{"id":"W4387885734","doi":"10.1109/tce.2023.3325128","title":"Intelligent Beamforming for UAV-Assisted IIoT Based on Hypergraph Inspired Explainable Deep Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Scalability; Computer science; Beamforming; Artificial intelligence; Hypergraph; Artificial neural network; Machine learning; Deep learning; Distributed computing; Wireless; Industrial Internet; Internet of Things; Telecommunications; Embedded system","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":[0.0001645154,0.0002309687,0.0001890961,0.0004416571,0.000454323,0.00005285786,0.0001337771,0.0001381916,0.00004850473],"category_scores_gemma":[0.00000838466,0.0002593051,0.0001453423,0.0009550516,0.00002653702,0.00009772035,6.753501e-7,0.0003722147,0.00008680409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002627695,"about_ca_system_score_gemma":0.00006035959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008988215,"about_ca_topic_score_gemma":0.00009239627,"domain_scores_codex":[0.9987214,0.00002305824,0.0002853888,0.0002806681,0.0001673658,0.0005221537],"domain_scores_gemma":[0.9992917,0.000225386,0.00004079818,0.0002762956,0.0000807331,0.00008512418],"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":[0.00004048442,0.00006848547,0.000005454088,0.00004101682,0.00006070534,5.727558e-7,0.00007725463,0.9006667,0.002152571,0.0001322931,0.00009246854,0.09666202],"study_design_scores_gemma":[0.0005227583,0.0001904995,0.00001595,0.0000270892,0.00006638705,0.000001981992,0.0000855523,0.9294476,0.03862811,0.00005239495,0.03069162,0.0002700608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01617454,0.000180159,0.981251,0.00009554769,0.0002523445,0.0005753273,0.00001821463,0.001064444,0.0003884412],"genre_scores_gemma":[0.9928531,0.000859787,0.004725502,0.00007187227,0.00002163167,0.0008797692,0.00008345919,0.0001027796,0.0004020673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9766786,"threshold_uncertainty_score":0.9999859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302658456594792,"score_gpt":0.2274167416847632,"score_spread":0.2143901571188153,"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."}}