{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:RMA4DYWLQJUTFDWNYJR7CPMA4F","short_pith_number":"pith:RMA4DYWL","canonical_record":{"source":{"id":"1710.10098","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-27T12:07:55Z","cross_cats_sorted":[],"title_canon_sha256":"5e1caf920dfc6c0963ceafe6d3330527d6a6a4e782a952aafb7fbd86273b5742","abstract_canon_sha256":"68055c721f6ea5a1e38142ad7e46f2dd1bffdbdfe1d240bcfb0a9730e9a92070"},"schema_version":"1.0"},"canonical_sha256":"8b01c1e2cb8269328ecdc263f13d80e14b46441aafbab1725a1a3d0f7d2b015d","source":{"kind":"arxiv","id":"1710.10098","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10098","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10098v1","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10098","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"pith_short_12","alias_value":"RMA4DYWLQJUT","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RMA4DYWLQJUTFDWN","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RMA4DYWL","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:RMA4DYWLQJUTFDWNYJR7CPMA4F","target":"record","payload":{"canonical_record":{"source":{"id":"1710.10098","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-27T12:07:55Z","cross_cats_sorted":[],"title_canon_sha256":"5e1caf920dfc6c0963ceafe6d3330527d6a6a4e782a952aafb7fbd86273b5742","abstract_canon_sha256":"68055c721f6ea5a1e38142ad7e46f2dd1bffdbdfe1d240bcfb0a9730e9a92070"},"schema_version":"1.0"},"canonical_sha256":"8b01c1e2cb8269328ecdc263f13d80e14b46441aafbab1725a1a3d0f7d2b015d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:54.757600Z","signature_b64":"u1pTYyJHV+GHcls1gAo8QZHO2yFv22k/ycuMgrT/4wMXkwOUix5CqwCthc7uWxuLLwFTdtyO8S1lmVcLCqBMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b01c1e2cb8269328ecdc263f13d80e14b46441aafbab1725a1a3d0f7d2b015d","last_reissued_at":"2026-05-18T00:31:54.757148Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:54.757148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.10098","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":"2026-05-18T00:31:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DoOF3triXPPZMf7SdmwXGaVCY5y9sfZuWGe2nI7Ge1j5bhka4u9QmBOWYEPXpQU8NWqe4r2StwSA3YupPAZIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T09:38:39.472598Z"},"content_sha256":"4e3ba8bcca7822d1d7e52251fe3d11faa7c092eb7d482a07e795f9d5d1835f64","schema_version":"1.0","event_id":"sha256:4e3ba8bcca7822d1d7e52251fe3d11faa7c092eb7d482a07e795f9d5d1835f64"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:RMA4DYWLQJUTFDWNYJR7CPMA4F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"C. Labreuche, K. Belahc\\`ene, N. Maudet, V. Mousseau, W. Ouerdane","submitted_at":"2017-10-27T12:07:55Z","abstract_excerpt":"The literature on Multiple Criteria Decision Analysis (MCDA) proposes several methods in order to sort alternatives evaluated on several attributes into ordered classes. Non Compensatory Sorting models (NCS) assign alternatives to classes based on the way they compare to multicriteria profiles separating the consecutive classes. Previous works have proposed approaches to learn the parameters of a NCS model based on a learning set. Exact approaches based on mixed integer linear programming ensures that the learning set is best restored, but can only handle datasets of limited size. Heuristic ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10098","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":"2026-05-18T00:31:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kN8q/8CnA8gFXbr0lthq4nZRrPzePAlnEcM+OgGmy83pshS5QW/ZWSua2bSTGQXKyRMDUgtwBMBYsq/+751qDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T09:38:39.473443Z"},"content_sha256":"6a585d5f02decd3cbac82f581e3466416c9f1a8c305ffa83aa821675877894be","schema_version":"1.0","event_id":"sha256:6a585d5f02decd3cbac82f581e3466416c9f1a8c305ffa83aa821675877894be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RMA4DYWLQJUTFDWNYJR7CPMA4F/bundle.json","state_url":"https://pith.science/pith/RMA4DYWLQJUTFDWNYJR7CPMA4F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RMA4DYWLQJUTFDWNYJR7CPMA4F/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":"2026-05-31T09:38:39Z","links":{"resolver":"https://pith.science/pith/RMA4DYWLQJUTFDWNYJR7CPMA4F","bundle":"https://pith.science/pith/RMA4DYWLQJUTFDWNYJR7CPMA4F/bundle.json","state":"https://pith.science/pith/RMA4DYWLQJUTFDWNYJR7CPMA4F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RMA4DYWLQJUTFDWNYJR7CPMA4F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:RMA4DYWLQJUTFDWNYJR7CPMA4F","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":"68055c721f6ea5a1e38142ad7e46f2dd1bffdbdfe1d240bcfb0a9730e9a92070","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-27T12:07:55Z","title_canon_sha256":"5e1caf920dfc6c0963ceafe6d3330527d6a6a4e782a952aafb7fbd86273b5742"},"schema_version":"1.0","source":{"id":"1710.10098","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10098","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10098v1","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10098","created_at":"2026-05-18T00:31:54Z"},{"alias_kind":"pith_short_12","alias_value":"RMA4DYWLQJUT","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RMA4DYWLQJUTFDWN","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RMA4DYWL","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:6a585d5f02decd3cbac82f581e3466416c9f1a8c305ffa83aa821675877894be","target":"graph","created_at":"2026-05-18T00:31:54Z","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":"The literature on Multiple Criteria Decision Analysis (MCDA) proposes several methods in order to sort alternatives evaluated on several attributes into ordered classes. Non Compensatory Sorting models (NCS) assign alternatives to classes based on the way they compare to multicriteria profiles separating the consecutive classes. Previous works have proposed approaches to learn the parameters of a NCS model based on a learning set. Exact approaches based on mixed integer linear programming ensures that the learning set is best restored, but can only handle datasets of limited size. Heuristic ap","authors_text":"C. Labreuche, K. Belahc\\`ene, N. Maudet, V. Mousseau, W. Ouerdane","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-27T12:07:55Z","title":"An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10098","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":"sha256:4e3ba8bcca7822d1d7e52251fe3d11faa7c092eb7d482a07e795f9d5d1835f64","target":"record","created_at":"2026-05-18T00:31:54Z","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":"68055c721f6ea5a1e38142ad7e46f2dd1bffdbdfe1d240bcfb0a9730e9a92070","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-27T12:07:55Z","title_canon_sha256":"5e1caf920dfc6c0963ceafe6d3330527d6a6a4e782a952aafb7fbd86273b5742"},"schema_version":"1.0","source":{"id":"1710.10098","kind":"arxiv","version":1}},"canonical_sha256":"8b01c1e2cb8269328ecdc263f13d80e14b46441aafbab1725a1a3d0f7d2b015d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8b01c1e2cb8269328ecdc263f13d80e14b46441aafbab1725a1a3d0f7d2b015d","first_computed_at":"2026-05-18T00:31:54.757148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:54.757148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u1pTYyJHV+GHcls1gAo8QZHO2yFv22k/ycuMgrT/4wMXkwOUix5CqwCthc7uWxuLLwFTdtyO8S1lmVcLCqBMBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:54.757600Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.10098","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e3ba8bcca7822d1d7e52251fe3d11faa7c092eb7d482a07e795f9d5d1835f64","sha256:6a585d5f02decd3cbac82f581e3466416c9f1a8c305ffa83aa821675877894be"],"state_sha256":"1c4e68895f55d9070bfe324594697c69694f25afb169e818966259bfeb92de50"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uTyx+IZV4FQimCAtlRB/eR3y3vUzwie/Gws4yLvtcwSLJLqZNMoy9c23Ej9QKjeJ57T1cvnBfTb7ejexz0HqCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T09:38:39.477782Z","bundle_sha256":"b47a3f71a20b9e5bc86d12c5723a32d956cf8fabace966772fe29513d4a11e36"}}