{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YDH5K5VFYLD7QKOUR4OSQJCF2H","short_pith_number":"pith:YDH5K5VF","schema_version":"1.0","canonical_sha256":"c0cfd576a5c2c7f829d48f1d282445d1f9db29d1b8f896b63561001d27d54dc1","source":{"kind":"arxiv","id":"2404.18525","version":2},"attestation_state":"computed","paper":{"title":"Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chiara Masiero, David Dandolo, Gian Antonio Susto, Valentina Zaccaria","submitted_at":"2024-04-29T09:11:41Z","abstract_excerpt":"While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with indust"},"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":"2404.18525","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-29T09:11:41Z","cross_cats_sorted":[],"title_canon_sha256":"cd9f7e11c52f1f9351c9d2c0ca02ddf1ab065a507e7b0f5edaf15d3a066becda","abstract_canon_sha256":"7b950535c9779b386a7047eb18e47b1374b9dd9368fad881c8c3d3ab19ebd968"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:42.506228Z","signature_b64":"OCa2j8iJMwZqv+kROnmfJQSi3oNCy3tt4ub20cxA8ltqZ8pyVm8AYC9MW3k53KhQlL2vfJiO9/7rDo4vZORSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0cfd576a5c2c7f829d48f1d282445d1f9db29d1b8f896b63561001d27d54dc1","last_reissued_at":"2026-07-05T09:25:42.505724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:42.505724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chiara Masiero, David Dandolo, Gian Antonio Susto, Valentina Zaccaria","submitted_at":"2024-04-29T09:11:41Z","abstract_excerpt":"While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with indust"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.18525","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/2404.18525/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":"2404.18525","created_at":"2026-07-05T09:25:42.505782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.18525v2","created_at":"2026-07-05T09:25:42.505782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.18525","created_at":"2026-07-05T09:25:42.505782+00:00"},{"alias_kind":"pith_short_12","alias_value":"YDH5K5VFYLD7","created_at":"2026-07-05T09:25:42.505782+00:00"},{"alias_kind":"pith_short_16","alias_value":"YDH5K5VFYLD7QKOU","created_at":"2026-07-05T09:25:42.505782+00:00"},{"alias_kind":"pith_short_8","alias_value":"YDH5K5VF","created_at":"2026-07-05T09:25:42.505782+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/YDH5K5VFYLD7QKOUR4OSQJCF2H","json":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H.json","graph_json":"https://pith.science/api/pith-number/YDH5K5VFYLD7QKOUR4OSQJCF2H/graph.json","events_json":"https://pith.science/api/pith-number/YDH5K5VFYLD7QKOUR4OSQJCF2H/events.json","paper":"https://pith.science/paper/YDH5K5VF"},"agent_actions":{"view_html":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H","download_json":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H.json","view_paper":"https://pith.science/paper/YDH5K5VF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.18525&json=true","fetch_graph":"https://pith.science/api/pith-number/YDH5K5VFYLD7QKOUR4OSQJCF2H/graph.json","fetch_events":"https://pith.science/api/pith-number/YDH5K5VFYLD7QKOUR4OSQJCF2H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H/action/storage_attestation","attest_author":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H/action/author_attestation","sign_citation":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H/action/citation_signature","submit_replication":"https://pith.science/pith/YDH5K5VFYLD7QKOUR4OSQJCF2H/action/replication_record"}},"created_at":"2026-07-05T09:25:42.505782+00:00","updated_at":"2026-07-05T09:25:42.505782+00:00"}