{"id":"W4396680540","doi":"10.1109/access.2024.3397783","title":"Enhancing Multivariate Time Series Classifiers Through Self-Attention and Relative Positioning Infusion","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Deep learning; Machine learning; Multivariate statistics; Block (permutation group theory); Artificial neural network; Task (project management); Deep neural networks; Time series; Pattern recognition (psychology); Data mining; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001026315,0.0007967611,0.0005545507,0.00085875,0.0002490696,0.0006952331,0.001019278,0.0005678575,0.001799434],"category_scores_gemma":[0.00288979,0.0002024322,0.0006367381,0.0009266867,0.0003012716,0.001529922,0.00111301,0.0008821078,0.0007840826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006321549,"about_ca_system_score_gemma":0.0005955674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005155156,"about_ca_topic_score_gemma":0.006856676,"domain_scores_codex":[0.9996598,0.00006155494,0.00001953817,0.0001057042,0.00009899901,0.00005437616],"domain_scores_gemma":[0.9991886,0.0003337238,0.00009405969,0.0001215958,0.0002215361,0.00004038029],"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":[0.0001771254,0.0001851999,0.004541398,0.00007392274,0.00009602868,0.00008365783,0.0001013175,0.2052246,0.02235576,0.005196841,0.005549689,0.7564144],"study_design_scores_gemma":[0.00000340826,0.00002594279,0.0009024547,0.000004118422,0.00001572611,0.00001707895,0.000008243465,0.9918161,0.005014757,0.001482705,0.0007051295,0.000004419012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07844881,0.0005942094,0.9137892,0.0002731371,0.0001278606,0.00004384984,0.0001957451,0.003042229,0.00348501],"genre_scores_gemma":[0.8286399,0.0003854253,0.1648113,0.0002556305,0.0001752861,0.00006637512,0.0007750449,0.0001985174,0.004692581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005155156,"threshold_uncertainty_score":0.01025027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01579003606636511,"score_gpt":0.2733667265059397,"score_spread":0.2575766904395746,"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."}}