{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T19:28:11Z","timestamp":1763580491873,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,26]]},"DOI":"10.1145\/3715669.3726787","type":"proceedings-article","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T06:57:59Z","timestamp":1748069879000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Device-Specific Style Transfer of Eye-Tracking Signals"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8088-9270","authenticated-orcid":false,"given":"Dillon J","family":"Lohr","sequence":"first","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8956-9947","authenticated-orcid":false,"given":"Dmytro","family":"Katrychuk","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7656-2662","authenticated-orcid":false,"given":"Samantha","family":"Aziz","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1144-6118","authenticated-orcid":false,"given":"Mehedi Hasan","family":"Raju","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7890-8842","authenticated-orcid":false,"given":"Oleg","family":"Komogortsev","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas State University, San Marcos, Texas, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,5,25]]},"reference":[{"key":"e_1_3_3_2_2_1","doi-asserted-by":"publisher","unstructured":"Richard Andersson Linnea Larsson Kenneth Holmqvist Martin Stridh and Marcus Nystr\u00f6m. 2017. One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms. Behavior Research Methods 49 2 (01 Apr 2017) 616\u2013637. 10.3758\/s13428-016-0738-9","DOI":"10.3758\/s13428-016-0738-9"},{"key":"e_1_3_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207722"},{"key":"e_1_3_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_3_2_5_1","doi-asserted-by":"publisher","unstructured":"Lee Friedman Mark\u00a0S. Nixon and Oleg\u00a0V. Komogortsev. 2017. Method to assess the temporal persistence of potential biometric features: Application to oculomotor gait face and brain structure databases. PLOS ONE 12 6 (06 2017) 1\u201342. 10.1371\/journal.pone.0178501","DOI":"10.1371\/journal.pone.0178501"},{"key":"e_1_3_3_2_6_1","doi-asserted-by":"publisher","unstructured":"Lee Friedman Ioannis Rigas Evgeny Abdulin and Oleg\u00a0V. Komogortsev. 2018. A novel evaluation of two related and two independent algorithms for eye movement classification during reading. Behavior Research Methods 50 4 (01 Aug 2018) 1374\u20131397. 10.3758\/s13428-018-1050-7","DOI":"10.3758\/s13428-018-1050-7"},{"key":"e_1_3_3_2_7_1","doi-asserted-by":"publisher","unstructured":"Henry Griffith Dillon Lohr Evgeny Abdulin and Oleg Komogortsev. 2021. GazeBase a large-scale multi-stimulus longitudinal eye movement dataset. Scientific Data 8 (07 2021). 10.1038\/s41597-021-00959-y","DOI":"10.1038\/s41597-021-00959-y"},{"key":"e_1_3_3_2_8_1","doi-asserted-by":"crossref","unstructured":"Hao Guan and Mingxia Liu. 2021. Domain adaptation for medical image analysis: a survey. IEEE Transactions on Biomedical Engineering 69 3 (2021) 1173\u20131185.","DOI":"10.1109\/TBME.2021.3117407"},{"key":"e_1_3_3_2_9_1","doi-asserted-by":"publisher","unstructured":"Brian Guenter Mark Finch Steven Drucker Desney Tan and John Snyder. 2012. Foveated 3D Graphics. ACM Trans. Graph. 31 6 Article 164 (nov 2012) 10\u00a0pages. 10.1145\/2366145.2366183","DOI":"10.1145\/2366145.2366183"},{"key":"e_1_3_3_2_10_1","doi-asserted-by":"crossref","unstructured":"D.