{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004019046,0.0001285469,0.0001676183,0.000150137,0.00009906902,0.0001824496,0.00009206182,0.00007571879,0.000003742876],"category_scores_gemma":[0.0000179067,0.0001196614,0.00003843732,0.0003792301,0.00001865917,0.0001447158,0.00006827593,0.0001191181,0.00001185282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005268163,"about_ca_system_score_gemma":0.00003125848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002538216,"about_ca_topic_score_gemma":0.00003510239,"domain_scores_codex":[0.9989141,0.00004631087,0.0002182092,0.0004224206,0.0001337584,0.0002651518],"domain_scores_gemma":[0.9994937,0.0001712383,0.00006561731,0.0001326401,0.00008470943,0.00005216473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007676391,0.000181274,0.005470596,0.000104101,0.00002035514,0.000004610733,0.003265591,0.7003053,0.0262034,0.007174745,0.000463894,0.2567294],"study_design_scores_gemma":[0.0006266629,0.0001399118,0.002886434,0.00005771031,0.000003083643,0.00002634156,0.0001396421,0.9928154,0.002147866,0.000453276,0.0005419332,0.0001617226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.561239,0.00006056042,0.4376471,0.0001134773,0.0001482579,0.0003589762,2.941879e-7,0.00008983003,0.0003425473],"genre_scores_gemma":[0.9515102,0.000002424501,0.04803924,0.0001374738,0.00004205764,0.00001235376,0.000002635089,0.00001129152,0.0002423301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3902712,"threshold_uncertainty_score":0.4879648,"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."}}