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Background Mammographic breast density is an important predictor of breast cancer, but its measurement has limitations related to subjectivity of visual evaluation or to difficult access for automatic volumetric measurement methods. Herein, we describe the design and clinical validation of Aguida, a software program for automated quantification of breast density from flat mammography images. Materials and methods The software program was developed in MatLab. After image segmentation separating the background from the breast image, the operator positions a cursor defining a region of interest on the pectoralis major muscle from the mediolateral oblique view. Then, in the craniocaudal view, the threshold for separation of the dense tissue is based on the optical density of the pectoral muscle, and the proportion of dense tissue is calculated by the program. Mammograms obtained from 2 different occasions in 291 women were used for clinical evaluation. Results The intraclass correlation coefficient (ICC) between breast density measurements by the software and by a radiologist was 0.96, with a bias of only 0.67 percentage points and a 95% limit of agreement of 13.5 percentage points; the ICC was 0.94 in the interobserver reliability assessment by two radiologists with different experience; and the ICC was 0.98 in the intraobserver reliability assessment. The distribution among the density classes was close to the values obtained with the volumetric software. Conclusions Measurement of breast density with the Aguida program from flat mammography images showed high agreement with the visual determination by radiologists, and high inter- and intra-observer reliability.

Given the actual volume and speed of financial transactions, financial fraud detection systems are constantly evolving based on new computational intelligence algorithms. Therefore, transaction monitoring and analysis prevent monetary losses caused by fraudsters. Since the fraud detection process is a labor-intensive task for human auditors given the huge amount of daily transactions processed by financial services information systems. Credit card is the financial product most explored in the financial fraud detection literature, while mobile money service is becoming a popular option for payments, fraud detection for such financial product has not yet been deeply explored. Therefore, it is interesting to optimize the auditing process and test new quantitative techniques, such as deep learning, to support human auditors before double-checking a suspicious transaction. Thus, we propose an integration of adversarial autoencoders and machine learning methods to perform an objective classification among three transaction types: regular, local, and global anomaly. The integration consists of using the autoencoder's generated latent vectors as features for the supervised learning algorithms. The experiments considered different latent vector space forms concerning their dimensionality and the clusters generated by a prior Gaussian mixture. The results show that some classifiers may accept latent characteristics well, getting better or similar performance when using all the original characteristics.

A substantial number of expert and intelligent systems rely on deep learning methods to solve problems in areas such as economics, physics, and medicine. Improving the accuracy of the activation functions used by such methods can directly and positively impact the overall performance and quality of the mentioned systems at no cost whatsoever. In this sense, enhancing the design of such theoretical fundamental blocks is of great significance as it immediately impacts a broad range of current and future real-world deep learning based applications. Therefore, in this paper, we turn our attention to the interworking between the activation functions and the batch normalization, which is practically a mandatory technique to train deep networks currently. We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models. These Convolutional Neural Networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learning computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments (p

The increasing demand for renewable energy has been promoting a rapid development of photovoltaic technologies. Given this, photoinduced thermal emission is being explored with the aim of improving solar cell performance by converting low-energy IR photons into visible light. Here, we report the light-induced blackbody emission from LnO2 (Ln = Pr and Tb) as a potential emitter for thermophotovoltaic applications. Lanthanide dioxides display broad IR absorption and a direct optical band gap of 1.49 (PrO2) and 1.51 eV (TbO2). These materials achieve a maximum temperature of ∼1500 K under a power density excitation of 160 W cm−2 and display a stable bright light emission. Thermal emission can be tuned from laser power density modulation.

Digital Libraries have been developed nowadays as a way to dispose digital information through the Internet. This is particularly very useful when the information comes from historical documents. This research takes place in the PROHIST Project [Mello et al., 2008] which aims the creation of a digital library with methods to preserve and broadcast images of historical documents. In general, the access to original documents has to be done carefully as, because of its age, the paper is more susceptible to the wear and tear over time. In order to make the documents more easily accessible, digitization comes as the most efficient solution. In a digital media, as digital images, the documents can be visualized and copied. This also helps the preservation of the documents as they are digitized in high resolution and in true color format. It is common to use JPEG file format (Sayood, 1996) to store these images ensuring a good space storage/quality ratio. However, even in this format, to access an archive of thousands of high quality true color images is not an easy task even with the extended use of broad band Internet. The storage space of the images can be reduced with its conversion to black-and-white images. In this bi-level format and stored using GIF file format, the size of the file can be five times lower than the original true color JPEG image. Binarization or thresholding (Parker, 1997) is the process that converts an image into black-and-white: a threshold value is defined and the colors above that value are converted into white, while the colors below it are converted into black. This is a very simple process in digital image processing when one has a document with black ink written on a white paper. Historical documents, however, have several types of noises. The degradation yellows the sheet of paper and creates some noise that is perceptible to the digitizing process. Even more, in some cases, the ink has faded. This is particularly important when the document is written on both sides of the paper. In some cases, the ink of one side interferes in the other creating an effect called “ink bleeding”. Because of these problems, it is very difficult to find the best threshold value that separates the colors that belong to the paper from the colors that belong to the ink. An example of such a document is presented in Figure 1-left. In this paper, we present a new thresholding algorithm for color quantization based on genetic algorithms and image fidelity metrics. These metrics are used to define the convergence point of the genetic algorithm. The quantized image is then binarized based on

SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context., arXiv admin note: text overlap with arXiv:2004.03373

This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV). SigNet is a state of the art Deep CNN model for feature representation in the HSV context and contains 2048 dimensions. Some of these dimensions may include redundant information in the dissimilarity representation space generated by the dichotomy transformation (DT) used by the writer-independent (WI) approach. The analysis is carried out on the GPDS-960 dataset. Experiments demonstrate that the proposed method is able to control overfitting during the search for the most discriminant representation.

