{"id":"W3107219844","doi":"10.1109/tifs.2020.3040877","title":"Secure Content Delivery in Two-Tier Cache-Enabled mmWave Heterogeneous Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Windsor","funders":"Xidian University; Natural Sciences and Engineering Research Council of Canada; Shenzhen University; National Natural Science Foundation of China","keywords":"Computer science; Cache; Computer network; Base station; Throughput; Transmission (telecommunications); Resource allocation; Stochastic geometry; Artificial noise; Secrecy; Heterogeneous network; Wireless network; Wireless; Telecommunications; Channel (broadcasting); Transmitter; Computer security","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":[],"consensus_categories":[],"category_scores_codex":[0.0001174425,0.0002059053,0.0002338975,0.0001375486,0.0001093895,0.00009332712,0.0001473374,0.0001341795,0.00003336028],"category_scores_gemma":[0.000004030397,0.0002247666,0.00007749325,0.0002588101,0.00005754536,0.0007206968,0.000004890931,0.0005585122,0.00001769877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008262588,"about_ca_system_score_gemma":0.00001509571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000100749,"about_ca_topic_score_gemma":0.0002063425,"domain_scores_codex":[0.9989809,0.00003840779,0.000469812,0.0001159001,0.0001705172,0.0002244156],"domain_scores_gemma":[0.9993979,0.00005096602,0.00006231134,0.0002418048,0.0001053834,0.0001416686],"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":[0.000146011,0.00006638464,0.0001009927,0.0002144817,0.0001217323,0.000007222019,0.01960022,0.9352573,0.0001668578,0.002470073,0.001361887,0.0404869],"study_design_scores_gemma":[0.0008313052,0.00006877822,0.0000490632,0.00004160142,0.00001756453,0.00001103777,0.0004464943,0.9872732,0.005721809,0.0004454972,0.004793612,0.0003000687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2744016,0.0002987723,0.7223113,0.0003659049,0.0002476325,0.0005382001,0.0001024865,0.0006418416,0.001092317],"genre_scores_gemma":[0.9975414,0.0008359592,0.0007029569,0.0007846547,0.00002129139,0.00005326816,0.00003975988,0.00001877914,0.000001885006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7231399,"threshold_uncertainty_score":0.9165717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865554697056604,"score_gpt":0.2142812575250939,"score_spread":0.1956257105545278,"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."}}