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On network backbone extraction for modeling online collective behavior.
(2022) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Silva, Ana Paula Couto da; Trevisan, Martino; Vassio, Luca; Drago, Idilio; Mellia, Marco; Almeida, Jussara Marques de
Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.
Watershed-ng : an extensible distributed stream processing framework.
(2016) Rocha, Rodrigo; Hott, Bruno; Dias, Vinícius; Ferreira, Renato; Meira Júnior, Wagner; Guedes Neto, Dorgival Olavo
Most high-performance data processing (a.k.a. big data) systems allow users to express their computation using abstractions (like MapReduce), which simplify the extraction of parallelism from applications. Most frameworks, however, do not allow users to specify how communication must take place: That element is deeply embedded into the run-time system abstractions, making changes hard to implement. In this work, we describe Wathershed-ng, our re-engineering of the Watershed system, a framework based on the filter–stream paradigm and originally focused on continuous stream processing. Like other big-data environments, Watershed provided object-oriented abstractions to express computation (filters), but the implementation of streams was a run-time system element. By isolating stream functionality into appropriate classes, combination of communication patterns and reuse of common message handling functions (like compression and blocking) become possible. The new architecture even allows the design of new communication patterns, for example, allowing users to choose MPI, TCP, or shared memory implementations of communication channels as their problem demands. Applications designed for the new interface showed reductions in code size on the order of 50% and above in some cases. The performance results also showed significant improvements, because some implementation bottlenecks were removed in the re-engineering process.
A fix-and-optimize heuristic for the ITC2021 sports timetabling problem.
(2022) Fonseca, George Henrique Godim da; Toffolo, Túlio Ângelo Machado
This paper addresses the general and challenging Sports Timetabling Problem proposed during the International Timetabling Competition of 2021 (ITC2021). The problem is expressed in a flexible format which enables modeling a number of real-world constraints that often occur in Sports Timetabling. An integer programming (IP) formulation and a fix-and-optimize heuristic are proposed to address the problem. The fix-and-optimize approach uses the IP formulation to heuristically decompose the problem into sub-problems and efficiently search on very large neighborhoods. The diverse ITC2021 benchmark instances were used to evaluate the proposed methods. The formulation resulted in proven optimal solutions for two instances. However, it failed to produce feasible solutions for most instances. The proposed fix-and-optimize, which uses an automatic sub-problem size calibration strategy, resulted in feasible solutions for 37 out of the 45 ITC2021 instances. Among these solutions, four are the best known in the literature. The proposed approach participated in the ITC2021 and was one of the finalists.
Animação gráfica da marcha humana a partir de dados do Kinect.
(2021) Leite, Edmo de Oliveira; Assis, Gilda Aparecida de; Yared, Glauco Ferreira Gazel
A análise da marcha humana a partir de dados biométricos tem aplicações em áreas como segurança, robótica bioinspirada e saúde. Sensores de movimento de baixo custo, como o Kinect, têm permitido a aquisição de dados biométricos da marcha em ambientes terrestres. Entretanto, esses equipamentos têm limitações que podem impactar na qualidade dos dados. Nesse cenário, diferentes técnicas de processamento de sinais podem ser aplicadas para reduzir o ruído. A visualização desses dados, originais ou processados, muitas vezes é realizada na forma de gráficos, tendo utilidade limitada para profissionais não experientes na análise de sinais. Nesse sentido, a visualização dos dados da marcha em um modelo tridimensional pode contribuir para melhorar a decisão dos profissionais, principalmente da saúde. Este trabalho tem como objetivo realizar a animação da marcha humana em um modelo tridimensional, a partir dos dados coletados pelo sensor Kinect 2.0. Para reduzir o ruído dos dados, foi realizado um pré-processamento com filtros de média móvel e Butterworth. Foram elaborados vídeos das animações conforme as vistas isométrica e lateral, que foram incorporados em um questionário on-line e avaliados em uma pesquisa de campo sobre artificialidade/naturalidade da animação, utilizando-se a técnica de pontuação média de opinião (mean opinion score [MOS]). Um total de 22 participantes, estudantes de computação, respondeu ao questionário on-line. A análise de variância simples (analysis of variance [Anova]) one way mostrou que os vídeos a partir das vistas isométrica e lateral processados com filtro de média móvel (janela = 15 e repetições = 3) que obtiveram maiores valores da métrica MOS foram avaliados como significativamente mais naturais do que outros vídeos, processados ou não.
Multiobjective planning of indoor Wireless Local Area Networks using subpermutation-based hybrid algorithms.
