology.org\/2020.pam-1.15"},{"issue":"1","key":"12_CR10","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1038\/s41746-023-00944-2","volume":"6","author":"M Felsch","year":"2023","unstructured":"Felsch, M., et al.: Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model. NPJ Digit. Med. 6(1), 198 (2023). https:\/\/doi.org\/10.1038\/s41746-023-00944-2","journal-title":"NPJ Digit. Med."},{"key":"12_CR11","doi-asserted-by":"publisher","unstructured":"Grafberger, S., Groth, P., Schelter, S.: Towards data-centric what-if analysis for native machine learning pipelines. In: Proceedings of the 6th Workshop on Data Management for End-to-End Machine Learning, DEEM 2022. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3533028.3533303","DOI":"10.1145\/3533028.3533303"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Grafberger, S., Groth, P., Schelter, S.: Automating and optimizing data-centric what-if analyses on native machine learning pipelines. Proc. ACM Manag. Data 1(2) (2023). https:\/\/doi.org\/10.1145\/3589273","DOI":"10.1145\/3589273"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Griebe, T., Hesenius, M., Gesth\u00fcsen, M., Gruhn, V.: Test automation for speech-based applications. In: New Trends in Software Methodologies, Tools and Techniques: Proceedings of the Fifteenth SoMeT_16, pp. 85\u2013100. IOS Press (2016). https:\/\/doi.org\/10.3233\/978-1-61499-674-3-85","DOI":"10.3233\/978-1-61499-674-3-85"},{"key":"12_CR14","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-22689-7_1","volume-title":"Intelligent Software Methodologies, Tools and Techniques","author":"T Griebe","year":"2015","unstructured":"Griebe, T., Hesenius, M., Gruhn, V.: Towards automated UI-tests for sensor-based mobile applications. In: Fujita, H., Guizzi, G. (eds.) SoMeT 2015. CCIS, vol. 532, pp. 3\u201317. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-22689-7_1"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., et al.: The many faces of robustness: a critical analysis of out-of-distribution generalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8340\u20138349 (2021)","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Hendrycks, D., Liu, X., Wallace, E., Dziedzic, A., Krishnan, R., Song, D.: Pretrained transformers improve out-of-distribution robustness. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2744\u20132751. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.244","DOI":"10.18653\/v1\/2020.acl-main.244"},{"key":"12_CR17","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (2019)"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15262\u201315271 (2021)","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"12_CR19","unstructured":"Hesenius, M., Book, M., Gruhn, V.: Test automation for gesture-based interfaces. In: HCI Engineering 2019 \u2013 Methods and Tools for Advanced Interactive Systems and Integration of Multiple Stakeholder Viewpoints. CEUR Workshop Proceedings (2019). http:\/\/ceur-ws.org\/Vol-2503\/paper1_5.pdf"},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"Hesenius, M., Griebe, T., Gries, S., Gruhn, V.: Automating UI tests for mobile applications with formal gesture descriptions. In: Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2014, pp. 213\u2013222. ACM, New York (2014). https:\/\/doi.org\/10.1145\/2628363.2628391","DOI":"10.1145\/2628363.2628391"},{"key":"12_CR21","doi-asserted-by":"publisher","unstructured":"Hesenius, M., Griebe, T., Gruhn, V.: Towards a behavior-oriented specification and testing language for multimodal applications. In: Proceedings of the 2014 ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS 2014, pp. 117\u2013122. Association for Computing Machinery, New York (2014). https:\/\/doi.org\/10.1145\/2607023.2610278","DOI":"10.1145\/2607023.2610278"},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Hesenius, M., Schwenzfeier, N., Meyer, O., Gruhn, V.: On the uncertainty in IoT-enabled business processes using artificial intelligence components. In: 2022 International Conference on Service Science (ICSS), pp. 80\u201387. IEEE, Zhuhai (2022). https:\/\/doi.org\/10.1109\/ICSS55994.2022.00021","DOI":"10.1109\/ICSS55994.2022.00021"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Hesenius, M., Schwenzfeier, N., Meyer, O., Koop, W., Gruhn, V.: Towards a software engineering process for developing data-driven applications. In: 2019 IEEE\/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE), pp. 35\u201341. IEEE, Montreal (2019). https:\/\/doi.org\/10.1109\/RAISE.2019.00014","DOI":"10.1109\/RAISE.2019.00014"},{"key":"12_CR24","doi-asserted-by":"publisher","unstructured":"Hofer, N., Sch\u00f6ttle, P., Rietzler, A., Stabinger, S.: Adversarial examples against a BERT ABSA model \u2013 fooling BERT with L33T, Misspellign, and punctuation,. In: Proceedings of the 16th International Conference on Availability, Reliability and Security, ARES 2021. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3465481.3465770","DOI":"10.1145\/3465481.3465770"},{"key":"12_CR25","doi-asserted-by":"publisher","unstructured":"Hu, Q., Ma, L., Xie, X., Yu, B., Liu, Y., Zhao, J.: DeepMutation++: a mutation testing framework for deep learning systems. In: 2019 34th IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 1158\u20131161 (2019). https:\/\/doi.org\/10.1109\/ASE.2019.00126","DOI":"10.1109\/ASE.2019.00126"},{"issue":"14","key":"12_CR26","doi-asserted-by":"publisher","first-page":"10123","DOI":"10.1007\/s00521-023-08459-3","volume":"35","author":"G Iglesias","year":"2023","unstructured":"Iglesias, G., Talavera, E., Gonz\u00e1lez-Prieto, \u00c1., Mozo, A., G\u00f3mez-Canaval, S.: Data augmentation techniques in time series domain: a survey and taxonomy. Neural Comput. Appl. 35(14), 10123\u201310145 (2023). https:\/\/doi.org\/10.1007\/s00521-023-08459-3","journal-title":"Neural Comput. Appl."},{"key":"12_CR27","doi-asserted-by":"publisher","unstructured":"Iwana, B.K., Uchida, S.: An empirical survey of data augmentation for time series classification with neural networks. Plos One 16(7) (2021). https:\/\/doi.org\/10.1371\/journal.pone.0254841","DOI":"10.1371\/journal.pone.0254841"},{"key":"12_CR28","unstructured":"Klumpp, M., et al.: Driving big data \u2013 integration and synchronization of data sources for artificial intelligence applications with the example of truck driver work stress and strain analysis. In: International Conference on Information Systems (ICIS) 2022 proceedings (2022). https:\/\/aisel.aisnet.org\/icis2022\/data_analytics\/data_analytics\/3"},{"key":"12_CR29","unstructured":"Koner, R., Sinhamahapatra, P., Roscher, K., G\u00fcnnemann, S., Tresp, V.: OODformer: out-of-distribution detection transformer. In: 32nd British Machine Vision Conference 2021, BMVC 2021, Online, 22\u201325 November 2021, p.\u00a0223. BMVA Press (2021). https:\/\/www.bmvc2021-virtualconference.com\/assets\/papers\/1391.pdf"},{"issue":"2","key":"12_CR30","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1177\/00220345211032524","volume":"101","author":"J K\u00fchnisch","year":"2022","unstructured":"K\u00fchnisch, J., Meyer, O., Hesenius, M., Hickel, R., Gruhn, V.: Caries detection on intraoral images using artificial intelligence. J. Dent. Res. 101(2), 158\u2013165 (2022). https:\/\/doi.org\/10.1177\/00220345211032524","journal-title":"J. Dent. Res."},{"key":"12_CR31","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.aiopen.2022.03.001","volume":"3","author":"B Li","year":"2022","unstructured":"Li, B., Hou, Y., Che, W.: Data augmentation approaches in natural language processing: a survey. AI Open 3, 71\u201390 (2022). https:\/\/doi.org\/10.1016\/j.aiopen.2022.03.001","journal-title":"AI Open"},{"key":"12_CR32","doi-asserted-by":"publisher","unstructured":"Ma, L., et al.: DeepMutation: mutation testing of deep learning systems. In: 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE), pp. 100\u2013111 (2018). https:\/\/doi.org\/10.1109\/ISSRE.2018.00021","DOI":"10.1109\/ISSRE.2018.00021"},{"issue":"8","key":"12_CR33","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1007\/s11263-023-01790-1","volume":"131","author":"J Mao","year":"2023","unstructured":"Mao, J., Shi, S., Wang, X., Li, H.: 3D object detection for autonomous driving: a comprehensive survey. Int. J. Comput. Vision 131(8), 1909\u20131963 (2023). https:\/\/doi.org\/10.1007\/s11263-023-01790-1","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"12_CR34","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.patcog.2011.06.019","volume":"45","author":"JG Moreno-Torres","year":"2012","unstructured":"Moreno-Torres, J.G., Raeder, T., Alaiz-Rodr\u00edguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Pattern Recogn. 45(1), 521\u2013530 (2012). https:\/\/doi.org\/10.1016\/j.patcog.2011.06.019","journal-title":"Pattern Recogn."},{"key":"12_CR35","doi-asserted-by":"publisher","unstructured":"Mozaffari, M.A., Zhang, X., Cheng, J., Guo, J.L.: GANSpiration: balancing targeted and serendipitous inspiration in user interface design with style-based generative adversarial network. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3491102.3517511","DOI":"10.1145\/3491102.3517511"},{"key":"12_CR36","doi-asserted-by":"publisher","unstructured":"Ojeda-Castelo, J.J., Capobianco-Uriarte, M.D.L.M., Piedra-Fernandez, J.A., Ayala, R.: A survey on intelligent gesture recognition techniques. IEEE Access: Pract. Innov. Open Solut. 10, 87135\u201387156 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3199358","DOI":"10.1109\/ACCESS.2022.3199358"},{"key":"12_CR37","unstructured":"Ovadia, Y., et al.: Can you trust your model\u2019s uncertainty? Evaluating predictive uncertainty under dataset shift. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a032. Curran Associates, Inc. (2019). