{"id":"W4315630387","doi":"10.1109/globecom48099.2022.10000829","title":"Object-Based Resolution Selection for Efficient Edge-Assisted Multi-Task Video Analytics","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Analytics; Video tracking; Bandwidth (computing); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Real-time computing; Computer vision; Latency (audio); Task (project management); Video processing; Computation; Data mining; Algorithm; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004173896,0.0005293614,0.0004170706,0.0005140849,0.0002630908,0.0006739981,0.0008758178,0.0004701983,0.001069997],"category_scores_gemma":[0.001310714,0.0002038077,0.0002660598,0.0004594084,0.0002456888,0.001143086,0.0007460635,0.0006177485,0.0004825106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526948,"about_ca_system_score_gemma":0.00033237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000766399,"about_ca_topic_score_gemma":0.001118789,"domain_scores_codex":[0.9997094,0.00004997962,0.00001659883,0.00006606546,0.0001128295,0.00004502675],"domain_scores_gemma":[0.9996166,0.0001292057,0.00005219741,0.00007263401,0.0000988387,0.00003058098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005392072,0.0002273202,0.002855702,0.000125514,0.00006379774,0.0002933098,0.0002135228,0.1426602,0.1729414,0.008418893,0.003694235,0.6679669],"study_design_scores_gemma":[0.00001768424,0.00007766802,0.0007752719,0.00000805892,0.00001456948,0.0001731708,0.0000378336,0.9524194,0.04207106,0.002449638,0.001937919,0.00001776768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02835276,0.0002652538,0.9693996,0.00005944521,0.00002208195,0.00003372864,0.0000256619,0.0005044864,0.001337015],"genre_scores_gemma":[0.476512,0.0002513659,0.5207119,0.0001078706,0.00004640423,0.00005746707,0.0001370734,0.0001142882,0.002061604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001069997,"threshold_uncertainty_score":0.003579497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05635500627788053,"score_gpt":0.32118535474641,"score_spread":0.2648303484685294,"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."}}