{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T03:16:52Z","timestamp":1777173412996,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"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":[[2023,6,18]]},"DOI":"10.1145\/3581791.3596853","type":"proceedings-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T17:52:21Z","timestamp":1686937941000},"page":"275-288","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["EMSAssist: An End-to-End Mobile Voice Assistant at the Edge for Emergency Medical Services"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0115-184X","authenticated-orcid":false,"given":"Liuyi","family":"Jin","sequence":"first","affiliation":[{"name":"Texas A&amp;M University, College Station, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1178-9548","authenticated-orcid":false,"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3902-8636","authenticated-orcid":false,"given":"Amran","family":"Haroon","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3976-4502","authenticated-orcid":false,"given":"Radu","family":"Stoleru","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, United States"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0828-6839","authenticated-orcid":false,"given":"Michael","family":"Middleton","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3990-4774","authenticated-orcid":false,"given":"Ziwei","family":"Zhu","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7603-8633","authenticated-orcid":false,"given":"Theodora","family":"Chaspari","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,6,18]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Report from 2018 warned of \"major damage\". https:\/\/www.bbc.com\/news\/world-us-canada-57621774","author":"BBC.","year":"2021","unstructured":"BBC. Florida building collapse : Report from 2018 warned of \"major damage\". https:\/\/www.bbc.com\/news\/world-us-canada-57621774 , 2021 . [Online; accessed 31-March-2022]. BBC. Florida building collapse: Report from 2018 warned of \"major damage\". https:\/\/www.bbc.com\/news\/world-us-canada-57621774, 2021. [Online; accessed 31-March-2022]."},{"key":"e_1_3_2_1_2_1","volume-title":"Surfside condominium collapse. https:\/\/en.wikipedia.org\/wiki\/Surfside_condominium_collapse","year":"2022","unstructured":"Wikipedia. Surfside condominium collapse. https:\/\/en.wikipedia.org\/wiki\/Surfside_condominium_collapse , 2022 . [Online; accessed 31-March-2022]. Wikipedia. Surfside condominium collapse. https:\/\/en.wikipedia.org\/wiki\/Surfside_condominium_collapse, 2022. [Online; accessed 31-March-2022]."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/DMP.0b013e31818aaf55"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2016.09.016"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPS.2018.00047"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357495.3357502"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8968233"},{"key":"e_1_3_2_1_8_1","volume-title":"Anaphylaxis: Emergency treatment. https:\/\/www.uptodate.com\/contents\/anaphylaxis-emergency-treatment","year":"2023","unstructured":"UpToDate. Anaphylaxis: Emergency treatment. https:\/\/www.uptodate.com\/contents\/anaphylaxis-emergency-treatment , 2023 . [Online; accessed 21-March-2023]. UpToDate. Anaphylaxis: Emergency treatment. https:\/\/www.uptodate.com\/contents\/anaphylaxis-emergency-treatment, 2023. [Online; accessed 21-March-2023]."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CloudNet.2015.7335296"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SEC.2018.00027"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICFC49376.2020.00018"},{"key":"e_1_3_2_1_12_1","first-page":"2703","volume-title":"29th USENIX Security Symposium (USENIX Security 20)","author":"Ahmed Shimaa","year":"2020","unstructured":"Shimaa Ahmed , Amrita Roy Chowdhury , Kassem Fawaz , and Parmesh Ramanathan . Preech : A system for Privacy-Preserving speech transcription . In 29th USENIX Security Symposium (USENIX Security 20) , pages 2703 -- 2720 . USENIX Association , August 2020 . Shimaa Ahmed, Amrita Roy Chowdhury, Kassem Fawaz, and Parmesh Ramanathan. Preech: A system for Privacy-Preserving speech transcription. In 29th USENIX Security Symposium (USENIX Security 20), pages 2703--2720. USENIX Association, August 2020."