Crowd detection from still images

The analysis of human crowds has widespread uses from law enforcement to urban engineering and traffic management. All of these require a crowd to first be detected, which is the problem addressed in this paper. Given an image, the algorithm we propose segments it into crowd and non-crowd regions. The main idea is to capture two key properties of crowds: (i) on a narrow scale, its basic element should look like a human (only weakly so, due to low resolution, occlusion, clothing variation etc.), while (ii) on a larger scale, a crowd inherently contains repetitive appearance elements. Our method exploits this by building a pyramid of sliding windows and quantifying how “crowd-like” each level of the pyramid is using an underlying statistical model based on quantized SIFT features. The two aforementioned crowd properties are captured by the resulting feature vector of window responses, describing the degree of crowd-like appearance around an image location as the surrounding spatial extent is increased.

A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.

Face Recognition from Video Using the Generic Shape-Illumination Manifold

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence “reillumination” algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature

Multitasking: multiple, domain-specific cognitive functions in a virtual environment

Multitasking among three or more different tasks is a ubiquitous requirement of everyday cognition, yet rarely is it addressed in research on healthy adults who have had no specific training in multitasking skills. Participants completed a set of diverse subtasks within a simulated shopping mall and office environment, the Edinburgh Virtual Errands Test (EVET). The aim was to investigate how different cognitive functions, such as planning, retrospective and prospective memory, and visuospatial and verbal working memory, contribute to everyday multitasking. Subtasks were chosen to be diverse, and predictions were derived from a statistical model of everyday multitasking impairments associated with frontal-lobe lesions (Burgess, Veitch, de Lacy Costello, & Shallice, 2000b). Multiple regression indicated significant independent contributions from measures of retrospective memory, visuospatial working memory, and online planning, but not from independent measures of prospective memory or verbal working memory. Structural equation modelling showed that the best fit to the data arose from three underlying constructs, with Memory and Planning having a weak link, but with both having a strong directional pathway to an Intent construct that reflected implementation of intentions. Participants who followed their preprepared plan achieved higher scores than those who altered their plan during multitask performance. This was true regardless of whether the plan was efficient or poor. These results substantially develop and extend the Burgess et al. (2000b) model to healthy adults and yield new insight into the poorly understood area of everyday multitasking. The findings also point to the utility of using virtual environments for investigating this form of complex human cognition.

Cooperative and competitive structures of trust relations in teams

This article argues that it is not just trust-generating but also trust-inhibiting mechanisms that operate in teams, and that these cooperative and competitive structures of interpersonal relations of trust within teams may affect team performance. Specifically, we propose that the presence of trust-generating structures (e.g., reciprocity, trusting in the referrals of others we trust, trusting in high performers and more experienced people) and the absence of trust-inhibiting structures (e.g., not trusting in the referrals of others we trust) are more likely to be associated with successful teams. Using exponential random graph models, a particular class of statistical model for social networks, we examine three professional sporting teams from the Australian Football League for the presence and absence of these mechanisms of interpersonal relations of trust. Quantitative network results indicate a differential presence of these postulated structures of trust relations in line with our hypotheses. Qualitative comparisons of these quantitative findings with team performance measures suggest a link between trust-generating and trust-inhibiting mechanisms of trust and team performance. Further theorization on other trust-inhibiting structures of trust relations and related empirical work is likely to shed further light on these connections.

Time correlated anomaly detection based on inferences

Anomaly detection techniques are used to find the presence of anomalous activities in a network by comparing traffic data activities against a "normal" baseline. Although it has several advantages which include detection of "zero-day" attacks, the question surrounding absolute definition of systems deviations from its "normal" behaviour is important to reduce the number of false positives in the system. This study proposes a novel multi-agent network-based framework known as Statistical model for Correlation and Detection (SCoDe), an anomaly detection framework that looks for timecorrelated anomalies by leveraging statistical properties of a large network, monitoring the rate of events occurrence based on their intensity. SCoDe is an instantaneous learning-based anomaly detector, practically shifting away from the conventional technique of having a training phase prior to detection. It does acquire its training using the improved extension of Exponential Weighted Moving Average (EWMA) which is proposed in this study. SCoDe does not require any previous knowledge of the network traffic, or network administrators chosen reference window as normal but effectively builds upon the statistical properties from different attributes of the network traffic, to correlate undesirable deviations in order to identify abnormal patterns. The approach is generic as it can be easily modified to fit particular types of problems, with a predefined attribute, and it is highly robust because of the proposed statistical approach. The proposed framework was targeted to detect attacks that increase the number of activities on the network server, examples which include Distributed Denial of Service (DDoS) and, flood and flash-crowd events. This paper provides a mathematical foundation for SCoDe, describing the specific implementation and testing of the approach based on a network log file generated from the cyber range simulation experiment of the industrial partner of this project.

The influence of yarn parameters on the ultraviolet protection of yarns

 Improving ultraviolet (UV) protection of textiles is essential to protect wearers against UV radiation induced risks. In addition to fabric parameters, yarn parameters are important factors affecting UV protection of textiles. This work is to examine the influence of yarn parameters on UV protection in order to set up a statistical model for predicting the UV protection of yarns. Wool yarns with different variables were used to test the ultraviolet protection factor (UPF) values for data analysis and the model verification. The model provides the optimized parameters for the UV protective fabric design. This work is helpful as a pre-cursor to the development of a more advanced optical model, which will look at understanding the penetration of UV light through fibres, yarns and fabrics.

