{"id":"W3175052831","doi":"10.1145/3468218.3469050","title":"Poisoning Attack Anticipation in Mobile Crowdsensing","year":2021,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Crowdsensing; Computer science; Anticipation (artificial intelligence); Robustness (evolution); Adversary; Server; Machine learning; Artificial intelligence; Computer security; Adversarial system; Mobile device; Mobile computing; Trilateration; Computer network; Engineering; World Wide Web","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":[0.0003064099,0.0001151477,0.000164462,0.0001110509,0.0001137364,0.000303177,0.0001830798,0.00006108636,0.00003059005],"category_scores_gemma":[0.00008995241,0.0001194221,0.0000522432,0.0006921953,0.00002100867,0.0003890629,0.0001648198,0.0001458827,0.00007741662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005388802,"about_ca_system_score_gemma":0.00009053093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004537448,"about_ca_topic_score_gemma":0.0001134394,"domain_scores_codex":[0.9987009,0.00009420948,0.0002621917,0.0004175701,0.0001907383,0.0003344151],"domain_scores_gemma":[0.9991705,0.0001093747,0.00005091744,0.0004960292,0.0001087218,0.00006440531],"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":[0.00001270759,0.0005122783,0.0426297,0.0001228223,0.00004560631,0.00211422,0.01178412,0.07218583,0.1884511,0.03104753,0.003694446,0.6473996],"study_design_scores_gemma":[0.001036333,0.00008048095,0.02420372,0.0003325118,0.000009351432,0.000402782,0.001264357,0.7893125,0.1692492,0.0007760001,0.01252523,0.0008075128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7881014,0.000244631,0.2013084,0.0003230129,0.0002986817,0.00007186538,1.870152e-7,0.0001923762,0.009459427],"genre_scores_gemma":[0.9571506,0.00001082163,0.04176016,0.0003637909,0.00004636766,0.000004439867,0.000002661338,0.00000952707,0.0006516536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7171267,"threshold_uncertainty_score":0.4869892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276464526289314,"score_gpt":0.2817655007386218,"score_spread":0.2590008554757287,"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."}}