Context-sensitive analysis without calling-context

Since Sharir and Pnueli, algorithms for context-sensitivity have been defined in terms of 'valid' paths in an interprocedural flow graph. The definition of valid paths requires atomic call and ret statements, and encapsulated procedures. Thus, the resulting algorithms are not directly applicable when behavior similar to call and ret instructions may be realized using non-atomic statements, or when procedures do not have rigid boundaries, such as with programs in low level languages like assembly or RTL. We present a framework for context-sensitive analysis that requires neither atomic call and ret instructions, nor encapsulated procedures. The framework presented decouples the transfer of control semantics and the context manipulation semantics of statements. A new definition of context-sensitivity, called stack contexts, is developed. A stack context, which is defined using trace semantics, is more general than Sharir and Pnueli's interprocedural path based calling-context. An abstract interpretation based framework is developed to reason about stack-contexts and to derive analogues of calling-context based algorithms using stack-context. The framework presented is suitable for deriving algorithms for analyzing binary programs, such as malware, that employ obfuscations with the deliberate intent of defeating automated analysis. The framework is used to create a context-sensitive version of Venable et al.'s algorithm for analyzing x86 binaries without requiring that a binary conforms to a standard compilation model for maintaining procedures, calls, and returns. Experimental results show that a context-sensitive analysis using stack-context performs just as well for programs where the use of Sharir and Pnueli's calling-context produces correct approximations. However, if those programs are transformed to use call obfuscations, a contextsensitive analysis using stack-context still provides the same, correct results and without any additional overhead. © Springer Science+Business Media, LLC 2011.

Erudição e ciência: as procelas de Júlio Ribeiro no Brasil oitocentista

Pós-graduação em História - FCLAS

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

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.

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.

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

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

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

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

A current-mode based Field-Programmable Analog Array for signal processing applications

This paper presents a new approach to develop Field Programmable Analog Arrays (FPAAs),(1) which avoids excessive number of programming elements in the signal path, thus enhancing the performance. The paper also introduces a novel FPAA architecture, devoid of the conventional switching and connection modules. The proposed FPAA is based on simple current mode sub-circuits. An uncompounded methodology has been employed for the programming of the Configurable Analog Cell (CAC). Current mode approach has enabled the operation of the FPAA presented here, over almost three decades of frequency range. We have demonstrated the feasibility of the FPAA by implementing some signal processing functions.

Planar waveguides based on nanocrystalline and Er3+ doped SnO2

Luminescent SnO2: x%mol Er3+ (x=0.1-2.0) thin films have been spin coated on borosilicate and silica substrates from water colloidal suspensions that could be prepared containing up to 40% in weight SnO2 nanocrystalline powders. High Resolution Transmission Electron Microscopy results show the well known SnO2 cassiterite structure and nanocrystallites around 10 nm in diameter, corroborating results from X-ray diffraction. Mono and multi layers have been prepared from the stable colloidal suspensions and films thickness was observed to increase linearly, up to 200 nm, with the colloidal suspensions nanoparticles amount. Excitation and emission spectra have been measured and Er3+ ions were found to be essentially incorporated into the cassiterite structure, substituting for Sn4+, for doping concentration lower than 0.05 mol%. Er3+ ions also appear segregated at the grains surface for higher doping concentration. The optical parameters (refractive index, thickness and propagating modes) of a waveguide sample were measured at 632.8 and 543.4 nm by the prism coupling technique. A monomodal waveguide was obtained with attenuation loss of 3.5 dB/cm along a 2.5 cm optical path.

