{"id":"W4411196631","doi":"10.1016/j.jnca.2025.104236","title":"Poisoning behavioral-based worker selection in mobile crowdsensing using generative adversarial networks","year":2025,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Crowdsensing; Adversarial system; Selection (genetic algorithm); Generative grammar; Generative adversarial network; Pedestrian; Artificial intelligence; Machine learning; Computer security; Human–computer interaction; Deep learning; Transport engineering","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":[0.002218002,0.001108044,0.002012152,0.0006903487,0.0008648509,0.001101126,0.002629549,0.00207285,0.001733594],"category_scores_gemma":[0.006582026,0.0008340676,0.0008893221,0.0005478531,0.001808961,0.001495982,0.003579297,0.001637703,0.0005198281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091512,"about_ca_system_score_gemma":0.00110926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00416345,"about_ca_topic_score_gemma":0.003644206,"domain_scores_codex":[0.9987437,0.000363034,0.00004224826,0.0003639448,0.000289606,0.0001975019],"domain_scores_gemma":[0.9963959,0.002485312,0.0002943589,0.0003435934,0.0002897127,0.0001912345],"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":[0.0002583609,0.000090813,0.001597805,0.00007170738,0.00005945204,0.000215998,0.000119093,0.9562594,0.003804815,0.008646921,0.00100498,0.02787058],"study_design_scores_gemma":[0.000005047258,0.00001532078,0.0000969902,0.000003025917,0.000003665661,0.00001783051,0.000007694286,0.9969923,0.0003791239,0.002392174,0.00008226091,0.000004545022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05236671,0.0002949756,0.9435956,0.0004526913,0.0001237054,0.000105349,0.00007402398,0.0005783956,0.002408561],"genre_scores_gemma":[0.9653415,0.00009397562,0.03029646,0.0001973566,0.00006536574,0.00008193667,0.00009507957,0.00005559827,0.003772696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00416345,"threshold_uncertainty_score":0.01173007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01190624434839746,"score_gpt":0.2936087360083873,"score_spread":0.2817024916599899,"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."}}