Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They Should be able to recognize human beings and each other, and to engage in social, interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction. behavior control and learning from environment. Learning processes described oil Science of Behavior Analysis may lead to the development of promising methods and Structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation. are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction. (C) 2009 Elsevier Inc. All rights reserved.

A closer look at the influence of tubular initial conditions on two-particle correlations

In a recent paper, the hydrodynamic code NEXSPheRIO was used in conjunction with STAR analysis methods to study two-particle correlations as a function of Delta(eta) and Delta phi. The various structures observed in the data were reproduced. In this work, we discuss the origin of these structures as well as present new results.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

Relative entropy measures applied to healthy and pathological voice characterization

Nowadays, noninvasive methods of diagnosis have increased due to demands of the population that requires fast, simple and painless exams. These methods have become possible because of the growth of technology that provides the necessary means of collecting and processing signals. New methods of analysis have been developed to understand the complexity of voice signals, such as nonlinear dynamics aiming at the exploration of voice signals dynamic nature. The purpose of this paper is to characterize healthy and pathological voice signals with the aid of relative entropy measures. Phase space reconstruction technique is also used as a way to select interesting regions of the signals. Three groups of samples were used, one from healthy individuals and the other two from people with nodule in the vocal fold and Reinke`s edema. All of them are recordings of sustained vowel /a/ from Brazilian Portuguese. The paper shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Relative entropy is well suited due to its sensibility to uncertainties, since the pathologies are characterized by an increase in the signal complexity and unpredictability. The results showed that the pathological groups had higher entropy values in accordance with other vocal acoustic parameters presented. This suggests that these techniques may improve and complement the recent voice analysis methods available for clinicians. (C) 2008 Elsevier Inc. All rights reserved.

Automatic contour extraction from 2D neuron images

This work describes a novel methodology for automatic contour extraction from 2D images of 3D neurons (e.g. camera lucida images and other types of 2D microscopy). Most contour-based shape analysis methods cannot be used to characterize such cells because of overlaps between neuronal processes. The proposed framework is specifically aimed at the problem of contour following even in presence of multiple overlaps. First, the input image is preprocessed in order to obtain an 8-connected skeleton with one-pixel-wide branches, as well as a set of critical regions (i.e., bifurcations and crossings). Next, for each subtree, the tracking stage iteratively labels all valid pixel of branches, tip to a critical region, where it determines the suitable direction to proceed. Finally, the labeled skeleton segments are followed in order to yield the parametric contour of the neuronal shape under analysis. The reported system was successfully tested with respect to several images and the results from a set of three neuron images are presented here, each pertaining to a different class, i.e. alpha, delta and epsilon ganglion cells, containing a total of 34 crossings. The algorithms successfully got across all these overlaps. The method has also been found to exhibit robustness even for images with close parallel segments. The proposed method is robust and may be implemented in an efficient manner. The introduction of this approach should pave the way for more systematic application of contour-based shape analysis methods in neuronal morphology. (C) 2008 Elsevier B.V. All rights reserved.

Insights into the Molecular Requirements for the Anti-obesity Activity of a Series of CB1 Ligands

Two-dimensional and 3D quantitative structure-activity relationships studies were performed on a series of diarylpyridines that acts as cannabinoid receptor ligands by means of hologram quantitative structure-activity relationships and comparative molecular field analysis methods. The quantitative structure-activity relationships models were built using a data set of 52 CB1 ligands that can be used as anti-obesity agents. Significant correlation coefficients (hologram quantitative structure-activity relationships: r 2 = 0.91, q 2 = 0.78; comparative molecular field analysis: r 2 = 0.98, q 2 = 0.77) were obtained, indicating the potential of these 2D and 3D models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted (calculated) values are in good agreement with the experimental results. The final quantitative structure-activity relationships models, along with the information obtained from 2D contribution maps and 3D contour maps, obtained in this study are useful tools for the design of novel CB1 ligands with improved anti-obesity potency.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Computerized identification of motor complications in Parkinson's disease

Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects. Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis. Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used. Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77). Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.

A influência dos ativos intangíveis na análise de risco de crédito de empresas de base tecnológica

Evolution is present in world dynamics. And it is just in such transformational environment where companies have been encapsulated. In an economy of knowledge, physical assets alone are unable to provide profits to meet shareholders' demands. Now there comes an invisible component with the purpose of defining strategies and impelling results: Intangible Assets. Banking financing systems, however, have not kept pace with this knowledge revolution and its resulting new income generation techniques. Credit analysis methods for most financing agents would not employ any intangible parameters in their methodology of study as yet. This paper seeks to discuss the importance of intangible assets by focusing their role of influencial factor in decisions to finance technology-based companies. By studying the credit risk classification system employed by FINEP, Brazil's Federal Agency for innovation development, we wished to suggest indicators for intangibles which might be put to use in the Financiadora.

