{"id":"W4405974465","doi":"10.1109/sec62691.2024.00046","title":"SecFePAS: Secure Facial-Expression-Based Pain Assessment with Deep Learning at the Edge","year":2024,"lang":"en","type":"article","venue":"","topic":"Trigeminal Neuralgia and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"HORIZON EUROPE Framework Programme; European Commission; McMaster University; University of Northern British Columbia","keywords":"Enhanced Data Rates for GSM Evolution; Facial expression; Computer science; Expression (computer science); Deep learning; Artificial intelligence; Face (sociological concept)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001997413,0.0001731111,0.0001668033,0.00005349928,0.0001932298,0.00004858269,0.00005502204,0.00005885465,0.001476172],"category_scores_gemma":[0.00002803727,0.00007671867,0.00008545334,0.0001796681,0.00005006117,0.000041186,0.00003089537,0.0002971294,0.0001471178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001326094,"about_ca_system_score_gemma":0.000109488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002351224,"about_ca_topic_score_gemma":0.0000359409,"domain_scores_codex":[0.9989282,0.000117996,0.0001259535,0.0002853007,0.0003225779,0.0002199484],"domain_scores_gemma":[0.9992548,0.0003704568,0.00002671011,0.000208914,0.00003172675,0.0001073596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00130529,0.0009835336,0.7050774,0.001344713,0.00112666,0.006356436,0.002218102,0.0009509077,0.03051516,0.0009440176,0.04105503,0.2081227],"study_design_scores_gemma":[0.009530673,0.007010417,0.1427032,0.00340587,0.00172026,0.0005019658,0.003257368,0.07956734,0.07237599,0.0001795677,0.6785892,0.001158142],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394636,0.001736215,0.01062894,0.00973656,0.0002990066,0.001175426,0.00001245177,0.0007536223,0.03619416],"genre_scores_gemma":[0.9754352,0.00001368086,0.000750777,0.0005999941,0.00007933332,0.0000659138,0.00007027958,0.00002974163,0.0229551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6375342,"threshold_uncertainty_score":0.9994366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009231158375715627,"score_gpt":0.2836242571908894,"score_spread":0.2743930988151738,"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."}}