{"id":"W4402401573","doi":"10.1109/jiot.2024.3457372","title":"FEDge-HAR: An Optimized Private Mobile Edge-Enabled IoT Paradigm for Privacy of Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Ningbo Municipal Bureau of Science and Technology","keywords":"Computer science; Internet of Things; Enhanced Data Rates for GSM Evolution; Edge computing; Computer security; Information privacy; Internet privacy; Activity recognition; Mobile telephony; Computer network; Human–computer interaction; Telecommunications; Artificial intelligence; Mobile radio","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":[0.001828251,0.0002830159,0.0006375797,0.0004864509,0.0001246164,0.0006607703,0.001300014,0.0001592038,0.0000832746],"category_scores_gemma":[0.0001311023,0.0002629031,0.0004414618,0.0003034719,0.00008918486,0.0027666,0.0001754732,0.0005744,0.00002363402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001817077,"about_ca_system_score_gemma":0.0001682652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001186332,"about_ca_topic_score_gemma":0.000005337207,"domain_scores_codex":[0.9973974,0.0003067824,0.0009060206,0.0005180053,0.000493976,0.0003778108],"domain_scores_gemma":[0.997604,0.0004507456,0.0008466203,0.0004778779,0.0004199328,0.0002008527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007189736,0.00131651,0.0001355945,0.001185916,0.001039541,0.0001039954,0.01808106,0.0001965707,0.4313235,0.001254801,0.006042552,0.538601],"study_design_scores_gemma":[0.003460265,0.002901518,0.0001823294,0.003008754,0.0001625538,0.001199851,0.0001327353,0.1822501,0.7742078,0.02560333,0.006065237,0.0008256007],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5318777,0.0001101858,0.4654381,0.0001653728,0.001639711,0.0004505983,0.00001316313,0.0001234913,0.0001816448],"genre_scores_gemma":[0.9809886,0.00001737551,0.01812955,0.0000567779,0.0003442651,0.0000754869,0.000005756654,0.00003897261,0.0003432781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5377754,"threshold_uncertainty_score":0.9999823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05172679627628167,"score_gpt":0.3181005615479278,"score_spread":0.2663737652716461,"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."}}