{"id":"W2123215628","doi":"10.1145/1753846.1754139","title":"Input precision for gaze-based graphical passwords","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Password; Gaze; Computer science; Usability; Human–computer interaction; Eye tracking; Point (geometry); Cognitive password; Computer vision; Cued speech; Artificial intelligence; Computer security; Password strength; One-time password; Mathematics; Psychology","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":[0.0002721403,0.00009337464,0.0001085451,0.0001391284,0.00009551855,0.00006617981,0.00072635,0.0001464248,0.00002035365],"category_scores_gemma":[0.0001531793,0.00007194797,0.00007700961,0.0002778171,0.00007679548,0.00009375068,0.00008491871,0.0002425343,0.00002617572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005417964,"about_ca_system_score_gemma":0.00004178865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005680005,"about_ca_topic_score_gemma":0.00004265839,"domain_scores_codex":[0.9991679,0.00001446997,0.0001318825,0.0003257992,0.0001314118,0.0002285581],"domain_scores_gemma":[0.9990528,0.0002315553,0.00003654604,0.000528146,0.0000902497,0.00006072128],"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":[0.00001089426,0.0001474267,0.004874049,0.000006872186,0.000006366318,0.000003606715,0.00001287886,0.000007395066,0.01799869,0.7116594,0.005232777,0.2600396],"study_design_scores_gemma":[0.002846459,0.001003845,0.1088123,0.00003952386,0.00001741093,0.00002942749,0.000008409331,0.2712569,0.1675507,0.2586752,0.1888703,0.0008895461],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07672725,0.00000406994,0.9154477,0.005214453,0.0005063162,0.0001277024,0.000001304486,0.0005964793,0.001374707],"genre_scores_gemma":[0.7672822,3.445377e-7,0.232172,0.0003258554,0.0000368904,0.00002943237,0.000001251827,0.000004711466,0.0001472048],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.690555,"threshold_uncertainty_score":0.2933953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204751343378055,"score_gpt":0.2632084626400981,"score_spread":0.2511609492063175,"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."}}