Brazil

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.

BRACIS 2020

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

running patterns

artificial neural networks

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 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.

Asymmetric Action Abstractions for Real-Time Planning in Extensive-Form Games

Statistical machine learning algorithms usually assume that there is considerably-size data to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data by relying on a model learned in a source domain where data is easy to obtain to be a starting point for the target domain. On the other hand, real-world data contains objects and their relations, usually gathered from noisy environment. Finding patterns through such uncertain relational data has been the focus of the Statistical Relational Learning (SRL) area. Thus, to address domains with scarce, relational, and uncertain data, in this paper, we propose TreeBoostler, an algorithm that transfers the SRL state-of-the-art Boosted Relational Dependency Networks learned in a source domain to the target domain. TreeBoostler first finds a mapping between pairs of predicates to accommodate the additive trees into the target vocabulary. After, it employs two theory revision operators devised to handle incorrect relational regression trees aiming at improving the performance of the mapped trees. In the experiments presented in this paper, TreeBoostler has successfully transferred knowledge among several distinct domains. Moreover, it performs comparably or better than learning from scratch methods in terms of accuracy and outperforms a transfer learning approach in terms of accuracy and runtime.

Transfer Learning by Mapping and Revising Boosted Relational Dependency Networks

The performance of a meta-heuristic on a search problem relies on the features of the problem instance, meaning that it does not exist a single method that always achieves the best performance. Therefore, approaches that automatically select an algorithm can improve the robustness of an application. Among the possible alternatives, the selection can be done with meta-learning, which aims at mapping the characteristics (meta-features) of problem instances to the performance of a set of algorithms. Some works have proposed the use of Fitness Landscape Analysis (FLA) to characterize the instances, where the structure of a problem is analysed by extracting measures of the search space, normally with a sample of solutions. This dissertation is composed by a series of incremental studies on meta-learning algorithm selection for the Quadratic Assignment Problem. We experimented with a variety of meta-features, giving emphasis on the FLA based metrics, and with a set of classic and novel meta-heuristics. The reported results show that this meta-learning approach is suitable for algorithm selection, yielding good and balanced classification performance with a low addition in computational effort.

Seleção Automática de Algoritmo para o Problema Quadrático de Alocação Usando Meta-aprendizagem e Métricas de Superfície de Aptidão

A classificação de textos tem sido estudada extensivamente nas últimas décadas e grande parte dos trabalhos relacionados ao tema são direcionados a classificação de rótulo único e ao aprendizado offline. Neste tipo de aprendizado, os documentos de texto são associados a apenas um rótulo e devem estar disponíveis com antecedência para o treinamento. Problemas reais de classificação de textos, no entanto, frequentemente envolvem instâncias multirrotuladas, que se tornam disponíveis continuamente e com padrões que mudam ao longo do tempo. Para manipular esses problemas, os classificadores idealmente deveriam ser capazes de predizer múltiplos rótulos para cada documento de texto e de atualizar o seu modelo preditivo de forma eficiente, para ser escalável mesmo com recursos de memória e tempo limitados, e ser rapidamente adaptável às mudanças nos padrões dos dados. Por isso, o aprendizado online e a classificação multirrótulo tem atraído grande interesse de pesquisa, uma vez que existem poucos métodos capazes de abordar os dois problemas simultaneamente e, assim, sempre é necessário recorrer às técnicas de transformação de problemas ou retreinar todo o modelo de predição quando novos documentos de texto se tornam disponíveis. Neste trabalho, é apresentado um método de classificação de textos baseado no princípio da descrição mais simples, que pode ser empregado em problemas de classificação multirrótulo sem a necessidade de transformá-los em problemas de rótulo único. Ele também apresenta a vantagem de considerar a existência de dependência entre os rótulos e de suportar o treinamento incremental naturalmente. O desempenho desse método foi avaliado empregando-o na tarefa de classificação em 15 aplicações de diferentes domínios e o resultado obtido foi comparado com os resultados de outros classificadores de referência na literatura, considerando cenários de aprendizado offline e online. Os resultados obtidos pelo método proposto são muito competitivos com os resultados obtidos pelos métodos estado-da-arte avaliados, uma vez que o ML-MDLText esteve
entre as duas melhores performances em termos acurácia de subconjunto e macro F-medida nos experimentos com aprendizado online.

ML-MDLText: um método de classificação de textos multirrótulo de aprendizado incremental

Communication has become increasingly dynamic with the popularization of social networks and applications that allow people to express themselves and communicate instantly. In this scenario, distributed representation models have their quality impacted by new words that appear frequently or that are derived from spelling errors. These words that are unknown by the models, known as out-of-vocabulary (OOV) words, need to be properly handled to not degrade the quality of the natural language processing (NLP) applications, which depend on the appropriate vector representation of the texts. To better understand this problem and finding the best techniques to handle OOV words, in this study, we present a comprehensive performance evaluation of deep learning models for representing OOV words. We performed an intrinsic evaluation using a benchmark dataset and an extrinsic evaluation using different NLP tasks: text categorization, named entity recognition, and part-of-speech tagging. Although the results indicated that the best technique for handling OOV words is different for each task, Comick, a deep learning method that infers the embedding based on the context and the morphological structure of the OOV word, obtained promising results.

Deep learning models for representing out-of-vocabulary words

In recent years, the advances in technology have produced datasets of increasing size, not only regarding the number of samples but also the number of features. Unfortunately, creating a sufﬁciently large amount of adequately labeled data with enough examples for each class is not easy. Labeling is a challenging, expensive, and time-consuming task. It is usually done manually, which may contribute to the insertion of noise and errors in the data. Hence, it is of great importance to put forward intelligent models that can beneﬁt from the distinct information that both labeled and unlabeled data can provide, since, for many applications, there is a plentiful amount of unlabeled data, but insufﬁcient labeled ones. Semi-Supervised Learning (SSL) is employed to achieve this. It is halfway between supervised and unsupervised learning. In this sense, we highlight two very inﬂuential models: Self-Organizing Maps (SOM) and Learning Vector Quantization (LVQ). SOM is a biologically inspired neural model that uses unsupervised and incremental learning to produce prototypes of the input data, whereas the LVQ can be seen as its supervised counterpart. The unsupervised characteristic of SOM makes it unfeasible to execute SSL. In that way, the current work proposes new models that incorporate standard concepts from LVQ to the SOM algorithm to build semi-supervised approaches. Such proposals can dynamically switch between the two types of learning at training time, according to the availability of labels and automatically adjust themselves to the local variance observed in each cluster. The experimental results show that the proposed models can surpass the performance of other traditional methods not only in terms of classiﬁcation but also regarding clustering quality. It also enhances the range of applications of SOM and LVQ-based models by combining them with Deep Learning in a synergic way to allow dealing with complex data structures, such as images and sound. Moreover, we explore forms of learning good representations of the input data, and manners to estimate the unsupervised error when no labels are provided. Our approaches demonstrated to be good at producing a meaningful topology and clustering prototypes that appropriately represent the data.

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