Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Automatic 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. © 2012 IEEE.

Automatic landslide recognition through Optimum-Path Forest

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.

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.

Speeding up optimum-path forest training by path-cost propagation

In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training. © 2012 ICPR Org Committee.

An iterated sample construction with path relinking method: Application to switch allocation in electrical distribution networks

We present a metaheuristic approach which combines constructive heuristics and local searches based on sampling with path relinking. Its effectiveness is demonstrated by an application to the problem of allocating switches in electrical distribution networks to improve their reliability. Our approach also treats the service restoration problem, which has to be solved as a subproblem, to evaluate the reliability benefit of a given switch allocation proposal. Comparisons with other metaheuristics and with a branch-and-bound procedure evaluate its performance. © 2012 Published by Elsevier Ltd.

Unfolding physics from the algebraic classification of spinor fields

After reviewing the Lounesto spinor field classification, according to the bilinear covariants associated to a spinor field, we call attention and unravel some prominent features involving unexpected properties about spinor fields under such classification. In particular, we pithily focus on the new aspects - as well as current concrete possibilities. They mainly arise when we deal with some non-standard spinor fields concerning, in particular, their applications in physics. © 2012 Elsevier B.V.

Microbiological control of moisturizing mask formulation added of hibiscus flowers, assai palm, black mulberry and papaw glycolic extracts

The microbiological control of moisturizing mask formulation added of hibiscus flowers, assai palm, black mulberry and papaw glycolic extracts, determining the number of viable microorganisms and possible presence of pathogenic. The moisturizing mask formulation was composed of zinc oxide (5. 0%) and moisturizing cream constituted of triceteareth-4 phosphate (and) cetyl alcohol (and) stearyl alcohol (and) sodium cetearyl sulfate (and) oleth-10 (qs 50g). To this formulation was added hibiscus flowers glycolic extract (2. 5%), assai palm glycolic extract (1. 5%), black mulberry glycolic extract (1. 5%) and papaw glycolic extract (2. 0%). The formulation was stored in aseptically clean recipients, away from humidity and light, in fresh and airy places. The results of the microbiological analysis on the counting of aerobic mesophilic microorganisms (bacteria and fungi), of the above mentioned formulation, revealed a bioburden < 10 CFU/mL in all samples. Such data indicate adequate microbiological quality of the tested products, according to official recommendations. Furthermore, it was not detected the presence of pathogenic microorganisms, assuring the harmlessness of the formulation. The results lead us to conclude that the formulation and raw materials analyzed did not present microbial contamination, evidenced for estimating the number of viable microorganisms (<10 UFC/g) and for researching pathogens.

Biochemical biomarkers in Scinax fuscovarius tadpoles exposed to a commercial formulation of the pesticide fipronil

One of the main pesticides used in the cultivation of sugarcane in São Paulo State, Brazil, is Regent®800WG, the main active compound of which is fipronil. Fipronil is a potent insecticide that eliminates pests, including insects resistant to pyrethroids, organophosphates (OP) and carbamates (CA). There is little known on the toxic effects of fipronil on non-target organisms, such as tadpoles of frogs. It is possible that this compound carries a high toxicity for these organisms, since the pesticide can be incorporated into aquatic environments during the rainy season, a time which coincides with the time of amphibian reproduction and the occurrence of tadpoles in the aquatic environment in this region. Thus, the pesticide could be contributing to the decline of amphibians in the northwest region of São Paulo state due to its wide use. This study aimed to test the influence of Regent®800WG on some biochemical systems of tadpoles (such as antioxidant defense systems) at different stages of development. The results of analysis from in vivo exposures demonstrated that only a few parameters in the groups exposed to fipronil responded to exposure to Regent®800WG, results which indicate that the pesticide instigates biochemical responses in tadpoles. Although catalase and glucose-6-phosphate dehydrogenase (G6PDH) were unchanged during the experiments, glutathione-S-transferase (GST) was inhibited in tadpoles, and the activity of glutathione reductase (GR) varied according to the exposure period and pesticide concentration. This data demonstrated the influence of the fipronil formulation on the metabolism of tadpoles, and showed that it can increase their susceptibility to environmental contaminants. © 2013 Elsevier Ltd. All rights reserved.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

The algebraic structure behind the derivative nonlinear Schrödinger equation

The Kaup-Newell (KN) hierarchy contains the derivative nonlinear Schrödinger equation (DNLSE) amongst others interesting and important nonlinear integrable equations. In this paper, a general higher grading affine algebraic construction of integrable hierarchies is proposed and the KN hierarchy is established in terms of an Ŝℓ2Kac-Moody algebra and principal gradation. In this form, our spectral problem is linear in the spectral parameter. The positive and negative flows are derived, showing that some interesting physical models arise from the same algebraic structure. For instance, the DNLSE is obtained as the second positive, while the Mikhailov model as the first negative flows. The equivalence between the latter and the massive Thirring model is also explicitly demonstrated. The algebraic dressing method is employed to construct soliton solutions in a systematic manner for all members of the hierarchy. Finally, the equivalence of the spectral problem introduced in this paper with the usual one, which is quadratic in the spectral parameter, is achieved by setting a particular automorphism of the affine algebra, which maps the homogeneous into principal gradation. © 2013 IOP Publishing Ltd.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Anthelmintic efficacy of an oral formulation of Aurixazol against gastrointestinal nematodes of naturally and experimentally infected sheep

As a result of the need to develop new active principles for the control of endoparasites in ruminants, the present in vivo study evaluated a formulation containing 24% Aurixazol (48 mg/kg), a parasiticide molecule based on disophenolate of levamisole. Two experiments were conducted: one evaluating the anthelmintic efficacy of 24% Aurixazol (48 mg/kg) against gastrointestinal nematodes in naturally infected sheep, compared to an association of ivermectin (0.2 mg/kg) + albendazole (5.0 mg/kg) + levamisole (7.5 mg/kg) (IAL), and a second one which evaluated the persistent efficacy of the same formulation against immature stages (L4) and adults of Haemonchus contortus in experimentally infected animals. In experiment I, against H. contortus, the formulation of Aurixazol and the IAL association reached efficacies (arithmetic means) of 99.32% and 96.11%, respectively. For Trichostrongylus colubriformis, the efficacy values were 88.92% and 98.08% for Aurixazol and the IAL association, respectively. Both formulations were totally effective against Oesophagostomum columbianum (100%). The results of the statistical analysis demonstrated that the mean parasitic burden of treated animals was significantly different (P ≤ 0.05) compared to the average number of helminths diagnosed in animals from the control group for H. contortus, T. colubriformis and O. columbianum. Comparing only the treated groups, it was possible to verify that the average number of H. contortus recovered from animals treated with Aurixazol was different (P ≤ 0.05) when compared to the mean amount recovered from sheep treated with the IAL association. When evaluating the prevention of H. contortus infection in experiment II, Aurixazol did not present preventive efficacy. Up until 21 days after treatment the groups treated with Aurixazol contained less adults and L4 of H. contortus (P ≤ 0.05) when compared to the non-medicated control group. However, future studies will be necessary to assess the effectiveness of Aurixazol against nematode strains resistant to levamisole and disophenol, but the efficacy results described in this study allow to state that Aurixazol can, associated with other measures, become an important tool in the control of sheep nematodes. © 2013.

Análise não-linear física de vigas de concreto armado utilizando o elemento finito prismático regular linear associado ao de barra

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)