Event-based graphical monitoring in the EpochX genetic programming framework

EpochX is a genetic programming framework with provision for event management – similar to the Java event model – allowing the notification of particular actions during the lifecycle of the evolutionary algorithm. It also provides a flexible Stats system to gather statistics measures. This paper introduces a graphical interface to the EpochX genetic programming framework, taking full advantage of EpochX's event management. A set of representation-independent and tree-dependent GUI components are presented, showing how statistic information can be presented in a rich format using the information provided by EpochX's Stats system

Useful academic references for data mining and usage statistics

Relates to the following software for analysing Blackboard stats http://www.edshare.soton.ac.uk/11134/ Is supporting material for the following podcast: http://youtu.be/yHxCzjiYBo

Teaching Stats for Data Science

“Data science” is a useful catchword for methods and concepts original to the field of statistics, but typically being applied to large, multivariate, observational records. Such datasets call for techniques not often part of an introduction to statistics: modeling, consideration of covariates, sophisticated visualization, and causal reasoning. This article re-imagines introductory statistics as an introduction to data science and proposes a sequence of 10 blocks that together compose a suitable course for extracting information from contemporary data. Recent extensions to the mosaic packages for R together with tools from the “tidyverse” provide a concise and readable notation for wrangling, visualization, model-building, and model interpretation: the fundamental computational tasks of data science

Labour stats 101: unemployment

This guide provides a brief overview of unemployment, an introduction to some of the key concepts and terminology, and lists some relevant data sources. This is one in a series of Quick Guides related to labour statistics, designed to provide a basic understanding of Australian labour market data. Other guides include labour force, employment and youth unemployment

Real-time Image Signal Processor Stats Management to Save Power and CPU Cycles

This publication describes techniques and apparatuses, implemented on a digital image capture device, directed at minimizing power consumption and central processing unit (CPU) cycles during image capture and processing events. An image sensor on the device captures a scene as a frame and generates raw image data. An on-device image signal processor (ISP) receives the raw image data and generates a statistics output (“stats output”) that includes image statistics for the frame. The stats output further includes a descriptive tag for the image statistics, saved in a header of the stats output. Software implemented on the device (e.g., a Statistics Manager) receives the stats output, parses the descriptive tag from the header, compares the descriptive tag to one or more previous descriptive tags, and determines if a change in the stats output is greater than a threshold. Upon determining that the change in the stats output is less than the threshold, the Statistics Manager determines that processing of the stats output by an Image Processing Module (e.g., 3A algorithms, other ISP software algorithms) is not necessary. Upon determining that the change in the stats output is greater than the threshold, the Statistics Manager determines processing of the stats output by the Image Processing Module is necessary. Through the use of such techniques and apparatuses, an image capture device can avoid unnecessary processing of stats outputs

"Why are books banned?" Banned Books Week infographic poster

This infographic poster made for Banned Books Week provides some reasons for why some books have been banned and which are the top banned books. Stats are from the American Library Association

The Use of Road Safety Data in Highway Authorities

The Road Safety Code of Good Practice (1989) suggested that highway authorities should collect supplementary information in addition to STATS 19 records and that there should be moves towards developing coordinated strategies for road safety planning. It finds that traffic flows, letters from the public, population statistics, police reported damage-only accidents and highway and land development sources are used most often. Shire Counties tend to collect more sources of data than other authorities. All have meetings with the Police, but the Shire Counties and the Non-English Authorities have more meetings with the hospitals compared to the Metropolitan Authorities and the London Boroughs. Only twenty-two Authorities had any evidence of under-reporting, of which only six provided any figures. The location of accidents was the STATS 19 variable considered to have the most number of errors

Labour stats 101: youth unemployment

This guide provides a brief overview of youth unemployment, an introduction to some of the key concepts and terminology used, and lists some relevant data sources. This is one in a series of Quick Guides related to labour statistics, designed to provide a basic understanding of the Australian labour market data. Other guides include labour force, unemployment and employment

2009 University of Northern Iowa Panther Basketball Postseason Media Guide

Contents: --2008-09 Schedule/Results --1--UNI Men\u27s Basketball Game Notes --1--2008-09 Missouri Valley Conference Standings --2--Head Coach Ben Jacobson --3--2008-09 MVC Award Winners --4--Statistical Matchup --5--UNI in MVC Stats/All Games --6--UNI in MVC Stats/Conference Games --7--UNI in NCAA Stats --8--UNI Single-Season Records --9-10--ESPN/USA Today Top 25 --11--UNI MVC Tournament History --12--UNI Postseason Tournament History --12--2008-09 UNI Men\u27s Basketball - UNI Combined Team Statistics - All Games --13--UNI Combined Team Statistics - Conference Games --14--2008-09 UNI Game-by-Game Stats --15--2008-09 UNI Opponent Game-by-Game Stats --16--UNI Team High/Low Analysis - All Games --17-20--UNI Player High/Low Analysis - All Games --21-22--UNI Opponent High/Low Analysis - All Games --23-24--2008-09 Recaps/Boxscores --25-36--Players\u27 Stats --37-46--NCAA Tournament: 2004-2005-2006 --47-61https://scholarworks.uni.edu/amg/1360/thumbnail.jp