Saulo Moraes Villela

Bio: Saulo Moraes Villela is an academic researcher from Universidade Federal de Juiz de Fora. The author has contributed to research in topics: Support vector machine & Perceptron. The author has an hindex of 5, co-authored 27 publications receiving 59 citations.

An analysis of the fees and pending time correlation in Ethereum

Abstract: Ethereum is a new blockchain‐based platform that is also capable of running smart contracts. Despite its increasing popularity, there is a lack of studies on characterizing this system, in special the fees paid by users and the respective delay to confirm the transactions, that is, the pending time. In this sense, we study the main features of Ethereum transactions and evaluate the common belief—for blockchain systems that rely on proof of work—that users who pay higher fees will have their transactions confirmed faster. Specifically, we collect information about 7.2 million of transactions in Ethereum and correlate their pending time to several fee‐related features. Moreover, we conduct our study evaluating different ranges of values for the features, such as default and unusual values adopted by users as well as clusters of users with similar behaviors. Our empirical analysis shows strong evidence that there is no clear correlation between fees‐related features and the pending time. Overall, we conclude from our investigation that transaction's features, including gas and gas price defined by users, cannot determine the pending time of transactions.

Feature selection from microarray data via an ordered search with projected margin

Abstract: Microarray experiments are capable of measuring the expression level of thousands of genes simultaneously. Dealing with this enormous amount of information requires complex computation. Support Vector Machines (SVM) have been widely used with great efficiency to solve classification problems that have high dimension. In this sense, it is plausible to develop new feature selection strategies for microarray data that are associated with this type of classifier. Therefore, we propose, in this paper, a new method for feature selection based on an ordered search process to explore the space of possible subsets. The algorithm, called Admissible Ordered Search (AOS), uses as evaluation function the margin values estimated for each hypothesis by a SVM classifier. An important theoretical contribution of this paper is the development of the projected margin concept. This value is computed as the margin vector projection on a lower dimensional subspace and is used as an upper bound for the current value of the hypothesis in the search process. This enables great economy in runtime and consequently efficiency in the search process as a whole. The algorithm was tested using five different microarray data sets yielding superior results when compared to three representative feature selection methods.

Analyzing Transaction Confirmation in Ethereum Using Machine Learning Techniques

Abstract: Ethereum has emerged as one of the most important cryptocurrencies in terms of the number of transactions. Given the recent growth of Ethereum, the cryptocurrency community and researchers are interested in understanding the Ethereum transactions behavior. In this work, we investigate a key aspect of Ethereum: the prediction of a transaction confirmation or failure based on its features. This is a challenging issue due to the small, but still relevant, fraction of failures in millions of recorded transactions and the complexity of the distributed mechanism to execute transactions in Ethereum. To conduct this investigation, we train machine learning models for this prediction, taking into consideration carefully balanced sets of confirmed and failed transactions. The results show high-performance models for classification of transactions with the best values of F1-score and area under the ROC curve approximately equal to 0.67 and 0.87, respectively. Also, we identified the gas used as the most relevant feature for the prediction.

Human action recognition using convolutional neural networks with symmetric time extension of visual rhythms

Abstract: Despite the expressive progress of deep learning models on the image classification task, they still need enhancement for efficient human action recognition. One way to achieve such gain is to augment the existing datasets. With this goal, we propose the usage of multiple Visual Rhythm crops, symmetrically extended in time and separated by a fixed stride. The symmetric extension preserves the video frame rate, which is crucial to not distort actions. The crops provide a 2D representation of the video volume matching the fixed input size of the 2D Convolutional Neural Network (CNN) employed. In addition, multiple crops with stride guarantee coverage of the entire video. Aiming to evaluate our method, a multi-stream strategy combining RGB and Optical Flow information is extended to include the Visual Rhythm. Accuracy rates fairly close to the state-of-the-art were obtained from the experiments with our method on the challenging UCF101 and HMDB51 datasets.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Proceedings of the National Academy of Sciences

Abstract: where A represents the magnetic vector potential, is an integral of the hydromagnetic equations. This -integral made it possible to formulate a variational principle for the force-free magnetic fields. The integral expresses the fact that motions cannot transform a given field in an entirely arbitrary different field, if the conductivity of the medium isconsidered infinite. In this paper we shall show that the full set of hydromagnetic equations admit five more integrals, besides the energy integral, if dissipative processes are absent. These integrals, as we shall presently verify, are I2 =fbHvdV, (2)

Cost-sensitive feature selection by optimizing F-measures

Abstract: Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional feature selection methods usually ignore the class imbalance problem, thus the selected features will be biased towards the majority class. Considering that F-measure is a more reasonable performance measure than accuracy for imbalanced data, this paper presents an effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures. Since F-measure optimization can be decomposed into a series of cost-sensitive classification problems, we investigate the cost-sensitive feature selection by generating and assigning different costs to each class with rigorous theory guidance. After solving a series of cost-sensitive feature selection problems, features corresponding to the best F-measure will be selected. In this way, the selected features will fully represent the properties of all classes. Experimental results on popular benchmarks and challenging real-world data sets demonstrate the significance of cost-sensitive feature selection for the imbalanced data setting and validate the effectiveness of the proposed method.

Hierarchical Feature Selection with Recursive Regularization

Abstract: In the big data era, the sizes of datasets have increased dramatically in terms of the number of samples, features, and classes. In particular, there exists usually a hierarchical structure among the classes. This kind of task is called hierarchical classification. Various algorithms have been developed to select informative features for flat classification. However, these algorithms ignore the semantic hyponymy in the directory of hierarchical classes, and select a uniform subset of the features for all classes. In this paper, we propose a new technique for hierarchical feature selection based on recursive regularization. This algorithm takes the hierarchical information of the class structure into account. As opposed to flat feature selection, we select different feature subsets for each node in a hierarchical tree structure using the parent-children relationships and the sibling relationships for hierarchical regularization. By imposing `2,1-norm regularization to different parts of the hierarchical classes, we can learn a sparse matrix for the feature ranking of each node. Extensive experiments on public datasets demonstrate the effectiveness of the proposed algorithm.

A Survey of State-of-the-Art on Blockchains: Theories, Modelings, and Tools

Abstract: To draw a roadmap of current research activities of the blockchain community, we first conduct a brief overview of state-of-the-art blockchain surveys published in the recent 5 years. We found that those surveys are basically studying the blockchain-based applications, such as blockchain-assisted Internet of Things (IoT), business applications, security-enabled solutions, and many other applications in diverse fields. However, we think that a comprehensive survey towards the essentials of blockchains by exploiting the state-of-the-art theoretical modelings, analytic models, and useful experiment tools is still missing. To fill this gap, we perform a thorough survey by identifying and classifying the most recent high-quality research outputs that are closely related to the theoretical findings and essential mechanisms of blockchain systems and networks. Several promising open issues are also summarized finally for future research directions. We wish this survey can serve as a useful guideline for researchers, engineers, and educators about the cutting-edge development of blockchains in the perspectives of theories, modelings, and tools.