{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:44:49Z","timestamp":1787017489651,"version":"build-2736575974"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFF1105102, and 2023YFF1105105"],"award-info":[{"award-number":["2023YFF1105102, and 2023YFF1105105"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62020106012, 62332008, 62106089, U1836218, and 62336004"],"award-info":[{"award-number":["62020106012, 62332008, 62106089, U1836218, and 62336004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"111 Project of Ministry of Education of China","award":["B12018"],"award-info":[{"award-number":["B12018"]}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","award":["EP\/N007743\/1, MURI\/EPSRC\/DSTL, and EP\/R018456\/1"],"award-info":[{"award-number":["EP\/N007743\/1, MURI\/EPSRC\/DSTL, and EP\/R018456\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>Video action recognition aims at classifying the action category in given videos. In general, semantic-relevant video frame pairs reflect significant action patterns such as object appearance variation and abstract temporal concepts like speed, rhythm, and so on. However, existing action recognition approaches tend to holistically extract spatiotemporal features. Though effective, there is still a risk of neglecting the crucial action features occurring across frames with a long-term temporal span. Motivated by this, in this article, we propose to perceive actions via frame pairs directly and devise a novel Nest Structure with frame pairs as basic units. Specifically, we decompose a video sequence into all possible frame pairs and hierarchically organize them according to temporal frequency and order, thus transforming the original video sequence into a Nest Structure. Through naturally decomposing actions, the proposed structure can flexibly adapt to diverse action variations such as speed or rhythm changes. Next, we devise a Temporal Pair Analysis module (TPA) to extract discriminative action patterns based on the proposed Nest Structure. The designed TPA module consists of a pair calculation part to calculate the pair features and a pair fusion part to hierarchically fuse the pair features for recognizing actions. The proposed TPA can be flexibly integrated into existing backbones, serving as a side branch to capture various action patterns from multi-level features. Extensive experiments show that the proposed TPA module can achieve consistent improvements over several typical backbones, reaching or updating CNN-based SOTA results on several challenging action recognition benchmarks.<\/jats:p>","DOI":"10.1145\/3652611","type":"journal-article","created":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T03:40:54Z","timestamp":1710646854000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Perceiving Actions via Temporal Video Frame Pairs"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-4870","authenticated-orcid":false,"given":"Rongchang","family":"Li","sequence":"first","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9015-3128","authenticated-orcid":false,"given":"Tianyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0310-5778","authenticated-orcid":false,"given":"Xiao-Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"Jiangnan University, Wuxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6701-1965","authenticated-orcid":false,"given":"Zhongwei","family":"Shen","sequence":"additional","affiliation":[{"name":"Suzhou University of Science and Technology, Suzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8110-9205","authenticated-orcid":false,"given":"Josef","family":"Kittler","sequence":"additional","affiliation":[{"name":"University of Surrey, Guildford, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"3173","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Alwassel Humam","year":"2021","unstructured":"Humam Alwassel, Silvio Giancola, and Bernard Ghanem. 2021. Tsp: Temporally-sensitive pretraining of video encoders for localization tasks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 3173\u20133183."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"e_1_3_1_4_2","unstructured":"Junyoung Chung Caglar Gulcehre Kyunghyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning."},{"key":"e_1_3_1_5_2","unstructured":"Iulia Duta Andrei Nicolicioiu and Marius Leordeanu. 2020. Dynamic regions graph neural networks for spatio-temporal reasoning. In ORLR Workshop Neural Information Processing Systems (NeurIPS\u201920)."},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Nour Eldin Elmadany Yifeng He and Ling Guan. 2021. Improving action recognition via temporal and complementary learning. ACM Transactions on Intelligent Systems and Technology 12 3 (2021) 1\u201324.","DOI":"10.1145\/3447686"},{"key":"e_1_3_1_7_2","unstructured":"Quanfu Fan Chun-Fu Richard Chen Hilde Kuehne Marco Pistoia and David Cox. 2019. More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00630"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Amy Fire and Song-Chun Zhu. 2015. Learning perceptual causality from video. ACM Transactions on Intelligent Systems and Technology 7 2 (2015) 1\u201322.","DOI":"10.1145\/2809782"},{"key":"e_1_3_1_11_2","unstructured":"Raghav Goyal Samira Ebrahimi Kahou Vincent Michalski Joanna Materzynska Susanne Westphal Heuna Kim Valentin Haenel Ingo Fruend Peter Yianilos Moritz Mueller-Freitag Florian Hoppe Christian Thurau Ingo Bax and Roland Memisevic. 