{"id":"W2995664421","doi":"10.1109/wcsp.2019.8927953","title":"Achieve Secure and Efficient Skyline Computation for Worker Selection in Mobile Crowdsensing","year":2019,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Skyline; Crowdsensing; Computer science; Selection (genetic algorithm); Scheme (mathematics); Probabilistic logic; Task (project management); Computation; Mobile device; Big data; Computer security; Data mining; Machine learning; Artificial intelligence; World Wide Web; Engineering; Algorithm","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":[0.001477363,0.0007209325,0.001281291,0.0005942754,0.001765677,0.001199456,0.001618219,0.001067623,0.001816669],"category_scores_gemma":[0.004472629,0.0003214718,0.00067915,0.001148582,0.000931514,0.002063237,0.003899685,0.0007900725,0.0007864427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009406238,"about_ca_system_score_gemma":0.00145013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413045,"about_ca_topic_score_gemma":0.001600098,"domain_scores_codex":[0.9976246,0.0005739902,0.0001207355,0.000596361,0.000686336,0.0003980034],"domain_scores_gemma":[0.9973283,0.000797387,0.00038915,0.00095594,0.0003493522,0.0001799156],"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":[0.004079053,0.000362224,0.008973606,0.0007727409,0.0002611471,0.001335899,0.002265135,0.3093753,0.1249212,0.1210459,0.01987536,0.4067326],"study_design_scores_gemma":[0.0001624037,0.0003012933,0.00136192,0.00002938473,0.00003711331,0.0004830724,0.0003437422,0.9153428,0.02315293,0.0503486,0.008374569,0.00006215671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04280111,0.0004827198,0.9517651,0.0004050878,0.00007875627,0.0001742985,0.0002488726,0.001163571,0.00288056],"genre_scores_gemma":[0.8896725,0.0002538395,0.1067648,0.0001950177,0.00008714051,0.0002520145,0.0002480845,0.00007070226,0.002455912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001816669,"threshold_uncertainty_score":0.007813096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005997924775083075,"score_gpt":0.236372869303097,"score_spread":0.2303749445280139,"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."}}