Gestão de imagens em movimento de matérias jornalísticas em banco de dados na TV Digital

Pós-graduação em Televisão Digital: Informação e Conhecimento - FAAC

Comparative analysis of different meat traceability systems using multiple criteria and a social network approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Meio ambiente e desenvolvimento: a construção do debate ambiental em O Correio da UNESCO (1972-1992)

Pós-graduação em História - FCLAS

Fluxo e tecnologias de informação no contexto brasileiro de inventário de ciclo de vida

Pós-graduação em Ciência da Informação - FFC

O papel da comunicação no processo de transferência de tecnologia da Embrapa para a comunidade indígena Campo Alegre de Boa Vista, RR

Pós-graduação em Comunicação - FAAC

Comunicação pública e acesso à informação: análise da atuação da SABESP em plataformas virtuais

This undergraduate research final paper analyzed the communication process carried out by the São Paulo State Water and Sanitation Company - Sabesp from the perspective of public communication, social capital and right to information theories. By monitoring Sabesp's institutional fanpage and website from 24 to 30 August 2015, it sought to assess the performance of Sabesp in disclosing public information on the context of the water supply crisis in São Paulo, concerning the fulfillment of requirements of the Right to Information Law (12.527/2011) and the need for interaction and dialogue between the institution and its stakeholders, taken as principles of public communication. The results suggest that digital media can enhance information flow and contribute to foster public relations and civic participation, but there are opportunities for Sabesp to improve communication and reach greater transparency

A gestão do fluxo das informações contábeis-financeiras como subsídio do processo de decisão: estudo de caso em uma instituição de ensino superior do terceiro setor

Pós-graduação em Ciência da Informação - FFC

O atraso na retroalimentação auditiva e seus efeitos nas disfluências típicas da gagueira

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise de integridade física de barragens de terra a partir da integração do método geofísico da eletrorresistividade com ensaios geotécnicos

Pós-graduação em Geociências e Meio Ambiente - IGCE

A criança nativa digital no contexto de filmes publicitários

Pós-graduação em Psicologia - FCLAS

JVM-based Techniques for Improving Java Observability

Observability measures the support of computer systems to accurately capture, analyze, and present (collectively observe) the internal information about the systems. Observability frameworks play important roles for program understanding, troubleshooting, performance diagnosis, and optimizations. However, traditional solutions are either expensive or coarse-grained, consequently compromising their utility in accommodating today’s increasingly complex software systems. New solutions are emerging for VM-based languages due to the full control language VMs have over program executions. Existing such solutions, nonetheless, still lack flexibility, have high overhead, or provide limited context information for developing powerful dynamic analyses. In this thesis, we present a VM-based infrastructure, called marker tracing framework (MTF), to address the deficiencies in the existing solutions for providing better observability for VM-based languages. MTF serves as a solid foundation for implementing fine-grained low-overhead program instrumentation. Specifically, MTF allows analysis clients to: 1) define custom events with rich semantics ; 2) specify precisely the program locations where the events should trigger; and 3) adaptively enable/disable the instrumentation at runtime. In addition, MTF-based analysis clients are more powerful by having access to all information available to the VM. To demonstrate the utility and effectiveness of MTF, we present two analysis clients: 1) dynamic typestate analysis with adaptive online program analysis (AOPA); and 2) selective probabilistic calling context analysis (SPCC). In addition, we evaluate the runtime performance of MTF and the typestate client with the DaCapo benchmarks. The results show that: 1) MTF has acceptable runtime overhead when tracing moderate numbers of marker events; and 2) AOPA is highly effective in reducing the event frequency for the dynamic typestate analysis; and 3) language VMs can be exploited to offer greater observability.

Análise de integridade física de barragens de terra a partir da integração do método geofísico da eletrorresistividade com ensaios geotécnicos

Pós-graduação em Geociências e Meio Ambiente - IGCE

A criança nativa digital no contexto de filmes publicitários

Pós-graduação em Psicologia - FCLAS

Apectos conceituais e metodológicos de redes sociais e sua influência no estudo de fluxos de informação

As ligações e interações propiciadas pelas redes sociais permitem compreender como ocorrem os fluxos de informação entre indivíduos e instituições que unem esforços na busca de metas comuns. O artigo apresenta aspectos conceituais sobre redes e redes sociais ressaltando que a estrutura e as relações de interação e intermediação entre os elos da rede impulsionam mudanças nos fluxos de informação. Descreve a metodologia de Análise de Redes Sociais (ARS) sinalizando como esta pode ser utilizada na área da Ciência da Informação para compreender os fluxos de informação que se configuram e re-configuram nas redes sociais a partir da estrutura de relacionamento

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.