(2023) Lima, Marlon Paolo; Takahashi, Ricardo Hiroshi Caldeira; Vieira, Marcos Augusto Menezes; Carrano, Eduardo Gontijo
Wireless Local Area Network (WLAN) has become the most popular technology for mobile Internet access in recent decades. This manuscript presents a novel approach, based on hybrid optimization algorithms, for planning WLANs. Two objective functions are optimized: to maximize network load balance and signal-to-noise ratio. In addition, constraints related to coverage, customer, and equipment demand are considered. A key aspect of the proposed algorithm is its new representation/decoding scheme, based on subpermutations, which considerably reduces the search space dimension. This structure guarantees feasibility of the obtained solutions and increases the computational efficiency of the method. Several tests were performed in two scenarios, one of them using real data from a large-scale WLAN. When compared to other three approaches, such results show that the proposed method provides solutions that reduce costs and improve the WLAN throughput.
Embedded real-time feature extraction for electrode inversion detection in telemedicine electrocardiograms.
(2020) Torres, Vitor Angelo Maria Ferreira; Silva, D. A.C.; Torres, Luiz Carlos Bambirra; Braga, Mateus Taulois; Cardoso, Mathues B. R.; Lino, Vinicius Terra; Torres, Frank Sill; Braga, Antônio de Pádua
Early detection of technical errors in medical examinations, especially in remote locations, is of utmost importance in order to avoid invalid measurements that would require costly and time consuming repeti- tions. This paper proposes a highly efficient method for the identification of an erroneous inversion of the measuring electrodes during a multichannel electrocardiogram. Therefore, a widely applied approach for heart beat detection is modified and approximated feature extraction techniques are employed. In con- trast to existing works, the improved heart beat identification requires no removal of baseline wandering and no amplitude related thresholds. Furthermore, a piecewise linear approximation of the baseline and basic calculations are sufficient for extracting the cardiac axis, which allows the construction of a clas- sifier capable of quickly detecting electrode reversals. Our implementation indicates that the proposed method has minimal hardware costs and is able to operate in real-time on a simple micro-controller.
Width optimization of RBF kernels for binary classification of support vector machines : a density estimation-based approach.
(2019) Menezes, Murilo V. F.; Torres, Luiz Carlos Bambirra; Braga, Antônio de Pádua
Kernels are often used for modelling non-linear data, developing a main role in models like the SVM. The optimization of its parameters to better fit each dataset is a frequently faced challenge: A bad choice of kernel parameters often implies a poor model. This problem is usually worked out using exhaustive search approaches, such as cross-validation. These methods, however, do not take into account existent information on data arrangement. This paper proposes an alternative approach, based on density estimation. By making use of density estimation methods to analyze the dataset structure, it is proposed a function over the kernel parameters. This function can be used to choose the parameters that best suit the data.
A novel hybrid feature selection algorithm for hierarchical classification.
(2021) Lima, Helen de Cássia Sousa da Costa; Otero, Fernando Esteban Barril; Merschmann, Luiz Henrique de Campos; Souza, Marcone Jamilson Freitas
Feature selection is a widespread preprocessing step in the data mining field. One of its purposes is to reduce the number of original dataset features to improve a predictive model’s performance. Despite the benefits of feature selection for the classification task, to the best of our knowledge, few studies in the literature address feature selection for the hierarchical classification context. This paper proposes a novel feature selection method based on the general variable neighborhood search metaheuristic, combining a filter and a wrapper step, wherein a global model hierarchical classifier evaluates feature subsets. We used twelve datasets from the proteins and images domains to perform computational experiments to validate the effect of the proposed algorithm on classification performance when using two global hierarchical classifiers proposed in the literature. Statistical tests showed that using our method for feature selection led to predictive performances that were consistently better than or equivalent to that obtained by using all features with the benefit of reducing the number of features needed, which justifies its efficiency for the hierarchical classification scenario.
A shape-aware retargeting approach to transfer human motion and appearance in monocular videos.
(2021) Gomes, Thiago Luange; Martins, Renato José; Ferreira, João Pedro Moreira; Azevedo, Rafael Augusto Vieira de; Torres, Guilherme Alvarenga; Nascimento, Erickson Rangel do
Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision. Despite the advances from recent image-to-image translation approaches, there are several transferring contexts where most end-to-end learning-based retargeting methods still perform poorly. Transferring human appearance from one actor to another is only ensured when a strict setup has been complied, which is generally built considering their training regime’s specificities. In this work, we propose a shape-aware approach based on a hybrid image-based rendering technique that exhibits competitive visual retargeting quality compared to state-of-the-art neural rendering approaches. The formulation leverages the user body shape into the retargeting while considering physical constraints of the motion in 3D and the 2D image domain. We also present a new video retargeting benchmark dataset composed of different videos with annotated human motions to evaluate the task of synthesizing people’s videos, which can be used as a common base to improve tracking the progress in the field. The dataset and its evaluation protocols are designed to evaluate retargeting methods in more general and challenging conditions. Our method is validated in several experiments, comprising publicly available videos of actors with different shapes, motion types, and camera setups. The dataset and retargeting code are publicly available to the community at: https://www.verlab.dcc.ufmg.br/retargeting-motion.
