{"id":"W3215475368","doi":"10.1109/tnse.2021.3130948","title":"Efficient Allocation of Resource-Intensive Mobile Cyber–Physical Social System Applications on a Heterogeneous Mobile Ad Hoc Cloud","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Hankuk University of Foreign Studies","keywords":"Computer science; Resource allocation; Distributed computing; Cloud computing; Computer network; Wireless ad hoc network; Resource management (computing); Key (lock); Mobile computing; Latency (audio); Wireless; Computer security; Telecommunications","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":[0.0002869834,0.000172047,0.0002402761,0.0001133246,0.0004699135,0.0000969851,0.0003260162,0.00006695581,0.000001636992],"category_scores_gemma":[0.00000145996,0.0001724614,0.00008893296,0.001197256,0.0001485974,0.00007764971,0.00001201885,0.0002103132,0.00000727019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001147062,"about_ca_system_score_gemma":0.0001543616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001652629,"about_ca_topic_score_gemma":9.796699e-7,"domain_scores_codex":[0.9983585,0.0000274294,0.0002521838,0.0005033928,0.0004744701,0.0003840095],"domain_scores_gemma":[0.9989099,0.000143744,0.00007287863,0.000392222,0.0003258341,0.0001554034],"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":[0.000008863911,0.00008233661,2.002668e-7,0.00002467965,0.00001329437,0.000008842385,0.0005757583,0.9308384,0.001066503,0.001185786,0.00004055118,0.06615481],"study_design_scores_gemma":[0.0001520686,0.0001588721,0.000006149277,0.00008578127,0.00002009219,0.00005161688,0.0002614075,0.9955369,0.002087738,0.00001225202,0.001455065,0.0001720813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07805127,0.000271907,0.9205201,0.00003908531,0.0004872173,0.0002998259,0.000007633495,0.000143456,0.0001795035],"genre_scores_gemma":[0.9976608,0.00004107456,0.001765087,0.00008319061,0.0001922843,0.0002164765,0.000001580582,0.00001247701,0.00002708726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9196095,"threshold_uncertainty_score":0.7032772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008915757605798961,"score_gpt":0.2176209749128689,"score_spread":0.20870521730707,"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."}}