{"id":"W4402159510","doi":"10.1109/tdsc.2024.3446587","title":"PulseAnomaly: Unsupervised Anomaly Detection on Avionic Platforms With Seasonality and Trend Modeling in Transformer Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Queen's University","funders":"Innovation for Defence Excellence and Security","keywords":"Anomaly detection; Avionics; Computer science; Anomaly (physics); Transformer; Data mining; Electrical engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007972927,0.0008760239,0.0005953461,0.00110056,0.0002522856,0.0004936334,0.001235861,0.0004943539,0.0005897086],"category_scores_gemma":[0.002208899,0.0002629554,0.00060722,0.0006726893,0.000259748,0.0008411987,0.0006352766,0.000853062,0.00029821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006959172,"about_ca_system_score_gemma":0.0006287058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01256638,"about_ca_topic_score_gemma":0.01570269,"domain_scores_codex":[0.9996941,0.00008375665,0.00001855349,0.0001002545,0.00006786314,0.00003554358],"domain_scores_gemma":[0.9991522,0.0004952054,0.0001158818,0.0000742632,0.00012476,0.00003777788],"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":[0.0003589926,0.0002587608,0.01759386,0.0000745077,0.000187762,0.0001620512,0.00008149334,0.7663964,0.004136622,0.001962361,0.003912294,0.2048749],"study_design_scores_gemma":[0.000002340362,0.00001139597,0.0004581198,9.109691e-7,0.000002904877,0.00001228547,0.00000294368,0.9986462,0.0003728688,0.0003423202,0.0001457133,0.000002014285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2436877,0.0004346896,0.7461841,0.0003692193,0.00008947703,0.0001335229,0.001319561,0.006417894,0.001363748],"genre_scores_gemma":[0.8711287,0.0002530253,0.122518,0.0001000717,0.00008560051,0.0001136688,0.003266469,0.0001834377,0.002351116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01256638,"threshold_uncertainty_score":0.02498651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01489354663741561,"score_gpt":0.2287670306058573,"score_spread":0.2138734839684417,"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."}}