{"id":"W4388040462","doi":"10.1109/pimrc56721.2023.10293847","title":"A transfer learning approach based on integrated feature extractor for anti-jamming in wireless networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Jamming; Computer science; Extractor; Transfer of learning; Reinforcement learning; Feature (linguistics); Wireless network; Wireless; Artificial intelligence; Throughput; Computer network; Engineering; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007687697,0.0008608896,0.0007447231,0.0006503165,0.0002998475,0.0004221097,0.001140283,0.0007068545,0.001235514],"category_scores_gemma":[0.001694706,0.0002914795,0.0005870446,0.0006537075,0.000493244,0.001352004,0.0009804743,0.00118393,0.0003431072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005436502,"about_ca_system_score_gemma":0.0006992516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00321306,"about_ca_topic_score_gemma":0.001940711,"domain_scores_codex":[0.9996191,0.00007042487,0.00002333206,0.0001118723,0.0001150278,0.0000602084],"domain_scores_gemma":[0.9995442,0.0001584545,0.00006618893,0.00005213548,0.0001491231,0.00002990647],"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":[0.0001831303,0.0002338072,0.001547763,0.00006313224,0.00009428627,0.0001203148,0.00007594084,0.4760912,0.01896197,0.003613662,0.001790324,0.4972245],"study_design_scores_gemma":[0.000003663773,0.00004209995,0.000150601,0.000001484958,0.000005599238,0.00001389321,0.000003469263,0.9975029,0.001522884,0.0005692129,0.0001796868,0.000004474817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02593153,0.0002231372,0.9720296,0.0001162949,0.0000411292,0.00003853802,0.00002455595,0.0008411243,0.0007539815],"genre_scores_gemma":[0.8225398,0.000266067,0.1722664,0.0002100162,0.00006725702,0.0001506581,0.0001654322,0.00008899781,0.004245504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00321306,"threshold_uncertainty_score":0.006388664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660387154949816,"score_gpt":0.2316143232026028,"score_spread":0.2150104516531047,"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."}}