Sinalização cromática da condição reprodutiva de fêmeas de segui comum (Callithrix jacchus)

As social animals, primates use different sensory modalities (acoustic, chemical, tactile and visual) to convey information about social and sexual status to conspecifics. Among these modalities, visual signals are widely used, especially color signals, since primates are the mammalian group that displays the greatest variety of colors in their skin and fur. Studies with Old World primate species suggest that hormonal variations are related to variations in the colors of individual faces and genitals. Therefore, chromatic cues can be used by conspecifics to identify the reproductive condition of an individual. To date, studies with the same approach are unknown for New World species. However, behavioral and physiological studies suggest that different New World primate species seem to perceive reproductive conditions such as the timing of female conception and gestation. Thus, in this study, our aim was to: i) identify whether there are chromatic cues on the skin of female common marmosets, (Callithrix jacchus) that indicate their reproductive condition; ii) define whether this chromatic variation can be perceived by all visual phenotypes known in this species; iii) identify if these chromatic cues can be perceived under different light intensity levels (dim, intermediate and high). For this, we selected 13 female common marmosets in four distinct reproductive conditions: pregnant female preceding parturition, postpartum mothers, noncycling and cycling females. The coloration of the skin in genital and thigh areas in females was measured using a spectrophotometer. Using mathematical models of visual perception, we calculated the values of quantum catch for each photoreceptor type known in this species, the visual opponency channels and color contrast between those body spots. Our results indicate the occurance of chromatic variations in the genital area during the weeks that precede and follow parturition, forming a U-pattern of variation perceptible to males and females in natural conditions of low and high luminosity. Furthermore, we observed distinct color patterns in the genital skin of pregnant and cycling females that indicate their reproductive conditions. Finally, we present evidence of color contrast in noncycling females that is higher than that of pregnant ones. This study suggests that there is a chromatic xii variation in the genital skin of females that can be perceived by conspecifics and that may be related to hormonal changes typical of pregnancy and the ovarian cycle

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Uma análise da aplicação do modelo de Rede Neural RePART em Comitês de classificadores

RePART (Reward/Punishment ART) is a neural model that constitutes a variation of the Fuzzy Artmap model. This network was proposed in order to minimize the inherent problems in the Artmap-based model, such as the proliferation of categories and misclassification. RePART makes use of additional mechanisms, such as an instance counting parameter, a reward/punishment process and a variable vigilance parameter. The instance counting parameter, for instance, aims to minimize the misclassification problem, which is a consequence of the sensitivity to the noises, frequently presents in Artmap-based models. On the other hand, the use of the variable vigilance parameter tries to smoouth out the category proliferation problem, which is inherent of Artmap-based models, decreasing the complexity of the net. RePART was originally proposed in order to minimize the aforementioned problems and it was shown to have better performance (higer accuracy and lower complexity) than Artmap-based models. This work proposes an investigation of the performance of the RePART model in classifier ensembles. Different sizes, learning strategies and structures will be used in this investigation. As a result of this investigation, it is aimed to define the main advantages and drawbacks of this model, when used as a component in classifier ensembles. This can provide a broader foundation for the use of RePART in other pattern recognition applications

Sistema não-Intrusivo para Estimação da Direção do olhar utilizando redes neurais artificiais

The fundamental senses of the human body are: vision, hearing, touch, taste and smell. These senses are the functions that provide our relationship with the environment. The vision serves as a sensory receptor responsible for obtaining information from the outside world that will be sent to the brain. The gaze reflects its attention, intention and interest. Therefore, the estimation of gaze direction, using computer tools, provides a promising alternative to improve the capacity of human-computer interaction, mainly with respect to those people who suffer from motor deficiencies. Thus, the objective of this work is to present a non-intrusive system that basically uses a personal computer and a low cost webcam, combined with the use of digital image processing techniques, Wavelets transforms and pattern recognition, such as artificial neural network models, resulting in a complete system that performs since the image acquisition (including face detection and eye tracking) to the estimation of gaze direction. The obtained results show the feasibility of the proposed system, as well as several feature advantages.