{"id":"W2914576459","doi":"10.1109/tnse.2018.2865183","title":"Trust-Based Social Networks with Computing, Caching and Communications: A Deep Reinforcement Learning Approach","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Economía y Competitividad","keywords":"Computer science; Reinforcement learning; Exploit; Leverage (statistics); Mobile edge computing; Distributed computing; Computation; Mobile device; Social network (sociolinguistics); Scheme (mathematics); Mobile social network; Artificial intelligence; Mobile computing; Computer network; Enhanced Data Rates for GSM Evolution; Social media; Computer security; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006776148,0.0001464645,0.0001308831,0.0001452312,0.001718305,0.00031924,0.0004850312,0.00004234119,6.020961e-7],"category_scores_gemma":[0.000004609938,0.000134314,0.00002481617,0.0007825785,0.0003169565,0.0003734396,0.00001820672,0.0003631461,7.516596e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005577132,"about_ca_system_score_gemma":0.00006282218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003560325,"about_ca_topic_score_gemma":0.000009692556,"domain_scores_codex":[0.9988311,0.00002924407,0.0001486813,0.0003338867,0.0002755664,0.0003815381],"domain_scores_gemma":[0.9993628,0.00007984293,0.00004894752,0.0003112766,0.00009215871,0.0001049373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007401256,0.00001292385,0.0000330426,0.000006327881,0.000009030685,5.833851e-7,0.0004535443,0.9798267,0.00008173906,0.0006993492,0.000005986448,0.01886339],"study_design_scores_gemma":[0.0002466849,0.0001461631,0.0001806622,0.00005332125,0.00001117878,0.00001515749,0.00005282612,0.99894,0.00003431367,0.000002898977,0.000144307,0.0001724745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01683821,0.0001172439,0.9821873,0.0001153577,0.0001719356,0.0001047858,8.220908e-8,0.0001921139,0.0002729263],"genre_scores_gemma":[0.9792463,0.00003179718,0.02040801,0.0001795537,0.0000986425,0.00001126041,4.559852e-7,0.000009165524,0.00001487541],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9624081,"threshold_uncertainty_score":0.9995813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240932805343792,"score_gpt":0.2096396408444556,"score_spread":0.1972303127910177,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}