{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T05:47:57Z","timestamp":1751435277347,"version":"3.28.0"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,10]]},"DOI":"10.1109\/iwqos54832.2022.9812864","type":"proceedings-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T19:27:44Z","timestamp":1657049264000},"page":"1-10","source":"Crossref","is-referenced-by-count":2,"title":["Improving disk failure detection accuracy via data augmentation"],"prefix":"10.1109","author":[{"given":"Wang","family":"Wang","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"}]},{"given":"Xuehai","family":"Tang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"}]},{"given":"Biyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"}]},{"given":"Wenjie","family":"Xiao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"}]},{"given":"Jizhong","family":"Han","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"}]},{"given":"Songlin","family":"Hu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.1109\/DSN48987.2021.00039"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1073\/pnas.0308738101"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.21437\/Interspeech.2012-65"},{"year":"2014","author":"zaremba","article-title":"Recurrent neural network regularization","key":"ref31"},{"year":"2018","author":"devlin","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding[J]","key":"ref30"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/BF00992696","article-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","volume":"8","author":"williams","year":"1992","journal-title":"Machine Learning"},{"year":"2009","author":"villani","article-title":"Optimal transport: old and new[M]","key":"ref36"},{"key":"ref35","first-page":"214","article-title":"Wasserstein generative adversarial networks[C]","author":"arjovsky","year":"2017","journal-title":"Int Conference on Machine Learning"},{"year":"2017","author":"arjovsky","article-title":"Towards principled methods for training generative adversarial networks[J]","key":"ref34"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1145\/3225058.3225106"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1186\/s40537-018-0151-6"},{"year":"2016","author":"cui","article-title":"Multi-scale convolutional neural networks for time series classification","key":"ref12"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1613\/jair.953"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1007\/11538059_91"},{"key":"ref15","first-page":"1322","article-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning","author":"he","year":"2008","journal-title":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) IEEE"},{"key":"ref16","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2017","journal-title":"2018 ICLR"},{"year":"2014","author":"goodfellow","article-title":"Generative adversarial networks","key":"ref17"},{"year":"2013","author":"kingma","article-title":"Auto-encoding variational bayes","key":"ref18"},{"year":"2015","author":"makhzani","article-title":"Adversarial autoencoders","key":"ref19"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/TNNLS.2014.2308321"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1109\/TC.2016.2538237"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1109\/ICDM.2011.137"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1109\/DSN.2014.44"},{"key":"ref6","first-page":"111","article-title":"HDDse: Enabling High-Dimensional Disk State Embedding for Generic Failure Detection System of Heterogeneous Disks in Large Data Centers","author":"zhang","year":"2020","journal-title":"2020 USENIX Annual Technical Conference (USENIX ATC)"},{"year":"2020","author":"gao","article-title":"RobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks","key":"ref29"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/SRDSW.2015.15"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1145\/2939672.2939699"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/TPDS.2020.2985346"},{"key":"ref2","first-page":"481","article-title":"Improving service availability of cloud systems by predicting disk error[C]","author":"xu","year":"2018","journal-title":"2018 USENIX Annual Technical Conference (USENIX ATC 18)"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/ICDCS47774.2020.00044"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1145\/1288783.1288785"},{"year":"2014","author":"mirza","article-title":"Conditional generative adversarial nets","key":"ref20"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1609\/aaai.v31i1.10804"},{"year":"2019","author":"yoon","journal-title":"Time-series generative adversarial networks","key":"ref21"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/ICCD46524.2019.00033"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1109\/IWQOS52092.2021.9521302"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/TKDE.2013.37"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/ICCD.2018.00089"}],"event":{"name":"2022 IEEE\/ACM 30th International Symposium on Quality of Service (IWQoS)","start":{"date-parts":[[2022,6,10]]},"location":"Oslo, Norway","end":{"date-parts":[[2022,6,12]]}},"container-title":["2022 IEEE\/ACM 30th International Symposium on Quality of Service (IWQoS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9812862\/9812863\/09812864.pdf?arnumber=9812864","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:44:39Z","timestamp":1659660279000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9812864\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/iwqos54832.2022.9812864","relation":{},"subject":[],"published":{"date-parts":[[2022,6,10]]}}}