{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:26I2J7OVB3R7PNLZNOWBVMQYCG","short_pith_number":"pith:26I2J7OV","schema_version":"1.0","canonical_sha256":"d791a4fdd50ee3f7b5796bac1ab21811a2195b14bf4a90f0dcf7c5d0e7c75e3f","source":{"kind":"arxiv","id":"1903.06235","version":2},"attestation_state":"computed","paper":{"title":"Learning Automata Based Q-learning for Content Placement in Cooperative Caching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Lei Jiao, Yuanwei Liu, Yue Chen, Zhong Yang","submitted_at":"2019-03-14T20:12:04Z","abstract_excerpt":"An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked f"},"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":"1903.06235","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-03-14T20:12:04Z","cross_cats_sorted":[],"title_canon_sha256":"8c50d68fe2b81aeb09ebb927f897e53ab651be60d479f63681085cf32fe3ded7","abstract_canon_sha256":"c0576cd829a302e5177628926538007c7304115601f083ce54e4e8d399e9d694"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:49.790864Z","signature_b64":"MamOD9dX5dDBCiS7bOblLdeE22OLPfLHCGVTWK44y/3W/GWaX5KQeAH7vbkB+jbHk/KgFjWNviO7lic8HbR2CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d791a4fdd50ee3f7b5796bac1ab21811a2195b14bf4a90f0dcf7c5d0e7c75e3f","last_reissued_at":"2026-05-17T23:49:49.790377Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:49.790377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Automata Based Q-learning for Content Placement in Cooperative Caching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Lei Jiao, Yuanwei Liu, Yue Chen, Zhong Yang","submitted_at":"2019-03-14T20:12:04Z","abstract_excerpt":"An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.06235","kind":"arxiv","version":2},"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":"1903.06235","created_at":"2026-05-17T23:49:49.790482+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.06235v2","created_at":"2026-05-17T23:49:49.790482+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.06235","created_at":"2026-05-17T23:49:49.790482+00:00"},{"alias_kind":"pith_short_12","alias_value":"26I2J7OVB3R7","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"26I2J7OVB3R7PNLZ","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"26I2J7OV","created_at":"2026-05-18T12:33:07.085635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.08812","citing_title":"Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG","json":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG.json","graph_json":"https://pith.science/api/pith-number/26I2J7OVB3R7PNLZNOWBVMQYCG/graph.json","events_json":"https://pith.science/api/pith-number/26I2J7OVB3R7PNLZNOWBVMQYCG/events.json","paper":"https://pith.science/paper/26I2J7OV"},"agent_actions":{"view_html":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG","download_json":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG.json","view_paper":"https://pith.science/paper/26I2J7OV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.06235&json=true","fetch_graph":"https://pith.science/api/pith-number/26I2J7OVB3R7PNLZNOWBVMQYCG/graph.json","fetch_events":"https://pith.science/api/pith-number/26I2J7OVB3R7PNLZNOWBVMQYCG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG/action/storage_attestation","attest_author":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG/action/author_attestation","sign_citation":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG/action/citation_signature","submit_replication":"https://pith.science/pith/26I2J7OVB3R7PNLZNOWBVMQYCG/action/replication_record"}},"created_at":"2026-05-17T23:49:49.790482+00:00","updated_at":"2026-05-17T23:49:49.790482+00:00"}