{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ESX2263H4KP2HATPKX7DZ4RJ5L","short_pith_number":"pith:ESX2263H","canonical_record":{"source":{"id":"2212.13067","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-26T09:45:41Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"44b521540fdf706064d55c50af064261088e9ff29a2d44c527b092de9b3fef01","abstract_canon_sha256":"34ce9b230d6485be33c31c542e4f890760db5e68d1adeec205ac64dd312feb77"},"schema_version":"1.0"},"canonical_sha256":"24afad7b67e29fa3826f55fe3cf229eafd5c821fcbbef63ca67e5f76cace01e3","source":{"kind":"arxiv","id":"2212.13067","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.13067","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"2212.13067v3","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.13067","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"ESX2263H4KP2","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"pith_short_16","alias_value":"ESX2263H4KP2HATP","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"pith_short_8","alias_value":"ESX2263H","created_at":"2026-07-05T05:59:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ESX2263H4KP2HATPKX7DZ4RJ5L","target":"record","payload":{"canonical_record":{"source":{"id":"2212.13067","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-26T09:45:41Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"44b521540fdf706064d55c50af064261088e9ff29a2d44c527b092de9b3fef01","abstract_canon_sha256":"34ce9b230d6485be33c31c542e4f890760db5e68d1adeec205ac64dd312feb77"},"schema_version":"1.0"},"canonical_sha256":"24afad7b67e29fa3826f55fe3cf229eafd5c821fcbbef63ca67e5f76cace01e3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:59:22.578714Z","signature_b64":"NWXe9E5OsxAPkVRT7MdDughtkZY9k4CCrkQ/qX2VmP27jkWS/ZcM9Y2HfZgblLHh9st8R8AMlCrX8hZs2MW/AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24afad7b67e29fa3826f55fe3cf229eafd5c821fcbbef63ca67e5f76cace01e3","last_reissued_at":"2026-07-05T05:59:22.578201Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:59:22.578201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.13067","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s5UX2QLpY0B6lGgkbHxKaehRC2J9nEQJYc3wdcStV8mIkm/jsqSC0dSCxSX8IfsEas4sIFU3SPa9mUAGMnBuBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T22:05:52.875929Z"},"content_sha256":"47c34dbac0eff82dcaef4dd1d8668f9ca7e60ebfb7c25c710804a36962af1f5d","schema_version":"1.0","event_id":"sha256:47c34dbac0eff82dcaef4dd1d8668f9ca7e60ebfb7c25c710804a36962af1f5d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ESX2263H4KP2HATPKX7DZ4RJ5L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Davide Cacciarelli, John Tyssedal, Murat Kulahci","submitted_at":"2022-12-26T09:45:41Z","abstract_excerpt":"Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regress"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.13067","kind":"arxiv","version":3},"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/2212.13067/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":"2026-07-05T05:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PmpZLbKsBl3P0SZg719/fzSw3Au37HnyOMYxrGzFg1RIegCVWeB0PHc00wZOS+6odSxLxKOhLyzH9v4ax60JDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T22:05:52.876441Z"},"content_sha256":"75f08f9f76d8c86c89d04d706ee70f0b78d80a1e0897843b4ecd296251210097","schema_version":"1.0","event_id":"sha256:75f08f9f76d8c86c89d04d706ee70f0b78d80a1e0897843b4ecd296251210097"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ESX2263H4KP2HATPKX7DZ4RJ5L/bundle.json","state_url":"https://pith.science/pith/ESX2263H4KP2HATPKX7DZ4RJ5L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ESX2263H4KP2HATPKX7DZ4RJ5L/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":"2026-08-09T22:05:52Z","links":{"resolver":"https://pith.science/pith/ESX2263H4KP2HATPKX7DZ4RJ5L","bundle":"https://pith.science/pith/ESX2263H4KP2HATPKX7DZ4RJ5L/bundle.json","state":"https://pith.science/pith/ESX2263H4KP2HATPKX7DZ4RJ5L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ESX2263H4KP2HATPKX7DZ4RJ5L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ESX2263H4KP2HATPKX7DZ4RJ5L","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":"34ce9b230d6485be33c31c542e4f890760db5e68d1adeec205ac64dd312feb77","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-26T09:45:41Z","title_canon_sha256":"44b521540fdf706064d55c50af064261088e9ff29a2d44c527b092de9b3fef01"},"schema_version":"1.0","source":{"id":"2212.13067","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.13067","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"2212.13067v3","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.13067","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"ESX2263H4KP2","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"pith_short_16","alias_value":"ESX2263H4KP2HATP","created_at":"2026-07-05T05:59:22Z"},{"alias_kind":"pith_short_8","alias_value":"ESX2263H","created_at":"2026-07-05T05:59:22Z"}],"graph_snapshots":[{"event_id":"sha256:75f08f9f76d8c86c89d04d706ee70f0b78d80a1e0897843b4ecd296251210097","target":"graph","created_at":"2026-07-05T05:59:22Z","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/2212.13067/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regress","authors_text":"Davide Cacciarelli, John Tyssedal, Murat Kulahci","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-26T09:45:41Z","title":"Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.13067","kind":"arxiv","version":3},"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":"sha256:47c34dbac0eff82dcaef4dd1d8668f9ca7e60ebfb7c25c710804a36962af1f5d","target":"record","created_at":"2026-07-05T05:59:22Z","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":"34ce9b230d6485be33c31c542e4f890760db5e68d1adeec205ac64dd312feb77","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-26T09:45:41Z","title_canon_sha256":"44b521540fdf706064d55c50af064261088e9ff29a2d44c527b092de9b3fef01"},"schema_version":"1.0","source":{"id":"2212.13067","kind":"arxiv","version":3}},"canonical_sha256":"24afad7b67e29fa3826f55fe3cf229eafd5c821fcbbef63ca67e5f76cace01e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24afad7b67e29fa3826f55fe3cf229eafd5c821fcbbef63ca67e5f76cace01e3","first_computed_at":"2026-07-05T05:59:22.578201Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:59:22.578201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NWXe9E5OsxAPkVRT7MdDughtkZY9k4CCrkQ/qX2VmP27jkWS/ZcM9Y2HfZgblLHh9st8R8AMlCrX8hZs2MW/AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:59:22.578714Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.13067","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47c34dbac0eff82dcaef4dd1d8668f9ca7e60ebfb7c25c710804a36962af1f5d","sha256:75f08f9f76d8c86c89d04d706ee70f0b78d80a1e0897843b4ecd296251210097"],"state_sha256":"655a88de22bd66c49d652d6a97645fc13904558cb65f484ab2d2d71d52f34362"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CoKiXaX7GIaUVtxcgzZYvl22Y+TrU+lDV/CkD6TxQlNnrJ2MHHh5M1tE9nTkrX5pNW/J4XfOih/W6YeygLlbAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T22:05:52.880441Z","bundle_sha256":"f35671d108175b781dbdcc6bd69bdbd155486f9670b03a5daf13ee9e115cd98b"}}