{"id":"W4317792686","doi":"10.1109/wsc57314.2022.10015433","title":"Using Deep Learning for Simulation of Real time Video Streaming Applications","year":2022,"lang":"en","type":"article","venue":"2022 Winter Simulation Conference (WSC)","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cloud computing; Analytics; Queueing theory; Big data; Real-time computing; Data modeling; Deep learning; Latency (audio); Artificial intelligence; Distributed computing; Machine learning; Data mining; Computer network; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004811017,0.0001709081,0.0002410141,0.0002472273,0.0006586416,0.000125156,0.0005841888,0.00005176264,0.00009269731],"category_scores_gemma":[0.00008410062,0.0002045473,0.0001217372,0.0005116059,0.00003145515,0.0004605387,0.0004923263,0.0002156308,0.00001112851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001555966,"about_ca_system_score_gemma":0.0001124043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002996568,"about_ca_topic_score_gemma":0.000001509692,"domain_scores_codex":[0.9982104,0.0001608183,0.0004960166,0.0004758862,0.0003618259,0.000295063],"domain_scores_gemma":[0.9981003,0.0007265228,0.0003898773,0.0003848163,0.0003371306,0.00006137161],"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":[0.00001667573,0.00003872017,0.0006107577,0.00001888655,0.0000181026,4.906897e-7,0.00143907,0.9408647,0.003465672,0.0007855858,0.00002078872,0.05272049],"study_design_scores_gemma":[0.0003519358,0.0001035721,0.0005044229,0.00001753853,0.00001776906,0.000001644692,0.0001101649,0.9917072,0.0002538696,0.001312664,0.005413145,0.0002060938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05421385,0.000019896,0.943496,0.00005251957,0.0007715368,0.0005069026,0.000002276587,0.0001654947,0.0007715136],"genre_scores_gemma":[0.9841655,8.933088e-7,0.01494366,0.00003717959,0.0004024227,0.00005871043,0.00005788739,0.00002037323,0.0003134096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9299516,"threshold_uncertainty_score":0.8341196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05010586110517674,"score_gpt":0.3175271352451167,"score_spread":0.2674212741399399,"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."}}