Representações de ciência e tecnologia em Gustavo Barroso (1909-1935): nacionalismo autoritário, eugenia e antissemitismo

This study focuses on the representations of science and technology in the thinking of the Brazilian intellectual Gustavo Barroso in the period between 1909 and 1935. As one of the main leaders of the Brazilian Integralism, we will discuss,especially, his eugenic vision, prior to his participation in integralism and the influence of this science in building his authoritarian and anti-Semitic thinking. We seek to realize Gustavo Barroso dialogue, in a context of profound social and political changes, with the search for a nationalism "truly Brazilian." The methodology used was of a documentary and literature research , highlighting the analysis of Gustavo Barroso books, which were used as primary sources: Intelligencia das Coisas(1923), Aquém da Atlântida (1931), Brasil Colônia de Banqueiros (1934), O Integralismo de Norte a Sul (1934), O Quarto Império (1935) e A Palavra e o Pensamento Integralista (1935), and, also, some of his articles surveyed in the National History Museum collection. The survey results show that in this period Gustavo Barroso went on to develop in his writings an eugenic and authoritarian political vision, later, in his integralist phase, linked strongly to an anti-Semitic view.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.