{"id":"W3130326228","doi":"10.1109/icdmw51313.2020.00045","title":"Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector","year":2020,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Dirichlet process; Computer science; Streaming data; Naive Bayes classifier; Detector; Parametric statistics; Artificial intelligence; Process (computing); Data mining; Bayesian probability; Anomaly (physics); Algorithm; Pattern recognition (psychology); Mathematics; Support vector machine; Statistics","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":[0.0001675602,0.0002061987,0.0002414779,0.0000482548,0.00008780469,0.000163364,0.001101066,0.0001109139,0.00009108254],"category_scores_gemma":[0.00005884332,0.0001530439,0.00008715039,0.0007162152,0.00002705401,0.0004573533,0.0001766662,0.0001996498,0.00008278208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001116747,"about_ca_system_score_gemma":0.00009420414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009766954,"about_ca_topic_score_gemma":0.000003202086,"domain_scores_codex":[0.9984955,0.00009257736,0.0002330071,0.0005891365,0.0002726135,0.0003172261],"domain_scores_gemma":[0.9990363,0.00004538291,0.0000769617,0.0004257295,0.0001089066,0.0003067584],"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":[0.0001243607,0.0003977587,0.003895578,0.0005024526,0.0002201375,0.0006621997,0.01258147,0.00003362212,0.07034545,0.3924964,0.07002667,0.4487139],"study_design_scores_gemma":[0.003295755,0.001370895,0.003908605,0.00009769987,0.00007893761,0.000202948,0.00008745831,0.4930543,0.236834,0.1501289,0.1074876,0.00345294],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004650163,0.0001759181,0.9711435,0.008741299,0.0001352121,0.0001978599,0.000002309897,0.0005246689,0.01442908],"genre_scores_gemma":[0.4701676,0.000002936904,0.5233914,0.00584646,0.0001443613,0.00001210513,0.000001415143,0.00001277506,0.000420959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4930207,"threshold_uncertainty_score":0.6240951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189801902305601,"score_gpt":0.2568452401752701,"score_spread":0.23786504994471,"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."}}