Brazil

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called \textit{low-level} classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has a facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as \textit{high-level} classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results.
In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure.
We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.

BRACIS 2020

A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

high-level classification

supervise learning

machine learning

The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020) will be held October 26–28th virtually at The Netherlands Institute for Sound and Vision in Hilversum, The Netherlands.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

This year, we especially invite works that will generate new insights into the quality (or qualities) of human-annotated datasets, including elements such as reliability and replicability of human computation and crowdsourcing experiments, novel metrics for aggregation of results, and holistic approaches to deal with bias, fairness and interpretability.

HCOMP 2020

HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. 


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.

Many works that apply Deep Neural Networks (DNNs) to Speech Emotion Recognition (SER) use single datasets or train and evaluate the models separately when using multiple datasets. Those datasets are constructed with specific guidelines and the subjective nature of the labels for SER makes it difficult to obtain robust and general models. We investigate how DNNs learn shared representations for different datasets in both multi-task and unified setups. We also analyse how each dataset benefits from others in different combinations of datasets and popular neural network architectures. We show that the long-standing belief of more data resulting in more general models doesn’t always hold for SER, as different dataset and meta-parameter combinations hold the best result for each of the analysed datasets.

Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition

Machine learning algorithms are widely used in credit card fraud detection systems due to their ability to distinguish between legitimate and fraudulent transactions. A known problem in this field is the high imbalance usually found in the classes, which can compromise the performance of the classifiers. The empirical studies found in the literature apply, at most, two sampling techniques. This article presents a comparative study of five classification models under five different methods of balancing the training sets. The best performance was obtained by random forest, which in addition to having the highest average F-score (0.867), proved to be considerably more robust than the other classifiers in relation to the choice of the balancing technique and attribute selection.

Empirical evaluation of classifiers and balancing methods for fraud detection in credit card transactions

Non-traumatic Vertebral Compression Fractures (VCFs) are generally caused by osteoporosis (benign VCFs) or metastatic cancer (malignant VCFs) and the success of the medical treatment strongly depends on a fast and correct classification of VCFs. Recently, methods for computer-aided diagnosis (CAD) based on machine learning have been proposed for classifying VCFs. In this work, we investigate the problem of clustering images of VCFs and the impact of feature selection by genetic algorithms on this task. The analysis of the clusters helps to understand the results of classifiers and difficulties of differentiating images of different classes by an expert. The results indicate that features selection improved the separability of clusters and purity. Feature selection also helps to understand which attributes are more important for analysing the images of vertebral bodies.

Impact of Feature Selection on Clustering Images of Vertebral Compression Fractures

This work presents an investigation on how to define Neural Networks (NN) architectures adopting a data-driven approach using clustering to create sub-labels to facilitate the learning process and to discover the number of neurons needed to compose the layers. We also increase the depth of the model aiming to represent the samples better, the more in-depth it flows into the model. We hypothesize that the clustering process identifies sub-regions in the feature space in which the samples belonging to the same cluster have strong similarities. We used seven benchmark datasets to validate our hypothesis using 10-fold cross validation 3 times. The proposed model increased the performance, while never decreased it, with statistical significance considering the p-value < 0.05 in comparison with a Multi-Layer Perceptron with a single hidden layer with approximately the same number of parameters of the architectures found by our approach.

Clustering for Data-driven Unraveling Artificial Neural Networks

The identification of running patterns, especially those associated to fatigue, can assist in designing more efficient workouts and preventing injuries in high performance sports. However, this task is not trivial due to the complexity of the patterns. An interesting alternative is to use Machine Learning methods, such as Artificial Neural Networks (ANNs), to classify running patterns. In this research, force signals are obtained by sensors coupled to the base of a low-cost open source treadmill. The goal is to indicate to the user the occurrence of fatigue during training based on running patterns. Different attributes are extracted from the time signals generated during training sessions. The examples are then classified by an ANN. The experimental results indicate that the ANN is able to classify running patterns with good accuracy. Experiments considering different scenarios indicate that the efficiency of the classifier was influenced by the day and stage of the training sessions.

Using Artificial Neural Networks to Classify Treadmill Running Patterns in High-Performance Sports

In this paper, we explore the fine-grained opinion identification and polarity classification tasks using twitter data on the COVID-19 pandemic in Brazilian Portuguese. We trained machine learning-based classifiers using a few different methods and tested how well they per-formed different tasks. For polarity classification, we tested a cross-domain strategy in order to measure the performance of the classifiers among different domains. For fine-grained opinion identification, we provide a taxonomy of opinion aspects and employed them in conjunction with machine learning methods. Based on the obtained results, we found that the cross-domain data improved the results of the polarity classification. For fine-grained opinion identification, the use of a domain taxonomy presented competitive results for the Portuguese language.

