{"id":"W2289706180","doi":"10.1109/icitst.2015.7412065","title":"Fingerprint security for protecting EMV payment cards","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer security; Payment; Identity theft; Computer science; Fingerprint (computing); Biometrics; Authentication (law); Eavesdropping; Counterfeit; Internet privacy; Credit card; Payment card; Issuing bank; Countermeasure; Phishing; The Internet; 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":[0.001209282,0.0000693668,0.00008819142,0.0001241392,0.0001084698,0.0001918185,0.0003934064,0.00004378022,0.000008747919],"category_scores_gemma":[0.0002983113,0.00005927325,0.00005326143,0.000523893,0.00001407489,0.000183199,0.0001739236,0.00008407775,0.00003933393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008145207,"about_ca_system_score_gemma":0.00009555225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001196667,"about_ca_topic_score_gemma":0.00001336354,"domain_scores_codex":[0.999087,0.00004620168,0.0001653617,0.0002732045,0.0002493298,0.0001789068],"domain_scores_gemma":[0.9991719,0.00004983714,0.00005630353,0.0003734795,0.0002213689,0.0001271248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002435951,0.000550149,0.001062324,0.0001353314,0.00006538887,0.000005656365,0.01919242,0.00003447847,0.0008268123,0.6033729,0.1014688,0.2732613],"study_design_scores_gemma":[0.001587432,0.0002571206,0.001737285,0.00001627037,0.00001011639,0.00001945022,0.0008609185,0.2650037,0.03712006,0.07863083,0.6141201,0.0006367406],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01051414,0.000069398,0.9820538,0.00213405,0.0005934873,0.0004871854,0.000002358036,0.0002193459,0.003926209],"genre_scores_gemma":[0.9412026,0.000001194781,0.05770158,0.0002499068,0.00006032809,0.00009292924,0.000001980923,0.000003619107,0.0006859013],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9306884,"threshold_uncertainty_score":0.2417093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06382096003522034,"score_gpt":0.3001066567977884,"score_spread":0.236285696762568,"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."}}