Probabilidade de financiamento por cartões de crédito híbridos

Esta pesquisa tem o objetivo de identificar as variáveis e sua influência na propensão à aquisição de crédito pessoal, propondo um modelo estatístico de propensão ao financiamento por cartões de crédito híbridos para maximização de contratação de crédito e otimização dos esforços de marketing. O estudo descritivo pode gerar insights para a compreensão da expansão do crédito ao consumo, sobretudo num contexto de escassez de opções de financiamento e limitação no canal de distribuição. Foram usados dados de uma base de clientes de uma instituição financeira com variáveis sócio demográficas e transacionai, e o modelo matemático foi seguido da validação de sua capacidade preditiva.

Uma investigação sobre o impacto dos accruals na variabilidade dos resultados nos diferentes contrastes cross-sectional nas firmas brasileiras de capital aberto

Esta dissertação tem como objetivo principal investigar o impacto dos accruals na variabilidade dos resultados corporativos (EVAR) que influenciam a aplicação prática do income smoothing nas firmas brasileiras de capital aberto. Inicialmente, é demonstrada a importância das demonstrações contábeis que devem ser evidenciadas em cumprimento aos princípios contábeis geralmente aceitos. Sua evidenciação deve representar a realidade econômico-financeira da firma para o processo de tomada de decisão dos acionistas e credores. Porém, em determinados momentos, os gestores se sentem motivados a praticar o gerenciamento dos resultados contábeis na tentativa de reduzir a variabilidade dos lucros por meio da utilização dos accruals. Os accruals correspondem à diferença entre o lucro líquido e o fluxo de caixa operacional. Nesse processo de redução da volatilidade dos resultados, os gestores se utilizam da prática do income smoothing procurando reduzir eventuais distorções no preço das ações da firma. A amostra neste estudo é composta por um grupo de 163 firmas de capital aberto listadas na Bovespa e que apresentaram informações financeiras no intervalo de 2000 a 2007, categorizadas por setores através de dados obtidos na Economática. O modelo estatístico utilizado na pesquisa foi a análise de regressão para explicar os diferentes modelos de cross-sectional. Os resultados desta pesquisa indicam que os accruals são significativos para explicar a variabilidade dos resultados corporativos (EVAR) de empresas brasileiras. Além disso, nossos resultados sugerem que o modelo estrutural de identificação do EVAR nas empresas brasileiras deve ser explicado por variáveis não contábeis diferentes das que são apresentadas pelas firmas norte-americanas.

Dual resource transfers and interruption in external debt service

Externai debt service requires a dual resource transfer. Trade surpluses have to be generated in order to make foreign exchange revenues available for debt repayment. In addition, with developing countries' externai debt being largely a public liability, debt service requires that resources can be effectively transferred from the private to the public sector. This paper derives a statistical model for dealing with dual constraints in the presence of binary dependent variables and applies it to the dual resource transfer problem. The results from the estimation of the model for a sample of 31 middle-income developing countries in the period of 1980 to 1990, strongly support the hypothesis that both externai and fiscal constraints are important in explaining externai debt service disruptions.

Modelagem analítica e experimental da filtração em meios porosos

Deep bed filtration occurs in several industrial and environmental processes like water filtration and soil contamination. In petroleum industry, deep bed filtration occurs near to injection wells during water injection, causing injectivity reduction. It also takes place during well drilling, sand production control, produced water disposal in aquifers, etc. The particle capture in porous media can be caused by different physical mechanisms (size exclusion, electrical forces, bridging, gravity, etc). A statistical model for filtration in porous media is proposed and analytical solutions for suspended and retained particles are derived. The model, which incorporates particle retention probability, is compared with the classical deep bed filtration model allowing a physical interpretation of the filtration coefficients. Comparison of the obtained analytical solutions for the proposed model with the classical model solutions allows concluding that the larger the particle capture probability, the larger the discrepancy between the proposed and the classical models

Genetic associations between weight at maturity and maturation rate with ages and weights at first and second calving in Canchim beef cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Efeito da linhagem citoplasmática sobre a produção de leite em bovinos da raça Caracu

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efeito da idade da vaca e da data juliana de nascimento sobre o ganho médio diário de bezerros de corte no período pré-desmame

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Qualidade em serviços de saúde: uma contribuição à definição de um modelo paramétrico e padrão de qualidade do tempo agendado para consulta ambulatorial

This work presents a study in quality of health care, with focus on consulting appointment. The main purpose is to define a statistical model and propose a quality grade of the consulting appointment time. The time considered is that from the day the patient get the appointment done to the day the consulting is realized. It is used reliability techniques and functions that has as main characteristic the analysis of data regarding the time of occurrence certain event. It is gathered a random sample of 1743 patients in the appointment system of a University Hospital - the Hospital Universitário Onofre Lopes - of the Federal University of Rio Grande do Norte, Brazil. The sample is randomly stratified in terms on clinical specialty. The data were analyzed against the parametric methods of the reliability statistics and the adjustment of the regression model resulted in the Weibull distribution being best fit to data. The quality grade proposed is based in the PAHO criteria for a consulting appointment and result that no clinic got the PAHO quality grade. The quality grade proposed could be used to define priority for improvement and as criteria to quality control