{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:L7SUW4ERO2QE6G3I2YYQW56EU4","short_pith_number":"pith:L7SUW4ER","canonical_record":{"source":{"id":"1907.12778","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T08:36:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f58d63224aea10b32f2696fbe02a30ba90800a2940076e676e240a15d0245512","abstract_canon_sha256":"a5d9673ebd77c16a4d9116dc3444b89d89c0768d78acf9e52c2374e33cdddd3c"},"schema_version":"1.0"},"canonical_sha256":"5fe54b709176a04f1b68d6310b77c4a7331aaab4c22c7c5703d4a177dba78e0b","source":{"kind":"arxiv","id":"1907.12778","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.12778","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"arxiv_version","alias_value":"1907.12778v3","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.12778","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"pith_short_12","alias_value":"L7SUW4ERO2QE","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"pith_short_16","alias_value":"L7SUW4ERO2QE6G3I","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"pith_short_8","alias_value":"L7SUW4ER","created_at":"2026-07-05T00:27:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:L7SUW4ERO2QE6G3I2YYQW56EU4","target":"record","payload":{"canonical_record":{"source":{"id":"1907.12778","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T08:36:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f58d63224aea10b32f2696fbe02a30ba90800a2940076e676e240a15d0245512","abstract_canon_sha256":"a5d9673ebd77c16a4d9116dc3444b89d89c0768d78acf9e52c2374e33cdddd3c"},"schema_version":"1.0"},"canonical_sha256":"5fe54b709176a04f1b68d6310b77c4a7331aaab4c22c7c5703d4a177dba78e0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:27:17.935665Z","signature_b64":"0cbXJbyctdR/5i+zXBq+t0sJMtBRgkPS6oKcqCZu4uX5ZOY6k/+TwBc8Is6xHStlpOgCnjLp5jqmVmAhntgnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fe54b709176a04f1b68d6310b77c4a7331aaab4c22c7c5703d4a177dba78e0b","last_reissued_at":"2026-07-05T00:27:17.935184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:27:17.935184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.12778","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:27:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m7dSDdRsUJbk/NN3g8TSLlZLI4RrLWAR+VsR7MXPyTvfI3C8pH1YTlc/b6vYFpOeUIFmUaW5U+f7HRhb+GbXDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T15:35:43.235834Z"},"content_sha256":"e4a9f455afa1891a3ba785c83572a221993b14049796a7da17ddb58dd4f74f00","schema_version":"1.0","event_id":"sha256:e4a9f455afa1891a3ba785c83572a221993b14049796a7da17ddb58dd4f74f00"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:L7SUW4ERO2QE6G3I2YYQW56EU4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An anomaly prediction framework for financial IT systems using hybrid machine learning methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jian Gao, Jingwen Wang, Jingxin Liu, Juntao Pu, Qinghong Yang, You Song, Zhongchen Miao","submitted_at":"2019-07-30T08:36:37Z","abstract_excerpt":"In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time consuming and extremely inefficient. To improve the efficiency and accuracy of system failure detection and thereby reduce the impact of system failures on financial services, we propose a novel machine learning-based framework to predict the occurrence of system exceptions and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.12778","kind":"arxiv","version":3},"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/1907.12778/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":"2026-07-05T00:27:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yz9D38rwY8ifr67y2Q0GzgdwhKhf2jGpLJF4iKN6VdCGI59k5Z7mxcBoM6y7TeQUCxKfoZr6HrKMABMKTf6HCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T15:35:43.236401Z"},"content_sha256":"7793ce9dc374d81bb43782e79dab380cebff27708a45fb9954da88dbb6b42755","schema_version":"1.0","event_id":"sha256:7793ce9dc374d81bb43782e79dab380cebff27708a45fb9954da88dbb6b42755"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L7SUW4ERO2QE6G3I2YYQW56EU4/bundle.json