\u00a0W. Hansen and Q. Ji. 2010. In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans Pattern Anal Mach Intell 32 3 (Mar 2010) 478\u2013500.","DOI":"10.1109\/TPAMI.2009.30"},{"key":"e_1_3_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_2_12_1","volume-title":"Advances in Neural Information Processing Systems","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0V. Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.), Vol.\u00a030. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/8a1d694707eb0fefe65871369074926d-Paper.pdf"},{"key":"e_1_3_3_2_13_1","doi-asserted-by":"publisher","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9 8 (11 1997) 1735\u20131780. 10.1162\/neco.1997.9.8.1735 arXiv:https:\/\/direct.mit.edu\/neco\/article-pdf\/9\/8\/1735\/813796\/neco.1997.9.8.1735.pdf","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_2_14_1","first-page":"9","volume-title":"Eye Tracking: A Comprehensive Guide to Methods and Measures","author":"Holmqvist Kenneth","year":"2011","unstructured":"Kenneth Holmqvist, Marcus Nystr\u00f6m, Richard Andersson, Richard Dewhurst, Halszka Jarodzka, and Joost van\u00a0de Weijer. 2011. Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford University Press, New York, Chapter\u00a02, 9\u201364. https:\/\/lup.lub.lu.se\/search\/publication\/1852359"},{"key":"e_1_3_3_2_15_1","doi-asserted-by":"publisher","unstructured":"Zhiming Hu Sheng Li Congyi Zhang Kangrui Yi Guoping Wang and Dinesh Manocha. 2020. DGaze: CNN-Based Gaze Prediction in Dynamic Scenes. IEEE Transactions on Visualization and Computer Graphics 26 5 (2020) 1902\u20131911. 10.1109\/TVCG.2020.2973473","DOI":"10.1109\/TVCG.2020.2973473"},{"key":"e_1_3_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"e_1_3_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-25976-3_23"},{"key":"e_1_3_3_2_19_1","unstructured":"Diederik\u00a0P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arxiv:https:\/\/arXiv.org\/abs\/1412.6980\u00a0[cs.LG]"},{"key":"e_1_3_3_2_20_1","doi-asserted-by":"crossref","unstructured":"Robert Konrad Anastasios Angelopoulos and Gordon Wetzstein. 2020. Gaze-Contingent Ocular Parallax Rendering for Virtual Reality. ACM Trans. Graph. 39 (2020). Issue 2.","DOI":"10.1145\/3361330"},{"key":"e_1_3_3_2_21_1","first-page":"108","volume-title":"The Neurology of Eye Movements (4 ed.)","author":"Leigh R\u00a0John","year":"2006","unstructured":"R\u00a0John Leigh and David\u00a0S Zee. 2006. The Neurology of Eye Movements (4 ed.). Oxford University Press, New York, Chapter\u00a03, 108\u2013187."},{"key":"e_1_3_3_2_22_1","doi-asserted-by":"crossref","unstructured":"Dillon Lohr Samantha Aziz Lee Friedman and Oleg\u00a0V Komogortsev. 2023. GazeBaseVR a large-scale longitudinal binocular eye-tracking dataset collected in virtual reality. Scientific Data 10 1 (2023).","DOI":"10.1038\/s41597-023-02075-5"},{"key":"e_1_3_3_2_23_1","doi-asserted-by":"crossref","unstructured":"Dillon Lohr Henry Griffith and Oleg\u00a0V Komogortsev. 2022. Eye know you: Metric learning for end-to-end biometric authentication using eye movements from a longitudinal dataset. IEEE Transactions on Biometrics Behavior and Identity Science 4 2 (2022) 276\u2013288.","DOI":"10.1109\/TBIOM.2022.3167633"},{"key":"e_1_3_3_2_24_1","doi-asserted-by":"crossref","unstructured":"Dillon Lohr and Oleg\u00a0V Komogortsev. 