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl066"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12617"},{"key":"e_1_3_2_1_17_1","volume-title":"ISCA","author":"Park Daniel","year":"2019","unstructured":"Daniel Park , William Chan , Yu Zhang , Chung-Cheng Chiu , Barret Zoph , Ekin Cubuk , and Quoc Le. SpecAugment : A simple data augmentation method for automatic speech recognition. In Interspeech 2019 . ISCA , Sep 2019 . Daniel Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin Cubuk, and Quoc Le. SpecAugment: A simple data augmentation method for automatic speech recognition. In Interspeech 2019. ISCA, Sep 2019."},{"key":"e_1_3_2_1_18_1","volume-title":"ISCA","author":"Gulati Anmol","year":"2020","unstructured":"Anmol Gulati , James Qin , Chung-Cheng Chiu , Niki Parmar , Yu Zhang , Jiahui Yu , Wei Han , Shibo Wang , Zhengdong Zhang , Yonghui Wu , and Ruoming Pang . Conformer : Convolution-augmented transformer for speech recognition. In Interspeech 2020 . ISCA , Oct 2020 . Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, and Ruoming Pang. Conformer: Convolution-augmented transformer for speech recognition. In Interspeech 2020. ISCA, Oct 2020."},{"key":"e_1_3_2_1_19_1","volume-title":"https:\/\/cloud.google.com\/speech-to-text\/","author":"Text Cloud","year":"2022","unstructured":"Google. Cloud Speech-to- Text . https:\/\/cloud.google.com\/speech-to-text\/ , 2022 . [Online; accessed 30-Nov-2022]. Google. Cloud Speech-to-Text. https:\/\/cloud.google.com\/speech-to-text\/, 2022. [Online; accessed 30-Nov-2022]."},{"key":"e_1_3_2_1_20_1","volume-title":"Recent advances in end-to-end automatic speech recognition. ArXiv, 2111.01690","author":"Li Jinyu","year":"2022","unstructured":"Jinyu Li . Recent advances in end-to-end automatic speech recognition. ArXiv, 2111.01690 , 2022 . Jinyu Li. Recent advances in end-to-end automatic speech recognition. ArXiv, 2111.01690, 2022."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178964"},{"key":"e_1_3_2_1_22_1","first-page":"128","volume-title":"Proceedings, Part XXXIV","author":"Panagiotakopoulos Theodoros","year":"2022","unstructured":"Theodoros Panagiotakopoulos , Pier Luigi Dovesi , Linus H\u00e4renstam-Nielsen , and Matteo Poggi . Online domain adaptation for semantic segmentation in ever-changing conditions. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022 , Proceedings, Part XXXIV , pages 128 -- 146 . Springer , 2022 . Theodoros Panagiotakopoulos, Pier Luigi Dovesi, Linus H\u00e4renstam-Nielsen, and Matteo Poggi. Online domain adaptation for semantic segmentation in ever-changing conditions. In Computer Vision-ECCV 2022:17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXXIV, pages 128--146. Springer, 2022."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054505"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00408"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01081"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.139"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458754"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-short.72"},{"key":"e_1_3_2_1_29_1","volume-title":"Number of voice assistants in use worldwide 2019--2023. https:\/\/www.statista.com\/statistics\/973815\/worldwide-digital-voice-assistant-in-use","year":"2022","unstructured":"Statista. Number of voice assistants in use worldwide 2019--2023. https:\/\/www.statista.com\/statistics\/973815\/worldwide-digital-voice-assistant-in-use , 2022 . [Online; accessed 01-Feb-2022]. Statista. Number of voice assistants in use worldwide 2019--2023. https:\/\/www.statista.com\/statistics\/973815\/worldwide-digital-voice-assistant-in-use, 2022. [Online; accessed 01-Feb-2022]."},{"key":"e_1_3_2_1_30_1","unstructured":"Department of Homeland Security. Snapshot: Public safety agencies pilot artificial intelligence to aid in first response. https:\/\/www.dhs.gov\/science-and-technology\/news\/2018\/10\/16\/snapshot-public-safety-agencies-pilot-artificial-intelligence 2018. [Online; accessed 01-Feb-2022].  