{"id":"W4293253993","doi":"10.1109/pst55820.2022.9851973","title":"Efficient and Privacy-preserving Worker Selection in Mobile Crowdsensing Over Tentative Future Trajectories","year":2022,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Crowdsensing; Computer science; Selection (genetic algorithm); Computer security; Internet privacy; Artificial intelligence","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":[0.0004765386,0.0001693096,0.0001842671,0.0002120746,0.0005673622,0.0002382822,0.000313521,0.00004277545,0.00007180581],"category_scores_gemma":[0.0000297614,0.0001633878,0.00004581994,0.0009931666,0.00003920119,0.0001721974,0.0007849087,0.0003612401,0.000001655817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001704394,"about_ca_system_score_gemma":0.00005751333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000180218,"about_ca_topic_score_gemma":0.0000735275,"domain_scores_codex":[0.9983217,0.0002053883,0.0002369836,0.000529395,0.0003452692,0.0003613122],"domain_scores_gemma":[0.9994094,0.000122883,0.00007886137,0.0002746703,0.00004383478,0.00007034389],"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":[0.0001759306,0.0007013135,0.07537533,0.0001057141,0.0001036226,0.0002259538,0.1041837,0.5604776,0.06412193,0.01139979,0.004919867,0.1782093],"study_design_scores_gemma":[0.00098651,0.0002232813,0.06890809,0.00004951509,0.00001062281,0.0002032015,0.0100679,0.9079296,0.004017773,0.0005714225,0.006456085,0.0005760097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747445,0.0004128065,0.02297994,0.0002471473,0.0004423419,0.0002665503,9.411562e-7,0.0001927071,0.0007130463],"genre_scores_gemma":[0.9900208,0.000005365279,0.009343541,0.0001741542,0.0001022366,0.00003856878,0.000001019065,0.00001467154,0.0002996943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.347452,"threshold_uncertainty_score":0.6662759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007550641055587889,"score_gpt":0.2351870034059932,"score_spread":0.2276363623504053,"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."}}