{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006524601,0.001065047,0.0007011627,0.0007709908,0.0005280302,0.0009808294,0.001543612,0.0009050067,0.008322568],"category_scores_gemma":[0.002468304,0.0004414999,0.0005815175,0.0004100771,0.0005380095,0.00180735,0.00319057,0.001247842,0.003546193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002906321,"about_ca_system_score_gemma":0.0004485819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004223138,"about_ca_topic_score_gemma":0.0003242723,"domain_scores_codex":[0.9988066,0.0001999607,0.0001464446,0.0001600982,0.0005241883,0.0001628026],"domain_scores_gemma":[0.998206,0.0002566353,0.0002039582,0.0008249992,0.0003484227,0.0001599888],"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":[0.005508445,0.0004865311,0.01248192,0.001915002,0.0003932727,0.00501101,0.001478576,0.01075984,0.3417387,0.02459949,0.07613343,0.5194937],"study_design_scores_gemma":[0.00059743,0.003375113,0.01301408,0.000339385,0.0003872709,0.01015745,0.0003254779,0.330587,0.4728805,0.009707909,0.1581951,0.0004332095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08446833,0.001568657,0.8194084,0.0004795724,0.0004400995,0.0011214,0.001218285,0.07997998,0.01131531],"genre_scores_gemma":[0.8590826,0.0007233023,0.1208958,0.0007182354,0.000172187,0.0006413582,0.001828095,0.0008845144,0.01505382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008322568,"threshold_uncertainty_score":0.02784175,"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."}}