Forecasting the CATS benchmark with the Double Vector Quantization method

The Double Vector Quantization method, a long-term forecasting method based on the SOM algorithm, has been used to predict the 100 missing values of the CATS competition data set. An analysis of the proposed time series is provided to estimate the dimension of the auto-regressive part of this nonlinear auto-regressive forecasting method. Based on this analysis experimental results using the Double Vector Quantization (DVQ) method are presented and discussed. As one of the features of the DVQ method is its ability to predict scalars as well as vectors of values, the number of iterative predictions needed to reach the prediction horizon is further observed. The method stability for the long term allows obtaining reliable values for a rather long-term forecasting horizon.Comment: Accepted for publication in Neurocomputing, Elsevie

Determinants in the on-line distribution of digital content: an exploratory analysis

This article shows the phases – and discusses the results – of an empirical analysis addressing the legal business models that are adopted online to distribute digital content

A Spatial Dynamic Model of Population Changes in a Vulnerable Coastal Environment

Achieving coastal sustainability in low-lying coastal areas is a great challenge. This study developed a spatial dynamic model to study the coupled natural-human responses in the form of population changes in the Lower Mississippi River Basin region. The goal was to identify the key social-economic factors (utility) and selected environmental factors (such as hazards damage, elevation, and subsidence rate) that affect population changes, as well as how population changes affect the local utility and the local environment reciprocally. The study area was partitioned into the “north’ and the “south” by a hypothetical boundary to test the differences of the emergence. Areal interpolation techniques with volume preserving property were used to integrate all the data acquired from different sources and defined in various formats into a unified 3 km by 3 km cellular space. An Elastic Net model was built to extract the rules and calibrate the parameters. Genetic Algorithms were applied to calibrate the neighborhood effects. A Monte Carlo approach using random sampling was used to conduct the uncertainty analysis. The final model yielded an accuracy of above 97% in projecting both the population changes and the developed area percentage changes from 2000 to 2010. A resilience assessment framework and a sustainability assessment framework were used to examine the simulated results from 2010 to 2050. The low-resilience areas were found to concentrate in the “south” in the central metropolitan areas of New Orleans. The sustainability analysis shows that high-resilience areas will always be sustainable. However, for the low-resilience areas, three sustainability conditions can occur depending on the mitigation budget: the tipping space, the mitigatable space, and the sustainable space. A Relative Land Price concept was defined to indicate the surplus value of a spatial unit due to its population and utility. The low-resilience areas were found to have higher Relative Land Prices mainly due to their high populations. In the short time-period simulation (2010-2050), the “south” will fall behind the “north” in population growth and developed land increase, and its average population was projected to be decreasing. However, in the long time-period simulation (2010-2210), its average population is able to bounce back from a certain population level. The results from this study will shed light on the relationships between coastal hazards and human responses and provide valuable insight into the development of optimal strategies for coastal sustainability

Classifying the suras by their lexical semantics :an exploratory multivariate analysis approach to understanding the Qur'an

PhD ThesisThe Qur'an is at the heart of Islamic culture. Careful, well-informed interpretation of it is fundamental both to the faith of millions of Muslims throughout the world, and also to the non-Islamic world's understanding of their religion. There is a long and venerable tradition of Qur'anic interpretation, and it has necessarily been based on literary-historical methods for exegesis of hand-written and printed text. Developments in electronic text representation and analysis since the second half of the twentieth century now offer the opportunity to supplement traditional techniques by applying the newly-emergent computational technology of exploratory multivariate analysis to interpretation of the Qur'an. The general aim of the present discussion is to take up that opportunity. Specifically, the discussion develops and applies a methodology for discovering the thematic structure of the Qur'an based on a fundamental idea in a range of computationally oriented disciplines: that, with respect to some collection of texts, the lexical frequency profiles of the individual texts are a good indicator of their semantic content, and thus provide a reliable criterion for their conceptual categorization relative to one another. This idea is applied to the discovery of thematic interrelationships among the suras that constitute the Qur'an by abstracting lexical frequency data from them and then analyzing that data using exploratory multivariate methods in the hope that this will generate hypotheses about the thematic structure of the Qur'an. The discussion is in eight main parts. The first part introduces the discussion. The second gives an overview of the structure and thematic content of the Qur'an and of the tradition of Qur'anic scholarship devoted to its interpretation. The third part xvi defines the research question to be addressed together with a methodology for doing so. The fourth reviews the existing literature on the research question. The fifth outlines general principles of data creation and applies them to creation of the data on which the analysis of the Qur'an in this study is based. The sixth outlines general principles of exploratory multivariate analysis, describes in detail the analytical methods selected for use, and applies them to the data created in part five. The seventh part interprets the results of the analyses conducted in part six with reference to the existing results in Qur'anic interpretation described in part two. And, finally, the eighth part draws conclusions relative to the research question and identifies directions along which the work presented in this study can be developed