Fast fault section estimation in distribution control centers using adaptive genetic algorithm

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

Pseudechis guttatus venom proteome: Insights into evolution and toxin clustering

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Automatic visual dictionary generation through optimum-path forest clusteringautomatic visual dictionary generation through optimum-path forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.

Effect of sublethal doses of the insecticide imidacloprid on adaptive traits of Drosophila melanogaster: Response to treatment over and after 15 consecutive generations

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

Identification of the notothenioid sister lineage illuminates the biogeographic history of an Antarctic adaptive radiation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Chemotactic signal transduction and phosphate metabolism as adaptive strategies during citrus canker induction by Xanthomonas citri

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

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)

Metrics for Association Rule Clustering Assessment

Issues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.

Numerical tests of an adaptive sectioned airfoil actuated by shape memory alloys

The main objective of this work is to illustrate an application of angular active control in a sectioned airfoil using shape memory alloys. In the proposed model, one wants to establish the shape of the airfoil profile based on the determination of an angle between its two sections. This angle is obtained by the effect of the shape memory of the alloy by passing an electric current that modifies the temperature of the wire through the Joule effect, changing the shape of the alloy. This material is capable of converting thermal energy into mechanical energy and once permanently deformed, the material can return to its original shape by heating. Due to the presence of nonlinear effects, especially in the mathematical model of the alloy, this work proposes the application of a control system based on fuzzy logic. Through numerical tests, the performance of the fuzzy controller is compared with an on-off controller applied in a sectioned airfoil model.

A comparison about evolutionary algorithms for optimum-path forest clustering optimization

In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.

Scalable Web Server Clustering Technologies

The exponential growth of the Internet, coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. Server clustering has emerged as a promising technique to build scalable Web servers. In this article we examine the seminal work, early products, and a sample of contemporary commercial offerings in the field of transparent Web server clustering. We broadly classify transparent server clustering into three categories.

A Generic Autonomous Clustering-Based Heterogeneous Waveband Switching Architecture in WDM Networks

Heterogeneous waveband switching (HeteroWBS) in WDM networks reduces the network operational costs. We propose an autonomous clustering-based HeteroWBS architecture to support the design of efficient HeteroWBS algorithms under dynamic traffic requests in such a network.

Layered Clustering Communication Protocol for Wireless Sensor Networks

In this paper, we propose a Layered Clustering Hierarchy (LCH) communication protocol for Wireless Sensor Networks (WSNs). The design of LCH has two goals: scalability and energy-efficiency. In LCH, the sensor nodes are organized as a layered clustering structure. Each layer runs a distributed clustering protocol. By randomizing the rotation of cluster heads in each layer, the energy load is distributed evenly across sensors in the network. Our simulations show that LCH is effective in densely deployed sensor networks. On average, 70% of live sensor nodes are involved directly in the clustering communication hierarchy. Moreover, the simulations also show that the energy load and dead nodes are distributed evenly over the network. As studies prove that the performance of LCH depends mainly on the distributed clustering protocol, the location of cluster heads and cluster size are two critical factors in the design of LCH.

A Comparison of Adaptive Strategies and Patterns of Victimization among Homeless Adolescents and Adults

One hundred and fifty-six homeless adolescents and 319 homeless adults interviewed directly on the streets and in shelters were compared for backgrounds of abuse, adaptations to life on the streets, and rates of criminal victimization when on the streets. Homeless adolescents were more likely to be from abusive family backgrounds, more likely to rely on deviant survival strategies, and more likely to be criminally victimized. A social learning model of adaptation and victimization on the streets was hypothesized. Although the model was supported for both homeless adults and adolescents, it was more strongly supported for adolescents than adults, and for males than females regardless of age.

A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity