{"id":"W2917631242","doi":"10.1109/glocom.2018.8647174","title":"Anomalous Path Detection for Spatial Crowdsourcing-Based Indoor Navigation System","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Crowdsourcing; Computer science; Hidden Markov model; Trajectory; Real-time computing; Scheme (mathematics); Path (computing); Data mining; Computer security; Artificial intelligence; Computer network; World Wide Web","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":[0.00009555135,0.0001190201,0.0001112702,0.0001032524,0.0001347026,0.00004002084,0.0000878225,0.0001506766,0.00001696201],"category_scores_gemma":[0.00002982434,0.0001121204,0.00004787099,0.0001666043,0.00004034795,0.00007085569,0.00000866732,0.00005837057,0.00004473156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001211643,"about_ca_system_score_gemma":0.00001122455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004245773,"about_ca_topic_score_gemma":0.00004335851,"domain_scores_codex":[0.9993795,0.000008703041,0.000188551,0.0001371163,0.00009360015,0.0001925897],"domain_scores_gemma":[0.9996324,0.00002752606,0.00003094658,0.0001646279,0.0001176048,0.00002691784],"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":[0.0006749685,0.0001963909,0.013994,0.005413372,0.0003334839,0.00003647055,0.001841605,0.09405556,0.2934918,0.01509667,0.006827644,0.568038],"study_design_scores_gemma":[0.0003766194,0.0001192828,0.0002559059,0.00003169883,0.000009387536,0.000004153888,0.0001187375,0.5756332,0.4224212,0.00005448182,0.0008516248,0.000123694],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2008333,0.00001244667,0.7950416,0.000009403218,0.0005455741,0.0002756545,0.0000104759,0.002164205,0.001107371],"genre_scores_gemma":[0.9974873,2.709395e-7,0.00207354,0.00001859779,0.000243926,0.00007922732,0.00002676883,0.00003266197,0.00003768538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.796654,"threshold_uncertainty_score":0.4572136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006210961457537821,"score_gpt":0.2004092674552484,"score_spread":0.1941983059977106,"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."}}