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Community detection in networks is a hard to solve combinatorial optimization problem with large-scale applications such as those based on flight networks. To approach this problem, a number of heuristics can be found in the literature, among them we highlight multilevel algorithms which are very efficient in detecting communities in larger networks. In this paper, we propose an improvement of a multilevel community detection algorithm that aims at finding communities so as to maximize the well-known modularity measure. The proposed improvement is the inclusion of a refinement phase to enhance the modularity value in flight networks we compiled. We presented a study case on the communities found for real flight networks. The vertices of the constructed networks correspond to airports in Europe, South America, United States of America and Canada and the arcs represent the flights whose weights indicate the number of flights between pairs of destinations. Experiments with these networks pointed to an improvement in the modularity of the communities found by the proposed algorithm when contrasted with its original version and the Lovain method. We visually identified consistent communities in the flight networks. Besides, in comparison to other reference algorithms on benchmark networks, ours was very competitive.

BRACIS 2020

Improved Multilevel Algorithm to Detect Communities in Flight Networks

flight networks.

multilevel algorithms

modularity maximization

community detection

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.

This work proposes a new approach to segmentation of hemorrhagic stroke (HS) based on morphological geodesic active contour method, with automatic initialization near to lesion region, without previous training, called Adaptive Morphological Geodesic Active Contour (Ada-MGAC). To evaluate the performance, we used 100 computed tomography images with HS of volunteers. These samples were compared against segmentation methods from specialized literature. A manual segmentation from a medical specialist was considered as the gold standard. The results indicate a competitive potential of Ada-MGAC and method showed a mean convergency time about 3 seconds, indicating a fast result to medical analysis. Thus, it is possible to conclude that the proposed approach can be used to aid medical diagnosis in the cerebral vascular accident.

New Adaptive Morphological Geodesic Active Contour Method for Segmentation of Hemorrhagic Stroke in Computed Tomography Image

Forecasting stock market prices is a hard task. The main reason for that is due to the fact that its environment is highly dynamic, intrinsically complex, and chaotic. Traditional economic theories suggest that trying to forecast short-term stock price movements is a wasted effort because the market is influenced by several external events and its behavior approximates a random walk. Recent studies that address the problem of stock market forecasting usually create specific prediction models for the price behavior of a single stock. In this work we propose a technique to predict price movements based on similar stock sets. Our goal is to build a model to identify whether the price tends to bullishness or bearishness in the near future, considering stock information from similar sets based on two sources of information: historical stock data and Google Trends news. Firstly, the proposed study applies a method to identify similar stock sets and then creates a forecasting model based on a LSTM (long short-term memory) for these sets. More specifically, two experiments were conducted: (1) using the K-Means algorithm to identify similar stock sets and then using a LSTM neural network to forecast stock price movements for these stock sets; (2) using the DBSCAN (Density-based spatial clustering) algorithm to identify similar stock sets and then using the same LSTM neural network to forecast stock price movements. The study was conducted over 51 stocks of the Brazilian stock market. The results show that the use of an algorithm to identify stock clusters yields an improvement of approximately 7% in accuracy and f1-score and 8% in recall and precision when compared to models for a single stock

Stocks Clustering Based on Textual Embeddings for Price Forecasting

The use of Semi-supervised Learning (SSL) methods have emerged as an efficient solution to smooth out the problem of availability of labelled instances. Several methods have been proposed in the literature and Self-training and Co-training are two well-known methods. The main aim is to use only a few labelled instances to define a model and to apply this model in a labelling process, in which unlabelled instances are labelled and included in the labelled set. However, the labelling process is always directly dependent on the selection of the unlabelled instances. Moreover, the selection criterion used to select and label new instances has an important effect in the performance of a semi-supervised method. In this paper, we propose a distance-weighted selection of unlabelled instances for Self-training and Co-training semi-supervised methods. In addition, we compare the standard Self-training and Co-training methods against the proposed versions of these two methods over 20 classification datasets.

A Distance-weighted Selection of Unlabelled Instances for Self-training and Co-training Semi-supervised Methods

Monte Carlo Tree Search (MCTS) parallelization is one of the many possible enhancements for MCTS algorithms. However, no work had been done on evaluating these methods in the rather new area of General Video Game Playing (GVGP), an area that challenges the creation of agents that can play any videogame even without prior knowledge about the video game they are going to play. 
In this presentation, we show how we have approached this research gap with the implementation of three different parallel MCTS agents for GVGP as well as the results we obtained from performing a set of experiments over them.

