{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004358796,0.0006045921,0.0006821537,0.0008821725,0.0006204055,0.0004415125,0.001077497,0.0005167193,0.0005237859],"category_scores_gemma":[0.001819967,0.0001776104,0.0003091876,0.0005880099,0.0003982153,0.0006439305,0.001064608,0.0004698611,0.000414251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006965595,"about_ca_system_score_gemma":0.0009353346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007099264,"about_ca_topic_score_gemma":0.006548984,"domain_scores_codex":[0.9992558,0.00009583609,0.00004579151,0.0001583603,0.0003183299,0.0001258939],"domain_scores_gemma":[0.9989723,0.0001528854,0.0002014952,0.0002362609,0.0003417607,0.00009530639],"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":[0.001462448,0.0002852905,0.06045043,0.0003294335,0.0002581281,0.002123686,0.001020058,0.2462654,0.1096666,0.007093198,0.009457606,0.5615878],"study_design_scores_gemma":[0.00002292574,0.0001286506,0.005186862,0.000009701818,0.00003443749,0.0004208472,0.0001217292,0.9717177,0.01796629,0.001760961,0.002586217,0.0000435854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3091724,0.000440135,0.6778234,0.0003438546,0.0001759933,0.000210523,0.0004643836,0.007523563,0.003845798],"genre_scores_gemma":[0.9672954,0.00006086939,0.03147418,0.00004918187,0.00001592062,0.00003818984,0.0001833165,0.00001902045,0.0008638614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007099264,"threshold_uncertainty_score":0.01411587,"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."}}