Brazil

Focal cortical dysplasia (FCD) is a local malformation of the cortex, the main cause of refractory epilepsy in childhood and one of the most common causes in adults. The surgery decision and planning depend on the FCD localization. Although recent studies have successfully detected FCD through artificial intelligence, no study investigates the relevance and prevalence of cortical features on FCD identification and the performance of different machine learning techniques. In this study, the proposed method constructed a voxel-based set of features, e.g., texture measure, border definition, cortical thickness.

BRACIS 2020

Brain Tissue Classification to Detect Focal Cortical Dysplasia in Magnetic Resonance Imaging

focal cortical dysplasia

refractory epilepsy

medical images

segmentation

machine learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.

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.

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

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.

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Next from BRACIS 2020

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

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MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM)

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