{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PE4QEQMIM4U2N34ITACS6C3QTE","short_pith_number":"pith:PE4QEQMI","schema_version":"1.0","canonical_sha256":"79390241886729a6ef8898052f0b709939395e3a3cb543ce981174f173884eaf","source":{"kind":"arxiv","id":"2402.08975","version":1},"attestation_state":"computed","paper":{"title":"Research and application of Transformer based anomaly detection model: A literature review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chunjie Zhou, Lansheng Han, Mingrui Ma","submitted_at":"2024-02-14T06:39:54Z","abstract_excerpt":"Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the dat"},"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":"2402.08975","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-14T06:39:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"96a6b0dfeb420f27d398e050930d8333968dc5d4ae9bfc77492e5704753ed7c3","abstract_canon_sha256":"b94a6ab781c94ec62311e18176999c6aa65a5a95644fcbf074122216a07a6c7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:45:06.389077Z","signature_b64":"Gh0wZ7z4dAH7kmOHy3IqI4NwkCX7EviHwpG2n8CV9W8WM1v4cvonieQRna42QZgTgjKlsYZeu5J/TSWSDUcEBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79390241886729a6ef8898052f0b709939395e3a3cb543ce981174f173884eaf","last_reissued_at":"2026-07-05T07:45:06.388511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:45:06.388511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Research and application of Transformer based anomaly detection model: A literature review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chunjie Zhou, Lansheng Han, Mingrui Ma","submitted_at":"2024-02-14T06:39:54Z","abstract_excerpt":"Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.08975","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/2402.08975/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":"2402.08975","created_at":"2026-07-05T07:45:06.388579+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.08975v1","created_at":"2026-07-05T07:45:06.388579+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.08975","created_at":"2026-07-05T07:45:06.388579+00:00"},{"alias_kind":"pith_short_12","alias_value":"PE4QEQMIM4U2","created_at":"2026-07-05T07:45:06.388579+00:00"},{"alias_kind":"pith_short_16","alias_value":"PE4QEQMIM4U2N34I","created_at":"2026-07-05T07:45:06.388579+00:00"},{"alias_kind":"pith_short_8","alias_value":"PE4QEQMI","created_at":"2026-07-05T07:45:06.388579+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.12982","citing_title":"Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE","json":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE.json","graph_json":"https://pith.science/api/pith-number/PE4QEQMIM4U2N34ITACS6C3QTE/graph.json","events_json":"https://pith.science/api/pith-number/PE4QEQMIM4U2N34ITACS6C3QTE/events.json","paper":"https://pith.science/paper/PE4QEQMI"},"agent_actions":{"view_html":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE","download_json":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE.json","view_paper":"https://pith.science/paper/PE4QEQMI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.08975&json=true","fetch_graph":"https://pith.science/api/pith-number/PE4QEQMIM4U2N34ITACS6C3QTE/graph.json","fetch_events":"https://pith.science/api/pith-number/PE4QEQMIM4U2N34ITACS6C3QTE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE/action/storage_attestation","attest_author":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE/action/author_attestation","sign_citation":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE/action/citation_signature","submit_replication":"https://pith.science/pith/PE4QEQMIM4U2N34ITACS6C3QTE/action/replication_record"}},"created_at":"2026-07-05T07:45:06.388579+00:00","updated_at":"2026-07-05T07:45:06.388579+00:00"}