{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T17:08:47Z","timestamp":1781284127919,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"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":[[2019,11,7]]},"DOI":"10.1145\/3318216.3363310","type":"proceedings-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T14:11:35Z","timestamp":1572876695000},"page":"209-221","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Collaborative cloud-edge computation for personalized driving behavior modeling"],"prefix":"10.1145","author":[{"given":"Xingzhou","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China and Wayne State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu","family":"Qiao","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangkai","family":"Liu","sequence":"additional","affiliation":[{"name":"Wayne State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfei","family":"Xu","sequence":"additional","affiliation":[{"name":"DENSO International America Inc"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weisong","family":"Shi","sequence":"additional","affiliation":[{"name":"Wayne State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Collaborative Learning on the Edges: A Case Study on Connected Vehicles. In 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19)","unstructured":"2019. Collaborative Learning on the Edges: A Case Study on Connected Vehicles. In 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19) . USENIX Association, Renton, WA. https:\/\/www.usenix.org\/conference\/hotedge19\/presentation\/lu 2019. Collaborative Learning on the Edges: A Case Study on Connected Vehicles. In 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19). USENIX Association, Renton, WA. https:\/\/www.usenix.org\/conference\/hotedge19\/presentation\/lu"},{"key":"e_1_3_2_1_2_1","volume-title":"Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning. arXiv preprint arXiv:1705.07356","author":"Abbasi-Asl Reza","year":"2017","unstructured":"Reza Abbasi-Asl and Bin Yu. 2017. Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning. arXiv preprint arXiv:1705.07356 ( 2017 ). Reza Abbasi-Asl and Bin Yu. 2017. Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning. arXiv preprint arXiv:1705.07356 (2017)."},{"key":"e_1_3_2_1_3_1","first-page":"231","article-title":"Methods to Determine a Vehicle Insurance Premium Based on Vehicle Operation Data Collected Via a Mobile Device","volume":"13","author":"Bowne Benjamin F","year":"2013","unstructured":"Benjamin F Bowne , Nicholas R Baker , Duane Lee Marzinzik , Matthew Eric Riley , Nick U Christopulos , Brian Mark Fields , J Lynn Wilson , Bryan T Wilkerson , David W Thurber , 2013 . Methods to Determine a Vehicle Insurance Premium Based on Vehicle Operation Data Collected Via a Mobile Device . US Patent App. 13\/763 , 231 . Benjamin F Bowne, Nicholas R Baker, Duane Lee Marzinzik, Matthew Eric Riley, Nick U Christopulos, Brian Mark Fields, J Lynn Wilson, Bryan T Wilkerson, David W Thurber, et al. 2013. Methods to Determine a Vehicle Insurance Premium Based on Vehicle Operation Data Collected Via a Mobile Device. US Patent App. 13\/763,231.","journal-title":"US Patent App."},{"key":"e_1_3_2_1_4_1","volume-title":"Retrieved","author":"Brendan McMahan Daniel Ramage","year":"2017","unstructured":"Daniel Ramage Brendan McMahan . 2017 . Federated Learning: Collaborative Machine Learning without Centralized Training Data . Retrieved Sep 20, 2019 from https:\/\/ai.googleblog.com\/2017\/04\/federated-learning-collaborative.html Daniel Ramage Brendan McMahan. 2017. Federated Learning: Collaborative Machine Learning without Centralized Training Data. Retrieved Sep 20, 2019 from https:\/\/ai.googleblog.com\/2017\/04\/federated-learning-collaborative.html"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"e_1_3_2_1_6_1","volume-title":"Retrieved","author":"Xsens Technologies","year":"2009","unstructured":"Xsens Technologies B.V. 2009 . Xsens Mti-G-700 user manual and technical documentation . Retrieved Sep 20, 2019 from https:\/\/projects.asl.ethz.ch\/datasets\/lib\/exe\/fetch.php?media=hardware:tiltinglaser:mti-g_user_manual_and_technical_documentation.pdf Xsens Technologies B.V. 2009. Xsens Mti-G-700 user manual and technical documentation. Retrieved Sep 20, 2019 from https:\/\/projects.asl.ethz.ch\/datasets\/lib\/exe\/fetch.php?media=hardware:tiltinglaser:mti-g_user_manual_and_technical_documentation.pdf"},{"key":"e_1_3_2_1_7_1","unstructured":"Emily L Denton Soumith Chintala Rob Fergus etal 2015. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in Neural Information Processing Systems (NIPS). 1486--1494.  Emily L Denton Soumith Chintala Rob Fergus et al. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in Neural Information Processing Systems (NIPS). 1486--1494."},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI).","author":"Ding Xiaohan","year":"2018","unstructured":"Xiaohan Ding , Guiguang Ding , Jungong Han , and Sheng Tang . 2018 . Auto-balanced filter pruning for efficient convolutional neural networks . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI). Xiaohan Ding, Guiguang Ding, Jungong Han, and Sheng Tang. 2018. Auto-balanced filter pruning for efficient convolutional neural networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI)."},{"key":"e_1_3_2_1_9_1","volume-title":"Edge-Based Discovery of Training Data for Machine Learning. In 2018 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, IEEE, 145--158","author":"Feng Ziqiang","year":"2018","unstructured":"Ziqiang Feng , Shilpa George , Jan Harkes , Padmanabhan Pillai , Roberta Klatzky , and Mahadev Satyanarayanan . 2018 . Edge-Based Discovery of Training Data for Machine Learning. In 2018 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, IEEE, 145--158 . Ziqiang Feng, Shilpa George, Jan Harkes, Padmanabhan Pillai, Roberta Klatzky, and Mahadev Satyanarayanan. 2018. Edge-Based Discovery of Training Data for Machine Learning. In 2018 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, IEEE, 145--158."},{"key":"e_1_3_2_1_10_1","unstructured":"Lex Fridman Daniel E. Brown Michael Glazer etal 2017. MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation. CoRR abs\/1711.06976 (2017). arXiv:1711.06976 http:\/\/arxiv.org\/abs\/1711.06976  Lex Fridman Daniel E. Brown Michael Glazer et al. 2017. MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation. CoRR abs\/1711.06976 (2017). arXiv:1711.06976 http:\/\/arxiv.org\/abs\/1711.06976"},{"key":"e_1_3_2_1_11_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS). 2672--2680.  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS). 2672--2680."},{"key":"e_1_3_2_1_12_1","unstructured":"Song Han Jeff Pool John Tran and William Dally. 2015. Learning both weights and connections for efficient neural network. In Advances in Neural Information Processing Systems (NIPS). 1135--1143.  Song Han Jeff Pool John Tran and William Dally. 2015. Learning both weights and connections for efficient neural network. In Advances in Neural Information Processing Systems (NIPS). 1135--1143."},{"key":"e_1_3_2_1_13_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556288.2557321"},{"key":"e_1_3_2_1_15_1","volume-title":"Hui Jiang, and Roland Memisevic.","author":"Im Daniel Jiwoong","year":"2016","unstructured":"Daniel Jiwoong Im , Chris Dongjoo Kim , Hui Jiang, and Roland Memisevic. 2016 . Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110 (2016). Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic. 2016. Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110 (2016)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2462084"},{"key":"e_1_3_2_1_17_1","volume-title":"Ananda Theertha Suresh, and Dave Bacon","author":"Kone\u010dn\u1ef3 Jakub","year":"2016","unstructured":"Jakub Kone\u010dn\u1ef3 , H Brendan McMahan , Felix X Yu , Peter Richt\u00e1rik , Ananda Theertha Suresh, and Dave Bacon . 2016 . Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016). Jakub Kone\u010dn\u1ef3, H Brendan McMahan, Felix X Yu, Peter Richt\u00e1rik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)."},{"key":"e_1_3_2_1_18_1","volume-title":"2017 IEEE Intelligent Vehicles Symposium (IV). 204--211","author":"Kuefler A.","unstructured":"A. Kuefler , J. Morton , T. Wheeler , and M. Kochenderfer . 2017. Imitating driver behavior with generative adversarial networks . In 2017 IEEE Intelligent Vehicles Symposium (IV). 204--211 . A. Kuefler, J. Morton, T. Wheeler, and M. Kochenderfer. 2017. Imitating driver behavior with generative adversarial networks. In 2017 IEEE Intelligent Vehicles Symposium (IV). 204--211."