{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6JHVROBCEMUCMMIDP7CJ7PFO3S","short_pith_number":"pith:6JHVROBC","schema_version":"1.0","canonical_sha256":"f24f58b82223282631037fc49fbcaedcaee0731a9368a444294d13155c3e85f4","source":{"kind":"arxiv","id":"2402.09729","version":1},"attestation_state":"computed","paper":{"title":"Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.AI","authors_text":"Danny H.K. Tsang, Jiadong Yu, Jun Zhang, Tailin Zhou","submitted_at":"2024-02-15T05:56:35Z","abstract_excerpt":"This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce a quality of experience (QoE) metric to measure user experience, which considers the MEC system's latency, user attention levels, and preferred resolutions. Then, a QoE maximization problem is formulated for resource allocation to ensure the highest possible user experience,which is cast as a reinforcement learning problem, aiming to learn a generalized policy applicable across diverse user environments for all MEC "},"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":"2402.09729","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-02-15T05:56:35Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"afcf917825b8a6b3c854555f98ea6da63be4c3942b805bd747c602e6353c2672","abstract_canon_sha256":"cdf4c05c668a67d8307904f8a3ec36f34134df3627f6f98b30095886197655b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:45:35.723060Z","signature_b64":"wzTOTrH7WghPcfMoDq6gD/4bOFGoEn7UhwPOdierY+pP/z7BOOgpxTpM4rPJSy0EwRxJ4u87FU6cqeyF+6E5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f24f58b82223282631037fc49fbcaedcaee0731a9368a444294d13155c3e85f4","last_reissued_at":"2026-07-05T07:45:35.722244Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:45:35.722244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.AI","authors_text":"Danny H.K. Tsang, Jiadong Yu, Jun Zhang, Tailin Zhou","submitted_at":"2024-02-15T05:56:35Z","abstract_excerpt":"This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce a quality of experience (QoE) metric to measure user experience, which considers the MEC system's latency, user attention levels, and preferred resolutions. Then, a QoE maximization problem is formulated for resource allocation to ensure the highest possible user experience,which is cast as a reinforcement learning problem, aiming to learn a generalized policy applicable across diverse user environments for all MEC "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.09729","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/2402.09729/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":"2402.09729","created_at":"2026-07-05T07:45:35.722649+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.09729v1","created_at":"2026-07-05T07:45:35.722649+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.09729","created_at":"2026-07-05T07:45:35.722649+00:00"},{"alias_kind":"pith_short_12","alias_value":"6JHVROBCEMUC","created_at":"2026-07-05T07:45:35.722649+00:00"},{"alias_kind":"pith_short_16","alias_value":"6JHVROBCEMUCMMID","created_at":"2026-07-05T07:45:35.722649+00:00"},{"alias_kind":"pith_short_8","alias_value":"6JHVROBC","created_at":"2026-07-05T07:45:35.722649+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.07211","citing_title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S","json":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S.json","graph_json":"https://pith.science/api/pith-number/6JHVROBCEMUCMMIDP7CJ7PFO3S/graph.json","events_json":"https://pith.science/api/pith-number/6JHVROBCEMUCMMIDP7CJ7PFO3S/events.json","paper":"https://pith.science/paper/6JHVROBC"},"agent_actions":{"view_html":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S","download_json":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S.json","view_paper":"https://pith.science/paper/6JHVROBC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.09729&json=true","fetch_graph":"https://pith.science/api/pith-number/6JHVROBCEMUCMMIDP7CJ7PFO3S/graph.json","fetch_events":"https://pith.science/api/pith-number/6JHVROBCEMUCMMIDP7CJ7PFO3S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S/action/storage_attestation","attest_author":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S/action/author_attestation","sign_citation":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S/action/citation_signature","submit_replication":"https://pith.science/pith/6JHVROBCEMUCMMIDP7CJ7PFO3S/action/replication_record"}},"created_at":"2026-07-05T07:45:35.722649+00:00","updated_at":"2026-07-05T07:45:35.722649+00:00"}