Histórias de empreendedores: significados, narrativas e artefatos

Compreender o processo de significação e ressignificação que o empreendedor atribui ao novo negócio através das etapas de busca, percepção e interpretação propostas por Daft e Weick (1984) ao longo do tempo. Design/metodologia/abordagem: Sob uma perspectiva interpretativista, este trabalho se valeu de abordagens qualitativas. Foram utilizadas téc nicas de análise de narrativas, antenarrativas e análise de dados qualitativos sobre entrevistas realizadas com 11 empreendedores digitais em dois momentos diferentes. Descobertas: as análises sugerem que o empreendedor ressignifica seu negócio com o passar do tempo, provavelmente em função do nível de sucesso do empreendimento. Esta pesquisa corrobora a proposição de Daft & Weick (1984) acerca da existência de um processo cíclico de busca, interpretação e ação que levaria a um contínuo de ressignificações. Implicações da pesquisa: Para os empreendedores (practioners), este trabalho lança luz sobre as possibilidades de uso das narrativas como recursos para influência e disseminação de significados (sensegiving). Para acadêmicos, este trabalho oferece melhor conceituação do constructo ‘artefato estratégico’, além de avançar na consolidação dos métodos de análise de narrativas. Originalidade/Valor: O valor dessa pesquisa se dá em fortalecer os métodos de análise de narrativas, além de apresentar um possível caminho para capturar os elementos de ressignificação. Além disso, ao propor um framework de análise e construção de narrativas empreendedoras, este trabalho pode auxiliar pesquisadores e practioners

O coração e o dragão: perspectivas da vida urbana em uma cidade fragmentada

This research aims to study dimensions of urban life in the contemporaneous city. It is an effort to understand the functioning of the contemporary city as an artifact that somehow affects social relations. The study focuses on the limits and possibilities of urbanity in the city today, understanding urbanity as a set of factors that favor wealth, diversity and spontaneity of public life. The research aims to show that cities today tend to criate fragmented urban life into at least one of the three urbanity dimensions: spatial dimension, social and temporal dimension. The study involves the analysis of two public spaces in Fortaleza (Praça do Ferreira and the open urban public spaces of the Centro Cultural Dragão do Mar), using Space Syntax Analysis methods and for Post Occupancy Evaluation procedures. Research shows that temporal dimension of urbanity is limited in the public spaces studied. In Praça do Ferreira, spatial and social dimensions are present, but their effects are limited by the temporal dimension. The Dragão do Mar, on the other hand, the spatial and social dimensions of urban life are more limited and more concentrated in time

Ecomorfologia de dez espécies de peixes marinhos mais abundantes de Galinhos/RN

Ecomorphology is a science based on the idea that morphological differences among species could be associated with distinct biological and environmental pressures suffered by them. These differences can be studied employing morphological and biometric indexes denominated Ecomorphological attributes , representing standards that express characteristics of the individual in relation to its environment, and can be interpreted as indicators of life habits or adaptations suffered due its occupation of different habitats. This work aims to contribute for the knowledge of the ecomorphology of the Brazilian marine ichthyofauna, specifically from Galinhos, located at Rio Grande do Norte state. 10 different species of fish were studied, belonging the families Gerreidae (Eucinostomus argenteus), Haemulidae (Orthopristis ruber,Pomadasyscorvinaeformis,Haemulonaurolineatum,Haemulonplumieri,Haemulonsteindachneri), Lutjanidae (Lutjanus synagris), Paralichthyidae (Syaciummicrurum), Bothidae (Bothus ocellatus) and Tetraodontidae (Sphoeroidestestudineus), which were obtained during five collections, in the period time of September/2004 to April/2005, utilizing three special nets. The ecomorphological study was performed at the laboratory. Eight to ten samples of each fish specie were measured. Fifteen morphological aspects were considered to calculate twelve ecomorphological attributes. Multivariate statistical analysis methods such as Principal Component Analysis (PCA) and Cluster Analysis were done to identify ecmorphological patterns to describe the data set obtained. As results, H.aurolineatumwas the most abundant specie found (23,03%) and S.testudineusthe less one with 0,23%. The 1st Principal component showed variation of 60,03% with influence of the ecomorphological attribute related to body morphology, while the 2nd PC with 23,25% variation had influence of the ecomorphological attribute related to oral morphology. The Cluster Analiysis promoted the identification of three distinct groups Perciformes, Pleuronectiformes and Tetraodontiformes. Based on the obtained data, considering morphological characters differences among the species studied, we suggest that all of them live at the medium (E.argenteus,O.rubber, P.corvinaeformis,H.aurolineatum,H.plumieri,H.steindachneri,L.synagris) and bottom (S.micrurum,B.ocellatus,S.testudineus) region of column water.

Estudo comparativo de carragenanas comerciais Kappa, Iota e Lambda no processo inflamatório em ratos :edema intraplantar e pleurisia

The Iota, Kappa and Lambda commercial carrageenans are rarely pure and normally contain varying amounts of the other types of carrageenans. The exact amount of impurity depends on the seaweed source and extraction procedure. Then, different analysis methods have been applied for determination of the main constituents of carrageenans because these three carrageenans are extensively used in food, cosmetic and pharmaceutical industry. The electrophoresis of these compounds proved that the carrageenans are constituted by sulfated polysaccharides. These compounds were characterized by colorimetric methods and was observed that the Lambda carrageenan shown the greater value (33.38%) of sulfate. These polymers were examined by means of 13C NMR spectroscopy and infrared spectra. The polysaccharides consisted mainly of units alternating of sulfated galactoses and anhydrogalactoses. The aim of the study was also to test the inflammatory action of these different polysaccharides. A suitable model of inflammation is acute sterile inflammation of the rat hind limb induced by carrageenan. Paw edema was induced by injecting carrageenans (κ, ι and λ) in saline into the hind paw of a male Wistar rats (175–200 g). The pathway to acute inflammation by carrageenan (kappa, iota and lambda) were expressed as time-edema dependence and measured by paw edema volume. For this purpose, was used an apparatus (pakymeter), which makes it possible to measure the inflammation (swelling of the rat foot) with sufficient accuracy. The results showed that κ-carrageenan (1%) have an edema of 3.7 mm and the paw edema increase was time and dose dependent; the ι-carrageenan (0.2%) caused an edema of 4 mm and the λ-carrageenan (1%) caused an edema of 3.6 mm. Other model was used in this study based in the inflammation of pleura for comparatives studies. Injection of carrageenans into the pleural cavity of rat induced an acute inflammatory response characterized by fluid accumulation in the pleural cavity, a large number of neutrophils and raised NO production. The levels of NO were measured by Griess reactive. The ι-carrageenan caused the greater inflammation, because it has high concentration of nitrite/nitrate (63.478 nmoles/rat), exudato volume (1.52 ml) and PMNs (4902 x 103 cells). Quantitative evaluation of inflammations of rats is a useful and important parameter for the evaluation of the efficacy of anti-inflammatory drugs

Sistemas de informação geográfica e análise espacial de dados como ferramentas para determinação de agregação espacial de doenças: a hanseníase como modelo

This work demonstrates the importance of using tools used in geographic information systems (GIS) and spatial data analysis (SDA) for the study of infectious diseases. Analysis methods were used to describe more fully the spatial distribution of a particular disease by incorporating the geographical element in the analysis. In Chapter 1, we report the historical evolution of these techniques in the field of human health and use Hansen s disease (leprosy) in Rio Grande do Norte as an example. In Chapter 2, we introduced a few basic theoretical concepts on the methodology and classified the types of spatial data commonly treated. Chapters 3 and 4 defined and demonstrated the use of the two most important techniques for analysis of health data, which are data point processes and data area. We modelled the case distribution of Hansen s disease in the city of Mossoró - RN. In the analysis, we used R scripts and made available routines and analitical procedures developed by the author. This approach can be easily used by researchers in several areas. As practical results, major risk areas in Mossoró leprosy were detected, and its association with the socioeconomic profile of the population at risk was found. Moreover, it is clearly shown that his approach could be of great help to be used continuously in data analysis and processing, allowing the development of new strategies to work might increase the use of such techniques in data analysis in health care

Técnicas de análise multivariada na determinação de grupos homogêneos de produtores de leite

Nesta pesquisa objetivou-se captar a variedade de situações tecnológicas para identificar grupos de produtores, o mais semelhante possível, no conjunto de variáveis e características selecionadas. Foram considerados 72 produtores, 8,33% da amostra total, selecionados conforme 29 variáveis relacionadas a fatores produtivos. Avaliaram-se as variáveis de melhor representatividade dentro de cada fator e suas comunalidades dentro do conjunto de fatores analisados. Para a avaliação desses resultados, foram utilizados métodos de análise fatorial em componentes principais. Posteriormente, aplicou-se o método de análise de agrupamentos. O pluralismo tecnológico requer análises de agrupamento para viabilizar intervenções técnicas diferenciadas, o que permite a consolidação de condições de sustentabilidade a partir das reais necessidades de incorporação tecnológica dos produtores.