{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T22:54:55Z","timestamp":1753052095472,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,4]]},"DOI":"10.1145\/3666025.3699346","type":"proceedings-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T18:48:26Z","timestamp":1730746106000},"page":"394-408","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5816-6837","authenticated-orcid":false,"given":"Kahou","family":"Tam","sequence":"first","affiliation":[{"name":"State Key Laboratory of IoTSC, University of Macau, Macau, Macao"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5220-1609","authenticated-orcid":false,"given":"Chunlin","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of IoTSC, University of Macau, Macau, Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2044-8289","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of IoTSC, University of Macau, Macau, Macao"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3830-4275","authenticated-orcid":false,"given":"Haikai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Vancouver, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9480-0356","authenticated-orcid":false,"given":"ChengZhong","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of IoTSC, University of Macau, Macau, Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"29677","article-title":"Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction","volume":"35","author":"Alam Samiul","year":"2022","unstructured":"Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang. 2022. Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction. Advances in Neural Information Processing Systems 35 (2022), 29677--29690.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of machine learning and systems 1","author":"Bonawitz Keith","year":"2019","unstructured":"Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Kone\u010dn\u1ef3, Stefano Mazzocchi, Brendan McMahan, et al. 2019. Towards federated learning at scale: System design. Proceedings of machine learning and systems 1 (2019), 374--388."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of ICML. PMLR","author":"Chen Jianfei","year":"2021","unstructured":"Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael Mahoney, and Joseph Gonzalez. 2021. Actnn: Reducing training memory footprint via 2-bit activation compressed training. In Proceedings of ICML. PMLR, 1803--1813."},{"key":"e_1_3_2_1_4_1","volume-title":"Training deep nets with sublinear memory cost. arXiv preprint arXiv:1604.06174","author":"Chen Tianqi","year":"2016","unstructured":"Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016. Training deep nets with sublinear memory cost. arXiv preprint arXiv:1604.06174 (2016)."},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of ICML. PMLR, 151--159","author":"Chen Wei","year":"2013","unstructured":"Wei Chen, Yajun Wang, and Yang Yuan. 2013. Combinatorial multi-armed bandit: General framework and applications. In Proceedings of ICML. PMLR, 151--159."},{"key":"e_1_3_2_1_6_1","volume-title":"Client selection in federated learning: Convergence analysis and power-of-choice selection strategies. arXiv preprint arXiv:2010.01243","author":"Cho Yae Jee","year":"2020","unstructured":"Yae Jee Cho, Jianyu Wang, and Gauri Joshi. 2020. Client selection in federated learning: Convergence analysis and power-of-choice selection strategies. arXiv preprint arXiv:2010.01243 (2020)."},{"key":"e_1_3_2_1_7_1","volume-title":"Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems 29","author":"Defferrard Michael","year":"2016","unstructured":"Michael Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_3_2_1_8_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_9_1","volume-title":"Heterofl: Computation and communication efficient federated learning for heterogeneous clients. arXiv preprint arXiv:2010.01264","author":"Diao Enmao","year":"2020","unstructured":"Enmao Diao, Jie Ding, and Vahid Tarokh. 2020. Heterofl: Computation and communication efficient federated learning for heterogeneous clients. arXiv preprint arXiv:2010.01264 (2020)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/1791834.1791836"},{"key":"e_1_3_2_1_11_1","first-page":"27434","article-title":"Ac-gc: Lossy activation compression with guaranteed convergence","volume":"34","author":"David Evans R","year":"2021","unstructured":"R David Evans and Tor Aamodt. 2021. Ac-gc: Lossy activation compression with guaranteed convergence. Advances in Neural Information Processing Systems 34 (2021), 27434--27448.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA45697.2020.00075"},{"key":"e_1_3_2_1_13_1","first-page":"12","article-title":"How much RAM does your Android phone really need in 2023? https:\/\/www.androidauthority.com\/how-much-ram-do-i-need-phone-3086661\/","volume":"2023","author":"Sims Gary","year":"2023","unstructured":"Gary Sims. 2023. How much RAM does your Android phone really need in 2023? https:\/\/www.androidauthority.com\/how-much-ram-do-i-need-phone-3086661\/. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3539765"},{"key":"e_1_3_2_1_15_1","first-page":"12","article-title":"samsung-galaxy-s21-ram-plus-update. https:\/\/developer.android.com\/topic\/performance\/memory-management","volume":"2023","year":"2023","unstructured":"Google. 2023. samsung-galaxy-s21-ram-plus-update. https:\/\/developer.android.com\/topic\/performance\/memory-management. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_16_1","first-page":"12","article-title":"System profiling, app tracing and trace analysis. https:\/\/perfetto.dev\/","volume":"2023","year":"2023","unstructured":"Google. 2023. System profiling, app tracing and trace analysis. https:\/\/perfetto.dev\/. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_17_1","first-page":"12","article-title":"UI\/Application Exerciser Monkey. https:\/\/developer.android.com\/studio\/test\/other-testing-tools\/monkey","volume":"2023","year":"2023","unstructured":"Google. 2023. UI\/Application Exerciser Monkey. https:\/\/developer.android.com\/studio\/test\/other-testing-tools\/monkey. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_18_1","first-page":"12","article-title":"ActivityManager. https:\/\/developer.android.com\/reference\/android\/app\/ActivityManager.AppTask","volume":"2023","year":"2024","unstructured":"Google. 2024. ActivityManager. https:\/\/developer.android.com\/reference\/android\/app\/ActivityManager.AppTask. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_19_1","first-page":"12","article-title":"oom-adj-score. https:\/\/developer.android.com\/topic\/performance\/memory-management#low-memory_killer","volume":"2023","year":"2024","unstructured":"Google. 2024. oom-adj-score. https:\/\/developer.android.com\/topic\/performance\/memory-management#low-memory_killer. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589038"},{"key":"e_1_3_2_1_21_1","first-page":"12","article-title":"ProcessList.java. https:\/\/android.googlesource.com\/platform\/frameworks\/base\/+\/6285a32\/services\/java\/com\/android\/server\/am\/ProcessList.java","volume":"2023","author":"Hackborn Dianne","year":"2013","unstructured":"Dianne Hackborn. 2013. ProcessList.java. https:\/\/android.googlesource.com\/platform\/frameworks\/base\/+\/6285a32\/services\/java\/com\/android\/server\/am\/ProcessList.java. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2020.101758"},{"key":"e_1_3_2_1_23_1","volume-title":"Se Jung Kwon, and Dongsoo Lee","author":"Heo Jung Hwan","year":"2023","unstructured":"Jung Hwan Heo, Jeonghoon Kim, Beomseok Kwon, Byeongwook Kim, Se Jung Kwon, and Dongsoo Lee. 2023. Rethinking channel dimensions to isolate outliers for low-bit weight quantization of large language models. arXiv preprint arXiv:2309.15531 (2023)."},{"key":"e_1_3_2_1_24_1","first-page":"12876","article-title":"Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout","volume":"34","author":"Horvath Samuel","year":"2021","unstructured":"Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Lane. 2021. Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout. Advances in Neural Information Processing Systems 34 (2021), 12876--12889.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378530"},{"key":"e_1_3_2_1_26_1","first-page":"12","article-title":"AI Benchmark: All About Deep Learning on Smartphones. https:\/\/ai-benchmark.com\/ranking.html","volume":"2023","author":"Ignatov Andrey","year":"2023","unstructured":"Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, and Luc Van Gool. 2023. AI Benchmark: All About Deep Learning on Smartphones. https:\/\/ai-benchmark.com\/ranking.html. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_27_1","first-page":"497","article-title":"Checkmate","volume":"2020","author":"Jain Paras","year":"2020","unstructured":"Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Joseph Gonzalez, Kurt Keutzer, and Ion Stoica. 2020. Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization. In Proceedings of Machine Learning and Systems 2020. 497--511.","journal-title":"Breaking the Memory Wall with Optimal Tensor Rematerialization. In Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of MLSys.","author":"Jiang Xiaotang","year":"2020","unstructured":"Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lv, and Zhihua Wu. 2020. MNN: A Universal and Efficient Inference Engine. In Proceedings of MLSys."},{"key":"e_1_3_2_1_29_1","volume-title":"Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14, 1--2 (2021), 1--210."},{"key":"e_1_3_2_1_30_1","volume-title":"Dynamic tensor rematerialization. arXiv preprint arXiv:2006.09616","author":"Kirisame Marisa","year":"2020","unstructured":"Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, and Zachary Tatlock. 2020. Dynamic tensor rematerialization. arXiv preprint arXiv:2006.09616 (2020)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01316-z"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of ACM ICML. PMLR, 11814--11827","author":"Lai Fan","year":"2022","unstructured":"Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, and Mosharaf Chowdhury. 2022. Fedscale: Benchmarking model and system performance of federated learning at scale. In Proceedings of ACM ICML. PMLR, 11814--11827."},{"key":"e_1_3_2_1_33_1","volume-title":"Oort: Efficient federated learning via guided participant selection. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 19--35.","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury. 2021. Oort: Efficient federated learning via guided participant selection. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21). 19--35."},{"key":"e_1_3_2_1_34_1","volume-title":"Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942","author":"Lan Zhenzhong","year":"2019","unstructured":"Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3079520"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483278"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517017"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3098664"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of USENIX ATC. 897--910","author":"Liang Yu","year":"2020","unstructured":"Yu Liang, Jinheng Li, Rachata Ausavarungnirun, Riwei Pan, Liang Shi, Tei-Wei Kuo, and Chun Jason Xue. 2020. Acclaim: Adaptive memory reclaim to improve user experience in android systems. In Proceedings of USENIX ATC. 897--910."