2022. Eye know you too: Toward viable end-to-end eye movement biometrics for user authentication. IEEE Transactions on Information Forensics and Security 17 (2022) 3151\u20133164.","DOI":"10.1109\/TIFS.2022.3201369"},{"key":"e_1_3_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCB62174.2024.10744483"},{"key":"e_1_3_3_2_26_1","unstructured":"Dillon\u00a0J. Lohr Lee Friedman and Oleg\u00a0V. Komogortsev. 2019. Evaluating the Data Quality of Eye Tracking Signals from a Virtual Reality System: Case Study using SMI\u2019s Eye-Tracking HTC Vive. arxiv:https:\/\/arXiv.org\/abs\/1912.02083\u00a0[cs.HC]"},{"key":"e_1_3_3_2_27_1","doi-asserted-by":"publisher","unstructured":"Silvia Makowski Paul Prasse David\u00a0R. Reich Daniel Krakowczyk Lena\u00a0A. J\u00e4ger and Tobias Scheffer. 2021. DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection Using Deep Neural Networks. IEEE Transactions on Biometrics Behavior and Identity Science 3 4 (2021) 506\u2013518. 10.1109\/TBIOM.2021.3116875","DOI":"10.1109\/TBIOM.2021.3116875"},{"key":"e_1_3_3_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302506.3310398"},{"key":"e_1_3_3_2_29_1","doi-asserted-by":"publisher","unstructured":"Marcus Nystr\u00f6m and Kenneth Holmqvist. 2010. An adaptive algorithm for fixation saccade and glissade detection in eyetracking data. Behavior Research Methods 42 1 (01 Feb 2010) 188\u2013204. 10.3758\/BRM.42.1.188","DOI":"10.3758\/BRM.42.1.188"},{"key":"e_1_3_3_2_30_1","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-59140-562-7.ch034"},{"key":"e_1_3_3_2_31_1","doi-asserted-by":"publisher","unstructured":"Paul Prasse Lena\u00a0A. J\u00e4ger Silvia Makowski Moritz Feuerpfeil and Tobias Scheffer. 2020. On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification. Procedia Computer Science 176 (2020) 2088\u20132097. 10.1016\/j.procs.2020.09.245 Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020.","DOI":"10.1016\/j.procs.2020.09.245"},{"key":"e_1_3_3_2_32_1","volume-title":"Dataset shift in machine learning","author":"Qui\u00f1onero-Candela Joaquin","year":"2022","unstructured":"Joaquin Qui\u00f1onero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil\u00a0D Lawrence. 2022. Dataset shift in machine learning. Mit Press."},{"key":"e_1_3_3_2_33_1","unstructured":"Mehedi\u00a0Hasan Raju Samantha Aziz Michael\u00a0J Proulx and Oleg\u00a0V Komogortsev. 2024a. Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2411.03708 (2024). https:\/\/arxiv.org\/abs\/2411.03708"},{"key":"e_1_3_3_2_34_1","doi-asserted-by":"publisher","unstructured":"Mehedi\u00a0H. Raju Lee Friedman Troy\u00a0M. Bouman and Oleg\u00a0V. Komogortsev. 2021a. Determining Which Sine Wave Frequencies Correspond to Signal and Which Correspond to Noise in Eye-Tracking Time-Series. Journal of Eye Movement Research 14 3 (2021) 1\u201319. 10.16910\/jemr.14.3.5","DOI":"10.16910\/jemr.14.3.5"},{"key":"e_1_3_3_2_35_1","doi-asserted-by":"publisher","unstructured":"Mehedi\u00a0H. Raju Lee Friedman Troy\u00a0M. Bouman and Oleg\u00a0V. Komogortsev. 2021b. Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic Savitzky-Golay IIR and FIR Digital Filters. Journal of Eye Movement Research 14 3 (2021) 1\u201316. 10.16910\/jemr.14.3.6","DOI":"10.16910\/jemr.14.3.6"},{"key":"e_1_3_3_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649902.3653353"},{"key":"e_1_3_3_2_37_1","unstructured":"Mehedi\u00a0Hasan Raju Lee Friedman Dillon\u00a0J Lohr and Oleg\u00a0V Komogortsev. 2024c. Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipeline. