{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:UC6XLJVP6MD4MSKWFHELDGOT3E","short_pith_number":"pith:UC6XLJVP","canonical_record":{"source":{"id":"1906.06446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-15T01:12:02Z","cross_cats_sorted":[],"title_canon_sha256":"3b75e3ab48b237146323db72108126654de99a3cadd3076f3ae259454e49f781","abstract_canon_sha256":"02179e415f42e39f223bb0fbbc3002b2aa170176a99d65902939d9397285a586"},"schema_version":"1.0"},"canonical_sha256":"a0bd75a6aff307c6495629c8b199d3d9089720f3f00656eba627c15b9f7aa1ac","source":{"kind":"arxiv","id":"1906.06446","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06446","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06446v1","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06446","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"pith_short_12","alias_value":"UC6XLJVP6MD4","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"UC6XLJVP6MD4MSKW","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"UC6XLJVP","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:UC6XLJVP6MD4MSKWFHELDGOT3E","target":"record","payload":{"canonical_record":{"source":{"id":"1906.06446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-15T01:12:02Z","cross_cats_sorted":[],"title_canon_sha256":"3b75e3ab48b237146323db72108126654de99a3cadd3076f3ae259454e49f781","abstract_canon_sha256":"02179e415f42e39f223bb0fbbc3002b2aa170176a99d65902939d9397285a586"},"schema_version":"1.0"},"canonical_sha256":"a0bd75a6aff307c6495629c8b199d3d9089720f3f00656eba627c15b9f7aa1ac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:13.654766Z","signature_b64":"BQXsqTUsk16JgH1OHpPTzOwVUgdH51cDTL+xM/Gt6n//gsMBSmOSE92SmCYNji242bdOi8o/CKai2kUxy6EPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0bd75a6aff307c6495629c8b199d3d9089720f3f00656eba627c15b9f7aa1ac","last_reissued_at":"2026-05-17T23:43:13.654165Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:13.654165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.06446","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":"2026-05-17T23:43:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kjxfc5FzlhQfmE0kccImI0tpHYjxwncKzTAaGfHD12cr2IshhR8jHDlt491Tm3o+Rqh08uvUTuZXbGa/KckVAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T03:29:43.096767Z"},"content_sha256":"0f97fbb9fc5790105e050c4cee4217b15df7ae8e0dc392840c574cfcca28dc82","schema_version":"1.0","event_id":"sha256:0f97fbb9fc5790105e050c4cee4217b15df7ae8e0dc392840c574cfcca28dc82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:UC6XLJVP6MD4MSKWFHELDGOT3E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Neural Network Approaches for Leather Defect Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng-Yan Yang, Chien An Wu, Cong Tue Le, Kun-hong Liu, Sze-Teng Liong, Tran Quang Binh, Yen-Chang Huang, Y.S. Gan","submitted_at":"2019-06-15T01:12:02Z","abstract_excerpt":"Genuine leather, such as the hides of cows, crocodiles, lizards and goats usually contain natural and artificial defects, like holes, fly bites, tick marks, veining, cuts, wrinkles and others. A traditional solution to identify the defects is by manual defect inspection, which involves skilled experts. It is time consuming and may incur a high error rate and results in low productivity. This paper presents a series of automatic image processing processes to perform the classification of leather defects by adopting deep learning approaches. Particularly, the leather images are first partitioned"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06446","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":""},"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":"2026-05-17T23:43:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HkaJcqNA9/jttDcNc/qACOs/Z93IHsoUaPLhBm+PKrxYAMGHJecG1y9drs8ODSqYNQ2QggeC7cUzRsGO/ML8BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T03:29:43.097118Z"},"content_sha256":"738ba27e1e62658f857915684422357e69d4fe119d65687e568837b25dee0132","schema_version":"1.0","event_id":"sha256:738ba27e1e62658f857915684422357e69d4fe119d65687e568837b25dee0132"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UC6XLJVP6MD4MSKWFHELDGOT3E/bundle.json","state_url":"https://