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/SEC.2018.00009"},{"key":"e_1_3_2_1_20_1","volume-title":"DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems 2017","author":"Ma Chunmei","year":"2017","unstructured":"Chunmei Ma , Xili Dai , Jinqi Zhu , Nianbo Liu , Huazhi Sun , and Ming Liu . 2017. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems 2017 ( 2017 ). Chunmei Ma, Xili Dai, Jinqi Zhu, Nianbo Liu, Huazhi Sun, and Ming Liu. 2017. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems 2017 (2017)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/SEC.2018.00014"},{"key":"e_1_3_2_1_22_1","volume-title":"Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784","author":"Mirza Mehdi","year":"2014","unstructured":"Mehdi Mirza and Simon Osindero . 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 ( 2014 ). Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)."},{"key":"e_1_3_2_1_23_1","volume-title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904","author":"Mogren Olof","year":"2016","unstructured":"Olof Mogren . 2016. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904 ( 2016 ). Olof Mogren. 2016. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904 (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"Usage-based insurance and telematics, https:\/\/www.naic.org. Retrieved","year":"2018","unstructured":"Naic. 2018. Usage-based insurance and telematics, https:\/\/www.naic.org. Retrieved Sep 3, 2018 from https:\/\/www.naic.org\/cipr_topics\/topic_usage_based_insurance.htm Naic. 2018. Usage-based insurance and telematics, https:\/\/www.naic.org. Retrieved Sep 3, 2018 from https:\/\/www.naic.org\/cipr_topics\/topic_usage_based_insurance.htm"},{"key":"e_1_3_2_1_25_1","volume-title":"Global status report on road safety","author":"World Health Organization","year":"2015","unstructured":"World Health Organization . 2015. Global status report on road safety 2015 . World Health Organization . World Health Organization. 2015. Global status report on road safety 2015. World Health Organization."},{"key":"e_1_3_2_1_26_1","volume-title":"Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific reports 6","author":"Pavlidis I","year":"2016","unstructured":"I Pavlidis , M Dcosta , S Taamneh , M Manser , T Ferris , R Wunderlich , E Akleman , and P Tsiamyrtzis . 2016. Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific reports 6 ( 2016 ), 25651. I Pavlidis, M Dcosta, S Taamneh, M Manser, T Ferris, R Wunderlich, E Akleman, and P Tsiamyrtzis. 2016. Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific reports 6 (2016), 25651."},{"key":"e_1_3_2_1_27_1","volume-title":"DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics. In IEEE International Conference on Computer Communications (INFOCOM).","author":"Ran Xukan","year":"2018","unstructured":"Xukan Ran , Haoliang Chen , Xiaodan Zhu , Zhenming Liu , and Jiasi Chen . 2018 . DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics. In IEEE International Conference on Computer Communications (INFOCOM). Xukan Ran, Haoliang Chen, Xiaodan Zhu, Zhenming Liu, and Jiasi Chen. 2018. DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics. In IEEE International Conference on Computer Communications (INFOCOM)."},{"key":"e_1_3_2_1_28_1","first-page":"1","article-title":"General data protection regulation","volume":"59","author":"Regulation Protection","year":"2016","unstructured":"Protection Regulation . 2016 . General data protection regulation . Official Journal of the European Union 59 (2016), 1 -- 88 . Protection Regulation. 2016. General data protection regulation. Official Journal of the European Union 59 (2016), 1--88.","journal-title":"Official Journal of the European Union"},{"key":"e_1_3_2_1_29_1","first-page":"339","article-title":"The emergence of edge computing","volume":"50","author":"Satyanarayanan Mahadev","year":"2017","unstructured":"Mahadev Satyanarayanan . 2017 . The emergence of edge computing . Computer 50 , 1 (2017), 339 . Mahadev Satyanarayanan. 2017. The emergence of edge computing. Computer 50, 1 (2017), 339.","journal-title":"Computer"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.226"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219985"},{"key":"e_1_3_2_1_33_1","volume-title":"CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles. In 2018 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, IEEE, 30-42","author":"Wang Yifan","year":"2018","unstructured":"Yifan Wang , Shaoshan Liu , Xiaopei Wu , and Weisong Shi . 