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.16399 (2024). https:\/\/arxiv.org\/abs\/2402.16399"},{"key":"e_1_3_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3517031.3532521"},{"key":"e_1_3_3_2_39_1","doi-asserted-by":"publisher","unstructured":"Jenelle Raynowska John-Ross Rizzo Janet\u00a0C Rucker Weiwei Dai Joel Birkemeier Julian Hershowitz Ivan Selesnick Laura\u00a0J Balcer Steven\u00a0L Galetta and Todd Hudson. 2018. Validity of low-resolution eye-tracking to assess eye movements during a rapid number naming task: performance of the eyetribe eye tracker. Brain Injury 32 2 (2018) 200\u2013208. 10.1080\/02699052.2017.1374469 arXiv:10.1080\/02699052.2017.1374469 PMID: 29211506.","DOI":"10.1080\/02699052.2017.1374469"},{"key":"e_1_3_3_2_40_1","doi-asserted-by":"crossref","unstructured":"Ioannis Rigas Lee Friedman and Oleg Komogortsev. 2018. Study of an Extensive Set of Eye Movement Features: Extraction Methods and Statistical Analysis. Journal of eye movement research 11 1 (20 Mar 2018) 10.16910\/jemr.11.1.3. https:\/\/pubmed.ncbi.nlm.nih.gov\/33828682 33828682[pmid].","DOI":"10.16910\/jemr.11.1.3"},{"key":"e_1_3_3_2_41_1","doi-asserted-by":"crossref","unstructured":"Ioannis Rigas and Oleg\u00a0V. Komogortsev. 2015. Eye Movement-Driven Defense against Iris Print-Attacks. Pattern Recogn. Lett. 68 P2 (dec 2015) 316\u2013326.","DOI":"10.1016\/j.patrec.2015.06.011"},{"key":"e_1_3_3_2_42_1","doi-asserted-by":"publisher","unstructured":"David Romo-Bucheli Philipp Seeb\u00f6ck Jos\u00e9\u00a0Ignacio Orlando Bianca\u00a0S. Gerendas Sebastian\u00a0M. Waldstein Ursula Schmidt-Erfurth and Hrvoje Bogunovi\u0107. 2019. Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina. Biomedical Optics Express 11 1 (December 2019) 346\u2013363. 10.1364\/BOE.379978","DOI":"10.1364\/BOE.379978"},{"key":"e_1_3_3_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/355017.355028"},{"key":"e_1_3_3_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00395"},{"key":"e_1_3_3_2_45_1","doi-asserted-by":"crossref","unstructured":"Immo Schuetz and Katja Fiehler. 2022. Eye tracking in virtual reality: Vive pro eye spatial accuracy precision and calibration reliability. Journal of Eye Movement Research 15 3 (2022) 10\u201316910.","DOI":"10.16910\/jemr.15.3.3"},{"key":"e_1_3_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/332040.332445"},{"key":"e_1_3_3_2_47_1","unstructured":"Kaleb\u00a0E Smith and Anthony\u00a0O Smith. 2020. Conditional GAN for timeseries generation. arxiv:https:\/\/arXiv.org\/abs\/2006.16477\u00a0[cs.LG]"},{"key":"e_1_3_3_2_48_1","unstructured":"Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens and Zbigniew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. arxiv:https:\/\/arXiv.org\/abs\/1512.00567\u00a0[cs.CV]"},{"key":"e_1_3_3_2_49_1","doi-asserted-by":"crossref","unstructured":"Rui Tian Xiao Li Wenxi Li Guojihong Li Keer Chen and Hanwei Dai. 2024. Using Pix2Pix conditional generative adversarial networks to generate personalized poster content: Style transfer and detail enhancement. Journal of Computational Methods in Sciences and Engineering (2024) 14727978241307472.","DOI":"10.1177\/14727978241307472"},{"key":"e_1_3_3_2_50_1","doi-asserted-by":"publisher","unstructured":"M\u00e9lodie Vidal Jayson Turner Andreas Bulling and Hans Gellersen. 2012. Wearable eye tracking for mental health monitoring. Computer Communications 35 11 (2012) 1306\u20131311. 