{"id":"W3184298967","doi":"10.13052/2245-1439.611","title":"Biometric Authentication Using Mouse and Eye Movement Data","year":2017,"lang":"en","type":"article","venue":"Journal of Cyber Security and Mobility","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Computer science; Biometrics; Salient; Artificial neural network; Eye movement; Artificial intelligence; Classifier (UML); Modalities; Authentication (law); Data set; Machine learning; Computer vision; Pattern recognition (psychology); Computer security","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":[0.001962542,0.0001012845,0.0002235826,0.0001738434,0.0003380286,0.0006142447,0.001168591,0.0000607112,0.000004662968],"category_scores_gemma":[0.0002978214,0.00008553193,0.00003948766,0.0001175192,0.0001401872,0.001657341,0.0007379726,0.0001488465,0.000001277896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003185411,"about_ca_system_score_gemma":0.00006223689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001478832,"about_ca_topic_score_gemma":0.00003028339,"domain_scores_codex":[0.9987077,0.0001033874,0.0004438776,0.0002820118,0.0003225081,0.0001405721],"domain_scores_gemma":[0.9975958,0.00005483809,0.0005988091,0.001383076,0.0002013378,0.0001661622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000265291,0.007937524,0.4124537,0.001873474,0.001221415,0.0001049007,0.2983833,0.00000619412,0.05453346,0.0886277,0.001951232,0.1326419],"study_design_scores_gemma":[0.002962766,0.000333027,0.5064755,0.0001887097,0.0001892139,0.0001579534,0.0008974752,0.4249741,0.004775853,0.04842227,0.009925011,0.0006980765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859664,0.0006366196,0.01178624,0.001088434,0.000298746,0.0001426978,0.00001378104,0.00001113088,0.00005590127],"genre_scores_gemma":[0.9981387,0.0001352973,0.00154116,0.00008370589,0.00006307997,7.51554e-7,0.000001398481,0.000003525593,0.00003232841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4249679,"threshold_uncertainty_score":0.5923175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06238039796970234,"score_gpt":0.3341156197576858,"score_spread":0.2717352217879834,"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."}}