{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NFL2S5TSHADQVRHMBX2OIDM7LB","short_pith_number":"pith:NFL2S5TS","schema_version":"1.0","canonical_sha256":"6957a9767238070ac4ec0df4e40d9f58612bf7c04b82216dd1039858fc2e7218","source":{"kind":"arxiv","id":"2209.00990","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.SP","authors_text":"Ali Etemad, Michael Rainbow, Setareh Rahimi Taghanaki","submitted_at":"2022-08-26T22:47:18Z","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"},"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":"2209.00990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2022-08-26T22:47:18Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"5ccdd021211b4ac5e60b5d33159a0579f8b3658587eafc352c0e4cecf5b49e6f","abstract_canon_sha256":"c3ad0835a69432ccd30f715098a25b02188d20eb54f6b2fbbe6f5ba8fc319318"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:26:16.410567Z","signature_b64":"r2j9wfbU3v3Lq33Es3i/zpxy6cfg1cKINpa9Qui8WFuM6xCa9/KVrO0J3UTP7aUsKJocyGh0fqmPB0mRCcesBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6957a9767238070ac4ec0df4e40d9f58612bf7c04b82216dd1039858fc2e7218","last_reissued_at":"2026-07-05T09:26:16.410148Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:26:16.410148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.SP","authors_text":"Ali Etemad, Michael Rainbow, Setareh Rahimi Taghanaki","submitted_at":"2022-08-26T22:47:18Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.00990","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/2209.00990/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":"2209.00990","created_at":"2026-07-05T09:26:16.410211+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.00990v1","created_at":"2026-07-05T09:26:16.410211+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.00990","created_at":"2026-07-05T09:26:16.410211+00:00"},{"alias_kind":"pith_short_12","alias_value":"NFL2S5TSHADQ","created_at":"2026-07-05T09:26:16.410211+00:00"},{"alias_kind":"pith_short_16","alias_value":"NFL2S5TSHADQVRHM","created_at":"2026-07-05T09:26:16.410211+00:00"},{"alias_kind":"pith_short_8","alias_value":"NFL2S5TS","created_at":"2026-07-05T09:26:16.410211+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.19940","citing_title":"Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB","json":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB.json","graph_json":"https://pith.science/api/pith-number/NFL2S5TSHADQVRHMBX2OIDM7LB/graph.json","events_json":"https://pith.science/api/pith-number/NFL2S5TSHADQVRHMBX2OIDM7LB/events.json","paper":"https://pith.science/paper/NFL2S5TS"},"agent_actions":{"view_html":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB","download_json":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB.json","view_paper":"https://pith.science/paper/NFL2S5TS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.00990&json=true","fetch_graph":"https://pith.science/api/pith-number/NFL2S5TSHADQVRHMBX2OIDM7LB/graph.json","fetch_events":"https://pith.science/api/pith-number/NFL2S5TSHADQVRHMBX2OIDM7LB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB/action/storage_attestation","attest_author":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB/action/author_attestation","sign_citation":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB/action/citation_signature","submit_replication":"https://pith.science/pith/NFL2S5TSHADQVRHMBX2OIDM7LB/action/replication_record"}},"created_at":"2026-07-05T09:26:16.410211+00:00","updated_at":"2026-07-05T09:26:16.410211+00:00"}