{"id":"W4312998458","doi":"10.1109/mass56207.2022.00070","title":"An Accurate and Energy-Efficient Anomaly Detection in Edge-Cloud Networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Cloud computing; Computer science; Anomaly detection; Anomaly (physics); Enhanced Data Rates for GSM Evolution; Boundary (topology); Data mining; Edge computing; Artificial intelligence; Mathematics","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":[0.0004695278,0.0001980207,0.0002105141,0.000281299,0.0004296443,0.000308466,0.0005654603,0.00007646744,0.00007280955],"category_scores_gemma":[0.000005298697,0.0002058133,0.00004669597,0.0003637685,0.00005246045,0.0002372992,0.0001437808,0.0003101194,0.000004959629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001677452,"about_ca_system_score_gemma":0.00005286129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001542264,"about_ca_topic_score_gemma":0.00007516941,"domain_scores_codex":[0.9981366,0.0002012794,0.0003956858,0.0006526341,0.00037518,0.0002385894],"domain_scores_gemma":[0.9991295,0.0000565414,0.0002066994,0.0003752907,0.0001085933,0.0001233848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000315941,0.0009369972,0.003079909,0.00005785027,0.000157355,0.00008939497,0.00153441,0.2263205,0.04605038,0.4621202,0.001209169,0.2581279],"study_design_scores_gemma":[0.0002912558,0.0005033294,0.0006228854,0.000018648,0.000004359307,0.00004410889,0.0004154751,0.9762251,0.0005652749,0.0004820503,0.02058204,0.0002454629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2290654,0.0005864018,0.7645486,0.0001963294,0.00229561,0.0004706233,0.00004284759,0.0002430903,0.002551093],"genre_scores_gemma":[0.9966927,0.0003158293,0.0002612622,0.0001292156,0.0001301113,0.001383007,0.00001824428,0.00001401675,0.001055645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7676272,"threshold_uncertainty_score":0.8392821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168579270106113,"score_gpt":0.2559509576161777,"score_spread":0.2390930306055664,"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."}}