{"id":"W4388079792","doi":"10.1109/pimrc56721.2023.10294071","title":"Predictive and Robust Field-of-View Selection for Virtual Reality Video Streaming","year":2023,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hubei Province; Peng Cheng Laboratory","keywords":"Computer science; Virtual reality; Selection (genetic algorithm); Video streaming; Field (mathematics); Multimedia; Human–computer interaction; Artificial intelligence; Real-time computing; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004976768,0.00006830454,0.0001223536,0.00006299421,0.00008962895,0.00005430507,0.0001504247,0.00004092317,0.000006049799],"category_scores_gemma":[0.0001045972,0.00006060501,0.00003637422,0.0003111664,0.00001596106,0.000374493,0.0001237569,0.00005088032,0.000002584856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000180121,"about_ca_system_score_gemma":0.00004874625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002185974,"about_ca_topic_score_gemma":0.00007083697,"domain_scores_codex":[0.9992403,0.00005552723,0.000183703,0.0002381864,0.0001313744,0.0001508939],"domain_scores_gemma":[0.9992424,0.0004153457,0.00005671809,0.0001595789,0.00008823042,0.00003767976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006794737,0.0001728908,0.003509107,0.0004034372,0.0001325626,0.000003531494,0.003457454,0.00297917,0.002441021,0.2666102,0.0319797,0.688243],"study_design_scores_gemma":[0.0005713769,0.001116449,0.01124334,0.00007423447,0.00002164695,0.00000531094,0.0006221217,0.9519928,0.02355398,0.008310516,0.002274139,0.0002141512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009031379,0.00001810138,0.9882638,0.001437525,0.0001218801,0.0002029092,0.000004407847,0.000158136,0.0007617962],"genre_scores_gemma":[0.9689512,0.00007290355,0.02951502,0.0004282929,0.00008670818,0.00005715714,0.000008817834,0.000006666134,0.000873238],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9599198,"threshold_uncertainty_score":0.24714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05220836871426057,"score_gpt":0.3451959044071402,"score_spread":0.2929875356928796,"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."}}