Working Paper 83 - Are African Countries Richer Than They Are Developed? A Multidimensional Analysis of Well-Being

Sen’s capability approach inspired a new conception of development and succeeded in the Human Development Index (HDI). On the basis of HDI critics, we propose to enlarge the number of variables and we use 9 indicators of Standard of Living and 9 indicators of Quality of Life that allows measuring two components of well-being and that can be divided into various fields (health, education, environment, etc.) to provide a finest measurement of poverty. The empirical results for 170 countries in 2000 are based on two different multidimensional analysis of poverty, the Totally Fuzzy Analysis and the Factorial Analysis of Correspondences. The conclusions depend on the considered method but are generally similar. The paper focuses on the African continent and shows that some countries are “richer” than “developed” or inversely. The correlation matrix between different indicators reveals that education is a key variable for defining poverty. Comparisons extended with HDI classification and GDP per capita classification prove that monetary poverty is related with all other dimensions of poverty and that the HDI takes into account its essential dimension even if it can’t be used to reduce some specific aspects as our original index.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA (IPM) MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL DAN REGRESI PROBIT ORDINAL (Studi Kasus Kabupaten/Kota di Jawa Tengah Tahun 2014)

Human Development Index (HDI) is one of the most important indicator to observe another dimensions of human development. The HDI is a measurement for achievement levels of the quality of human development. In conjuction with economic development, human development needs analysis avialibility and proper policymaking, as well as evaluating the on going development process, to find out its capability to improve the quality of life as an object of development. This study analyze HDI in the Districts/Cities of Central Java in 2014. The Central Java’s HDI data is categorized as low, medium, and high. The HDI presumed to be affected by many factors, such as high school participation rates, middle school graduates percentage, percentage of household with clean water access, numbers of health facility, open unemployment rate,and labour force participation rate. This study used the ordinal logistic regression and the ordinal probit regression as its statical analysis method. The result showed that factors affecting HDI in the Districts/Cities of Central Java in 2014 are percentage of household with clean water access and numbers of health facility. To evaluate the performance of ordinal logistic regression and the ordinal probit regression, researcher uses classification accuracy and AIC. Based on reasearch classification accuracy and AIC of each methods, the result showed that both the ordinal logistic regression and the ordinal probit regression has good result in analyzing factors affecting Human Development Index in the Districts/Cities of Central Java in 2014. Keywords: HDI, Ordinal Logistic Regression, Ordinal Probit Regression, Classification Accuracy, AI

Consequences of Data Error in Aggregate Indicators: Evidence from the Human Development Index

This paper examines the consequences of data error in data series used to construct aggregate indicators. Using the most popular indicator of country level economic development, the Human Development Index (HDI), we identify three separate sources of data error. We propose a simple statistical framework to investigate how data error may bias rank assignments and identify two striking consequences for the HDI. First, using the cutoff values used by the United Nations to assign a country as ‘low’, ‘medium’, or ‘high’ developed, we find that currently up to 45% of developing countries are misclassified. Moreover, by replicating prior development/macroeconomic studies, we find that key estimated parameters such as Gini coefficients and speed of convergence measures vary by up to 100% due to data error.measurement error, international comparative statistics

Trends in Human and Economic Development Across Countries

Convergence of the Human Development Index (HDI) and per capita income were tested between 1975 and 1998 using a yearly adjustment model. The results indicated convergence for HDI and divergence for income. Furthermore, there was an increase over the years in the average HDI for every group of countries as classified by HDI from low to high. The only group of countries with significant increase in income were the rich countries.

Corporation robots

Nowadays, various robots are built to perform multiple tasks. Multiple robots working together to perform a single task becomes important. One of the key elements for multiple robots to work together is the robot need to able to follow another robot. This project is mainly concerned on the design and construction of the robots that can follow line. In this project, focuses on building line following robots leader and slave. Both of these robots will follow the line and carry load. A Single robot has a limitation on handle load capacity such as cannot handle heavy load and cannot handle long size load. To overcome this limitation an easier way is to have a groups of mobile robots working together to accomplish an aim that no single robot can do alon

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe