Roseli A. F. Romero

Particle competition for complex network community detection

In many real situations, randomness is considered to be uncertainty or even confusion which impedes human beings from making a correct decision. Here we study the combined role of randomness and determinism in particle dynamics for complex network community detection. In the proposed model, particles walk in the network and compete with each other in such a way that each of them tries to possess as many nodes as possible. Moreover, we introduce a rule to adjust the level of randomness of particle walking in the network, and we have found that a portion of randomness can largely improve the community detection rate. Computer simulations show that the model has good community detection performance and at the same time presents low computational complexity.

Recognition of Human Motions for Imitation and Control of a Humanoid Robot

Most of the works in the field of teleoperation of humanoid robots do not use the whole-body of the robot to perform some task. This work treats the teleoperation of a humanoid robot through the recognition of human motions by using a natural interface mechanism. We propose the use of simple mathematical techniques to allow, in a satisfactory level, the user order in real-time a robot to mimic a person, to walk, to manipulate the environment with its hands and to perform some predetermined behaviors. Also, we provide an appropriate discussion around the findings of this research. The results of the experiments show a satisfactory performance for the system developed.

A multiple linear regression and partial least squares study of flavonoid compounds with anti-HIV activity

Abstract The molecular orbital semi-empirical method PM3 was employed to calculate a set of molecular properties (variables or descriptors) of 21 flavonoid compounds with anti-HIV activity. The correlation between biological activity and structural properties was obtained by using the multiple linear regression and partial least squares methods. The model obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with anti-HIV activity were: electronegativity (χ) and the charges on atoms C3 and C7 (Q3 and Q7, respectively). These variables led to a physical explanation of electronic molecular property contributions to HIV inhibitory potency.

Selecting salient objects in real scenes: An oscillatory correlation model

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system.

A neural networks study of quinone compounds with trypanocidal activity

AbstractThis work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO−1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency. FigureCompound component maps, where each map shows the calculated descriptors

ARCHITECT-R: a system for reconfigurable robots design

An increasing interest in the design of mobile robots has been observed in recent years, which is mainly motivated by technological advances that may allow their application to consumer markets, in addition to industrial areas.Although sophisticated techniques have been developed, choosing the appropriate hardware-software partitioning and programming robot functions are still very complex tasks.Current approaches often involve the design and implementation of hardwired solutions, with the associated problems of a long development cycle and inflexibility.In this paper we present a framework called ARCHITECT-R, which aims to design and program specialized hardware for robots based on FPGAs. We also present the first results obtained using this framework.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein.

Robotic Architecture Inspired on Behavior Analysis

Social robots are embodied agents that are part of a heterogeneous group: a society of robots or humans. They are able to recognize human beings and each other, and engage in social interactions. They possess histories and they explicitly communicate and learn from interactions. The construction of social robots may strongly benefit from using a robotic architecture. However, a robotic architecture for sociable robots must have structures and mechanism to allow social interaction control and learning from environment. In this paper, we propose a robotic architecture inspired on Behavior Analysis. Methods and structures of the proposed architecture are presented and discussed. The architecture was evaluated on a Skinner Box simulator and the obtained results shown that the architecture is able to produce appropriate behavior and to learn from social interaction.

Generation of composed musical structures through recurrent neural networks based on chaotic inspiration

In this work, an Elman recurrent neural network is used for automatic musical structure composition based on the style of a music previously learned during the training phase. Furthermore, a small fragment of a chaotic melody is added to the input layer of the neural network as an inspiration source to attain a greater variability of melodies. The neural network is trained by using the BPTT (back propagation through time) algorithm. Some melody measures are also presented for characterizing the melodies provided by the neural network and for analyzing the effect obtained by the insertion of chaotic inspiration in relation to the original melody characteristics. Specifically, a similarity melodic measure is considered for contrasting the variability obtained between the learned melody and each one of the composite melodies by using different quantities of inspiration musical notes.

Model of top-down / bottom-up visual attention for location of salient objects in specific domains

There are several real situations in which it is useful to have a system able to detect a specific target or a salient object and its localization in a given image in autonomous way. To guide the attention based on known characteristics of an object and primitive information of the image is not a trivial task for visual attention. Several works about visual attention have been developed, which focused in bottom-up or top-down in an isolate manner. We propose in this work a model of visual attention combining characteristics bottom-up and top-down. The proposed model is composed of four components: the training and recognition of known objects, the object segmentation of the input image, the self-organizing of information top-down and bottom-up in a single map and a network of neurons with excitatory connections and inhibitory connections to generate the map of salient attribute for the location of salient objects. Thanks to this combination it was possible to detect, to identify and to locate the salient objects of the image. Several tests have been applied to synthetic images to verify the viability of the model as a mechanism of selection of objects as a part of a visual attention system. The results demonstrate the effectiveness of the model.