{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:25:59Z","timestamp":1759335959952,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031546044"},{"type":"electronic","value":"9783031546051"}],"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\/978-3-031-54605-1_39","type":"book-chapter","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:43:10Z","timestamp":1709811790000},"page":"605-623","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Drawing the\u00a0Same Bounding Box Twice? Coping Noisy Annotations in\u00a0Object Detection with\u00a0Repeated Labels"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5344-4172","authenticated-orcid":false,"given":"David","family":"Tschirschwitz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9915-0057","authenticated-orcid":false,"given":"Christian","family":"Benz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8425-5161","authenticated-orcid":false,"given":"Morris","family":"Florek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2613-2572","authenticated-orcid":false,"given":"Henrik","family":"Norderhus","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9033-2217","authenticated-orcid":false,"given":"Benno","family":"Stein","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4815-0118","authenticated-orcid":false,"given":"Volker","family":"Rodehorst","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"issue":"10","key":"39_CR1","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1109\/TMI.2011.2147795","volume":"30","author":"AJ Asman","year":"2011","unstructured":"Asman, A.J., Landman, B.A.: Robust statistical label fusion through consensus level, labeler accuracy, and truth estimation (collate). IEEE Trans. Med. Imaging 30(10), 1779\u20131794 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR2","doi-asserted-by":"publisher","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2019). https:\/\/doi.org\/10.1109\/tpami.2019.2956516. https:\/\/dx.doi.org\/10.1109\/tpami.2019.2956516","DOI":"10.1109\/tpami.2019.2956516"},{"key":"39_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"39_CR4","unstructured":"Chen, K., et al.: MMDetection: open MMLab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019)"},{"key":"39_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster R-CNN for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3339\u20133348 (2018)","DOI":"10.1109\/CVPR.2018.00352"},{"key":"39_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1290\u20131299 (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"39_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, Y., et al.: Flow: a dataset and benchmark for floating waste detection in inland waters. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10953\u201310962 (2021)","DOI":"10.1109\/ICCV48922.2021.01077"},{"issue":"1","key":"39_CR8","first-page":"20","volume":"28","author":"AP Dawid","year":"1979","unstructured":"Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer error-rates using the EM algorithm. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 28(1), 20\u201328 (1979)","journal-title":"J. Roy. Stat. Soc.: Ser. C (Appl. Stat.)"},{"issue":"8","key":"39_CR9","doi-asserted-by":"publisher","first-page":"9981","DOI":"10.1109\/TITS.2021.3096943","volume":"23","author":"D Feng","year":"2021","unstructured":"Feng, D., et al.: Labels are not perfect: inferring spatial uncertainty in object detection. IEEE Trans. Intell. Transp. Syst. 23(8), 9981\u20139994 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Gao, J., Wang, J., Dai, S., Li, L.J., Nevatia, R.: Note-RCNN: noise tolerant ensemble RCNN for semi-supervised object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9508\u20139517 (2019)","DOI":"10.1109\/ICCV.2019.00960"},{"key":"39_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/978-3-031-20053-3_24","volume-title":"Computer Vision \u2013 ECCV 2022","author":"Z Gao","year":"2022","unstructured":"Gao, Z., et al.: Learning from multiple annotator noisy labels via sample-wise label fusion. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13684, pp. 407\u2013422. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20053-3_24"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Guan, M., Gulshan, V., Dai, A., Hinton, G.: Who said what: modeling individual labelers improves classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11756"},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"39_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759","volume":"65","author":"D Karimi","year":"2020","unstructured":"Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)","journal-title":"Med. Image Anal."