Capability outcomes from educational and ICT capability inputs : an analysis of ICT use in informal education in Kenya

In the field of Information and Communication Technologies for Development (ICT4D) ICT use in education is well studied. Education is often seen as a pre-requisite for development and ICTs are believed to aid in education, e.g. to make it more accessible and to increase its quality. In this paper we study the access and use of ICT in a study circle (SC) education program in the south coast of Kenya. The study is qualitative reporting results based on interviews and observations with SC participants, government officers and SC coordinators and teachers. The study builds on the capability approach perspective of development where individuals’ opportunities and ability to live a life that they value are focused. The aim of the study is to investigate the capability outcomes enabled through the capability inputs access and use of ICT in education as well as the factors that enabled and/or restricted the outcomes. Findings show that many opportunities have been enabled such as an increase in the ability to generate an income, learning benefits, community development and basic human development (e.g. literacy and self-confidence). However, conversion factors such as a poorly developed infrastructure and poor IT literacy prevent many of the individuals from taking full advantage of the ICT and the opportunities it enables. 

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.