{"id":"W2395661343","doi":"10.4018/ijssoe.2016010102","title":"Enhancing CAPTCHA Security Using Interactivity, Dynamism, and Mouse Movement Patterns","year":2016,"lang":"en","type":"article","venue":"International Journal of Systems and Service-Oriented Engineering","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"CAPTCHA; Computer science; Usability; Interactivity; Human–computer interaction; Dynamism; Matching (statistics); Task (project management); Benchmark (surveying); Artificial intelligence; World Wide Web; Engineering","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":[0.0004013324,0.0001508397,0.0002260976,0.000267757,0.00003949653,0.0002277651,0.0003806695,0.00005159773,0.000003303137],"category_scores_gemma":[0.00003307931,0.0001140801,0.0000472667,0.00009551275,0.000008216023,0.0009155816,0.0002116673,0.0001180218,0.000001380036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001428509,"about_ca_system_score_gemma":0.00002786232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002532794,"about_ca_topic_score_gemma":0.00004940041,"domain_scores_codex":[0.998597,0.00004001358,0.0005501106,0.0001917302,0.0004547031,0.000166445],"domain_scores_gemma":[0.9988157,0.00008599657,0.0003161897,0.0001576139,0.0004725316,0.0001520178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001014062,0.0004967599,0.04381999,0.001345461,0.001929737,0.0004088367,0.1060475,0.002412217,0.767585,0.0695299,0.00004977713,0.006273382],"study_design_scores_gemma":[0.001509503,0.00008275374,0.003371967,0.002054232,0.00002261184,0.0008581622,0.001021143,0.981918,0.007064972,0.0001868233,0.001549686,0.000360106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6482111,0.0001358703,0.3496042,0.0005742286,0.00137505,0.00006254439,0.000009222342,0.00002278976,0.00000491461],"genre_scores_gemma":[0.999059,0.00007156947,0.0005270378,0.0001037736,0.0001983004,0.000002817523,7.56425e-7,0.00001179528,0.00002494939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9795058,"threshold_uncertainty_score":0.4652053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00821322354173082,"score_gpt":0.2279165986601343,"score_spread":0.2197033751184035,"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."}}