},{"key":"e_1_3_2_1_40_1","volume-title":"20th USENIX Conference on File and Storage Technologies (FAST 22)","author":"Liang Yu","year":"2022","unstructured":"Yu Liang, Riwei Pan, Tianyu Ren, Yufei Cui, Rachata Ausavarungnirun, Xianzhang Chen, Changlong Li, Tei-Wei Kuo, and Chun Jason Xue. 2022. {CacheSifter}: Sifting Cache Files for Boosted Mobile Performance and Lifetime. In 20th USENIX Conference on File and Storage Technologies (FAST 22). 445--459."},{"key":"e_1_3_2_1_41_1","volume-title":"SWAM: Revisiting Swap and OOMK for Improving Application Responsiveness on Mobile Devices. arXiv preprint arXiv:2306.08345","author":"Lim Geunsik","year":"2023","unstructured":"Geunsik Lim, Donghyun Kang, MyungJoo Ham, and Young Ik Eom. 2023. SWAM: Revisiting Swap and OOMK for Improving Application Responsiveness on Mobile Devices. arXiv preprint arXiv:2306.08345 (2023)."},{"key":"e_1_3_2_1_42_1","volume-title":"Awq: Activation-aware weight quantization for llm compression and acceleration. arXiv preprint arXiv:2306.00978","author":"Lin Ji","year":"2023","unstructured":"Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han. 2023. Awq: Activation-aware weight quantization for llm compression and acceleration. arXiv preprint arXiv:2306.00978 (2023)."},{"key":"e_1_3_2_1_43_1","volume-title":"Proceedings of ACM ICML. PMLR, 14139--14152","author":"Liu Xiaoxuan","year":"2022","unstructured":"Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, et al. 2022. GACT: Activation compressed training for generic network architectures. In Proceedings of ACM ICML. PMLR, 14139--14152."},{"key":"e_1_3_2_1_44_1","volume-title":"Proceedings of AISTATS. PMLR, 1273--1282","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Proceedings of AISTATS. PMLR, 1273--1282."},{"key":"e_1_3_2_1_45_1","volume-title":"Federated learning: Collaborative machine learning without centralized training data. Google Research Blog 3","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan and Daniel Ramage. 2017. Federated learning: Collaborative machine learning without centralized training data. Google Research Blog 3 (2017)."},{"key":"e_1_3_2_1_46_1","first-page":"12","article-title":"High voltage power moniter. https:\/\/www.msoon.com\/","volume":"2023","author":"Solutions Inc Monsoon","year":"2023","unstructured":"Inc Monsoon Solutions. 2023. High voltage power moniter. https:\/\/www.msoon.com\/. Accessed: 2023.12.","journal-title":"Accessed"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517351.2517354"},{"key":"e_1_3_2_1_48_1","volume-title":"Luke Carlson, Filip Granqvist, Chris Vandevelde, et al.","author":"Paulik Matthias","year":"2021","unstructured":"Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, et al. 2021. Federated evaluation and tuning for on-device personalization: System design & applications. arXiv preprint arXiv:2102.08503 (2021)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378505"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538917"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264948"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178487.3178491"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538928"},{"key":"e_1_3_2_1_55_1","unstructured":"Wikipedia contributors. Year the page was last edited. 68-95-99.7 rule. https:\/\/en.wikipedia.org\/wiki\/68%E2%80%9395%E2%80%9399.7_rule Accessed: Access date."},{"key":"e_1_3_2_1_56_1","volume-title":"PowerInfer-2: Fast Large Language Model Inference on a Smartphone. arXiv preprint arXiv:2406.06282","author":"Xue Zhenliang","year":"2024","unstructured":"Zhenliang Xue, Yixin Song, Zeyu Mi, Le Chen, Yubin Xia, and Haibo Chen. 2024. PowerInfer-2: Fast Large Language Model Inference on a Smartphone. arXiv preprint arXiv:2406.06282 (2024)."},{"key":"e_1_3_2_1_57_1","unstructured":"Chengliang Zhang Suyi Li Junzhe Xia Wei Wang Feng Yan and Yang Liu. 2020. {BatchCrypt}: Efficient homomorphic encryption for {Cross-Silo} federated learning. In 2020 USENIX annual technical conference (USENIX ATC 20). 493--506."},{"key":"e_1_3_2_1_58_1","volume-title":"Character-level convolutional networks for text classification. Advances in neural information processing systems 28","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"}],"event":{"name":"SenSys '24: 22nd ACM Conference on Embedded Networked Sensor Systems","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture","SIGBED ACM Special Interest Group on Embedded Systems","SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"],"location":"Hangzhou China","acronym":"SenSys '24"},"container-title":["Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3666025.3699346","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3666025.3699346","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:10Z","timestamp":1750295890000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3666025.3699346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":59,"alternative-id":["10.1145\/3666025.3699346","10.1145\/3666025"],"URL":"https:\/\/doi.org\/10.1145\/3666025.3699346","relation":{},"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"2024-11-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}