{"id":"W3215010342","doi":"10.1109/tfuzz.2021.3130311","title":"Fuz-Spam: Label Smoothing-Based Fuzzy Detection of Spammers in Internet of Things","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Japan Society for the Promotion of Science; Chongqing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Spamming; Computer science; Artificial intelligence; Machine learning; Fuzzy logic; Data mining; The Internet; World Wide Web","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":[0.001409702,0.0006351186,0.000835585,0.001430608,0.0007947368,0.0008370632,0.0008971334,0.001242556,0.0006806286],"category_scores_gemma":[0.003700598,0.0002375098,0.000623832,0.0005976648,0.0009519327,0.001497952,0.001156805,0.001032202,0.0003384649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006552419,"about_ca_system_score_gemma":0.0006711505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002390468,"about_ca_topic_score_gemma":0.002740553,"domain_scores_codex":[0.999108,0.0001776039,0.00004722986,0.0002046013,0.0003606675,0.0001018133],"domain_scores_gemma":[0.9986065,0.0005370401,0.0001970409,0.0002080104,0.0003745098,0.00007698916],"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":[0.001029675,0.0004997565,0.01995803,0.0002889184,0.0002229748,0.000682176,0.0007472706,0.2100398,0.04785958,0.01730672,0.009031669,0.6923333],"study_design_scores_gemma":[0.00001373759,0.0001019012,0.001954111,0.00001305209,0.00002645369,0.0001891427,0.00005715834,0.9781836,0.01131361,0.007039828,0.001084547,0.00002286984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1386951,0.000467217,0.8549838,0.0004700301,0.0001250414,0.0001213736,0.0001297988,0.002374996,0.002632493],"genre_scores_gemma":[0.8437194,0.0002148665,0.1525114,0.0003443334,0.0001041244,0.00005968139,0.0002997901,0.00006490648,0.002681605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002390468,"threshold_uncertainty_score":0.007455289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203475875465673,"score_gpt":0.2327914706668393,"score_spread":0.212443883120272,"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."}}