{"id":"W2789249458","doi":"10.1109/infocom.2018.8485939","title":"SecTap: Secure Back of Device Input System for Mobile Devices","year":2018,"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":"University of Victoria","funders":"","keywords":"Computer science; Mobile device; Usability; Android (operating system); Cursor (databases); Obfuscation; Adversary; Side channel attack; Accelerometer; Computer security; Embedded system; Human–computer interaction; Cryptography; Artificial intelligence; Operating system","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":[0.0003093126,0.0001023788,0.0001899193,0.000074266,0.00007579378,0.00008808757,0.0007869373,0.00006467807,0.00005032374],"category_scores_gemma":[0.00001108911,0.00008360554,0.00007100496,0.0003013258,0.00004033601,0.0002680363,0.0001122332,0.00003283808,0.0003297817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002385533,"about_ca_system_score_gemma":0.00005366357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004931305,"about_ca_topic_score_gemma":0.0001459669,"domain_scores_codex":[0.9989607,0.00005175359,0.0003282258,0.0002816574,0.0001920213,0.000185672],"domain_scores_gemma":[0.9987679,0.00009196319,0.0001440904,0.0005425289,0.0003794852,0.00007401853],"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":[0.00005884342,0.0006016452,0.01028291,0.006648008,0.0003594728,0.000003322366,0.230748,0.00001180522,0.01344121,0.698673,0.02676824,0.01240364],"study_design_scores_gemma":[0.001442093,0.0008548821,0.001662495,0.0003779434,0.00003577888,0.00004613314,0.00314706,0.6431001,0.08908248,0.0007556742,0.2588491,0.0006462472],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3040041,0.0002444678,0.674714,0.0004093104,0.001453532,0.001468491,0.00001879553,0.0004100337,0.01727724],"genre_scores_gemma":[0.9920616,7.574724e-7,0.006744904,0.0001642532,0.0001259718,0.00005133861,0.000002635623,0.000007486237,0.000841091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6979172,"threshold_uncertainty_score":0.4238787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104003343265782,"score_gpt":0.270868218954617,"score_spread":0.2498281855219592,"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."}}