{"id":"W2796390756","doi":"10.1007/978-3-319-89656-4_13","title":"Mobile App for Detection of Counterfeit Banknotes","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Counterfeit; Computer science; Banknote; Android (operating system); Mobile phone; Authentication (law); Mobile apps; Embedded system; Artificial intelligence; Computer security; Telecommunications; Operating system; World Wide Web","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":[0.0002158212,0.0008283324,0.0005642626,0.001185167,0.0002398025,0.0006504696,0.0005982359,0.0009203241,0.01930255],"category_scores_gemma":[0.0008026814,0.0002255776,0.0002373459,0.0003305411,0.00008349388,0.0006662221,0.0005969879,0.0003855636,0.01406214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000795784,"about_ca_system_score_gemma":0.0001209162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003312575,"about_ca_topic_score_gemma":0.0007452292,"domain_scores_codex":[0.9997782,0.00002681697,0.00001174181,0.00005035577,0.00009563933,0.00003722355],"domain_scores_gemma":[0.999532,0.000170735,0.00006357999,0.00003551055,0.0001494089,0.00004876274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002647179,0.0005992368,0.02344138,0.0009497861,0.0001236956,0.002188308,0.0004030148,0.0003237489,0.08339823,0.001353062,0.1349162,0.7496561],"study_design_scores_gemma":[0.0004229187,0.004840852,0.2043272,0.001534443,0.001075528,0.02965787,0.001904315,0.1072184,0.3035291,0.005074229,0.3399718,0.0004434497],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6450633,0.01820423,0.139259,0.001758536,0.002927866,0.001378,0.02173532,0.06619573,0.103478],"genre_scores_gemma":[0.811268,0.0032302,0.0639247,0.001326474,0.0008767552,0.0004649383,0.006222212,0.0004470569,0.1122397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01930255,"threshold_uncertainty_score":0.06457347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01970480984471162,"score_gpt":0.2578125667119416,"score_spread":0.23810775686723,"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."}}