{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T07:09:34Z","timestamp":1763536174306,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":83,"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":[{"name":"the Guangzhou Municipal Science and Technology Project","award":["2023A03J0671"],"award-info":[{"award-number":["2023A03J0671"]}]},{"name":"HKUST(GZ) Industrial Informatics and Intelligence Institute"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612003","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:30Z","timestamp":1698391650000},"page":"4269-4280","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Uncertainty-Driven Dynamic Degradation Perceiving and Background Modeling for Efficient Single Image Desnowing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6837-886X","authenticated-orcid":false,"given":"Sixiang","family":"Chen","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8255-2997","authenticated-orcid":false,"given":"Tian","family":"Ye","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3118-5381","authenticated-orcid":false,"given":"Chenghao","family":"Xue","sequence":"additional","affiliation":[{"name":"Jimei University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7618-9733","authenticated-orcid":false,"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9567-5531","authenticated-orcid":false,"given":"Yun","family":"Liu","sequence":"additional","affiliation":[{"name":"Southwest University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1577-1732","authenticated-orcid":false,"given":"Erkang","family":"Chen","sequence":"additional","affiliation":[{"name":"Jimei University, Xiamne, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3871-663X","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou & New Territories, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","author":"Badrinarayanan Vijay","year":"2017","unstructured":"Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, Vol. 39, 12 (2017), 2481--2495."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-011-0421-7"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00575"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108213"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00169"},{"key":"e_1_3_2_1_6_1","volume-title":"Attention in attention network for image super-resolution. arXiv preprint arXiv:2104.09497","author":"Chen Haoyu","year":"2021","unstructured":"Haoyu Chen, Jinjin Gu, and Zhi Zhang. 2021b. Attention in attention network for image super-resolution. arXiv preprint arXiv:2104.09497 (2021)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00797"},{"key":"e_1_3_2_1_8_1","volume-title":"Simple baselines for image restoration. arXiv preprint arXiv:2204.04676","author":"Chen Liangyu","year":"2022","unstructured":"Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. 2022a. Simple baselines for image restoration. arXiv preprint arXiv:2204.04676 (2022)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00027"},{"key":"e_1_3_2_1_10_1","volume-title":"SnowFormer: Scale-aware Transformer via Context Interaction for Single Image Desnowing. arXiv preprint arXiv:2208.09703","author":"Chen Sixiang","year":"2022","unstructured":"Sixiang Chen, Tian Ye, Yun Liu, Erkang Chen, Jun Shi, and Jingchun Zhou. 2022c. SnowFormer: Scale-aware Transformer via Context Interaction for Single Image Desnowing. arXiv preprint arXiv:2208.09703 (2022)."},{"key":"e_1_3_2_1_11_1","volume-title":"MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing. arXiv preprint arXiv:2207.05621","author":"Chen Sixiang","year":"2022","unstructured":"Sixiang Chen, Tian Ye, Yun Liu, Taodong Liao, Yi Ye, and Erkang Chen. 2022d. MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing. arXiv preprint arXiv:2207.05621 (2022)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58589-1_45"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00416"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01713"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"e_1_3_2_1_16_1","volume-title":"Snow Mask Guided Adaptive Residual Network for Image Snow Removal. arXiv preprint arXiv:2207.04754","author":"Cheng Bodong","year":"2022","unstructured":"Bodong Cheng, Juncheng Li, Ying Chen, Shuyi Zhang, and Tieyong Zeng. 2022. Snow Mask Guided Adaptive Residual Network for Image Snow Removal. arXiv preprint arXiv:2207.04754 (2022)."},{"key":"e_1_3_2_1_17_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00127"},{"key":"e_1_3_2_1_19_1","volume-title":"Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration. arXiv","author":"Chen","year":"2022","unstructured":"Chen et al. 2022. Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration. arXiv (2022)."},{"key":"e_1_3_2_1_20_1","first-page":"2395","article-title":"Military robot for reconnaissance and surveillance using image processing","volume":"4","author":"Ghouse Zubair","year":"2017","unstructured":"Zubair Ghouse, Nishika Hiwrale, and Nihar Ranjan. 2017. Military robot for reconnaissance and surveillance using image processing. International Research Journal of Engineering and Technology, Vol. 4, 5 (2017), 2395--0072.","journal-title":"International Research Journal of Engineering and Technology"},{"key":"e_1_3_2_1_21_1","volume-title":"Image quality assessment for perceptual image restoration: A new dataset, benchmark and metric. arXiv preprint arXiv:2011.15002","author":"Gu Jinjin","year":"2020","unstructured":"Jinjin Gu, Haoming Cai, Haoyu Chen, Xiaoxing Ye, Jimmy Ren, and Chao Dong. 2020. Image quality assessment for perceptual image restoration: A new dataset, benchmark and metric. arXiv preprint arXiv:2011.15002 (2020)."},{"key":"e_1_3_2_1_22_1","volume-title":"European Conference on Computer Vision. Springer, 633?651","author":"Gu Jinjin","year":"2020","unstructured":"Jinjin Gu, Haoming Cai, Haoyu Chen, Xiaoxing Ye, Jimmy Ren, and Chao Dong. 2020. Pipal: a large-scale image quality assessment dataset for perceptual image restoration. In European Conference on Computer Vision. Springer, 633?651."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108232"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19973"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00595"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19800-7_10"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350855"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00955"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547829"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV) Workshops.","author":"Huang Jie","year":"2018","unstructured":"Jie Huang, Pengfei Zhu, Mingrui Geng, Jiewen Ran, Xingguang Zhou, Chen Xing, Pengfei Wan, and Xiangyang Ji. 2018. Range Scaling Global U-Net for Perceptual Image Enhancement on Mobile Devices. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops."},{"volume-title":"Intelligent Manufacturing and Energy Sustainability","author":"Ismail Rakshana","key":"e_1_3_2_1_31_1","unstructured":"Rakshana Ismail and Senthil Muthukumaraswamy. 2021. Military Reconnaissance and Rescue Robot with Real-Time Object Detection. In Intelligent Manufacturing and Energy Sustainability. Springer, 637--648."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25188"},{"key":"e_1_3_2_1_33_1","unstructured":"Yeying Jin Beibei Lin Wending Yan Wei Ye Yuan Yuan and Robby T. Tan. 2023 b. Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution. arxiv: 2308.01738 [cs.CV]"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00498"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the Asian Conference on Computer Vision. 2041--2058","author":"Jin Yeying","year":"2022","unstructured":"Yeying Jin, Wending Yan, Wenhan Yang, and Robby T Tan. 2022a. Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal. In Proceedings of the Asian Conference on Computer Vision. 2041--2058."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19836-6_23"},{"key":"e_1_3_2_1_37_1","volume-title":"ShadowDiffusion: Diffusion-based Shadow Removal using Classifier-driven Attention and Structure Preservation. arXiv preprint arXiv:2211.08089","author":"Jin Yeying","year":"2022","unstructured":"Yeying Jin, Wenhan Yang, Wei Ye, Yuan Yuan, and Robby T Tan. 2022c. ShadowDiffusion: Diffusion-based Shadow Removal using Classifier-driven Attention and Structure Preservation. arXiv preprint arXiv:2211.08089 (2022)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00218"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00324"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"e_1_3_2_1_41_1","volume-title":"NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer. arXiv preprint arXiv:2305.09533","author":"Liu Yun","year":"2023","unstructured":"Yun Liu, Zhongsheng Yan, Sixiang Chen, Tian Ye, Wenqi Ren, and Erkang Chen. 2023. NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer. arXiv preprint arXiv:2305.09533 (2023)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2806202"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_44_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV).","author":"Hu Yu Xueyang Fu Keyu Yan","year":"2022","unstructured":"Keyu Yan Hu Yu Xueyang Fu Aiping Liu Xian Wei Feng Zhao Man Zhou, Jie Huang. 2022. Spatial-Frequency Domain Information Integration for\u00a0Pan-Sharpening. In Proceedings of the European Conference on Computer Vision (ECCV)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01688"},{"key":"e_1_3_2_1_46_1","first-page":"16398","article-title":"Uncertainty-driven loss for single image super-resolution","volume":"34","author":"Ning Qian","year":"2021","unstructured":"Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, and Guangming Shi. 2021. Uncertainty-driven loss for single image super-resolution. Advances in Neural Information Processing Systems, Vol. 34 (2021), 16398--16409.