In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy. Current out-of-distribution (OOD) detection approaches usually do not directly fix the SoftMax loss drawbacks, but rather build techniques to circumvent it. Unfortunately, those methods usually produce undesired side effects (e.g., classification accuracy drop, additional hyperparameters, slower inferences, and collecting extra data). In the opposite direction, we propose replacing SoftMax loss with a novel loss function that does not suffer from the mentioned weaknesses. The proposed IsoMax loss is isotropic (exclusively distance-based) and provides high entropy posterior probability distributions. Replacing the SoftMax loss by IsoMax loss requires no model or training changes. Additionally, the models trained with IsoMax loss produce as fast and energy-efficient inferences as those trained using SoftMax loss. Moreover, no classification accuracy drop is observed. The proposed method does not rely on outlier/background data, hyperparameter tuning, temperature calibration, feature extraction, metric learning, adversarial training, ensemble procedures, or generative models. Our experiments showed that IsoMax loss works as a seamless SoftMax loss drop-in replacement that significantly improves neural networks' OOD detection performance. Hence, it may be used as a baseline OOD detection approach to be combined with current or future OOD detection techniques to achieve even higher results., Accepted for publication in the IEEE Transactions on Neural Networks and Learning Systems: Special Issue on Deep Learning for Anomaly Detection

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation. Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context. In this work, we present a white-box analysis of this approach highlighting how it handles the challenges, the dynamic selection of references through fusion function, and its application for transfer learning. All the analyses are carried out at the instance level using the instance hardness (IH) measure. The experimental results show that, using the IH analysis, we were able to characterize “good” and “bad” quality skilled forgeries as well as the frontier region between positive and negative samples. This enables futures investigations on methods for improving discrimination between genuine signatures and skilled forgeries by considering these characterizations.

The detection of objects considering a 6DoF pose is a common requirement to build virtual and augmented reality applications. It is usually a complex task which requires real-time processing and high precision results for adequate user experience. Recently, different deep learning techniques have been proposed to detect objects in 6DoF in RGB images. However, they rely on high complexity networks, requiring a computational power that prevents them from working on mobile devices. In this paper, we propose an approach to reduce the complexity of 6DoF detection networks while maintaining accuracy. We used Knowledge Distillation to teach portables Convolutional Neural Networks (CNN) to learn from a real-time 6DoF detection CNN. The proposed method allows real-time applications using only RGB images while decreasing the hardware requirements. We used the LINEMOD dataset to evaluate the proposed method, and the experimental results show that the proposed method reduces the memory requirement by almost 99\% in comparison to the original architecture with the cost of reducing half the accuracy in one of the metrics. Code is available at https://github.com/heitorcfelix/singleshot6Dpose., This paper was accepted by 2020 International Joint Conference on Neural Networks (IJCNN)

Most of the automatic fire alarm systems detect the fire presence through sensors like thermal, smoke, or flame. One of the new approaches to the problem is the use of images to perform the detection. The image approach is promising since it does not need specific sensors and can be easily embedded in different devices. However, besides the high performance, the computational cost of the used deep learning methods is a challenge to their deployment in portable devices. In this work, we propose a new deep learning architecture that requires fewer floating-point operations (flops) for fire recognition. Additionally, we propose a portable approach for fire recognition and the use of modern techniques such as inverted residual block, convolutions like depth-wise, and octave, to reduce the model's computational cost. The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops. One of our models presents 71\% fewer parameters than FireNet, while still presenting competitive accuracy and AUROC performance. The proposed methods are evaluated on FireNet and FiSmo datasets. The obtained results are promising for the implementation of the model in a mobile device, considering the reduced number of flops and parameters acquired., Comment: Accepted in the IEEE International Joint Conference on Neural Networks (IJCNN), 2020

A recent study has presented a model, called the increasing-decreasing-linear (IDL) model, which is able to efficiently predict the high-frequency stock market. Nevertheless, a drawback arises from the IDL's learning process, which consists of its costly methodology to circumvent the non-differentiability problem of increasing and decreasing operators. In this sense, trying to reduce the computational cost of the IDL design, we propose evolutionary learning machines, using the genetic algorithm, the particle swarm optimizer, the backtracking search algorithm, the firefly algorithm and the cuckoo search, to design the IDL model. Five relevant high-frequency time series from the Brazilian stock market are used to assess performance, and the achieved results have demonstrated better prediction performance with smaller computational cost when compared to those achieved by the IDL model designed by its classical learning process, as well as to those achieved by some relevant prediction models presented in the literature.

Supervised machine learning and opinion lexicon are the most frequent approaches for opinion mining, but they require considerable effort to prepare the training data and to build the opinion lexicon, respectively. In this paper, a novel unsupervised clustering approach is proposed for opinion mining. Three swarm algorithms based on Particle Swarm Optimization are evaluated using three corpora with different levels of complexity with respect to size, number of opinions, domains, languages, and class balancing. K-means and Agglomerative clustering algorithms, as well as, the Artificial Bee Colony and Cuckoo Search swarm-based algorithms were selected for comparison. The proposed swarm-based algorithms achieved better accuracy using the word bigram feature model as the pre-processing technique, the Global Silhouette as optimization function, and on datasets with two classes: positive and negative. Although the swarm-based algorithms obtained lower result for datasets with three classes, they are still competitive considering that neither labeled data, nor opinion lexicons are required for the opinion clustering approach.

