{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:Z65AANG2MTTHM2TNGNZJ7JIII3","short_pith_number":"pith:Z65AANG2","canonical_record":{"source":{"id":"1903.10022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-24T17:09:28Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"7d4b098e7b1daff796841c4284c0f980fca30febad5939b9512284bd74ba643b","abstract_canon_sha256":"57048247cb9a7f23b1065e771288aba1c719411df361c236f5cea317d6905388"},"schema_version":"1.0"},"canonical_sha256":"cfba0034da64e6766a6d33729fa50846f5bc91a9f5630e5de610db7a01d5fffd","source":{"kind":"arxiv","id":"1903.10022","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.10022","created_at":"2026-05-17T23:50:34Z"},{"alias_kind":"arxiv_version","alias_value":"1903.10022v1","created_at":"2026-05-17T23:50:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.10022","created_at":"2026-05-17T23:50:34Z"},{"alias_kind":"pith_short_12","alias_value":"Z65AANG2MTTH","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Z65AANG2MTTHM2TN","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Z65AANG2","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:Z65AANG2MTTHM2TNGNZJ7JIII3","target":"record","payload":{"canonical_record":{"source":{"id":"1903.10022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-24T17:09:28Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"7d4b098e7b1daff796841c4284c0f980fca30febad5939b9512284bd74ba643b","abstract_canon_sha256":"57048247cb9a7f23b1065e771288aba1c719411df361c236f5cea317d6905388"},"schema_version":"1.0"},"canonical_sha256":"cfba0034da64e6766a6d33729fa50846f5bc91a9f5630e5de610db7a01d5fffd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:34.759531Z","signature_b64":"HDQi5h3uKijeusZi3x139YqpjdC/bGRks3cFN5Wh1nN+jEoK/DKMH7dts7zPkYtvVv+ucqy5+ZFoNT7y0JM1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfba0034da64e6766a6d33729fa50846f5bc91a9f5630e5de610db7a01d5fffd","last_reissued_at":"2026-05-17T23:50:34.758846Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:34.758846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.10022","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":"2026-05-17T23:50:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lx5Nc8FITEtJic8/ApU92mzq/KIUCez4whOO2PJWRqNEv/+JQk5rVQ9dIB2ub021w+kHunV3qO92NNJA3Z5dAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T07:50:50.422846Z"},"content_sha256":"e3780aa92827a60f5a4abbc789c0acd56d8d3c5289bb9bb500ed06be8a2a55b8","schema_version":"1.0","event_id":"sha256:e3780aa92827a60f5a4abbc789c0acd56d8d3c5289bb9bb500ed06be8a2a55b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:Z65AANG2MTTHM2TNGNZJ7JIII3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cesar Hervas-Martinez, Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk","submitted_at":"2019-03-24T17:09:28Z","abstract_excerpt":"Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case, the main challenge is to estimate the label associated to new synthetic patterns. This paper studies the effect of generating synthetic data by convex combination of patterns and the use of these as unsupervised information in a semi-supervised learning framework with support vector machines, avoiding thus the need to label synthetic examples. We perform experiments on a total of 53 binary classification datasets. Ou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.10022","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":"2026-05-17T23:50:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NPoWt8WLomhxxBLDC7YPw4tlzRBAbdSpOVMrac52X98fRRnoNBbgPbZ7B6Z9NI0NYF01nj0k7wFErV0RlK+hDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T07:50:50.423413Z"},"content_sha256":"34684d4c32efa719e23686e454c07af1c1ef0b64d1a036c0ba9dc86be7d21fbd","schema_version":"1.0","event_id":"sha256:34684d4c32efa719e23686e454c07af1c1ef0b64d1a036c0ba9dc86be7d21fbd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z65AANG2MTTHM2TNGNZJ7JIII3/bundle.json","state_url":"https://pith.science/pith/Z65AANG2MTTHM2TNGNZJ7JIII3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z65AANG2MTTHM2TNGNZJ7JIII3/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":"2026-06-09T07:50:50Z","links":{"resolver":"https://pith.science/pith/Z65AANG2MTTHM2TNGNZJ7JIII3","bundle":"https://pith.science/pith/Z65AANG2MTTHM2TNGNZJ7JIII3/bundle.json","state":"https://pith.science/pith/Z65AANG2MTTHM2TNGNZJ7JIII3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z65AANG2MTTHM2TNGNZJ7JIII3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:Z65AANG2MTTHM2TNGNZJ7JIII3","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":"57048247cb9a7f23b1065e771288aba1c719411df361c236f5cea317d6905388","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-24T17:09:28Z","title_canon_sha256":"7d4b098e7b1daff796841c4284c0f980fca30febad5939b9512284bd74ba643b"},"schema_version":"1.0","source":{"id":"1903.10022","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.10022","created_at":"2026-05-17T23:50:34Z"},{"alias_kind":"arxiv_version","alias_value":"1903.10022v1","created_at":"2026-05-17T23:50:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.10022","created_at":"2026-05-17T23:50:34Z"},{"alias_kind":"pith_short_12","alias_value":"Z65AANG2MTTH","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Z65AANG2MTTHM2TN","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Z65AANG2","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:34684d4c32efa719e23686e454c07af1c1ef0b64d1a036c0ba9dc86be7d21fbd","target":"graph","created_at":"2026-05-17T23:50:34Z","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":"Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case, the main challenge is to estimate the label associated to new synthetic patterns. This paper studies the effect of generating synthetic data by convex combination of patterns and the use of these as unsupervised information in a semi-supervised learning framework with support vector machines, avoiding thus the need to label synthetic examples. We perform experiments on a total of 53 binary classification datasets. Ou","authors_text":"Cesar Hervas-Martinez, Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-24T17:09:28Z","title":"Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.10022","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:e3780aa92827a60f5a4abbc789c0acd56d8d3c5289bb9bb500ed06be8a2a55b8","target":"record","created_at":"2026-05-17T23:50:34Z","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":"57048247cb9a7f23b1065e771288aba1c719411df361c236f5cea317d6905388","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-24T17:09:28Z","title_canon_sha256":"7d4b098e7b1daff796841c4284c0f980fca30febad5939b9512284bd74ba643b"},"schema_version":"1.0","source":{"id":"1903.10022","kind":"arxiv","version":1}},"canonical_sha256":"cfba0034da64e6766a6d33729fa50846f5bc91a9f5630e5de610db7a01d5fffd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cfba0034da64e6766a6d33729fa50846f5bc91a9f5630e5de610db7a01d5fffd","first_computed_at":"2026-05-17T23:50:34.758846Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:50:34.758846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HDQi5h3uKijeusZi3x139YqpjdC/bGRks3cFN5Wh1nN+jEoK/DKMH7dts7zPkYtvVv+ucqy5+ZFoNT7y0JM1Dg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:50:34.759531Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.10022","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e3780aa92827a60f5a4abbc789c0acd56d8d3c5289bb9bb500ed06be8a2a55b8","sha256:34684d4c32efa719e23686e454c07af1c1ef0b64d1a036c0ba9dc86be7d21fbd"],"state_sha256":"ac477bc3fc598e88bed289e0151ae3652fdea69626ffac9723ebd2af1cdaf770"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YGKmaaRS02GqCgQkvBM0BpKe9chyhjETwpQ9QxDx2Rt7gCOcZVMGEL5FbamtXefTqe84nk/ztQSJULGohrIMCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T07:50:50.427345Z","bundle_sha256":"5c0ccf174771e0b8b2b62c3a42e6020a1926e382fdd5428aa7ea6fc37778d4e4"}}