{"id":"W3129650768","doi":"10.1109/jiot.2021.3059637","title":"Continuous Probabilistic Skyline Query for Secure Worker Selection in Mobile Crowdsensing","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Computer science; Outsourcing; Probabilistic logic; Encryption; Skyline; Security analysis; Computer security; Scheme (mathematics); Reliability (semiconductor); Cloud computing; Process (computing); Selection (genetic algorithm); Data mining; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002418372,0.0007923439,0.001698686,0.0006305925,0.001469252,0.001211908,0.001979812,0.001288741,0.001858741],"category_scores_gemma":[0.006107972,0.0003694318,0.0008084148,0.001155697,0.001010719,0.003058548,0.003657576,0.0009810349,0.0006411297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001020408,"about_ca_system_score_gemma":0.001484982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002447581,"about_ca_topic_score_gemma":0.002121854,"domain_scores_codex":[0.9965581,0.0009840149,0.000245553,0.0007769221,0.000964986,0.0004702962],"domain_scores_gemma":[0.9965653,0.001407443,0.000442125,0.0009244186,0.0004132367,0.000247501],"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":[0.005808508,0.000495801,0.01312818,0.001099264,0.0003209481,0.002599911,0.004260535,0.3629425,0.08989655,0.0832812,0.01850781,0.4176588],"study_design_scores_gemma":[0.0001641362,0.0003272118,0.001625551,0.00002666689,0.000045625,0.0006352619,0.0005065805,0.9579811,0.008436755,0.02389719,0.006285702,0.00006821979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0701474,0.001151473,0.9235162,0.000616201,0.0001290574,0.0002825056,0.0003689188,0.001190904,0.002597296],"genre_scores_gemma":[0.9363239,0.0003424195,0.06034324,0.0002056733,0.0001195282,0.00022768,0.0002832481,0.00004926754,0.002104983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002447581,"threshold_uncertainty_score":0.01278973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087284501309056,"score_gpt":0.2486157159356649,"score_spread":0.2377428709225744,"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."}}