{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:43:01Z","timestamp":1768344181957,"version":"3.49.0"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62206250, 62036009, 62276237, 62001418, 62371421"],"award-info":[{"award-number":["62206250, 62036009, 62276237, 62001418, 62371421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ22F020007, LD24F020005, LDT23F0202, LDT23F02021F02"],"award-info":[{"award-number":["LQ22F020007, LD24F020005, LDT23F0202, LDT23F02021F02"]}]},{"name":"Ten Thousand Talent Program of Zhejiang Province"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>Weakly supervised temporal action localization (WTAL) aims to classify and localize actions in untrimmed videos with only video-level labels. Recent studies have attempted to obtain more accurate temporal boundaries by exploiting latent action instances in ambiguous snippets or propagating representative action features. However, empirically handcrafted ambiguous snippet extraction and the imprecise alignment of representative snippet propagation lead to challenges in modeling the completeness of actions for these methods. In this article, we propose a Discriminative Action Snippet Propagation Network (DASP-Net) to accurately discover ambiguous snippets in videos and propagate discriminative instance-level features throughout the video for improving action completeness. Specifically, we introduce a novel discriminative feature propagation module for capturing the global contextual attention and propagating the action concept across the whole video by perceiving the discriminative action snippets with instance information from the same video. Simultaneously, we incorporate denoised pseudo-labels as supervision, where we correct the controversial prediction based on the feature space distribution during training, thereby alleviating false detection caused by noise background features. Furthermore, we design an ambiguous feature mining module, which maximizes the feature affinity information of action and background in ambiguous snippets to generate more accurate latent action and background snippets and learns more precise action instance boundaries through contrastive learning of action and background snippets. Extensive experiments show that DASP-Net achieves state-of-the-art results on THUMOS14 and ActivityNet1.2 datasets.<\/jats:p>","DOI":"10.1145\/3643815","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T12:00:32Z","timestamp":1706702432000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Discriminative Action Snippet Propagation Network for Weakly Supervised Temporal Action Localization"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8302-1338","authenticated-orcid":false,"given":"Yuanjie","family":"Dang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8427-9178","authenticated-orcid":false,"given":"Chunxia","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6122-0574","authenticated-orcid":false,"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6032-5083","authenticated-orcid":false,"given":"Dongdong","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4545-7197","authenticated-orcid":false,"given":"Nan","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2077-9608","authenticated-orcid":false,"given":"Ronghua","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2555-343X","authenticated-orcid":false,"given":"Ruohong","family":"Huan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"e_1_3_1_4_2","first-page":"695","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201914)","author":"Ciptadi Arridhana","year":"2014","unstructured":"Arridhana Ciptadi, Matthew S. Goodwin, and James M. Rehg. 2014. Movement pattern histogram for action recognition and retrieval. In Proceedings of the European Conference on Computer Vision (ECCV\u201914). Springer, 695\u2013710."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3567828"},{"key":"e_1_3_1_6_2","first-page":"19999","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Gao Junyu","year":"2022","unstructured":"Junyu Gao, Mengyuan Chen, and Changsheng Xu. 2022. Fine-grained temporal contrastive learning for weakly-supervised temporal action localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 19999\u201320009."},{"key":"e_1_3_1_7_2","first-page":"13925","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"He Bo","year":"2022","unstructured":"Bo He, Xitong Yang, Le Kang, Zhiyu Cheng, Xin Zhou, and Abhinav Shrivastava. 2022. ASM-Loc: Action-aware segment modeling for weakly-supervised temporal action localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13925\u201313935."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_1_9_2","first-page":"255","volume-title":"Proceedings of the16th European Conference on Computer Vision\u2013ECCV 2020","author":"He Tong","year":"2020","unstructured":"Tong He, Yifan Liu, Chunhua Shen, Xinlong Wang, and Changming Sun. 2020. Instance-aware embedding for point cloud instance segmentation. In Proceedings of the16th European Conference on Computer Vision\u2013ECCV 2020. Springer, 255\u2013270."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475298"},{"issue":"9","key":"e_1_3_1_11_2","first-page":"5729","article-title":"Two-branch relational prototypical network for weakly supervised temporal action localization","volume":"44","author":"Huang Linjiang","year":"2021","unstructured":"Linjiang Huang, Yan Huang, Wanli Ouyang, and Liang Wang. 2021. Two-branch relational prototypical network for weakly supervised temporal action localization. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 9 (2021), 5729\u20135746.