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Author Cristina L. Abad; Yi Lu; Roy H. Campbell pdf  url
openurl 
  Title DARE: Adaptive Data Replication for Efficient Cluster Scheduling Type Conference Article
  Year 2011 Publication IEEE International Conference on Cluster Computing, 2011 Abbreviated Journal  
  Volume Issue Pages 159 - 168  
  Keywords MapReduce, replication, scheduling, locality  
  Abstract Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively.  
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  Language English Summary Language English Original Title  
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  Notes Approved (down) yes  
  Call Number cidis @ cidis @ Serial 21  
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Author Jorge Alvarez Tello; Mireya Zapata; Dennys Paillacho pdf  openurl
  Title Kinematic optimization of a robot head movements for the evaluation of human-robot interaction in social robotics. Type Conference Article
  Year 2019 Publication 10th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences (AHFE 2019), Washington D.C.; United States. Advances in Intelligent Systems and Computing Abbreviated Journal  
  Volume 975 Issue Pages 108-118  
  Keywords  
  Abstract This paper presents the simplification of the head movements from

the analysis of the biomechanical parameters of the head and neck at the

mechanical and structural level through CAD modeling and construction with

additive printing in ABS/PLA to implement non-verbal communication strategies and establish behavior patterns in the social interaction. This is using in the

denominated MASHI (Multipurpose Assistant robot for Social Human-robot

Interaction) experimental robotic telepresence platform, implemented by a

display with a fish-eye camera along with the mechanical mechanism, which

permits 4 degrees of freedom (DoF). In the development of mathematicalmechanical modeling for the kinematics codification that governs the robot and

the autonomy of movement, we have the Pitch, Roll, and Yaw movements, and

the combination of all of them to establish an active communication through

telepresence. For the computational implementation, it will be show the rotational matrix to describe the movement.
 
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  Notes Approved (down) yes  
  Call Number gtsi @ user @ Serial 108  
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Author Shendry Rosero Vásquez pdf  openurl
  Title Reconocimiento facial: técnicas tradicionales y técnicas de aprendizaje profundo, un análisis. (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). M.Sc. thesis Type Book Chapter
  Year 2019 Publication Ediciones FIEC-ESPOL Abbreviated Journal  
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  Corporate Author Ph.D. Angel Sappa, Director de tesis & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis  
  Publisher Place of Publication Editor  
  Language Español Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes Approved (down) yes  
  Call Number gtsi @ user @ Serial 114  
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Author Suárez P. pdf  openurl
  Title Processing and Representation of Multispectral Images Using Deep Learning Techniques Type Magazine Article
  Year 2021 Publication In Electronic Letters on Computer Vision and Image Analysis Abbreviated Journal  
  Volume Vol. 19 Issue Issue 2 Pages pp. 5-8  
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  Corporate Author Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis  
  Publisher Place of Publication Editor  
  Language Español Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved (down) yes  
  Call Number cidis @ cidis @ Serial 122  
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Author Dennys Paillacho; Nayeth I. Solorzano Alcivar; Jonathan S. Paillacho Corredores url  isbn
openurl 
  Title LOLY 1.0: A Proposed Human-Robot-Game Platform Architecture for the Engagement of Children with Autism in the Learning Process Type Book Chapter
  Year 2021 Publication The international Conference on Systems and Information Sciences (ICCIS 2020), julio 27-29. Advances in Intelligent Systems and Computing. Abbreviated Journal  
  Volume 1273 Issue Pages 225-238  
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  Series Volume Series Issue Edition  
  ISSN 21945357 ISBN 978-303059193-9 Medium  
  Area Expedition Conference  
  Notes Approved (down) yes  
  Call Number cidis @ cidis @ Serial 185  
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Author Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa url  openurl
  Title A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. Type Journal Article
  Year 2022 Publication Journal of Manufacturing Systems Abbreviated Journal  
  Volume Vol. 64 Issue Pages pp. 497-507  
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  Notes Approved (down) yes  
  Call Number cidis @ cidis @ Serial 184  
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Author Velesaca, Henry O.; Suárez, Patricia L.; Sappa, Angel D.; Carpio, Dario; Rivadeneira, Rafael E.; Sanchez, Angel url  openurl
  Title Review on Common Techniques for Urban Environment Video Analytics. Type Conference Article
  Year 2022 Publication WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI 2022) Abbreviated Journal  
  Volume Issue Porto Alegre: Sociedade Brasileira de Computação Pages 107-118  
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  Notes Approved (down) yes  
  Call Number cidis @ cidis @ Serial 192  
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla pdf  openurl
  Title Human Pose Estimation through A Novel Multi-View Scheme Type Conference Article
  Year 2022 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 Abbreviated Journal  
  Volume 5 Issue Pages 855-862  
  Keywords Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach  
  Abstract This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human

pose estimation problem. The proposed approach first obtains the human body joints of a set of images,

which are captured from different views at the same time. Then, it enhances the obtained joints by using a

multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from

another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed

for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and

comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements

in the accuracy of body joints estimations.
 
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  Notes Approved (down) yes  
  Call Number cidis @ cidis @ Serial 169  
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Author Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. url  openurl
  Title LDC: Lightweight Dense CNN for Edge Detection. Type Journal Article
  Year 2022 Publication IEEE Access journal Abbreviated Journal  
  Volume Vol. 10 Issue Pages pp. 68281-68290  
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  Notes Approved (down) yes  
  Call Number cidis @ cidis @ Serial 183  
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Author Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco pdf  openurl
  Title Computer vision for image understanding. A comprehensive review Type Conference Article
  Year 2019 Publication International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador Abbreviated Journal  
  Volume Issue Pages 248-259  
  Keywords  
  Abstract Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.  
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  Notes Approved (down) no  
  Call Number gtsi @ user @ Serial 97  
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