{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:14:03Z","timestamp":1775135643787,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599424","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:13:58Z","timestamp":1691172838000},"page":"2109-2119","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7153-1878","authenticated-orcid":false,"given":"Shuo","family":"Sun","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3369-219X","authenticated-orcid":false,"given":"Xinrun","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3490-1088","authenticated-orcid":false,"given":"Wanqi","family":"Xue","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9510-1300","authenticated-orcid":false,"given":"Xiaoxuan","family":"Lou","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7064-7438","authenticated-orcid":false,"given":"Bo","family":"An","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.05.013"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015432"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.02.006"},{"key":"e_1_3_2_2_4_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML). 1489--1499","author":"Chauhan Lakshay","year":"2020","unstructured":"Lakshay Chauhan , John Alberg , and Zachary Lipton . 2020 . Uncertainty-aware lookahead factor models for quantitative investing . In Proceedings of the 37th International Conference on Machine Learning (ICML). 1489--1499 . Lakshay Chauhan, John Alberg, and Zachary Lipton. 2020. Uncertainty-aware lookahead factor models for quantitative investing. In Proceedings of the 37th International Conference on Machine Learning (ICML). 1489--1499."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330663"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/640"},{"key":"e_1_3_2_2_7_1","volume-title":"Orhan Firat, et al.","author":"Du Nan","year":"2021","unstructured":"Nan Du , Yanping Huang , Andrew M Dai , Simon Tong , Dmitry Lepikhin , Yuanzhong Xu , Maxim Krikun , Yanqi Zhou , Adams Wei Yu , Orhan Firat, et al. 2021 . GLaM : Efficient scaling of language models with mixture-of-experts. arXiv preprint arXiv:2112.06905 (2021). Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, et al. 2021. GLaM: Efficient scaling of language models with mixture-of-experts. arXiv preprint arXiv:2112.06905 (2021)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1978.tb02041.x"},{"key":"e_1_3_2_2_9_1","volume-title":"Learning factored representations in a deep mixture of experts. arXiv preprint arXiv:1312.4314","author":"Eigen David","year":"2013","unstructured":"David Eigen , Marc'Aurelio Ranzato , and Ilya Sutskever . 2013. Learning factored representations in a deep mixture of experts. arXiv preprint arXiv:1312.4314 ( 2013 ). David Eigen, Marc'Aurelio Ranzato, and Ilya Sutskever. 2013. Learning factored representations in a deep mixture of experts. arXiv preprint arXiv:1312.4314 (2013)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.2307\/2325486"},{"key":"e_1_3_2_2_11_1","volume-title":"Enhancing stock movement prediction with adversarial training. arXiv preprint arXiv:1810.09936","author":"Feng Fuli","year":"2018","unstructured":"Fuli Feng , Huimin Chen , Xiangnan He , Ji Ding , Maosong Sun , and Tat-Seng Chua . 2018. Enhancing stock movement prediction with adversarial training. arXiv preprint arXiv:1810.09936 ( 2018 ). Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2018. Enhancing stock movement prediction with adversarial training. arXiv preprint arXiv:1810.09936 (2018)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3309547"},{"key":"e_1_3_2_2_13_1","volume-title":"Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757","author":"Fort Stanislav","year":"2019","unstructured":"Stanislav Fort , Huiyi Hu , and Balaji Lakshminarayanan . 2019. Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757 ( 2019 ). Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan. 2019. Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757 (2019)."},{"key":"e_1_3_2_2_14_1","volume-title":"Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al.","author":"Gawlikowski Jakob","year":"2021","unstructured":"Jakob Gawlikowski , Cedrique Rovile Njieutcheu Tassi , Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al. 2021 . A survey of uncertainty in deep neural networks. arXiv preprint arXiv:2107.03342 (2021). Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al. 2021. A survey of uncertainty in deep neural networks. arXiv preprint arXiv:2107.03342 (2021)."},{"key":"e_1_3_2_2_15_1","volume-title":"International Conference on Learning Representations.","author":"Gontijo-Lopes Raphael","year":"2022","unstructured":"Raphael Gontijo-Lopes , Yann Dauphin , and Ekin Dogus Cubuk . 2022 . No One Representation to Rule Them All: Overlapping Features of Training Methods . In International Conference on Learning Representations. Raphael Gontijo-Lopes, Yann Dauphin, and Ekin Dogus Cubuk. 2022. No One Representation to Rule Them All: Overlapping Features of Training Methods. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.58871"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159690"},{"key":"e_1_3_2_2_18_1","volume-title":"Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109","author":"Huang Gao","year":"2017","unstructured":"Gao Huang , Yixuan Li , Geoff Pleiss , Zhuang Liu , John E Hopcroft , and Kilian Q Weinberger . 2017. Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109 ( 2017 ). Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger. 2017. Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109 (2017)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"e_1_3_2_2_20_1","volume-title":"33th Conference on Neural Information Processing Systems (NeurIPS).","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke , Qi Meng , Thomas Finley , Taifeng Wang , Wei Chen , Weidong Ma , Qiwei Ye , and Tie-Yan Liu . 2017 . Lightgbm: A highly efficient gradient boosting decision tree . In 33th Conference on Neural Information Processing Systems (NeurIPS). Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In 33th Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_21_1","volume-title":"Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems 7","author":"Krogh Anders","year":"1994","unstructured":"Anders Krogh and Jesper Vedelsby . 1994. Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems 7 ( 1994 ). Anders Krogh and Jesper Vedelsby. 1994. Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems 7 (1994)."},{"key":"e_1_3_2_2_22_1","volume-title":"Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30","author":"Lakshminarayanan Balaji","year":"2017","unstructured":"Balaji Lakshminarayanan , Alexander Pritzel , and Charles Blundell . 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 ( 2017 ). Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_23_1","volume-title":"Deep learning. Nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. Nature 521, 7553 ( 2015 ), 436--444. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467358"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/628"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220007"},{"key":"e_1_3_2_2_27_1","volume-title":"2010 International Conference on Computer Information Systems and Industrial Management Applications. 132--136","author":"Naeini Mahdi Pakdaman","year":"2010","unstructured":"Mahdi Pakdaman Naeini , Hamidreza Taremian , and Homa Baradaran Hashemi . 2010 . Stock market value prediction using neural networks . In 2010 International Conference on Computer Information Systems and Industrial Management Applications. 132--136 . Mahdi Pakdaman Naeini, Hamidreza Taremian, and Homa Baradaran Hashemi. 2010. Stock market value prediction using neural networks. In 2010 International Conference on Computer Information Systems and Industrial Management Applications. 132--136."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"e_1_3_2_2_29_1","first-page":"512","article-title":"What is being transferred in transfer learning","volume":"33","author":"Neyshabur Behnam","year":"2020","unstructured":"Behnam Neyshabur , Hanie Sedghi , and Chiyuan Zhang . 2020 . What is being transferred in transfer learning ? Advances in Neural Information Processing Systems 33 (2020), 512 -- 523 . Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang. 2020. What is being transferred in transfer learning? Advances in Neural Information Processing Systems 33 (2020), 512--523.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00299-5"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/3013545.3013549"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"e_1_3_2_2_33_1","volume-title":"34th Conference on Neural Information Processing Systems (NeurIPS).","author":"Prokhorenkova Liudmila","year":"2018","unstructured":"Liudmila Prokhorenkova , Gleb Gusev , Aleksandr Vorobev , Anna Veronika Dorogush , and Andrey Gulin . 2018 . CatBoost: Unbiased boosting with categorical features . In 34th Conference on Neural Information Processing Systems (NeurIPS). Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: Unbiased boosting with categorical features. In 34th Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/366"},{"key":"e_1_3_2_2_35_1","volume-title":"37th Conference on Neural Information Processing Systems (NeurIPS).","author":"Ruiz Carlos Riquelme","year":"2021","unstructured":"Carlos Riquelme Ruiz , Joan Puigcerver , Basil Mustafa , Maxim Neumann , Rodolphe Jenatton , Andr\u00e9 Susano Pinto , Daniel Keysers , and Neil Houlsby . 2021 . Scaling vision with sparse mixture of experts . In 37th Conference on Neural Information Processing Systems (NeurIPS). Carlos Riquelme Ruiz, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, Andr\u00e9 Susano Pinto, Daniel Keysers, and Neil Houlsby. 2021. Scaling vision with sparse mixture of experts. In 37th Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.676"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.643"},{"key":"e_1_3_2_2_38_1","volume-title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538","author":"Shazeer Noam","year":"2017","unstructured":"Noam Shazeer , Azalia Mirhoseini , Krzysztof Maziarz , Andy Davis , Quoc Le , Geoffrey Hinton , and Jeff Dean . 