{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:MGYUVXYRTKVZQFCQI4DZRIBURR","short_pith_number":"pith:MGYUVXYR","schema_version":"1.0","canonical_sha256":"61b14adf119aab981450470798a0348c6f81b819aba02efb0effdb0c1007d553","source":{"kind":"arxiv","id":"1710.04749","version":2},"attestation_state":"computed","paper":{"title":"Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.CV","authors_text":"Vijay Manikandan Janakiraman","submitted_at":"2017-10-12T23:42:00Z","abstract_excerpt":"Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of sca"},"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":"1710.04749","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-12T23:42:00Z","cross_cats_sorted":["cs.AI","stat.AP","stat.ML"],"title_canon_sha256":"a71a9270ace3630bb095bb22e5fc1adb4ae5e5b91c711cab3bde5dda16dc3645","abstract_canon_sha256":"e37c623df20fb74d34224675c025a612643a5810ece48842d453868fc18185db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:52.830641Z","signature_b64":"wRhP5rh7oqFQrFlGafjfaSRfB9ZyXDEz/pIX9bYW2yC52clz14tmL+WeJ39MEB2zt3PfyeEj3moLh1ASOVRgBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61b14adf119aab981450470798a0348c6f81b819aba02efb0effdb0c1007d553","last_reissued_at":"2026-05-18T00:23:52.830084Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:52.830084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.CV","authors_text":"Vijay Manikandan Janakiraman","submitted_at":"2017-10-12T23:42:00Z","abstract_excerpt":"Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of sca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04749","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":""},"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":"1710.04749","created_at":"2026-05-18T00:23:52.830148+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04749v2","created_at":"2026-05-18T00:23:52.830148+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04749","created_at":"2026-05-18T00:23:52.830148+00:00"},{"alias_kind":"pith_short_12","alias_value":"MGYUVXYRTKVZ","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"MGYUVXYRTKVZQFCQ","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"MGYUVXYR","created_at":"2026-05-18T12:31:31.346846+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/MGYUVXYRTKVZQFCQI4DZRIBURR","json":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR.json","graph_json":"https://pith.science/api/pith-number/MGYUVXYRTKVZQFCQI4DZRIBURR/graph.json","events_json":"https://pith.science/api/pith-number/MGYUVXYRTKVZQFCQI4DZRIBURR/events.json","paper":"https://pith.science/paper/MGYUVXYR"},"agent_actions":{"view_html":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR","download_json":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR.json","view_paper":"https://pith.science/paper/MGYUVXYR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04749&json=true","fetch_graph":"https://pith.science/api/pith-number/MGYUVXYRTKVZQFCQI4DZRIBURR/graph.json","fetch_events":"https://pith.science/api/pith-number/MGYUVXYRTKVZQFCQI4DZRIBURR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR/action/storage_attestation","attest_author":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR/action/author_attestation","sign_citation":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR/action/citation_signature","submit_replication":"https://pith.science/pith/MGYUVXYRTKVZQFCQI4DZRIBURR/action/replication_record"}},"created_at":"2026-05-18T00:23:52.830148+00:00","updated_at":"2026-05-18T00:23:52.830148+00:00"}