{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WAS53XDCDAQB6DGTHNTZHRTFXF","short_pith_number":"pith:WAS53XDC","schema_version":"1.0","canonical_sha256":"b025dddc6218201f0cd33b6793c665b975d856c118a6c8f3970dc93ed2c8d9cd","source":{"kind":"arxiv","id":"1807.07963","version":2},"attestation_state":"computed","paper":{"title":"Deep Transfer Learning for Cross-domain Activity Recognition","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"eess.IV","authors_text":"Jindong Wang, Meiyu Huang, Vincent W. Zheng, Yiqiang Chen","submitted_at":"2018-07-20T06:09:44Z","abstract_excerpt":"Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate"},"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":"1807.07963","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.IV","submitted_at":"2018-07-20T06:09:44Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0046c6f226c4a701f55c6f4bc5cd537f9347e71c0198b734fc5653be97164814","abstract_canon_sha256":"e1644f03047dad415961284ad9cde25cfd7158032f59bd7c42872f078b01dec6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:49.045985Z","signature_b64":"MAKQ7r1KFu6tANnj75XJjdygnwpuG/eVhYlBhamYNZRHwdCpT4Lj7M3tL33lqeZlk+8pNSNgTUiQh2oZVLJYDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b025dddc6218201f0cd33b6793c665b975d856c118a6c8f3970dc93ed2c8d9cd","last_reissued_at":"2026-05-18T00:07:49.045274Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:49.045274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Transfer Learning for Cross-domain Activity Recognition","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"eess.IV","authors_text":"Jindong Wang, Meiyu Huang, Vincent W. Zheng, Yiqiang Chen","submitted_at":"2018-07-20T06:09:44Z","abstract_excerpt":"Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.07963","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1807.07963","created_at":"2026-05-18T00:07:49.045402+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.07963v2","created_at":"2026-05-18T00:07:49.045402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.07963","created_at":"2026-05-18T00:07:49.045402+00:00"},{"alias_kind":"pith_short_12","alias_value":"WAS53XDCDAQB","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"WAS53XDCDAQB6DGT","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"WAS53XDC","created_at":"2026-05-18T12:32:59.047623+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF","json":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF.json","graph_json":"https://pith.science/api/pith-number/WAS53XDCDAQB6DGTHNTZHRTFXF/graph.json","events_json":"https://pith.science/api/pith-number/WAS53XDCDAQB6DGTHNTZHRTFXF/events.json","paper":"https://pith.science/paper/WAS53XDC"},"agent_actions":{"view_html":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF","download_json":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF.json","view_paper":"https://pith.science/paper/WAS53XDC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.07963&json=true","fetch_graph":"https://pith.science/api/pith-number/WAS53XDCDAQB6DGTHNTZHRTFXF/graph.json","fetch_events":"https://pith.science/api/pith-number/WAS53XDCDAQB6DGTHNTZHRTFXF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF/action/storage_attestation","attest_author":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF/action/author_attestation","sign_citation":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF/action/citation_signature","submit_replication":"https://pith.science/pith/WAS53XDCDAQB6DGTHNTZHRTFXF/action/replication_record"}},"created_at":"2026-05-18T00:07:49.045402+00:00","updated_at":"2026-05-18T00:07:49.045402+00:00"}