},{"key":"39_CR15","unstructured":"Khetan, A., Lipton, Z.C., Anandkumar, A.: Learning from noisy singly-labeled data. arXiv preprint arXiv:1712.04577 (2017)"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Khodabandeh, M., Vahdat, A., Ranjbar, M., Macready, W.G.: A robust learning approach to domain adaptive object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 480\u2013490 (2019)","DOI":"10.1109\/ICCV.2019.00057"},{"issue":"12","key":"39_CR17","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.1109\/TMI.2010.2057442","volume":"29","author":"TR Langerak","year":"2010","unstructured":"Langerak, T.R., van der Heide, U.A., Kotte, A.N., Viergever, M.A., Van Vulpen, M., Pluim, J.P.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (simple). IEEE Trans. Med. Imaging 29(12), 2000\u20132008 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR18","unstructured":"Le, K.H., Tran, T.V., Pham, H.H., Nguyen, H.T., Le, T.T., Nguyen, H.Q.: Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis. arXiv preprint arXiv:2203.10611 (2022)"},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Li, M., Xu, Y., Cui, L., Huang, S., Wei, F., Li, Z., Zhou, M.: DocBank: a benchmark dataset for document layout analysis. arXiv preprint arXiv:2006.01038 (2020)","DOI":"10.18653\/v1\/2020.coling-main.82"},{"key":"39_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"39_CR21","unstructured":"Michaelis, C., et al.: Benchmarking robustness in object detection: autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484 (2019)"},{"key":"39_CR22","unstructured":"Nguyen, D.B., Nguyen, H.Q., Elliott, J., KeepLearning, Nguyen, N.T., Culliton, P.: VinBigData chest X-ray abnormalities detection (2020). https:\/\/kaggle.com\/competitions\/vinbigdata-chest-xray-abnormalities-detection"},{"issue":"1","key":"39_CR23","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1038\/s41597-022-01498-w","volume":"9","author":"HQ Nguyen","year":"2022","unstructured":"Nguyen, H.Q., et al.: VinDr-CXR: an open dataset of chest X-rays with radiologist\u2019s annotations. Sci. Data 9(1), 429 (2022)","journal-title":"Sci. Data"},{"key":"39_CR24","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., Yuille, A.: Detectors: detecting objects with recursive feature pyramid and switchable atrous convolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10213\u201310224 (2021)","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"39_CR25","doi-asserted-by":"publisher","unstructured":"Ramamonjison, R., Banitalebi-Dehkordi, A., Kang, X., Bai, X., Zhang, Y.: SimROD: a simple adaptation method for robust object detection. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, pp. 3550\u20133559. IEEE, October 2021. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00355. https:\/\/ieeexplore.ieee.org\/document\/9711168\/","DOI":"10.1109\/ICCV48922.2021.00355"},{"key":"39_CR26","doi-asserted-by":"crossref","unstructured":"Raykar, V.C., et al.: Supervised learning from multiple experts: whom to trust when everyone lies a bit. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 889\u2013896 (2009)","DOI":"10.1145\/1553374.1553488"},{"key":"39_CR27","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28 (2015)"},{"key":"39_CR28","doi-asserted-by":"crossref","unstructured":"Rodrigues, F., Pereira, F.: Deep learning from crowds. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11506"},{"key":"39_CR29","doi-asserted-by":"crossref","unstructured":"Sheng, V.S., Provost, F., Ipeirotis, P.G.: Get another label? Improving data quality and data mining using multiple, noisy labelers. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 614\u2013622 (2008)","DOI":"10.1145\/1401890.1401965"},{"key":"39_CR30","doi-asserted-by":"crossref","unstructured":"Sheng, V.S., Zhang, J.: Machine learning with crowdsourcing: a brief summary of the past research and future directions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9837\u20139843 (2019)","DOI":"10.1609\/aaai.v33i01.33019837"},{"key":"39_CR31","unstructured":"Sinha, V.B., Rao, S., Balasubramanian, V.N.: Fast Dawid-Skene: a fast vote aggregation scheme for sentiment classification. arXiv preprint arXiv:1803.02781 (2018)"},{"key":"39_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104117","volume":"107","author":"R Solovyev","year":"2021","unstructured":"Solovyev, R., Wang, W., Gabruseva, T.: Weighted boxes fusion: ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021)","journal-title":"Image Vis. Comput."