{"id":"W4376851419","doi":"10.1109/jstsp.2023.3276595","title":"User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Carleton University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Cache; Scalability; Scheduling (production processes); Computer network; Virtual reality; Mobile edge computing; Frame (networking); Enhanced Data Rates for GSM Evolution; Server; Real-time computing; Multimedia; Artificial intelligence; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003111734,0.0004840511,0.0005501785,0.0003599792,0.0005385736,0.0008343477,0.001230824,0.0005481398,0.000803157],"category_scores_gemma":[0.001231082,0.0002075108,0.0003024118,0.0006302657,0.0003770741,0.001371376,0.0008171814,0.0005631432,0.0002224295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008199993,"about_ca_system_score_gemma":0.0005987492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005352062,"about_ca_topic_score_gemma":0.007123509,"domain_scores_codex":[0.9996396,0.0001002167,0.00001498682,0.00007601034,0.00009760955,0.00007154915],"domain_scores_gemma":[0.9994999,0.0001654777,0.00004749419,0.0001145598,0.000124395,0.00004805183],"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":[0.0007140285,0.000288522,0.005015367,0.0002242525,0.0001367118,0.0007851551,0.0004258456,0.530115,0.0914278,0.05690284,0.007865277,0.3060992],"study_design_scores_gemma":[0.000006723831,0.00004454061,0.0004445455,0.000004480114,0.0000141444,0.0001156455,0.00004050567,0.9899576,0.004113219,0.003676925,0.001568195,0.00001335017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07632703,0.001680456,0.9159288,0.0003932567,0.00007044831,0.00005217838,0.00005824568,0.0009582221,0.004531465],"genre_scores_gemma":[0.9162052,0.0005440016,0.08130662,0.000125458,0.00004653763,0.00003219776,0.00006030301,0.00005535922,0.001624279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005352062,"threshold_uncertainty_score":0.01064181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461911258985467,"score_gpt":0.2867099660388082,"score_spread":0.2620908534489535,"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."}}