{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T04:11:50Z","timestamp":1781064710352,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"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":[[2022,5,30]]},"DOI":"10.1145\/3494110.3528242","type":"proceedings-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T04:08:22Z","timestamp":1653365302000},"page":"21-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Transformers for End-to-End InfoSec Tasks"],"prefix":"10.1145","author":[{"given":"Ethan M.","family":"Rudd","sequence":"first","affiliation":[{"name":"Mandiant Inc., Reston, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Saidur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip","family":"Tully","sequence":"additional","affiliation":[{"name":"Mandiant Inc., Reston, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66402-6_6"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISI.2016.7745451"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7841028"},{"key":"e_1_3_2_2_5_1","volume-title":"IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 1--6.","author":"Alzhrani Khudran","year":"2017","unstructured":"Khudran Alzhrani , Ethan M Rudd , C Edward Chow , and Terrance E Boult . 2017 . Automated us diplomatic cables security classification: Topic model pruning vs. classification based on clusters . In IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 1--6. Khudran Alzhrani, Ethan M Rudd, C Edward Chow, and Terrance E Boult. 2017. Automated us diplomatic cables security classification: Topic model pruning vs. classification based on clusters. In IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 1--6."},{"key":"e_1_3_2_2_6_1","volume-title":"EMBER: an open dataset for training static pe malware machine learning models . arXiv preprint arXiv:1804.04637","author":"Anderson Hyrum S","year":"2018","unstructured":"Hyrum S Anderson and Phil Roth . 2018. EMBER: an open dataset for training static pe malware machine learning models . arXiv preprint arXiv:1804.04637 ( 2018 ). Hyrum S Anderson and Phil Roth. 2018. EMBER: an open dataset for training static pe malware machine learning models . arXiv preprint arXiv:1804.04637 (2018)."},{"key":"e_1_3_2_2_7_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 ( 2014 ). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"key":"e_1_3_2_2_8_1","volume-title":"Longformer: The long-document transformer . arXiv preprint arXiv:2004.05150","author":"Beltagy Iz","year":"2020","unstructured":"Iz Beltagy , Matthew E Peters , and Arman Cohan . 2020 . Longformer: The long-document transformer . arXiv preprint arXiv:2004.05150 (2020). Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer . arXiv preprint arXiv:2004.05150 (2020)."},{"key":"e_1_3_2_2_9_1","volume-title":"Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509","author":"Child Rewon","year":"2019","unstructured":"Rewon Child , Scott Gray , Alec Radford , and Ilya Sutskever . 2019. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509 ( 2019 ). Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509 (2019)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2019.00017"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2019.e01802"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1285"},{"key":"e_1_3_2_2_13_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics , 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 4171--4186."},{"key":"e_1_3_2_2_14_1","volume-title":"2019 a. Automatic Malware Description via Attribute Tagging and Similarity Embedding. arXiv preprint arXiv:1905.06262","author":"Ducau Felipe N","year":"2019","unstructured":"Felipe N Ducau , Ethan M Rudd , Tad M Heppner , Alex Long , and Konstantin Berlin . 2019 a. Automatic Malware Description via Attribute Tagging and Similarity Embedding. arXiv preprint arXiv:1905.06262 ( 2019 ). Felipe N Ducau, Ethan M Rudd, Tad M Heppner, Alex Long, and Konstantin Berlin. 2019 a. Automatic Malware Description via Attribute Tagging and Similarity Embedding. arXiv preprint arXiv:1905.06262 (2019)."},{"key":"e_1_3_2_2_15_1","volume-title":"2019 b. SMART: Semantic Malware Attribute Relevance Tagging . (2019). arxiv","author":"Ducau Felipe N","year":"1905","unstructured":"Felipe N Ducau , Ethan M Rudd , Tad M Heppner , Alex Long , and Konstantin Berlin . 2019 b. SMART: Semantic Malware Attribute Relevance Tagging . (2019). arxiv : 1905 .06262 Felipe N Ducau, Ethan M Rudd, Tad M Heppner, Alex Long, and Konstantin Berlin. 2019 b. SMART: Semantic Malware Attribute Relevance Tagging . (2019). arxiv: 1905.06262"},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 3280--3291","author":"Fu Daniel","year":"2020","unstructured":"Daniel Fu , Mayee Chen , Frederic Sala , Sarah Hooper , Kayvon Fatahalian , and Christopher R\u00e9 . 2020 . Fast and three-rious: Speeding up weak supervision with triplet methods . In International Conference on Machine Learning (ICML). PMLR, 3280--3291 . Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher R\u00e9. 2020. Fast and three-rious: Speeding up weak supervision with triplet methods. In International Conference on Machine Learning (ICML). PMLR, 3280--3291."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-40667-1_20"},{"key":"e_1_3_2_2_19_1","volume-title":"Reformer: The Efficient Transformer. In International Conference on Learning Representations (ICLR) .","author":"Kitaev Nikita","year":"2020","unstructured":"Nikita Kitaev , Lukasz Kaiser , and Anselm Levskaya . 