{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:JMHLL2HVJKVKDGFH3LB3PPZV4Y","short_pith_number":"pith:JMHLL2HV","canonical_record":{"source":{"id":"2102.12354","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T15:40:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"05c6f374d5c9bb1c40f43544b183607aff3956d3b00bc287d7e79a3b49b53821","abstract_canon_sha256":"e91044127abe46ecd30edda32571327b6a696b5dc42aa51322e46f8bebdfb676"},"schema_version":"1.0"},"canonical_sha256":"4b0eb5e8f54aaaa198a7dac3b7bf35e62d5a615cc51a59d8b6c725d60d3bff3b","source":{"kind":"arxiv","id":"2102.12354","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.12354","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"arxiv_version","alias_value":"2102.12354v1","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12354","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"pith_short_12","alias_value":"JMHLL2HVJKVK","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"pith_short_16","alias_value":"JMHLL2HVJKVKDGFH","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"pith_short_8","alias_value":"JMHLL2HV","created_at":"2026-07-05T02:18:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:JMHLL2HVJKVKDGFH3LB3PPZV4Y","target":"record","payload":{"canonical_record":{"source":{"id":"2102.12354","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T15:40:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"05c6f374d5c9bb1c40f43544b183607aff3956d3b00bc287d7e79a3b49b53821","abstract_canon_sha256":"e91044127abe46ecd30edda32571327b6a696b5dc42aa51322e46f8bebdfb676"},"schema_version":"1.0"},"canonical_sha256":"4b0eb5e8f54aaaa198a7dac3b7bf35e62d5a615cc51a59d8b6c725d60d3bff3b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:18:11.889885Z","signature_b64":"4sNhC6PHRLEWs/Un6HKz0pdZAhMeTxbbjwF/oZis1dmwHcQDhpbi5o9IeKDvii9+AGkaKjdEdc4zDrmGipkLDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b0eb5e8f54aaaa198a7dac3b7bf35e62d5a615cc51a59d8b6c725d60d3bff3b","last_reissued_at":"2026-07-05T02:18:11.889495Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:18:11.889495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.12354","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":"2026-07-05T02:18:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IlYm5Oec/7siFjsubdgbjyvEVzYLk1YBUqgGTpyh+xpd/mT7s3fpYSqabfycgwV77yKftrl32+G/JnQpDfsQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T16:11:32.132107Z"},"content_sha256":"84dd92f435ff0989b8afca9073b95bf41cbd5f0f21da51b44f6aa56de71990a3","schema_version":"1.0","event_id":"sha256:84dd92f435ff0989b8afca9073b95bf41cbd5f0f21da51b44f6aa56de71990a3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:JMHLL2HVJKVKDGFH3LB3PPZV4Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Impact of Interpretability Methods in Active Image Augmentation Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cleber Zanchettin, Flavio Santos, Leonardo Matos, Paulo Novais","submitted_at":"2021-02-24T15:40:54Z","abstract_excerpt":"Robustness is a significant constraint in machine learning models. The performance of the algorithms must not deteriorate when training and testing with slightly different data. Deep neural network models achieve awe-inspiring results in a wide range of applications of computer vision. Still, in the presence of noise or region occlusion, some models exhibit inaccurate performance even with data handled in training. Besides, some experiments suggest deep learning models sometimes use incorrect parts of the input information to perform inference. Activate Image Augmentation (ADA) is an augmentat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12354","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/2102.12354/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":"2026-07-05T02:18:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GWJ7WZimlxO37mM74PsKjoFmmi9nORn4/QEkQVQKJJ9tZlvjNrKALM2lv16u3gsvlwvc17OSbanlnPl6GJr0DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T16:11:32.133052Z"},"content_sha256":"c45c83a8781f86b6f3bb4a3c969767e53c055e73de9d8f45f14445cd1bda840c","schema_version":"1.0","event_id":"sha256:c45c83a8781f86b6f3bb4a3c969767e53c055e73de9d8f45f14445cd1bda840c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JMHLL2HVJKVKDGFH3LB3PPZV4Y/bundle.json","state_url":"https://pith.science