{"id":"W4285236576","doi":"10.1109/tnet.2022.3183231","title":"CharmSeeker: Automated Pipeline Configuration for Serverless Video Processing","year":2022,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Shanghai Jiao Tong University; British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Computer science; Leverage (statistics); Pipeline transport; Pipeline (software); Cloud computing; Scalability; Distributed computing; Key (lock); Real-time computing; Embedded system; Artificial intelligence; Database; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00120819,0.001781429,0.0007490077,0.0009815436,0.000733518,0.001204103,0.002892774,0.0009793959,0.00687845],"category_scores_gemma":[0.00443938,0.0008159225,0.0005733411,0.0006693499,0.0008546961,0.002070876,0.001695987,0.001449301,0.001810782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289804,"about_ca_system_score_gemma":0.002199715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004878262,"about_ca_topic_score_gemma":0.00781346,"domain_scores_codex":[0.9989178,0.0001901624,0.00005513781,0.000280624,0.0003855101,0.0001708621],"domain_scores_gemma":[0.9987007,0.0005400106,0.0001529376,0.0002774991,0.0002155394,0.0001132704],"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":[0.001234836,0.0005175593,0.00380819,0.000478691,0.0001561653,0.0004079016,0.0002993433,0.3917277,0.06921695,0.009882568,0.04428829,0.477982],"study_design_scores_gemma":[0.000081558,0.0001017647,0.0003837742,0.00001233108,0.00001142916,0.00006288697,0.00003367295,0.9778519,0.01479828,0.003396677,0.003233798,0.00003190782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03951605,0.0004483837,0.8703574,0.0002175886,0.0001012571,0.000309597,0.0003601872,0.08298351,0.005705948],"genre_scores_gemma":[0.490395,0.0001647496,0.5016195,0.0002139554,0.00003287778,0.0002675412,0.0007686405,0.003141609,0.003396241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00687845,"threshold_uncertainty_score":0.02301067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04848542494560888,"score_gpt":0.3192706637491375,"score_spread":0.2707852388035287,"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."}}