{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LKCHZ3JLOMDL7AVVXKFS5AIG6C","short_pith_number":"pith:LKCHZ3JL","schema_version":"1.0","canonical_sha256":"5a847ced2b7306bf82b5ba8b2e8106f0a25e86342964719956da5561f75914ce","source":{"kind":"arxiv","id":"2508.08280","version":1},"attestation_state":"computed","paper":{"title":"MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Dongil Kim, Seonyoung Kim","submitted_at":"2025-08-01T05:27:44Z","abstract_excerpt":"Deep learning has emerged as the most promising approach in various fields; however, when the distributions of training and test data are different (domain shift), the performance of deep learning models can degrade. Semi-supervised domain adaptation (SSDA) is a major approach for addressing this issue, assuming that a fully labeled training set (source domain) is available, but the test set (target domain) provides labels only for a small subset. In this study, we propose a novel two-step momentum encoder-utilized SSDA framework, MoSSDA, for multivariate time-series classification. Time serie"},"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":"2508.08280","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-01T05:27:44Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"a32f03d9ac77953483bbe83e28bc826f7ebe435a3125add56bb438ded60508d0","abstract_canon_sha256":"4b56008a0d4b2e19f47b3429960e6f8ef3c9af09ae9a7265e0a3dd2fc18ee5ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:25.288055Z","signature_b64":"YGbOap2sNMG54wwqQzXc1k5ZExwdEDKRebHzh5EBGngQNhPBf3rDWb2S8DLS0VjSiVS7lMSJfVyaQjvcEQuuCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a847ced2b7306bf82b5ba8b2e8106f0a25e86342964719956da5561f75914ce","last_reissued_at":"2026-07-05T11:52:25.287589Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:25.287589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Dongil Kim, Seonyoung Kim","submitted_at":"2025-08-01T05:27:44Z","abstract_excerpt":"Deep learning has emerged as the most promising approach in various fields; however, when the distributions of training and test data are different (domain shift), the performance of deep learning models can degrade. Semi-supervised domain adaptation (SSDA) is a major approach for addressing this issue, assuming that a fully labeled training set (source domain) is available, but the test set (target domain) provides labels only for a small subset. In this study, we propose a novel two-step momentum encoder-utilized SSDA framework, MoSSDA, for multivariate time-series classification. Time serie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.08280","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/2508.08280/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":"2508.08280","created_at":"2026-07-05T11:52:25.287653+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.08280v1","created_at":"2026-07-05T11:52:25.287653+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.08280","created_at":"2026-07-05T11:52:25.287653+00:00"},{"alias_kind":"pith_short_12","alias_value":"LKCHZ3JLOMDL","created_at":"2026-07-05T11:52:25.287653+00:00"},{"alias_kind":"pith_short_16","alias_value":"LKCHZ3JLOMDL7AVV","created_at":"2026-07-05T11:52:25.287653+00:00"},{"alias_kind":"pith_short_8","alias_value":"LKCHZ3JL","created_at":"2026-07-05T11:52:25.287653+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.08269","citing_title":"emg2tendon: From sEMG Signals to Tendon Control in Musculoskeletal Hands","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C","json":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C.json","graph_json":"https://pith.science/api/pith-number/LKCHZ3JLOMDL7AVVXKFS5AIG6C/graph.json","events_json":"https://pith.science/api/pith-number/LKCHZ3JLOMDL7AVVXKFS5AIG6C/events.json","paper":"https://pith.science/paper/LKCHZ3JL"},"agent_actions":{"view_html":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C","download_json":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C.json","view_paper":"https://pith.science/paper/LKCHZ3JL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.08280&json=true","fetch_graph":"https://pith.science/api/pith-number/LKCHZ3JLOMDL7AVVXKFS5AIG6C/graph.json","fetch_events":"https://pith.science/api/pith-number/LKCHZ3JLOMDL7AVVXKFS5AIG6C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C/action/storage_attestation","attest_author":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C/action/author_attestation","sign_citation":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C/action/citation_signature","submit_replication":"https://pith.science/pith/LKCHZ3JLOMDL7AVVXKFS5AIG6C/action/replication_record"}},"created_at":"2026-07-05T11:52:25.287653+00:00","updated_at":"2026-07-05T11:52:25.287653+00:00"}