Documents written in natural language constitute a major part of the artefacts produced during the software engineering life cycle. Studies indicate that more than 80% of enterprise data is stored in some sort of unstructured form, mainly as text. Therefore, the growth of user-generated content, especially from social media, provides a huge amount of data which allows discovering the experiences, opinions, and feelings of users. Text mining refers to the set of tools, techniques, and algorithms adopted to extract useful information from unstructured data. Considering that Portuguese ranks among the ten most spoken languages, and it is the second most common in Twitter, this study aims to map current primary studies that relate to the application of text mining for Portuguese. A systematic mapping method was applied and 6075 primary studies were retrieved up to the year 2014. A total of 203 studies were included, from which more than 60% analyse texts written in Brazilian variant. The majority of studies focus on the text classification task. Support vector machine and Naive Bayes appear as main the algorithms. Folha de Sao Paulo and Publico newspapers appear as main corpora, followed by the Portuguese Attorney General's Office corpus and Twitter.

The dilation-erosion-linear perceptron is a hybrid morphological neuron which has been recently proposed in the literature to solve some prediction problems. However, a drawback arises from such model for building mappings to solve tasks with complex input-output nonlinear relationships within effort estimation problems. In this sense, to overcome this limitation, we present a particular class of hybrid multilayer perceptrons, called the multilayer dilation-erosion-linear perceptron (MDELP), to deal with software development effort estimation problems. Each processing unit of the proposed model is composed of a mix between a hybrid morphological operator (given by a balanced combination between dilation and erosion operators) and a linear operator. According to Pessoa and Maragos’s ideas, we propose a descending gradient-based learning process to train the proposed model. Besides, we conduct an experimental analysis using relevant datasets of software development effort estimation and the achieved results are discussed and compared, according to MMRE and PRED25 measures, to those obtained by classical and state of the art models presented in the literature.

Identifying the language of a text is an important step for several natural language processing applications. State-of-the-art language identification (LID) systems perform very well when discriminating between unrelated languages on standard datasets. However, the LID task has a bottleneck when discriminating between similar languages or language varieties. Furthermore, LID has also proven to be very challenging when dealing with short texts such as the ones from Twitter. In this paper, we propose the use of smoothed n-gram language models to classify tweets in both Brazilian and European Portuguese variants. Word and character n-gram language models were combined and evaluated through five different classifiers. We have compared the smoothed n-gram language models together with the Term Frequency and Inverse Document Frequency weighting scheme. This paper also proposes an ensemble model, in which the class labels output were combined using majority voting and algebraic combiners. The best configuration reached accuracy of 92.71% using an ensemble model, which combines Lidstone (0.1) character 6-gram, Good–Turing word unigram, and Witten–Bell word bigram models, together with the Log-Likelihood Ratio estimation method.

The dilation–erosion perceptron (DEP) is a hybrid morphological processing unit, composed of a balanced combination between dilation and erosion morphological operators, recently presented in the literature to solve some problems. However, a drawback arises from such model for building complex decision surfaces for non-linearly separable data. In this sense, to overcome this drawback, we present a particular class of morphological neural networks with multilayer structure, called the dilation–erosion neural network (DENN), to deal with binary classification problems. Each processing unit of the DENN is composed by a DEP processing unit. Also, a descending gradient-based learning process is presented to train the DENN, according to ideas from Pessoa and Maragos. Furthermore, we conduct an experimental analysis with the DENN using a relevant set of binary classification problems, and the obtained results indicate similar or superior classification performance to those achieved by classical and state of the art models presented in the literature.

Introduction The Ts65Dn mouse is the most widely used animal model of Down syndrome (DS). Differences in autonomic regulation of heart rate variability (HRV) in individuals with DS have been hypothesized. Pharmacological studies in animal models may help us understand mechanisms underlying observed changes in HRV in people with DS. Objective To investigate the use a new, noninvasive technique to assess cardiac autonomic modulation in Ts65Dn mice under the effect of adrenergic and cholinergic agonists. Method We recorded electrocardiograms (ECGs) from 12 Ts65Dn and 12 euploid control mice. A 30‐min baseline recording was followed by the injection of an adrenergic (isoproterenol [Iso]) or cholinergic (carbachol [CCh]) agonist. Heart rate and HRV were analyzed using a series of methods customized for mice. Results and Discussion The ECG apparatus described here allowed us to detect noninvasively long series of heartbeats in freely‐moving animals. During baseline conditions, the yield of detectable heartbeats was 3%–27% of the estimated total number of events, which increased to 35%–70% during the 15‐min period after either Iso or CCh injections. Ts65Dn mice displayed a robust enhanced Iso‐induced negative chronotropic rebound response compared with euploid control mice. We observed a significantly smaller CCh response in Ts65Dn versus control euploid mice in the 6‐ to 10‐min‐interval postcarbachol injection. Conclusion This work showed that the techniques described here are sufficient for this type of study. However, future studies involving the use of more selective pharmacological agents and/or genetic manipulations will be key to advance a mechanistic understanding of cardiac autonomic regulation in DS., We used a new, noninvasive technique to assess cardiac autonomic modulation in the Ts65Dn mouse model of Down syndrome under the effect of adrenergic and cholinergic agonists. This new ECG recording apparatus has the potential to be very useful to investigators studying heart rate and heart rate variability in rodent models of human disorders. However, future studies involving the use of more selective pharmacological agents and/or genetic manipulations of the mouse models of DS will be necessary to complement this work.

The lack of labeled data and the need to update the learning model are the two main challenges when performing Opinion Mining with data streams. Usual Opinion Mining approaches are not able to deal with these challenges, nor with the characteristics brought by this kind of data. Moreover, the occurrence of changes (drifts) in the concepts and/or opinions is another issue. Possible alternative solutions to these problems are: the use of Semi-supervised learning, such as Active Learning, which labels selected data rather than the entire data set; or Multiple Classifiers Systems, which combines different classifiers and are well suited to deal with drifts. In this study, we combined these two approaches, proposing the use of eight Active Learning sampling strategies as a generation method for Multiple Classifiers Systems. The Active Learning approach requires the choice of a strategy to select the instances, and each strategy results in the creation of a distinct classification model. Our method was evaluated in 14 Twitter stream data sets and the results showed that it can be better for Opinion Mining with data streams than popular ensemble generation methods present in the literature, such as Bagging and AdaBoost.

Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., classification accuracy drop, slower energy-inefficient inferences). We argue that these issues are a consequence of the SoftMax loss anisotropy and disagreement with the maximum entropy principle. Thus, we propose the IsoMax loss and the entropic score. The seamless drop-in replacement of the SoftMax loss by IsoMax loss requires neither additional data collection nor hyperparameter validation. The trained models do not exhibit classification accuracy drop and produce fast energy-efficient inferences. Moreover, our experiments show that training neural networks with IsoMax loss significantly improves their OOD detection performance. The IsoMax loss exhibits state-of-the-art performance under the mentioned conditions (fast energy-efficient inference, no classification accuracy drop, no collection of outlier data, and no hyperparameter validation), which we call the seamless OOD detection task. In future work, current OOD detection methods may replace the SoftMax loss with the IsoMax loss to improve their performance on the commonly studied non-seamless OOD detection problem., Accepted for publication in The International Joint Conference on Neural Networks (IJCNN), 2021

There are two main problems when performing Opinion Mining (OM) with data streams: the lack of labeled data and the need to update the learning model. The most used OM techniques cannot deal well with these challenges, so, an alternative is to use semi-supervised methods, such as the Active Learning, which is a method to label only selected data rather than the entire data set; however, it requires the choice of a sampling strategy to select the data to be labeled. In this paper, we evaluated eight strategies in ten data sets, in order to identify the best ones for OM with Twitter streams. According to our experiments, the Entropy strategy showed the best results, but it selects a large number of instances to be labeled, requiring further investigation.

When compared to Writer-Dependent (WD) Handwritten Signature Verification, in which a model is trained for each individual writer, the Writer-Independent (WI) approach offers greater scalability, since only a single model is trained for all users from a dissimilarity space generated by the dichotomy transformation. However, many samples from the dissimilarity space are redundant and have little influence during the training of the verification model. This work investigates whether prototype selection (PS) preprocessing can be used in the space resulting from the dichotomy transformation without degrading the performance of the classifier. Furthermore, an investigation is also performed to examine the use of a WI classifier in a transfer learning scenario, i.e., where the classifier is trained in one dataset, and is used to verify signatures in other datasets. The experiments reported herein show that the use of prototype selection in the dissimilarity space allows a reduction in the complexity of the classifier without degrading its generalization performance. In addition, the results show that the WI classifier is scalable enough to be used in a transfer learning approach, with a resulting performance comparable to that of a classifier trained and tested in the same dataset. An analysis of the results obtained based on the instance hardness (IH) measure and dendrogram diagrams is performed in order to better understand the behavior of the resulting dichotomy transformation.

Made available in DSpace on 2019-10-03T17:31:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2019. Added 1 bitstream(s) on 2019-10-04T16:20:46Z : No. of bitstreams: 1 S1807-59322019000100264.pdf: 973926 bytes, checksum: 7adab7e44ab58177364c74382fbaf3d5 (MD5) OBJECTIVES: We aimed to evaluate the effects of cataract surgery on cardiac autonomic modulation.METHODS: A cross-sectional and analytical study was conducted at the Hospital Maria Braido in the city of São Caetano do Sul, São Paulo, between 2015 and 2016. We investigated 19 patients of both sexes who were all over 50 years old; all patients had a diagnosis of senile or bilateral cataracts and were recommended to undergo implantation of the intraocular lens. Heart rate variability (HRV) was evaluated before, during and after cataract surgery.RESULTS: There were no significant changes in the time and geometric domains of HRV before, during or after surgery. The high-frequency (HF) band in normalized units (nu) on the spectral analysis significantly increased (p=0.02, Cohen’s d=0.9, large effect size). However, the low-frequency (LF) band in nu significantly decreased during surgery (p=0.02, Cohen’s d=0.9, large effect size).CONCLUSION: Throughout the intraocular lens implantation cataract surgery, there was an increase in parasympathetic modulation and a decrease in the sympathetic component of the heart rate (HR). We propose that this result is attributable to the supine position of the patients during surgery and the trigeminal reflex. Centro Universitario Saude ABC Laboratorio de Delineamento de Estudos e Escrita Cientifica Escola Superior de Ciencias da Santa Casa de Misericordia Universidade Federal do Acre Programa de Mestrado em Ciencias da Saude da Amazonia Universidade Estadual Paulista Centro de Estudos do Sistema Nervoso Autonomo Faculdade de Filosofia e Ciencias Universidade de Sao Paulo Faculdade de Saude Publica Programa de Pos-Graduacao em Epidemiologia Universidade Federal de Sao Paulo Escola Paulista de Medicina Oxford Brookes University, Headington Campus School of Health and Life Sciences Department of Biological and Medical Sciences University of Limerick Graduate Entry Medical School Universidade Estadual Paulista Centro de Estudos do Sistema Nervoso Autonomo Faculdade de Filosofia e Ciencias

The Software Effort Estimation (SEE) process has been approached in different ways in the literature, including models built from Machine Learning (ML). The combination of these models (Ensemble) is an important research topic in ML, and has lead to improvements in accuracy compared to individual models. This paper proposes heterogeneous and dynamic ensemble selection (DES) models, composed by a set of regressors dynamically selected by classifiers to estimate software development effort. In the training phase, a pool of regression algorithms is trained using training data and a validation data set to validate the models. Next, some classifiers are trained to identify the best regression model from the pool for each training instance. In the test phase each trained classifier is used to dynamically select a regressor model from the pool for predicting the effort for each test instance. The final prediction is given by the combination of the predictions of the regressors selected by the classifiers. An experimental analysis considering a relevant set of software effort estimation problems is reported. The experiments demonstrate that the proposed method outperforms individual regressors and some state of the art models of the literature.

