{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:XR5MTNUV4IUUUQ5UKY4HX2ZWMU","short_pith_number":"pith:XR5MTNUV","canonical_record":{"source":{"id":"1706.09308","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-28T14:16:56Z","cross_cats_sorted":[],"title_canon_sha256":"04d8c523ee9cbc787d214c8005662a6dee0b506c2bb898f2022815abd34c7105","abstract_canon_sha256":"dd521904e6fe4c262681b3081805ebe63eea1af514e7e04ab74b42b26e8987b2"},"schema_version":"1.0"},"canonical_sha256":"bc7ac9b695e2294a43b456387beb36651b9bf931f4b43856a552c7661b4f1872","source":{"kind":"arxiv","id":"1706.09308","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.09308","created_at":"2026-05-18T00:40:18Z"},{"alias_kind":"arxiv_version","alias_value":"1706.09308v2","created_at":"2026-05-18T00:40:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.09308","created_at":"2026-05-18T00:40:18Z"},{"alias_kind":"pith_short_12","alias_value":"XR5MTNUV4IUU","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XR5MTNUV4IUUUQ5U","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XR5MTNUV","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:XR5MTNUV4IUUUQ5UKY4HX2ZWMU","target":"record","payload":{"canonical_record":{"source":{"id":"1706.09308","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-28T14:16:56Z","cross_cats_sorted":[],"title_canon_sha256":"04d8c523ee9cbc787d214c8005662a6dee0b506c2bb898f2022815abd34c7105","abstract_canon_sha256":"dd521904e6fe4c262681b3081805ebe63eea1af514e7e04ab74b42b26e8987b2"},"schema_version":"1.0"},"canonical_sha256":"bc7ac9b695e2294a43b456387beb36651b9bf931f4b43856a552c7661b4f1872","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:18.930795Z","signature_b64":"01RLXuNI4FsQOsXMAX/Q5KA3E6ExUDqLTeGQQ/jYU7qKIzxDnMdkA7WKqwSA4gDa2Q8QpbyWTtlBPL6dY5d5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc7ac9b695e2294a43b456387beb36651b9bf931f4b43856a552c7661b4f1872","last_reissued_at":"2026-05-18T00:40:18.930118Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:18.930118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.09308","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:40:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k3u1c9UyqXozDnMGQnMyPvoEqymIzykuQ52oQGVNK8FY9TzKAlbuIfPBhHJjfra+iqPdhbZ9HslBZ17XDwQuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T18:59:05.087876Z"},"content_sha256":"04e51655b6ce0fce883fc3ff1cb5994dde65adef866e66a2f7add04a0b38aaa3","schema_version":"1.0","event_id":"sha256:04e51655b6ce0fce883fc3ff1cb5994dde65adef866e66a2f7add04a0b38aaa3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:XR5MTNUV4IUUUQ5UKY4HX2ZWMU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A New Urban Objects Detection Framework Using Weakly Annotated Sets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Claudio Silva, Eric Keiji, Gabriel Ferreira, Roberto M. Cesar Jr","submitted_at":"2017-06-28T14:16:56Z","abstract_excerpt":"Urban informatics explore data science methods to address different urban issues intensively based on data. The large variety and quantity of data available should be explored but this brings important challenges. For instance, although there are powerful computer vision methods that may be explored, they may require large annotated datasets. In this work we propose a novel approach to automatically creating an object recognition system with minimal manual annotation. The basic idea behind the method is to use large input datasets using available online cameras on large cities. A off-the-shelf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.09308","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:40:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"goJAi1FJmYo0plqbZ9vxtpzrkKQjdPWqeFB4Xc7vuKl9DdhQynNExetaC4IfIHcI5QLHVLFkdxmib4FB0skNDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T18:59:05.088499Z"},"content_sha256":"61318c2c4394806ce2a1fd78fc37841665e36790e278c2a8c632d8e341a17825","schema_version":"1.0","event_id":"sha256:61318c2c4394806ce2a1fd78fc37841665e36790e278c2a8c632d8e341a17825"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XR5MTNUV4IUUUQ5UKY4HX2ZWMU/bundle.json","state_url":"https://pith.science/pith/XR5MTNUV4IUUUQ5UKY4HX2ZWMU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XR5MTNUV4IUUUQ5UKY4HX2ZWMU/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":"2026-06-07T18:59:05Z","links":{"resolver":"https://pith.science/pith/XR5MTNUV4IUUUQ5UKY4HX2ZWMU","bundle":"https://pith.science/pith/XR5MTNUV4IUUUQ5UKY4HX2ZWMU/bundle.json","state":"https://pith.science/pith/XR5MTNUV4IUUUQ5UKY4HX2ZWMU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XR5MTNUV4IUUUQ5UKY4HX2ZWMU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:XR5MTNUV4IUUUQ5UKY4HX2ZWMU","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":"dd521904e6fe4c262681b3081805ebe63eea1af514e7e04ab74b42b26e8987b2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-28T14:16:56Z","title_canon_sha256":"04d8c523ee9cbc787d214c8005662a6dee0b506c2bb898f2022815abd34c7105"},"schema_version":"1.0","source":{"id":"1706.09308","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.09308","created_at":"2026-05-18T00:40:18Z"},{"alias_kind":"arxiv_version","alias_value":"1706.09308v2","created_at":"2026-05-18T00:40:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.09308","created_at":"2026-05-18T00:40:18Z"},{"alias_kind":"pith_short_12","alias_value":"XR5MTNUV4IUU","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XR5MTNUV4IUUUQ5U","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XR5MTNUV","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:61318c2c4394806ce2a1fd78fc37841665e36790e278c2a8c632d8e341a17825","target":"graph","created_at":"2026-05-18T00:40:18Z","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":"Urban informatics explore data science methods to address different urban issues intensively based on data. The large variety and quantity of data available should be explored but this brings important challenges. For instance, although there are powerful computer vision methods that may be explored, they may require large annotated datasets. In this work we propose a novel approach to automatically creating an object recognition system with minimal manual annotation. The basic idea behind the method is to use large input datasets using available online cameras on large cities. A off-the-shelf","authors_text":"Claudio Silva, Eric Keiji, Gabriel Ferreira, Roberto M. Cesar Jr","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-28T14:16:56Z","title":"A New Urban Objects Detection Framework Using Weakly Annotated Sets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.09308","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":"sha256:04e51655b6ce0fce883fc3ff1cb5994dde65adef866e66a2f7add04a0b38aaa3","target":"record","created_at":"2026-05-18T00:40:18Z","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":"dd521904e6fe4c262681b3081805ebe63eea1af514e7e04ab74b42b26e8987b2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-28T14:16:56Z","title_canon_sha256":"04d8c523ee9cbc787d214c8005662a6dee0b506c2bb898f2022815abd34c7105"},"schema_version":"1.0","source":{"id":"1706.09308","kind":"arxiv","version":2}},"canonical_sha256":"bc7ac9b695e2294a43b456387beb36651b9bf931f4b43856a552c7661b4f1872","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc7ac9b695e2294a43b456387beb36651b9bf931f4b43856a552c7661b4f1872","first_computed_at":"2026-05-18T00:40:18.930118Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:40:18.930118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"01RLXuNI4FsQOsXMAX/Q5KA3E6ExUDqLTeGQQ/jYU7qKIzxDnMdkA7WKqwSA4gDa2Q8QpbyWTtlBPL6dY5d5AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:40:18.930795Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.09308","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:04e51655b6ce0fce883fc3ff1cb5994dde65adef866e66a2f7add04a0b38aaa3","sha256:61318c2c4394806ce2a1fd78fc37841665e36790e278c2a8c632d8e341a17825"],"state_sha256":"d0288b1313387eed63f4f368b1fd0b4d5368f7d69d9918207e99ba34c3fc8926"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IU9e3Rq6zCkpbpU0VLxdK2mI4qiN0OIxsw2XuDK41Rp5+T13oIOcsU05AJLaaCeyMl7HYzGVZo7JTW7X2YkRAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T18:59:05.091635Z","bundle_sha256":"ca6b3e77db573905ad287fe8d4dd6a3bb852e42b985d692d73315d740ea3e296"}}