{"id":"W4396523200","doi":"10.1109/tvt.2024.3394909","title":"Achieving Efficient and Privacy-Preserving Worker Selection With Arbitrary Spatial Ranges for Vehicular Crowdsensing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Computer science; Bloom filter; Upload; Scheme (mathematics); Crowdsensing; Computation; Selection (genetic algorithm); Cryptography; Location awareness; Similarity (geometry); Overhead (engineering); Mobile device; Information privacy; Data mining; Distributed computing; Computer network; Computer security; Artificial intelligence; Algorithm; Image (mathematics)","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":[0.001409449,0.0006495342,0.001013818,0.0005887653,0.001629337,0.001143111,0.001730473,0.001066891,0.001224344],"category_scores_gemma":[0.004493945,0.0003074756,0.0006291791,0.001015382,0.0009821778,0.002264699,0.004291693,0.000866198,0.000592305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026248,"about_ca_system_score_gemma":0.001852697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001814091,"about_ca_topic_score_gemma":0.002059603,"domain_scores_codex":[0.9973774,0.0005572762,0.0001767945,0.0004821499,0.0009780134,0.0004283254],"domain_scores_gemma":[0.9971949,0.0007526855,0.0003639404,0.001153827,0.0003563328,0.0001782317],"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":[0.002710313,0.0003510382,0.008856903,0.0004857363,0.0001931072,0.00190366,0.002911843,0.2418157,0.1677171,0.1319686,0.01043647,0.4306496],"study_design_scores_gemma":[0.0001480183,0.0003629922,0.001247287,0.0000452344,0.0000576354,0.001018188,0.0006617454,0.8666007,0.05873173,0.05764274,0.0133703,0.0001134195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05043495,0.0004048987,0.9442119,0.0003651103,0.0000680495,0.0002065389,0.0001612703,0.0009642152,0.003183068],"genre_scores_gemma":[0.9192853,0.0002165746,0.07708213,0.0001549585,0.00004625763,0.00016774,0.0001570227,0.00003450766,0.002855543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001814091,"threshold_uncertainty_score":0.007453978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007842734417457916,"score_gpt":0.2225498839795691,"score_spread":0.2147071495621112,"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."}}