Department of Homeland Security. Snapshot: Public safety agencies pilot artificial intelligence to aid in first response. https:\/\/www.dhs.gov\/science-and-technology\/news\/2018\/10\/16\/snapshot-public-safety-agencies-pilot-artificial-intelligence 2018. [Online; accessed 01-Feb-2022]."},{"key":"e_1_3_2_1_31_1","volume-title":"S&T, canadian counterparts evaluate audrey in use case. https:\/\/www.dhs.gov\/science-and-technology\/news\/2019\/08\/13\/snapshot-st-canadian-counterparts-evaluate-audrey-use-case","author":"Department of Homeland Security. Snapshot","year":"2019","unstructured":"Department of Homeland Security. Snapshot : S&T, canadian counterparts evaluate audrey in use case. https:\/\/www.dhs.gov\/science-and-technology\/news\/2019\/08\/13\/snapshot-st-canadian-counterparts-evaluate-audrey-use-case , 2019 . [Online; accessed 01-Feb-2022]. Department of Homeland Security. Snapshot: S&T, canadian counterparts evaluate audrey in use case. https:\/\/www.dhs.gov\/science-and-technology\/news\/2019\/08\/13\/snapshot-st-canadian-counterparts-evaluate-audrey-use-case, 2019. [Online; accessed 01-Feb-2022]."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i08.7048"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CHASE52844.2021.00014"},{"key":"e_1_3_2_1_34_1","volume-title":"April","author":"Mann Clay","year":"2015","unstructured":"Clay Mann , Lauren Kane , Mengtao Dai , and Karen Jacobson . Description of the 2012 NEMSIS Public-Release Research Dataset. Prehospital Emergency Care, 19(2):232--240 , April 2015 . Clay Mann, Lauren Kane, Mengtao Dai, and Karen Jacobson. Description of the 2012 NEMSIS Public-Release Research Dataset. Prehospital Emergency Care, 19(2):232--240, April 2015."},{"key":"e_1_3_2_1_35_1","volume-title":"Promoting innovation in emergency medical services. https:\/\/emsinnovations.org\/","author":"Munjal Kevin","year":"2018","unstructured":"Kevin Munjal , Hugh Chapin , Taylor Miller , Christopher Kahn , Lynne Richardson , and James Dunford on behalf of the Promoting Innovation in EMS Steering Committee . Promoting innovation in emergency medical services. https:\/\/emsinnovations.org\/ , 2018 . [Online; accessed 31-March-2022]. Kevin Munjal, Hugh Chapin, Taylor Miller, Christopher Kahn, Lynne Richardson, and James Dunford on behalf of the Promoting Innovation in EMS Steering Committee. Promoting innovation in emergency medical services. https:\/\/emsinnovations.org\/, 2018. [Online; accessed 31-March-2022]."},{"key":"e_1_3_2_1_36_1","first-page":"17","volume-title":"Proceedings. AMIA Symposium","author":"Aronson Alan","year":"2001","unstructured":"Alan Aronson . Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program . Proceedings. AMIA Symposium , pages 17 -- 21 , 2001 . Alan Aronson. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proceedings. AMIA Symposium, pages 17--21, 2001."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_2_1_38_1","volume-title":"Contextnet: Improving convolutional neural networks for automatic speech recognition with global context. ArXiv, abs\/2005.03191","author":"Han Wei","year":"2020","unstructured":"Wei Han , Zhengdong Zhang , Yu Zhang , Jiahui Yu , Chung-Cheng Chiu , James Qin , Anmol Gulati , Ruoming Pang , and Yonghui Wu . Contextnet: Improving convolutional neural networks for automatic speech recognition with global context. ArXiv, abs\/2005.03191 , 2020 . Wei Han, Zhengdong Zhang, Yu Zhang, Jiahui Yu, Chung-Cheng Chiu, James Qin, Anmol Gulati, Ruoming Pang, and Yonghui Wu. Contextnet: Improving convolutional neural networks for automatic speech recognition with global context. ArXiv, abs\/2005.03191, 2020."