},{"key":"39_CR33","doi-asserted-by":"publisher","first-page":"8135","DOI":"10.1109\/TNNLS.2022.3152527","volume":"34","author":"H Song","year":"2022","unstructured":"Song, H., Kim, M., Park, D., Shin, Y., Lee, J.G.: Learning from noisy labels with deep neural networks: a survey. IEEE Trans. Neural Netw. Learn. Syst. 34, 8135\u20138153 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"39_CR34","doi-asserted-by":"crossref","unstructured":"Tanno, R., Saeedi, A., Sankaranarayanan, S., Alexander, D.C., Silberman, N.: Learning from noisy labels by regularized estimation of annotator confusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11244\u201311253 (2019)","DOI":"10.1109\/CVPR.2019.01150"},{"key":"39_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-031-16788-1_22","volume-title":"Pattern Recognition","author":"D Tschirschwitz","year":"2022","unstructured":"Tschirschwitz, D., Klemstein, F., Stein, B., Rodehorst, V.: A dataset for analysing complex document layouts in the digital humanities and its evaluation with Krippendorff\u2019s alpha. In: Andres, B., Bernard, F., Cremers, D., Frintrop, S., Goldl\u00fccke, B., Ihrke, I. (eds.) DAGM GCPR 2022. LNCS, vol. 13485, pp. 354\u2013374. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16788-1_22"},{"key":"39_CR36","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Robust object detection via instance-level temporal cycle confusion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9143\u20139152 (2021)","DOI":"10.1109\/ICCV48922.2021.00901"},{"key":"39_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Y., Guo, Y., Fang, L., Wang, S.: Data-uncertainty guided multi-phase learning for semi-supervised object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4568\u20134577 (2021)","DOI":"10.1109\/CVPR46437.2021.00454"},{"issue":"7","key":"39_CR38","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903\u2013921 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR39","unstructured":"Whitehill, J., Wu, T.F., Bergsma, J., Movellan, J., Ruvolo, P.: Whose vote should count more: optimal integration of labels from labelers of unknown expertise. In: Advances in Neural Information Processing Systems 22 (2009)"},{"key":"39_CR40","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Rethinking classification and localization for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10186\u201310195 (2020)","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"39_CR41","doi-asserted-by":"crossref","unstructured":"Wu, Z., Suresh, K., Narayanan, P., Xu, H., Kwon, H., Wang, Z.: Delving into robust object detection from unmanned aerial vehicles: a deep nuisance disentanglement approach. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1201\u20131210 (2019)","DOI":"10.1109\/ICCV.2019.00129"},{"key":"39_CR42","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1369\u20131378 (2017)","DOI":"10.1109\/ICCV.2017.153"},{"key":"39_CR43","doi-asserted-by":"publisher","unstructured":"Zhang, H., Wang, J.: Towards adversarially robust object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, South Korea, pp. 421\u2013430. IEEE, October 2019. https:\/\/doi.org\/10.1109\/ICCV.2019.00051. https:\/\/ieeexplore.ieee.org\/document\/9009990\/","DOI":"10.1109\/ICCV.2019.00051"},{"key":"39_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, H., Arik, S.O., Lee, H., Pfister, T.: Distilling effective supervision from severe label noise. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9294\u20139303 (2020)","DOI":"10.1109\/CVPR42600.2020.00931"},{"issue":"5","key":"39_CR45","doi-asserted-by":"publisher","first-page":"541","DOI":"10.14778\/3055540.3055547","volume":"10","author":"Y Zheng","year":"2017","unstructured":"Zheng, Y., Li, G., Li, Y., Shan, C., Cheng, R.: Truth inference in crowdsourcing: is the problem solved? Proc. VLDB Endow. 10(5), 541\u2013552 (2017)","journal-title":"Proc. VLDB Endow."},{"key":"39_CR46","doi-asserted-by":"crossref","unstructured":"Zhong, X., Tang, J., Yepes, A.J.: PubLayNet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1015\u20131022. IEEE (2019)","DOI":"10.1109\/ICDAR.2019.00166"},{"issue":"3","key":"39_CR47","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s10462-004-0751-8","volume":"22","author":"X Zhu","year":"2004","unstructured":"Zhu, X., Wu, X.: Class noise vs. attribute noise: a quantitative study. Artif. Intell. Rev. 22(3), 177 (2004)","journal-title":"Artif. Intell. Rev."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-54605-1_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:13:12Z","timestamp":1709813592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-54605-1_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031546044","9783031546051"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-54605-1_39","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 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heidelberg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"19 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"45","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dagm-gcpr.de\/year\/2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"76","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":"40","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":"0","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":"3","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":"5","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)"}}]}}