{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:QLVIIN2EK3KVJWHY6JLNKQOYQL","short_pith_number":"pith:QLVIIN2E","schema_version":"1.0","canonical_sha256":"82ea84374456d554d8f8f256d541d882f6ca00acd535221f72e4795f25fb37a9","source":{"kind":"arxiv","id":"2208.03647","version":1},"attestation_state":"computed","paper":{"title":"BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Yifan Hu, Yu Wang","submitted_at":"2022-08-07T05:48:48Z","abstract_excerpt":"The development of IoT technology enables a variety of sensors can be integrated into mobile devices. Human Activity Recognition (HAR) based on sensor data has become an active research topic in the field of machine learning and ubiquitous computing. However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced. Considering the limited sensor resources and the high cost of manually labeled sensor data, human activity recognition is facing the challenge of highly imbalanced activity datasets. In this paper, we pr"},"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":"2208.03647","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-07T05:48:48Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"b07e2fdd1b52a517c6402ccd714c729012e770a5318ee5d9a2ac1d5074e8c9e0","abstract_canon_sha256":"5eeac986162226bae720cfe4b85ce2bb5bfb63a71a7f6e2bdbd62d33caa7da73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:47:19.345112Z","signature_b64":"q6DbHH7UhPrrQLoUlspq5HrM3G9+Q6gUZi+cuHnnJljmHSVL4QIzPJOzA6E5L3Lpb/lENLzfDX736HuVJOzgDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82ea84374456d554d8f8f256d541d882f6ca00acd535221f72e4795f25fb37a9","last_reissued_at":"2026-07-05T04:47:19.344732Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:47:19.344732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Yifan Hu, Yu Wang","submitted_at":"2022-08-07T05:48:48Z","abstract_excerpt":"The development of IoT technology enables a variety of sensors can be integrated into mobile devices. Human Activity Recognition (HAR) based on sensor data has become an active research topic in the field of machine learning and ubiquitous computing. However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced. Considering the limited sensor resources and the high cost of manually labeled sensor data, human activity recognition is facing the challenge of highly imbalanced activity datasets. In this paper, we pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.03647","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/2208.03647/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":"2208.03647","created_at":"2026-07-05T04:47:19.344789+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.03647v1","created_at":"2026-07-05T04:47:19.344789+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.03647","created_at":"2026-07-05T04:47:19.344789+00:00"},{"alias_kind":"pith_short_12","alias_value":"QLVIIN2EK3KV","created_at":"2026-07-05T04:47:19.344789+00:00"},{"alias_kind":"pith_short_16","alias_value":"QLVIIN2EK3KVJWHY","created_at":"2026-07-05T04:47:19.344789+00:00"},{"alias_kind":"pith_short_8","alias_value":"QLVIIN2E","created_at":"2026-07-05T04:47:19.344789+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/QLVIIN2EK3KVJWHY6JLNKQOYQL","json":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL.json","graph_json":"https://pith.science/api/pith-number/QLVIIN2EK3KVJWHY6JLNKQOYQL/graph.json","events_json":"https://pith.science/api/pith-number/QLVIIN2EK3KVJWHY6JLNKQOYQL/events.json","paper":"https://pith.science/paper/QLVIIN2E"},"agent_actions":{"view_html":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL","download_json":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL.json","view_paper":"https://pith.science/paper/QLVIIN2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.03647&json=true","fetch_graph":"https://pith.science/api/pith-number/QLVIIN2EK3KVJWHY6JLNKQOYQL/graph.json","fetch_events":"https://pith.science/api/pith-number/QLVIIN2EK3KVJWHY6JLNKQOYQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL/action/storage_attestation","attest_author":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL/action/author_attestation","sign_citation":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL/action/citation_signature","submit_replication":"https://pith.science/pith/QLVIIN2EK3KVJWHY6JLNKQOYQL/action/replication_record"}},"created_at":"2026-07-05T04:47:19.344789+00:00","updated_at":"2026-07-05T04:47:19.344789+00:00"}