Brazil

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.

BRACIS 2020

Sequencing Operator Counts with State-Space Search

classical planning

operator-counting framework

state-space search

artificial intelligence

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

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.

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.

A Clustering Visualization Query Language

The DBSCAN algorithm is a traditional density-based clustering
method. This algorithm allows to identify groups of different topological for-
mats and also to discard isolated noises. The DBSCAN usually presents good
results, however, it performs several calculations of distances in the clustering
process, causing low efficiency, and its application in large datasets is not re-
commended. This work presents a new sampling method that makes it possible
to apply DBSCAN to a reduced set of examples, in order to approximate the ori-
ginal result of the algorithm applied to the entire dataset. Therefore, the method
makes it possible to execute large datasets with results similar to DBSCAN on
entire dataset, however, with greater computational efficiency.

A biased sampling method for applying DBSCAN

We describe an intelligent triage system for emergency rooms; the system interacts with patients and classifies them by priority level and with respect to medical specialty. The system consists of a conversational interface, coupled with sensors and a physical robot-like platform, and classifiers that operate on symptoms and measurements so as to select a medical specialty and to output a priority level. Tests with human subjects demonstrated that our Healthbot system was well received and in fact preferred to alternatives. Tests have also shown that the classifiers reached accuracy consistent with a doctor’s output.

AUTOMATED EMERGENCY ROOM TRIAGE: HELPING PATIENTS GET THE BEST TREATMENT

Sample bias is a common issue on traditional machine learning studies but rarely considered when discussing meta-learning. It happens when the
training data sample lacks or overemphasizes one or more characteristics, compared to others. Herewith, models trained on such data may become inaccurate
for some instances. This work aims to analyze this issue in the meta-learning
context. Indeed, in most of the meta-learning literature, a random sample of
datasets is taken for building meta-models. Nonetheless, there is no discussion over a possible side-effect bias not controlled in such random sampling.
This work aims to analyze these effects, in order not only to discuss their consequences, but also to start a debate over the need of their consideration in
meta-learning research.

Sample Bias Effect on Meta-learning

The huge amount of textual data made available every day on the internet encouraged research in several areas that automatically process and analyze these texts. One of the most popular areas is Sentiment Analysis (SA), but although SA has been a topic widely discussed in recent years, it still faces a shortage of available resources in the Brazilian Portuguese language. This work presents the steps for a complete process of sentiment analysis and classification, from the development of a dataset in Brazilian Portuguese (in the books domain) to the sentiment classification, using some of the main classifiers in the literature and different feature extraction methods.

A Sentiment Classification Approach for Books Reviews in Brazilian Portuguese Using Different Feature Extraction Methods

In this work, a tool for inferring the degree of difficulty of cooking recipes will be proposed. The inference will be made by the textual classification of the recipe preparation methods. The tool will be a fundamental piece to the development of a context-aware cooking recipe recommendation system. Some of the main classifiers in Text Classification literature will be adopted, in addition to different feature extraction methods. An experimental evaluation is performed, in order to select the best approaches to compose the system.

Sequencing Operator Counts with State-Space Search

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