Brazil

The most recent successful time series prediction models are
a combination of three elements: traditional stochastic models, machine
learning models and signal processing techniques. CEEMDAN-LSTM
models have combined empirical mode decomposition and long shortterm memory neural networks to achieve state-of-the-art results for financial data. In this work, we propose a generalized CEEMDAN-LSTM
architecture for time series forecasting capable of dealing with exogenous features as input, and the consequences of input data growth, such
as convergence difficulties. Our model was applied to time series from
10 of the most liquid Brazilian stocks, and results show that accuracy
is overall improved when compared to the original single feature input
CEEMDAN-LSTM architecture.

BRACIS 2020

Financial time series forecasting via CEEMDAN-LSTM with exogenous features

ceemdan

forecasting

time series

neural networks

lstm

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.

Data Augmentation methods - a family of techniques designed
for synthetic generation of training data - have shown remarkable
results in various Deep Learning and Machine Learning tasks. Despite its
widespread and successful adoption within the computer vision community,
data augmentation techniques designed for natural language processing
(NLP) tasks have exhibited much slower advances and limited
success in achieving performance gains. As a consequence, with the exception
of applications of back-translation to machine translation tasks,
these techniques have not been as thoroughly explored by the wider
NLP community. Recent research on the subject also still lacks a proper
practical understanding of the relationship between data augmentation
and several important aspects of model design, such as hyperparameters
and regularization parameters. In this paper, we perform a comprehensive
study of NLP data augmentation techniques, comparing their relative
performance under different settings. We also propose Deep Back-
Translation, a novel NLP data augmentation technique and apply it to
benchmark datasets. We analyze the quality of the synthetic data generated,
evaluate its performance gains and compare all of these aspects
to previous existing data augmentation procedures.

DeepBT and NLP Data Augmentation Techniques: a new proposal and a comprehensive study

Suicidal ideation is one of the main predictors of the risk of suicide attempt and can be described as thoughts, ideas, planning, and desire to commit suicide. Fast detection of such ideation in early stages is essential for effective treatment. Many expressions of suicidal ideation can be found in publications in social networks, especially by young people. Previous works explore the automatic detection of suicidal ideation in social networks for the English language using machine learning algorithms. In this work, we present the first exploration of machine learning algorithms for suicidal ideation detection for the Portuguese language. We compared three classifiers in Twitter data: SVM, LSTM, and BERT (multilingual and Portuguese). Results suggest that BERT is effective for suicidal ideation identification in Portuguese data, achieving 79% of F1 score and less than 9% false negative score.

Machine learning for suicidal ideation identification on Twitter for the Portuguese language

The Winograd Challenge has been advocated as a test of computer understanding with respect to commonsense reasoning. The challenge is based on Winograd Schemas: sentences that contain correferential ambiguities. Most Winograd Schemas are relatively easy for human subjects, and today the best computer systems for the Winograd Challenge can work close to human performance. In this paper, we examine the assumptions behind the Winograd Challenge, and investigate how far we can push the difficulty level of Winograd Schemas, proposing various strategies to build really challenging schemas.

The Winograd Schemas from Hell

Evolutionary algorithms are a non-deterministic approach to solving optimization problems that cannot be solved in polynomial time, like the classic NP-Hard problems. The Permutation Flow Shop (PFS) is a combinatorial optimization problem related to the production environment. In the PFS, jobs must be processed by machines, maintaining the same processing flow on the considered machines. In this work, we adopt the multiobjective approach for the PFS, minimizing the makespan and total flowtime. NSGA-II with Tabu Search were considered in this hybrid approach and applied to 11 instances of PSF with different dimensions. We include an analysis on the use of prohibition rules in Tabu Search and its restrictiveness. The results were analyzed using the IGD and Empirical Attainment Functions metrics and compared with the canonical NSGA-II.

Hybrid Algorithm for the Multi-objective Permutation Flow Shop Problem

This paper proposes a temporal analysis for music metadata using a generative probabilistic model for collections of discrete dataset such as text corpora. This method is also a topic model that is used for discovering abstract topics from a collection of documents. The method is then applied to audio metadata and song lyrics extracted with Echo Nest engine, Spotify and Lyrics Genius API. Song data time series are generated by grouping data items by release date, genre and dominant topics (from LDA analysis). Using a technique from Network Theory we visualise how these topics, in this case genres, are related with each other through time.

