Aircraft accident in Brazil: Boeing vs. Legacy?

Air accidents represent a small proportion of the flights registered worldwide. Airplane collisions in the air are rare. In September of 2006, a Boeing 737-800 collided in midair with a Legacy Jet. It was the largest accident registered in the history of Brazilian aviation until that time. The present study explores aspects of press coverage of the accident. Data and information reported in the media about the accident from September 2006 to August 2007 were collected and discussed. Media coverage called attention to two unusual aspects: politicisation of the discussion, culminating in the opening of congressional inquiries, and equally the concomitance of police investigations interfering in the work of agencies responsible for the official accident investigation. Emphasis on assigning guilt and establishing penalties may close the windows of opportunity an accident had opened for discussions on the improvement of air safety. In Brazil, political imperatives and organizational pressures have interfered and the possibilities of organizational learning from the accident have been drastically curtailed.

Uma análise sobre o suporte dos fatores de recursos humanos à gestão ambiental: estudo de caso em um hotel do interior do Estado de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influências dos fatores de recursos humanos para a inovação em serviços de informação: estudo de casos

Pós-graduação em Engenharia de Produção - FEB

Saúde e segurança do trabalhador do barro em arranjos produtivos locais: o caso do artesanato de barro nos bairros Olarias e Poti Velho na cidade de Teresina - Piauí

Pós-graduação em Geografia - IGCE

Indicador multivariado policórico: proposta de indicador para o diagnóstico da aprendizagem organizacional

Pós-graduação em Engenharia Mecânica - FEG

Gestão do Conhecimento aplicada à redes de Fast-Food

The knowledge currently stands as one of the intangible assets of fundamental importance in the competitive market in which it is organizations. Knowledge management has become mandatory in the competitive environment of today, as a tool to systematize knowledge within the organization. Underscoring the importance of this management process, this thesis aimed to identify possible problems and generate recommendations for improving the performance of Knowledge Management. The objective was achieved by conducting the analysis of a case study of a fast-food franchise through a case study in the ground plan of all the franchise restaurants. For the preparation of the case study was required to respect a theoretical review on the introduction of the concepts of knowledge management in fast-food restaurants, in addition to reviewing the theory on the concepts of organizational learning and on Standardization. When finalizing the theoretical review and analyze the case study was proposed recommendations and highlight difficulties and good practice found in the analyzed organization, and procedures for demonstrating the success of knowledge management in organizations

Acidentes de trabalho em hospital público de alta complexidade no interior paulista: estudo das concepções de segurança

Pós-graduação em Saúde Coletiva - FMB

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

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

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

A video-based biometric authentication for e-learning web applications

In the last years there was an exponential growth in the offering of Web-enabled distance courses and in the number of enrolments in corporate and higher education using this modality. However, the lack of efficient mechanisms that assures user authentication in this sort of environment, in the system login as well as throughout his session, has been pointed out as a serious deficiency. Some studies have been led about possible biometric applications for web authentication. However, password based authentication still prevails. With the popularization of biometric enabled devices and resultant fall of prices for the collection of biometric traits, biometrics is reconsidered as a secure remote authentication form for web applications. In this work, the face recognition accuracy, captured on-line by a webcam in Internet environment, is investigated, simulating the natural interaction of a person in the context of a distance course environment. Partial results show that this technique can be successfully applied to confirm the presence of users throughout the course attendance in an educational distance course. An efficient client/server architecture is also proposed. © 2009 Springer Berlin Heidelberg.

Autenticação biométrica de usuários em sistemas de E-learning baseada em reconhecimento de faces a partir de vídeo

Pós-graduação em Ciência da Computação - IBILCE

Spatial memory in sedentary and trained diabetic rats: Molecular mechanisms

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

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.