},{"key":"e_1_3_2_1_39_1","volume-title":"Google's neural machine translation system: Bridging the gap between human and machine translation. ArXiv, abs\/1609.08144","author":"Wu Yonghui","year":"2016","unstructured":"Yonghui Wu , Mike Schuster , Z. Chen , Quoc V. Le , Mohammad Norouzi , Wolfgang Macherey , Maxim Krikun , Yuan Cao , Qin Gao , Klaus Macherey , Jeff Klingner , Apurva Shah , Melvin Johnson , Xiaobing Liu , Lukasz Kaiser , Stephan Gouws , Yoshikiyo Kato , Taku Kudo , Hideto Kazawa , Keith Stevens , George Kurian , Nishant Patil , Wei Wang , Cliff Young , Jason R. Smith , Jason Riesa , Alex Rudnick , Oriol Vinyals , Gregory S. Corrado , Macduff Hughes , and Jeffrey Dean . Google's neural machine translation system: Bridging the gap between human and machine translation. ArXiv, abs\/1609.08144 , 2016 . Yonghui Wu, Mike Schuster, Z. Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason R. Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Gregory S. Corrado, Macduff Hughes, and Jeffrey Dean. Google's neural machine translation system: Bridging the gap between human and machine translation. ArXiv, abs\/1609.08144, 2016."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.160"},{"key":"e_1_3_2_1_41_1","volume-title":"ArXiv, 1810","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . BERT : Pre-training of deep bidirectional transformers for language understanding . ArXiv, 1810 .04805, 2018 . Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv, 1810.04805, 2018."},{"key":"e_1_3_2_1_42_1","first-page":"02984","article-title":"a compact task-agnostic bert for resource-limited devices","author":"Sun Zhiqing","year":"2004","unstructured":"Zhiqing Sun , Hongkun Yu , Xiaodan Song , Renjie Liu , Yiming Yang , and Denny Zhou . MobileBERT : a compact task-agnostic bert for resource-limited devices . ArXiv , 2004 . 02984 , 2020. Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. MobileBERT: a compact task-agnostic bert for resource-limited devices. ArXiv, 2004.02984, 2020.","journal-title":"ArXiv"},{"key":"e_1_3_2_1_43_1","volume-title":"Your complete reference source. https:\/\/www.radioreference.com\/","author":"Radioreference LLC.","year":"2022","unstructured":"RadioReference.com LLC. Radioreference .com : Your complete reference source. https:\/\/www.radioreference.com\/ , 2022 . [Online; accessed 30-March-2022]. RadioReference.com LLC. Radioreference.com: Your complete reference source. https:\/\/www.radioreference.com\/, 2022. [Online; accessed 30-March-2022]."},{"key":"e_1_3_2_1_44_1","volume-title":"TensorFlowASR: Almost state-of-the-art automatic speech recognition in tensorflow 2. https:\/\/github.com\/TensorSpeech\/TensorFlowASR","author":"Nguyen Huy Le","year":"2022","unstructured":"Huy Le Nguyen . TensorFlowASR: Almost state-of-the-art automatic speech recognition in tensorflow 2. https:\/\/github.com\/TensorSpeech\/TensorFlowASR , 2022 . [Online; accessed 30-March-2022]. Huy Le Nguyen. TensorFlowASR: Almost state-of-the-art automatic speech recognition in tensorflow 2. https:\/\/github.com\/TensorSpeech\/TensorFlowASR, 2022. [Online; accessed 30-March-2022]."},{"key":"e_1_3_2_1_45_1","volume-title":"Sequence transduction with recurrent neural networks. ArXiv, 1211.3711","author":"Graves Alex","year":"2012","unstructured":"Alex Graves . Sequence transduction with recurrent neural networks. ArXiv, 1211.3711 , 2012 . Alex Graves. Sequence transduction with recurrent neural networks. ArXiv, 1211.3711, 2012."},{"key":"e_1_3_2_1_46_1","volume-title":"https:\/\/github.com\/google-research\/bert","author":"Research Google","year":"2018","unstructured":"Google Research . BERT. https:\/\/github.com\/google-research\/bert , 2018 . [Online; accessed 30-Nov-2022]. Google Research. BERT. https:\/\/github.com\/google-research\/bert, 2018. [Online; accessed 30-Nov-2022]."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"e_1_3_2_1_48_1","first-page":"05342","article-title":"Modeling clinical notes and predicting hospital readmission","author":"Huang Kexin","year":"1904","unstructured":"Kexin Huang , Jaan Altosaar , and Rajesh Ranganath . ClinicalBERT : Modeling clinical notes and predicting hospital readmission . ArXiv , 1904 . 05342 , 2019. Kexin Huang, Jaan Altosaar, and Rajesh Ranganath. ClinicalBERT: Modeling clinical notes and predicting hospital readmission. ArXiv, 1904.05342, 2019.","journal-title":"ArXiv"},{"key":"e_1_3_2_1_49_1","first-page":"4604","volume-title":"Medical Inference and Disease Name Recognition. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"He Yun","year":"2020","unstructured":"Yun He , Ziwei Zhu , Yin Zhang , Qin Chen , and James Caverlee . Infusing Disease Knowledge into BERT for Health Question Answering , Medical Inference and Disease Name Recognition. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 4604 -- 4614 , Online , November 2020 . Association for Computational Linguistics. Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, and James Caverlee. Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4604--4614, Online, November 2020. Association for Computational Linguistics."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-5006"},{"key":"e_1_3_2_1_51_1","volume-title":"Proceedings of the AAAI Conference on ArtificialIntelligence, 35(8):6679--6687","author":"Sercan","year":"2021","unstructured":"Sercan \u00d6. Arik and Tomas Pfister. Tabnet: Attentive interpretable tabular learning . Proceedings of the AAAI Conference on ArtificialIntelligence, 35(8):6679--6687 , May 2021 . Sercan \u00d6. Arik and Tomas Pfister. Tabnet: Attentive interpretable tabular learning. Proceedings of the AAAI Conference on ArtificialIntelligence, 35(8):6679--6687, May 2021."},{"key":"e_1_3_2_1_52_1","first-page":"18932","article-title":"Revisiting deep learning models for tabular data","volume":"34","author":"Gorishniy Yury","year":"2021","unstructured":"Yury Gorishniy , Ivan Rubachev , Valentin Khrulkov , and Artem Babenko . Revisiting deep learning models for tabular data . Advances in Neural Information Processing Systems , 34 : 18932 -- 18943 , 2021 . Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems, 34:18932--18943, 2021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327546.3327555"},{"key":"e_1_3_2_1_54_1","volume-title":"Master cog document effective 03.11.22. https:\/\/www.austintexas.gov\/sites\/default\/files\/files\/OCMO\/COGMasterDocument2022.pdf","author":"Austin","year":"2022","unstructured":"Austin EMS. Master cog document effective 03.11.22. https:\/\/www.austintexas.gov\/sites\/default\/files\/files\/OCMO\/COGMasterDocument2022.pdf , 2022 . [Online; accessed 05-November-2022]. Austin EMS. Master cog document effective 03.11.22. https:\/\/www.austintexas.gov\/sites\/default\/files\/files\/OCMO\/COGMasterDocument2022.pdf, 2022. [Online; accessed 05-November-2022]."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw177"},{"key":"e_1_3_2_1_56_1","volume-title":"https:\/\/tfhub.dev\/tensorflow\/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT\/1","author":"Hub Tensorflow","year":"2022","unstructured":"Tensorflow Hub . MobileBERT_en_uncased_l-24_h-128_b-512_a-4_f-4_opt. https:\/\/tfhub.dev\/tensorflow\/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT\/1 , 2022 . [Online; accessed 30-March-2022]. Tensorflow Hub. MobileBERT_en_uncased_l-24_h-128_b-512_a-4_f-4_opt. https:\/\/tfhub.dev\/tensorflow\/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT\/1, 2022. [Online; accessed 30-March-2022]."},{"key":"e_1_3_2_1_57_1","first-page":"381","volume-title":"Proceedings of the 13th International Conference on Neural Information Processing Systems, NIPS'00","author":"Caruana Rich","year":"2000","unstructured":"Rich Caruana , Steve Lawrence , and Lee Giles . Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping . In Proceedings of the 13th International Conference on Neural Information Processing Systems, NIPS'00 , page 381 -- 387 , Cambridge, MA, USA , 2000 . MIT Press. Rich Caruana, Steve Lawrence, and Lee Giles. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. In Proceedings of the 13th International Conference on Neural Information Processing Systems, NIPS'00, page 381--387, Cambridge, MA, USA, 2000. MIT Press."},{"key":"e_1_3_2_1_58_1","first-page":"24392","article-title":"Understanding and improving early stopping for learning with noisy labels","volume":"34","author":"Bai Yingbin","year":"2021","unstructured":"Yingbin Bai , Erkun Yang , Bo Han , Yanhua Yang , Jiatong Li , Yinian Mao , Gang Niu , and Tongliang Liu . Understanding and improving early stopping for learning with noisy labels . Advances in Neural Information Processing Systems , 34 : 24392 -- 24403 , 2021 . Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, and Tongliang Liu. Understanding and improving early stopping for learning with noisy labels. Advances in Neural Information Processing Systems, 34:24392--24403, 2021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_59_1","volume-title":"Deploy machine learning models on mobile and iot devices. https:\/\/www.tensorflow.org\/lite","author":"TensorFlow Lite Google","year":"2022","unstructured":"Google TensorFlow Lite . Deploy machine learning models on mobile and iot devices. https:\/\/www.tensorflow.org\/lite , 2022 . [Online; accessed 31-March-2022]. Google TensorFlow Lite. Deploy machine learning models on mobile and iot devices. https:\/\/www.tensorflow.org\/lite, 2022. [Online; accessed 31-March-2022]."},{"key":"e_1_3_2_1_60_1","volume-title":"Model optimization. https:\/\/www.tensorflow.org\/lite\/performance\/model_optimization","author":"TensorFlow Lite Google","year":"2022","unstructured":"Google TensorFlow Lite . Model optimization. https:\/\/www.tensorflow.org\/lite\/performance\/model_optimization , 2022 . [Online; accessed 31-March-2022]. Google TensorFlow Lite. Model optimization. https:\/\/www.tensorflow.org\/lite\/performance\/model_optimization, 2022. [Online; accessed 31-March-2022]."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_62_1","volume-title":"bert_en_uncased_l-12_h-768_a-12. https:\/\/tfhub.dev\/tensorflow\/bert_en_uncased_L-12_H-768_A-12\/4","author":"Hub Tensorflow","year":"2022","unstructured":"Tensorflow Hub . bert_en_uncased_l-12_h-768_a-12. https:\/\/tfhub.dev\/tensorflow\/bert_en_uncased_L-12_H-768_A-12\/4 , 2022 . [Online; accessed 30-March-2022]. Tensorflow Hub. bert_en_uncased_l-12_h-768_a-12. https:\/\/tfhub.dev\/tensorflow\/bert_en_uncased_L-12_H-768_A-12\/4, 2022. [Online; accessed 30-March-2022]."},{"key":"e_1_3_2_1_63_1","volume-title":"experts\/bert\/pubmed. https:\/\/tfhub.dev\/google\/experts\/bert\/pubmed\/2","author":"Hub Tensorflow","year":"2022","unstructured":"Tensorflow Hub . experts\/bert\/pubmed. https:\/\/tfhub.dev\/google\/experts\/bert\/pubmed\/2 , 2022 . [Online; accessed 30-March-2022]. Tensorflow Hub. experts\/bert\/pubmed. https:\/\/tfhub.dev\/google\/experts\/bert\/pubmed\/2, 2022. [Online; accessed 30-March-2022]."},{"key":"e_1_3_2_1_64_1","volume-title":"8th International Conference on Learning Representations, ICLR 2020","author":"Lan Zhenzhong","year":"2020","unstructured":"Zhenzhong Lan , Mingda Chen , Sebastian Goodman , Kevin Gimpel , Piyush Sharma , and Radu Soricut . ALBERT : A lite BERT for self-supervised learning of language representations . In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia, April 26--30 , 2020 . OpenReview.net, 2020. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. ALBERT: A lite BERT for self-supervised learning of language representations. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net, 2020."},{"key":"e_1_3_2_1_65_1","volume-title":"https:\/\/tfhub.dev\/google\/albert_base\/2","author":"Hub Tensorflow","year":"2022","unstructured":"Tensorflow Hub . ALBERT_base. https:\/\/tfhub.dev\/google\/albert_base\/2 , 2022 . [Online; accessed 30-March-2022]. Tensorflow Hub. ALBERT_base. https:\/\/tfhub.dev\/google\/albert_base\/2, 2022. [Online; accessed 30-March-2022]."}],"event":{"name":"MobiSys '23: 21st Annual International Conference on Mobile Systems, Applications and Services","location":"Helsinki Finland","acronym":"MobiSys '23","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581791.3596853","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:31Z","timestamp":1750178191000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581791.3596853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":64,"alternative-id":["10.1145\/3581791.3596853","10.1145\/3581791"],"URL":"https:\/\/doi.org\/10.1145\/3581791.3596853","relation":{},"subject":[],"published":{"date-parts":[[2023,6,18]]},"assertion":[{"value":"2023-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}