{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KZQUTPYTYAQC4FMLMIN34R5L7W","short_pith_number":"pith:KZQUTPYT","schema_version":"1.0","canonical_sha256":"566149bf13c0202e158b621bbe47abfda2aa3dda7c42856f9088bad1a9dcd7b8","source":{"kind":"arxiv","id":"2307.11413","version":2},"attestation_state":"computed","paper":{"title":"A Video-based Detector for Suspicious Activity in Examination with OpenPose","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jimmy Mbelwa, Michael Zimba, Reuben Moyo, Stanley Ndebvu","submitted_at":"2023-07-21T08:15:39Z","abstract_excerpt":"Examinations are a crucial part of the learning process, and academic institutions invest significant resources into maintaining their integrity by preventing cheating from students or facilitators. However, cheating has become rampant in examination setups, compromising their integrity. The traditional method of relying on invigilators to monitor every student is impractical and ineffective. To address this issue, there is a need to continuously record exam sessions to monitor students for suspicious activities. However, these recordings are often too lengthy for invigilators to analyze effec"},"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":"2307.11413","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-21T08:15:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c35b4f5fe7cf60109e9e9a303f27600add4acd6b182d73abb9baef6c06ce5eb0","abstract_canon_sha256":"e1699926264b775a965cbb45307393f958d99775553a4453ae6c8e44dbe855b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:44:08.464338Z","signature_b64":"WoW3p5ED9PsamLzxBPAou24rQ14Vj+yevvF/oULHtFaEadYfgKfYbFXb4LcluyPHDWZw04GuzUlf/S4qv0tDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"566149bf13c0202e158b621bbe47abfda2aa3dda7c42856f9088bad1a9dcd7b8","last_reissued_at":"2026-07-05T06:44:08.463825Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:44:08.463825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Video-based Detector for Suspicious Activity in Examination with OpenPose","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jimmy Mbelwa, Michael Zimba, Reuben Moyo, Stanley Ndebvu","submitted_at":"2023-07-21T08:15:39Z","abstract_excerpt":"Examinations are a crucial part of the learning process, and academic institutions invest significant resources into maintaining their integrity by preventing cheating from students or facilitators. However, cheating has become rampant in examination setups, compromising their integrity. The traditional method of relying on invigilators to monitor every student is impractical and ineffective. To address this issue, there is a need to continuously record exam sessions to monitor students for suspicious activities. However, these recordings are often too lengthy for invigilators to analyze effec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.11413","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.11413/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":"2307.11413","created_at":"2026-07-05T06:44:08.463887+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.11413v2","created_at":"2026-07-05T06:44:08.463887+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.11413","created_at":"2026-07-05T06:44:08.463887+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZQUTPYTYAQC","created_at":"2026-07-05T06:44:08.463887+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZQUTPYTYAQC4FML","created_at":"2026-07-05T06:44:08.463887+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZQUTPYT","created_at":"2026-07-05T06:44:08.463887+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16234","citing_title":"A Two-Stage, Object-Centric Deep Learning Framework for Robust Exam Cheating Detection","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W","json":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W.json","graph_json":"https://pith.science/api/pith-number/KZQUTPYTYAQC4FMLMIN34R5L7W/graph.json","events_json":"https://pith.science/api/pith-number/KZQUTPYTYAQC4FMLMIN34R5L7W/events.json","paper":"https://pith.science/paper/KZQUTPYT"},"agent_actions":{"view_html":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W","download_json":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W.json","view_paper":"https://pith.science/paper/KZQUTPYT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.11413&json=true","fetch_graph":"https://pith.science/api/pith-number/KZQUTPYTYAQC4FMLMIN34R5L7W/graph.json","fetch_events":"https://pith.science/api/pith-number/KZQUTPYTYAQC4FMLMIN34R5L7W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W/action/storage_attestation","attest_author":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W/action/author_attestation","sign_citation":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W/action/citation_signature","submit_replication":"https://pith.science/pith/KZQUTPYTYAQC4FMLMIN34R5L7W/action/replication_record"}},"created_at":"2026-07-05T06:44:08.463887+00:00","updated_at":"2026-07-05T06:44:08.463887+00:00"}