Learning to dance : a graph convolutional adversarial network to generate realistic dance motions from audio.
(2021) Ferreira, João Pedro Moreira; Coutinho, Thiago Malta; Gomes, Thiago Luange; Silva Neto, José Francisco da; Azevedo, Rafael Augusto Vieira de; Martins, Renato José; Nascimento, Erickson Rangel do
Synthesizing human motion through learning techniques is becoming an increasingly popular approach to alleviating the requirement of new data capture to produce animations. Learning to move naturally from music, i.e., to dance, is one of the more complex motions humans often perform effortlessly. Each dance movement is unique, yet such movements maintain the core characteristics of the dance style. Most approaches addressing this problem with classical convolutional and recursive neural models un- dergo training and variability issues due to the non-Euclidean geometry of the motion manifold struc- ture. In this paper, we design a novel method based on graph convolutional networks to tackle the problem of automatic dance generation from audio information. Our method uses an adversarial learn- ing scheme conditioned on the input music audios to create natural motions preserving the key move- ments of different music styles. We evaluate our method with three quantitative metrics of generative methods and a user study. The results suggest that the proposed GCN model outperforms the state-of- the-art dance generation method conditioned on music in different experiments. Moreover, our graph- convolutional approach is simpler, easier to be trained, and capable of generating more realistic mo- tion styles regarding qualitative and different quantitative metrics. It also presented a visual movement perceptual quality comparable to real motion data. The dataset and project are publicly available at: https://www.verlab.dcc.ufmg.br/motion-analysis/cag2020.
Multi-objective neural network model selection with a graph-based large margin approach.
(2022) Torres, Luiz Carlos Bambirra; Castro, Cristiano Leite de; Rocha, Honovan Paz; Almeida, Gustavo Matheus de; Braga, Antônio de Pádua
This work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.
Extreme wavelet fast learning machine for evaluation of the default profle on financial transactions.
(2020) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra
Extreme learning machines enable multilayered neural networks to perform activities to facilitate the process and business dynamics. It acts in pattern classifcation, linear regression problems, and time series prediction. The fnancial area needs efcient models that can perform businesses in a short time. Credit card fraud and debits occur regularly, and efective decision making can avoid signifcant obstacles for both clients and fnancial companies. This paper proposes a training model for multilayer networks where the weights of the training algorithm are defned by the nature and characteristics of the dataset using the concepts of the wavelet transform. The traditional algorithm of weights’ defnition of the output layer is changed to a regularized method that acts more quickly in the description of the weights of the output layer. Finally, several activation functions are applied to the model to verify its efciency in several scenarios. This model was subjected to an extensive dataset and comparing to diferent machine learning approaches. Its answers were satisfactory in a short-time execution, proving that the Extreme Learning Machine works effciently to identify possible profles of defaulters in payments in the fnancial relationships involving a credit card.
Large margin gaussian mixture classifier with a Gabriel graph geometric representation of data set structure.
(2020) Torres, Luiz Carlos Bambirra; Castro, Cristiano Leite de; Coelho, Frederico Gualberto Ferreira; Braga, Antônio de Pádua
This brief presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the data set from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometrical support vectors (SVs) (analogous to support vector machines (SVMs) SVs) are obtained in order to yield the final large margin solution from a Gaussian mixture model. Experiments with 20 data sets have shown that the solutions obtained with the proposed method are statistically equivalent to those obtained with SVMs. However, the present method does not require optimization and can also be extended to large data sets using the cascade SVM concept.
Enhancing performance of Gabriel graph-based classifiers by a hardware co-processor for embedded system applications.
(2020) Arias Garcia, Janier; Mafra, Augusto Amaral; Gade, Liliane dos Reis; Coelho, Frederico Gualberto Ferreira; Castro, Cristiano Leite de; Torres, Luiz Carlos Bambirra; Braga, Antônio de Pádua
It is well known that there is an increasing interest in edge computing to reduce the distance between cloud and end devices, especially for Machine Learning (ML) methods. However, when related to latency-sensitive applications, little work can be found in ML literature on suitable embedded systems implementations. This paper presents new ways to implement the decision rule of a large margin classifier based on Gabriel graphs as well as an efficient implementation of this on an embedded system. The proposed approach uses the nearest neighbor method as the decision rule, and the implementation starts from an RTL pipeline architecture developed for binary large margin classifiers and proposes the integration in a hardware/software co-design. Results showed that the proposed approach was statistically similar to the classifier and had a speedup factor of up to 8x compared to the classifier executed in software, with performance suitable for ML latency-sensitive applications.
Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs.
(2020) Torres, Vitor Angelo Maria Ferreira; Jaimes, Brayan Rene Acevedo; Ribeiro, Eduardo da Silva; Braga, Mateus Taulois; Shiguemori, Elcio Hideit; Velho, Haroldo Fraga de Campos; Torres, Luiz Carlos Bambirra; Braga, Antônio de Pádua
This work presents a combined weightless neural network architecture for deforestation surveillance and visual navigation of Unmanned Aerial Vehicles (UAVs). Binary images, which are required for position estimation and UAV navigation, are provided by the deforestation surveillance circuit. Learned models are evaluated in a real UAV flight over a green countryside area, while deforestation surveillance is assessed with an Amazon forest benchmarking image data. Small utilization percentage of Field Programmable Gate Arrays (FPGAs) allows for a higher degree of parallelization and block processing of larger regions of input images.
Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function.
(2019) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra; Guimarães, Augusto Júnio; Araújo, Vanessa Souza; Araújo, Vinicius Jonathan Silva; Rezende, Thiago Silva
This paper proposes the use of fuzzification functions based on clustering of data based on their density to perform the granularization of the input space. The neurons formed in this layer are built through the density centers obtained with the input data of the model. In the second layer, the nullneurons aggregate the generated neurons in the first layer and allow the creation of if/then fuzzy rules. Even in the second layer, a regularization function is activated to determine the essential nullneurons. The concepts of extreme learning machine generate the weights used in the third layer, but with a regularizing factor. Finally, in the third layer, represented by an artificial neural network, it has a single neuron that the activation function uses robust functions to carry out the model. To verify the new training approach for fuzzy neural networks, we performed real and synthetic database tests for the pattern classification, which led to the conclusion that the data density-based approach the use of regularization factors in the second model layer and neurons with more robust activation functions allowed better results compared to other classifiers that use the concepts of extreme learning machine.
A fuzzy data reduction cluster method based on boundary information for large datasets.
(2019) Silva, Gustavo Rodrigues Lacerda; Cirino Neto, Paulo; Torres, Luiz Carlos Bambirra; Braga, Antônio de Pádua
The fuzzy c-means algorithm (FCM) is aimed at computing the membership degree of each data point to its corresponding cluster center. This computation needs to calculate the distance matrix between the cluster center and the data point. The main bottleneck of the FCM algorithm is the computing of the membership matrix for all data points. This work presents a new clustering method, the bdrFCM (boundary data reduction fuzzy c-means). Our algorithm is based on the original FCM proposal, adapted to detect and remove the boundary regions of clusters. Our implementation efforts are directed in two aspects: processing large datasets in less time and reducing the data volume, maintaining the quality of the clusters. A significant volume of real data application ([106 records) was used, and we identified that bdrFCM implementation has good scalability to handle datasets with millions of data points.
Pulsar detection for wavelets soda and regularized fuzzy neural networks based on andneuron and robust activation function.
(2019) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra; Guimarães, Augusto Júnio; Araújo, Vanessa Souza
The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.
Wearables and detection of falls : a comparison of machine learning methods and sensors positioning.
(2022) Pinto, Arthur Bernardo Assumpção; Assis, Gilda Aparecida de; Torres, Luiz Carlos Bambirra; Beltrame, Thomas; Domingues, Diana Maria Gallicchio
Wearable sensors have many applications to provide assistance for older adults. We aimed to identify the best combination of machine learning algorithms and body regions to attach one wearable for real-time falls detection from a public dataset where volunteers performed daily activities and simulated falls. Accuracy and comfort of the combination of wearables and algorithms were assessed. Raw data from the accelerometer and gyroscope were used for both training and testing stages. We evaluated the confusion matrix between all wear- ables at each of the different body regions (Ankle, Right Pocket, Belt, Neck, and Wrist) for the following machine learning algorithms: Multilayer Perceptron (MLP), Random Forest, XGBoost, and Long Short Term Memory (LSTM) deep neural network. The accuracy was compared by ANOVA two-way repeated measures statistical test. This work has two main technical contributions. First, our results demonstrated the highest accuracy in identifying falls when the sensors were positioned on the neck or ankle. Second, when the machine learning algorithms to detect fall was compared, LSTM deep neural network and Random Forest showed statistically higher accuracy than MLP and XGBoost. Besides, a comfort analysis based on the literature concluded that neck and wrist are the most comfortable regions to wear wearables.
A hierarchical network-oriented analysis of user participation in misinformation spread on WhatsApp.
(2022) Nobre, Gabriel Peres; Ferreira, Carlos Henrique Gomes; Almeida, Jussara Mendes de
WhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries. In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation in the platform.

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