Considerando que o Imposto Predial e Territorial Urbano (IPTU) é uma das principais receitas próprias municipais, mas apresenta desigualdade arrecadatória entre municípios brasileiros, este trabalho realiza um diagnóstico comparativo entre seis capitais nordestinas e propõe soluções baseadas em experiências de outras localidades. O estudo examina fatores como indicadores econômicos, mercado imobiliário, qualidade cadastral, níveis de avaliação imobiliária e inadimplência. Em seguida, apresentam-se estratégias fundamentadas em tecnologia da informação e modelos matemáticos, destacando a importância de bases de dados consolidadas, integração institucional e uso de técnicas estatísticas e aprendizado de máquina para detectar padrões e inconsistências, bem como apoiar avaliações imobiliárias automatizadas. Os resultados revelam desempenhos contrastantes entre os municípios, relacionados sobretudo à qualidade cadastral, à defasagem das avaliações imobiliárias e aos níveis de inadimplência. Conclui-se que muitos municípios têm potencial para ampliar a arrecadação do IPTU mediante investimentos em soluções tecnológicas capazes de qualificar cadastros, aprimorar diagnósticos e orientar ações de cobrança mais eficientes, contribuindo para o aumento da receita municipal e para a ampliação da capacidade de investimento público.

This work demonstrates the development of a prototype system that could improve urban and digital accessibility for People with Disability - PwD. The research involves using an academic questionnaire to enable a wider perspective of the central subject and assessing the effectiveness of the proposed solution. The main goal is to help social integration and make the future solution implementable for smart cities through an experiment in an academic campus. The dissertation is related to technological and social issues that might be used as a resource by researchers, public or private bodies interested in more profound studies. The document recognizes the lack of studies on this subject, especially in Brazil because there are few available articles. The prototype system is designed to tackle the issues faced by persons with disability in urban settings through platforms, applications and mobile phones as it proposes a comprehensive set of solutions when addressing accessibility for PwDs. In turn, this research fosters knowledge advancements in the field of urban accessibility and provides vital recommendations for developing inclusive technologies and policies.

This research explores the potential of gamified educational applications as motivational tools for distance learning students. The study draws from the research conducted by Fernando da Silva Pontes in his dissertation, which examines the impact of gamification on student motivation and engagement in the context of distance education. The paper provides a comprehensive overview of the theoretical foundations of gamification and its application in educational settings, synthesizing existing research on the impact of gamified educational applications on student motivation, engagement, and learning outcomes. The findings highlight the significant contribution of gamification in fostering a collaborative and interactive learning environment for distance education students. The study underscores the transformative potential of gamification in addressing the challenges of student motivation and engagement in distance education, paving the way for future advancements in educational practices and research. The paper provides insights into the potential of gamification in enhancing the learning experience for distance education students, contributing to the understanding of the effectiveness of gamified educational applications in motivating and engaging students in the context of distance learning.

The rapid growing application of language models (LLMs) in education offers exciting prospects for personalized learning and interactive experiences. However, a critical challenge emerges - the risk of "hallucinations," where LLMs generate factually incorrect or misleading information. This paper proposes Comparative and Cross-Verification Prompting (CCVP), a novel technique specifically designed to mitigate hallucinations in educational LLMs. CCVP leverages the strengths of multiple LLMs, a Principal Language Model (PLM) and Auxiliary Language Models (ALMs), to verify the accuracy and educational relevance of the PLM's response to a prompt. Through a series of prompts and assessments, CCVP harnesses the diverse perspectives of various LLMs and incorporates human expertise for intricate cases. This method addresses the limitations of relying on a single model and fosters critical thinking skills in learners within the educational context. We detail the CCVP approach with examples specifically applicable to educational settings, such as geography. We also discuss its strengths and limitations, including computational cost, data reliance, and ethical considerations. We highlight its potential applications in educational disciplines, including fact-checking content, detecting bias, and promoting responsible LLM use. CCVP presents a promising avenue for ensuring the accuracy and trustworthiness of LLM-generated educational content. Further research and development will refine its scalability, address potential biases, and solidify its position as a vital tool for harnessing the power of LLMs while fostering responsible knowledge dissemination in education.

O presente artigo propoe um modelo de predicao para o mercado acionario baseado na Logica Fuzzy . O objetivo do estudo visa proporcionar aos negociadores a oportunidade de simular uma negociacao no mercado acionario antes de sua efetividade. Para tanto, foram utilizados dados historicos da bolsa de valores do Brasil, coletados a partir de site especializado como fonte para realizacao dos calculos referentes aos indicadores tecnicos utilizados (RSI, MACD e o Indice Beta). A seguir, estes resultados foram aplicados ao Sistema de Controle Fuzzy para simulacao. Foram utilizados dois ativos reais de empresas, PETR4 e VALE5. As simulacoes demonstraram que o modelo fornece resultados coerentes com a movimentacao do mercado real.

