{"id":"W2970063543","doi":"10.1109/iccc.2019.00019","title":"Using EEG to Predict and Analyze Password Memorability","year":2019,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Password; Computer science; Electroencephalography; Artificial intelligence; Speech recognition; Computer security; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003677956,0.0004348937,0.0002546216,0.0006410381,0.00008757137,0.000471477,0.000130343,0.0004192469,0.001160652],"category_scores_gemma":[0.003879653,0.0001165503,0.0002137762,0.000310599,0.0001333567,0.0003842366,0.0002025626,0.0003082458,0.000348507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007641496,"about_ca_system_score_gemma":0.000103054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008041388,"about_ca_topic_score_gemma":0.001208387,"domain_scores_codex":[0.9998212,0.00004398493,0.00001784264,0.00004452166,0.000051192,0.00002115514],"domain_scores_gemma":[0.9987452,0.0006846158,0.0002654258,0.00006855841,0.0001711504,0.00006499825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002698279,0.001223446,0.5967051,0.0003128336,0.0003386966,0.0003622014,0.0004084775,0.006330369,0.1301477,0.0001523712,0.001080797,0.2602397],"study_design_scores_gemma":[0.00006713489,0.002762105,0.9111462,0.00003640056,0.0001400882,0.0008257571,0.0003197749,0.06168869,0.02211643,0.0003246012,0.0005323715,0.00004047179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99273,0.000148411,0.006066465,0.00003990487,0.00001534585,0.00004234201,0.0003331,0.00008285452,0.0005415677],"genre_scores_gemma":[0.9956042,0.0001719264,0.003495774,0.00001523286,0.00001492524,0.000025111,0.000293306,0.000005673989,0.0003739543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001160652,"threshold_uncertainty_score":0.003882766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02696228571735825,"score_gpt":0.2746880555572216,"score_spread":0.2477257698398634,"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."}}