{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002571976,0.0008256551,0.000864119,0.0005815089,0.0008247693,0.001613553,0.001219643,0.001853231,0.0009065238],"category_scores_gemma":[0.01108225,0.0003608258,0.0006479989,0.000356529,0.002112467,0.002079962,0.002014563,0.001554175,0.0002276915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553984,"about_ca_system_score_gemma":0.001043193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003525818,"about_ca_topic_score_gemma":0.001630935,"domain_scores_codex":[0.9977063,0.0007400437,0.00008233434,0.000465231,0.0006064412,0.0003996678],"domain_scores_gemma":[0.9925674,0.004395079,0.001439742,0.000552659,0.0007555602,0.0002895228],"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":[0.0003205056,0.00008667111,0.005861438,0.0001037689,0.00007178103,0.0005875556,0.0002044566,0.9559745,0.004468861,0.0158607,0.0008788896,0.0155809],"study_design_scores_gemma":[0.000007862262,0.00009656412,0.0007441129,0.000009273395,0.00001099302,0.0001035609,0.00006140916,0.990468,0.001731508,0.006343535,0.0004085136,0.00001486156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3486256,0.0007884701,0.6397514,0.00165942,0.0002161786,0.0002685262,0.0001639565,0.0007755781,0.007750857],"genre_scores_gemma":[0.9938887,0.00009744484,0.005062744,0.00009597409,0.00002119924,0.0000262188,0.000020929,0.000009976124,0.0007767528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003525818,"threshold_uncertainty_score":0.01360202,"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."}}