2018 . CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles. In 2018 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, IEEE, 30-42 . Yifan Wang, Shaoshan Liu, Xiaopei Wu, and Weisong Shi. 2018. CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles. In 2018 IEEE\/ACM Symposium on Edge Computing (SEC). IEEE, IEEE, 30-42."},{"key":"e_1_3_2_1_34_1","unstructured":"Wei Wen Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2016. Learning structured sparsity in deep neural networks. In Advances in Neural Information Processing Systems (NIPS). 2074--2082.  Wei Wen Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2016. Learning structured sparsity in deep neural networks. In Advances in Neural Information Processing Systems (NIPS). 2074--2082."},{"key":"e_1_3_2_1_35_1","volume-title":"Xsens North America Inc, https:\/\/www.xsens.com\/products\/mti-g-710\/. Retrieved","year":"2018","unstructured":"Xsens. 2018. Xsens North America Inc, https:\/\/www.xsens.com\/products\/mti-g-710\/. Retrieved Jun 4, 2018 from https:\/\/www.xsens.com\/products\/mti-g-710\/ Xsens. 2018. Xsens North America Inc, https:\/\/www.xsens.com\/products\/mti-g-710\/. Retrieved Jun 4, 2018 from https:\/\/www.xsens.com\/products\/mti-g-710\/"},{"key":"e_1_3_2_1_36_1","volume-title":"Yuting Cheng, Mu Lin, Lorenzo Torresani, et al.","author":"You Chuang-Wen","year":"2013","unstructured":"Chuang-Wen You , Nicholas D Lane , Fanglin Chen , Rui Wang , Zhenyu Chen , Thomas J Bao , Martha Montes-de Oca , Yuting Cheng, Mu Lin, Lorenzo Torresani, et al. 2013 . Carsafe app: Alerting drowsy and distracted drivers using dual cameras on smartphones. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, ACM, New York, NY, USA , 13--26. Chuang-Wen You, Nicholas D Lane, Fanglin Chen, Rui Wang, Zhenyu Chen, Thomas J Bao, Martha Montes-de Oca, Yuting Cheng, Mu Lin, Lorenzo Torresani, et al. 2013. Carsafe app: Alerting drowsy and distracted drivers using dual cameras on smartphones. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, ACM, New York, NY, USA, 13--26."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020462"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2018.00131"},{"key":"e_1_3_2_1_39_1","volume-title":"USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18)","author":"Zhang Xingzhou","year":"2018","unstructured":"Xingzhou Zhang , Yifan Wang , and Weisong Shi . 2018 . pCAMP: Performance Comparison of Machine Learning Packages on the Edges . In USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18) . USENIX Association, Boston, MA. https:\/\/www.usenix.org\/conference\/hotedge18\/presentation\/zhang Xingzhou Zhang, Yifan Wang, and Weisong Shi. 2018. pCAMP: Performance Comparison of Machine Learning Packages on the Edges. In USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18). USENIX Association, Boston, MA. https:\/\/www.usenix.org\/conference\/hotedge18\/presentation\/zhang"},{"key":"e_1_3_2_1_40_1","volume-title":"To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878","author":"Zhu Michael","year":"2017","unstructured":"Michael Zhu and Suyog Gupta . 2017. To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 ( 2017 ). Michael Zhu and Suyog Gupta. 2017. To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 (2017)."}],"event":{"name":"SEC '19: The Fourth ACM\/IEEE Symposium on Edge Computing","location":"Arlington Virginia","acronym":"SEC '19","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","IEEE-CS\\DATC IEEE Computer Society"]},"container-title":["Proceedings of the 4th ACM\/IEEE Symposium on Edge Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3318216.3363310","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3318216.3363310","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:40Z","timestamp":1750204480000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3318216.3363310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"references-count":40,"alternative-id":["10.1145\/3318216.3363310","10.1145\/3318216"],"URL":"https:\/\/doi.org\/10.1145\/3318216.3363310","relation":{},"subject":[],"published":{"date-parts":[[2019,11,7]]},"assertion":[{"value":"2019-11-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}