{"id":"W2593891526","doi":"10.1109/icse-nier.2017.12","title":"Building Usage Profiles Using Deep Neural Nets","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Profiling (computer programming); Software; Convolutional neural network; Task (project management); Artificial neural network; Construct (python library); Deep learning","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":[0.0005357568,0.001168347,0.0005590002,0.003366481,0.0003223842,0.0007789518,0.0006897003,0.0006853979,0.001508176],"category_scores_gemma":[0.004075499,0.0005184709,0.0005471783,0.00177707,0.0001625271,0.001728152,0.0006831458,0.0009958223,0.001515573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109874,"about_ca_system_score_gemma":0.0007280001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02005365,"about_ca_topic_score_gemma":0.03787365,"domain_scores_codex":[0.9993631,0.00009993724,0.0000557675,0.0002211684,0.0001493273,0.0001107907],"domain_scores_gemma":[0.9984277,0.0005341184,0.0002547363,0.0001797926,0.0004843523,0.0001193365],"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":[0.0005909106,0.0008180701,0.1073658,0.0002720429,0.0002142953,0.0004499401,0.0004411029,0.1434527,0.01189268,0.0023553,0.01597498,0.7161723],"study_design_scores_gemma":[0.00000661896,0.00004829826,0.009900359,0.00002905695,0.00001709309,0.00005703998,0.00007916862,0.9824809,0.003304585,0.002372643,0.001688884,0.00001537276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5089695,0.001028984,0.459141,0.0004996992,0.0001110839,0.0002829415,0.009612523,0.01101979,0.009334451],"genre_scores_gemma":[0.878897,0.0003934855,0.102305,0.0001504474,0.00003445823,0.0001714854,0.01352223,0.0002711068,0.004254812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02005365,"threshold_uncertainty_score":0.03987384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0497947721742919,"score_gpt":0.3320615793785307,"score_spread":0.2822668072042388,"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."}}