{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4IX3SXCFUXXXJY43JIMLTHE45J","short_pith_number":"pith:4IX3SXCF","schema_version":"1.0","canonical_sha256":"e22fb95c45a5ef74e39b4a18b99c9cea66861423480e60e79fd03a4d7b939bc8","source":{"kind":"arxiv","id":"2311.09514","version":1},"attestation_state":"computed","paper":{"title":"Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Srivatsa P, Thomas Pl\\\"otz","submitted_at":"2023-11-16T02:43:13Z","abstract_excerpt":"There has been a resurgence of applications focused on Human Activity Recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they especially suffer from a practical limitation: they require successful pre-segmentation of continuous sens"},"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":"2311.09514","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-16T02:43:13Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"807a223e06aa8a3308c95f51f50c4d384d6b35afab09222c8fc285a4833085ab","abstract_canon_sha256":"897024c6e5d0fb6987fdfc9402130d44a16499d78ae2d6b3824af1db2bc3c2fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:13:29.105586Z","signature_b64":"UAMsZhBTs3ZZtj44387ywvkvFxd/hmfU9pc+UIjFzcpcAhM6AWIxherAoT+TwMuIw+PaIAgkGlU/eHJbygXaCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e22fb95c45a5ef74e39b4a18b99c9cea66861423480e60e79fd03a4d7b939bc8","last_reissued_at":"2026-07-05T07:13:29.105044Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:13:29.105044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Srivatsa P, Thomas Pl\\\"otz","submitted_at":"2023-11-16T02:43:13Z","abstract_excerpt":"There has been a resurgence of applications focused on Human Activity Recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they especially suffer from a practical limitation: they require successful pre-segmentation of continuous sens"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.09514","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/2311.09514/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":"2311.09514","created_at":"2026-07-05T07:13:29.105103+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.09514v1","created_at":"2026-07-05T07:13:29.105103+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.09514","created_at":"2026-07-05T07:13:29.105103+00:00"},{"alias_kind":"pith_short_12","alias_value":"4IX3SXCFUXXX","created_at":"2026-07-05T07:13:29.105103+00:00"},{"alias_kind":"pith_short_16","alias_value":"4IX3SXCFUXXXJY43","created_at":"2026-07-05T07:13:29.105103+00:00"},{"alias_kind":"pith_short_8","alias_value":"4IX3SXCF","created_at":"2026-07-05T07:13:29.105103+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02841","citing_title":"TRACE: Temporal Reasoning over Context and Evidence for Activity Recognition in Smart Homes","ref_index":67,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J","json":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J.json","graph_json":"https://pith.science/api/pith-number/4IX3SXCFUXXXJY43JIMLTHE45J/graph.json","events_json":"https://pith.science/api/pith-number/4IX3SXCFUXXXJY43JIMLTHE45J/events.json","paper":"https://pith.science/paper/4IX3SXCF"},"agent_actions":{"view_html":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J","download_json":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J.json","view_paper":"https://pith.science/paper/4IX3SXCF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.09514&json=true","fetch_graph":"https://pith.science/api/pith-number/4IX3SXCFUXXXJY43JIMLTHE45J/graph.json","fetch_events":"https://pith.science/api/pith-number/4IX3SXCFUXXXJY43JIMLTHE45J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J/action/storage_attestation","attest_author":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J/action/author_attestation","sign_citation":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J/action/citation_signature","submit_replication":"https://pith.science/pith/4IX3SXCFUXXXJY43JIMLTHE45J/action/replication_record"}},"created_at":"2026-07-05T07:13:29.105103+00:00","updated_at":"2026-07-05T07:13:29.105103+00:00"}