O ensino colaborativo como facilitador da inclusão da criança com deficiência intelectual na educação infantil

This paper presents aspects of Early Childhood Education in the perspective of Inclusive Education, considering collaborative teaching as a relevant strategy in this mode. Thus, had been aimed to review the literature on collaborative learning in kindergarten and verify the knowledge of teachers on this pedagogical strategy. The survey was conducted in Child Education Centers and Schools municipal daycare, totaling 4 units and 45 participants. Empirical data were collected through a closed questionnaire, tabulated and analyzed qualitative and qualitatively. The results indicate the recognition of preschool teachers in need of support and assistance regarding collaborative teaching, highlighting the need for continuous training in the performance of special education teacher in this context.

Ensino da história e cultura afro-brasileira por meio do atletismo: contribuições de um curso de extensão à distância para professores de educação física

Pós-graduação em Desenvolvimento Humano e Tecnologias - IBRC

A newly recognized syndrome of marfanoid habitus; Long face; Hypotelorism; Long, thin nose; Long, thin hands and feet; and a specific pattern of language and learning disabilities

Here, we report on a newly recognized syndrome in a Brazilian family with three affected women, who had a Marfanoid habitus; long face; hypotelorism; long, thin nose; long, thin hands and feet; and language and learning disabilities. The disorder is compatible with autosomal dominant inheritance. (C) 2007 Wiley-Liss, Inc.

Pattern recognition issues on anisotropic smoothed particle hydrodynamics

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

HOW FAR do WE GET USING MACHINE LEARNING BLACK-BOXES?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Pressure drop correlation for oil-refrigerant R134a mixture flashing flow in a small diameter tube

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Optic Flow Contribution to Locomotion Adjustments in Obstacle Avoidance

Locomotion generates a visual movement pattern characterized as optic flow. To explore how the locomotor adjustments are affected by this pattern, an experimental paradigm was developed to eliminate optic flow during obstacle avoidance. The aim was to investigate the contribution of optic flow in obstacle avoidance by using a stroboscopic lamp. Ten young adults walked on an 8m pathway and stepped over obstacles at two heights. Visual sampling was determined by a stroboscopic lamp (static and dynamic visual sampling). Three-dimensional kinematics data showed that the visual information about self-motion provided by the optic flow was crucial for estimating the distance from and the height of the obstacle. Participants presented conservative behavior for obstacle avoidance under experimental visual sampling conditions, which suggests that optic flow favors the coupling of vision to adaptive behavior for obstacle avoidance.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Contemporary pollen flow, mating patterns and effective population size inferred from paternity analysis in a small fragmented population of the Neotropical tree Copaifera langsdorffii Desf. (Leguminosae-Caesalpinioideae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Desenvolvimento de um objeto de aprendizagem para análise de sistemas de energia elétrica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)