{"id":"W4386123921","doi":"10.1109/tsmc.2023.3301001","title":"Convolutional Features-Based Broad Learning With LSTM for Multidimensional Facial Emotion Recognition in Human–Robot Interaction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Pooling; Convolutional neural network; Artificial intelligence; Computer science; Feature (linguistics); Pattern recognition (psychology); Convolution (computer science); Scale (ratio); Speech recognition; Artificial neural network","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":[0.000358958,0.0007743417,0.0005233049,0.000390349,0.0001987843,0.0003618544,0.0008714649,0.0005631437,0.001924195],"category_scores_gemma":[0.0005787759,0.00028722,0.0006731501,0.0004298269,0.000226872,0.0009861905,0.0007273261,0.0007306597,0.0005926091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005212235,"about_ca_system_score_gemma":0.000508755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005306607,"about_ca_topic_score_gemma":0.006720283,"domain_scores_codex":[0.9997728,0.00002534256,0.00001500129,0.0000769874,0.00005887907,0.00005088809],"domain_scores_gemma":[0.9998958,0.00002447178,0.00001310866,0.00001725099,0.00004061987,0.000008649163],"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":[0.0005101505,0.0002633712,0.002283158,0.000203501,0.0002160692,0.0003625589,0.0001408132,0.1528814,0.09585059,0.001755966,0.009110527,0.7364219],"study_design_scores_gemma":[0.00001004006,0.0001174923,0.001665696,0.000009506014,0.00004392858,0.00008596815,0.00002897632,0.9811554,0.01448595,0.001122809,0.00125573,0.00001854403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1433443,0.002450858,0.8423809,0.0003868759,0.0002926194,0.0001348688,0.0006838381,0.00593962,0.004386164],"genre_scores_gemma":[0.8744878,0.0008096853,0.1166702,0.0003741914,0.00005755192,0.0001516452,0.001392676,0.00009451354,0.005961685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005306607,"threshold_uncertainty_score":0.01055145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515160252177361,"score_gpt":0.2638456770987739,"score_spread":0.2386940745770003,"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."}}