Para mensurar a qualidade do biodiesel no Brasil, vários parâmetros são estabelecidos. Alguns deles como viscosidade, índice de iodo, número de cetano, densidade são importantes pois caracterizam funções importantes de como o biodiesel irá reagir no motores. Neste estudo, foram usadas Redes Neurais Artificiais (RNAs) para predizer o índice de viscosidade do biodiesel. Para este fim foram utilizadas 13 compostos de esteres deácidos graxos como entrada para as RNAs, com vários algoritmos de convergência do tipo feedforward tendo como sáıda das redes os índices de viscosidade.

The economic growth is a consensus in any country.To grow economically, it is necessary to channel the revenues for investment.One way of raising is the capital market and the stock exchanges.In this context, predicting the behavior of shares in the stock exchange is not a simple task, as itinvolves variables not always known and can undergo various influences, from the collective emotion to high-profile news.Such volatility can represent considerable financial losses for investors.In order to anticipate such changes in the market, it has been proposed various mechanisms trying to predict the behavior of an asset in the stock market, based on previously existing information.Such mechanisms include statistical data only, without considering the collective feeling.This paper is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an attempt to predict the active behaviour.

The stock exchange is an important apparatus for economic growth as it is an opportunity for investors to acquire equity and, at the same time, provide resources for organizations expansions. On the other hand, a major concern regarding entering this market is related with the dynamic in which deals are made since the pricing of shares happens in a smart and oscillatory way. Due to this context, several researchers are studying techniques in order to predict the stock exchange, maximize profits and reduce risks. Thus, this study proposes a linear regression model for stock exchange prediction which, combined with financial indicators, provides support decision-making by investors.

The stock exchange is an important apparatus for economic growth as it is an opportunity for investors to acquire equity and, at the same time, provide resources for organizations expansions.On the other hand, a major concern regarding entering this market is related with the dynamic in which deals are made since the pricing of shares happens in a smart and oscillatory way.Due to this context, several researchers are studying techniques in order to predict the stock exchange, maximize profits and reduce risks.Thus, this study proposes a linear regression model for stock exchange prediction which, combined with financial indicators, provides support decision-making by investors.

Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to high-profile news. Such volatility, can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information. Such mechanisms include statistical data only, without considering the collective feeling. This article, is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem.

This work aims the implementation of a system to support decision-making to buy and sells share on the São Paulo Stock Exchange. To do that it was used a genetic algorithm with the help of technical analysis indicators. The Indicators allow us to detect trends, being widely used in the prediction of prices. The prediction of high or low at the stock market is a complex problem that involves a wide range of factors, ranging from the feeling of each market participant to a natural disaster. Thus, this system is not intended to be a crucial tool for decision making, but rather a supporting tool that if in a combination with other methods of analysis (such as fundamental analysis for example) and privileged information, can provide a higher accuracy for a solution of this problem.

Este trabalho tem por objetivo apresentar um modelo de predicao para o mercado acionario baseado na Logica Fuzzy que contribua com os negociadores desta ordem na simulacao das operacoes antes de sua concretizacao. Para atingir esse proposito, foi utilizado dados historicos da Bolsa de Valores de Sao Paulo para realizacao dos calculos matematicos necessarios referente aos indicadores tecnicos utilizados, RSI - Relative Strenght Index, MACD - Moving Average Convergence/Divergence e o Indice Beta. A seguir, os dados foram transformados em variaveis linguisticas originando o conjunto de dados fuzzy para serem aplicados ao Sistema de Controle Fuzzy. Foram utilizados dois ativos reais de empresas, PETR4 e VALE5 para as simulacoes. Os resultados demonstraram que o modelo fornece resultados coerentes com a movimentacao do mercado em operacao. Dessa forma, podendo ser utilizado como uma ferramenta de apoio na tomada de decisao no mercado de acoes.

Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of back propagation of back propagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this caseiodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm. Keywords—Artificial Neural Networks, Biodiesel, Iodine Value, Prediction.

The evaluation systems of Brazilian basiceducation has evolved over the years, but still needsimprovements related to the resources used in the estimatesof the evaluation. One of the biggest advances has been theinclusion of the Item Response Theory (IRT) to thesesystems, providing a statistical analysis based on items asmain elements of the test. Although the Item ResponseTheory is a great quantifier in educational evaluation, thereare still factors that inhibit its use in large scale, such as thefact of the customization of each IRT application and thecomplexity of the mathematical methods involved. Thispaper has the purpose of a system modeling called VirtualTaneb, which uses the IRT as the basis of its evaluationprocess. This software will be implemented later, working onthe analysis of students' knowledge of issues related to thegeometry taught in the 4th grade of elementary school.Index Terms ⎯ Educational evaluation. software agent. itemresponse theory.

