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Background: Adolescents start to understand nonliteral language and begin to use language to maintain social bonds in varying social contexts. Aim: This study aims to profile pragmatic judgment abilities in typically developing adolescents. Materials and Methods: The present study was conducted on 120 typically developing adolescents across the age range of 10–16 years. The formulated story consisted of 29 multiple-choice judgment questions to assess an adolescent's ability to judge the use of pragmatics skills by different characters in the story. Results: There was a statistically significant difference between the groups determined by the one-way analysis of variance (F [5114] =4.83, P = 0.00). Conclusion: With increasing grades, adolescents spend a reasonable time socializing with peers and others, promoting the development of pragmatic skills. As children and adolescents grow, perceptions of others improve, along with their ability to identify the complexity of contentious topics and differing points of view.

Several Apriori algorithm implementations for mining association rules have been proposed in the literature using the Hadoop-MapReduce framework and, more recently, Spark. However, none of the works have made a detailed assessment of its performance, for example, comparing it with other implementations in various characteristics of data sets. In this work, we present a review of the main algorithms proposed for Hadoop-MapReduce and compared their implementations in a single environment under several different situations. Moreover, these algorithms had their implementations adapted to Spark, and also compared under the same circumstances. Based on the results of the experiments, we present a framework for recommending the Apriori implementation most appropriate for solving a given problem, according to the data set characteristics and minimum required support. The results show that Spark implementations overcome Hadoop-MapReduce implementations at runtime in most experiments. However, there is no single implementation that is the best in all the evaluated situations.

We propose a framework for disaster situation modeling in smart city using micro-reporting from the most efficient and reliable sources of multimedia, people with mobile phones. By using micro-reports generated from a crowdsourced mobile phone application and EventShop to analyze all kinds of Zika related data, TargetZIKA aims to combat the spread of the Zika virus in Brazil. EventShop is an open-source platform which provides a generic infrastructure for ingesting and aggregating heterogeneous spatio-temporal data streams derived from various sources like in situ sensors and social media. By using the foremost technologies for disaster situation mapping, we develop not only a method to rate the dangerous states and locations in Brazil but also a tool for simple visualization of the smart-citizen's environment.

A necessity to improve the quality of textile products and reduce production costs associated with losses of raw materials in the processing requires the development of automated quality inspection systems for all steps of textile production. Promising is the use of contactless methods of nondestructive testing based on the methods of vision.The object of research is a device to inspect the shape of textile packages by shadow projection method for monitoring in real time.A series of experiments and theoretical research are conducted aimed at the study of structural parameters of the device to inspect the shape of the packing by the shadow projection method, providing the required accuracy. On the basis of the mutual arrangement of the structural elements of the light source, camera shutter and inspected bobbin, the impact of each of them on the scale transformation error for inspection of the package shape of cross winding is defined.This result allows to select the mutual arrangement of design elements and to set their permissible variations of devices to inspect the package shape of cross winding by the shadow projection method.Inspection of package shape in the process of their developments will prevent the formation of defective packages. It will increase a percentage of defect packages and the loss of raw material in the textile industry, which ultimately will raise its efficiency., Проведено аналіз структури похибок при контролі форми пакувань хрестової мотки методом тіньової проекції. Отримані вирази для окремих складових похибки параметрів форми, що дозволяє обґрунтовано підійти до призначення допусків на взаємне розташування окремих елементів пристрою. Проведено аналіз пристроїв реєстрації зображень з точки зору можливості їх застосування в апаратному комплексі для реєстрації параметрів форми пакувань хрестової мотки., Проведен анализ структуры погрешностей при контроле формы паковок крестовой намотки методом теневой проекции. Получены выражения для отдельных составляющих погрешности параметров формы, что позволяет обоснованно подойти к назначению допусков на взаимное расположение отдельных элементов устройства. Проведен анализ устройств регистрации изображений с точки зрения возможности их применения в аппаратном комплексе для регистрации параметров формы паковок крестовой мотки.

Data clustering is one of the most popular techniques in data mining. It is a process of partitioning an unlabeled dataset into groups, where each group contains objects which are similar to each other with respect to a certain similarity measure and different from those of other groups. Clustering high-dimensional data is the cluster analysis of data which have anywhere from a few dozen to many thousands of dimensions. Such high-dimensional data spaces are often encountered in areas such as medicine, bioinformatics, biology, recommendation systems and the clustering of text documents. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as the slowness of their convergence and their sensitivity to initialization values. Particle Swarm Optimization (PSO) is a population-based globalized search algorithm that uses the principles of the social behavior of swarms. PSO produces better results in complicated and multi-peak problems. This paper presents a literature survey on the PSO algorithm and its variants to clustering high-dimensional data. An attempt is made to provide a guide for the researchers who are working in the area of PSO and high-dimensional data clustering.

Hasil belajar siswa kelas V SD Negeri 054875 Sei Limbat pada pokok bahasan bangun datar pada tahun ajaran 2014/2015 mempunyai hasil yang rendah. Hal ini diduga karena pendekatan pembelajaran pada saat itu belum tepat. Penelitian tindakan kelas dengan 2 silkus dilakukan dan pembelajaran Contextual Teaching and Learning (CTL) dirancang untuk meningkatkan hasil belajar siswa dalam mata pelajaran matematika. Berdasarkan hasil belajar siswa hasil penelitianmenunjukkan bahwa pendekatan CTL dapat meningkatkan hasil belajar siswa padapokok bahasan bangun datar. Nilai rata-rata yang dicapai siswa adalah 7,2 dengan ketuntasan belajar 78,5%.

Number of defects remaining in a system provides an insight into the quality of the system. Defect detection systems predict defects by using software metrics and data mining techniques. Clustering analysis is adopted to build the software defect prediction models. Cluster ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. In this paper, the clustering ensemble using Particle Swarm Optimization algorithm (PSO) solution is proposed to improve the prediction quality. An empirical study shows that the PSO can be a good choice to build defect prediction software models.

This paper presents a particle swarm optimization (PSO) as a tool for loss reduction study. This issue can be formulated as a nonlinear optimization problem. The proposed application consists of using a developed optimal power flow based on loss minimization function by expanding the original PSO. The study is carried out in two steps. First, by using the tangent vector technique, the critical area of the power system is identified under the point of view of voltage instability. Second, once this area is identified, the PSO technique calculates the amount of shunt reactive power compensation that takes place in each bus. The proposed approach has been examined and tested with promising numerical results using the IEEE 118-bus system.

We report the results of an aggressive salvage regimen in 2 patients with advanced-stage leukemic-phase mantle cell lymphoma who were refractory to previous conventional therapies. We combined multiple phases of a cytoreductive regimen including rituximab and sequential high-dose treatment with autologous stem cell transplantation (ASCT). The regimen consisted of a debulking phase with fludarabine, idarubicin, high-dose cytarabine, and high-dose methotrexate; a mobilization and in vivo purging phase with rituximab, cyclophosphamide, and granulocyte colony-stimulating factor; high-dose sequential chemotherapy with etoposide, mitoxantrone, and melphalan followed by ASCT; and, finally, posttransplantation consolidation with rituximab for treatment of minimal residual disease. With this regimen, these 2 refractory patients with multiple poor prognostic factors are in complete remission at 41 and 42 months following transplantation. Although the fact that these 2 patients are still in remission beyond 3 years after ASCT is encouraging, we need a longer follow-up to comment on their long-term survival.

This paper presents the real-time summarization of scheduled soccer games from flows of Twitter stream. During events, many messages (tweets) are sent describing and expressing opinions about the game. The proposed approach shrinks the stream of tweets in real-time, and consists of two main steps: (i) the sub-event detection step, which determines if something new has occurred, and (ii) the tweet selection step, which picks a few representative tweets to describe each sub-event. We compare the automatic summaries generated in some of the soccer games of Brazilian, Spanish and England (2013-2014) Leagues with the live reports offered by ESPN! Sports, Globo Esporte, Yahoo! Sports and livematch.Com web site. The results show that the proposed approach is efficient and can produce real-time summarization with good quality.

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas. The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. So far, many contributions have been made to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, the Particle Swarm Optimization algorithm (PSO) is proposed to solve the consensus clustering problem. We find that the particle swarm clustering algorithm is efficient for this problem. An empirical study compares the accuracy of our proposed algorithms with other consensus clustering methods including voting on five data sets. The experimental results show that the PSO consensus clustering method produces clustering's that are as good as, and often better than, these other methods.

The survey of the progress of Cercospora leaf spot becomes potentially useful and understandable in understanding the disease process and in decision making for control measures. In the last years, computer programs have helped to elucidate what factors are biotic or abiotic more representative. The aim of this work was to investigate, using knowledge extraction techniques, which phenological and environmental attributes most influence on the occurrence of Cercospora leaf spot on coffee trees in southern Minas Gerais, under two tillage systems: conventional and organic. For this, data were organized incidence of Cercospora leaf spot in both cropping systems, with climatic data and phenological crop in a period of five years of evaluation. Then an algorithm based on knowledge extraction decision tree was used to obtain the attributes that most favor the occurrence of Cercospora leaf spot. The generated models were 60% hit rate and showed that the average temperature of the attribute was greater influence on the entire data and the conventional culture system. In organic management, the precipitation and phenology are the factors that most influence the occurrence of disease. O levantamento do progresso da cercosporiose torna-se potencialmente útil e compreensível no entendimento da doença e no processo de tomada de decisão para medidas de controle. Nos últimos anos, programas computacionais têm ajudado a elucidar quais fatores bióticos ou abióticos são mais representativos. Objetivou-se, neste trabalho, investigar,utilizando técnicas de extração do conhecimento, quais atributos ambientais e fenológicos mais influenciam na ocorrência da cercosporiose em cafeeiros no Sul de Minas Gerais, sob dois sistemas de cultivo: convencional e orgânico. Para isso, foram organizados dados de incidência de cercosporiose nos dois sistemas de cultivo, com dados climáticos e fenológicos da cultura,em um período de cinco anos de avaliação. Em seguida, um algoritmo de extração do conhecimento baseado em árvore de decisão foi utilizado para obter os atributos que mais favorecem a ocorrência da cercosporiose. Os modelos gerados tiveram 60% de taxa de acerto e mostraram que a temperatura média foi o atributo de maior influência na totalidade dos dados e para o sistema convencional de cultivo. No manejo orgânico, a precipitação mensal e a fenologia são os fatores que mais interferem na ocorrência da doença.

Twitter is a micro logging service where worldwide users publish and share their feelings. However, sentiment analysis for Twitter messages ('tweets') is regarded as a challenging problem because tweets are short and informal. In this paper, we apply a novel approach for automatically classifying the sentiment and emotions of Twitter messages. These messages are hierarchically categorized on basis of neutrality, polarity (positive or negative) and presence of various emotions. The hierarchical classification approach (HC) is a specialization of the well-known flat classification task. The main difference between them is that when using HC, examples must be assigned to classes organized in a previously defined class hierarchy, while traditional flat classification does not take into account the hierarchical information. We applied our model to posts collected from Twitter regarding the 2011 season of the Brazilian Soccer League. Our results show that the proposed method outperforms the corresponding flat approach in emotion classification.

When the Fuzzy Set Theory was proposed by Lotfi Zadeh in a seminal paper published in 1965, he noted that the technological resources available until then were not able to automate the activities related to industrial, biological or chemical problems. These activities use typically analog data which are inappropriate to be handled in a digital computer that works with well-defined numerical data, i.e. , discrete values.

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas, such as text application and bioinformatics data. In this paper we propose the use of a novel algorithm for clustering data that we call hybrid particle swarm optimization with mutation (HPSOM), which is based on PSO. The HPSOM basically uses PSO and incorporates the mutation process often used in GA to allow the search to escape from local optima. It is shown how the PSO/HPSOM can be used to find the centroids of a user-specified number of clusters. The new algorithm is evaluated on five benchmark data sets. The proposed method is compared with the K-means (KM) clustering technique and the standard PSO algorithm. The results show that the algorithm is efficient and produces compact clusters.

Multi-label classification learning first arose in the context of text categorization, where each document may belong to several classes simultaneously and has attracted significant attention lately, as a consequence of both the challenge it represents and its relevance in terms of application scenarios. In this paper, we propose a new hybrid approach, Multi Label K-Nearest Michigan Particle Swarm Optimization (ML-KMPSO), that is based on two strategies: Michigan Particle Swarm Optimization (MPSO) and ML-KNN. We evaluated the performance of ML-KMPSO using two real-world datasets and the results show that our proposal matches or outperforms well-established multi-label classification learning algorithms.