Parallel Monte Carlo Tree Search in General Video Game Playing

Despite the availability of benchmark machine learning (ML) repositories (e.g., UCI, OpenML), there is no standard evaluation strategy yet capable of pointing out which is the best set of datasets to serve as gold standard to test different ML algorithms. In recent studies, Item Response Theory (IRT) has emerged as a new approach to elucidate what should be a good ML benchmark. This work applied IRT to explore the well-known OpenML-CC18 benchmark to identify how suitable it is on the evaluation of classifiers. Several classifiers ranging from classical to ensembles ones were evaluated using IRT models, which could simultaneously estimate dataset difficulty and classifiers' ability. The Glicko-2 rating system was applied on the top of IRT to summarize the innate ability and aptitude of classifiers. It was observed that not all datasets from OpenML-CC18 are really useful to evaluate classifiers. Most datasets evaluated in this work (84%) contain easy instances in general (e.g., around 10% of difficult instances only). Also, 80% of the instances in half of this benchmark are very discriminating ones, which can be of great use for pairwise algorithm comparison, but not useful to push classifiers abilities. This paper presents this new evaluation methodology based on IRT as well as the tool decodIRT, developed to guide IRT estimation over ML benchmarks.

Decoding machine learning benchmarks

AGM’s belief revision is one of the main paradigms in the study of belief change operations. Despite its popularity and importance to the area, it is well recognized that AGM’s work relies on a strong idealisation of the agent’s capabilities and on the nature of beliefs themselves. Particularly, it is well-recognized in the area of Epistemology that Belief and Knowledge are hyperintensional notions, but to our knowledge only a few works have explicitly considered how hyperintensionality affects belief change. In this work, we investigate belief change operations based on Berto’s topic-sensitive framework and provide three different pseudo- contraction operations to account for the hyperintensional behaviour of beliefs. Our work highlights the connection of a foundational hyperintensional theory of belief with the results of AGM Belief Change. Also we propose and characterise different possible contraction-like operations in this framework.

Towards a Theory of Hyperintensional Belief Change

Recent advances in language representation using neural networkshave made it viable to transfer the learned internal states of large pretrained language models (LMs) to downstream natural language processing (NLP) tasks. This transfer learning approach improves the overall performance on many tasks and is highly beneficial when labeled data is scarce, making pretrained LMs valuable resources specially for languages with few annotated training examples. In this work, we train BERT (Bidirectional Encoder Representations from Transformers) models for Brazilian Portuguese, which we nickname BERTimbau. We evaluate our models on three downstream NLP tasks: sentence textual similarity, recognizing textual entailment, and named entity recognition. Our models improve the state-of-the-art in all of these tasks, outperforming Multilingual BERT and confirming the effectiveness of large pretrained LMs for Portuguese. We release our models to the community hoping to provide strong baselines for future NLP research: https://github.com/neuralmind-ai/portuguese-bert.

BERTimbau: pretrained BERT models for Brazilian Portuguese

The increasing adoption of industrial robots to boost production efficiency is turning human-robot collaborative scenarios much more frequent. In this context, technical factory workers need to be safe at all times from collisions and prepare for emergencies and potential accidents. Another trend in industrial automation is the usage of machine learning techniques - specifically, deep learning algorithms - for image classification. Following these tendencies, this work evaluates the application of deep learning models to detect physical collision in human-robot interactions. Security camera images are used as the primary information source for intelligent collision detection. Unlike other proposed approaches in the literature that apply sensors like Light Detection And Ranging (LIDAR), Laser Range Finder (LRF), or torque sensors from robots, this work does not consider extra sensors, using only 2D cameras. Results show more than 99% of accuracy in the evaluated scenarios, revealing that approaches adopting deep learning algorithms could be promising for human-robot collision avoidance in industrial scenarios. The proposed models may support safety in industrial environments and reduce the impact of collision accidents.

Assessing Deep Learning Models for Human-Robot Collaboration Collision Detection in Industrial Environments

Performing analytical tasks over graph data has become increasingly interesting due to the ubiquity and large availability of relational information. However, unlike images or sentences, there is no notion of sequence in networks. Nodes (and edges) follow no absolute order, and it is hard for traditional machine learning (ML) algorithms to recognize a pattern and generalize their predictions on this type of
data. Graph Neural Networks (GNN) successfully tackled this problem. They became popular after the generalization of the convolution concept to the graph domain. However, they possess a large number of hyperparameters and their design and optimization is currently hand-made, based on heuristics or empirical intuition. Neural Architecture Search (NAS) methods appear as an interesting solution to this problem. In this direction, this paper compares two NAS methods for optimizing GNN: one based on reinforcement learning and a second based on evolutionary algorithms. Results consider 7 datasets over two search spaces and show that both methods obtain similar accuracies to a random search, raising the question of how many of the search space dimensions are actually relevant to the problem.

Neural Architecture Search in Graph Neural Networks

A major challenge in image classification tasks using Ma-chine Learning, and in particular when using deep neural networks, is domain shifting in deployment. This happens when images during usageare capture in different conditions from those used during training. In this paper, we show that despite previous works on the diagnosis of apple tree diseases using standard Convolutional Neural Networks displaying high accuracy, they do so only when no domain shift is present. When the trained model is asked to classify photos of apples taken in the wild, a 22% reduction in F1 score is observed. We propose to treat the task as a segmentation problem and test two different approaches, showing that using Mask R-CNN allows not only to improve performance in the original domain by 3%, but also significantly reduce losses in the new domain (only 6% reduction in F1 score). We establish segmentation as an important alternative towards improving diagnosis of apple tree diseases from photos.

Improved Multilevel Algorithm to Detect Communities in Flight Networks

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