{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.001329109,0.0003720186,0.0003781799,0.0002951388,0.002276906,0.0003511046,0.005408987,0.0001028652,0.0001299483],"category_scores_gemma":[0.0002075841,0.0004625283,0.0002424809,0.00287455,0.0002088597,0.0003281315,0.002126722,0.0006278257,0.00003285616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00187902,"about_ca_system_score_gemma":0.0009260631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004782298,"about_ca_topic_score_gemma":0.0007134952,"domain_scores_codex":[0.9959996,0.0009012116,0.000749966,0.0008804438,0.000789717,0.0006790788],"domain_scores_gemma":[0.9950214,0.0002815913,0.0005058023,0.003379295,0.0006598284,0.0001520609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006197312,0.01199333,0.004970825,0.000309068,0.0006925811,0.0000235127,0.001558181,0.1094792,0.06422486,0.3681123,0.2533108,0.1847056],"study_design_scores_gemma":[0.0009577068,0.0003388715,0.001312319,0.0000243028,0.00005542979,0.00002195295,0.0001224146,0.9568525,0.001741768,0.0009355277,0.03713835,0.000498858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001650526,0.0005585734,0.9900601,0.002169408,0.0007675127,0.00143895,0.0004728604,0.0009851633,0.001896923],"genre_scores_gemma":[0.7537213,0.0000619214,0.2429608,0.0006851207,0.00002794825,0.001797499,0.0003580484,0.00002198522,0.0003654094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8473732,"threshold_uncertainty_score":0.9999722,"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."}}