{"id":"W2748934360","doi":"10.1145/3139243.3139252","title":"Detecting Location Fraud in Indoor Mobile Crowdsensing","year":2017,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowdsensing; Computer science; Identification (biology); Mobile device; Location data; Computer security; Data mining; Mobile computing; Artificial intelligence; Computer network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005991793,0.0001337448,0.0001602982,0.0001356419,0.0006649204,0.0009700751,0.000720589,0.00008361522,0.000006765785],"category_scores_gemma":[0.0002900679,0.0001306959,0.00004042554,0.0001825395,0.00005907171,0.0007857308,0.0003125338,0.0002061025,0.00006046756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006317512,"about_ca_system_score_gemma":0.00006199368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005617039,"about_ca_topic_score_gemma":0.0002975451,"domain_scores_codex":[0.9987468,0.00004823443,0.0002513028,0.0004199791,0.000186704,0.0003469481],"domain_scores_gemma":[0.9983248,0.00008703885,0.0001686544,0.001252726,0.00009477876,0.00007196657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008002877,0.00008102424,0.03070457,0.00004372214,0.00001163695,0.0001015108,0.002849213,0.00333809,0.02125966,0.00536786,0.0001941372,0.9360406],"study_design_scores_gemma":[0.001360652,0.0001352718,0.1069452,0.0004181025,0.000007920604,0.0001479585,0.0007363143,0.7359837,0.1478923,0.00300582,0.002436823,0.000929913],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7188129,0.00005642906,0.2706191,0.0002328336,0.000411684,0.0001559812,7.684022e-8,0.0001951424,0.009515768],"genre_scores_gemma":[0.9792749,0.000002484299,0.02019319,0.0001354658,0.00008828277,0.000009477394,2.436917e-7,0.0000112412,0.0002847567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9351107,"threshold_uncertainty_score":0.9354457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529700479088996,"score_gpt":0.2651161529137469,"score_spread":0.2498191481228569,"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."}}