{"id":"W3036219156","doi":"10.1109/access.2020.3003869","title":"Cancelable Biometrics Using Deep Learning as a Cloud Service","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Cloud computing; Computer science; Scalability; Popularity; Software deployment; Computer security; Authentication (law); Service (business); Deep learning; Artificial intelligence; Distributed computing; Database; Software engineering; 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":[0.0002392161,0.0001117578,0.000145643,0.0004621022,0.0002233254,0.0008686804,0.001757353,0.00007563551,0.00007038146],"category_scores_gemma":[0.0001848643,0.0001167032,0.00004181664,0.01359401,0.00001867509,0.001000733,0.0003310356,0.0002073909,0.0002817558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006713295,"about_ca_system_score_gemma":0.0001051503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001237861,"about_ca_topic_score_gemma":0.00002768873,"domain_scores_codex":[0.9986455,0.00007180466,0.0002189885,0.0004165954,0.0003837276,0.0002633552],"domain_scores_gemma":[0.9990342,0.0000752519,0.0001443783,0.000305829,0.0002455415,0.0001948002],"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":[0.0001402683,0.001124053,0.05563593,0.001861242,0.0005121622,0.0005090906,0.04004065,0.04153337,0.09625695,0.04125229,0.03751458,0.6836194],"study_design_scores_gemma":[0.0004111813,0.00004196098,0.001081623,0.00001268163,0.00001423329,0.00001084736,0.00008432398,0.8970746,0.01772288,0.0007231838,0.08244721,0.0003752095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1858058,0.0004600667,0.8081633,0.002808731,0.001346369,0.0001267032,0.000001919847,0.0002720581,0.00101498],"genre_scores_gemma":[0.9873372,0.00005212636,0.006187831,0.006072246,0.0002345887,0.000004571595,0.000002965517,0.00001122466,0.00009726642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8555413,"threshold_uncertainty_score":0.8376705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1061599104239778,"score_gpt":0.3352558814257913,"score_spread":0.2290959710018134,"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."}}