{"id":"W4251923949","doi":"10.22215/etd/2018-12718","title":"Configurable FPGA-Based Outlier Detection for Time Series Data","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Field-programmable gate array; Outlier; Computer science; MATLAB; Anomaly detection; Floating point; Series (stratigraphy); Embedded system; Real-time computing; Computer hardware; Algorithm; Data mining; Artificial intelligence","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":[0.0002223683,0.0002176116,0.000209822,0.0001426813,0.0003446888,0.0002513815,0.001411042,0.0002595423,0.0002293892],"category_scores_gemma":[0.00003342987,0.0002084312,0.00008470303,0.0002726416,0.00002841814,0.0005341141,0.0000677488,0.0001170934,0.0002950286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003820855,"about_ca_system_score_gemma":0.0001567245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004591332,"about_ca_topic_score_gemma":0.0002245675,"domain_scores_codex":[0.998579,0.00001705171,0.000288082,0.0007264428,0.000174184,0.0002152913],"domain_scores_gemma":[0.9977264,0.00004500498,0.0002196285,0.00163455,0.000312747,0.0000616461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002731908,0.0003343964,0.000003154534,0.0005092892,0.0001773054,0.000001760194,0.0002761113,0.00001503865,0.0482238,0.0308626,0.2600451,0.6592782],"study_design_scores_gemma":[0.0001557764,0.0002267995,0.00002664699,0.00002724339,0.00003058827,0.000002736876,0.00003013933,0.05750526,0.4509972,0.003101804,0.4875441,0.0003516805],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001766932,0.00002294034,0.9777227,0.0002089796,0.0003806716,0.0008353771,0.00008785784,0.0009392552,0.01962549],"genre_scores_gemma":[0.0268936,0.00003008157,0.5188549,0.0006392257,0.0006920805,0.00197026,0.007199958,0.00012812,0.4435918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6589265,"threshold_uncertainty_score":0.8499578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163044197972791,"score_gpt":0.2884225539059227,"score_spread":0.2667921119261948,"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."}}