{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2IJW7DITYF53XPBJ4Z2NO4XOZG","short_pith_number":"pith:2IJW7DIT","schema_version":"1.0","canonical_sha256":"d2136f8d13c17bbbbc29e674d772eec9835ea73868da772d7063cb31ba533ecc","source":{"kind":"arxiv","id":"2208.10462","version":1},"attestation_state":"computed","paper":{"title":"Shapelet-Based Counterfactual Explanations for Multivariate Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Omar Bahri, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi","submitted_at":"2022-08-22T17:33:31Z","abstract_excerpt":"As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their black-box nature. The Explainable Artificial Intelligence (XAI) paradigm has gained a lot of momentum lately due to its ability to reduce models opacity. XAI methods have not only increased stakeholders' trust in the decision process but also helped developers ensure its fairness. Recent efforts have been invested in creating transparent models and post-hoc explanations. However, fewer methods have been developed for "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2208.10462","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-22T17:33:31Z","cross_cats_sorted":[],"title_canon_sha256":"ef43a75afc81300e6ba922594599800b94ae257aa3d39e3cb707efde71813894","abstract_canon_sha256":"0407966b0a2e94d131700cc7d21c51fb00dcbca1fd329a962be6924e93f574ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:50:29.061735Z","signature_b64":"SR7oOrYSUZLbEbNWNsB20oaKBxsIZjd5mNYsvZNtTCtJJfIUQNxCDymkGVY0mIvmDE5lYMkdaUUs/nWPM9c3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2136f8d13c17bbbbc29e674d772eec9835ea73868da772d7063cb31ba533ecc","last_reissued_at":"2026-07-05T04:50:29.061328Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:50:29.061328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Shapelet-Based Counterfactual Explanations for Multivariate Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Omar Bahri, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi","submitted_at":"2022-08-22T17:33:31Z","abstract_excerpt":"As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their black-box nature. The Explainable Artificial Intelligence (XAI) paradigm has gained a lot of momentum lately due to its ability to reduce models opacity. XAI methods have not only increased stakeholders' trust in the decision process but also helped developers ensure its fairness. Recent efforts have been invested in creating transparent models and post-hoc explanations. However, fewer methods have been developed for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.10462","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/2208.10462/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2208.10462","created_at":"2026-07-05T04:50:29.061384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.10462v1","created_at":"2026-07-05T04:50:29.061384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.10462","created_at":"2026-07-05T04:50:29.061384+00:00"},{"alias_kind":"pith_short_12","alias_value":"2IJW7DITYF53","created_at":"2026-07-05T04:50:29.061384+00:00"},{"alias_kind":"pith_short_16","alias_value":"2IJW7DITYF53XPBJ","created_at":"2026-07-05T04:50:29.061384+00:00"},{"alias_kind":"pith_short_8","alias_value":"2IJW7DIT","created_at":"2026-07-05T04:50:29.061384+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.04009","citing_title":"Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG","json":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG.json","graph_json":"https://pith.science/api/pith-number/2IJW7DITYF53XPBJ4Z2NO4XOZG/graph.json","events_json":"https://pith.science/api/pith-number/2IJW7DITYF53XPBJ4Z2NO4XOZG/events.json","paper":"https://pith.science/paper/2IJW7DIT"},"agent_actions":{"view_html":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG","download_json":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG.json","view_paper":"https://pith.science/paper/2IJW7DIT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.10462&json=true","fetch_graph":"https://pith.science/api/pith-number/2IJW7DITYF53XPBJ4Z2NO4XOZG/graph.json","fetch_events":"https://pith.science/api/pith-number/2IJW7DITYF53XPBJ4Z2NO4XOZG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG/action/storage_attestation","attest_author":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG/action/author_attestation","sign_citation":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG/action/citation_signature","submit_replication":"https://pith.science/pith/2IJW7DITYF53XPBJ4Z2NO4XOZG/action/replication_record"}},"created_at":"2026-07-05T04:50:29.061384+00:00","updated_at":"2026-07-05T04:50:29.061384+00:00"}