Algumas propriedades do biodiesel, como índice de iodo, viscosidade e densidade, podem sofrer variações conforme as estruturas moleculares dos seus ésteres constituintes. O objetivo do pre-sente estudo é avaliar e comparar três métodos de convergência no treinamento supervisionado de redes neurais com arquitetura MLP na predição de propriedades de biodiesel. Os métodos aplicados foram os de BFGS, Gradiente Descendente e Gradiente Conjugado. Dados do LAPQAP e da literatura foram padronizados, organizados e armazenados no Sistema Oleodata, sendo divididos em três partes: 70% para o treinamento da rede, 15% para a fase de validação e 15% para a de teste. As variáveis de entrada foram os percentuais de cada éster de ácido graxo que compõe as amostras de biodiesel, e as variáveis de saída foram o índice de iodo, a viscosidade e a densidade. As seguintes funções de ativação foram previamente testadas: tangente hiperbólica, seno, identidade, exponencial e logística. Além disso, foram fixadas a quantidade de 1000 redes treinadas para cada variação do estudo e 1000 ciclos de treinamento para cada rede. Em seguida, foram variados os métodos de convergência: BFGS, Gradiente Descenden-te e Gradiente Conjugado. Após a obtenção do método de convergência, um novo treinamento foi feito para a otimização da quantidade de neurônios, variando-se de 5 a 15. Ao final de todos os treinamentos a rede com melhores desempenhos foi treinada pelo método de BFGS e apresentava 10 neurônios ocul-tos, função logística em ambas as camadas oculta e de saída, com um coeficiente médio superior a 0,88. Palavras-chave: Parâmetros de Qualidade. Método de BFGS. Método de Gradiente Descendente. Mé-todo de Gradiente Conjugado. EVALUATION OF CONVERGENCE METHODS IN TRAINING OF ARTIFICIAL NEURAL NETWORKS APPLIED TO PREDICTION OF IODINE INDEX, VISCOSITY AND DENSITY IN BIODIESEL ABSTRACT: Some properties of biodiesel, like iodine index, viscosity and density, can vary with the mo-lecular structures of its esters. The present study evaluates and compares three convergence methods of supervised training of neural networks with MLP architecture on prediction of biodiesel properties. The methods applied were BFGS, Descent Gradient and Conjugate Gradient. Data from LAPQAP and from literature were standardized, organized and stored into the Oleodata System, being divided in three parts: 70% for networks training, 15% for validation phase and 15% for test. The input variables were the per-cent of each fatty ester which compose the biodiesel samples, and the output variables were iodine index, viscosity and density. The following activation functions were previously tested: hyperbolic tangent, sine, identity, exponential and logistic. Moreover, it was fixed the quantity of 1000 networks trained for each va-riation of the study and 1000 cycles of training for each network. Then, convergence methods were varied: BFGS, Descent Gradient and Conjugate Gradient. After selected the best convergence method, a new training was carried out for optimization of neurons quantity, ranging from 5 to 15. At the end of training, the network with best performances was trained by BFGS method and presented 10 hidden neurons, logistic function in both hidden and output layers, with a mean coefficient higher than 0,88. KEYWORDS: Quality Parameters. BFGS Method. Descent Gradient Method. Conjugate Gradient Method. EVALUACIÓN DE MÉTODOS DE CONVERGENCIA EN EL ENTRENAMIENTO DE REDES NEURONALES ARTIFICIALES APLICADAS A LA PREDICCIÓN DEL ÍNDICE DE YODO, LA VISCOSIDAD Y LA DENSIDAD DE BIODIESEL RESUMEN: Algunas propiedades del biodiesel, como índice de yodo, viscosidad y densidad, pueden va-riar con las estructuras moleculares de sus ésteres. El presente estudio evalúa y compara tres métodos de convergencia de entrenamiento supervisado de las redes neuronales con la arquitectura MLP en la predicción de las propiedades del biodiesel. Los métodos aplicados fueron BFGS, Gradiente de Descen-so y Gradiente Conjugado. Los datos de LAPQAP y de la literatura fueron estandarizados, organizado y almacenado en el Sistema de Oleodata, siendo divididos en tres partes: 70% para el entrenamiento de redes, 15% para la fase de validación y 15% para el test. Las variables de entrada fueron el porcentaje de cada éster de grasa que componen las muestras de biodiesel, y las variables de salida fueron el índice de yodo, la viscosidad y la densidad. Las siguientes funciones de activación fueron probadas previamente: tangente hiperbólica, seno, identidad, exponencial y logística. Además, se determinó la cantidad de 1.000 redes entrenadas para cada variación del estudio y 1000 ciclos de formación para cada red. Entonces, los métodos de convergencia fueron variados: BFGS, Gradiente de Descenso y Gradiente Conjugado. Después de seleccionar el mejor método de convergencia, un nuevo entrenamiento fue realizado para la optimización de la cantidad de neuronas, que van de 5 a 15. Al final de la formación, la red con las mejores actuaciones fue entrenada por el método BFGS y presentó 10 neuronas ocultas, la función logística en capas encubiertas y de salida, con un coeficiente medio superior a 0,88. PALABRAS CLAVE: Parámetros de Calidad. Método de BFGS. Método del Gradiente de Descenso. Método del Gradiente Conjugado.

