{"id":"W4378982855","doi":"10.1016/j.future.2023.05.031","title":"Human-to-human interaction behaviors sensing based on complex-valued neural network using Wi-Fi channel state information","year":2023,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Ministry of Electronics and Information technology; King Saud University; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Channel state information; Channel (broadcasting); Wireless; SIGNAL (programming language); Telecommunications; Human–computer interaction","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":[0.0002220327,0.0002638448,0.0002470392,0.000495561,0.0004655761,0.0003866387,0.0001346361,0.0001470321,0.00000539802],"category_scores_gemma":[0.000003567447,0.000272963,0.00006588319,0.0006831688,0.00001304227,0.0003915463,0.00004218433,0.0001976746,0.00005909587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002001734,"about_ca_system_score_gemma":0.00001060684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004182665,"about_ca_topic_score_gemma":0.00003544029,"domain_scores_codex":[0.9985293,0.0000922336,0.0005379816,0.000208096,0.0002962413,0.0003361289],"domain_scores_gemma":[0.9993516,0.000012252,0.0001214639,0.0002949484,0.0001530278,0.00006677353],"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":[0.000002896653,0.000004286947,0.00003004363,0.00004970536,0.00001202367,0.000003466751,0.0004219831,0.9347419,0.001982788,0.0001352518,0.06121371,0.001401986],"study_design_scores_gemma":[0.0002881525,0.00007590135,0.00048346,0.00007361075,0.00001085639,0.000007889674,0.0001456485,0.9903769,0.0009058409,0.000002518282,0.007349825,0.0002794001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2407195,0.00001091892,0.7267436,0.00006892902,0.02992351,0.0005215214,0.0000222638,0.001945447,0.00004429089],"genre_scores_gemma":[0.9808487,0.000001014586,0.002593937,0.0003111292,0.01514769,0.00002161222,0.001005371,0.00005021349,0.00002031106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7401292,"threshold_uncertainty_score":0.9999723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03790034113270989,"score_gpt":0.2660466771599597,"score_spread":0.2281463360272498,"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."}}