{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:7CATNA5NSV5ZRQ5J2QUAICIQIP","short_pith_number":"pith:7CATNA5N","canonical_record":{"source":{"id":"1905.11590","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-28T03:25:45Z","cross_cats_sorted":[],"title_canon_sha256":"7e692b5b83d8c01d36f109c4f96fb16c67155c1c151ec8a4a5564ef0c9b7f3b8","abstract_canon_sha256":"044e259349759bef3836aabf1e3a9d0b2596e7ea04896e118c65c98640fed8c7"},"schema_version":"1.0"},"canonical_sha256":"f8813683ad957b98c3a9d42804091043e95d6373bf365277cfba9a64901d1929","source":{"kind":"arxiv","id":"1905.11590","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11590","created_at":"2026-05-17T23:44:52Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11590v1","created_at":"2026-05-17T23:44:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11590","created_at":"2026-05-17T23:44:52Z"},{"alias_kind":"pith_short_12","alias_value":"7CATNA5NSV5Z","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"7CATNA5NSV5ZRQ5J","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"7CATNA5N","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:7CATNA5NSV5ZRQ5J2QUAICIQIP","target":"record","payload":{"canonical_record":{"source":{"id":"1905.11590","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-28T03:25:45Z","cross_cats_sorted":[],"title_canon_sha256":"7e692b5b83d8c01d36f109c4f96fb16c67155c1c151ec8a4a5564ef0c9b7f3b8","abstract_canon_sha256":"044e259349759bef3836aabf1e3a9d0b2596e7ea04896e118c65c98640fed8c7"},"schema_version":"1.0"},"canonical_sha256":"f8813683ad957b98c3a9d42804091043e95d6373bf365277cfba9a64901d1929","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:52.913494Z","signature_b64":"boRjzB9QGUBeFGwYC9HmAppa3T4dSLjXY3LZ1pis3iiw9suwtFGmo9OTr6MSDkAIEiPqYrvEnIsV0XJ/YSgWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8813683ad957b98c3a9d42804091043e95d6373bf365277cfba9a64901d1929","last_reissued_at":"2026-05-17T23:44:52.912965Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:52.912965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.11590","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":"2026-05-17T23:44:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sdoaD2RExO0jUP3NE7bkBIZTV7FiYVIS1p91Uusm6u3hEv2STG56iGaMHgzFKL47YgJhcTeXxciTRb/lb+6gDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T00:37:08.350629Z"},"content_sha256":"eb6b0b3d7a8cea0465ca6c8b368f992bd13263124a294e47d0976c8beb86cb3e","schema_version":"1.0","event_id":"sha256:eb6b0b3d7a8cea0465ca6c8b368f992bd13263124a294e47d0976c8beb86cb3e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:7CATNA5NSV5ZRQ5J2QUAICIQIP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Review of Semi Supervised Learning Theories and Recent Advances","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Enmei Tu, Jie Yang","submitted_at":"2019-05-28T03:25:45Z","abstract_excerpt":"Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning. The main idea of semi-supervised learning is to introduce unlabeled samples into the model training process to avoid performance (or model) degeneration due to insufficiency of labeled samples. Semi-supervised learning has been applied successfully in many fields. This paper reviews the development process and main theories of semi-supervised learning, as well as its recent advances and importance in solving real-wor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11590","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":"2026-05-17T23:44:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t48ODN2A8Q/3Q+ZVjuP80E1gH778fFFtvaRyLKy9pXjaIa3objEhczUgO40LgD2J7NIQEdDwUHxq+62PwgayCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T00:37:08.350986Z"},"content_sha256":"48d2bc1ea2a5d15e443709182e5b6c3fe976deaeca1559534745431dad50cc95","schema_version":"1.0","event_id":"sha256:48d2bc1ea2a5d15e443709182e5b6c3fe976deaeca1559534745431dad50cc95"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7CATNA5NSV5ZRQ5J2QUAICIQIP/bundle.json","state_url":"https://pith.science/pith/7CATNA5NSV5ZRQ5J2QUAICIQIP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7CATNA5NSV5ZRQ5J2QUAICIQIP/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":"2026-06-03T00:37:08Z","links":{"resolver":"https://pith.science/pith/7CATNA5NSV5ZRQ5J2QUAICIQIP","bundle":"https://pith.science/pith/7CATNA5NSV5ZRQ5J2QUAICIQIP/bundle.json","state":"https://pith.science/pith/7CATNA5NSV5ZRQ5J2QUAICIQIP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7CATNA5NSV5ZRQ5J2QUAICIQIP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7CATNA5NSV5ZRQ5J2QUAICIQIP","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":"044e259349759bef3836aabf1e3a9d0b2596e7ea04896e118c65c98640fed8c7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-28T03:25:45Z","title_canon_sha256":"7e692b5b83d8c01d36f109c4f96fb16c67155c1c151ec8a4a5564ef0c9b7f3b8"},"schema_version":"1.0","source":{"id":"1905.11590","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11590","created_at":"2026-05-17T23:44:52Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11590v1","created_at":"2026-05-17T23:44:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11590","created_at":"2026-05-17T23:44:52Z"},{"alias_kind":"pith_short_12","alias_value":"7CATNA5NSV5Z","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"7CATNA5NSV5ZRQ5J","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"7CATNA5N","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:48d2bc1ea2a5d15e443709182e5b6c3fe976deaeca1559534745431dad50cc95","target":"graph","created_at":"2026-05-17T23:44:52Z","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":"Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning. The main idea of semi-supervised learning is to introduce unlabeled samples into the model training process to avoid performance (or model) degeneration due to insufficiency of labeled samples. Semi-supervised learning has been applied successfully in many fields. This paper reviews the development process and main theories of semi-supervised learning, as well as its recent advances and importance in solving real-wor","authors_text":"Enmei Tu, Jie Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-28T03:25:45Z","title":"A Review of Semi Supervised Learning Theories and Recent Advances"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11590","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:eb6b0b3d7a8cea0465ca6c8b368f992bd13263124a294e47d0976c8beb86cb3e","target":"record","created_at":"2026-05-17T23:44:52Z","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":"044e259349759bef3836aabf1e3a9d0b2596e7ea04896e118c65c98640fed8c7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-28T03:25:45Z","title_canon_sha256":"7e692b5b83d8c01d36f109c4f96fb16c67155c1c151ec8a4a5564ef0c9b7f3b8"},"schema_version":"1.0","source":{"id":"1905.11590","kind":"arxiv","version":1}},"canonical_sha256":"f8813683ad957b98c3a9d42804091043e95d6373bf365277cfba9a64901d1929","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f8813683ad957b98c3a9d42804091043e95d6373bf365277cfba9a64901d1929","first_computed_at":"2026-05-17T23:44:52.912965Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:52.912965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"boRjzB9QGUBeFGwYC9HmAppa3T4dSLjXY3LZ1pis3iiw9suwtFGmo9OTr6MSDkAIEiPqYrvEnIsV0XJ/YSgWAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:52.913494Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.11590","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb6b0b3d7a8cea0465ca6c8b368f992bd13263124a294e47d0976c8beb86cb3e","sha256:48d2bc1ea2a5d15e443709182e5b6c3fe976deaeca1559534745431dad50cc95"],"state_sha256":"ef7519849a142d38b64685d21a985f74061c0758aa6569391d5d06b737a5230b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D8K6yyMOQo0P07R+alZEnQ/6ebnhzJVHCe9POZ2a6v0hA2XL6OEYcZcvpiG1af/eZbMPPcqebg8joJcRoStxCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T00:37:08.352909Z","bundle_sha256":"2f100c2fdac8fd57863995fc50acd76400d639947b96926f35f694646543f468"}}