At present, several artificial intelligence (AI) techniques are used to identify complex systems. The data collected is extremely important, as it enables the evaluation, prediction and correction variables' behavior in any given process. The most recent methods tend to associate such techniques in order to obtain models that are continuously closer to those desired. This paper presents a method based on polynomial neural networks and fuzzy logics, optimized by a technique known as particle swarm optimization. The idea consists in generating a final structure that is compact, flexible and capable of producing good results when applied to resolving system identification problems and time series forecasting.

The success of fuzzy application to solve the control problems depends on a number of parameters, such as fuzzy rules and membership functions. The problem of generating desirable fuzzy rules is very important in the development of fuzzy systems, which are usually decided upon subjectively. This paper describes a very simple and straightforward fuzzy rule generation and optimization technique by using the Particle Swarm Optimization Algorithm (PSO). The proposed algorithm can obtain a set of fuzzy rules which cover the examples set in iterative process. The proposed method is tested with promising results.

This paper proposes two new data clustering approaches using the particle swarm optimization algorithm (PSO). It is shown how the PSO can be used to find centroids of a user specified number of clusters. The proposed approaches are an attempt to improve the Merwe and Engelbrecht method using different fitness functions and considering the situation where data is uniformly distributed. The data clustering PSO algorithm, using the original and proposed fitness functions is evaluated on well known data sets. Notable improvements on the results were achieved by the modifications, this shows the potential of the PSO, not only on data clustering but also on the several areas it can be applied.

This paper presents a particle swarm optimization (PSO) as an efficient approach for loss reduction study. This issue can be formulated as a nonlinear optimization problem. The proposed approach employs the PSO algorithm for optimal setting of optimal power flow (OPF) based on loss minimization (LM) function. The study is carried out in two steps. First, by using the tangent vector technique, the critical area of the power system is identified under the point of view of voltage instability. Second, once this area is identified, the PSO technique calculates the amount of shunt reactive power compensation that takes place in each bus. The proposed approach has been examined and tested on the standard IEEE 30-bus test system. The results are promising and show the effectives of the proposed approach.

The success of fuzzy application to solve the control problems depends on a number of parameters, such as fuzzy membership functions. One way to improve the performance of the fuzzy reasoning model is made by optimizing the membership functions and the use of evolutionary algorithms. In this paper a Hybrid Particle Swarm Optimization (HPSOM) algorithm is used to optimize the fuzzy membership functions. The HPSOM is able to generate an optimal set of parameters for fuzzy reasoning model based on either, their initial subjective selection, or on a random selection. The purpose of this paper is to present and discuss a different strategy for the membership functions automatic adjustment, using HPSOM algorithm. The proposed approach has been examined and tested with promising results using an application designed to park a vehicle into a garage, beginning from any start position.

Este artigo apresentada uma introdução sobre UML “Unified Modeling Language” que é a padronização das metodologias de desenvolvimento de sistemas baseados na orientação a objetos. A UML incorpora as noções do desenvolvimento de software totalmente visual.

The use of fuzzy logic to solve control problems have been increasing considerably in the past years. The successfulness of fuzzy application depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. One way to improve the performance of the fuzzy reasoning model is the use of genetic algorithm. In this paper it is shown that a particle swarm optimization (PSO) algorithm learning mechanism, supplements the performance of fuzzy reasoning model. The PSO is able to generate an optimal set of parameters for fuzzy reasoning model based on either, their initial subjective selection, or on a random selection. The purpose of this paper is to present and discuss a strategy for the membership functions automatic adjustment, using PSO algorithms, and presents an application designed to park a vehicle into a garage, beginning from any start position.

Data mining is a process to discover useful patterns in large volumes of data through the application of appropriated algorithms, tools, and techniques. However, building scalable, extensible, and easy-to-use data mining systems has proved to be a very difficult task. This paper presents the web platform called SMINER, which aims at interoperability and facility of integration in the development of data mining applications. The platform is based on the paradigm of service-oriented architecture SOA using the web services standard for extensibility and interoperability. This platform is composed of two main components: 1 web services, which implement data mining algorithms; 2 a user web interface, which can be used for modelling applications that use the services of the platform. Experimental results demonstrate that our SOA-based platform makes it easy to construct a flexible and scalable data mining system.