{"id":"W4405179107","doi":"10.1109/tmc.2024.3514214","title":"Joint Encoding and Enhancement for Low-Light Video Analytics in Mobile Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Analytics; Encoding (memory); Enhanced Data Rates for GSM Evolution; Mobile telephony; Computer network; Telecommunications; Artificial intelligence; Mobile radio; Data science","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":[0.0007924996,0.0002226406,0.0002910225,0.0002981024,0.0002612636,0.000429137,0.0002716335,0.00008379434,0.00001222516],"category_scores_gemma":[0.00000352757,0.0002224677,0.0001406647,0.0005693164,0.0000320119,0.0003717386,0.00001422166,0.0003441856,0.00001040627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000195531,"about_ca_system_score_gemma":0.00008336113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001588154,"about_ca_topic_score_gemma":0.00001768524,"domain_scores_codex":[0.9980742,0.00008310127,0.0005490864,0.0006412757,0.0002089235,0.0004433781],"domain_scores_gemma":[0.9989988,0.0004328076,0.00006683876,0.000358327,0.00004881736,0.00009436214],"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":[0.00001000462,0.0002931353,0.000006047286,0.0003078259,0.00006607758,0.00002987645,0.002553362,0.5373002,0.003554413,0.00124582,0.0002385992,0.4543947],"study_design_scores_gemma":[0.0003141129,0.0002824371,0.00001137745,0.0004512827,0.00001986646,0.00001004021,0.0001586734,0.9654022,0.03110608,0.0002062601,0.001786666,0.0002510131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01726965,0.0008875885,0.9791096,0.000226821,0.001441456,0.0007600535,0.000003476764,0.000191034,0.0001102742],"genre_scores_gemma":[0.9842436,0.0001528204,0.01488105,0.0002122137,0.0001305937,0.0002321754,0.000001611853,0.00001993018,0.0001260051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.966974,"threshold_uncertainty_score":0.9071968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02730909917065205,"score_gpt":0.3075135664525185,"score_spread":0.2802044672818665,"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."}}