{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:43:03Z","timestamp":1778082183305,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE200101610"],"award-info":[{"award-number":["DE200101610"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3611808","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:12Z","timestamp":1698391632000},"page":"1167-1178","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2180-7204","authenticated-orcid":false,"given":"Zixin","family":"Wang","sequence":"first","affiliation":[{"name":"The University of Queensland, Brisbane, QLD, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6272-2971","authenticated-orcid":false,"given":"Yadan","family":"Luo","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, QLD, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9385-144X","authenticated-orcid":false,"given":"Zhi","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, QLD, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5414-8276","authenticated-orcid":false,"given":"Sen","family":"Wang","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, QLD, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-4949","authenticated-orcid":false,"given":"Zi","family":"Huang","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, QLD, Australia"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Hospedales","author":"Bohdal Ondrej","year":"2021","unstructured":"Ondrej Bohdal, Yongxin Yang, and Timothy M. Hospedales. 2021. Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error. CoRR, Vol. abs\/2106.09613 (2021). [arXiv]2106.09613 https:\/\/arxiv.org\/abs\/2106.09613"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00859"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475258"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413813"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_3_2_1_7_1","volume-title":"Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation. CoRR","author":"Cui Shuhao","year":"2021","unstructured":"Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian. 2021. Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation. CoRR, Vol. abs\/2107.06154 (2021). [arXiv]2107.06154 https:\/\/arxiv.org\/abs\/2107.06154"},{"key":"e_1_3_2_1_8_1","first-page":"1","article-title":"The comparison and evaluation of forecasters","volume":"32","author":"DeGroot Morris H","year":"1983","unstructured":"Morris H DeGroot and Stephen E Fienberg. 1983. The comparison and evaluation of forecasters. Journal of the Royal Statistical Society: Series D (The Statistician), Vol. 32, 1--2 (1983), 12--22.","journal-title":"Journal of the Royal Statistical Society: Series D (The Statistician)"},{"key":"e_1_3_2_1_9_1","volume-title":"Where and how to transfer: knowledge aggregation-induced transferability perception for unsupervised domain adaptation","author":"Dong Jiahua","year":"2021","unstructured":"Jiahua Dong, Yang Cong, Gan Sun, Zhen Fang, and Zhengming Ding. 2021a. Where and how to transfer: knowledge aggregation-induced transferability perception for unsupervised domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"Confident Anchor-Induced Multi-Source Free Domain Adaptation. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Dong Jiahua","year":"2021","unstructured":"Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, and Tongliang Liu. 2021b. Confident Anchor-Induced Multi-Source Free Domain Adaptation. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 2848--2860. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/168908dd3227b8358eababa07fcaf091-Abstract.html"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017. On Calibration of Modern Neural Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 1321--1330. http:\/\/proceedings.mlr.press\/v70\/guo17a.html"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_13_1","volume-title":"Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Huang Jiaxing","year":"2021","unstructured":"Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. 2021. Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 3635--3649. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/1dba5eed8838571e1c80af145184e515-Abstract.html"},{"key":"e_1_3_2_1_14_1","volume-title":"Soft Calibration Objectives for Neural Networks. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Karandikar Archit","year":"2021","unstructured":"Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, and Becca Roelofs. 2021. Soft Calibration Objectives for Neural Networks. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 29768--29779. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/f8905bd3df64ace64a68e154ba72f24c-Abstract.html"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00019"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2945942"},{"key":"e_1_3_2_1_17_1","volume-title":"Hao Song, and Peter A. Flach.","author":"Kull Meelis","year":"2019","unstructured":"Meelis Kull, Miquel Perell\u00f3-Nieto, Markus K\u00e4ngsepp, Telmo de Menezes e Silva Filho, Hao Song, and Peter A. Flach. 2019. Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration. CoRR, Vol. abs\/1910.12656 (2019). [arXiv]1910.12656 http:\/\/arxiv.org\/abs\/1910.12656"},{"key":"e_1_3_2_1_18_1","volume-title":"Verified Uncertainty Calibration. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Kumar Ananya","year":"2019","unstructured":"Ananya Kumar, Percy Liang, and Tengyu Ma. 2019. Verified Uncertainty Calibration. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds.). 3787--3798. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/f8c0c968632845cd133308b1a494967f-Abstract.html"},{"key":"e_1_3_2_1_19_1","volume-title":"Balancing Discriminability and Transferability for Source-Free Domain Adaptation. In International Conference on Machine Learning, ICML 2022","volume":"11728","author":"Kundu Jogendra Nath","year":"2022","unstructured":"Jogendra Nath Kundu, Akshay R. Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, and Venkatesh Babu Radhakrishnan. 2022. Balancing Discriminability and Transferability for Source-Free Domain Adaptation. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 11710--11728. https:\/\/proceedings.mlr.press\/v162\/kundu22a.html"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00696"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_26"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01130"},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","volume":"6039","author":"Liang Jian","year":"2020","unstructured":"Jian Liang, Dapeng Hu, and Jiashi Feng. 2020. Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 6028--6039. http:\/\/proceedings.mlr.press\/v119\/liang20a.html"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01268"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00688"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3064379"},{"key":"e_1_3_2_1_28_1","volume-title":"Revisiting the Calibration of Modern Neural Networks. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Minderer Matthias","year":"2021","unstructured":"Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic. 2021. Revisiting the Calibration of Modern Neural Networks. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 15682--15694. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/8420d359404024567b5aefda1231af24-Abstract.html"},{"key":"e_1_3_2_1_29_1","volume-title":"Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Mukhoti Jishnu","year":"2020","unstructured":"Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, and Puneet K. Dokania. 2020. Calibrating Deep Neural Networks using Focal Loss. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/aeb7b30ef1d024a76f21a1d40e30c302-Abstract.html"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/2888116.2888120"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"e_1_3_2_1_32_1","volume-title":"The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020","volume":"3229","author":"Park Sangdon","year":"2020","unstructured":"Sangdon Park, Osbert Bastani, James Weimer, and Insup Lee. 2020. Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation. In The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy] (Proceedings of Machine Learning Research, Vol. 108), Silvia Chiappa and Roberto Calandra (Eds.). PMLR, 3219--3229. http:\/\/proceedings.mlr.press\/v108\/park20b.html"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"John Platt et al. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers Vol. 10 3 (1999) 61--74.","DOI":"10.7551\/mitpress\/1113.003.0008"},{"key":"e_1_3_2_1_34_1","volume-title":"S4T: Source-free domain adaptation for semantic segmentation via self-supervised selective self-training. CoRR","author":"Prabhu Viraj","year":"2021","unstructured":"Viraj Prabhu, Shivam Khare, Deeksha Kartik, and Judy Hoffman. 2021. S4T: Source-free domain adaptation for semantic segmentation via self-supervised selective self-training. CoRR, Vol. abs\/2107.10140 (2021). [arXiv]2107.10140 https:\/\/arxiv.org\/abs\/2107.10140"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_5"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00780"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00154"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"e_1_3_2_1_41_1","volume-title":"On Calibration and Out-of-Domain Generalization. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Wald Yoav","year":"2021","unstructured":"Yoav Wald, Amir Feder, Daniel Greenfeld, and Uri Shalit. 2021. On Calibration and Out-of-Domain Generalization. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 2215--2227. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/118bd558033a1016fcc82560c65cca5f-Abstract.html"},{"key":"e_1_3_2_1_42_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Wang Dequan","year":"2021","unstructured":"Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, and Trevor Darrell. 2021. Tent: Fully Test-Time Adaptation by Entropy Minimization. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=uXl3bZLkr3c"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240512"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME52920.2022.9859733"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2302.13824"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME52920.2022.9859581"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475384"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475482"},{"key":"e_1_3_2_1_52_1","volume-title":"DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI","author":"Yu Fei","year":"2021","unstructured":"Fei Yu, Mo Zhang, Hexin Dong, Sheng Hu, Bin Dong, and Li Zhang. 2021. DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 10754--10762. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17285"},{"key":"e_1_3_2_1_53_1","volume-title":"Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001","author":"Zadrozny Bianca","year":"2001","unstructured":"Bianca Zadrozny and Charles Elkan. 2001. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, Carla E. Brodley and Andrea Pohoreckyj Danyluk (Eds.). Morgan Kaufmann, 609--616."},{"key":"e_1_3_2_1_54_1","volume-title":"Deep Long-Tailed Learning: A Survey. CoRR","author":"Zhang Yifan","year":"2021","unstructured":"Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. 2021. Deep Long-Tailed Learning: A Survey. CoRR, Vol. abs\/2110.04596 (2021). [arXiv]2110.04596 https:\/\/arxiv.org\/abs\/2110.04596"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3179021"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"e_1_3_2_1_58_1","volume-title":"PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. In 9th International Conference on Learning Representations, ICLR 2021","author":"Zou Yuliang","year":"2021","unstructured":"Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister. 2021. PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=-TwO99rbVRu"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3611808","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3611808","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:10:38Z","timestamp":1755821438000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3611808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":58,"alternative-id":["10.1145\/3581783.3611808","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3611808","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}