{"id":"W4294975018","doi":"10.1109/embc48229.2022.9871958","title":"Affective State Recognition with Convolutional Autoencoders","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Overfitting; Convolutional neural network; Feature extraction; Pattern recognition (psychology); Deep learning; Machine learning; Activity recognition; Classifier (UML); Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006650194,0.0001958184,0.000265253,0.0002174316,0.000116017,0.000005592573,0.0004120918,0.00009932882,0.00349725],"category_scores_gemma":[0.0001204365,0.0001541921,0.000123477,0.0004431063,0.0003249869,0.00008344676,0.0001005823,0.0007409653,0.00002020752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001812005,"about_ca_system_score_gemma":0.00009758498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004384065,"about_ca_topic_score_gemma":0.0001751776,"domain_scores_codex":[0.9984697,0.0002234692,0.0003663551,0.0003510227,0.0003142455,0.0002751881],"domain_scores_gemma":[0.9989724,0.0002274289,0.000232386,0.000189467,0.0003198029,0.00005849555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.003875174,0.003544653,0.08438218,0.0004116198,0.007345393,0.000043192,0.1440733,0.1289658,0.07909743,0.04014395,0.4860187,0.02209857],"study_design_scores_gemma":[0.05065035,0.01000998,0.3731478,0.002318786,0.0008700457,0.001619141,0.08965606,0.1293107,0.002652791,0.03650614,0.297835,0.005423187],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443645,0.0001937483,0.03438101,0.00518622,0.007968435,0.0006819845,0.0009737555,0.0001340826,0.006116278],"genre_scores_gemma":[0.9945683,0.00008846138,0.001014949,0.0006826273,0.000216959,0.0002056973,0.0005627029,0.00002265847,0.002637574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2887656,"threshold_uncertainty_score":0.9974137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04866092246013864,"score_gpt":0.314470922844961,"score_spread":0.2658100003848223,"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."}}