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_47_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_48_1","volume-title":"2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 1--6.","author":"Pei Soo-Chang","year":"2014","unstructured":"Soo-Chang Pei, Yu-Tai Tsai, and Chen-Yu Lee. 2014. Removing rain and snow in a single image using saturation and visibility features. In 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 1--6."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00231"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_51_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_52_1","volume-title":"Vision Transformers for Single Image Dehazing. arXiv preprint arXiv:2204.03883","author":"Song Yuda","year":"2022","unstructured":"Yuda Song, Zhuqing He, Hui Qian, and Xin Du. 2022. Vision Transformers for Single Image Dehazing. arXiv preprint arXiv:2204.03883 (2022)."},{"key":"e_1_3_2_1_53_1","volume-title":"NIMA: Neural image assessment","author":"Talebi Hossein","year":"2018","unstructured":"Hossein Talebi and Peyman Milanfar. 2018. NIMA: Neural image assessment. IEEE transactions on image processing, Vol. 27, 8 (2018), 3998--4011."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00651"},{"key":"e_1_3_2_1_55_1","volume-title":"Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing. arXiv preprint arXiv:2210.16057","author":"Tong Ming","year":"2022","unstructured":"Ming Tong, Yongzhen Wang, Peng Cui, Xuefeng Yan, and Mingqiang Wei. 2022. Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing. arXiv preprint arXiv:2210.16057 (2022)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00239"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2708502"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_1"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01251"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01351"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00860"},{"key":"e_1_3_2_1_64_1","volume-title":"Proceedings of the Asian Conference on Computer Vision. 366--381","author":"Ye Tian","year":"2022","unstructured":"Tian Ye, Sixiang Chen, Yun Liu, Yi Ye, Jinbin Bai, and Erkang Chen. 2022a. Towards real-time high-definition image snow removal: Efficient pyramid network with asymmetrical encoder-decoder architecture. In Proceedings of the Asian Conference on Computer Vision. 366--381."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00064"},{"key":"e_1_3_2_1_66_1","volume-title":"Perceiving and Modeling Density for Image Dehazing. In European Conference on Computer Vision. Springer, 130--145","author":"Ye Tian","year":"2022","unstructured":"Tian Ye, Yunchen Zhang, Mingchao Jiang, Liang Chen, Yun Liu, Sixiang Chen, and Erkang Chen. 2022c. Perceiving and Modeling Density for Image Dehazing. In European Conference on Computer Vision. Springer, 130--145."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548410"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548410"},{"key":"e_1_3_2_1_69_1","volume-title":"Fahad Shahbaz Khan, and Ming-Hsuan Yang","author":"Zamir Syed Waqas","year":"2021","unstructured":"Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. 2021a. Restormer: Efficient Transformer for High-Resolution Image Restoration. arXiv preprint arXiv:2111.09881 (2021)."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00564"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548359"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3104166"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2839891"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2426416"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.224"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-42051-1_33"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547924"},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547774"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00184"},{"key":"e_1_3_2_1_81_1","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Yu Hu","year":"2022","unstructured":"man zhou, Hu Yu, Jie Huang, Feng Zhao, Jinwei Gu, Chen Change Loy, Deyu Meng, and Chongyi Li. 2022. Deep Fourier Up-Sampling. In Advances in Neural Information Processing Systems, Vol. 35. Curran Associates, Inc., 22995--23008."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.276"},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00554"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Ottawa ON Canada","acronym":"MM '23"},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612003","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612003","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T23:58:20Z","timestamp":1755820700000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":83,"alternative-id":["10.1145\/3581783.3612003","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612003","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"}}]}}