{"id":"W2607252581","doi":"10.1109/les.2017.2695118","title":"HypAp: A Hypervolume-Based Approach for Refining the Design of Embedded Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Embedded Systems Letters","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Multi-objective optimization; Mathematical optimization; Process (computing); Pareto principle; MPSoC; Genetic algorithm; Cluster analysis; Optimization problem; Face (sociological concept); Algorithm; Artificial intelligence; System on a chip; Machine learning; Mathematics; Embedded system","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","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.008734008,0.0007853055,0.001443731,0.0004120106,0.001337619,0.001888807,0.007142434,0.0004094826,0.000001164753],"category_scores_gemma":[0.0006515872,0.0006062376,0.0004975742,0.0003790761,0.000437575,0.001084196,0.0002632503,0.0004996733,0.00002114147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002778565,"about_ca_system_score_gemma":0.000311939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005924299,"about_ca_topic_score_gemma":0.000003219813,"domain_scores_codex":[0.9920096,0.00225627,0.001794348,0.001407577,0.001343359,0.001188888],"domain_scores_gemma":[0.9886378,0.00180925,0.002579774,0.006186504,0.0005451557,0.0002415238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002226008,0.0003295536,0.0004093433,0.003080459,0.0006887958,0.00008828341,0.004163328,0.3367119,0.5305386,0.01563159,0.1064008,0.00173472],"study_design_scores_gemma":[0.001437367,0.0002506723,0.00002772913,0.0006795215,0.00008686212,0.000115328,0.0004484741,0.9799067,0.01442835,0.00005701428,0.001665905,0.0008960463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002424605,0.0005352607,0.9870945,0.0005386244,0.003602705,0.003824204,0.00003686178,0.0007966822,0.00114652],"genre_scores_gemma":[0.8835653,0.00000369859,0.1116798,0.0004683086,0.0007060468,0.002890757,0.00000968087,0.0001341913,0.0005422076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8811407,"threshold_uncertainty_score":0.9999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07360325167212002,"score_gpt":0.2822132825428419,"score_spread":0.2086100308707219,"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."}}