{"id":"W4417292807","doi":"10.1186/s13677-025-00821-1","title":"Security-aware computation offloading in internet of vehicles: a multi-agent reinforcement learning algorithm with attention mechanism","year":2025,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computation offloading; Computation; Task (project management); Reinforcement learning; Energy consumption; Layer (electronics); Latency (audio); Feature (linguistics); Key (lock); Application layer","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":[0.0007514763,0.000143294,0.0003504315,0.0003049268,0.0001551844,0.0001190689,0.0003089072,0.00004804691,8.532024e-8],"category_scores_gemma":[0.00001214706,0.0001258564,0.00005840387,0.0005471692,0.0000336585,0.0002506303,0.0001524311,0.0002739031,5.735985e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009741008,"about_ca_system_score_gemma":0.00006652134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004284493,"about_ca_topic_score_gemma":0.000001352871,"domain_scores_codex":[0.998372,0.0001043437,0.000854164,0.0002301884,0.0002466188,0.0001926475],"domain_scores_gemma":[0.9983895,0.0001646591,0.0009449695,0.0001278869,0.0003180391,0.00005498839],"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":[0.00003561206,0.000365646,0.009296501,0.001256715,0.0002090447,0.00002105074,0.004589219,0.5589199,0.001031648,0.03293279,0.0001712508,0.3911707],"study_design_scores_gemma":[0.0007894962,0.0001521337,0.000473971,0.001365104,0.00001626479,0.00005524647,0.0005410126,0.9938063,0.0002056311,0.0008272111,0.001645707,0.0001219268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04879228,0.001015413,0.9486767,0.00005848005,0.001054151,0.0002906643,1.253917e-7,0.00003140051,0.00008075555],"genre_scores_gemma":[0.9672815,0.0000489394,0.03230014,0.00001678174,0.0002933296,0.000008150032,0.00000192877,0.000006773637,0.00004252034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9184892,"threshold_uncertainty_score":0.5132275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01003987785064213,"score_gpt":0.2699538571386763,"score_spread":0.2599139792880342,"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."}}