Brazil

A prática jurídica Brasileira tem convergido para o processamento de litígios por meio eletrônico, com a consequente diminuição progressiva das ações processadas fisicamente. A Constituição Federal, no artigo 5º, inciso V assegura a indenização pelo dano cível decorrente da violação de Direitos, como o Direito à vida, à liberdade, à igualdade, à segurança e à propriedade. Portanto, este estudo apresenta a ontologia a qual permite (utilizando os padrões da Web Semântica como a Lógica de Descrições) verificar se o caso em análise corresponde a um Dano Cível Indenizável. Os resultados alcançados até então apontam para a possibilidade da aplicação da Ontologia na prática jurídica/forense.

BRACIS 2020

SIMULANDO INDENIZAÇÃO EM AÇÕES CIVIS POR MEIO DE ONTOLOGIAS BASEADAS NA LÓGICA DESCRITIVA

lógica de descrições

indenização

dano cível

ontologia

### Welcome to the Brazilian Symposium on Multimedia Systems and Web 
Promoted annually by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year, we will be celebrating the 27th edition of WebMedia, to be held from **November 5th to 12th, 2021**, organized by the Federal University of Minas Gerais and Federal University of São João Del Rei. For the second time in its history, the event will be held online, with interaction between the participants being done through tools and web platforms.
 <iframe width="560" height="315" src="https://www.youtube.com/embed/2GvWKRUDtAY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> 
The Webmedia 2021 schedule will feature:
 
* Technical sessions for the presentation of scientific articles.
* National and international lectures
* Panel with Companies
* Workshops
* Short courses
* Project meetings open to the community
 

The topics of interest for paper submission are:
 
* Web and Social Networks
* Multimedia
* Digital TV, Ubiquitous and Mobile Computing
 

In addition to the main track, WebMedia 2021 will feature the Tools and Applications Workshop, the Scientific Initiation Papers Contest, the Theses and Dissertations Contest, the RNP CT-Video Workshop.
 
The organization of WebMedia 2021 is under the responsibility of the [Department of Computer Science at UFMG (DCC/UFMG)](http://dcc.ufmg.br/) and [Department of Computer Science at UFSJ (DCOMP/UFSJ)](https://dcomp.ufsj.edu.br/)
 
**#PartiuWebMedia2021 #WebMedia2021**

WebMedia 2021

The Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

technical paper

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

Popular mobile applications receive millions of user reviews.
These reviews contain relevant information, such as problem reports and improvement suggestions. The reviews information is a valuable knowledge source for software requirements engineering since the analysis of the reviews feedback helps to make strategic decisions in order to improve the app quality. However, due to the large volume of texts, the manual extraction of the relevant information is an impracticable task. In this paper, we investigate and compare textual representation models for app reviews classification. We discuss different aspects and approaches for the reviews representation, analyzing from the classic Bag-of-Words models to the most recent state-of-the-art Pre-trained Neural Language models. Our findings show that the classic Bag-of-Words model, combined with a careful analysis of text pre-processing techniques, is still a competitive model. However, pre-trained neural language models showed to be more advantageous since it obtains good classification performance, provides significant dimensionality reduction, and deals more adequately with semantic proximity between the reviews' texts, especially the multilingual neural language models.

From Bag-of-Words to Pre-trained Neural Language Models: Improving Automatic Classification of App Reviews for Requirements Engineering

Stock market forecasting has been a quite popular challenge in machine learning research. Recently, studies have been using deep learning techniques, such as Convolutional Neural Networks (CNN), to perform regressions in prices or classification in the trading signal as an operation indication. 
However, these works have not yet achieved a satisfactory financial result. In this paper, we propose a financially profitable stock market method that uses a new approach called Multichannel CNN Trading Classifier (MCNN-TC).
The model was evaluated using data from the Brazilian stock market. The results indicate a promising financial trading performance compared to the Buy and Hold strategy and good classification metrics.

Stock Trading Classifier with Multichannel Convolutional Neural Network

Trading in the stock market always comes with the challenge of deciding
the best action to take on each time step. The problem is intensified by the
theory that it is not possible to predict stock market time series as all information
related to the stock price is already contained in it. In this work we propose a
novel model called Discrete Wavelet Transform Gated Recurrent Unit Network
(DWT-GRU). The model learns from the data to choose between buying, holding
and selling, and when to execute them. The proposed model was compared to
other recurrent neural networks, with and without wavelets preprocessing, and
the buy and hold strategy. The results shown that the DWT-GRU outperformed
all the set baselines in the analysed stocks of the Brazilian stock market.

Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market

Heuristic search is the most common approach to solve classical planning tasks optimally. OpSeq is an innovative approach to solve planning tasks that decomposes the planning task into a master problem and a subproblem. The master problem generates an assignment of integer counts for each task operator, and the subproblem verifies if a plan satisfying these counts exists. If a plan does not exist, OpSeq learns a new constraint to inform the master problem. OpSeq solves the subproblem using a SAT solver. In this dissertation, we propose a new solver OpSearch: an A*-based approach to sequence operator counts that uses the frontier of the search to learn a constraint on failure. 
We show that OpSearch solves the subproblem better than OpSeq. It solves more planning tasks, scales better, and learns constraints more informative than OpSeq. We prove that OpSearch only learns constraints that maintain all optimal solutions. OpSearch extends the research on an entirely new method to solve planning tasks. Also, our solver opens many new lines of research based on decomposition, such as fast and stronger anytime lower bounds, new methods for agile planning, and new approaches to solve diverse planning.

Sequencing Operator Counts with State-Space Search

In this work, a hybrid partitional clustering algorithm between K-Means and Group Search Optimization is employed to deal with automatic disease segmentation in leaf images, as part of an automatic plant disease classification system: the GSO-KM. An unsupervised image clustering approach is adopted to deal with the problem. GSO-KM is compared to five algorithms from data clustering literature through twelve images, which present variant degrees of disease, using four evaluation metrics. Experimental analysis showed that GSO-KM is able to solve the image segmentation problem properly for the evaluated scenario.

Image Clustering Based on a Hybrid Group Search Optimization and K-Means Approach for Automatic Plant Disease Segmentation

In this paper, a hybrid Otsu and improved Particle Swarm Optimization (PSO) algorithm is presented to deal with multilevel color image thresholding problem, named APSOW. In APSOW, the historical information represented by the local best solutions found so far by PSO population are permuted among the current population, using a randomized greedy process. APSOW also implements a weedout operator to prune the worst individuals from the population. The proposed APSOW is compared to other hybrid EAs and Otsu approaches from literature (include standard PSO model) through twelve benchmark color image problems, showing its potential and robustness.

A Multilevel Thresholding Approach Based on Improved Particle Swarm Optimization for Color Image Segmentation

Recipe sharing websites have become very popular in the past years, allowing individuals to use such systems in an attempt to find a desired recipe. But sometimes finding recipes which best fit the user’s wishes, while still satisfying his food restrictions, may become a very time consuming and difficult task. In this work, we propose a recipe multi-label classification approach as part of a recipe recommendation system for people with food restrictions, in an attempt to automatically identify whether an input recipe or list of ingredients fits into one or more food restrictions, satisfying both user’s expectations and needs. The experimental evaluation includes two approaches for feature selection, as a manner to reduce the computational costs for the proposed system.

A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification

Electricity consumption is growing rapidly worldwide. Renewable
energy resources, such as solar energy, play a crucial role in this scenario,
contributing to satisfy demand sustainability. Although the share of
Photovoltaic (PV) power generation has increased in the past years, PV
systems are quite sensitive to climatic and meteorological conditions, leading
to undesirable power production variability. In order to improve energy grid
stability, reliability, and management, accurate forecasting models that relate
operational conditions to power output are needed. In this work we evaluate
the performance of regression methods applied to forecast short term (next
day) energy production of a PV Plant. Specifically, we consider five regression
methods and different configurations of feature sets. Our results suggest that
MLP and SVR provide the best forecasting results, in general. Also, although
features based on different solar irradiance levels play a key role in predicting
power generation, the use of additional features can improve prediction results.

Comparative Study of Photovoltaic Power Forecasting Methods

A utilização de técnicas de aprendizado de máquina popularizou-se nos últimos anos nas mais diversas áreas e aplicações. No entanto, a aplicação de tais técnicas ainda depende de um profissional especializado, que execute a sequência de tarefas necessárias para sua execução. Na área de banco de dados, o SQL democratizou a utilização de sistemas gerenciadores de bancos de dados (SGBD) para o usuário final através de uma linguagem declarativa simples, que se assemelha à linguagem natural. Neste artigo, é proposta a linguagem Clustering Visualization Query Language (CVQL), que estende o SQL para a realização de agrupamentos e visualização destes resultados. Agrupamento é uma técnica utilizada para dividir os dados em grupos que compartilham características similares. A visualização destes agrupamentos em diferentes formas é uma funcionalidade essencial para o usuário final analisar os grupos gerados. O CVQL foi implementado utilizando o SGBD MySQL e a biblioteca scikit-learn. Para apresentar o sistema, é adotado um exemplo de consulta com apenas 8 linhas sobre uma base de dados real de casos de Covid-19 nos EUA. Para o processamento da sequência de tarefas executadas pela consulta seriam necessárias 140 linhas de código na linguagem Python, que demonstra a utilidade do sistema.

SIMULANDO INDENIZAÇÃO EM AÇÕES CIVIS POR MEIO DE ONTOLOGIAS BASEADAS NA LÓGICA DESCRITIVA

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Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

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SIMULANDO INDENIZAÇÃO EM AÇÕES CIVIS POR MEIO DE ONTOLOGIAS BASEADAS NA LÓGICA DESCRITIVA

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Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

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An Ontological Model and Services for Capturing and Tracking Provenance in Decentralized Social Networks

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PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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