Follow up do Coro de Anões de Itabaianinha - SE

Os objetivos deste Projeto de extensão são: 1. Propiciar a atuação fonoaudiológica, visando a capacitação e qualificação de recursos humanos para à melhoria da qualidade de vida e voz em anões de Itabaianinha, Sergipe, Brasil. 2. Analisar acusticamente , perceptivo-auditivamente , escores de qualidade de vida em voz e a auto percepção sobre o efeito a longo prazo da terapia com Exercício do trato Vocal Semi ocluído (ETVSO) e do treinamento de coral em anões de Itabaianinha, Sergipe, Brasil

Image Re-ranking Acceleration On Gpus

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7 × from serial implementation considering the overall algorithm and up to 36 × on its core steps. © 2013 IEEE.176183Brazilian Computer Society (SBC),Brazilian Funding Agencies CAPES,CNPq,et al.,IEEE Computer Society Through the Technical Committees,on Computer Architecture (TCCA) and TCSCConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Datta, R., Joshi, D., Li, J., Wang, J.Z., Image retrieval: Ideas, influences, and trends of the new age (2008) ACM Computing Surveys, 40 (2), pp. 51-560McDonald, S., Tait, J., Search strategies in content-based image retrieval (2003) 26th ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR'03), pp. 80-87Ferreira, C.D., Dos Santos, J.A., Da Torres, S.R., Gonçalves, M.A., Rezende, R.C., Fan, W., Relevance feedback based on genetic programming for image retrieval (2011) Pattern Recogninion Letters, 32 (1), pp. 27-37Dos Santos, J.A., Ferreira, C.D., Da Torres, S.R., Gonçalves, M.A., Lamparelli, R.A., A relevance feedback method based on genetic programming for classification of remote sensing images (2011) Information Sciences, 181 (13), pp. 2671-2684Pedronette, D.C.G., Da Torres, S.R., Image re-ranking and rank aggregation based on similarity of ranked lists (2013) Pattern Recognition, , to appear http://dx.doi.org/10.1016/j.patcog.2013.01.004Yang, X., Prasad, L., Latecki, L., Affinity learning with diffusion on tensor product graph (2012) Pattern Analysis and Machine Intelligence, PP (99), p. 1. , IEEE Transactions onYang, X., Latecki, L.J., Affinity learning on a tensor product graph with applications to shape and image retrieval (2011) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2011), pp. 2369-2376Pedronette, D.C.G., Da Torres, S.R., Exploiting pairwise recommendation and clustering strategies for image re-ranking (2012) Information Sciences, 207, pp. 19-34Jegou, H., Schmid, C., Harzallah, H., Verbeek, J., Accurate image search using the contextual dissimilarity measure (2010) IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (1), pp. 2-11Pedronette, D.C.G., Da Torres, S.R., Borin, E., Breternitz, M., Efficient image re-ranking computation on GPUs (2012) Int. 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Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps

A Unified Model for Accelerating Unsupervised Iterative Re-Ranking Algorithms

Despite the continuous advances in image retrieval technologies, performing effective and efficient content-based searches remains a challenging task. Unsupervised iterative re-ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more efficient than related approaches based on diffusion processes, these re-ranking algorithms can still be computationally costly, demanding the specification and implementation of efficient big multimedia analysis approaches. Such demand associated with the significant potential for parallelization and highly effective results achieved by recently proposed re-ranking algorithms creates the need for exploiting efficiency vs effectiveness trade-offs. In this article, we introduce a class of unsupervised iterative re-ranking algorithms and present a model that can be used to guide their implementation and optimization for parallel architectures. We also analyze the impact of the parallelization on the performance of four algorithms that belong to the proposed class: Contextual Spaces, RL-Sim, Contextual Re-ranking, and Cartesian Product of Ranking References. The experiments show speedups that reach up to 6.0×,16.1×,3.3×, and7.1×for each algorithm, respectively. These results demonstrate that the proposed parallel programming model can be successfully applied to various algorithms and used to improve the performance of multimedia retrieval systems

A unified model for accelerating unsupervised iterative re‐ranking algorithms

Despite the continuous advances in image retrieval technologies, performing effective and efficient content‐based searches remains a challenging task. Unsupervised iterative re‐ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more efficient than related approaches based on diffusion processes, these re‐ranking algorithms can still be computationally costly, demanding the specification and implementation of efficient big multimedia analysis approaches. Such demand associated with the significant potential for parallelization and highly effective results achieved by recently proposed re‐ranking algorithms creates the need for exploiting efficiency vs effectiveness trade‐offs. In this article, we introduce a class of unsupervised iterative re‐ranking algorithms and present a model that can be used to guide their implementation and optimization for parallel architectures. We also analyze the impact of the parallelization on the performance of four algorithms that belong to the proposed class: Contextual Spaces, RL‐Sim, Contextual Re‐ranking, and Cartesian Product of Ranking References. The experiments show speedups that reach up to 6.0×, 16.1×, 3.3×, and 7.1× for each algorithm, respectively. These results demonstrate that the proposed parallel programming model can be successfully applied to various algorithms and used to improve the performance of multimedia retrieval systems3214CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP307560/2016‐3; 484254/2012‐0; 308194/2017‐9; 140653/2017‐188881.145912/2017‐012018/15597‐6; 2017/25908‐6; 2014/12236‐1; 2015/24494‐8; 2016/50250‐1; 2017/20945‐0; 2019/19312‐9; 2013/50155‐0; 2013/50169‐1; 2014/50715‐9The authors thank AMD, FAEPEX, CAPES (grant #88881.145912/2017‐01), FAPESP (grants #2018/15597‐6, 2017/25908‐6, #2014/12236‐1, #2015/24494‐8, #2016/50250‐1, #2017/20945‐0, and #2019/19312‐9), the FAPESP‐Microsoft Virtual Institute (grants #2013/50155‐0, #2013/50169‐1, and #2014/50715‐9), and CNPq (grants #307560/2016‐3, #484254/2012‐0, #308194/2017‐9, and #140653/2017‐1) for the financial suppor