{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:05:53Z","timestamp":1740099953413,"version":"3.37.3"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100011038","name":"Office of the Director of National Intelligence","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011039","name":"Intelligence Advanced Research Projects Activity","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011039","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,10]]},"DOI":"10.1109\/bigdata50022.2020.9378327","type":"proceedings-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T21:10:21Z","timestamp":1616188221000},"page":"1089-1096","source":"Crossref","is-referenced-by-count":0,"title":["MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data"],"prefix":"10.1109","author":[{"given":"Suwen","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louis","family":"Faust","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sidney","family":"D'Mello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gonzalo","family":"Martinez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/2806890"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011409"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/366"},{"key":"ref12","first-page":"139","article-title":"One-class svms for document classification","volume":"2","author":"manevitz","year":"2001","journal-title":"Journal of Machine Learning Research"},{"key":"ref13","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10)"},{"article-title":"Bpr: Bayesian personalized ranking from implicit feedback","year":"2012","author":"rendle","key":"ref14"},{"article-title":"Representation learning on variable length and incomplete wearable-sensory time series","year":"2020","author":"wu","key":"ref15"},{"article-title":"Learning to diagnose with lstm recurrent neural networks","year":"2015","author":"lipton","key":"ref16"},{"key":"ref17","article-title":"Personalized multitask learning for predicting tomorrow’s mood, stress, and health","author":"taylor","year":"2017","journal-title":"IEEE Transactions on Affective Computing"},{"key":"ref18","first-page":"71","article-title":"Expanding the criterion domain to include elements of contextual performance","author":"borman","year":"1993","journal-title":"Personnel Selection in Organizations San Francisco Jossey-Bass"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5465\/AMJ.2007.24634438"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8513102"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1136\/bjsm.2009.069476"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00351"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186058"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(03)00003-5"},{"key":"ref29","first-page":"1","article-title":"Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction","author":"ristic","year":"2008","journal-title":"2008 11th International Conference on Information Fusion FUSION"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2017.11.016"},{"article-title":"Deep structured energy based models for anomaly detection","year":"2016","author":"zhai","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2004.1334558"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098086"},{"article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","year":"2018","author":"zong","key":"ref9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835783"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref22","article-title":"A tutorial on energy-based learning","volume":"1","author":"lecun","year":"2006","journal-title":"Predicting Structured Data"},{"article-title":"Lstm-based encoder-decoder for multi-sensor anomaly detection","year":"2016","author":"malhotra","key":"ref21"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00027"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3186580"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s11704-015-4478-2","article-title":"Exploiting multi-channels deep convolutional neural networks for multivariate time series classification","volume":"10","author":"zheng","year":"2016","journal-title":"Frontiers of Computer Science"},{"key":"ref25","article-title":"Lstm-based ecg classification for continuous monitoring on personal wearable devices","author":"saadatnejad","year":"2019","journal-title":"IEEE Journal of Biomedical and Health Informatics"}],"event":{"name":"2020 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2020,12,10]]},"location":"Atlanta, GA, USA","end":{"date-parts":[[2020,12,13]]}},"container-title":["2020 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9377717\/9377728\/09378327.pdf?arnumber=9378327","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T15:57:46Z","timestamp":1656345466000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9378327\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/bigdata50022.2020.9378327","relation":{},"subject":[],"published":{"date-parts":[[2020,12,10]]}}}