{"id":"W4393186491","doi":"10.1109/vcc60689.2023.10475057","title":"Feature Engineering for Highly Irregular Network Traffic Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Ericsson (Canada); Carleton University","funders":"","keywords":"Computer science; Feature (linguistics); Feature engineering; Artificial intelligence; Data mining; 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":[0.0003359172,0.0001148192,0.0001390967,0.00009536768,0.00009883357,0.0001320385,0.0003828318,0.00007933659,0.000005244409],"category_scores_gemma":[0.00002061382,0.00009832231,0.0001434441,0.0006556593,0.000006032828,0.0001818249,0.00006573759,0.00009696273,0.00003957876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002232213,"about_ca_system_score_gemma":0.00001406506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.749203e-7,"about_ca_topic_score_gemma":0.000006185375,"domain_scores_codex":[0.9990347,0.00001167141,0.0001376645,0.0003016176,0.0001677463,0.0003466211],"domain_scores_gemma":[0.9996209,0.00007889885,0.00003496883,0.0001566551,0.00005120398,0.00005732996],"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":[0.000001264989,0.000005051881,0.000005432965,0.00000944622,0.00003274158,0.000002928121,0.0001459424,0.7563974,0.00002452952,0.165123,0.07393405,0.004318221],"study_design_scores_gemma":[0.0001179642,0.00003633265,0.0001990702,0.00001752448,0.00001095446,0.000003287021,0.00001387817,0.9634974,0.00003240916,0.00001211614,0.03595818,0.0001008579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01939452,0.00006644501,0.9766598,0.001375662,0.0009921392,0.0001373562,0.0000019373,0.001252308,0.0001197997],"genre_scores_gemma":[0.9711747,0.000005020217,0.02492296,0.00009971706,0.0006100493,0.0000240311,0.00002502317,0.00001278028,0.00312577],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9517801,"threshold_uncertainty_score":0.4009468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008361969518336964,"score_gpt":0.2032997583806149,"score_spread":0.194937788862278,"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."}}