{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009269945,0.0009410151,0.001300163,0.0003564337,0.0004703525,0.0008659356,0.001307638,0.001167165,0.001533789],"category_scores_gemma":[0.001983828,0.0004535037,0.0005557101,0.0003298671,0.0007157268,0.0008451979,0.001114842,0.001456453,0.000209986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008373102,"about_ca_system_score_gemma":0.001750434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01104636,"about_ca_topic_score_gemma":0.005512528,"domain_scores_codex":[0.9996071,0.00009741462,0.00002143077,0.0001043744,0.00006993158,0.00009965391],"domain_scores_gemma":[0.9990807,0.0005386775,0.0001057076,0.0000404148,0.0001462531,0.0000882551],"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":[0.00009855336,0.00008581519,0.001017644,0.00006247756,0.00004179177,0.00008730385,0.00007799811,0.9542032,0.001196955,0.003683854,0.000942951,0.03850146],"study_design_scores_gemma":[0.00001064442,0.00001685778,0.0000442788,0.000002422009,0.000004063744,0.000004863754,0.000004704716,0.9990466,0.0001008198,0.0006684404,0.0000941094,0.000002145464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0618087,0.000548285,0.9324006,0.000524398,0.00008611888,0.00009056409,0.00003521546,0.0005829855,0.003923142],"genre_scores_gemma":[0.9430224,0.0001846999,0.05381382,0.0002174785,0.00004010146,0.0001344232,0.0000571557,0.00003865283,0.002491249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01104636,"threshold_uncertainty_score":0.02196413,"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."}}