'Beheading', Rule Manipulation and Fraud: The Approval of Election Results in Brazil, 1894-1930

Studies of electoral fraud tend to focus their analyses only on the pre-electoral or electoral phases. By examining the Brazilian First Republic (1889-1930), this article shifts the focus to a later phase, discussing a particular type of electoral fraud that has been little explored by the literature, namely, that perpetrated by the legislatures themselves during the process of giving final approval to election results. The Brazilian case is interesting because of a practice known as degola ('beheading') whereby electoral results were altered when Congress decided on which deputies to certify as duly elected. This has come to be seen as a widespread and standard practice in this period. However, this article shows that this final phase of rubber-stamping or overturning election results was important not because of the number of degolas, which was actually much lower than the literature would have us believe, but chiefly because of their strategic use during moments of political uncertainty. It argues that the congressional certification of electoral results was deployed as a key tool in ensuring the political stability of the Republican regime in the absence of an electoral court.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

Dynamic Features for Iris Recognition

The human eye is sensitive to visible light. Increasing illumination on the eye causes the pupil of the eye to contract, while decreasing illumination causes the pupil to dilate. Visible light causes specular reflections inside the iris ring. On the other hand, the human retina is less sensitive to near infra-red (NIR) radiation in the wavelength range from 800 nm to 1400 nm, but iris detail can still be imaged with NIR illumination. In order to measure the dynamic movement of the human pupil and iris while keeping the light-induced reflexes from affecting the quality of the digitalized image, this paper describes a device based on the consensual reflex. This biological phenomenon contracts and dilates the two pupils synchronously when illuminating one of the eyes by visible light. In this paper, we propose to capture images of the pupil of one eye using NIR illumination while illuminating the other eye using a visible-light pulse. This new approach extracts iris features called "dynamic features (DFs)." This innovative methodology proposes the extraction of information about the way the human eye reacts to light, and to use such information for biometric recognition purposes. The results demonstrate that these features are discriminating features, and, even using the Euclidean distance measure, an average accuracy of recognition of 99.1% was obtained. The proposed methodology has the potential to be "fraud-proof," because these DFs can only be extracted from living irises.