{"id":"W3091775538","doi":"10.1109/tce.2020.3029955","title":"Mobile Match on Card Active Authentication Using Touchscreen Biometric","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Touchscreen; Computer science; Authentication (law); Mobile device; Login; Smart card; Biometrics; Overhead (engineering); Embedded system; Computer hardware; Computer security; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0001328064,0.0002256868,0.0002423763,0.0003967436,0.0002594796,0.0001544898,0.0006176014,0.0001179818,0.00003890879],"category_scores_gemma":[0.000009046252,0.0002341082,0.0001523214,0.001844835,0.00004620241,0.0003090901,0.000004050479,0.0003892818,0.0003897758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002411692,"about_ca_system_score_gemma":0.0002184739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000363753,"about_ca_topic_score_gemma":0.00001116997,"domain_scores_codex":[0.9981034,0.0001410889,0.0003282382,0.000568157,0.0004462517,0.0004129077],"domain_scores_gemma":[0.9988167,0.0001229138,0.0001249468,0.0005939101,0.0001333003,0.0002082895],"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":[0.0006710491,0.003892989,0.0001189269,0.0003326839,0.002153946,0.0000326388,0.1384177,0.008290642,0.1685421,0.01924095,0.001717055,0.6565893],"study_design_scores_gemma":[0.001056717,0.001027287,0.00005291872,0.00003675121,0.0001403644,0.00002323844,0.0002608511,0.7817627,0.1957647,0.0003347908,0.01890494,0.0006347401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08397584,0.0002661789,0.9135443,0.0007599985,0.000376266,0.0005727657,0.00003067477,0.0003250065,0.0001489833],"genre_scores_gemma":[0.9980004,0.0001259974,0.001013067,0.0005888114,0.00002936081,0.00006991505,0.000004119525,0.00002498047,0.0001433015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9140246,"threshold_uncertainty_score":0.9546655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03578144127722082,"score_gpt":0.2735957283535536,"score_spread":0.2378142870763328,"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."}}