Identifying Fine-Grained Opinion and Classifying Polarity on Coronavirus Pandemic

Goal Recognition is the task of recognizing the intended goal of autonomous agents or humans by observing their behavior in an environment. Over the past years, most existing approaches to goal and plan recognition have been ignoring the need to deal with imperfections regarding the domain model that formalizes the environment where autonomous agents behave. In this work, we introduce the problem of goal recognition over imperfect domain models, and develop solution approaches that explicitly deal with two distinct types of imperfect domains models: (1) incomplete discrete domain models that have possible, rather than known, preconditions and effects in action descriptions; and (2) approximate continuous domain models, where the transition function is approximated from past observations and not well- defined. We develop novel goal recognition approaches over imperfect domains models by leveraging and adapting existing recognition approaches from the literature. Experiments and evaluation over these two types of imperfect domains models show that our novel goal recognition approaches are accurate in comparison to baseline approaches from the literature, at several levels of observability and imperfections.

Goal Recognition over Imperfect Domain Models

Stochastic Shortest Path(SSP) is the most popular framework to model sequential decision-making problems under stochasticity. However, decisions for real problems should consider risk sensitivity to provide robust decisions taking into account bad scenarios. SSPs that deal with risk are called Risk-Sensitive SSPs (RSSSPs), and an interesting framework from a theoretical perspective con- siders Expected Utility Theory under an exponential utility function. However, from a practical perspective, exponential utility function causes overflow or underflow in computer implementation even in small state spaces. In this paper, we make use of LogSumExp technique to solve RSSSPs under exponential utility in practice within Value Iteration, Policy Iteration, and Linear Programming algorithms. Experiments were performed on a toy problem to show scalability of the proposed algorithms.

Risk Sensitive Stochastic Shortest Path and LogSumExp: from theory to practice

Com o crescimento da computação quântica como um paradigma de processamento de informação, modelos quânticos de redes neurais têm sido propostos para unir os benefícios da computação quântica com os benefícios das Redes Neurais Artificiais (RNA). Existe uma dificuldade de implementar a não-linearidade dos neurônios em computadores quânticos, uma vez que tradicionalmente a computação quântica possui apenas operadores unitários. Há algumas propostas na literatura de modelos de neurônios que simulam essa não-linearidade, mas elas aparecem simulando alguma função não-linear específica, como a função de limiar ou arco-tangente. Há ainda as RNAs que possuem comportamento de memória associativa, fazendo a recuperação de informação a partir de uma entrada igual ou parecida com seus padrões armazenados.
Nesse trabalho, três novos modelos inteligentes quânticos são descritos. O primeiro deles é um modelo de neurônio que executa qualquer função não-linear discreta. O modelo proposto permite que o neurônio possa simular o comportamento dos neurônios clássicos, assim como utilizar dos recursos intrínsecos da computação quântica como superposição e emaranhamento. Esse modelo também permite incorporar informações espaço-temporais. O segundo modelo proposto é uma memória associativa com uso de operadores não-lineares e o terceiro modelo é uma memória associativa com operadores lineares, utilizando os algoritmos quânticos de Grover e transformada inversa de Fourier. Esses modelos de memória permitem reconhecer informações próximas ou iguais ao conteúdo que está na memória e possui custo linear de operação.

Avanços em Redes Neurais Quânticas

Action abstractions restrict the number of legal actions available for real-time planning in multi-unit zero-sum extensive-form games, thus allowing algorithms to focus their search on a set of promising actions. Even though unabstracted game trees can lead to optimal policies, due to real-time constraints and the tree size, they are not a practical choice. In this context, we introduce an action abstraction scheme we call asymmetric abstraction. Similarly to unabstracted spaces,asymmetrically-abstracted spaces can have theoretical advantages over regularly abstracted spaces while still allowing search algorithms to derive effective strategies in practice, even in large-scale games. Further, asymmetric abstractions allow search algorithms to “pay more attention” to some aspects of the game by unevenly dividing the algorithm’s search effort amongst different aspects of the game. We also introduce four algorithms that search in asymmetrically-abstracted game trees to evaluate the effectiveness of our abstraction schemes. An extensive set of experiments in a real-time strategy game developed for research purposes shows that search algorithms using asymmetric abstractions are able to outperform all other search algorithms tested.

A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

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A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

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Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition

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Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy

Stay up to date with the latest Underline news!

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