{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007155249,0.0002097245,0.0002290488,0.0001711801,0.001539355,0.0003213223,0.0008394133,0.00005597275,0.00004507016],"category_scores_gemma":[0.000006331413,0.0002343863,0.0001423049,0.0007301526,0.00002415335,0.0005923889,0.00002100939,0.0003418457,0.000009691254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002143487,"about_ca_system_score_gemma":0.0001678112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003122756,"about_ca_topic_score_gemma":0.00002013427,"domain_scores_codex":[0.9979267,0.0001936076,0.000447926,0.0005362671,0.0004538478,0.0004416669],"domain_scores_gemma":[0.9987806,0.0002135806,0.0001992418,0.0005943365,0.0001303487,0.00008191226],"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":[0.00009899339,0.0004356417,0.00001134212,0.0001107618,0.0000555031,0.00001700034,0.001973577,0.339607,0.002261932,0.0006351735,0.003391961,0.6514011],"study_design_scores_gemma":[0.0007793784,0.0001645364,0.00002831952,0.00006034365,0.0000294536,0.00002241532,0.0001970546,0.9685056,0.004413598,0.0007956399,0.02469881,0.0003048835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008587291,0.0001393597,0.9926288,0.002592917,0.001963477,0.0005646359,0.00001423831,0.001073547,0.0001643072],"genre_scores_gemma":[0.9789092,0.00001617544,0.0181222,0.001593999,0.0003214601,0.0005270329,0.0000272461,0.00003050812,0.0004521541],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9780505,"threshold_uncertainty_score":0.9997605,"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."}}