{"id":"W4386857726","doi":"10.1109/jiot.2023.3314667","title":"CLSA: Contrastive-Learning-Based Survival Analysis for Popularity Prediction in MEC Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Popularity; Encoder; Deep learning; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Architecture; Artificial neural network; Latency (audio); Network architecture; Machine learning; Computer network; 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":[0.0009610952,0.0009643541,0.0007396138,0.0008774302,0.0003442978,0.0005290678,0.001362926,0.0008868339,0.002035208],"category_scores_gemma":[0.004414481,0.0003228653,0.0006996035,0.0006379073,0.0006373731,0.0012193,0.0008604325,0.001449018,0.0004579037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279036,"about_ca_system_score_gemma":0.0008637001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01527189,"about_ca_topic_score_gemma":0.01445555,"domain_scores_codex":[0.9997669,0.00005092079,0.00001595294,0.00006953075,0.00004891917,0.00004778249],"domain_scores_gemma":[0.9983493,0.0009742914,0.0001591716,0.00008723399,0.000353012,0.00007702543],"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":[0.0002499671,0.0001353123,0.009440283,0.0001180624,0.00009229874,0.0001951685,0.000107773,0.8423315,0.003704915,0.007372451,0.004107465,0.1321447],"study_design_scores_gemma":[0.000001705637,0.000009137148,0.0001758654,0.000002982228,0.00000310762,0.00000680141,0.000003353809,0.9983864,0.0001807083,0.001140025,0.00008784667,0.000001999942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1326036,0.0009997789,0.8604426,0.000678835,0.0001066205,0.00009159169,0.0007009538,0.001996181,0.002379803],"genre_scores_gemma":[0.932058,0.000388325,0.06164821,0.0003340652,0.00007595128,0.000134004,0.001527703,0.000111548,0.003722084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01527189,"threshold_uncertainty_score":0.030366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02554359801536098,"score_gpt":0.2579305983749547,"score_spread":0.2323870003595938,"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."}}