{"id":"W4225700085","doi":"10.1109/tfuzz.2022.3167158","title":"Fuzzy Active Learning to Detect OpenCL Kernel Heterogeneous Machines in Cyber Physical Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"National Center for Research and Development; National Research Foundation of Korea","keywords":"Computer science; Machine learning; Artificial intelligence; Multi-core processor; Data mining; Parallel computing","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":[0.001136643,0.0007195803,0.0006715613,0.001827061,0.0004552931,0.001043499,0.001192678,0.0006734421,0.0006757321],"category_scores_gemma":[0.003804501,0.0003075354,0.0005219536,0.0006015566,0.0005724137,0.001241997,0.0007087407,0.0007122831,0.0001176209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001069678,"about_ca_system_score_gemma":0.0004429534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003120336,"about_ca_topic_score_gemma":0.002735091,"domain_scores_codex":[0.9994766,0.00009030326,0.00003596774,0.0001298538,0.0001932319,0.00007396461],"domain_scores_gemma":[0.99839,0.0008788852,0.0002553061,0.00009634533,0.0003277239,0.00005175726],"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":[0.000418301,0.0002049512,0.01244197,0.000143023,0.00009435567,0.0002875612,0.0002752134,0.6924374,0.01900712,0.005522466,0.0008761976,0.2682914],"study_design_scores_gemma":[0.000002074724,0.00001661051,0.0005562059,0.000004385744,0.000004963712,0.00001530276,0.00001500922,0.9954626,0.002465368,0.00136129,0.00009252653,0.000003725428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1426393,0.0003319462,0.8547562,0.0001332816,0.00003344592,0.00006423372,0.00005065276,0.0006501788,0.001340833],"genre_scores_gemma":[0.9415968,0.00007352671,0.05756444,0.00004268272,0.00001568238,0.00003930886,0.00005324767,0.000026348,0.0005879669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003120336,"threshold_uncertainty_score":0.007761121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411534116422408,"score_gpt":0.2366867659415057,"score_spread":0.2225714247772816,"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."}}