Brazil

The Software Project Scheduling Problem (SPSP) aims to allocate employees to tasks in the development of a software project, such that the cost and duration, two conflicting goals, are minimized. The dynamic model of SPSP, called DSPSP, considers that some unpredictable events may occur during the project life cycle, like the arrival of new tasks, which implies on schedule updating along the project. In the context of Search-Based Software Engineering, this work proposes the use of dynamic optimization strategies, based on memory, together with the particle swarm optimization algorithm (PSO) to solve the DSPSP. The results suggest that the addition of these dynamic strategies improves the quality of the solutions in comparison with the application of the PSO algorithm only.

BRACIS 2020

Dynamic Software Project Scheduling Problemwith PSO and Dynamic Strategies Based onMemory

dynamic optimization

dynamic software project scheduling problem

particle swarm optimization

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.

technical paper

Data clustering is one of the most primitive tasks in pattern recognition, although it is known to be a NP-hard grouping task. Given its complexity, standard clustering methods, such as the partitional data clustering algorithms, are easily trapped in local minimum solutions, due to their lack of good global searching mechanisms. Evolutionary Algorithms (EAs) and Swarm Intelligence (SIs) methods, such
as Group Search Optimization (GSO) and Backtracking Search Optimization (BSA), are commonly employed to deal with clustering task, given their capabilities to handle global search problems. In this work, a new hybrid evolutionary algorithm between GSO and BSA is presented, named BGSO, to tackle clustering problem, which combines the best features of GSO and the historical mechanisms of BSA. Also, BGSO is developed in the context of Automatic Clustering approach, which means that it is able to predict the best number of nal clusters, so no prior assumption about the data set at hand is required. The proposed approach is compared to standard GSO, BSA and other three EAs and SIs from the literature by means of nine real-world problems, showing promising results considering four clustering metrics.

Backtracking Group Search Optimization: A Hybrid Approach for Automatic Data Clustering

Real-world discrete problems are often also dynamic making them very challenging to be optimized. Here we focus on the employment of evolutionary algorithms to deal with such problems. In the last few years, many evolutionary optimization algorithms have investigated dynamic problems, being that some of the most recent papers investigate formulations with more than one objective to be optimized at the
same time. Although evolutionary optimization had revealed very competitive algorithms in different applications, both multiobjective formulations and dynamic problems need to apply specic strategies to perform well. In this work, we investigate four algorithms proposed for dynamic multiobjective problems: DNSGA-II, MOEA/D-KF, MS-MOEA and DNSGA-III. The rst three were previously proposed in the literature, where they were applied just in continuous problems. We aim to observe the behavior of these algorithms in a discrete problem: the Dynamic Multiobjective Knapsack Problems (DMKP). Our results have shown that some of them are also promising for applying to problems with discrete space.

Applying Dynamic Evolutionary Optimization to the Multiobjective Knapsack Problem

The physiological signs are a reliable source to identify stress states, and wearable sensors provide precise identification of physiological signs associated with the stress occurrence. The literature review shows that the use of physiological signs as a source for stress patterns identification is still a critical investigation subject. Few studies evaluate the effect of combining several different signals and the implications of the data acquisition procedures and details. The objective of this article is to investigate the possible integration of data obtained from heart rate variability (HFV), electrocardiographic (ECG), electrodermal activity (EDA) and electromyography (EMG) to detect stress patterns, considering a new experimental protocol to data acquisition. The data acquisition involved the Trier Social Stress Test (TSST), wearable sensor monitoring, and complementary stress perception instruments, resulting in a publicly available dataset. This dataset was evaluated using different machine learning classifiers, considering the obtained annotated data and exploring different physiological features and its combinations.

Evaluating a New Approach to Data Fusion in Wearable Physiological Sensors for Stress Monitoring

The aquatic environment is subject to a series of contaminants resulting from anthropogenic activity which may compromise biota health and the quality of water resources. There is an imperative need for cost-effective, accurate and online solutions for monitoring aquatic environments. The present work proposes the construction of aquatic pollution biosensors for detecting both petrochemical and anti-fouling paint compounds based on the behavioral analysis of Perna perna mussels through multiple classifier systems. Networks of mussels instrumented with Hall effect sensors and magnets were exposed to Water-Accommodated Fraction of diesel and micro-particles of anti-fouling paint. The hourly behavioral parameters average amplitude, transition frequency and amount of motion reversals were used to infer the contamination status (polluted or not) through voting classifiers. Results presented high accuracy (95.8%) in predicting diesel pollution and non-pollution while lack of data and intrinsic characteristics of anti-fouling paints provided less significant results for detecting its compounds. This paper has demonstrated a promising use of artificial intelligence in the construction of aquatic pollution biosensors using behavioral analysis of bivalve mollusks.

