Modularization in Belief-Desire-Intention agent programming and artifact-based environments.

This article proposes an extension for the Agents and Artifacts meta-model to enable modularization. We adopt the Belief-Desire-Intention (BDI) model of agency to represent independent and reusable units of code by means of modules. The key idea behind our proposal is to take advantage of the syntactic notion of namespace, , a unique symbol identifier to organize a set of programming elements.

Wearables for Engagement Detection in Learning Environments: A Review.

Appropriate teaching-learning strategies lead to student engagement during learning activities. Scientific progress and modern technology have made it possible to measure engagement in educational settings by reading and analyzing student physiological signals through sensors attached to wearables. This work is a review of current student engagement detection initiatives in the educational domain.

How good is crude MDL for solving the bias-variance dilemma? An empirical investigation based on Bayesian networks.

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly).

Evaluation of the diagnostic power of thermography in breast cancer using Bayesian network classifiers.

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages.