{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:T5WWO4Y5I4YF3L7JFZGKBQVOGK","short_pith_number":"pith:T5WWO4Y5","schema_version":"1.0","canonical_sha256":"9f6d67731d47305dafe92e4ca0c2ae3297900e4ba3d0a29bb23b35abfe461882","source":{"kind":"arxiv","id":"2311.07765","version":1},"attestation_state":"computed","paper":{"title":"FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Egemen \\.I\\c{s}g\\\"uder, \\\"Ozlem Durmaz \\.Incel","submitted_at":"2023-11-13T21:31:07Z","abstract_excerpt":"Motion sensors integrated into wearable and mobile devices provide valuable information about the device users. Machine learning and, recently, deep learning techniques have been used to characterize sensor data. Mostly, a single task, such as recognition of activities, is targeted, and the data is processed centrally at a server or in a cloud environment. However, the same sensor data can be utilized for multiple tasks and distributed machine-learning techniques can be used without the requirement of the transmission of data to a centre. This paper explores Federated Transfer Learning in a Mu"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2311.07765","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-13T21:31:07Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f9abcbecdd74649eb2cfe4bda7af978142c07c6199c7b55fcd31a8a67df3f297","abstract_canon_sha256":"799bd924ed273a46173ae5044409555763a694b18e6f81261bf87a8c2fcba0bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:38.813105Z","signature_b64":"Ekj0WseNP0byqHB/Q7W36orBFa0Z9wKTqIfTVxU0WQnkYpc/kpAHJqdvXZHSCDxwZ7bPn8TLv/MjqAGOdnhCDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f6d67731d47305dafe92e4ca0c2ae3297900e4ba3d0a29bb23b35abfe461882","last_reissued_at":"2026-07-05T07:12:38.812771Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:38.812771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Egemen \\.I\\c{s}g\\\"uder, \\\"Ozlem Durmaz \\.Incel","submitted_at":"2023-11-13T21:31:07Z","abstract_excerpt":"Motion sensors integrated into wearable and mobile devices provide valuable information about the device users. Machine learning and, recently, deep learning techniques have been used to characterize sensor data. Mostly, a single task, such as recognition of activities, is targeted, and the data is processed centrally at a server or in a cloud environment. However, the same sensor data can be utilized for multiple tasks and distributed machine-learning techniques can be used without the requirement of the transmission of data to a centre. This paper explores Federated Transfer Learning in a Mu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07765","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.07765/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2311.07765","created_at":"2026-07-05T07:12:38.812820+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.07765v1","created_at":"2026-07-05T07:12:38.812820+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07765","created_at":"2026-07-05T07:12:38.812820+00:00"},{"alias_kind":"pith_short_12","alias_value":"T5WWO4Y5I4YF","created_at":"2026-07-05T07:12:38.812820+00:00"},{"alias_kind":"pith_short_16","alias_value":"T5WWO4Y5I4YF3L7J","created_at":"2026-07-05T07:12:38.812820+00:00"},{"alias_kind":"pith_short_8","alias_value":"T5WWO4Y5","created_at":"2026-07-05T07:12:38.812820+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06917","citing_title":"Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK","json":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK.json","graph_json":"https://pith.science/api/pith-number/T5WWO4Y5I4YF3L7JFZGKBQVOGK/graph.json","events_json":"https://pith.science/api/pith-number/T5WWO4Y5I4YF3L7JFZGKBQVOGK/events.json","paper":"https://pith.science/paper/T5WWO4Y5"},"agent_actions":{"view_html":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK","download_json":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK.json","view_paper":"https://pith.science/paper/T5WWO4Y5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.07765&json=true","fetch_graph":"https://pith.science/api/pith-number/T5WWO4Y5I4YF3L7JFZGKBQVOGK/graph.json","fetch_events":"https://pith.science/api/pith-number/T5WWO4Y5I4YF3L7JFZGKBQVOGK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK/action/storage_attestation","attest_author":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK/action/author_attestation","sign_citation":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK/action/citation_signature","submit_replication":"https://pith.science/pith/T5WWO4Y5I4YF3L7JFZGKBQVOGK/action/replication_record"}},"created_at":"2026-07-05T07:12:38.812820+00:00","updated_at":"2026-07-05T07:12:38.812820+00:00"}