The Computational Analysis of Bulgarian Dialect Pronunciation

The paper presents a computational analysis of Bulgarian dialect variation, concentrating on pronunciation differences. It describes the phonetic data set compiled during the project* ‘Measuring Linguistic Unity and Diversity in Europe’ that consists of the pronunciations of 157 words collected at 197 sites from all over Bulgaria. We also present the results of analyzing this data set using various quantitative methods and compare them to the traditional scholarship on Bulgarian dialects. The results have shown that various dialectometrical techniques clearly identify east-west division of the country along the ‘jat’ border, as well as the third group of varieties in the Rodopi area. The rest of the groups specified in the traditional atlases either were not confirmed or were confirmed with a low confidence.

Business Modeling of the Application Architecture of the Bulgarian Folklore Artery

In an attempt to answer the need of wider accessibility and popularization of the treasury of Bulgarian folklore, a team from the Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences has planned to develop the Bulgarian folklore artery within the national project ―Knowledge Technologies for Creation of Digital Presentation and Significant Repositories of Folklore Heritage‖. This paper presents the process of business modeling of the application architecture of the Bulgarian folklore artery, which aids requirements analysis, application design and its software implementation. The folklore domain process model is made in the context of the target social applications—e-learning, virtual expositions of folklore artifacts, research, news, cultural/ethno-tourism, etc. The basic processes are analyzed and modeled and some inferences are made for the use cases and requirements specification of the Bulgarian folklore artery application. As a conclusion the application architecture of the Bulgarian folklore artery is presented.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.