{"id":"W2924604683","doi":"10.1109/twc.2019.2944165","title":"Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Wireless Communications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Polytechnique Montréal; Université du Québec à Montréal","funders":"Quỹ Đổi mới sáng tạo Vingroup; Tập đoàn Vingroup - Công ty CP","keywords":"Computer science; Leverage (statistics); Cloud computing; Computation; Data compression; Distributed computing; Resource allocation; Computation offloading; Data compression ratio; Real-time computing; Algorithm; Image compression; Computer network; Edge computing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004327499,0.0006296638,0.0005504382,0.0002919031,0.0004653361,0.0007457731,0.0007427941,0.0004921939,0.0007673261],"category_scores_gemma":[0.001154769,0.0001891515,0.0002699024,0.0006405676,0.0005789933,0.0007370429,0.0007890839,0.0005272421,0.0001195239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008534583,"about_ca_system_score_gemma":0.001136454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008117914,"about_ca_topic_score_gemma":0.01136376,"domain_scores_codex":[0.9996641,0.00005918561,0.00001312247,0.00005001435,0.0001033674,0.0001103086],"domain_scores_gemma":[0.9996878,0.0001573942,0.00003389505,0.00004523028,0.00004605486,0.00002976493],"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":[0.0003512393,0.0001314057,0.001375451,0.0001128805,0.00003789895,0.0002595214,0.00009956327,0.8524843,0.01388884,0.01160002,0.002117463,0.1175413],"study_design_scores_gemma":[0.000009193378,0.00002449048,0.0003152001,0.000003885096,0.000005433076,0.00002799452,0.00002286891,0.9930034,0.003052108,0.003258738,0.0002719844,0.000004727501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1744954,0.0006201434,0.816984,0.0003565938,0.00005996943,0.0001488204,0.0001174976,0.00047125,0.006746291],"genre_scores_gemma":[0.9546562,0.0001500052,0.04409438,0.00006103322,0.00001880444,0.00003093255,0.00004947431,0.00002362444,0.0009155223],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008117914,"threshold_uncertainty_score":0.01614136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065240791860589,"score_gpt":0.3233019293477388,"score_spread":0.2167778501616799,"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."}}