Intelligent Classifiers on the Construction of Pollution Biosensors Based on Bivalves Behavior

Classification tasks using imbalanced datasets are not challenging on their own. Classification models perform poorly on the minority class when the datasets present other difficulties, such as class overlap and complex decision border. Data complexity measures can identify such difficulties, better dealing with imbalanced datasets. They can capture information about data overlapping, neighborhood, and linearity. Even though they were recently decomposed by classes to deal with imbalanced datasets, their high computational cost prevents their use on applications with a time restriction, such as recommendation systems or high dimensional datasets. In this paper, we use a Meta-Learning approach to estimate the decomposed data complexity measures. We show that the simulated measures assess the difficulty of the dataset after applying preprocessing techniques to different sample sizes. We also show that this approach is significantly faster than computing the original measures, with a statistically similar estimation error for both classes.

Simulating Complexity Measures on Imbalanced Datasets

Classification algorithms have been extensively studied in many of the major scientific investigations in recent decades. Many of these algorithms are designed for supervised learning, which requires labeled instances to achieve effective learning models. However, in many of the real human processes, data labeling is expensive and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without a significant loss of model performance. This paper presents the Semi-Supervised Learning C4.5 algorithm (SSL-C4.5) designed to work in scenarios where only a small part of the data is labeled. SSL-C4.5 was implemented over the J48 implementation of the C4.5 algorithm available at the WEKA platform. The J48 was modified incorporating a metric for semi-supervised learning. This metric aims at inducing decision tree models able to analyze and extract information from the entire training dataset, including instances of unlabeled data in scenarios where they are the majority. The assessment performed using eight different benchmark datasets showed that the new proposal has achieved promising results compared to the supervised version of C4.5.

SSL-C4.5: Implementation of a classification algorithm for semi-supervised learning based on C4.5

Assessing the hardness of each instance in a problem is an important
meta-knowledge which may leverage advances in Machine Learning.
In classification problems, an instance can be regarded as difficult
if it gets systematically misclassified by a diverse set of classification
techniques with different biases. The instance hardness measures were
proposed with the aim of relating data characteristics to this notion of
intrinsic difficulty of the instances. There are also in the literature a large
set of measures which are dedicated at describing the difficulty of a classification
problem from a dataset-level perspective. In this paper these
measures are decomposed at the instance-level, giving a perspective of
how each individual example in a dataset contributes to its overall complexity.
Experiments on synthetic and benchmark datasets demonstrate
the proposed measures can provide a complementary instance hardness
perspective when compared to those from related literature.

Measuring instance hardness using data complexity measures

The increasingly relevance of data streams in the context of machine learning and artificial intelligence has motivated this paper which discusses and draws necessary relationships between the concepts of data streams and time series in attempt to build on theoretical foundations to support online learning in such scenarios. We unify the concepts of data streams and time series by assessing their definitions in the literature and discuss the major implications of this claim on the way that data streams research and practice is carried out, showing that many common assumptions are incorrect or unnecessary. We analyzed six data sources typically used in benchmark data-stream classification and found that none of those meet the requirements and assumptions qualifying them for online learning.

Data Streams are Time Series: Challenging Assumptions

Given the most recent events involving the fast spreading of COVID-19, policy makers around the world have been challenged with the difficult task of developing efficient strategies to contain the dissemination of the disease among the population, sometimes by taking severe measures to restrict local activities, both socially and economically. Within this context, models which can help on predicting the spread evolution of COVID-19 in a specific region would surely help the authorities on their planning. In this paper, we introduce a semi-supervised regression model which makes use of a correlations-based temporal network, by considering the evolution of COVID-19 in different world regions, in order to predict the evolution of new confirmed cases and deaths in 27 federal units of Brazil. In this approach, each node in the network represents the COVID-19 time series in a specific region, and the edges are created according to the variations similarity between each pair of nodes, at each new time step. The results obtained, by predicting the weekly new confirmed cases and deaths in each region, are promising, with a median and mean absolute percentage error of 21% and 24%, respectively, when predicting new cases, and a median and mean absolute percentage error of 16% and 23%, respectively, when predicting new deaths, for the considered period.

Predicting the Evolution of COVID-19 Cases and Deaths Through a Correlations-Based Temporal Network

The source code similarity problem consists in defining iftwo given distinct codes are the same program or not. This problem isvaluable for polymorphic malware detection, which can generate distinctversions of code applying rules of obfuscation that change the originalcode. A technique to measure the similarity between source codes isto model the codes as program dependency graphs (PDG) and find analignment between the graphs that maximizes a similarity function. Thiswork investigates the performance of distinct similarity metrics applied toa genetic algorithm (GA). To distinguish between similar codes and non-similar codes, we use the k-nearest-neighbors (KNN) algorithm based onthe similarity of the alignment found by the GA. The experiments areconducted with a database proposed by this work, where the source codeswere retrieved from the Codeforces website and obfuscated by the toolCXX-obfuscator.

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