{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:11:30Z","timestamp":1790701890984,"version":"4.1.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Agency for Science, Technology and Research under its Career Development","award":["C210112046"],"award-info":[{"award-number":["C210112046"]}]},{"name":"A*STAR Singapore under its NRF AME Young Individual Research","award":["A2084c0167"],"award-info":[{"award-number":["A2084c0167"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tim.2024.3398094","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T13:31:57Z","timestamp":1717162317000},"page":"1-10","source":"Crossref","is-referenced-by-count":17,"title":["WiCAU: Comprehensive Partial Adaptation With Uncertainty-Aware for WiFi-Based Cross-Environment Activity Recognition"],"prefix":"10.1109","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3697-0733","authenticated-orcid":false,"given":"Wei","family":"Cui","sequence":"first","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8493-0712","authenticated-orcid":false,"given":"Keyu","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0762-6562","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1719-0328","authenticated-orcid":false,"given":"Zhenghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107561"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3069927"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3183112"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3289547"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3616494"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3216419"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jai.2022.100007"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3294004"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2878233"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2941527"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2871445"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2018.8422895"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16103"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241548"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3326285.3329054"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413590"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796782"},{"key":"ref19","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. IMCL","author":"Long"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2832198"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.755"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref24","first-page":"1","article-title":"Label efficient learning of transferable representations acrosss domains and tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Luo"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.469"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00288"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00851"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2964173"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00310"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_38"},{"key":"ref31","first-page":"14860","article-title":"Adversarial reweighting for partial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gu"},{"key":"ref32","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700082"},{"key":"ref34","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref35","first-page":"1","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Gulrajani"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.106534"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.09.111"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref39","first-page":"1","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Long"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_9"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/19\/10367905\/10545342.pdf?arnumber=10545342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T15:46:06Z","timestamp":1719330366000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10545342\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/tim.2024.3398094","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}