Mechanistic insights into the reaction between VO2+ and propene based on a DFT study

Calculations based on density functional theory have been carried out to investigate the free energy profiles at singlet and triplet electronic states associated with the gas-phase ion/molecule reactions of VO2++ ((1)A(1)/(3)A) with propene. The complex potential energy Surfaces, including Six reaction pathways (three dehydrogenation and three oxygen transfer processes), have been explored and analyzed. Along dehydrogenation reactive channels, three final products can be obtained: V(OH)(2)(+) ((1)Sigma(+)/(3)Sigma(-)) and allene (path Dehl), being the most kinetically and thermodynamically favorable reaction pathway, V(OH)(2)(+) ((1)Sigma(+)/(3)Sigma(-)) and propyne (path Deh2),and VO2+ ((1)A(1)/(3)A) and H-2 plus allene (path Deh3). The oxyoenation processes can yield its final products Vo(+) ((1)Delta/(3)Sigma) and acetone (path Ox1), VO+ ((1)Delta/(3)Sigma 2) and propanaldehyde (path Ox2), and VO+ ((1)Delta/(3)Sigma) and H-2 and propenaldehyde (path Ox3). Both paths Deh1 and Deh2 are associated with two consecutive hydrogen transfer processes from carbon atoms of the propene fragment to vanadyl oxygen atoms, while in path Deh3 the second hydrogen migration takes place to the vanadiurn atorn followed by the formation ola hydrogen molecule. Both paths Ox1 and Ox2 comprise an intramolecular hydrogen transfer between the ethylenic moiety of the propene fragment, while two consecutive hydrogen transfer processes take place from the propene fragment to oxygen and vanadium atoms of the vanadyl moiety along path Ox3. Three crossing points between both electronic states take place along path Deh1 (CP-Deh1) and path Deh2 (CP-Deh2) and in the entrance channel of oxidation processes (CP-Ox). A comparison with previous works on related reactions VO2+ + C2H4, VO2 + C2H6, and VO2+ + C3H8 allows us to rationalize the different reactivity patterns.

Programmable PFC based hybrid multipulse power rectifier for ultra clean power application

A novel hybrid three-phase rectifier is proposed. It is capable to achieve high input power factor (PF) and low total harmonic input currents distortion (THDI). The proposed hybrid high power rectifier is composed by a standard three-phase six-pulse diode rectifier (Graetz bridge) with a parallel connection of single-phase Sepic rectifiers in each three-phase rectifier leg. Such topology results in a structure capable of programming the input current waveform and providing conditions for obtaining high input power factor and low harmonic current distortion. In order to validate the proposed hybrid rectifier, this work describes its principles, with detailed operation, simulation, experimental results, and discussions on power rating of the required Sepic converters as related to the desired total harmonic current distortion. It is demonstrated that only a fraction of the output power is processed through the Sepic converters, making the proposed solution economically viable for very high power installations, with fast investment payback. Moreover, retrofitting to existing installations is also feasible since the parallel path can be easily controlled by integration with the existing dc-link. A prototype has been implemented in the laboratory and it was fully demonstrated to both operate with excellent performance and be feasibly implemented in higher power applications.

Optimum-Path Forest Classifier for Large Scale Biometric Applications

This paper addresses biometric identification using large databases, in particular, iris databases. In such applications, it is critical to have low response time, while maintaining an acceptable recognition rate. Thus, the trade-off between speed and accuracy must be evaluated for processing and recognition parts of an identification system. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. The existing Gauss-Laguerre Wavelet based coding scheme is used for iris encoding. The performance of the OPF and two other - Hamming and Bayesian - classifiers, is compared using small, medium, and large-scale databases. Such a comparison shows that the OPF has faster response for large-scale databases, thus performing better than the more accurate, but slower, classifiers.

A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.

A novel programmable PFC based hybrid rectifier for ultra clean power application

A novel hybrid high power rectifier capable to achieve unity power factor is proposed in this paper. Single-phase SEPIC rectifiers are associated in parallel with each leg of three-phase 6-pulse diode rectifier resulting in a programmable input current waveform structure. In this paper it is described the principles of operation of the proposed converter with detailed simulation and experimental results. For a total harmonic distortion of the input line current (THDI) less than 2% the rated power of the SEPIC rectifiers is 33%. Therefore, power rating of the SEPIC parallel converters is a fraction of the output power, on the range of 20% to 33% of the nominal output power, making the proposed solution economically viable for high power installations, with fast pay back of the investment. Moreover, retrofits to existing installations are also possible with this proposed topology, since the parallel path can be easily controlled by integration with the already existing de-link. Experimental results are presented for a 3 kW implemented prototype, in order to verify the developed analysis.