{"id":"W4230773722","doi":"10.32920/ryerson.14644191","title":"Inverse biometrics for keystroke dynamics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Biometrics; Keystroke dynamics; Computer science; Keystroke logging; Process (computing); Interface (matter); Task (project management); Data mining; Human–computer interaction; Artificial intelligence; Computer security; Engineering; Password","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003448857,0.0001787149,0.0002763894,0.0004656732,0.000062411,0.0007717201,0.001432952,0.0002471939,0.0000199258],"category_scores_gemma":[0.0001261243,0.0001777523,0.0002276743,0.0006644086,0.00002099946,0.0001428654,0.001358582,0.0001969948,0.00003474345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001461003,"about_ca_system_score_gemma":0.0002404367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008813423,"about_ca_topic_score_gemma":0.0001966261,"domain_scores_codex":[0.9984842,0.00005051632,0.0003430915,0.000606008,0.0002936481,0.0002225188],"domain_scores_gemma":[0.998077,0.0001084568,0.0001609405,0.001195947,0.0003393496,0.0001183126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008272343,0.0009644744,0.002898299,0.003201277,0.0006374794,0.0000398669,0.05677037,0.000125869,0.0002196234,0.8408961,0.03878425,0.05545411],"study_design_scores_gemma":[0.0001605057,0.00001417041,0.0000996853,0.00003146232,0.00001134581,0.000003825174,0.0001915577,0.9885377,0.0001386617,0.003069644,0.00750979,0.0002316167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006830037,0.0001020705,0.9858899,0.00150954,0.002766048,0.0004734114,0.00003778785,0.0002558216,0.002135352],"genre_scores_gemma":[0.799031,0.00006217014,0.1898049,0.0008841201,0.0001630448,0.0001790618,0.0003754721,0.00002772231,0.009472517],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9884118,"threshold_uncertainty_score":0.7441715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810973976384259,"score_gpt":0.277893626862153,"score_spread":0.2397838870983104,"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."}}