{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002104223,0.0009549595,0.001486259,0.002661025,0.0009694065,0.001542352,0.00199966,0.00207407,0.0003331161],"category_scores_gemma":[0.008122557,0.0003264744,0.0006158903,0.002297622,0.001089989,0.001491592,0.002367736,0.0008558313,0.000527197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123301,"about_ca_system_score_gemma":0.0008128881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003592481,"about_ca_topic_score_gemma":0.002658805,"domain_scores_codex":[0.9964311,0.00102449,0.0002297554,0.0007126344,0.001225144,0.0003769124],"domain_scores_gemma":[0.9943759,0.001805089,0.001543007,0.001249717,0.0007438118,0.0002823874],"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":[0.002950628,0.0009067685,0.1902975,0.0008837912,0.0005158428,0.00679769,0.001538437,0.2732006,0.04492368,0.0127225,0.01362173,0.4516408],"study_design_scores_gemma":[0.00006018186,0.0001862356,0.01851705,0.00007204474,0.00005817299,0.001813591,0.0007360073,0.9461321,0.01708347,0.009096876,0.006172831,0.00007139545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7747787,0.002454962,0.2112082,0.00142199,0.0003299261,0.0003906842,0.001461965,0.001888189,0.006065408],"genre_scores_gemma":[0.9768648,0.0002782759,0.02139308,0.0001249564,0.00005800169,0.00004396305,0.0004884181,0.00001571046,0.0007329014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003592481,"threshold_uncertainty_score":0.01112831,"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."}}