pith.science/pith/UC6XLJVP6MD4MSKWFHELDGOT3E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UC6XLJVP6MD4MSKWFHELDGOT3E/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":"2026-05-28T03:29:43Z","links":{"resolver":"https://pith.science/pith/UC6XLJVP6MD4MSKWFHELDGOT3E","bundle":"https://pith.science/pith/UC6XLJVP6MD4MSKWFHELDGOT3E/bundle.json","state":"https://pith.science/pith/UC6XLJVP6MD4MSKWFHELDGOT3E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UC6XLJVP6MD4MSKWFHELDGOT3E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UC6XLJVP6MD4MSKWFHELDGOT3E","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":"02179e415f42e39f223bb0fbbc3002b2aa170176a99d65902939d9397285a586","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-15T01:12:02Z","title_canon_sha256":"3b75e3ab48b237146323db72108126654de99a3cadd3076f3ae259454e49f781"},"schema_version":"1.0","source":{"id":"1906.06446","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06446","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06446v1","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06446","created_at":"2026-05-17T23:43:13Z"},{"alias_kind":"pith_short_12","alias_value":"UC6XLJVP6MD4","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"UC6XLJVP6MD4MSKW","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"UC6XLJVP","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:738ba27e1e62658f857915684422357e69d4fe119d65687e568837b25dee0132","target":"graph","created_at":"2026-05-17T23:43:13Z","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"},"paper":{"abstract_excerpt":"Genuine leather, such as the hides of cows, crocodiles, lizards and goats usually contain natural and artificial defects, like holes, fly bites, tick marks, veining, cuts, wrinkles and others. A traditional solution to identify the defects is by manual defect inspection, which involves skilled experts. It is time consuming and may incur a high error rate and results in low productivity. This paper presents a series of automatic image processing processes to perform the classification of leather defects by adopting deep learning approaches. Particularly, the leather images are first partitioned","authors_text":"Cheng-Yan Yang, Chien An Wu, Cong Tue Le, Kun-hong Liu, Sze-Teng Liong, Tran Quang Binh, Yen-Chang Huang, Y.S. Gan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-15T01:12:02Z","title":"Efficient Neural Network Approaches for Leather Defect Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06446","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":"sha256:0f97fbb9fc5790105e050c4cee4217b15df7ae8e0dc392840c574cfcca28dc82","target":"record","created_at":"2026-05-17T23:43:13Z","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":"02179e415f42e39f223bb0fbbc3002b2aa170176a99d65902939d9397285a586","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-15T01:12:02Z","title_canon_sha256":"3b75e3ab48b237146323db72108126654de99a3cadd3076f3ae259454e49f781"},"schema_version":"1.0","source":{"id":"1906.06446","kind":"arxiv","version":1}},"canonical_sha256":"a0bd75a6aff307c6495629c8b199d3d9089720f3f00656eba627c15b9f7aa1ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0bd75a6aff307c6495629c8b199d3d9089720f3f00656eba627c15b9f7aa1ac","first_computed_at":"2026-05-17T23:43:13.654165Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:13.654165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BQXsqTUsk16JgH1OHpPTzOwVUgdH51cDTL+xM/Gt6n//gsMBSmOSE92SmCYNji242bdOi8o/CKai2kUxy6EPCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:13.654766Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.06446","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f97fbb9fc5790105e050c4cee4217b15df7ae8e0dc392840c574cfcca28dc82","sha256:738ba27e1e62658f857915684422357e69d4fe119d65687e568837b25dee0132"],"state_sha256":"61387ee094f20172f436bf0c27f317e9a13bcc58b82714328603c19b8cc551f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KuqQos1+r7fa3KotVogxQbPhmpVyd+cfkyXVD8PWovKcXyA9jVgisZiEveKgWpeiAPoVZ6V4+t0KM7uNR0G1Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T03:29:43.099181Z","bundle_sha256":"16434d4e7d0991b015beca57687ac652682f5bf4668b0060bb880c2685758279"}}