{"id":"W4312575430","doi":"10.1109/cloudsummit54781.2022.00019","title":"Context-Aware Feature Selection using Denoising Auto-Encoder for Fault Detection in Cloud Environments","year":2022,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Feature selection; Discriminative model; Feature (linguistics); Computer science; Context (archaeology); Artificial intelligence; Feature engineering; Encoder; Machine learning; Cloud computing; Fault detection and isolation; Data mining; Autoencoder; Feature extraction; Pattern recognition (psychology); Deep learning","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":[0.0002013913,0.0001020064,0.00009740696,0.0001449548,0.0006378819,0.00006524259,0.0002529473,0.00006115636,0.00002804795],"category_scores_gemma":[0.000005474676,0.0001120173,0.00005942624,0.0004847429,0.00001330334,0.0002637482,0.000148142,0.0002086302,0.000002621374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004143566,"about_ca_system_score_gemma":0.00002894758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001481907,"about_ca_topic_score_gemma":0.00009003151,"domain_scores_codex":[0.9990711,0.00005221668,0.0001638822,0.0003569391,0.0001616169,0.0001942839],"domain_scores_gemma":[0.9996372,0.00002911359,0.00008973815,0.0001917969,0.00001763218,0.00003453247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000828562,0.0005297744,0.002172494,0.00003334787,0.00005119998,0.000005003912,0.001256815,0.06218442,0.4316231,0.0192916,0.002481568,0.4802878],"study_design_scores_gemma":[0.0002893966,0.0001300003,0.0004550438,0.000003776557,0.000005770949,0.00004914388,0.000219793,0.8897937,0.06887348,0.00154335,0.0384471,0.0001894208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03698493,0.00002046047,0.9618056,0.0002816773,0.0001515689,0.0004666083,0.000003324218,0.0002177031,0.00006810746],"genre_scores_gemma":[0.9709955,0.000002454511,0.02737927,0.0003030878,0.00004937446,0.0003120921,0.000003017074,0.00001182416,0.0009433762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9344264,"threshold_uncertainty_score":0.4906136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740648514003165,"score_gpt":0.2585826171115599,"score_spread":0.2411761319715282,"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."}}