{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:A5IN7XCXYPAGU6TGZ746AFBNX2","short_pith_number":"pith:A5IN7XCX","schema_version":"1.0","canonical_sha256":"0750dfdc57c3c06a7a66cff9e0142dbe95f03305fd6a46491edcf34a0e2b337e","source":{"kind":"arxiv","id":"1903.10909","version":2},"attestation_state":"computed","paper":{"title":"Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jun He, Kun Wang, Lei Zhang","submitted_at":"2019-03-24T05:47:31Z","abstract_excerpt":"Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attention-based convolutional neural network for human recognition from weakly labeled data. The proposed attention model can focus on labeled activity among a long sequence of sensor data, and while filter out a large amount of background noise signals. In experiment on the weakly labeled dataset, we show "},"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":"1903.10909","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-24T05:47:31Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8eedd8e70b42b08bc848eb27c8381099fa706bf90f9207f26d2cdcc296c1205a","abstract_canon_sha256":"ef716d6498143f5fc1e18c04c0eae96c9af8b52f188d27be8f5a361d97d91fa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:53.248714Z","signature_b64":"vat1UKQkfj/Q4cF6ZrXHr/Pg9tSRbhwsPAp04Jb12vrZ8p53+/g1mX/uSzjGsmf3J/DUh0zBGfXjDAFY9QQQAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0750dfdc57c3c06a7a66cff9e0142dbe95f03305fd6a46491edcf34a0e2b337e","last_reissued_at":"2026-05-17T23:41:53.248253Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:53.248253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jun He, Kun Wang, Lei Zhang","submitted_at":"2019-03-24T05:47:31Z","abstract_excerpt":"Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attention-based convolutional neural network for human recognition from weakly labeled data. The proposed attention model can focus on labeled activity among a long sequence of sensor data, and while filter out a large amount of background noise signals. In experiment on the weakly labeled dataset, we show "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.10909","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1903.10909","created_at":"2026-05-17T23:41:53.248326+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.10909v2","created_at":"2026-05-17T23:41:53.248326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.10909","created_at":"2026-05-17T23:41:53.248326+00:00"},{"alias_kind":"pith_short_12","alias_value":"A5IN7XCXYPAG","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"A5IN7XCXYPAGU6TG","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"A5IN7XCX","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2","json":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2.json","graph_json":"https://pith.science/api/pith-number/A5IN7XCXYPAGU6TGZ746AFBNX2/graph.json","events_json":"https://pith.science/api/pith-number/A5IN7XCXYPAGU6TGZ746AFBNX2/events.json","paper":"https://pith.science/paper/A5IN7XCX"},"agent_actions":{"view_html":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2","download_json":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2.json","view_paper":"https://pith.science/paper/A5IN7XCX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.10909&json=true","fetch_graph":"https://pith.science/api/pith-number/A5IN7XCXYPAGU6TGZ746AFBNX2/graph.json","fetch_events":"https://pith.science/api/pith-number/A5IN7XCXYPAGU6TGZ746AFBNX2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2/action/storage_attestation","attest_author":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2/action/author_attestation","sign_citation":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2/action/citation_signature","submit_replication":"https://pith.science/pith/A5IN7XCXYPAGU6TGZ746AFBNX2/action/replication_record"}},"created_at":"2026-05-17T23:41:53.248326+00:00","updated_at":"2026-05-17T23:41:53.248326+00:00"}