{"id":"W4387951256","doi":"10.1109/ccece58730.2023.10288951","title":"Lightweight Model for Emotion Detection from Facial Expression in Online Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Facial expression; Deep learning; Architecture; Inference; Artificial intelligence; Process (computing); Face (sociological concept); Online learning; Enhanced Data Rates for GSM Evolution; Learning environment; Expression (computer science); State (computer science); Machine learning; Emotion recognition; Emotion detection; Affective computing; Multimedia; Mathematics education; Psychology","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":[0.0003578617,0.000729061,0.0004689025,0.0004170897,0.0001771323,0.000416373,0.0008587877,0.0004645107,0.002001197],"category_scores_gemma":[0.0006559107,0.0002084257,0.0005685597,0.0002642038,0.0001535624,0.0005760882,0.0004685686,0.0009346509,0.001094467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005303171,"about_ca_system_score_gemma":0.0004184742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005115661,"about_ca_topic_score_gemma":0.00786394,"domain_scores_codex":[0.9998189,0.00002681957,0.000009009341,0.00006135771,0.00004165536,0.00004229488],"domain_scores_gemma":[0.9998872,0.00002762919,0.0000114285,0.00001215204,0.00005257489,0.000008967533],"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":[0.000644846,0.000630366,0.00906,0.0001227817,0.0002053276,0.0001518605,0.0001153814,0.1948406,0.0390868,0.002048843,0.01065752,0.7424356],"study_design_scores_gemma":[0.000008002877,0.00005545755,0.001984894,0.000007122388,0.00002571508,0.00002492681,0.00001665287,0.993387,0.003112219,0.0006681315,0.0007023273,0.000007507998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2020456,0.00182503,0.7831613,0.0007659658,0.0004748557,0.0001802535,0.001016109,0.003416546,0.007114271],"genre_scores_gemma":[0.9215128,0.0007148724,0.06670413,0.0003197072,0.00009766567,0.0002121088,0.001236331,0.00007230759,0.009130069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005115661,"threshold_uncertainty_score":0.01017177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06111772261021102,"score_gpt":0.3373193236906229,"score_spread":0.2762016010804119,"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."}}