{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000585959,0.0002464922,0.0002964492,0.0005272383,0.0004617205,0.0002058347,0.0001310326,0.0001657159,0.000003437297],"category_scores_gemma":[0.000009664554,0.0002299013,0.00007890567,0.0004421709,0.00004549297,0.0002092758,0.000003039702,0.000373204,0.00004467387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001306179,"about_ca_system_score_gemma":0.00005228185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008995284,"about_ca_topic_score_gemma":0.0001388994,"domain_scores_codex":[0.9979704,0.0003244313,0.0004189845,0.000538963,0.0004094799,0.0003377336],"domain_scores_gemma":[0.9990873,0.0002526683,0.0001946001,0.00020744,0.0001521032,0.0001058583],"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":[0.0001472451,0.0001642569,0.0002407451,0.000299248,0.00005370996,0.00001234538,0.0007778592,0.9823841,0.001776753,0.001088678,0.0002830007,0.01277206],"study_design_scores_gemma":[0.002369854,0.0007063109,0.003112795,0.0007590013,0.00002568135,0.00009737458,0.000563232,0.9884502,0.0003881669,0.00002331152,0.00311522,0.0003888055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1994045,0.00006688832,0.797407,0.0001428931,0.001572763,0.0007851884,0.00002681085,0.0003708684,0.0002231504],"genre_scores_gemma":[0.9963638,0.000008906889,0.0006453376,0.00002374937,0.0001258017,0.0002573442,0.0000689989,0.00002853264,0.002477541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7969593,"threshold_uncertainty_score":0.9375102,"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."}}