{"id":"W4405351316","doi":"10.1109/comst.2024.3516819","title":"A Survey on Semantic Communication Networks: Architecture, Security, and Privacy","year":2024,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Architecture; Computer security; Internet privacy; Computer network; Geography","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"],"consensus_categories":[],"category_scores_codex":[0.01006979,0.0003143748,0.0003980636,0.0002772222,0.0006594156,0.0009946595,0.003380955,0.0001646558,0.000004997277],"category_scores_gemma":[0.0007089425,0.00030551,0.0001054407,0.001248705,0.0003342266,0.0003074575,0.001777985,0.0008213754,0.00006365171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007832598,"about_ca_system_score_gemma":0.0001370769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007974938,"about_ca_topic_score_gemma":0.001097716,"domain_scores_codex":[0.9899002,0.00817225,0.0005791623,0.0006218147,0.0003166613,0.0004099217],"domain_scores_gemma":[0.9841811,0.01095253,0.0001296507,0.004275633,0.0003106378,0.0001504692],"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":[0.00004552156,0.0009333392,0.00533698,0.0001505131,0.0006106205,0.00002446943,0.01015844,0.007070018,0.0002005407,0.08035479,0.03479741,0.8603173],"study_design_scores_gemma":[0.0008104158,0.0002722408,0.08494868,0.0013872,0.00007627626,0.00004781565,0.00002952513,0.8361613,0.0001603981,0.03261547,0.04220259,0.001288141],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03708949,0.02406288,0.9257661,0.00325587,0.004842314,0.0009316076,0.00007238147,0.001498845,0.002480553],"genre_scores_gemma":[0.9926703,0.00318066,0.00324718,0.000190347,0.0003691987,0.00005387309,0.0001465789,0.00003712126,0.0001047604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9555808,"threshold_uncertainty_score":0.9999397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0492379884772208,"score_gpt":0.3150688360559589,"score_spread":0.2658308475787381,"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."}}