{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:BSCCO7NNMJ7MGGAHJVRKECI7ZL","short_pith_number":"pith:BSCCO7NN","schema_version":"1.0","canonical_sha256":"0c84277dad627ec318074d62a2091fcac8b1ee94c525519b60148d736b79c1e4","source":{"kind":"arxiv","id":"1706.08924","version":1},"attestation_state":"computed","paper":{"title":"Cross-Country Skiing Gears Classification using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aliaa Rassem, Mohamed Saleh, Mohammed El-Beltagy","submitted_at":"2017-06-27T16:14:00Z","abstract_excerpt":"Human Activity Recognition has witnessed a significant progress in the last decade. Although a great deal of work in this field goes in recognizing normal human activities, few studies focused on identifying motion in sports. Recognizing human movements in different sports has high impact on understanding the different styles of humans in the play and on improving their performance. As deep learning models proved to have good results in many classification problems, this paper will utilize deep learning to classify cross-country skiing movements, known as gears, collected using a 3D accelerome"},"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":"1706.08924","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-27T16:14:00Z","cross_cats_sorted":[],"title_canon_sha256":"9bb013cfb63e54f2dcb4ca7398a1891e7de833d0ae704eaaf0b976a096a9d2fd","abstract_canon_sha256":"32c37bb6ebd91900294541ce4443ae0abe7155a3eb5c70a32361aacc09742f11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:30.564280Z","signature_b64":"VwkaNRRjAb0QSKxhk8oy6l6ia4hH/frUmlnyxzzJ+YLODXQzkrz9N5umELzDRuLvYO5YOBB69XuAyzPiThIpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c84277dad627ec318074d62a2091fcac8b1ee94c525519b60148d736b79c1e4","last_reissued_at":"2026-05-18T00:41:30.563638Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:30.563638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-Country Skiing Gears Classification using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aliaa Rassem, Mohamed Saleh, Mohammed El-Beltagy","submitted_at":"2017-06-27T16:14:00Z","abstract_excerpt":"Human Activity Recognition has witnessed a significant progress in the last decade. Although a great deal of work in this field goes in recognizing normal human activities, few studies focused on identifying motion in sports. Recognizing human movements in different sports has high impact on understanding the different styles of humans in the play and on improving their performance. As deep learning models proved to have good results in many classification problems, this paper will utilize deep learning to classify cross-country skiing movements, known as gears, collected using a 3D accelerome"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.08924","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":"1706.08924","created_at":"2026-05-18T00:41:30.563727+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.08924v1","created_at":"2026-05-18T00:41:30.563727+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.08924","created_at":"2026-05-18T00:41:30.563727+00:00"},{"alias_kind":"pith_short_12","alias_value":"BSCCO7NNMJ7M","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_16","alias_value":"BSCCO7NNMJ7MGGAH","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_8","alias_value":"BSCCO7NN","created_at":"2026-05-18T12:31:08.081275+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/BSCCO7NNMJ7MGGAHJVRKECI7ZL","json":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL.json","graph_json":"https://pith.science/api/pith-number/BSCCO7NNMJ7MGGAHJVRKECI7ZL/graph.json","events_json":"https://pith.science/api/pith-number/BSCCO7NNMJ7MGGAHJVRKECI7ZL/events.json","paper":"https://pith.science/paper/BSCCO7NN"},"agent_actions":{"view_html":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL","download_json":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL.json","view_paper":"https://pith.science/paper/BSCCO7NN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.08924&json=true","fetch_graph":"https://pith.science/api/pith-number/BSCCO7NNMJ7MGGAHJVRKECI7ZL/graph.json","fetch_events":"https://pith.science/api/pith-number/BSCCO7NNMJ7MGGAHJVRKECI7ZL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL/action/storage_attestation","attest_author":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL/action/author_attestation","sign_citation":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL/action/citation_signature","submit_replication":"https://pith.science/pith/BSCCO7NNMJ7MGGAHJVRKECI7ZL/action/replication_record"}},"created_at":"2026-05-18T00:41:30.563727+00:00","updated_at":"2026-05-18T00:41:30.563727+00:00"}