{"id":"W4383200176","doi":"10.1109/jiot.2023.3292319","title":"Transferability of Machine Learning Algorithm for IoT Device Profiling and Identification","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research and Productivity Council; University of New Brunswick","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Profiling (computer programming); Transferability; Algorithm; Machine learning; Identification (biology); Artificial intelligence; Internet of Things; Data mining; Embedded system; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003542138,0.001104734,0.0008325712,0.001560692,0.0005281463,0.001191123,0.001190302,0.001496026,0.00123239],"category_scores_gemma":[0.01384017,0.0002556928,0.0009258933,0.0008760494,0.0008436189,0.002063693,0.001756873,0.002080763,0.000569565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009750015,"about_ca_system_score_gemma":0.000701296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002391838,"about_ca_topic_score_gemma":0.0007946827,"domain_scores_codex":[0.9981315,0.000722201,0.0001452405,0.0004422562,0.0004231732,0.0001357069],"domain_scores_gemma":[0.9956782,0.002943804,0.0002658679,0.0004544769,0.0005680216,0.00008955605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003153186,0.0003719682,0.006941158,0.0001065616,0.0001296224,0.0002521396,0.0002020386,0.6109933,0.004915838,0.005605903,0.001571006,0.3685951],"study_design_scores_gemma":[0.000003612564,0.00005545561,0.0004079783,0.000004555126,0.000006262582,0.00002482276,0.00001554233,0.9947628,0.001399572,0.003129102,0.0001847801,0.000005507342],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0713663,0.0005201219,0.9237011,0.0004233529,0.00006757205,0.0001354281,0.00005858145,0.0016385,0.002089085],"genre_scores_gemma":[0.9005088,0.0002757333,0.09681284,0.0001739543,0.00005420318,0.0001770558,0.0002150588,0.00008642681,0.001695981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003542138,"threshold_uncertainty_score":0.01873285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03590139028415922,"score_gpt":0.2922806529478848,"score_spread":0.2563792626637256,"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."}}