/pith/JMHLL2HVJKVKDGFH3LB3PPZV4Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JMHLL2HVJKVKDGFH3LB3PPZV4Y/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":"2026-08-23T16:11:32Z","links":{"resolver":"https://pith.science/pith/JMHLL2HVJKVKDGFH3LB3PPZV4Y","bundle":"https://pith.science/pith/JMHLL2HVJKVKDGFH3LB3PPZV4Y/bundle.json","state":"https://pith.science/pith/JMHLL2HVJKVKDGFH3LB3PPZV4Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JMHLL2HVJKVKDGFH3LB3PPZV4Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:JMHLL2HVJKVKDGFH3LB3PPZV4Y","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":"e91044127abe46ecd30edda32571327b6a696b5dc42aa51322e46f8bebdfb676","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T15:40:54Z","title_canon_sha256":"05c6f374d5c9bb1c40f43544b183607aff3956d3b00bc287d7e79a3b49b53821"},"schema_version":"1.0","source":{"id":"2102.12354","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.12354","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"arxiv_version","alias_value":"2102.12354v1","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12354","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"pith_short_12","alias_value":"JMHLL2HVJKVK","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"pith_short_16","alias_value":"JMHLL2HVJKVKDGFH","created_at":"2026-07-05T02:18:11Z"},{"alias_kind":"pith_short_8","alias_value":"JMHLL2HV","created_at":"2026-07-05T02:18:11Z"}],"graph_snapshots":[{"event_id":"sha256:c45c83a8781f86b6f3bb4a3c969767e53c055e73de9d8f45f14445cd1bda840c","target":"graph","created_at":"2026-07-05T02:18:11Z","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/2102.12354/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Robustness is a significant constraint in machine learning models. The performance of the algorithms must not deteriorate when training and testing with slightly different data. Deep neural network models achieve awe-inspiring results in a wide range of applications of computer vision. Still, in the presence of noise or region occlusion, some models exhibit inaccurate performance even with data handled in training. Besides, some experiments suggest deep learning models sometimes use incorrect parts of the input information to perform inference. Activate Image Augmentation (ADA) is an augmentat","authors_text":"Cleber Zanchettin, Flavio Santos, Leonardo Matos, Paulo Novais","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T15:40:54Z","title":"On the Impact of Interpretability Methods in Active Image Augmentation Method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12354","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":"sha256:84dd92f435ff0989b8afca9073b95bf41cbd5f0f21da51b44f6aa56de71990a3","target":"record","created_at":"2026-07-05T02:18:11Z","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":"e91044127abe46ecd30edda32571327b6a696b5dc42aa51322e46f8bebdfb676","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-24T15:40:54Z","title_canon_sha256":"05c6f374d5c9bb1c40f43544b183607aff3956d3b00bc287d7e79a3b49b53821"},"schema_version":"1.0","source":{"id":"2102.12354","kind":"arxiv","version":1}},"canonical_sha256":"4b0eb5e8f54aaaa198a7dac3b7bf35e62d5a615cc51a59d8b6c725d60d3bff3b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b0eb5e8f54aaaa198a7dac3b7bf35e62d5a615cc51a59d8b6c725d60d3bff3b","first_computed_at":"2026-07-05T02:18:11.889495Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:18:11.889495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4sNhC6PHRLEWs/Un6HKz0pdZAhMeTxbbjwF/oZis1dmwHcQDhpbi5o9IeKDvii9+AGkaKjdEdc4zDrmGipkLDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:18:11.889885Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.12354","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84dd92f435ff0989b8afca9073b95bf41cbd5f0f21da51b44f6aa56de71990a3","sha256:c45c83a8781f86b6f3bb4a3c969767e53c055e73de9d8f45f14445cd1bda840c"],"state_sha256":"116fbc0d472573bcb9cd09b11e9a3eeaaf4c61424d62f0b0de834d9cfa2c58ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+n2ZRYRKGKKsERRMn5+D7oW8jXSY8EQfJoArrXKejzTz3tfkj6qNN8cXcMyOff2kBccGgtJJCNjpltzo/13FAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T16:11:32.138674Z","bundle_sha256":"483a16a07aa5fb4702dcc9f988931838f62ef7e2c8bdabab83a80add9224935b"}}