{"id":"W4389382548","doi":"10.1109/twc.2023.3335362","title":"Hybrid Online–Offline Learning for Task Offloading in Mobile Edge Computing Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Korea Evaluation Institute of Industrial Technology; National Fire Agency; Ministry of the Interior and Safety; Canada Research Chairs","keywords":"Computer science; Mobile edge computing; Edge computing; Task (project management); Mobile computing; Enhanced Data Rates for GSM Evolution; Computer network; Multimedia; Distributed computing; Human–computer interaction; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000673633,0.001034727,0.001079061,0.000270644,0.000579256,0.0008736894,0.001484408,0.0008808609,0.001655875],"category_scores_gemma":[0.002521501,0.0003557391,0.0003089039,0.0004391129,0.0008070978,0.001612029,0.001088307,0.001010141,0.0002269282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008006873,"about_ca_system_score_gemma":0.001179971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006058926,"about_ca_topic_score_gemma":0.005935789,"domain_scores_codex":[0.999465,0.0001167762,0.00002307705,0.0001250417,0.00009713788,0.0001729688],"domain_scores_gemma":[0.9990237,0.0005638154,0.00008751029,0.0001007301,0.0001431204,0.0000810427],"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":[0.0001918928,0.0001413554,0.0007938609,0.00005234728,0.00001499444,0.00009874969,0.00005435848,0.9455007,0.002228167,0.003961959,0.0009094633,0.04605211],"study_design_scores_gemma":[0.000002340634,0.00001025924,0.00003537582,0.0000010245,0.000001091848,0.000005461625,0.000004970761,0.9987985,0.0002125049,0.0008769602,0.00005004239,0.000001521709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09317804,0.0004914359,0.9021268,0.0003315439,0.00008310241,0.0000634643,0.0000459656,0.000532207,0.003147454],"genre_scores_gemma":[0.9691949,0.0001149216,0.02888524,0.000108151,0.00003401,0.00004322701,0.0000400169,0.00004036931,0.001539215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006058926,"threshold_uncertainty_score":0.01204735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213213987880737,"score_gpt":0.3009210403637541,"score_spread":0.2587889004849467,"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."}}