{"id":"W2923319240","doi":"10.3390/s19061449","title":"Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; China Scholarship Council","keywords":"Computer science; Quality of service; Computer network; Polling; Software-defined networking; OpenFlow; Differentiated services; Distributed computing; Queueing theory; Middleware (distributed applications); Message queue; Network packet; Packet loss","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":[0.0002779642,0.0002263237,0.0003061798,0.0002410618,0.0001281665,0.0003742698,0.0006440756,0.0001168264,0.00002431165],"category_scores_gemma":[0.00005667868,0.0002082585,0.00008110639,0.0008166254,0.000009898035,0.0003363387,0.0002961438,0.0002473991,0.0000583632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007856957,"about_ca_system_score_gemma":0.00005286977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001189831,"about_ca_topic_score_gemma":0.0007070923,"domain_scores_codex":[0.998136,0.00006530475,0.0003121926,0.0006263434,0.0002586182,0.000601535],"domain_scores_gemma":[0.9989783,0.0001456531,0.0001186724,0.0004956566,0.000124319,0.0001374706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002050127,0.0002324407,0.5021704,0.000608001,0.00007962638,0.0000367783,0.005800136,0.4742391,0.003655333,0.001789619,0.0008990747,0.01028456],"study_design_scores_gemma":[0.0008554374,0.0001411984,0.006769826,0.0002264098,0.000009511478,0.00000768693,0.0001511798,0.9741358,0.0005353154,0.0004483758,0.01634499,0.0003742874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895433,0.000185232,0.007814642,0.0005042164,0.0008398302,0.0006632446,0.000006297495,0.0003112231,0.0001320105],"genre_scores_gemma":[0.95226,0.000006905301,0.04454444,0.0007570792,0.00009833655,0.00002483531,0.00003315101,0.00004786853,0.002227381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4998967,"threshold_uncertainty_score":0.8492537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0243001847373345,"score_gpt":0.2434844038776944,"score_spread":0.2191842191403599,"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."}}