EVIV: An end-to-end verifiable Internet voting system

Traditionally, a country's electoral system requires the voter to vote at a specific day and place, which conflicts with the mobility usually seen in modern live styles. Thus, the widespread of Internet (mobile) broadband access can be seen as an opportunity to deal with this mobility problem, i.e. the adoption of an Internet voting system can make the live of voter's much more convenient; however, a widespread Internet voting systems adoption relies on the ability to develop trustworthy systems, i.e. systems that are verifiable and preserve the voter's privacy. Building such a system is still an open research problem. Our contribution is a new Internet voting system: EVIV, a highly sound End-to-end Verifiable Internet Voting system, which offers full voter's mobility and preserves the voter's privacy from the vote casting PC even if the voter votes from a public PC, such as a PC at a cybercafe or at a public library. Additionally, EVIV has private vote verification mechanisms, in which the voter just has to perform a simple match of two small strings (4-5 alphanumeric characters), that detect and protect against vote manipulations both at the insecure vote client platform and at the election server side. (c) 2012 Elsevier Ltd. All rights reserved.

A experiência de marca na fidelização do consumidor: o caso Starbucks Portugal

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

Tudo no lugar certo: trabalho de projecto

Trabalho de Projecto submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro - especialização em Teatro e Comunidade.

Projeto de desenvolvimento de App de apoio ao cliente para operador de telecomunicações

Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

Main causes of death in Dande, Angola: results from verbal autopsies of deaths occurring during 2009–2012

bACKGROUND - The Dande Health and Demographic Surveillance System (HDSS) located in Bengo province, Angola, covers nearly 65,500 residents living in approximately 19,800 households. This study aims to describe the main causes of deaths (CoD) occurred within the HDSS, from 2009 to 2012, and to explore associations between demographic or socioeconomic factors and broad mortality groups (Group I-Communicable diseases, maternal, perinatal and nutritional conditions; Group II-Non-communicable diseases; Group III-Injuries; IND-Indeterminate). Methods - Verbal Autopsies (VA) were performed after death identification during routine HDSS visits. Associations between broad groups of CoD and sex, age, education, socioeconomic position, place of residence and place of death, were explored using chi-square tests and fitting logistic regression models. Results - From a total of 1488 deaths registered, 1009 verbal autopsies were performed and 798 of these were assigned a CoD based on the 10th revision of the International Classification of Diseases (ICD-10). Mortality was led by CD (61.0%), followed by IND (18.3%), NCD (11.6%) and INJ (9.1%). Intestinal infectious diseases, malnutrition and acute respiratory infections were the main contributors to under-five mortality (44.2%). Malaria was the most common CoD among children under 15 years old (38.6%). Tuberculosis, traffic accidents and malaria led the CoD among adults aged 15–49 (13.5%, 10.5 % and 8.0% respectively). Among adults aged 50 or more, diseases of the circulatory system (23.2%) were the major CoD, followed by tuberculosis (8.2%) and malaria (7.7%). CD were more frequent CoD among less educated people (adjusted odds ratio, 95% confidence interval for none vs. 5 or more years of school: 1.68, 1.04–2.72). Conclusion - Infectious diseases were the leading CoD in this region. Verbal autopsies proved useful to identify the main CoD, being an important tool in settings where vital statistics are scarce and death registration systems have limitations.

An Evaluation of Local Feature Combiners for Robot Visual Localization

In the last decade, local image features have been widely used in robot visual localization. To assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image to those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, we compare several candidate combiners with respect to their performance in the visual localization task. A deeper insight into the potential of the sum and product combiners is provided by testing two extensions of these algebraic rules: threshold and weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance. The voting method, whilst competitive to the algebraic rules in their standard form, is shown to be outperformed by both their modified versions.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.