{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:03:29Z","timestamp":1743105809840,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030617240"},{"type":"electronic","value":"9783030617257"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61725-7_26","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T13:05:25Z","timestamp":1604322325000},"page":"217-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Prediction Confidence in Learning-Enabled Autonomous Systems"],"prefix":"10.1007","author":[{"given":"Dimitrios","family":"Boursinos","sequence":"first","affiliation":[]},{"given":"Xenofon","family":"Koutsoukos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1016\/j.procs.2013.05.365","volume":"18","author":"D Allaire","year":"2013","unstructured":"Allaire, D., et al.: An offline\/online DDDAS capability for self-aware aerospace vehicles. Procedia Comput. Sci. 18, 1959\u20131968 (2013). 2013 International Conference on Computational Science","journal-title":"Procedia Comput. Sci."},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"2518","DOI":"10.1016\/j.procs.2015.05.360","volume":"51","author":"A Aved","year":"2015","unstructured":"Aved, A., Blasch, E.: Multi-int query language for DDDAS designs. Procedia Comput. Sci. 51, 2518\u20132532 (2015)","journal-title":"Procedia Comput. Sci."},{"key":"26_CR3","volume-title":"Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications","author":"V Balasubramanian","year":"2014","unstructured":"Balasubramanian, V., Ho, S.S., Vovk, V.: Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2014)","edition":"1"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Blasch, E., Seetharaman, G., Darema, F.: Dynamic data driven applications systems (DDDAS) modeling for automatic target recognition. In: Sadjadi, F.A., Mahalanobis, A. (eds.) Automatic Target Recognition XXIII, International Society for Optics and Photonics, SPIE, vol. 8744, pp. 165\u2013174 (2013)","DOI":"10.1117\/12.2016338"},{"key":"26_CR5","unstructured":"Boursinos, D., Koutsoukos, X.: Assurance monitoring of cyber-physical systems with machine learning components. arXiv preprint arXiv:2001.05014 (2020)"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Boursinos, D., Koutsoukos, X.: Trusted confidence bounds for learning enabled cyber-physical systems. arXiv preprint arXiv:2003.05107 (2020)","DOI":"10.1109\/SPW50608.2020.00053"},{"issue":"3","key":"26_CR7","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1109\/JPROC.2004.842783","volume":"93","author":"F Darema","year":"2005","unstructured":"Darema, F.: Grid computing and beyond: the context of dynamic data driven applications systems. Proc. IEEE 93(3), 692\u2013697 (2005)","journal-title":"Proc. IEEE"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Fujimoto, R.M., et al.: Dynamic data driven application systems for smart cities and urban infrastructures. In: 2016 Winter Simulation Conference (WSC), pp. 1143\u20131157. IEEE (2016)","DOI":"10.1109\/WSC.2016.7822172"},{"key":"26_CR9","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70, pp. 1321\u20131330. JMLR.org (2017)"},{"key":"26_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-24261-3_7","volume-title":"Similarity-Based Pattern Recognition","author":"E Hoffer","year":"2015","unstructured":"Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84\u201392. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24261-3_7"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Papadopoulos, H.: Inductive conformal prediction: theory and application to neural networks. In: Tools in artificial intelligence. IntechOpen (2008)","DOI":"10.5772\/6078"},{"key":"26_CR12","unstructured":"Papernot, N., McDaniel, P.: Deep k-nearest neighbors: towards confident, interpretable and robust deep learning. arXiv preprint arXiv:1803.04765 (2018)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances In Large Margin Classifiers, pp. 61\u201374. MIT Press (1999)","DOI":"10.7551\/mitpress\/1113.003.0008"},{"issue":"9","key":"26_CR14","doi-asserted-by":"publisher","first-page":"4325","DOI":"10.1109\/JSTARS.2016.2560220","volume":"9","author":"B Uzkent","year":"2016","unstructured":"Uzkent, B., Hoffman, M.J., Vodacek, A.: Integrating hyperspectral likelihoods in a multidimensional assignment algorithm for aerial vehicle tracking. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(9), 4325\u20134333 (2016)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Xuan, H., Stylianou, A., Pless, R.: Improved embeddings with easy positive triplet mining. arXiv preprint arXiv:1904.04370 (2019)","DOI":"10.1109\/WACV45572.2020.9093432"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 694\u2013699. ACM, New York (2002)","DOI":"10.1145\/775047.775151"}],"container-title":["Lecture Notes in Computer Science","Dynamic Data Driven Applications Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61725-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T20:04:34Z","timestamp":1723838674000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61725-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030617240","9783030617257"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61725-7_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DDDAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Dynamic Data Driven Application Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Boston, MA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dddas2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/dddas-conf\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}