{"id":"W4405844668","doi":"10.1109/tdsc.2024.3523561","title":"Blockchain-Enabled Secure Offloading for VEC: A Multi-Agent Reinforcement Learning Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Info-communications Media Development Authority; National Natural Science Foundation of China; Ministry of Education - Singapore; National Research Foundation Singapore","keywords":"Blockchain; Computer science; Reinforcement learning; Distributed computing; Computer network; Computer security; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006206996,0.0003248503,0.0003151094,0.0003732874,0.001155335,0.0003563518,0.0005057644,0.000261196,0.000009458498],"category_scores_gemma":[0.00001118789,0.0003145665,0.0001739331,0.0006919553,0.00006303665,0.0001562372,0.00002424976,0.0008434608,0.00001367504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000950582,"about_ca_system_score_gemma":0.00008141395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002916412,"about_ca_topic_score_gemma":0.00001147916,"domain_scores_codex":[0.9978154,0.00006220324,0.0004201971,0.0008975748,0.0002292769,0.0005752883],"domain_scores_gemma":[0.99894,0.0002976631,0.00008769936,0.0004378834,0.00009643911,0.0001403083],"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":[0.00002997644,0.0003661296,0.000009606947,0.000577124,0.0003465057,0.00003621389,0.007538938,0.7628508,0.001834482,0.07251926,0.0003358026,0.1535552],"study_design_scores_gemma":[0.0005630462,0.0001603665,0.000002377908,0.0001455437,0.00004587443,0.00009744605,0.0002550523,0.9874397,0.004918352,0.0006715011,0.005357923,0.00034283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01743911,0.0007990994,0.978825,0.0004615719,0.0004753972,0.0007310215,0.00000636074,0.001041879,0.0002205559],"genre_scores_gemma":[0.925465,0.00005682052,0.07363097,0.0001260071,0.00006949669,0.0001411599,0.000005919583,0.00003263469,0.0004719921],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9080259,"threshold_uncertainty_score":0.9999306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01797817310997997,"score_gpt":0.2527724400217966,"score_spread":0.2347942669118166,"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."}}