{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:WAN4LAMSOWSITSARUXGEM7UFFN","short_pith_number":"pith:WAN4LAMS","canonical_record":{"source":{"id":"2204.13574","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-28T15:44:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4aac9acb48d6626065be1b5331776f4ea2f6ac292ba70fdf5a0c322d2caa25f7","abstract_canon_sha256":"7ed5ced97d3c1cf87c883d6e956895358c844308178c3aaa0513d60bf27cea2e"},"schema_version":"1.0"},"canonical_sha256":"b01bc5819275a489c811a5cc467e852b77578319c35a80361d0454ef7e3a3334","source":{"kind":"arxiv","id":"2204.13574","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.13574","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"arxiv_version","alias_value":"2204.13574v2","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.13574","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"pith_short_12","alias_value":"WAN4LAMSOWSI","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"pith_short_16","alias_value":"WAN4LAMSOWSITSAR","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"pith_short_8","alias_value":"WAN4LAMS","created_at":"2026-07-05T04:19:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:WAN4LAMSOWSITSARUXGEM7UFFN","target":"record","payload":{"canonical_record":{"source":{"id":"2204.13574","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-28T15:44:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4aac9acb48d6626065be1b5331776f4ea2f6ac292ba70fdf5a0c322d2caa25f7","abstract_canon_sha256":"7ed5ced97d3c1cf87c883d6e956895358c844308178c3aaa0513d60bf27cea2e"},"schema_version":"1.0"},"canonical_sha256":"b01bc5819275a489c811a5cc467e852b77578319c35a80361d0454ef7e3a3334","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:19:08.767807Z","signature_b64":"s6o0tYIhXO4SquQ1mvE5w+9dSybihgGZTpyKX0DVUyweioK7LAhi6RuacMvuoIe01QI+9lK2/t8gae9swcTzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b01bc5819275a489c811a5cc467e852b77578319c35a80361d0454ef7e3a3334","last_reissued_at":"2026-07-05T04:19:08.767386Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:19:08.767386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.13574","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:19:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IPLW6BER3Sy1OY0TrgIVeYHn56crGH8O6oYINKFJm8R2hfCed6spZ9iWM8t/8a3VDcz20LDm7PQZ7i961YfeCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:13:46.315265Z"},"content_sha256":"c8b955a16fa59a248783ef1add9284737e0f47440e42e9156563c1bc961b504f","schema_version":"1.0","event_id":"sha256:c8b955a16fa59a248783ef1add9284737e0f47440e42e9156563c1bc961b504f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:WAN4LAMSOWSITSARUXGEM7UFFN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Explainable Regression Framework for Predicting Remaining Useful Life of Machines","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Imran Khan, Jebran Khan, Kashif Ahmad, Nasir Ahmad, Talhat Khan","submitted_at":"2022-04-28T15:44:12Z","abstract_excerpt":"Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of machine components. These ML algorithms are generally used as a black box with a total focus on the performance without identifying the potential causes behind the algorithms' decisions and their working mechanism. We believe, the performance (in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the trust of the stakeholders in ML prediction rather more insi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.13574","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/2204.13574/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:19:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W3DWYeXYc0jx/z6BAHMSQZaS7poAXYHvU/ADujselYIBTfOEfrnZxEifl0mV70EUhgfB4wM6HvEOmMwWVd2cBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:13:46.316145Z"},"content_sha256":"79cad621104a44c7fb179027e47a29d351f5a3f41a8aa1eaca1a0b31807c4954","schema_version":"1.0","event_id":"sha256:79cad621104a44c7fb179027e47a29d351f5a3f41a8aa1eaca1a0b31807c4954"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WAN4LAMSOWSITSARUXGEM7UFFN/bundle.json","state_url":"https://pith.science/pith/WAN4LAMSOWSITSARUXGEM7UFFN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WAN4LAMSOWSITSARUXGEM7UFFN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-20T07:13:46Z","links":{"resolver":"https://pith.science/pith/WAN4LAMSOWSITSARUXGEM7UFFN","bundle":"https://pith.science/pith/WAN4LAMSOWSITSARUXGEM7UFFN/bundle.json","state":"https://pith.science/pith/WAN4LAMSOWSITSARUXGEM7UFFN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WAN4LAMSOWSITSARUXGEM7UFFN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WAN4LAMSOWSITSARUXGEM7UFFN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7ed5ced97d3c1cf87c883d6e956895358c844308178c3aaa0513d60bf27cea2e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-28T15:44:12Z","title_canon_sha256":"4aac9acb48d6626065be1b5331776f4ea2f6ac292ba70fdf5a0c322d2caa25f7"},"schema_version":"1.0","source":{"id":"2204.13574","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.13574","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"arxiv_version","alias_value":"2204.13574v2","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.13574","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"pith_short_12","alias_value":"WAN4LAMSOWSI","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"pith_short_16","alias_value":"WAN4LAMSOWSITSAR","created_at":"2026-07-05T04:19:08Z"},{"alias_kind":"pith_short_8","alias_value":"WAN4LAMS","created_at":"2026-07-05T04:19:08Z"}],"graph_snapshots":[{"event_id":"sha256:79cad621104a44c7fb179027e47a29d351f5a3f41a8aa1eaca1a0b31807c4954","target":"graph","created_at":"2026-07-05T04:19:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2204.13574/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of machine components. These ML algorithms are generally used as a black box with a total focus on the performance without identifying the potential causes behind the algorithms' decisions and their working mechanism. We believe, the performance (in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the trust of the stakeholders in ML prediction rather more insi","authors_text":"Imran Khan, Jebran Khan, Kashif Ahmad, Nasir Ahmad, Talhat Khan","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-28T15:44:12Z","title":"An Explainable Regression Framework for Predicting Remaining Useful Life of Machines"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.13574","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c8b955a16fa59a248783ef1add9284737e0f47440e42e9156563c1bc961b504f","target":"record","created_at":"2026-07-05T04:19:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7ed5ced97d3c1cf87c883d6e956895358c844308178c3aaa0513d60bf27cea2e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-04-28T15:44:12Z","title_canon_sha256":"4aac9acb48d6626065be1b5331776f4ea2f6ac292ba70fdf5a0c322d2caa25f7"},"schema_version":"1.0","source":{"id":"2204.13574","kind":"arxiv","version":2}},"canonical_sha256":"b01bc5819275a489c811a5cc467e852b77578319c35a80361d0454ef7e3a3334","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b01bc5819275a489c811a5cc467e852b77578319c35a80361d0454ef7e3a3334","first_computed_at":"2026-07-05T04:19:08.767386Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:19:08.767386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s6o0tYIhXO4SquQ1mvE5w+9dSybihgGZTpyKX0DVUyweioK7LAhi6RuacMvuoIe01QI+9lK2/t8gae9swcTzCg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:19:08.767807Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.13574","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8b955a16fa59a248783ef1add9284737e0f47440e42e9156563c1bc961b504f","sha256:79cad621104a44c7fb179027e47a29d351f5a3f41a8aa1eaca1a0b31807c4954"],"state_sha256":"22e139184b9c7681fba04d02f9a29dacce4acf5e4ed9b079badb30a9d77c0026"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Ev64fIw8isFUoEnQnjSjWWcasKSwSxP6fZokamF810QybrxSIuNww05Tvq16GAzZk2ziYfcny2EXYshfKgaBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T07:13:46.321384Z","bundle_sha256":"e2ec56057ee4b4f4187812f080de267fee28ad7357792fdc4390461cc478346b"}}