Semantic segmentation is an essential task in computer vision that aims to label each image pixel. Several of the actual best approaches in this context are based on deep neural networks. For example, SegNet is a deep encoderdecoder architecture approach whose results were disruptive because it is fast and performs well. However, this architecture fails to finedelineating the edges between the objects of interest in the image. We propose some modifications in the SegNet-Basic architecture by using a post-processing segmentation layer (using Conditional Random Fields) and by transferring high resolution features combined to the decoder network. The proposed method was evaluated in the dataset CamVid. Moreover, it was compared with important variants of SegNet and showed to be able to improve the overall accuracy of SegNet-Basic by up to 17.5%.

The use of features extracted using a deep convolutional neural network (CNN) combined with a writer-dependent (WD) SVM classifier resulted in significant improvement in performance of handwritten signature verification (HSV) when compared to the previous state-of-the-art methods. In this work it is investigated whether the use of these CNN features provide good results in a writer-independent (WI) HSV context, based on the dichotomy transformation combined with the use of an SVM writer-independent classifier. The experiments performed in the Brazilian and GPDS datasets show that (i) the proposed approach outperformed other WI-HSV methods from the literature, (ii) in the global threshold scenario, the proposed approach was able to outperform the writer-dependent method with CNN features in the Brazilian dataset, (iii) in an user threshold scenario, the results are similar to those obtained by the writer-dependent method with CNN features.

We present two new hybrids of FCM and improved self-adaptive PSO.The methods are based on the FCM-PSO algorithm.We use FCM to initialize one particle to achieve better results in less iterations.The new methods are compared to FCM-PSO using many real and synthetic datasets.The proposed methods consistently outperform FCM-PSO in three evaluation metrics. Fuzzy clustering has become an important research field with many applications to real world problems. Among fuzzy clustering methods, fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency, although it shows some weaknesses, particularly its tendency to fall into local minima. To tackle this shortcoming, many optimization-based fuzzy clustering methods have been proposed in the literature. Some of these methods are based solely on a metaheuristic optimization, such as particle swarm optimization (PSO) whereas others are hybrid methods that combine a metaheuristic with a traditional partitional clustering method such as FCM. It is demonstrated in the literature that methods that hybridize PSO and FCM for clustering have an improved accuracy over traditional partitional clustering approaches. On the other hand, PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques. Another problem with PSO-based clustering is that the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. In this paper we introduce two hybrid methods for fuzzy clustering that aim to deal with these shortcomings. The methods, referred to as FCM-IDPSO and FCM2-IDPSO, combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions. Experiments using two synthetic data sets and eight real-world data sets are reported and discussed. The experiments considered the proposed methods as well as some recent PSO-based fuzzy clustering methods. The results show that the methods introduced in this paper provide comparable or in many cases better solutions than the other methods considered in the comparison and were much faster than the other state of the art PSO-based methods.

In this paper, a new sequential learning algorithm is constructed by combining the Online Sequential Extreme Learning Machine (OS-ELM) and Kalman filter regression. The Kalman Online Sequential Extreme Learning Machine (KOSELM) handles the problem of multicollinearity of the OS-ELM, which can generate poor predictions and unstable models. The KOSELM learns the training data one-by-one or chunk-by-chunk by adjusting the variance of the output weights through the Kalman filter. The performance of the proposed algorithm has been validated on benchmark regression datasets, and the results show that KOSELM can achieve a higher learning accuracy than OS-ELM and its related extensions. A statistical validation for the differences of the accuracy for all algorithms is performed, and the results confirm that KOSELM has better stability than ReOS-ELM, TOSELM and LS-IELM.

Software development effort estimation is the process of predicting the effort required to develop a software system. In order to improve the estimation accuracy, many different models have been proposed in the literature. Multiple classification systems represent an important field of research for machine learning. In order to estimate software development effort, this paper proposes a heterogeneous and dynamic ensemble selection model, composed by a set of regressors dynamically selected by classifiers. Along with the proposed method it is conducted an experimental analysis involving a relevant set of software effort estimation problems, which has led to better results than those achieved by classical and state of the art models previously presented.

In this work, we investigate speaker-specific filter banks for text-independent speaker verification. The proposed method performs an heuristic search for the best filter-bank configuration using the Artificial Bee Colony (ABC) algorithm and a proper fitness function for the standard i-vectors/PLDA-based speaker verification system. Furthermore, filter-bank decorrelated amplitudes are used instead of the cepstral coefficients produced by Discrete Cosine Transform (DCT). In the experiments, the proposed method is compared to standard Mel and linear scales in both cases where the decorrelation is performed using DCT and high-pass filtering. The comparison is performed on the MIT Mobile Device Speaker Verification Corpus in a gender-dependent trial scheme. The proposed method outperformed the baseline systems in almost all the test sets for both genders. Performance gains of 4.6% and 26.0% are achieved for male and female speakers, respectively.

This paper makes use of diverse domains’ datasets to analyze the impact of image complexity and diversity on the task of transfer learning in deep neural networks. As the availability of labels and quality instances for several domains are still scarce, it is imperative to use the knowledge acquired from similar problems to improve classifier performance by transferring the learned parameters. We performed a statistical analysis through several experiments in which the convolutional neural networks (LeNet-5, AlexNet, VGG-11 and VGG-16) were trained and transferred to different target tasks layer by layer. We show that when working with complex low-quality images and small datasets, fine-tuning the transferred features learned from a low complexity source dataset gives the best results.

Novelty detection is a problem with a large number of relevant applications. For some applications, the main interest is the prevention or detection of undesired states. In some cases, these undesired states are not known in advance; in others, such as machine monitoring, for instance, machine breakdown may be very rare and examples of this event may be unavailable. In such cases, the most widely accepted approach is to model the normal behavior of the system in order to subsequently detect unknown events. This is the basic concept of One-Class Classification (OCC). In some preliminary works we have proposed the Feature Boundaries Detector for One-Class Classification FBDOCC method, which operates by examining each problem feature at a time. In this paper we propose an extensive study of the behavior of the FBDOCC and introduce another version of the method, namely FBDOCC2. This work, also considers the use of the Particle Swarm Optimization (PSO) algorithm to find the best parameter configuration of the proposed method. Furthermore, this work also introduces a procedure to improve the training time performance without degrading the quality of the classification as well as some other contributions hereafter described. A number of experiments were carried out with synthetic and real datasets aiming at comparing both versions of the FBDOCC method with the most recent and effective OCC methods, namely: SVDD, One-class SVM, Least Squares One-class SVM, Kernel PCA, Gaussian Process Prior OCC, Condensed Nearest Neighbor Data Description and One-class Random Forests. The evaluation metrics considered in the experiments are: (i) the Area Under the ROC Curve (AUC); (ii) the Matthews Correlation Coefficient (MCC); (iii) the training time; and (iv) the prototype reduction rate. Regarding the metrics AUC and MCC, the first FBDOCC version has presented the best overall results among all the methods whereas the FBDOCC2 obtained results comparable to the best methods in some experiments where the standard FBDOCC yielded a poorer performance. FBDOCC was the faster method to train in comparison to the other methods in all but one dataset. In addition, FBDOCC was much faster than all methods based on support vector machines.

Introduction Previous studies have shown that relaxation music increases the heart's parasympathetic modulation as well as reducing its sympathetic activity. However, what is lacking in the literature is information on the acute effects of different intensities of music on cardiac autonomic regulation. We aimed to evaluate the acute effects of baroque and heavy-metal music on cardiac autonomic regulation at different intensities. Method The study was performed in 16 healthy men aged between 18 and 25 years. The main outcomes were the geometric indices of heart-rate variability (HRV) [i.e. triangular index (RRtri); triangular interpolation of RR intervals (TINN) and Poincare plot: SD1, SD2 and SD1/SD2 ratio]. First, HRV was recorded at rest for 10 min. The volunteers were then exposed to baroque or heavy-metal music for 5 min through an earphone; subjects were exposed to each song at three different sound levels (60–70, 70–80 and 80–90 decibels). After the first song, subjects remained at rest for 5 min before being exposed to the next song. The sequence of songs and sound intensity were randomised for each individual. Results Musical auditory stimulation with baroque music did not influence the geometric indices of HRV. The same was observed with heavy-metal musical auditory stimulation at the three sound-level ranges. Conclusion Musical auditory stimulation at different sound intensities did not influence the geometric indices of HRV in men.

Cardiopulmonary bypass (CPB) is often associated with renal dysfunction, as measured by plasma creatinine levels and hemodialysis rates. Aim. To compare creatinine clearance (CrCl), estimated with the Cockroft and Gault formula, between patients undergoing off-pump coronary artery bypass grafting (OPCAB) versus on-pump CABG (on-CAB). Material and methods. Between April 2008 and April 2009, 119 patients underwent coronary bypass graft surgery. Fifty-eight (58) of these patients underwent OPCAB while 61 had on-CAB. Creatinine clearance, plasma creatinine levels, and clinical outcome were compared between the groups. A creatinine clearance value of 50 mL/minute was accepted as the lowest limit of normal renal function. Results. There were two hospital deaths caused by sepses after pulmonary infection. Creatinine clearance (Preoperative OPCAB 73,64±33,72 x on-CAB 75,70±34,30mL/min; discharge OPCAB 75,73±35,07 x on-CAB 79,07±34,71 mL/ min; p=0,609), and creatinine levels (Preoperative OPCAB 1,04±0,38 x on-CAB 1,13±0,53 mg/dL; discharge OPCAB 1,12±0,79 x on-CAB 1,04±0,29mg/dL; p=0,407) did not show statistically inter-group differences. Conclusion. Deterioration in renal function is associated with higher rates of postoperative complications. No significant difference in CrCl could be demonstrated between the groups.

The aim of this study was to investigate acute cardiac response and heart rate variability (HRV) when listening to differing forms of music. Eleven healthy men aged between 18 and 25 years old were included in the study. HRV was recorded at rest for ten minutes with no music, then were asked to listen to classical baroque or heavy metal music for a period of 20 min. It was noted that heart rate variability did not affect HRV indices for time and frequency. In conclusion, music with different tempos does not influence cardiac autonomic regulation in men. However more studies are suggested to explore this topic in greater detail.