Lack of security is a constant concern in open distributed systems. Because of this problem, many tools for evaluating vulnerabilities of networks, as well as for their protection, are being developed and largely deployed; for example, techniques for encryption, antivirus, firewall, and IDSs (Intrusion Detection Systems). Among these, there are IDSs that are increasingly conceived, designed, and implemented. Currently, IDSs are created using software agents. Although IDSs can provide intrusion detection and countermeasures against threats, they are subjected to attacks. Thus, it is necessary to ensure security for IDSs. This chapter proposes a framework for providing self-security, self-reliability of message exchange, self-reliability of components, and self-integrity for IDSs based on agents. The proposed solution is implemented as an extension of the IDS-NIDIA (Network Intrusion Detection System Based on Intelligent Agents), whose architecture has an intelligent agent society that communicates in a cooperative way in a distributed environment. Some tests show the applicability of the proposed solution.

In the search for alternative fuels that can gradually replace petroleum derivatives, biodiesel is highlighted as a substitute for diesel and it is defined as a biofuel obtained from transesterification of triglycerides. Despite of several advantages over mineral diesel, an elementary disadvantage of biodiesel is its low oxidative stability, which is strongly influenced by the unsaturation degree of its fatty acid methyl esters (FAMEs) and by the conditions to which biodiesel is exposed during storage, transporting and handling. The present work focuses on the optimization and application of artificial neural networks (ANNs) on prediction of viscosity, iodine value and induction period of biodiesel, properties that directly reflect its level of degradation, to evaluate the oxidative stability. The input variables were the percentages of the 13 most common FAMEs in biodiesels and the transesterification does not change the fatty esters profile of the raw material. In this case the ANN method allows predicting viscosity, iodine value and induction period, either before transesterification, after synthesis of biodiesel or during the storage. Therefore, this method can be useful as a tool to evaluate the potential of raw materials to produce a biodiesel with good oxidative stability and to reach improvements concerning official methods. The optimization process of the ANN occurred in three steps: test of algorithms for adjusting weights, test of stopping condition and test of activation functions, and the physicochemical properties were treated independently. For the set of test samples, which simulates real samples, the application of the optimized ANNs provided results with root mean squared errors (RMSE) of 0.55 mm(2)/s, 3.49 g/100 g and 0.89 h for viscosity, iodine value and induction period, respectively, what ensures the feasibility of the proposed method. A comparison between the proposed method and linear methods from literature, both based on the biodiesel composition indicates that our ANN model is much more adequate to the problem addressed. (C) 2014 Elsevier Ltd. All rights reserved.

The research aims to create an application that uses techniques from Machine Learning to extract and collate data geolocated - collected a Social Network, aiming to promote the Social Recommendation users. Existing research in the field of social recommendation deficiencies remain regarding the effectiveness of the filtered data. This paper presents a study and implementation using Text Mining techniques as a proposal for resolution of problems found in social recommendation and more effective results.

The performance of both the traditional linear regression and Artificial Neural Network (ANN) techniques has been compared to check the validity to predict the properties of biodiesel and mixtures of diesel and biodiesel. We present on this paper a review on statistical and ANN applications to the Biodiesel quality. A case study is also presented showing the prediction of oxidative stability of Biodiesel using, for the first time, other official quality parameters instead of the chemical composition as input data. In this sense, our hope is that this paper would complement a series of recent review papers and catalyze future research in this rapidly evolving area.

This paper intends to create an application that performs the Data Mining with textual information linked to geolocation data. The structure of the information is distributed in heterogeneous and complex scenario that presents a Social Network. The purpose is that on the final extraction of information, the results are worked for Social Recommendation. However, the recommender systems present some failures in the filtering of the results and the way they are suggested to users. Then, the article presents a methodology of social recommendation to Location-Based Social Network with text mining techniques, and expose issues that still need to research more effective and consolidated results.