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/8558cb408c1d76621371888657d2eb1d-Paper.pdf"},{"issue":"6","key":"12_CR38","doi-asserted-by":"publisher","first-page":"5193","DOI":"10.1007\/s10664-020-09881-0","volume":"25","author":"V Riccio","year":"2020","unstructured":"Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., Tonella, P.: Testing machine learning based systems: a systematic mapping. Empir. Softw. Eng. 25(6), 5193\u20135254 (2020). https:\/\/doi.org\/10.1007\/s10664-020-09881-0","journal-title":"Empir. Softw. Eng."},{"key":"12_CR39","doi-asserted-by":"publisher","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3) (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","DOI":"10.1007\/s11263-015-0816-y"},{"key":"12_CR40","unstructured":"Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M.H., Sabokrou, M.: A unified survey on anomaly, novelty, open-set, and out of-distribution detection: solutions and future challenges. Trans. Mach. Learn. Res. (2022)"},{"key":"12_CR41","doi-asserted-by":"publisher","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (2020). https:\/\/doi.org\/10.48550\/arXiv.1910.01108. arXiv preprint","DOI":"10.48550\/arXiv.1910.01108"},{"key":"12_CR42","unstructured":"Schelter, S., Rukat, T., Biessmann, F.: JENGA: a framework to study the impact of data errors on the predictions of machine learning models. In: EDBT 2021 Industrial and Application Track (2021)"},{"key":"12_CR43","doi-asserted-by":"publisher","unstructured":"Shaw, A., Anthony, L.: Analyzing the articulation features of children\u2019s touchscreen gestures. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, ICMI 2016, pp. 333\u2013340. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2993148.2993179","DOI":"10.1145\/2993148.2993179"},{"issue":"1","key":"12_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J. Big Data"},{"key":"12_CR45","doi-asserted-by":"publisher","unstructured":"Song, D., Wang, Z., Huang, Y., Ma, L., Zhang, T.: DeepLens: interactive out-of-distribution data detection in NLP models. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1\u201317 (2023). https:\/\/doi.org\/10.1145\/3544548.3580741","DOI":"10.1145\/3544548.3580741"},{"key":"12_CR46","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: 2nd International Conference on Learning Representations, ICLR 2014 (2014)"},{"key":"12_CR47","doi-asserted-by":"publisher","unstructured":"Tian, Z., Chen, J., Zhu, Q., Yang, J., Zhang, L.: Learning to construct better mutation faults. In: Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering, ASE 2022, Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3551349.3556949","DOI":"10.1145\/3551349.3556949"},{"key":"12_CR48","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017)"},{"issue":"8","key":"12_CR49","doi-asserted-by":"publisher","first-page":"8052","DOI":"10.1109\/TKDE.2022.3178128","volume":"35","author":"J Wang","year":"2023","unstructured":"Wang, J., et al.: Generalizing to unseen domains: a survey on domain generalization. IEEE Trans. Knowl. Data Eng. 35(8), 8052\u20138072 (2023). https:\/\/doi.org\/10.1109\/TKDE.2022.3178128","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"12_CR50","unstructured":"Wynne, M., Hellesoy, A., Tooke, S.: The Cucumber Book: Behaviour-Driven Development for Testers and Developers. Pragmatic Bookshelf (2017)"},{"issue":"4","key":"12_CR51","doi-asserted-by":"publisher","first-page":"4396","DOI":"10.1109\/TPAMI.2022.3195549","volume":"45","author":"K Zhou","year":"2023","unstructured":"Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Loy, C.C.: Domain generalization: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4396\u20134415 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3195549","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"12_CR52","doi-asserted-by":"publisher","unstructured":"Zou, A., Wang, Z., Kolter, J.Z., Fredrikson, M.: Universal and transferable adversarial attacks on aligned language models (2023). https:\/\/doi.org\/10.48550\/arXiv.2307.15043. arXiv preprint","DOI":"10.48550\/arXiv.2307.15043"}],"container-title":["Lecture Notes in Computer Science","Engineering Interactive Computer Systems. EICS 2023 International Workshops and Doctoral Consortium"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-59235-5_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T13:06:42Z","timestamp":1723036002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-59235-5_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031592348","9783031592355"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-59235-5_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"8 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Engineering Interactive Computer Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Swansea","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eics2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eics.acm.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}