{"id":"W3106791815","doi":"10.1109/dsaa49011.2020.00017","title":"Ensemble of Hierarchical Temporal Memory for Anomaly Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Research and Development; Science and Engineering Research Council","keywords":"Anomaly detection; Computer science; Univariate; Encoder; Anomaly (physics); Multivariate statistics; Artificial intelligence; Ensemble learning; Data mining; Pattern recognition (psychology); Machine 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":[0.0000707896,0.00005428486,0.00009103813,0.00003464882,0.00006448234,0.00001959618,0.0002671784,0.00004049275,0.00001097277],"category_scores_gemma":[0.0000167544,0.00005093156,0.00007341337,0.0002478922,0.0000232947,0.0001226759,0.00006719917,0.0000521797,0.000007765876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007824193,"about_ca_system_score_gemma":0.0000231946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002351309,"about_ca_topic_score_gemma":0.000005145374,"domain_scores_codex":[0.9994525,0.00001150005,0.0001645174,0.0002013806,0.00007738156,0.00009267889],"domain_scores_gemma":[0.9995991,0.00003578277,0.00005674297,0.0001846653,0.00005551076,0.00006819174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004475365,0.0001095319,0.000230884,0.00006294123,0.00002055947,8.875801e-7,0.0003717264,0.00004429889,0.3788048,0.1777399,0.002969889,0.4395998],"study_design_scores_gemma":[0.0001771062,0.0004060597,0.0003119374,0.000001662433,0.000003551408,0.000004106429,0.00001930192,0.1156856,0.8632401,0.007428771,0.01262152,0.0001003593],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01277422,0.000006800751,0.9821138,0.002099299,0.00002587623,0.0002587949,0.000001616613,0.0002983397,0.002421271],"genre_scores_gemma":[0.8235134,0.000001091241,0.1758321,0.0003893017,0.00004204884,0.00006479429,6.922929e-7,0.000003899333,0.0001526092],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8107392,"threshold_uncertainty_score":0.2076929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437757581031336,"score_gpt":0.2524361550823725,"score_spread":0.2280585792720592,"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."}}