{"id":"W4410768835","doi":"10.1016/j.comnet.2025.111374","title":"CNN-DAG-Editor: A Convolutional Neural Network offloading analyzer with Multi-Objective Dynamic Adaptive Resource Competitive Swarm Optimization","year":2025,"lang":"en","type":"article","venue":"Computer Networks","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"National Key Research and Development Program of China; China Scholarship Council; Fudan University","keywords":"Computer science; Swarm behaviour; Convolutional neural network; Spectrum analyzer; Resource (disambiguation); Artificial intelligence; Computer network; Telecommunications","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":[0.0005496675,0.0005195069,0.0005578434,0.0002938268,0.0007994595,0.000426463,0.001140499,0.0002311355,0.000003573183],"category_scores_gemma":[0.00001761505,0.0004940971,0.0001812995,0.001741451,0.0002271693,0.0005599768,0.001007009,0.0006190919,0.000009511348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000465007,"about_ca_system_score_gemma":0.0002540628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002353804,"about_ca_topic_score_gemma":0.00001670027,"domain_scores_codex":[0.9965007,0.0003644552,0.0005371583,0.001123067,0.0004882423,0.0009863725],"domain_scores_gemma":[0.9976452,0.0006218664,0.0003103302,0.000638913,0.0005985422,0.0001851255],"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":[0.00007764041,0.00007988895,0.0008376071,0.0000101601,0.0001925272,0.00002860739,0.0003343054,0.9599142,5.021065e-7,0.002766998,0.02646263,0.009294943],"study_design_scores_gemma":[0.001170021,0.0002000252,0.003931095,0.0003839438,0.00004797878,0.00002467626,0.00003078545,0.9874886,0.000002254659,0.0001186105,0.006079787,0.0005222245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000553393,0.0006316375,0.9771759,0.0002822201,0.01896346,0.0004849637,0.000001431408,0.0005376331,0.001369352],"genre_scores_gemma":[0.352711,0.00002235341,0.6217021,0.002561918,0.02234955,0.00005827301,0.0001065795,0.00007376933,0.0004144085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3554738,"threshold_uncertainty_score":0.9997511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00728712140067076,"score_gpt":0.2196266947613425,"score_spread":0.2123395733606717,"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."}}