Made available in DSpace on 2015-02-24T13:58:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2014Bitstream added on 2015-02-24T14:07:54Z : No. of bitstreams: 1 ISSN1016-5169-2014-42-02-139-146.pdf: 1477941 bytes, checksum: ecd9db0f05750ce3e8fa5d812e77f88e (MD5) Objectives: The effects of chronic music auditory stimulation on the cardiovascular system have been investigated in the literature. However, data regarding the acute effects of different styles of music on cardiac autonomic regulation are lacking. The literature has indicated that auditory stimulation with white noise above 50 dB induces cardiac responses. We aimed to evaluate the acute effects of classical baroque and heavy metal music of different intensities on cardiac autonomic regulation. Study design: The study was performed in 16 healthy men aged 18-25 years. All procedures were performed in the same soundproof room. We analyzed heart rate variability (HRV) in time (standard deviation of normal-to-normal R-R intervals [SDNN], root-mean square of differences [RMSSD] and percentage of adjacent NN intervals with a difference of duration greater than 50 ms [pNN50]) and frequency (low frequency [LF], high frequency [HF] and LF/HF ratio) domains. HRV was recorded at rest for 10 minutes. Subsequently, the volunteers were exposed to one of the two musical styles (classical baroque or heavy metal music) for five minutes through an earphone, followed by a five-minute period of rest, and then they were exposed to the other style for another five minutes. The subjects were exposed to three equivalent sound levels (60- 70dB, 70-80dB and 80-90dB). The sequence of songs was randomized for each individual. Results: Auditory stimulation with heavy metal music did not influence HRV indices in the time and frequency domains in the three equivalent sound level ranges. The same was observed with classical baroque musical auditory stimulation with the three equivalent sound level ranges. Conclusion: Musical auditory stimulation of different intensities did not influence cardiac autonomic regulation in men. Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Fonoaudiologia, Faculdade de Filosofia e Ciências - Campus de Marília, Marilia, Av. Hygino Muzzi Filho, 737, Campus Universitário, CEP 17525900, SP, Brasil Department of Physiotherapy and Occupational Therapy, Faculty of Philosophy and Sciences, UNESP (Marília), SP Department of Speech Language and Hearing Therapy, Faculty of Philosophy and Sciences, UNESP (Presidente Prudente), SP Department of Physiotherapy, Faculty of Sciences and Technology, Unesp, Presidente Prudente, SP

Supervised machine learning (ML) and lexicon-based are the most frequent approaches for opinion mining (OM), but they require considerable effort for preparing the training data and to build the opinion lexicon, respectively. This paper presents two unsupervised approaches for OM based on Particle Swarm Optimization (PSO). The PSO-based approaches were evaluated by eighteen experiments with different corpora types, domains, language, class balancing and pre-processing techniques. The proposed approaches achieved better accuracy on twelve experiments. Best results were obtained on corpora with a reduced number of dimensions and for specific domains. Best accuracy (0.79) was obtained by Discrete IDPSO on the OBCC corpus, outperforming supervised ML and lexicon-based approaches for this corpus.

Twitter is one of the most used social media with users generating about 1 million messages per day. As a result of the expansion of this microblog, there is a diversity of languages used by users and many studies aimed at identifying the language of tweets. The third most used language on Twitter is Portuguese, a pluricentric language with two national standard varieties: Brazilian Portuguese and European Portuguese. Identifying a language variety may positively impact various Natural Language Processing tasks, but accomplishing this task is still regarded as one of the bottlenecks in this area, especially when combined with another bottleneck, language identification applied to short texts. Thus, given these challenges, this paper provides a current view on the automatic discrimination of the two main Portuguese language varieties on Twitter texts by using an acknowledged approach with different techniques and features in order to get an optimum configuration to fit our problem. Results reached 0.9271 for accuracy using an ensemble method, which combines character 6-grams and word unigrams and bigrams.

Efforts have been made in financial markets to deal with price movement predicting. Recent studies have shown that the market can be outperformed by methodologies with the aid of science. In other words, it has been shown that methods based on computational intelligence can be more profitable than a buy-and-hold strategy. This paper proposes a probabilistic and dynamic chart pattern recognition hybrid system, by using soft computing techniques, such as Linearized Fuzzy C-Medoids (LFCMdd), Perceptually Important Points (PIP), Dynamic Time Warping (DTW) and Probabilistic Support Vector Machines (PSVM), to predict three important foreign exchange rates. Also, comparisons are performed against trading systems built with the use of technical indicators, optimized by Genetic Algorithms (GA). The experiments were performed with the sliding window strategy. The results showed that the proposed methodology outperformed the GA-based methodology, with mean return (ROI) of 277.6% against 20.96% for the GA-based.

The growth of social media and user-generated content (UGC) on the Internet provides a huge quantity of information that allows discovering the experiences, opinions, and feelings of users or customers. Opinion Mining (OM) is a sub-field of text mining in which the main task is to extract opinions from UGC. Given that Portuguese is one of the most common spoken languages in the world, and it is also the second most frequent on Twitter, the goal of this work is to plot the landscape of current studies that relates the application of OM for Portuguese. A systematic mapping review (SMR) method was applied to search, select and to extract data from the included studies. Manual and automated searches retrieved 6075 studies up to year 2014, from which 25 articles were included. Almost 70 % of all approaches focus on the Brazilian Portuguese variant. Naive Bayes and Support Vector Machine were the main classifiers and SentiLex-PT was the most used lexical resource. Portugal and Brazil are the main contributors in processing the Portuguese language.

Unstructured data accounts for more than 80 % of enterprise data and is growing at an annual exponential rate of 60 %. Text mining refers to the process of discovering new, previously unknown and potentially useful information from a variety of unstructured data including user-generated text content (UGTC). Given that Portuguese language is one of the most common languages in the world, and it is also the second most frequent language on Twitter, the goal of this work is to plot the landscape of current studies that relates the application of text mining to UGTC in the Portuguese language. The systematic mapping review method was applied to search, select, and to extract data from the included studies. Our manual and automated searches retrieved 6075 studies up to year 2014, from which 35 were included in the study. Text classification concentrates 79 % of all text mining tasks, having the Naive Bayes as the main classifier and Twitter as the main data source.

This paper presents a novel method for smoke detection in videos based on three main steps: background removal, color classification, and temporal/spatial persistence. First, the regions of interest are determined by detecting objects in movement through background subtraction. After that, the moving objects are classified according to their color. Only objects with smoke colors are analyzed by the last stage, which carries out analysis of space movement, verifying the temporal persistence of pixels of interest in the video frames. Pixels that satisfy the rules defined in the three stages are considered pixels of smoke. The system considered in this work is compared with other existing systems, through tests with a database of videos that contains smoke images and videos that do not contain smoke images, but contain objects that look like smoke.

The growth of social media and user-generated content (UGC) on the Internet provides a huge quantity of information that allows discovering the experiences, opinions, and feelings of users or customers. These electronic Word of Mouth statements expressed on the web are prevalent in business and service industry to enable a customer to share his/her point of view. However, it is impossible for humans to fully understand it in a reasonable amount of time. Opinion mining (also known as Sentiment Analysis) is a sub-field of text mining in which the main task is to extract opinions from UGC. Thus, this work presents an open source pipeline to analyze the costumer’s opinion or sentiment in Twitter about products and services offered by Brazilian companies. The pipeline is based on General Architecture for Text Engineering (GATE) framework and the proposed hybrid method combines lexicon-based, supervised learning, and rule-based approaches. Case studies performed on Twitter real data achieved precision of almost 70 %.

In this paper we propose a hybrid methodology to design morphological-rank-linear (MRL) perceptrons in the problem of software development cost estimation (SDCE). In this methodology, we use a modified genetic algorithm (MGA) to optimize the parameters of the MRL perceptron, as well as to select an optimal input feature subset of the used databases, aiming at a higher accuracy level for SDCE problems. Besides, for each individual of MGA, a gradient steepest descent method is used to further improve the MRL perceptron parameters supplied by MGA. Finally, we conduct an experimental analysis with the proposed methodology using six well-known benchmark databases of software projects, where two relevant performance metrics and a fitness function are used to assess the performance of the proposed methodology, which is compared to classical machine learning models presented in the literature.

This work presents a shift-invariant morphological system to solve the problem of software development cost estimation (SDCE). It consists of a hybrid morphological model, which is a linear combination between a morphological-rank (MR) operator (nonlinear) and a Finite Impulse Response (FIR) operator (linear), referred to as morphological-rank-linear (MRL) filter. A gradient steepest descent method to adjust the MRL filter parameters (learning process), using the Least Mean Squares (LMS) algorithm, and a systematic approach to overcome the problem of non-differentiability of the morphological-rank operator are used to improve the numerical robustness of the training algorithm. Furthermore, an experimental analysis is conducted with the proposed system using the NASA software project database, and in the experiments, two relevant performance metrics and an evaluation function are used to assess its performance. The results obtained are compared to models recently presented in literature, showing superior performance of this kind of morphological systems for the SDCE problem.

Context: In software industry, project managers usually rely on their previous experience to estimate the number men/hours required for each software project. The accuracy of such estimates is a key factor for the efficient application of human resources. Machine learning techniques such as radial basis function (RBF) neural networks, multi-layer perceptron (MLP) neural networks, support vector regression (SVR), bagging predictors and regression-based trees have recently been applied for estimating software development effort. Some works have demonstrated that the level of accuracy in software effort estimates strongly depends on the values of the parameters of these methods. In addition, it has been shown that the selection of the input features may also have an important influence on estimation accuracy. Objective: This paper proposes and investigates the use of a genetic algorithm method for simultaneously (1) select an optimal input feature subset and (2) optimize the parameters of machine learning methods, aiming at a higher accuracy level for the software effort estimates. Method: Simulations are carried out using six benchmark data sets of software projects, namely, Desharnais, NASA, COCOMO, Albrecht, Kemerer and Koten and Gray. The results are compared to those obtained by methods proposed in the literature using neural networks, support vector machines, multiple additive regression trees, bagging, and Bayesian statistical models. Results: In all data sets, the simulations have shown that the proposed GA-based method was able to improve the performance of the machine learning methods. The simulations have also demonstrated that the proposed method outperforms some recent methods reported in the recent literature for software effort estimation. Furthermore, the use of GA for feature selection considerably reduced the number of input features for five of the data sets used in our analysis. Conclusions: The combination of input features selection and parameters optimization of machine learning methods improves the accuracy of software development effort. In addition, this reduces model complexity, which may help understanding the relevance of each input feature. Therefore, some input parameters can be ignored without loss of accuracy in the estimations.

In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure.

For further processing of document images, the ink pixels must be separated from the background pixels. This paper presents a new method for thresholding images of historical documents. The main objective is to create monochromatic images with high quality at low processing time. This allows easier access to the contents of the image files. One important problem arises when the document is written on both sides of the paper. The thresholding process can lose the contents of the document completely if the separation between the ink and the background is not correctly defined. We present a new efficient algorithm for binarization of historical documents and we analyze its performance by comparing it to other nineteen classic thresholding algorithms using measures like precision, recall, accuracy, specificity and a fidelity index. Our method achieved better results than other well-known algorithms.

This paper investigates the use of probabilistic neural networks trained with the dynamic decay adjustment algorithm (PNN-DDA) for novelty detection tasks. PNN-DDA is a fast, constructive neural model originally developed and investigated for standard classification tasks. The training algorithm is controlled by two parameters, @q^+ and @q^-. Simulations employing four data sets from the UCI machine learning repository are reported. The results show that parameter @q^- considerably influences the performance of PNN-DDA for novelty detection, and furthermore, that PNN-DDA achieves performance comparable to NNDD with the advantage of producing much smaller classifiers.

Two consecutive cases of ectopic opening of the vas deferens into Müllerian duct cysts are presented, with descriptions of the presentation, physical examination, radiographic and ultrasonographic images, surgical findings and treatment undertaken. In both cases, ablation of the cyst and ligature of the vas deferens was the technique employed. Both patients are currently well, with normal urinary flow and no urinary tract infections, 3 years after surgery. Ectopic opening of the vas deferens into Müllerian duct cysts is a rare entity but should always be considered when a child presents with purulent urinary discharge and abdominal tumor. Surgery is the only treatment recommended.