2017. Proceedings of the IEEE International Conference on Computer Vision (ICCV\u201917). 5842\u20135850."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00685"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Berthold K. P. Horn and Brian G. Schunck. 1981. Determining optical flow. Artificial Intelligence 17 1-3 (1981) 185\u2013203.","DOI":"10.1016\/0004-3702(81)90024-2"},{"key":"e_1_3_1_15_2","first-page":"10010","volume-title":"Proceedings of the 2020 25th International Conference on Pattern Recognition.","author":"Huang Guoxi","year":"2021","unstructured":"Guoxi Huang and Adrian G. Bors. 2021. Region-based non-local operation for video classification. In Proceedings of the 2020 25th International Conference on Pattern Recognition.IEEE, 10010\u201310017."},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Shuiwang Ji Wei Xu Ming Yang and Kai Yu. 2012. 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 1 (2012) 221\u2013231.","DOI":"10.1109\/TPAMI.2012.59"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00209"},{"key":"e_1_3_1_18_2","first-page":"0","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","author":"Kanojia Gagan","year":"2019","unstructured":"Gagan Kanojia, Sudhakar Kumawat, and Shanmuganathan Raman. 2019. Attentive spatio-temporal representation learning for diving classification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 0\u20130."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"e_1_3_1_20_2","unstructured":"Will Kay Joao Carreira Karen Simonyan Brian Zhang Chloe Hillier Sudheendra Vijayanarasimhan Fabio Viola Tim Green Trevor Back Paul Natsev Mustafa Suleyman and Andrew Zisserman. 2017. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950."},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Takashi Kitamura Chen Sun Jared Martin Lacey J. Kitch Mark J. Schnitzer and Susumu Tonegawa. 2015. Entorhinal cortical ocean cells encode specific contexts and drive context-specific fear memory. Neuron 87 6 (2015) 1317\u20131331.","DOI":"10.1016\/j.neuron.2015.08.036"},{"key":"e_1_3_1_22_2","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (2012) 1097\u20131105."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"e_1_3_1_24_2","first-page":"345","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Kwon Heeseung","year":"2020","unstructured":"Heeseung Kwon, Manjin Kim, Suha Kwak, and Minsu Cho. 2020. Motionsqueeze: Neural motion feature learning for video understanding. In Proceedings of the European Conference on Computer Vision. Springer, 345\u2013362."},{"key":"e_1_3_1_25_2","first-page":"204","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Lan Zhengzhong","year":"2015","unstructured":"Zhengzhong Lan, Ming Lin, Xuanchong Li, Alex G. Hauptmann, and Bhiksha Raj. 2015. Beyond gaussian pyramid: Multi-skip feature stacking for action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 204\u2013212."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01339"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00332"},{"key":"e_1_3_1_28_2","unstructured":"Kunchang Li Xianhang Li Yali Wang Jun Wang and Yu Qiao. 2020. CT-Net: Channel tensorization network for video classification. In International Conference on Learning Representations."},{"key":"e_1_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Rong-Chang Li Xiao-Jun Wu Cong Wu Tian-Yang Xu and Josef Kittler. 2021. Dynamic information enhancement for video classification. Image and Vision Computing 114 (2021) 104244.","DOI":"10.1016\/j.imavis.2021.104244"},{"key":"e_1_3_1_30_2","unstructured":"Rong-Chang Li Tianyang Xu Xiao-Jun Wu and Josef Kittler. 2021. Video is graph: Structured graph module for video action recognition. arXiv:2110.05904. Retrieved from https:\/\/arxiv.org\/abs\/2110.05904"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00099"},{"key":"e_1_3_1_32_2","volume-title":"Proceedings of the European Conference on Computer Vision. 513\u2013528","author":"Li Yingwei","year":"2018","unstructured":"Yingwei Li, Yi Li, and Nuno Vasconcelos. 2018. Resound: Towards action recognition without representation bias. In Proceedings of the European Conference on Computer Vision. 513\u2013528."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00718"},{"key":"e_1_3_1_34_2","first-page":"11669","volume-title":"Proceedings of the AAAI","author":"Liu Zhaoyang","year":"2020","unstructured":"Zhaoyang Liu, Donghao Luo, Yabiao Wang, Limin Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Tong Lu. 2020. TEINet: Towards an efficient architecture for video recognition. In Proceedings of the AAAI. 11669\u201311676."},{"key":"e_1_3_1_35_2","first-page":"13708","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Liu Zhaoyang","year":"2021","unstructured":"Zhaoyang Liu, Limin Wang, Wayne Wu, Chen Qian, and Tong Lu. 2021. Tam: Temporal adaptive module for video recognition. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 13708\u201313718."},{"key":"e_1_3_1_36_2","first-page":"5512","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Luo Chenxu","year":"2019","unstructured":"Chenxu Luo and Alan L. Yuille. 2019. Grouped spatial-temporal aggregation for efficient action recognition. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 5512\u20135521."},{"key":"e_1_3_1_37_2","unstructured":"Huaishao Luo Lei Ji Botian Shi Haoyang Huang Nan Duan Tianrui Li Jason Li Taroon Bharti and Ming Zhou. 2020. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv:2002.06353. Retrieved from https:\/\/arxiv.org\/abs\/2002.06353"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00990"},{"key":"e_1_3_1_39_2","first-page":"581","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Peng Xiaojiang","year":"2014","unstructured":"Xiaojiang Peng, Changqing Zou, Yu Qiao, and Qiang Peng. 2014. Action recognition with stacked fisher vectors. In Proceedings of the European Conference on Computer Vision. Springer, 581\u2013595."},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","unstructured":"Xin Qin Jindong Wang Yiqiang Chen Wang Lu and Xinlong Jiang. 2022. Domain generalization for activity recognition via adaptive feature fusion. ACM Transactions on Intelligent Systems and Technology 14 1 (2022) 1\u201321.","DOI":"10.1145\/3552434"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00055"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.590"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00269"},{"key":"e_1_3_1_45_2","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Two-stream convolutional networks for action recognition in videos. Advances in Neural Information Processing Systems 27 (2014)."},{"key":"e_1_3_1_46_2","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Retrieved from https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"e_1_3_1_47_2","unstructured":"Khurram Soomro Amir Roshan Zamir and Mubarak Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402."},{"key":"e_1_3_1_48_2","first-page":"1102","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sudhakaran Swathikiran","year":"2020","unstructured":"Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz. 2020. Gate-shift networks for video action recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1102\u20131111."},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00675"},{"key":"e_1_3_1_52_2","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.441"},{"key":"e_1_3_1_54_2","first-page":"352","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Heng","year":"2020","unstructured":"Heng Wang, Du Tran, Lorenzo Torresani, and Matt Feiszli. 2020. Video modeling with correlation networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 352\u2013361."},{"key":"e_1_3_1_55_2","first-page":"1895","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Limin","year":"2021","unstructured":"Limin Wang, Zhan Tong, Bin Ji, and Gangshan Wu. 2021. TDN: Temporal difference networks for efficient action recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1895\u20131904."},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00677"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"e_1_3_1_59_2","first-page":"13214","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Zhengwei","year":"2021","unstructured":"Zhengwei Wang, Qi She, and Aljosa Smolic. 2021. Action-net: Multipath excitation for action recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13214\u201313223."},{"key":"e_1_3_1_60_2","first-page":"363","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Weng Junwu","year":"2020","unstructured":"Junwu Weng, Donghao Luo, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Xudong Jiang, and Junsong Yuan. 2020. Temporal distinct representation learning for action recognition. In Proceedings of the European Conference on Computer Vision. Springer, 363\u2013378."},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00067"},{"key":"e_1_3_1_63_2","doi-asserted-by":"crossref","unstructured":"Jingran Zhang Fumin Shen Xing Xu and Heng Tao Shen. 2020. Temporal reasoning graph for activity recognition. IEEE Transactions on Image Processing 29 (2020) 5491\u20135506.","DOI":"10.1109\/TIP.2020.2985219"},{"key":"e_1_3_1_64_2","volume-title":"Proceedings of the ICLR 2020","author":"Zhang S.","year":"2020","unstructured":"S. Zhang, S. Guo, W. Huang, M. R. Scott, and L. Wang. 2020. V4D:4D convolutional neural networks for video-level representation learning. In Proceedings of the ICLR 2020."},{"key":"e_1_3_1_65_2","unstructured":"Yue Zhao Yuanjun Xiong and Dahua Lin. 2018. Trajectory convolution for action recognition. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_49"},{"key":"e_1_3_1_67_2","doi-asserted-by":"crossref","unstructured":"Yu Zhu Wenbin Chen and Guodong Guo. 2015. Fusing multiple features for depth-based action recognition. ACM Transactions on Intelligent Systems and Technology (TIST) 6 2 (2015) 1\u201320.","DOI":"10.1145\/2629483"},{"key":"e_1_3_1_68_2","doi-asserted-by":"crossref","unstructured":"Yisheng Zhu Hu Han Guangcan Liu and Qingshan Liu. 2021. Collaborative local-global learning for temporal action proposal. ACM Transactions on Intelligent Systems and Technology (TIST) 12 5 (2021) 1\u201314.","DOI":"10.1145\/3466181"},{"key":"e_1_3_1_69_2","doi-asserted-by":"crossref","unstructured":"M. Zolfaghari K. Singh and T. Brox. 2018. ECO: Efficient convolutional network for online video understanding. European Conference on Computer Vision (2018).","DOI":"10.1007\/978-3-030-01216-8_43"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652611","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652611","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:03:30Z","timestamp":1750277010000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":68,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3652611"],"URL":"https:\/\/doi.org\/10.1145\/3652611","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,17]]},"assertion":[{"value":"2023-03-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}