{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7L4JOQ3SO2IR36PZCFUV5MSKZA","short_pith_number":"pith:7L4JOQ3S","schema_version":"1.0","canonical_sha256":"faf897437276911df9f911695eb24ac801f759cf51ec4e785619aba72fbd344d","source":{"kind":"arxiv","id":"1907.03250","version":1},"attestation_state":"computed","paper":{"title":"Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hassan Ghasemzadeh, Houman Homayoun, Mahdi Pedram, Marjan Nourollahi, Seyed Ali Rokni","submitted_at":"2019-07-07T08:40:22Z","abstract_excerpt":"Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration "},"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":"1907.03250","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-07T08:40:22Z","cross_cats_sorted":[],"title_canon_sha256":"3ece828d6ca49666f7ed71d2ccfd00c6f2bec2d1da4722c1c787e73f2cad6564","abstract_canon_sha256":"237159b40fe9eaaaafb01bd3324206427f87858c3fa73dffcbf05817b8d9706b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:16.754550Z","signature_b64":"JwH9Lc/1ApMAQ+uH1cgayKyrzmIkFWdZbdv5Cv0/a3KCLGeZtRS2yAt9XWqmXu9m5sSuKdUjdGtCmKcRaIzDDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"faf897437276911df9f911695eb24ac801f759cf51ec4e785619aba72fbd344d","last_reissued_at":"2026-05-17T23:41:16.754154Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:16.754154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hassan Ghasemzadeh, Houman Homayoun, Mahdi Pedram, Marjan Nourollahi, Seyed Ali Rokni","submitted_at":"2019-07-07T08:40:22Z","abstract_excerpt":"Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03250","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":""},"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":"1907.03250","created_at":"2026-05-17T23:41:16.754214+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03250v1","created_at":"2026-05-17T23:41:16.754214+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03250","created_at":"2026-05-17T23:41:16.754214+00:00"},{"alias_kind":"pith_short_12","alias_value":"7L4JOQ3SO2IR","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7L4JOQ3SO2IR36PZ","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7L4JOQ3S","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/7L4JOQ3SO2IR36PZCFUV5MSKZA","json":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA.json","graph_json":"https://pith.science/api/pith-number/7L4JOQ3SO2IR36PZCFUV5MSKZA/graph.json","events_json":"https://pith.science/api/pith-number/7L4JOQ3SO2IR36PZCFUV5MSKZA/events.json","paper":"https://pith.science/paper/7L4JOQ3S"},"agent_actions":{"view_html":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA","download_json":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA.json","view_paper":"https://pith.science/paper/7L4JOQ3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03250&json=true","fetch_graph":"https://pith.science/api/pith-number/7L4JOQ3SO2IR36PZCFUV5MSKZA/graph.json","fetch_events":"https://pith.science/api/pith-number/7L4JOQ3SO2IR36PZCFUV5MSKZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA/action/storage_attestation","attest_author":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA/action/author_attestation","sign_citation":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA/action/citation_signature","submit_replication":"https://pith.science/pith/7L4JOQ3SO2IR36PZCFUV5MSKZA/action/replication_record"}},"created_at":"2026-05-17T23:41:16.754214+00:00","updated_at":"2026-05-17T23:41:16.754214+00:00"}