{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NFL2S5TSHADQVRHMBX2OIDM7LB","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":"c3ad0835a69432ccd30f715098a25b02188d20eb54f6b2fbbe6f5ba8fc319318","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2022-08-26T22:47:18Z","title_canon_sha256":"5ccdd021211b4ac5e60b5d33159a0579f8b3658587eafc352c0e4cecf5b49e6f"},"schema_version":"1.0","source":{"id":"2209.00990","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.00990","created_at":"2026-07-05T09:26:16Z"},{"alias_kind":"arxiv_version","alias_value":"2209.00990v1","created_at":"2026-07-05T09:26:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.00990","created_at":"2026-07-05T09:26:16Z"},{"alias_kind":"pith_short_12","alias_value":"NFL2S5TSHADQ","created_at":"2026-07-05T09:26:16Z"},{"alias_kind":"pith_short_16","alias_value":"NFL2S5TSHADQVRHM","created_at":"2026-07-05T09:26:16Z"},{"alias_kind":"pith_short_8","alias_value":"NFL2S5TS","created_at":"2026-07-05T09:26:16Z"}],"graph_snapshots":[{"event_id":"sha256:2733ef7a354c6e0d5d1a4fb1ee513c072ae5a46a0e98dc5b99e50be376fcd5c3","target":"graph","created_at":"2026-07-05T09:26:16Z","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/2209.00990/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We aim to develop a model that learns strong representations from accelerometer signals, in order to perform robust human activity classification, while reducing the model's reliance on class labels. Specifically, we intend to enable cross-dataset transfer learning such that our network pre-trained on a particular dataset can perform effective activity classification on other datasets (successive to a small amount of fine-tuning). To tackle this problem, we design ou","authors_text":"Ali Etemad, Michael Rainbow, Setareh Rahimi Taghanaki","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2022-08-26T22:47:18Z","title":"Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.00990","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:b51e19e098b33e7cb7f75593f0af7276dfa3b3a8e1bab30e1be3e4dee8f68504","target":"record","created_at":"2026-07-05T09:26:16Z","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":"c3ad0835a69432ccd30f715098a25b02188d20eb54f6b2fbbe6f5ba8fc319318","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2022-08-26T22:47:18Z","title_canon_sha256":"5ccdd021211b4ac5e60b5d33159a0579f8b3658587eafc352c0e4cecf5b49e6f"},"schema_version":"1.0","source":{"id":"2209.00990","kind":"arxiv","version":1}},"canonical_sha256":"6957a9767238070ac4ec0df4e40d9f58612bf7c04b82216dd1039858fc2e7218","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6957a9767238070ac4ec0df4e40d9f58612bf7c04b82216dd1039858fc2e7218","first_computed_at":"2026-07-05T09:26:16.410148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:26:16.410148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r2j9wfbU3v3Lq33Es3i/zpxy6cfg1cKINpa9Qui8WFuM6xCa9/KVrO0J3UTP7aUsKJocyGh0fqmPB0mRCcesBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:26:16.410567Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.00990","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b51e19e098b33e7cb7f75593f0af7276dfa3b3a8e1bab30e1be3e4dee8f68504","sha256:2733ef7a354c6e0d5d1a4fb1ee513c072ae5a46a0e98dc5b99e50be376fcd5c3"],"state_sha256":"7a745958f06ce7eeb286036543fe7344acbf43cc27d0e723dafb8b02b02489f7"}