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Nathan Inkawhich, C. T., Justice Wheelwright, Oliver Nina, Dylan Bowald, Angel Sappa, Erik Blasch. (2025). 4th Multi-modal Aerial View Image Challenge: SAR CLASSIFICATION – PBVS 2025. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025, .
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Dylan Bowald, J. W., Oliver Nina, Angel Sappa, Riad Hammoud, Erik Blasch, Nathan Inkawhich. (2025). 3th Multi-modal Aerial View Image Challenge: Sensor Domain Translation – PBVS 2025. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025.
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Patricia Suarez & Angel D. Sappa. (2025). Lightweight Architecture for Fruit Quality Estimation in the Infrared Domain. In 5th International Conference on Innovations in Computational Intelligence and Computer Vision ICICV 2025.
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Henry O. Velesaca Hector Villegas, and A. D. S. (2025). Exploring Camouflaged Object Detection Techniques for Invasive Vegetation Monitoring. In 14th International Conference on Data Science, Technology and Applications DATA 2025.
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Kevin E. Munoz, L. N. S., Steven S. Araujo, and Boris X. Vintimilla. (2025). Stereo Vision Techniques: A Comparative Study of Traditional and Machine Learning-Based Approaches. In 5th International Conference on Computer Vision and Robotics CVR 2025.
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Kevin E. Muñoz Loberlly N. Salazar Steven S. Araujo Boris X. Vintimilla. (2025). Detecting and Characterizing Human Interactions to EnhanceHuman-Robot Engagement. In 3rd International Conference on Robotics, Control and Vision Engineering RCVE 2025.
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Cristina L. Abad, Yi Lu, & Roy H. Campbell. (2011). DARE: Adaptive Data Replication for Efficient Cluster Scheduling. In IEEE International Conference on Cluster Computing, 2011 (pp. 159–168).
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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Jorge Alvarez Tello, Mireya Zapata, & Dennys Paillacho. (2019). Kinematic optimization of a robot head movements for the evaluation of human-robot interaction in social robotics. In 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 (Vol. 975, pp. 108–118).
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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Shendry Rosero Vásquez. (2019). Reconocimiento facial: técnicas tradicionales y técnicas de aprendizaje profundo, un análisis. (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). M.Sc. thesis. In Ediciones FIEC-ESPOL.
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Suárez P. (2021). Processing and Representation of Multispectral Images Using Deep Learning Techniques. In Electronic Letters on Computer Vision and Image Analysis, Vol. 19(Issue 2), pp. 5–8.
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