{"id":"W4313016332","doi":"10.1109/dasc/picom/cbdcom/cy55231.2022.9927942","title":"Enhancing Biometric Security with Combinatorial and Permutational Multi-Fingerprint Authentication Strategies","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Chicoutimi; Concordia University of Edmonton","funders":"","keywords":"Biometrics; Computer science; Fingerprint (computing); Authentication (law); Fingerprint recognition; Computer security; Scheme (mathematics); Data mining; Mathematics","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":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","sts"],"category_scores_codex":[0.005276357,0.00174333,0.001894985,0.004946306,0.004644323,0.00340177,0.004859765,0.0007892735,0.0001071701],"category_scores_gemma":[0.001092269,0.001675987,0.0001817829,0.005208429,0.005017526,0.0009983609,0.005671865,0.003597417,0.00003801429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005574294,"about_ca_system_score_gemma":0.001519214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007147678,"about_ca_topic_score_gemma":0.0001860724,"domain_scores_codex":[0.987515,0.0007473993,0.002307146,0.005355398,0.002133521,0.001941584],"domain_scores_gemma":[0.9905341,0.002163926,0.002163017,0.002659759,0.001606585,0.0008725748],"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":[0.0005656314,0.001710923,0.004258261,0.0003467553,0.0006481916,0.0003100515,0.01038419,0.0007059231,0.003030975,0.6615975,0.001379466,0.3150621],"study_design_scores_gemma":[0.006909041,0.007729919,0.007738097,0.001534603,0.0003507872,0.00172537,0.01420351,0.8813706,0.01839951,0.01395726,0.04070525,0.005376057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9161317,0.001426074,0.06948834,0.002887618,0.006335801,0.00198234,0.0003488348,0.000817016,0.0005822416],"genre_scores_gemma":[0.9942641,0.0005394472,0.002149719,0.002208433,0.0003686713,0.00004778942,0.0001826148,0.00009365474,0.0001455984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8806646,"threshold_uncertainty_score":0.9995313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03987941056858585,"score_gpt":0.2833913463491,"score_spread":0.2435119357805141,"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."}}