{"id":"W3217202204","doi":"10.1145/3489849.3489883","title":"BreachMob: Detecting Vulnerabilities in Physical Environments Using Virtual Reality","year":2021,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Virtual reality; Computer science; Physical security; Computer security; Realization (probability); Human–computer interaction; Property (philosophy); Secure coding; Immersion (mathematics); Virtual machine; Information security; Software security assurance; Security service","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":[0.001358098,0.0007730224,0.0005000095,0.0010639,0.000782947,0.002391328,0.001180244,0.001479132,0.003738191],"category_scores_gemma":[0.005466633,0.0006741199,0.0006519579,0.0003497541,0.001504388,0.002129321,0.00444627,0.00120873,0.001025949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003441946,"about_ca_system_score_gemma":0.0004696368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001720657,"about_ca_topic_score_gemma":0.002269974,"domain_scores_codex":[0.9986388,0.0005801366,0.00003373736,0.0001388364,0.0004700363,0.000138541],"domain_scores_gemma":[0.9984074,0.0006941663,0.0001485857,0.0004225533,0.0001877032,0.0001396866],"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":[0.001392583,0.0006859127,0.01677764,0.001566941,0.0003181166,0.003028465,0.04380283,0.03350732,0.2151466,0.06526446,0.02763719,0.5908719],"study_design_scores_gemma":[0.0004555306,0.002863982,0.0545622,0.001581841,0.0003974289,0.01342758,0.02598972,0.361495,0.1549587,0.07809293,0.3051592,0.001015942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1894242,0.0007390051,0.777601,0.001079922,0.0001231412,0.0004821449,0.0003793551,0.01019273,0.01997846],"genre_scores_gemma":[0.6814708,0.0004372609,0.3112223,0.0002742516,0.00001636632,0.0002860746,0.0003144781,0.0006599647,0.005318549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003738191,"threshold_uncertainty_score":0.01250553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452361091649149,"score_gpt":0.266263130467097,"score_spread":0.2417395195506055,"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."}}