{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:XB23IR4NL44Q57L7WFYTYURSP5","short_pith_number":"pith:XB23IR4N","canonical_record":{"source":{"id":"1804.07481","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:03:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7c897647b073c04c25dfddc255a7743801b30b9abe622f20c57d694514eba276","abstract_canon_sha256":"62f8bf7928fe631a023ab0192ffa4b68743c4338ceafd36e073c70ae686433e9"},"schema_version":"1.0"},"canonical_sha256":"b875b4478d5f390efd7fb1713c52327f7bc8f1da7a03fc929d467bf0c48e553b","source":{"kind":"arxiv","id":"1804.07481","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.07481","created_at":"2026-05-18T00:17:58Z"},{"alias_kind":"arxiv_version","alias_value":"1804.07481v1","created_at":"2026-05-18T00:17:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.07481","created_at":"2026-05-18T00:17:58Z"},{"alias_kind":"pith_short_12","alias_value":"XB23IR4NL44Q","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XB23IR4NL44Q57L7","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XB23IR4N","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:XB23IR4NL44Q57L7WFYTYURSP5","target":"record","payload":{"canonical_record":{"source":{"id":"1804.07481","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:03:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7c897647b073c04c25dfddc255a7743801b30b9abe622f20c57d694514eba276","abstract_canon_sha256":"62f8bf7928fe631a023ab0192ffa4b68743c4338ceafd36e073c70ae686433e9"},"schema_version":"1.0"},"canonical_sha256":"b875b4478d5f390efd7fb1713c52327f7bc8f1da7a03fc929d467bf0c48e553b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:58.698702Z","signature_b64":"vy3SLXKlERFqrci056toOo2ci3ml2NC2LgtvLAIMRWTsV7NXAhOBIPx1ce8d/XCdHwAlHpbdwNOlrTTWtS5kDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b875b4478d5f390efd7fb1713c52327f7bc8f1da7a03fc929d467bf0c48e553b","last_reissued_at":"2026-05-18T00:17:58.697993Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:58.697993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.07481","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":"2026-05-18T00:17:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DjPAoTghyYnMxVeNdUTuADfuKJs/oTKiRzCdwmARguNBJQTtvDHETz0DG6ececgkbatpMeMM+cETadUuldb4DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T04:47:39.166005Z"},"content_sha256":"319c03b718c9cd2f69a96d74b37f90661d133968a7947ff5c7133171f86ad4fb","schema_version":"1.0","event_id":"sha256:319c03b718c9cd2f69a96d74b37f90661d133968a7947ff5c7133171f86ad4fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:XB23IR4NL44Q57L7WFYTYURSP5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fabirzio Carcillo, Gianluca Bontempi, Olivier Caelen, Yann-A\\\"el Le Borgne","submitted_at":"2018-04-20T08:03:52Z","abstract_excerpt":"Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.07481","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":"2026-05-18T00:17:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qne9yIMrglZ3vVWM2fQlUKwpYO0N9k8a5cPfYFM+abdzxHN/DA2M6OI+TUlhV+YeX0Tf9tE+zFBYM6jTG0FQBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T04:47:39.166371Z"},"content_sha256":"a5230538185b5c208835dc8e5bb1bffcfa08dacd57de209404321e8e2b100a53","schema_version":"1.0","event_id":"sha256:a5230538185b5c208835dc8e5bb1bffcfa08dacd57de209404321e8e2b100a53"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XB23IR4NL44Q57L7WFYTYURSP5/bundle.json","state_url":"https://pith.science/pith/XB23IR4NL44Q57L7WFYTYURSP5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XB23IR4NL44Q57L7WFYTYURSP5/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":"2026-06-30T04:47:39Z","links":{"resolver":"https://pith.science/pith/XB23IR4NL44Q57L7WFYTYURSP5","bundle":"https://pith.science/pith/XB23IR4NL44Q57L7WFYTYURSP5/bundle.json","state":"https://pith.science/pith/XB23IR4NL44Q57L7WFYTYURSP5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XB23IR4NL44Q57L7WFYTYURSP5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:XB23IR4NL44Q57L7WFYTYURSP5","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":"62f8bf7928fe631a023ab0192ffa4b68743c4338ceafd36e073c70ae686433e9","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:03:52Z","title_canon_sha256":"7c897647b073c04c25dfddc255a7743801b30b9abe622f20c57d694514eba276"},"schema_version":"1.0","source":{"id":"1804.07481","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.07481","created_at":"2026-05-18T00:17:58Z"},{"alias_kind":"arxiv_version","alias_value":"1804.07481v1","created_at":"2026-05-18T00:17:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.07481","created_at":"2026-05-18T00:17:58Z"},{"alias_kind":"pith_short_12","alias_value":"XB23IR4NL44Q","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XB23IR4NL44Q57L7","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XB23IR4N","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:a5230538185b5c208835dc8e5bb1bffcfa08dacd57de209404321e8e2b100a53","target":"graph","created_at":"2026-05-18T00:17:58Z","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":"Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an ","authors_text":"Fabirzio Carcillo, Gianluca Bontempi, Olivier Caelen, Yann-A\\\"el Le Borgne","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:03:52Z","title":"Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.07481","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":"sha256:319c03b718c9cd2f69a96d74b37f90661d133968a7947ff5c7133171f86ad4fb","target":"record","created_at":"2026-05-18T00:17:58Z","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":"62f8bf7928fe631a023ab0192ffa4b68743c4338ceafd36e073c70ae686433e9","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:03:52Z","title_canon_sha256":"7c897647b073c04c25dfddc255a7743801b30b9abe622f20c57d694514eba276"},"schema_version":"1.0","source":{"id":"1804.07481","kind":"arxiv","version":1}},"canonical_sha256":"b875b4478d5f390efd7fb1713c52327f7bc8f1da7a03fc929d467bf0c48e553b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b875b4478d5f390efd7fb1713c52327f7bc8f1da7a03fc929d467bf0c48e553b","first_computed_at":"2026-05-18T00:17:58.697993Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:17:58.697993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vy3SLXKlERFqrci056toOo2ci3ml2NC2LgtvLAIMRWTsV7NXAhOBIPx1ce8d/XCdHwAlHpbdwNOlrTTWtS5kDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:17:58.698702Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.07481","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:319c03b718c9cd2f69a96d74b37f90661d133968a7947ff5c7133171f86ad4fb","sha256:a5230538185b5c208835dc8e5bb1bffcfa08dacd57de209404321e8e2b100a53"],"state_sha256":"89c262aa7e2a323299c836798d040ae8f448f48c574e707909fb98f8c89f8988"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Egmzt396e0phvxzN56wyYJbHJriCj3rNzzuG5wmaQUASX2NOtVuJsPOvhwFqvtbnmPo5Jq7ArGIKCR2XQILaCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T04:47:39.170064Z","bundle_sha256":"85b174329f7dd04825f46439176585b2ea5afe42f5f896dc3f53061da247b12b"}}