International Economic Integration entails collaborative efforts among nations to overcome barriers and achieve shared benefits. The development of analytical models is crucial for comprehending the interaction of countries as a collective entity. In South America, MERCOSUR stands out, comprising Argentina, Brazil, Paraguay, and Uruguay. In this study, Economic Complexity metrics were applied to analyze productive capacities within MERCOSUR. The results underscore integration's significance by forming economic blocs, capabilities are expanded, enriching diversity and the scope of production. Economic complexity serves as a pivotal tool for assessing interdependence and enhancing countries' global positioning. In summary, this work highlights how economic integration, exemplified by MERCOSUR, enhances productive capacity, propelling development, and competitiveness in an interconnected world. Leveraging a novel computational mathematical model, this study offers insights into the complex dynamics of international economic integration, shedding light on strategies to foster growth and collaboration in a rapidly evolving global landscape.

In general, Multiobjective Optimization Problems (MOPs) with conflicting objectives requires a Decision Maker to select a solution from a set of alternatives in the Pareto Front. A visual approach is a valuable alternative to analyze the several options that may exist. Thus, to support the visual analysis, this work proposes to combine a clustering method based on the shape of the solutions together with the parallel coordinate graph. The shape of a solution is defined as the positions of the ordered values of the normalized objectives that correspond to a solution. The method proposed in this work, allows exploring groups of solutions with similar shapes, to generate visual summaries with a high degree of representativeness, and to calculate statistical data of the groups and to identify the solutions. Also, this paper presents a software tool called Tava. This tool was developed to perform the visual analysis interactively.

The Kinect sensor is a device that can recognize the user's hands and body movements without controllers or marks. Although Kinect was originally designed for the Xbox 360 game console, programming tools for PC appeared shortly after its release. As a result, many papers and applications explore Kinect's technology nowadays, in areas such as robotics and medicine. Kinect can be specially useful for pre-school education, since the human-computer interaction with hand movements supports the concept of hand-eye coordination, an ability to be developed during the pre-school age. This work presents a prototype that uses Kinect and includes educational games for pre-schoolers. This prototype was tested in an educational center for pre-school children, and was evaluated positively. From this experience we concluded that these kind of tools has a high potential in the educational field, whereas there is a need of interaction standards to aid the development of such kind of applications.

In face recognition systems that work with databases of thousands of images it is not practical to compare the query image against each stored image to determine similarity. The increasing use of these systems in various fields creates the need to explore mechanisms for efficient and effective search in terms of use of computing resources and percentage of hits. In order to reduce the load of face recognition systems and improve their response time, several alternatives to reduce or eliminate the need for an exhaustive search of images were developed. Indexing methods are one of these alternatives. This paper presents a new indexing technique based on permutations which, optimizes storage of images and accelerates the search process to predict the similarity between objects. The proposed method shows improved behaviour when compared against other techniques representing the state of the art using the FERET database.

In the last decades, a large number of metrics has been proposed to compare the performance of different evolutionary approaches in multi-objective optimization. This situation leads to difficulties when comparisons among the output of different algorithms are needed and appropriate metrics must be selected to perform those comparisons. Hence, no complete agreement on what metrics should be used exists. This paper presents a review and analysis of 54 multi-objective-optimization metrics in the specialized literature, discussing the usage, tendency and advantages/disadvantages of the most cited ones in order to give researchers enough information when choosing metrics is necessary. The review process performed in this work indicates that the hypervolume is the most used metric, followed by the generational distance, the epsilon indicator and the inverted generational distance.

A network design is robust if it is able to deal with any traffic requirement under certain bounds and physical network conditions. The robust network design is a complex problem of growing importance where, in general, the only information available are traffic bounds of the network links. This work proposes a Genetic Algorithm to design robust networks with optimal capacity of links considering a stable routing with uncertain traffic that can be divided in k sub-routes. The uncertain traffic is handled by usign the hose model which imposes a maximum input/output traffic for each network node. Experimental results for a set of instances with different number of routes show the convenience of a stable routing against a non-divisible routing (k = 1). However, an increasing of the k value implies an increasing of the number of viable solutions, thus, a trade-off relation between k and the quality of solutions obtained by the proposed algorithm was detected.

Survivability against link failures in WDM optical networks with dynamic traffic, i.e. requirements that change in time, has become very important because it is a more realistic approach than static traffic. This work evaluates the efficiency of the protection method based on p-cycles or pre-configured protection cycles, with a multi-objective approach, optimizing simultaneously network resources as well as the amount of needed reconfigurations in the nodes when working capacity changes. With this purpose we have developed a new multi-objective genetic algorithm which calculates both objectives. We also show experimental results, analyzing the effects of the small-batch size of requirements in each network reconfiguration, and the stability of p-cycles, i.e. the variation of these parameters in each time period.

This work presents a new mining model to train Radial Basis Neural Network (RBNN) for short term prediction. Training is performed in two phases. First, weights of the radial basis function (RBF) are trained and in the second phase, a competitive cooperative coevolutionary multiobjective algorithm is used to determine the parameters for each RBF node. The model has been applied to real problems in time series prediction and the obtained results are similar to those obtained by a model representing the state-of-the-art in bioinspired algorithms for time series prediction.