{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Q7VANNU26UHLKBLMITSEUX4AHB","short_pith_number":"pith:Q7VANNU2","schema_version":"1.0","canonical_sha256":"87ea06b69af50eb5056c44e44a5f80385157c2ed68571fa4e83db2b28bbca0d3","source":{"kind":"arxiv","id":"1907.03107","version":3},"attestation_state":"computed","paper":{"title":"CoAug-MR: An MR-based Interactive Office Workstation Design System via Augmented Multi-Person Collaboration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.HC","authors_text":"Kuk-Jin Yoon, Lin Wang","submitted_at":"2019-07-06T10:19:04Z","abstract_excerpt":"Digital prototyping and evaluation using 3D modeling and digital human models are becoming more practical for customizing products to the preference of a user. However, the 3D modeling is less accessible to casual users, and digital human models suffer from insufficient body data and less intuitive illustration on how people use the product or how it accommodates to their body. Recently, VR-supported 'Do It Yourself' design has achieved real-time ergonomic evaluation with users themselves by capturing their poses, however, it lacks reliability and quality of design. In this paper, we explore a"},"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":"1907.03107","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2019-07-06T10:19:04Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"00cc898685e51afc77c5db8ff8192d25c71f8f5f7dfbfcde8413839009cfe759","abstract_canon_sha256":"e2ef12175b25225b26af9abe8933d06f212b8361cb3772d16c7b6a1406608a79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:04:59.703983Z","signature_b64":"5qTubPtu9tvLSYq1bEZ5Av5WWrZLzCUIRlxJwMVFfMLGX/oU+uZohxL6WNa3KP2CYiu59RP5U6frRzmImxzIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87ea06b69af50eb5056c44e44a5f80385157c2ed68571fa4e83db2b28bbca0d3","last_reissued_at":"2026-07-05T01:04:59.703501Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:04:59.703501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CoAug-MR: An MR-based Interactive Office Workstation Design System via Augmented Multi-Person Collaboration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.HC","authors_text":"Kuk-Jin Yoon, Lin Wang","submitted_at":"2019-07-06T10:19:04Z","abstract_excerpt":"Digital prototyping and evaluation using 3D modeling and digital human models are becoming more practical for customizing products to the preference of a user. However, the 3D modeling is less accessible to casual users, and digital human models suffer from insufficient body data and less intuitive illustration on how people use the product or how it accommodates to their body. Recently, VR-supported 'Do It Yourself' design has achieved real-time ergonomic evaluation with users themselves by capturing their poses, however, it lacks reliability and quality of design. In this paper, we explore a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03107","kind":"arxiv","version":3},"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/1907.03107/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":"1907.03107","created_at":"2026-07-05T01:04:59.703554+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03107v3","created_at":"2026-07-05T01:04:59.703554+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03107","created_at":"2026-07-05T01:04:59.703554+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q7VANNU26UHL","created_at":"2026-07-05T01:04:59.703554+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q7VANNU26UHLKBLM","created_at":"2026-07-05T01:04:59.703554+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q7VANNU2","created_at":"2026-07-05T01:04:59.703554+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.17011","citing_title":"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB","json":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB.json","graph_json":"https://pith.science/api/pith-number/Q7VANNU26UHLKBLMITSEUX4AHB/graph.json","events_json":"https://pith.science/api/pith-number/Q7VANNU26UHLKBLMITSEUX4AHB/events.json","paper":"https://pith.science/paper/Q7VANNU2"},"agent_actions":{"view_html":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB","download_json":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB.json","view_paper":"https://pith.science/paper/Q7VANNU2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03107&json=true","fetch_graph":"https://pith.science/api/pith-number/Q7VANNU26UHLKBLMITSEUX4AHB/graph.json","fetch_events":"https://pith.science/api/pith-number/Q7VANNU26UHLKBLMITSEUX4AHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB/action/storage_attestation","attest_author":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB/action/author_attestation","sign_citation":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB/action/citation_signature","submit_replication":"https://pith.science/pith/Q7VANNU26UHLKBLMITSEUX4AHB/action/replication_record"}},"created_at":"2026-07-05T01:04:59.703554+00:00","updated_at":"2026-07-05T01:04:59.703554+00:00"}