","state_url":"https://pith.science/pith/L7SUW4ERO2QE6G3I2YYQW56EU4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L7SUW4ERO2QE6G3I2YYQW56EU4/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":"2026-08-10T15:35:43Z","links":{"resolver":"https://pith.science/pith/L7SUW4ERO2QE6G3I2YYQW56EU4","bundle":"https://pith.science/pith/L7SUW4ERO2QE6G3I2YYQW56EU4/bundle.json","state":"https://pith.science/pith/L7SUW4ERO2QE6G3I2YYQW56EU4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L7SUW4ERO2QE6G3I2YYQW56EU4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:L7SUW4ERO2QE6G3I2YYQW56EU4","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":"a5d9673ebd77c16a4d9116dc3444b89d89c0768d78acf9e52c2374e33cdddd3c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T08:36:37Z","title_canon_sha256":"f58d63224aea10b32f2696fbe02a30ba90800a2940076e676e240a15d0245512"},"schema_version":"1.0","source":{"id":"1907.12778","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.12778","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"arxiv_version","alias_value":"1907.12778v3","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.12778","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"pith_short_12","alias_value":"L7SUW4ERO2QE","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"pith_short_16","alias_value":"L7SUW4ERO2QE6G3I","created_at":"2026-07-05T00:27:17Z"},{"alias_kind":"pith_short_8","alias_value":"L7SUW4ER","created_at":"2026-07-05T00:27:17Z"}],"graph_snapshots":[{"event_id":"sha256:7793ce9dc374d81bb43782e79dab380cebff27708a45fb9954da88dbb6b42755","target":"graph","created_at":"2026-07-05T00:27:17Z","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/1907.12778/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time consuming and extremely inefficient. To improve the efficiency and accuracy of system failure detection and thereby reduce the impact of system failures on financial services, we propose a novel machine learning-based framework to predict the occurrence of system exceptions and ","authors_text":"Jian Gao, Jingwen Wang, Jingxin Liu, Juntao Pu, Qinghong Yang, You Song, Zhongchen Miao","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T08:36:37Z","title":"An anomaly prediction framework for financial IT systems using hybrid machine learning methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.12778","kind":"arxiv","version":3},"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:e4a9f455afa1891a3ba785c83572a221993b14049796a7da17ddb58dd4f74f00","target":"record","created_at":"2026-07-05T00:27:17Z","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":"a5d9673ebd77c16a4d9116dc3444b89d89c0768d78acf9e52c2374e33cdddd3c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-30T08:36:37Z","title_canon_sha256":"f58d63224aea10b32f2696fbe02a30ba90800a2940076e676e240a15d0245512"},"schema_version":"1.0","source":{"id":"1907.12778","kind":"arxiv","version":3}},"canonical_sha256":"5fe54b709176a04f1b68d6310b77c4a7331aaab4c22c7c5703d4a177dba78e0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fe54b709176a04f1b68d6310b77c4a7331aaab4c22c7c5703d4a177dba78e0b","first_computed_at":"2026-07-05T00:27:17.935184Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:27:17.935184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0cbXJbyctdR/5i+zXBq+t0sJMtBRgkPS6oKcqCZu4uX5ZOY6k/+TwBc8Is6xHStlpOgCnjLp5jqmVmAhntgnBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:27:17.935665Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.12778","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4a9f455afa1891a3ba785c83572a221993b14049796a7da17ddb58dd4f74f00","sha256:7793ce9dc374d81bb43782e79dab380cebff27708a45fb9954da88dbb6b42755"],"state_sha256":"e9b49e5896f360a06c448a9e20b8c9d35c50eaaf83fb69a35f07ca2728a5e28b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bGb4H4hPIXkG75TjFBHTXBJ6feakmAUdiyQO5JVenl9hg7SfaBm8RpPIKtj1vhQmsw198hKg0HhO0Rchpu0gBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T15:35:43.241065Z","bundle_sha256":"3ec72198585f20e915697fa9afc311b110ff2ddca850157f14e6445c2bd04f38"}}