{"id":"W4414405478","doi":"10.1109/iccworkshops67674.2025.11162172","title":"Efficient Adversarial Detection Frameworks for Vehicle-to-Microgrid Services in Edge Computing","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Science and Engineering Research Council","keywords":"Inference; Benchmark (surveying); Process (computing); Adversarial system; Evasion (ethics); Enhanced Data Rates for GSM Evolution; Edge computing; Edge device; Key (lock)","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":[0.001128047,0.0008550916,0.000728839,0.0004841501,0.0004379042,0.0008660821,0.001635555,0.0008705516,0.002232976],"category_scores_gemma":[0.002952303,0.0003711329,0.0006569235,0.0002986424,0.0008154033,0.001481385,0.001565621,0.001569418,0.0004479611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140701,"about_ca_system_score_gemma":0.001255641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00603565,"about_ca_topic_score_gemma":0.005405562,"domain_scores_codex":[0.9994405,0.0001719709,0.00001919165,0.0001027729,0.000178172,0.0000874094],"domain_scores_gemma":[0.9990042,0.0005257063,0.00009853822,0.0001449485,0.0001711204,0.00005557061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004525323,0.00002211188,0.0003725071,0.00001700349,0.00001724276,0.00004157072,0.00002973955,0.9638449,0.001496888,0.008776642,0.0008150029,0.02452114],"study_design_scores_gemma":[0.000001134314,0.000003574802,0.00001814707,9.469618e-7,0.000001078689,0.000006417301,0.000002286798,0.9980655,0.000379063,0.001387889,0.0001327527,0.000001192758],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01461799,0.0001173849,0.9823909,0.0001690134,0.00002233106,0.00003339456,0.00003223641,0.001112668,0.001504057],"genre_scores_gemma":[0.7479398,0.0002153297,0.2466693,0.0002358998,0.00004200069,0.00009731278,0.0001615792,0.0003152818,0.004323618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00603565,"threshold_uncertainty_score":0.01200104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007046615663747078,"score_gpt":0.2705852907643029,"score_spread":0.2635386751005558,"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."}}