{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:A6KIQQTRFAE2VVQZJQDPMQXBQB","short_pith_number":"pith:A6KIQQTR","canonical_record":{"source":{"id":"2303.05957","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-10T14:45:37Z","cross_cats_sorted":[],"title_canon_sha256":"cc4872af600a64449b84e3feaf50013ec6ac9be646a4d0719503bb61b2997736","abstract_canon_sha256":"7950df9c4debc6b35c1b1675bced384dde44380727d5248b22266f8a0c00d157"},"schema_version":"1.0"},"canonical_sha256":"07948842712809aad6194c06f642e18044b50ce5fa212d433290f56daa3103bb","source":{"kind":"arxiv","id":"2303.05957","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.05957","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"arxiv_version","alias_value":"2303.05957v1","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05957","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"pith_short_12","alias_value":"A6KIQQTRFAE2","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"pith_short_16","alias_value":"A6KIQQTRFAE2VVQZ","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"pith_short_8","alias_value":"A6KIQQTR","created_at":"2026-07-05T05:49:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:A6KIQQTRFAE2VVQZJQDPMQXBQB","target":"record","payload":{"canonical_record":{"source":{"id":"2303.05957","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-10T14:45:37Z","cross_cats_sorted":[],"title_canon_sha256":"cc4872af600a64449b84e3feaf50013ec6ac9be646a4d0719503bb61b2997736","abstract_canon_sha256":"7950df9c4debc6b35c1b1675bced384dde44380727d5248b22266f8a0c00d157"},"schema_version":"1.0"},"canonical_sha256":"07948842712809aad6194c06f642e18044b50ce5fa212d433290f56daa3103bb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:59.157919Z","signature_b64":"+8pHQa8jLrnM09vu3T1KCzyJNUIDiRoxE9scubCHKpMyHEhQe8gjKiloOK/DhcbpnFnMTS4ubiWegn151RbjCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07948842712809aad6194c06f642e18044b50ce5fa212d433290f56daa3103bb","last_reissued_at":"2026-07-05T05:49:59.157447Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:59.157447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.05957","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:49:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L+yQ9LjdHP5AbQuzj+vF4EpZsp1XidPOgunh2mu4nmWNFFYp2uyJZhOhYyEoeQEBKOFjshq8CRGzzyCJIEYuDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T00:18:33.077788Z"},"content_sha256":"15e579ef2a503ae11cbb1b59379c3e373ed60249c51eb577276c6381b1579868","schema_version":"1.0","event_id":"sha256:15e579ef2a503ae11cbb1b59379c3e373ed60249c51eb577276c6381b1579868"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:A6KIQQTRFAE2VVQZJQDPMQXBQB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Imad L. Al-Qadi, Zehui Zhu","submitted_at":"2023-03-10T14:45:37Z","abstract_excerpt":"This article proposes a deep neural network, namely CrackPropNet, to measure crack propagation on asphalt concrete (AC) specimens. It offers an accurate, flexible, efficient, and low-cost solution for crack propagation measurement using images collected during cracking tests. CrackPropNet significantly differs from traditional deep learning networks, as it involves learning to locate displacement field discontinuities by matching features at various locations in the reference and deformed images. An image library representing the diversified cracking behavior of AC was developed for supervised"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05957","kind":"arxiv","version":1},"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/2303.05957/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":"2026-07-05T05:49:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SZcBU80uHHTX4h72x23kgX5pVTCqhvVHSakU/QD7Vljc8HHDVEKqqKGQVz5wGXBtKtkgBiOVTwO5YHpphm+iCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T00:18:33.078749Z"},"content_sha256":"940188a2e5d8815dbdde62086f45c54f27aeacde58787fd24a804206581e6fe0","schema_version":"1.0","event_id":"sha256:940188a2e5d8815dbdde62086f45c54f27aeacde58787fd24a804206581e6fe0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A6KIQQTRFAE2VVQZJQDPMQXBQB/bundle.json","state_url":"https://pith.science/pith/A6KIQQTRFAE2VVQZJQDPMQXBQB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A6KIQQTRFAE2VVQZJQDPMQXBQB/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":"2026-08-19T00:18:33Z","links":{"resolver":"https://pith.science/pith/A6KIQQTRFAE2VVQZJQDPMQXBQB","bundle":"https://pith.science/pith/A6KIQQTRFAE2VVQZJQDPMQXBQB/bundle.json","state":"https://pith.science/pith/A6KIQQTRFAE2VVQZJQDPMQXBQB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A6KIQQTRFAE2VVQZJQDPMQXBQB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:A6KIQQTRFAE2VVQZJQDPMQXBQB","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":"7950df9c4debc6b35c1b1675bced384dde44380727d5248b22266f8a0c00d157","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-10T14:45:37Z","title_canon_sha256":"cc4872af600a64449b84e3feaf50013ec6ac9be646a4d0719503bb61b2997736"},"schema_version":"1.0","source":{"id":"2303.05957","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.05957","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"arxiv_version","alias_value":"2303.05957v1","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05957","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"pith_short_12","alias_value":"A6KIQQTRFAE2","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"pith_short_16","alias_value":"A6KIQQTRFAE2VVQZ","created_at":"2026-07-05T05:49:59Z"},{"alias_kind":"pith_short_8","alias_value":"A6KIQQTR","created_at":"2026-07-05T05:49:59Z"}],"graph_snapshots":[{"event_id":"sha256:940188a2e5d8815dbdde62086f45c54f27aeacde58787fd24a804206581e6fe0","target":"graph","created_at":"2026-07-05T05:49:59Z","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/2303.05957/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This article proposes a deep neural network, namely CrackPropNet, to measure crack propagation on asphalt concrete (AC) specimens. It offers an accurate, flexible, efficient, and low-cost solution for crack propagation measurement using images collected during cracking tests. CrackPropNet significantly differs from traditional deep learning networks, as it involves learning to locate displacement field discontinuities by matching features at various locations in the reference and deformed images. An image library representing the diversified cracking behavior of AC was developed for supervised","authors_text":"Imad L. Al-Qadi, Zehui Zhu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-10T14:45:37Z","title":"Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05957","kind":"arxiv","version":1},"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":"sha256:15e579ef2a503ae11cbb1b59379c3e373ed60249c51eb577276c6381b1579868","target":"record","created_at":"2026-07-05T05:49:59Z","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":"7950df9c4debc6b35c1b1675bced384dde44380727d5248b22266f8a0c00d157","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-10T14:45:37Z","title_canon_sha256":"cc4872af600a64449b84e3feaf50013ec6ac9be646a4d0719503bb61b2997736"},"schema_version":"1.0","source":{"id":"2303.05957","kind":"arxiv","version":1}},"canonical_sha256":"07948842712809aad6194c06f642e18044b50ce5fa212d433290f56daa3103bb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07948842712809aad6194c06f642e18044b50ce5fa212d433290f56daa3103bb","first_computed_at":"2026-07-05T05:49:59.157447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:49:59.157447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+8pHQa8jLrnM09vu3T1KCzyJNUIDiRoxE9scubCHKpMyHEhQe8gjKiloOK/DhcbpnFnMTS4ubiWegn151RbjCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:49:59.157919Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.05957","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15e579ef2a503ae11cbb1b59379c3e373ed60249c51eb577276c6381b1579868","sha256:940188a2e5d8815dbdde62086f45c54f27aeacde58787fd24a804206581e6fe0"],"state_sha256":"a1459a4d120a49ffe62343f226685df631cd5c2c0118c14967ff435c4b180ef9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Q2chUin2nG3RCp2wbLqIf0b8BtM1JqTDpKgapEB33vAZxekmnULWDgj4A+gnIjuj4MDAsGxCYsmUTBjAoqSCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T00:18:33.084077Z","bundle_sha256":"a183b314e30ee735c65d5eb3f0913bc5bff08206f0d3b719749c2d37e82b899b"}}