Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

3D geovisualisation techniques applied in spatial data mining

The increase in the number of spatial data collected has motivated the development of geovisualisation techniques, aiming to provide an important resource to support the extraction of knowledge and decision making. One of these techniques are 3D graphs, which provides a dynamic and flexible increase of the results analysis obtained by the spatial data mining algorithms, principally when there are incidences of georeferenced objects in a same local. This work presented as an original contribution the potentialisation of visual resources in a computational environment of spatial data mining and, afterwards, the efficiency of these techniques is demonstrated with the use of a real database. The application has shown to be very interesting in interpreting obtained results, such as patterns that occurred in a same locality and to provide support for activities which could be done as from the visualisation of results. © 2013 Springer-Verlag.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

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

Paleometry: a brand new area in brazilian science

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

Evolutionary stages of environmental management in computer companies located in Brazil: case studies

This study aims at identifying which environmental management practices computer companies located in Brazil are adopting, and classifying these companies according to the evolutionary stages of environmental management. For such, the case study research method was used at three companies. Results show that environmental management in the sector is geared towards compliance with laws and eco-efficiency, aimed at cutting costs. It was concluded that classification does not occur in a linear manner with defined borders, and that the multinational companies located in Brazil are greatly influenced by their foreign head offices, which are at more advanced evolutionary stages.

Preserve and protect in a science museum

Reflections on the university campus usually focus on its relevancy as a research and teaching area; however, the need for preservation, protection, maintenance and cleaning only become visible in the event of inadequacy or lack thereof. The aim of this study is to address the characteristics of the preservation and security measures performed at the Science and Technology Park of the University of São Paulo (Parque de Ciência e Tecnologia da Universidade de São Paulo), agency subjected to the Dean’s Office for Culture and University Extension (Pró-Reitoria de Cultura e Extensão Universitária). Because of its history and location, the Park requires special care. The Park’s land formerly housed the Astronomical Observatory of São Paulo and the Institute of Astronomy and Geophysics of the University of São Paulo (Instituto de Astronomia e Geofísica – IAG-USP), within the Fontes do Ipiranga State Park (Parque Estadual das Fontes do Ipiranga – PEFI), in the city of São Paulo. Preservation and reconversion activities relative to historical buildings are developed at the Park. The institution’s location and its specificities require security in its borders, as well as in relation to the users of the park.

Swarm intelligence in computer vision: an application to object tracking

Tesis en inglés. Eliminadas las páginas en blanco del pdf

Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

Gender classification in large databases

[EN]In this paper, we address the challenge of gender classi - cation using large databases of images with two goals. The rst objective is to evaluate whether the error rate decreases compared to smaller databases. The second goal is to determine if the classi er that provides the best classi cation rate for one database, improves the classi cation results for other databases, that is, the cross-database performance.

Improving gender classification accuracy in the wild

[EN]In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Diff erent descriptors, resolutions and classfii ers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

An study on re-identification in RGB-D imagery

[EN]Re-identi fication is commonly accomplished using appearance features based on salient points and color information. In this paper, we make an study on the use of di fferent features exclusively obtained from depth images captured with RGB-D cameras. The results achieved, using simple geometric features extracted in a top-view setup, seem to provide useful descriptors for the re-identi fication task.

Automatic initialization for facial analysis in interactive robotics

[EN]This paper focuses on four different initialization methods for determining the initial shape for the AAM algorithm and their particular performance in two different classification tasks with respect to either the facial expression DaFEx database and to the real world data obtained from a robot’s point of view.

Useful computer vision techniques for human-robot interaction

[ES]This paper describes some simple but useful computer vision techniques for human-robot interaction. First, an omnidirectional camera setting is described that can detect people in the surroundings of the robot, giving their angular positions and a rough estimate of the distance. The device can be easily built with inexpensive components. Second, we comment on a color-based face detection technique that can alleviate skin-color false positives. Third, a simple head nod and shake detector is described, suitable for detecting affirmative/negative, approval/dissaproval, understanding/disbelief head gestures.