Temporal analysis and visualisation of music metadata and topic modelling of song lyrics

O objetivo da presente pesquisa consiste em verificar a eficácia das armas de fogo, fuzil e pistola, em eventos de tiroteios em massa em ambiente fechado e quais os parâmetros que influenciam no número de vítimas, a fim de apoiar a análise e elaboração de políticas públicas. Para isso, realizou-se a Simulação Multiagente através do software NetLogo. Os resultados mostram que o tipo de arma utilizada no evento tem grande influência no número de vítimas. Além disso, também observou-se que a diferença no número de vítimas das duas armas diminui à medida em que se aumenta a quantidade de munição e se diminui a dimensão da porta de saída, devido ao efeito funil.

Multiagent Simulation to Support Disarmament Policies

The increase in transmission rates required for new Internet-related services is driving further studies on network optimization. Optimization algorithms with local search have shown promise in an attempt to design networks that satisfy different objectives such as: cost, blocking probability, energy expenditure and robustness indicators. This work proposes a new strategy for selecting individuals for local search, aiming to improve Pareto's quality in terms of hypervolume, and also to obtain efficiency gains. The proposed strategy was compared with a base method, in a simulated scenario of a backbone network for the state of Pernambuco. According to the results, the proposed strategy was able to make the algorithm more effective, improving its performance in all objectives, and more efficient; surpassing the base method in both aspects studied.

A Novel Strategy for Selecting Individuals in Manyobjective Algorithms with Local Search Applied to the Network Design Problem

This work presents the development of an explainable image classifier,
trained for the task of determining whether a cell is infected by malaria. The
classifier consists of a residual neural network, with a classification accuracy
of 96%, trained on the National Institute of Health Malaria Dataset. Explainable
Artificial Intelligence (XAI) techniques are employed to make classifications
more interpretable. These explanations are generated by using two different
methodologies: Local Interpretable Model Agnostic Explanations (LIME) and
SquareGrid. The explanations provide novel, key insights into the decision making
patterns of such a high performing model for a medical task.

Explainable Classifications for Images of Cells Infected by Malaria

We propose an integrated framework, named Multi-Document Aspect-based Sentiment Extractive Summarization (MD-ASES for short), to automatically generate extractive review summaries based on aspects of a large database with reviews of items such as films, businesses, and companies. Such summaries are got by extracting a subset of sentences as they are in the reviews, based on some relevance criteria. In MD-ASES, initially sentences are grouped in terms of aspects identified as predominant in the reviews. Then, sentences are selected by the similarity of the sentiment expressed about a particular aspect to the overall sentiment of the dataset reviews.
Our results show that MD-ASES can successfully preserve the average sentiment of the reviews while including the most important aspects in the summary.

A Framework for Multi-Document Extractive Summarization of Reviews with Aspect-Based Sentiment Analysis

Reinforcement Learning (RL) is a well-known technique to train autonomous
agents through interactions with the environment. However, the learning process
has a high sample-complexity to infer an effective policy, especially when multiple agents are simultaneously actuating in the environment. We here propose to take advantage of previous knowledge, so as to accelerate learning in multiagent RL problems. Agents may reuse knowledge gathered from previously solved tasks, and they may also receive guidance from more experienced friendly agents to learn faster. However, specifying a framework to integrate knowledge reuse into the learning process requires answering challenging research questions, such as: How to abstract task solutions to reuse them later in similar yet different tasks? How to define when advice should be given? How to select the previous task most similar to the new one and map correspondences? and How to defined if received advice is trustworthy? Although many methods exist to reuse knowledge from a specific knowledge source, the literature is composed of methods very specialized in their own scenario that are not compatible. We propose in this thesis to reuse knowledge both from previously solved tasks and from communication with other agents. In order to accomplish our goal, we propose several flexible methods to enable each of those two types of knowledge reuse. Our proposed methods include: Ad Hoc Advising, an inter-agent advising framework, where agents can share knowledge among themselves through action suggestions; and an extension of the object-oriented representation to multiagent RL and methods to leverage it for reusing knowledge. Combined, our methods provide ways to reuse knowledge from both previously solved tasks and other agents with state-of-the-art performance. Our contributions are first steps towards more flexible and broadly applicable multiagent transfer learning methods, where agents will be able to consistently combine reused knowledge from multiple sources, including solved tasks and other learning agents.

Financial time series forecasting via CEEMDAN-LSTM with exogenous features

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Financial time series forecasting via CEEMDAN-LSTM with exogenous features

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