{"id":"W4200249961","doi":"10.23919/cnsm52442.2021.9615580","title":"Network Assurance in Intent-Based Networking Data Centers with Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Construct (python library); Artificial intelligence; Convolutional neural network; Machine learning; Data center; Data modeling; Artificial neural network; Recurrent neural network; Network architecture; Component (thermodynamics); Data mining; Computer network; Database","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":[0.002866493,0.0007085534,0.0006451409,0.0007686587,0.0004617574,0.0008729558,0.001384322,0.0007308202,0.0006900938],"category_scores_gemma":[0.009348216,0.0004380268,0.0005024648,0.0005120428,0.0007371489,0.002647927,0.001349388,0.001489763,0.0001688563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00128057,"about_ca_system_score_gemma":0.001041887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004936142,"about_ca_topic_score_gemma":0.003784228,"domain_scores_codex":[0.999077,0.0003004524,0.00006236294,0.0001773745,0.0002573515,0.0001253894],"domain_scores_gemma":[0.9963864,0.001561045,0.0007120149,0.0005167367,0.0006724742,0.0001513865],"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":[0.0001773515,0.0001283771,0.00617336,0.00006843013,0.00003555699,0.00008993607,0.0002324988,0.8397648,0.003361764,0.01142411,0.001376381,0.1371674],"study_design_scores_gemma":[0.00000224672,0.00001830808,0.0001670702,0.000002569324,0.000002772314,0.000006392575,0.000007383092,0.9967586,0.0005482266,0.002370629,0.0001126375,0.000003098059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05827808,0.0002357064,0.9389948,0.0004411558,0.00003082374,0.00005388029,0.00004095284,0.001187645,0.0007368626],"genre_scores_gemma":[0.9241808,0.0001278789,0.07466715,0.000108609,0.00003815031,0.00004287653,0.0001064907,0.00004559963,0.0006824477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004936142,"threshold_uncertainty_score":0.01515967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341287356746359,"score_gpt":0.2344785104269992,"score_spread":0.2110656368595357,"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."}}