10.1016\/j.comcom.2011.11.002","DOI":"10.1016\/j.comcom.2011.11.002"},{"key":"e_1_3_3_2_51_1","doi-asserted-by":"publisher","unstructured":"Pauli Virtanen Ralf Gommers Travis\u00a0E. Oliphant Matt Haberland Tyler Reddy David Cournapeau Evgeni Burovski Pearu Peterson Warren Weckesser Jonathan Bright St\u00e9fan\u00a0J. van der Walt Matthew Brett Joshua Wilson K.\u00a0Jarrod Millman Nikolay Mayorov Andrew R.\u00a0J. Nelson Eric Jones Robert Kern Eric Larson C\u00a0J Carey \u0130lhan Polat Yu Feng Eric\u00a0W. Moore Jake VanderPlas Denis Laxalde Josef Perktold Robert Cimrman Ian Henriksen E.\u00a0A. Quintero Charles\u00a0R. Harris Anne\u00a0M. Archibald Ant\u00f4nio\u00a0H. Ribeiro Fabian Pedregosa Paul van Mulbregt and SciPy 1.0 Contributors. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17 (2020) 261\u2013272. 10.1038\/s41592-019-0686-2","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_3_3_2_52_1","doi-asserted-by":"crossref","unstructured":"Zhe Xiao Xin Chen and Li Zhou. 2024. Music performance style transfer for learning expressive musical performance. Signal Image and Video Processing 18 1 (2024) 889\u2013898.","DOI":"10.1007\/s11760-023-02788-5"},{"key":"e_1_3_3_2_53_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Xu Yanyu","year":"2018","unstructured":"Yanyu Xu, Yanbing Dong, Junru Wu, Zhengzhong Sun, Zhiru Shi, Jingyi Yu, and Shenghua Gao. 2018. Gaze Prediction in Dynamic 360\u00b0 Immersive Videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_3_2_54_1","doi-asserted-by":"publisher","unstructured":"Raimondas Zemblys Diederick\u00a0C. Niehorster Oleg Komogortsev and Kenneth Holmqvist. 2018. Using machine learning to detect events in eye-tracking data. Behavior Research Methods 50 1 (01 Feb 2018) 160\u2013181. 10.3758\/s13428-017-0860-3","DOI":"10.3758\/s13428-017-0860-3"},{"key":"e_1_3_3_2_55_1","doi-asserted-by":"publisher","unstructured":"Yongtuo Zhang Wen Hu Weitao Xu Chun\u00a0Tung Chou and Jiankun Hu. 2018. Continuous Authentication Using Eye Movement Response of Implicit Visual Stimuli. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1 4 Article 177 (jan 2018) 22\u00a0pages. 10.1145\/3161410","DOI":"10.1145\/3161410"},{"key":"e_1_3_3_2_56_1","doi-asserted-by":"crossref","unstructured":"Qijie Zhao Xinming Yuan Dawei Tu and Jianxia Lu. 2015. Eye moving behaviors identification for gaze tracking interaction. Journal on Multimodal User Interfaces 9 2 (2015) 89\u2013104.","DOI":"10.1007\/s12193-014-0171-2"},{"key":"e_1_3_3_2_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"}],"event":{"name":"ETRA '25: 2025 Symposium on Eye Tracking Research and Applications","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"],"location":"Tokyo Japan","acronym":"ETRA '25"},"container-title":["Proceedings of the 2025 Symposium on Eye Tracking Research and Applications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715669.3726787","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:19:13Z","timestamp":1750295953000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715669.3726787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,25]]},"references-count":56,"alternative-id":["10.1145\/3715669.3726787","10.1145\/3715669"],"URL":"https:\/\/doi.org\/10.1145\/3715669.3726787","relation":{},"subject":[],"published":{"date-parts":[[2025,5,25]]},"assertion":[{"value":"2025-05-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}