{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:2QA7QRQ3LQTS2JQ6APYD5JJZX3","short_pith_number":"pith:2QA7QRQ3","canonical_record":{"source":{"id":"2111.01371","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-11-02T04:59:57Z","cross_cats_sorted":[],"title_canon_sha256":"0d1b544a8dcaaaec4effa57c75aaf163525e8c30c2d7be32a8b9f1e2b7b88b65","abstract_canon_sha256":"107fc618c1c808522e97b995305a709e899177b2a62700ccd473a84d0ef63fca"},"schema_version":"1.0"},"canonical_sha256":"d401f8461b5c272d261e03f03ea539bef2049c72fc4525571de58e2cba362e2a","source":{"kind":"arxiv","id":"2111.01371","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.01371","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"arxiv_version","alias_value":"2111.01371v1","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.01371","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"pith_short_12","alias_value":"2QA7QRQ3LQTS","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"pith_short_16","alias_value":"2QA7QRQ3LQTS2JQ6","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"pith_short_8","alias_value":"2QA7QRQ3","created_at":"2026-07-05T03:28:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:2QA7QRQ3LQTS2JQ6APYD5JJZX3","target":"record","payload":{"canonical_record":{"source":{"id":"2111.01371","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-11-02T04:59:57Z","cross_cats_sorted":[],"title_canon_sha256":"0d1b544a8dcaaaec4effa57c75aaf163525e8c30c2d7be32a8b9f1e2b7b88b65","abstract_canon_sha256":"107fc618c1c808522e97b995305a709e899177b2a62700ccd473a84d0ef63fca"},"schema_version":"1.0"},"canonical_sha256":"d401f8461b5c272d261e03f03ea539bef2049c72fc4525571de58e2cba362e2a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:28:02.175688Z","signature_b64":"lkAGrVxbR4NGAidShCNw9PA50aM6RlPHwb96V1JUZ0gZHpV7vrmXcM4mAgAK7jSaLI6tjmmpKARUy3S4ZxKBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d401f8461b5c272d261e03f03ea539bef2049c72fc4525571de58e2cba362e2a","last_reissued_at":"2026-07-05T03:28:02.175234Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:28:02.175234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.01371","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":"2026-07-05T03:28:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QQE+doWaJ1kpAoK1oCm9uaTlk58YqYGi47v5+6RTttO2/ptaBSIPM1gfWFKB9XLHQIMZaLMmQUHkqRIeIg33Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:06:26.945472Z"},"content_sha256":"f71887ede1de943a18aac701e5d60e8df40237390278a8ee8915e7a120d116a3","schema_version":"1.0","event_id":"sha256:f71887ede1de943a18aac701e5d60e8df40237390278a8ee8915e7a120d116a3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:2QA7QRQ3LQTS2JQ6APYD5JJZX3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fan Li, Pin Wang, Xiaoheng Zhang, Yongming Li","submitted_at":"2021-11-02T04:59:57Z","abstract_excerpt":"Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.01371","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/2111.01371/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":"2026-07-05T03:28:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ewH036mfM2p9ij8KXwD5TQt6djFrYvZm4WO7luAcm3gA+QRw2IXPe+OXH3PPf7GriA2PXswUfO7FDCPa0GFLBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:06:26.947348Z"},"content_sha256":"d9407fc8cf9501b9fdd1679f3773320dc7dc52d272ae387685f3cd8a665d37ae","schema_version":"1.0","event_id":"sha256:d9407fc8cf9501b9fdd1679f3773320dc7dc52d272ae387685f3cd8a665d37ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2QA7QRQ3LQTS2JQ6APYD5JJZX3/bundle.json","state_url":"https://pith.science/pith/2QA7QRQ3LQTS2JQ6APYD5JJZX3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2QA7QRQ3LQTS2JQ6APYD5JJZX3/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":"2026-08-23T01:06:26Z","links":{"resolver":"https://pith.science/pith/2QA7QRQ3LQTS2JQ6APYD5JJZX3","bundle":"https://pith.science/pith/2QA7QRQ3LQTS2JQ6APYD5JJZX3/bundle.json","state":"https://pith.science/pith/2QA7QRQ3LQTS2JQ6APYD5JJZX3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2QA7QRQ3LQTS2JQ6APYD5JJZX3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:2QA7QRQ3LQTS2JQ6APYD5JJZX3","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":"107fc618c1c808522e97b995305a709e899177b2a62700ccd473a84d0ef63fca","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-11-02T04:59:57Z","title_canon_sha256":"0d1b544a8dcaaaec4effa57c75aaf163525e8c30c2d7be32a8b9f1e2b7b88b65"},"schema_version":"1.0","source":{"id":"2111.01371","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.01371","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"arxiv_version","alias_value":"2111.01371v1","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.01371","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"pith_short_12","alias_value":"2QA7QRQ3LQTS","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"pith_short_16","alias_value":"2QA7QRQ3LQTS2JQ6","created_at":"2026-07-05T03:28:02Z"},{"alias_kind":"pith_short_8","alias_value":"2QA7QRQ3","created_at":"2026-07-05T03:28:02Z"}],"graph_snapshots":[{"event_id":"sha256:d9407fc8cf9501b9fdd1679f3773320dc7dc52d272ae387685f3cd8a665d37ae","target":"graph","created_at":"2026-07-05T03:28:02Z","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/2111.01371/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides,","authors_text":"Fan Li, Pin Wang, Xiaoheng Zhang, Yongming Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-11-02T04:59:57Z","title":"Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.01371","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:f71887ede1de943a18aac701e5d60e8df40237390278a8ee8915e7a120d116a3","target":"record","created_at":"2026-07-05T03:28:02Z","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":"107fc618c1c808522e97b995305a709e899177b2a62700ccd473a84d0ef63fca","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-11-02T04:59:57Z","title_canon_sha256":"0d1b544a8dcaaaec4effa57c75aaf163525e8c30c2d7be32a8b9f1e2b7b88b65"},"schema_version":"1.0","source":{"id":"2111.01371","kind":"arxiv","version":1}},"canonical_sha256":"d401f8461b5c272d261e03f03ea539bef2049c72fc4525571de58e2cba362e2a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d401f8461b5c272d261e03f03ea539bef2049c72fc4525571de58e2cba362e2a","first_computed_at":"2026-07-05T03:28:02.175234Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:28:02.175234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lkAGrVxbR4NGAidShCNw9PA50aM6RlPHwb96V1JUZ0gZHpV7vrmXcM4mAgAK7jSaLI6tjmmpKARUy3S4ZxKBBA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:28:02.175688Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.01371","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f71887ede1de943a18aac701e5d60e8df40237390278a8ee8915e7a120d116a3","sha256:d9407fc8cf9501b9fdd1679f3773320dc7dc52d272ae387685f3cd8a665d37ae"],"state_sha256":"8664e17dd4635bc5c78e2a22c111ff8ffb07dfcf4950ac928590e6ea4696b57a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eLja2VE+6d7AEEu3hXtJM8MCwarsTD2V+d907OIxvEF7CsTAh3HotroHGan2H0lEP4eLH/TqKcz+u/HauMfFCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T01:06:26.953512Z","bundle_sha256":"b650b5f48f447a6789405d75882498bcaf36ad96362db3271ba27afab6b66a98"}}