{"id":"W4306763817","doi":"10.1145/3551661.3561371","title":"Performance Evaluation of Edge Computing-Aided IoT Augmented Reality Systems","year":2022,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Augmented reality; Server; Edge computing; Enhanced Data Rates for GSM Evolution; Queueing theory; Latency (audio); Mobile edge computing; Virtual reality; Distributed computing; Real-time computing; Embedded system; Computer network; Human–computer interaction; Artificial intelligence; Telecommunications","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":[0.001104104,0.0008458694,0.0007593185,0.0006905836,0.0006794014,0.001258834,0.0008571538,0.0006245117,0.003635332],"category_scores_gemma":[0.00387285,0.0001798732,0.0003163187,0.0008282319,0.0004124693,0.0009006208,0.0007938308,0.0003695755,0.0005377752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001150217,"about_ca_system_score_gemma":0.0007139579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006803158,"about_ca_topic_score_gemma":0.004119617,"domain_scores_codex":[0.998926,0.0002558065,0.00007601485,0.0001407648,0.0003310631,0.0002704151],"domain_scores_gemma":[0.9973401,0.001400714,0.0001956104,0.0001756681,0.0007473518,0.0001405823],"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":[0.001959501,0.0003303769,0.004139502,0.0002985155,0.00007628426,0.0002091308,0.00007698456,0.9264193,0.01218177,0.002048059,0.001869677,0.05039084],"study_design_scores_gemma":[0.00002247944,0.0004809927,0.00147996,0.000008556304,0.00002317723,0.00004908997,0.00005407658,0.9919947,0.005182534,0.0002470696,0.0004433,0.00001403932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9308524,0.001502696,0.04895411,0.0003583698,0.0001740602,0.0001801916,0.0003784138,0.001064004,0.0165358],"genre_scores_gemma":[0.9964871,0.0001280994,0.00264717,0.0000205746,0.000006945023,0.00001519425,0.0001152473,0.00001453723,0.0005651205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006803158,"threshold_uncertainty_score":0.0135271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07341872403018206,"score_gpt":0.301540114910842,"score_spread":0.2281213908806599,"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."}}