{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:51:00Z","timestamp":1773247860735,"version":"3.50.1"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p>\n            Teleworking has become a social gain following the COVID-19 lock-downs. In many professions, remote work is becoming a common practice, either at the employee's home or in a shared space nearby. However, this creates an implicit private\/work-life tension as private activities may be carried out during work time and vice versa. Detecting\n            <jats:italic toggle=\"yes\">boundary crossings<\/jats:italic>\n            is of outmost relevance - they serve as evidence of the workers' breaks and right to rest. However, this must be achieved without excessive surveillance. Existing activity recognition techniques either do not address the border crossing problem or require a priori training.\n          <\/jats:p>\n          <jats:p>\n            To address this issue, this article proposes\n            <jats:italic toggle=\"yes\">TELESAFE<\/jats:italic>\n            , a boundary crossing detector solution for teleworking.\n            <jats:italic toggle=\"yes\">TELESAFE<\/jats:italic>\n            does not require any training nor instrumentation of the teleworker home and can be run locally in resource-constrained devices. To illustrate its suitability, it is applied on electric consumption trails so as to enable self and third-party assessment (e.g., work inspectors) on working conditions. Results on real-world datasets show a\n            <jats:italic toggle=\"yes\">Fscore<\/jats:italic>\n            over 90% for identifying private activities involving one or more devices with usage patterns of varying lengths. Interestingly,\n            <jats:italic toggle=\"yes\">TELESAFE<\/jats:italic>\n            outperforms Machine and Deep-Learning approaches in the most complex settings, without the burden of training.\n          <\/jats:p>","DOI":"10.14778\/3725688.3725690","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:19:21Z","timestamp":1756477161000},"page":"1565-1578","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["<i>TELESAFE<\/i>\n            : Detecting Private\/Work Boundary Crossings in Energy Consumption Trails in Telework"],"prefix":"10.14778","volume":"18","author":[{"given":"Haoying","family":"Zhang","sequence":"first","affiliation":[{"name":"INSA CVL, Inria, Univ. Orl\u00e9ans"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariem","family":"Brahem","sequence":"additional","affiliation":[{"name":"Inria, INSA CVL, Univ. 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