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 ( 2017 ). Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.04.298"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/514"},{"key":"e_1_3_2_2_41_1","volume-title":"9th International Conference on Learning Representations (ICLR).","author":"Wang Xudong","year":"2020","unstructured":"Xudong Wang , Long Lian , Zhongqi Miao , Ziwei Liu , and Stella Yu . 2020 . Long-tailed recognition by routing diverse distribution-aware experts . In 9th International Conference on Learning Representations (ICLR). Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu. 2020. Long-tailed recognition by routing diverse distribution-aware experts. In 9th International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_42_1","volume-title":"Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). 552--562","author":"Wang Xin","year":"2020","unstructured":"Xin Wang , Fisher Yu , Lisa Dunlap , Yi-An Ma , Ruth Wang , Azalia Mirhoseini , Trevor Darrell , and Joseph E Gonzalez . 2020 . Deep mixture of experts via shallow embedding . In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). 552--562 . Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, and Joseph E Gonzalez. 2020. Deep mixture of experts via shallow embedding. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). 552--562."},{"key":"e_1_3_2_2_43_1","volume-title":"International Conference on Learning Representations.","author":"Wen Yeming","year":"2019","unstructured":"Yeming Wen , Dustin Tran , and Jimmy Ba . 2019 . BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning . In International Conference on Learning Representations. Yeming Wen, Dustin Tran, and Jimmy Ba. 2019. BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_44_1","volume-title":"Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. In International Conference on Learning Representations.","author":"Wen Yeming","year":"2018","unstructured":"Yeming Wen , Paul Vicol , Jimmy Ba , Dustin Tran , and Roger Grosse . 2018 . Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. In International Conference on Learning Representations. Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse. 2018. Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_45_1","volume-title":"Hyperparameter ensembles for robustness and uncertainty quantification. Advances in Neural Information Processing Systems","author":"Wenzel Florian","year":"2020","unstructured":"Florian Wenzel , Jasper Snoek , Dustin Tran , and Rodolphe Jenatton . 2020. Hyperparameter ensembles for robustness and uncertainty quantification. Advances in Neural Information Processing Systems ( 2020 ), 6514--6527. Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton. 2020. Hyperparameter ensembles for robustness and uncertainty quantification. Advances in Neural Information Processing Systems (2020), 6514--6527."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20842"},{"key":"e_1_3_2_2_47_1","volume-title":"Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al.","author":"Wortsman Mitchell","year":"2022","unstructured":"Mitchell Wortsman , Gabriel Ilharco , Samir Yitzhak Gadre , Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. 2022 . Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. arXiv preprint arXiv:2203.05482 (2022). Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. 2022. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. arXiv preprint arXiv:2203.05482 (2022)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2006.345431"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00058"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450032"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1183"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.23919\/ChiCC.2017.8027964"},{"key":"e_1_3_2_2_53_1","volume-title":"Qlib: An AI-oriented quantitative investment platform. arXiv preprint arXiv:2009.11189","author":"Yang Xiao","year":"2020","unstructured":"Xiao Yang , Weiqing Liu , Dong Zhou , Jiang Bian , and Tie-Yan Liu . 2020 . Qlib: An AI-oriented quantitative investment platform. arXiv preprint arXiv:2009.11189 (2020). Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, and Tie-Yan Liu. 2020. Qlib: An AI-oriented quantitative investment platform. arXiv preprint arXiv:2009.11189 (2020)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467297"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098117"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599424","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599424","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:36Z","timestamp":1750178256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":55,"alternative-id":["10.1145\/3580305.3599424","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599424","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}