{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:25PLHIMOK4ZW64VPU43SLROCFJ","short_pith_number":"pith:25PLHIMO","schema_version":"1.0","canonical_sha256":"d75eb3a18e57336f72afa73725c5c22a717c1291003acd1b5af7df9ae6b10450","source":{"kind":"arxiv","id":"2503.07882","version":1},"attestation_state":"computed","paper":{"title":"ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Aaron Tartz, Baris Aksanli, Cagla Ipek Kocal, Onat Gungor, Tajana Rosing","submitted_at":"2025-03-10T21:55:50Z","abstract_excerpt":"Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adversarial attacks, emphasizing the need for resilient methods that ensure robust performance and efficient model selection. We introduce ReLATE, a framework that identifies robust learners based on dataset similarity, reduces computational overhead, and enhances resilience. ReLATE maintains multiple deep learning models in well-known adversarial attack scenarios, capturing model performance. ReLATE identifies the most"},"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":"2503.07882","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-10T21:55:50Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"3f55266848f2df65694da69c8ceb6bd462d8ef8497448274d7b862e90d5a501e","abstract_canon_sha256":"50282e1999936f6a7bab903c5f54379049fa45d08b01493cc23554e4c1358425"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:28:45.724835Z","signature_b64":"fxiPXxCVxOfToZ0NXASisd/dGb0u3IeS7R03MK7j+t8SZSYu+8+h8zdREgASOxnZSplDYGDoEFsrCe0U3HP4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d75eb3a18e57336f72afa73725c5c22a717c1291003acd1b5af7df9ae6b10450","last_reissued_at":"2026-07-05T10:28:45.724241Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:28:45.724241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Aaron Tartz, Baris Aksanli, Cagla Ipek Kocal, Onat Gungor, Tajana Rosing","submitted_at":"2025-03-10T21:55:50Z","abstract_excerpt":"Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adversarial attacks, emphasizing the need for resilient methods that ensure robust performance and efficient model selection. We introduce ReLATE, a framework that identifies robust learners based on dataset similarity, reduces computational overhead, and enhances resilience. ReLATE maintains multiple deep learning models in well-known adversarial attack scenarios, capturing model performance. ReLATE identifies the most"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.07882","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/2503.07882/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":"2503.07882","created_at":"2026-07-05T10:28:45.724318+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.07882v1","created_at":"2026-07-05T10:28:45.724318+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.07882","created_at":"2026-07-05T10:28:45.724318+00:00"},{"alias_kind":"pith_short_12","alias_value":"25PLHIMOK4ZW","created_at":"2026-07-05T10:28:45.724318+00:00"},{"alias_kind":"pith_short_16","alias_value":"25PLHIMOK4ZW64VP","created_at":"2026-07-05T10:28:45.724318+00:00"},{"alias_kind":"pith_short_8","alias_value":"25PLHIMO","created_at":"2026-07-05T10:28:45.724318+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.19456","citing_title":"ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ","json":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ.json","graph_json":"https://pith.science/api/pith-number/25PLHIMOK4ZW64VPU43SLROCFJ/graph.json","events_json":"https://pith.science/api/pith-number/25PLHIMOK4ZW64VPU43SLROCFJ/events.json","paper":"https://pith.science/paper/25PLHIMO"},"agent_actions":{"view_html":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ","download_json":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ.json","view_paper":"https://pith.science/paper/25PLHIMO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.07882&json=true","fetch_graph":"https://pith.science/api/pith-number/25PLHIMOK4ZW64VPU43SLROCFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/25PLHIMOK4ZW64VPU43SLROCFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ/action/storage_attestation","attest_author":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ/action/author_attestation","sign_citation":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ/action/citation_signature","submit_replication":"https://pith.science/pith/25PLHIMOK4ZW64VPU43SLROCFJ/action/replication_record"}},"created_at":"2026-07-05T10:28:45.724318+00:00","updated_at":"2026-07-05T10:28:45.724318+00:00"}