{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:I5DUI7ELZANWYCNL6L2BBN6KNH","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":"19d34ad00b64b47a8e81d50e894ffc0d1d76abcaed637b4a35ede7bd5084a609","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-07-20T19:10:24Z","title_canon_sha256":"617658c9ce1c404fcd0f0b83fb4d6a28d96ce3d5181e58e43fb6062f24f06a5a"},"schema_version":"1.0","source":{"id":"2507.16845","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.16845","created_at":"2026-07-05T11:52:03Z"},{"alias_kind":"arxiv_version","alias_value":"2507.16845v2","created_at":"2026-07-05T11:52:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16845","created_at":"2026-07-05T11:52:03Z"},{"alias_kind":"pith_short_12","alias_value":"I5DUI7ELZANW","created_at":"2026-07-05T11:52:03Z"},{"alias_kind":"pith_short_16","alias_value":"I5DUI7ELZANWYCNL","created_at":"2026-07-05T11:52:03Z"},{"alias_kind":"pith_short_8","alias_value":"I5DUI7EL","created_at":"2026-07-05T11:52:03Z"}],"graph_snapshots":[{"event_id":"sha256:8495baaf96198bd48199a080487f77daa51ce688c5cbc7b964af050c5f6bc1ba","target":"graph","created_at":"2026-07-05T11:52:03Z","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/2507.16845/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN. By introducing semi supervised learning modules such as Mix Match, Co-Refinement, and Co Refurbishing, we aim to enhance the detection performance while reducing dependence on manual annotations. With the add-on semi-supervised modules, the accuracy rate of the MFCC+CNN model is 92.9%, an increa","authors_text":"In-Ho Ra, Ravi Sankar, Xiaoran Xu","cross_cats":["cs.LG","cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-07-20T19:10:24Z","title":"Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16845","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:ffdc5f6e67d21fae0c43bda21e8c4abcf86740249fec361f8a13deb213cf4747","target":"record","created_at":"2026-07-05T11:52:03Z","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":"19d34ad00b64b47a8e81d50e894ffc0d1d76abcaed637b4a35ede7bd5084a609","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-07-20T19:10:24Z","title_canon_sha256":"617658c9ce1c404fcd0f0b83fb4d6a28d96ce3d5181e58e43fb6062f24f06a5a"},"schema_version":"1.0","source":{"id":"2507.16845","kind":"arxiv","version":2}},"canonical_sha256":"4747447c8bc81b6c09abf2f410b7ca69e5e4bc74e1262859cf9f860068fa601b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4747447c8bc81b6c09abf2f410b7ca69e5e4bc74e1262859cf9f860068fa601b","first_computed_at":"2026-07-05T11:52:03.202158Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:52:03.202158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fC54Bj5UKi2FvhSWK6QJHfwlCGoc+sfpEMIMz7OIA5vOPBuyBT/y2Z6r4rpcvw5NikFDeOArVeYVXMrHZJ5wAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:52:03.202640Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.16845","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ffdc5f6e67d21fae0c43bda21e8c4abcf86740249fec361f8a13deb213cf4747","sha256:8495baaf96198bd48199a080487f77daa51ce688c5cbc7b964af050c5f6bc1ba"],"state_sha256":"c8d48cbac396109393a72588428056ea13b6b321ce8d9001fc50602b8cc73d5d"}