2020 . Reformer: The Efficient Transformer. In International Conference on Learning Representations (ICLR) . Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2020. Reformer: The Efficient Transformer. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_2_20_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NeurIPS). 1097--1105.  Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NeurIPS). 1097--1105."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW50608.2020.00018"},{"key":"e_1_3_2_2_22_1","volume-title":"International Conference on Learning Representations (ICLR) .","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 . In International Conference on Learning Representations (ICLR) . 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. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_2_23_1","volume-title":"URLNet: Learning a URL representation with deep learning for malicious URL detection . arXiv preprint arXiv:1802.03162","author":"Le Hung","year":"2018","unstructured":"Hung Le , Quang Pham , Doyen Sahoo , and Steven CH Hoi . 2018. URLNet: Learning a URL representation with deep learning for malicious URL detection . arXiv preprint arXiv:1802.03162 ( 2018 ). Hung Le, Quang Pham, Doyen Sahoo, and Steven CH Hoi. 2018. URLNet: Learning a URL representation with deep learning for malicious URL detection . arXiv preprint arXiv:1802.03162 (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"I-MAD: A Novel Interpretable Malware Detector Using Hierarchical Transformer . arXiv preprint arXiv:1909.06865","author":"Li Miles Q","year":"2019","unstructured":"Miles Q Li , Benjamin Fung , Philippe Charland , and Steven HH Ding . 2019. I-MAD: A Novel Interpretable Malware Detector Using Hierarchical Transformer . arXiv preprint arXiv:1909.06865 ( 2019 ). Miles Q Li, Benjamin Fung, Philippe Charland, and Steven HH Ding. 2019. I-MAD: A Novel Interpretable Malware Detector Using Hierarchical Transformer . arXiv preprint arXiv:1909.06865 (2019)."},{"key":"e_1_3_2_2_25_1","volume-title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach . arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu , Myle Ott , Naman Goyal , Jingfei Du , Mandar Joshi , Danqi Chen , Omer Levy , Mike Lewis , Luke Zettlemoyer , and Veselin Stoyanov . 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach . arXiv preprint arXiv:1907.11692 ( 2019 ). Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach . arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_2_26_1","volume-title":"Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems (NeurIPS). 13--23.","author":"Lu Jiasen","year":"2019","unstructured":"Jiasen Lu , Dhruv Batra , Devi Parikh , and Stefan Lee . 2019 . Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems (NeurIPS). 13--23. Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems (NeurIPS). 13--23."},{"key":"e_1_3_2_2_27_1","volume-title":"Italian Conference on Cyber Security (ITASEC)","volume":"2315","author":"Luca Demetrio","year":"2019","unstructured":"Demetrio Luca , Battista Biggio , Lagorio Giovanni , Fabio Roli , and Armando Alessandro . 2019 . Explaining vulnerabilities of deep learning to adversarial malware binaries . In Italian Conference on Cyber Security (ITASEC) , Vol. 2315 . Demetrio Luca, Battista Biggio, Lagorio Giovanni, Fabio Roli, and Armando Alessandro. 2019. Explaining vulnerabilities of deep learning to adversarial malware binaries. In Italian Conference on Cyber Security (ITASEC), Vol. 2315."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178304"},{"key":"e_1_3_2_2_29_1","volume-title":"et almbox","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa , Ga\u00ebl Varoquaux , Alexandre Gramfort , Vincent Michel , Bertrand Thirion , Olivier Grisel , Mathieu Blondel , Peter Prettenhofer , Ron Weiss , Vincent Dubourg , et almbox . 2011 . Scikit-learn : Machine learning in Python. the Journal of machine Learning research , Vol. 12 (2011), 2825--2830. Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et almbox. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research , Vol. 12 (2011), 2825--2830."},{"key":"e_1_3_2_2_30_1","volume-title":"Trex: Learning execution semantics from micro-traces for binary similarity . arXiv preprint arXiv:2012.08680","author":"Pei Kexin","year":"2020","unstructured":"Kexin Pei , Zhou Xuan , Junfeng Yang , Suman Jana , and Baishakhi Ray . 2020 . Trex: Learning execution semantics from micro-traces for binary similarity . arXiv preprint arXiv:2012.08680 (2020). Kexin Pei, Zhou Xuan, Junfeng Yang, Suman Jana, and Baishakhi Ray. 2020. Trex: Learning execution semantics from micro-traces for binary similarity . arXiv preprint arXiv:2012.08680 (2020)."},{"key":"e_1_3_2_2_31_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training.  Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training."},{"key":"e_1_3_2_2_32_1","volume-title":"Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence .","author":"Raff Edward","year":"2018","unstructured":"Edward Raff , Jon Barker , Jared Sylvester , Robert Brandon , Bryan Catanzaro , and Charles K Nicholas . 2018 . Malware detection by eating a whole EXE . In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence . Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, and Charles K Nicholas. 2018. Malware detection by eating a whole EXE. In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence ."},{"key":"e_1_3_2_2_33_1","unstructured":"Edward Raff William Fleming Richard Zak Hyrum Anderson Bill Finlayson Charles Nicholas and Mark McLean. 2019. KiloGrams: Very Large N-Grams for Malware Classification. In Learning and Mining for Cybersecurity (LEMINCS) .  Edward Raff William Fleming Richard Zak Hyrum Anderson Bill Finlayson Charles Nicholas and Mark McLean. 2019. KiloGrams: Very Large N-Grams for Malware Classification. In Learning and Mining for Cybersecurity (LEMINCS) ."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3039691"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3305-0"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.14778\/3157794.3157797"},{"key":"e_1_3_2_2_37_1","volume-title":"ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation. In USENIX Security Symposium . 303--320","author":"Rudd Ethan M","year":"2019","unstructured":"Ethan M Rudd , Felipe N Ducau , Cody Wild , Konstantin Berlin , and Richard Harang . 2019 . ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation. In USENIX Security Symposium . 303--320 . Ethan M Rudd, Felipe N Ducau, Cody Wild, Konstantin Berlin, and Richard Harang. 2019. ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation. In USENIX Security Symposium . 303--320."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_2"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/THS.2018.8574202"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2016.2636078"},{"key":"e_1_3_2_2_41_1","volume-title":"Malicious URL detection using machine learning: A survey. arXiv preprint arXiv:1701.07179","author":"Sahoo Doyen","year":"2017","unstructured":"Doyen Sahoo , Chenghao Liu , and Steven CH Hoi . 2017. Malicious URL detection using machine learning: A survey. arXiv preprint arXiv:1701.07179 ( 2017 ). Doyen Sahoo, Chenghao Liu, and Steven CH Hoi. 2017. Malicious URL detection using machine learning: A survey. arXiv preprint arXiv:1701.07179 (2017)."},{"key":"e_1_3_2_2_42_1","volume-title":"eXpose: A character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys . arXiv preprint arXiv:1702.08568","author":"Saxe Joshua","year":"2017","unstructured":"Joshua Saxe and Konstantin Berlin . 2017. eXpose: A character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys . arXiv preprint arXiv:1702.08568 ( 2017 ). Joshua Saxe and Konstantin Berlin. 2017. eXpose: A character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys . arXiv preprint arXiv:1702.08568 (2017)."},{"key":"e_1_3_2_2_43_1","first-page":"1","article-title":"Weaponizing data science for social engineering: Automated E2E spear phishing on Twitter","volume":"37","author":"Seymour John","year":"2016","unstructured":"John Seymour and Philip Tully . 2016 . Weaponizing data science for social engineering: Automated E2E spear phishing on Twitter . Black Hat USA , Vol. 37 (2016), 1 -- 39 . John Seymour and Philip Tully. 2016. Weaponizing data science for social engineering: Automated E2E spear phishing on Twitter. Black Hat USA , Vol. 37 (2016), 1--39.","journal-title":"Black Hat USA"},{"key":"e_1_3_2_2_44_1","volume-title":"Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning. In ACM SIGSAC Conference on Computer and Communications Security (CCS). 1131--1148","author":"Sirinam Payap","year":"2019","unstructured":"Payap Sirinam , Nate Mathews , Mohammad Saidur Rahman , and Matthew Wright . 2019 . Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning. In ACM SIGSAC Conference on Computer and Communications Security (CCS). 1131--1148 . Payap Sirinam, Nate Mathews, Mohammad Saidur Rahman, and Matthew Wright. 2019. Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning. In ACM SIGSAC Conference on Computer and Communications Security (CCS). 1131--1148."},{"key":"e_1_3_2_2_45_1","volume-title":"Adaptive Attention Span in Transformers. In Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 331--335","author":"Sukhbaatar Sainbayar","year":"2019","unstructured":"Sainbayar Sukhbaatar , Edouard Grave , Piotr Bojanowski , and Armand Joulin . 2019 . Adaptive Attention Span in Transformers. In Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 331--335 . Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, and Armand Joulin. 2019. Adaptive Attention Span in Transformers. In Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 331--335."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.335"},{"key":"e_1_3_2_2_47_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS). 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS). 5998--6008."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489147"}],"event":{"name":"ASIA CCS '22: ACM Asia Conference on Computer and Communications Security","location":"Nagasaki Japan","acronym":"ASIA CCS '22","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 1st Workshop on Robust Malware Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3494110.3528242","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3494110.3528242","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:45Z","timestamp":1750188645000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3494110.3528242"}},"subtitle":["A Feasibility Study"],"short-title":[],"issued":{"date-parts":[[2022,5,30]]},"references-count":48,"alternative-id":["10.1145\/3494110.3528242","10.1145\/3494110"],"URL":"https:\/\/doi.org\/10.1145\/3494110.3528242","relation":{},"subject":[],"published":{"date-parts":[[2022,5,30]]},"assertion":[{"value":"2022-05-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}