{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003965605,0.0005421436,0.0003927648,0.0003531858,0.0003152929,0.0005489285,0.0009076881,0.0006660513,0.001639351],"category_scores_gemma":[0.001324501,0.0003076656,0.0004584896,0.0003105421,0.0003801985,0.0006492083,0.0003804811,0.0009179561,0.0001476595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123772,"about_ca_system_score_gemma":0.001048546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02469181,"about_ca_topic_score_gemma":0.01518318,"domain_scores_codex":[0.9998438,0.00003746404,0.000009688131,0.00003365497,0.00004309717,0.00003227259],"domain_scores_gemma":[0.9995603,0.0002477744,0.00003794496,0.00003385319,0.00009334338,0.00002690582],"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":[0.00001785369,0.00002635916,0.0006320799,0.00001004485,0.000007361468,0.00001462292,0.00000900095,0.9950104,0.0006799521,0.000843194,0.00009558996,0.002653593],"study_design_scores_gemma":[6.208359e-7,0.000002128634,0.0000230699,3.108983e-7,3.700229e-7,6.330325e-7,6.841502e-7,0.9996808,0.0001527563,0.0001145093,0.00002375421,4.197814e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3371729,0.0002507453,0.6495506,0.0004647742,0.00009201629,0.000117578,0.0004041234,0.00207995,0.009867287],"genre_scores_gemma":[0.9596782,0.00009123098,0.03775718,0.00005646301,0.000007392864,0.00008443516,0.0001962154,0.00004336612,0.00208542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02469181,"threshold_uncertainty_score":0.04909617,"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."}}