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00790"},{"key":"e_1_3_1_13_2","first-page":"3272","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Huang Linjiang","year":"2022","unstructured":"Linjiang Huang, Liang Wang, and Hongsheng Li. 2022. Weakly supervised temporal action localization via representative snippet knowledge propagation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3272\u20133281."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00069"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16256"},{"key":"e_1_3_1_16_2","first-page":"547","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Islam Ashraful","year":"2020","unstructured":"Ashraful Islam and Richard Radke. 2020. Weakly supervised temporal action localization using deep metric learning. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 547\u2013556."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475261"},{"key":"e_1_3_1_19_2","unstructured":"Yu-Gang Jiang Jingen Liu A. Roshan Zamir George Toderici Ivan Laptev Mubarak Shah and Rahul Sukthankar. 2014. THUMOS Challenge: Action Recognition with a Large Number of Classes. Retrieved from http:\/\/crcv.ucf.edu\/THUMOS14\/ Accessed 7\/20\/2023."},{"key":"e_1_3_1_20_2","first-page":"2311","volume-title":"Proceedings of the IEEE International Conference on Image Processing (ICIP\u201922)","author":"Kim Sunkyung","year":"2022","unstructured":"Sunkyung Kim, Hyesong Choi, and Dongbo Min. 2022. Sequential cross attention based multi-task learning. In Proceedings of the IEEE International Conference on Image Processing (ICIP\u201922). IEEE, 2311\u20132315."},{"key":"e_1_3_1_21_2","first-page":"3524","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Singh Krishna Kumar","year":"2017","unstructured":"Krishna Kumar Singh and Yong Jae Lee. 2017. Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 3524\u20133533."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6793"},{"key":"e_1_3_1_23_2","first-page":"1854","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Lee Pilhyeon","year":"2021","unstructured":"Pilhyeon Lee, Jinglu Wang, Yan Lu, and Hyeran Byun. 2021. Weakly-supervised temporal action localization by uncertainty modeling. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35, 1854\u20131862."},{"key":"e_1_3_1_24_2","first-page":"1346","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Lee Yong Jae","year":"2012","unstructured":"Yong Jae Lee, Joydeep Ghosh, and Kristen Grauman. 2012. Discovering important people and objects for egocentric video summarization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1346\u20131353."},{"key":"e_1_3_1_25_2","first-page":"19914","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Li Jingjing","year":"2022","unstructured":"Jingjing Li, Tianyu Yang, Wei Ji, Jue Wang, and Li Cheng. 2022. Exploring denoised cross-video contrast for weakly-supervised temporal action localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 19914\u201319924."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548300"},{"key":"e_1_3_1_27_2","first-page":"1","volume-title":"Proceedings of the IEEE International Conference on Multimedia and Expo (ICME\u201922)","author":"Lin Hezheng","year":"2022","unstructured":"Hezheng Lin, Xing Cheng, Xiangyu Wu, and Dong Shen. 2022. Cat: Cross attention in vision transformer. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME\u201922). 1\u20136."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_1"},{"key":"e_1_3_1_29_2","first-page":"8635","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Liu Chen","year":"2021","unstructured":"Chen Liu, Yanwei Fu, Chengming Xu, Siqian Yang, Jilin Li, Chengjie Wang, and Li Zhang. 2021. Learning a few-shot embedding model with contrastive learning. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35, 8635\u20138643."},{"key":"e_1_3_1_30_2","first-page":"1298","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Liu Daochang","year":"2019","unstructured":"Daochang Liu, Tingting Jiang, and Yizhou Wang. 2019. Completeness modeling and context separation for weakly supervised temporal action localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1298\u20131307."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16551"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00984"},{"key":"e_1_3_1_33_2","first-page":"420","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201920)","author":"Ma Fan","year":"2020","unstructured":"Fan Ma, Linchao Zhu, Yi Yang, Shengxin Zha, Gourab Kundu, Matt Feiszli, and Zheng Shou. 2020. SF-Net: Single-frame supervision for temporal action localization. In Proceedings of the European Conference on Computer Vision (ECCV\u201920). Springer, 420\u2013437."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2005.854410"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00877"},{"key":"e_1_3_1_36_2","first-page":"6752","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Nguyen Phuc","year":"2018","unstructured":"Phuc Nguyen, Ting Liu, Gautam Prasad, and Bohyung Han. 2018. Weakly supervised action localization by sparse temporal pooling network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6752\u20136761."},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00560"},{"key":"e_1_3_1_38_2","first-page":"11205","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Pan Tian","year":"2021","unstructured":"Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, and Wei Liu. 2021. Videomoco: Contrastive video representation learning with temporally adversarial examples. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 11205\u201311214."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_35"},{"key":"e_1_3_1_40_2","unstructured":"Sanqing Qu Guang Chen Zhijun Li Lijun Zhang Fan Lu and Alois Knoll. 2021. ACM-Net: Action context modeling network for weakly-supervised temporal action localization. arXiv:2104.02967. Retrieved from https:\/\/arxiv.org\/abs\/2104.02967"},{"key":"e_1_3_1_41_2","first-page":"2394","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Ren Huan","year":"2023","unstructured":"Huan Ren, Wenfei Yang, Tianzhu Zhang, and Yongdong Zhang. 2023. Proposal-based multiple instance learning for weakly-supervised temporal action localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2394\u20132404."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00109"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548077"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_10"},{"key":"e_1_3_1_45_2","first-page":"1049","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Shou Zheng","year":"2016","unstructured":"Zheng Shou, Dongang Wang, and Shih-Fu Chang. 2016. Temporal action localization in untrimmed videos via multi-stage CNNs. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1049\u20131058."},{"issue":"6","key":"e_1_3_1_46_2","first-page":"7559","article-title":"Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition","volume":"45","author":"Shu Xiangbo","year":"2022","unstructured":"Xiangbo Shu, Binqian Xu, Liyan Zhang, and Jinhui Tang. 2022. Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 6 (2022), 7559\u20137576.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_47_2","first-page":"6479","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sultani Waqas","year":"2018","unstructured":"Waqas Sultani, Chen Chen, and Mubarak Shah. 2018. Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6479\u20136488."},{"key":"e_1_3_1_48_2","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9 86 (2008) 2579\u20132605."},{"key":"e_1_3_1_49_2","first-page":"18878","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Yu","year":"2023","unstructured":"Yu Wang, Yadong Li, and Hongbin Wang. 2023. Two-stream networks for weakly-supervised temporal action localization with semantic-aware mechanisms. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 18878\u201318887."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3567827"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3175605"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3247103"},{"key":"e_1_3_1_54_2","first-page":"9070","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Xu Yunlu","year":"2019","unstructured":"Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng, Jianwen Xie, Yi Niu, Shiliang Pu, and Fei Wu. 2019. Segregated temporal assembly recurrent networks for weakly supervised multiple action detection. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33, 9070\u20139078."},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261659"},{"key":"e_1_3_1_56_2","first-page":"3090","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Yang Zichen","year":"2022","unstructured":"Zichen Yang, Jie Qin, and Di Huang. 2022. ACGNet: Action complement graph network for weakly-supervised temporal action localization. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36, 3090\u20133098."},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00637"},{"key":"e_1_3_1_58_2","unstructured":"Yuan Yuan Yueming Lyu Xi Shen Ivor W. Tsang and Dit-Yan Yeung. 2019. Marginalized average attentional network for weakly-supervised learning. arXiv:1905.08586. Retrieved from https:\/\/arxiv.org\/abs\/1905.08586"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00719"},{"key":"e_1_3_1_60_2","first-page":"37","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201920)","author":"Zhai Yuanhao","year":"2020","unstructured":"Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang, Junsong Yuan, and Gang Hua. 2020. Two-stream consensus network for weakly-supervised temporal action localization. In Proceedings of the European Conference on Computer Vision (ECCV\u201920). Springer, 37\u201354."},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01575"},{"key":"e_1_3_1_62_2","first-page":"2914","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Zhao Yue","year":"2017","unstructured":"Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xiaoou Tang, and Dahua Lin. 2017. Temporal action detection with structured segment networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2914\u20132923."},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240511"},{"key":"e_1_3_1_64_2","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"Zhou Dingfu","year":"2020","unstructured":"Dingfu Zhou, Xibin Song, Yuchao Dai, Junbo Yin, Feixiang Lu, Miao Liao, Jin Fang, and Liangjun Zhang. 2020. IAFA: Instance-aware feature aggregation for 3D object detection from a single image. In Proceedings of the Asian Conference on Computer Vision."},{"key":"e_1_3_1_65_2","first-page":"6028","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Zhou Jianxiong","year":"2023","unstructured":"Jianxiong Zhou and Ying Wu. 2023. Temporal feature enhancement dilated convolution network for weakly-supervised temporal action localization. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 6028\u20136037."}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643815","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3643815","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:57:46Z","timestamp":1750294666000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,8]]},"references-count":64,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3643815"],"URL":"https:\/\/doi.org\/10.1145\/3643815","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,8]]},"assertion":[{"value":"2023-07-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}