Assessing the Number of Components in Mixture Models: a Review.

Despite the widespread application of finite mixture models, the decision of how many classes are required to adequately represent the data is, according to many authors, an important, but unsolved issue. This work aims to review, describe and organize the available approaches designed to help the selection of the adequate number of mixture components (including Monte Carlo test procedures, information criteria and classification-based criteria); we also provide some published simulation results about their relative performance, with the purpose of identifying the scenarios where each criterion is more effective (adequate).Finite mixture; number of mixture components; information criteria; simulation studies.

Democracy and Economic Development: a Fuzzy Classification Approach

The aim of this work is to (1) analyse whether countries differ on political indicators (democracy, rule of law, government effectiveness and corruption) and (2) study whether countries with different political profiles are associated with different levels of economic, human development and gender-related development indicators. Using a fuzzy classification approach (fuzzy k-means algorithm), we propose a typology of 124 countries based on 10 political variables. Six segments are identified; these political groups implicate the access to different levels of economic and human development. In this study evidence of a positive but not perfect relationship between democracy and economic and human development is observed, thus presenting new insights for the understanding of the heterogeneity of behaviors relatively to political indicators.Democracy, Economic Development, Fuzzy k-means

Ways of Applying Artificial Intelligence in Software Engineering

As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use