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Nayeth I. Solorzano Alcivar, R. L., Stalyn Gonzabay Yagual, & Boris X. Vintimilla. (2020). Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language. In ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020 (Vol. 77).
Abstract: This paper explains how Natural Language (NL) processing by computers, through smart
programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written
statements. The study recognized the need to improve the implemented web platform using a
dashboard in which we collected a set of extensive data to measure assessment factors of using
children´s educational games. In this case, applying NL is a strategy to give assessments, build, and
display more precise written statements to enhance the understanding of children´s gaming behavior.
We propose the development of a new tool to assess the use of written explanations rather than a
statistical representation of feedback information for the comprehension of parents and teachers with
a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim
explanations of children´s behavior playing educational videogames as NL interpretation instead of
statistical representations. An educational series of digital game applications for mobile devices,
identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a
dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary
schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing
the degrees of playability and usability factors obtained from the data produced when children play a
MIDI game. The results obtained are presented in a comprehensive guiding evaluation report
applying NL for parents and teachers. These guiding evaluations are useful to enhance children's
learning understanding related to the school curricula applied to ludic digital games.
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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2015). Sensor Fault Detection and Diagnosis for autonomous vehicles. In 2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 (Vol. 30, pp. 1–6). EDP Sciences.
Abstract: In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.
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Dennis G. Romero, A. F. Neto, T. F. Bastos, & Boris X. Vintimilla. (2012). RWE patterns extraction for on-line human action recognition through window-based analysis of invariant moments. In 5th Workshop in applied Robotics and Automation (RoboControl).
Abstract: This paper presents a method for on-line human action recognition on video sequences. An analysis based on Mahalanobis distance is performed to identify the “idle” state, which defines the beginning and end of the person movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.
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Milton Mendieta, F. Panchana, B. Andrade, B. Bayot, C. Vaca, Boris X. Vintimilla, et al. (2018). Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. In IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador (pp. 1–6).
Abstract: The identification of shrimp organs in biology using
histological images is a complex task. Shrimp histological images
poses a big challenge due to their texture and similarity among
classes. Image classification by using feature engineering and
convolutional neural networks (CNN) are suitable methods to
assist biologists when performing organ detection. This work
evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-
Words (PBOW) models for image classification leveraging big
data techniques; and transfer learning for the same classification
task by using a pre-trained CNN. A comparative analysis
of these two different techniques is performed, highlighting
the characteristics of both approaches on the shrimp organs
identification problem.
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Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2018). Near InfraRed Imagery Colorization. In 25 th IEEE International Conference on Image Processing, ICIP 2018 (pp. 2237–2241).
Abstract: This paper proposes a stacked conditional Generative
Adversarial Network-based method for Near InfraRed
(NIR) imagery colorization. We propose a variant architecture
of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model.
We show that this new architecture/loss-function yields better
generalization and representation of the generated colored IR
images. The proposed approach is evaluated on a large test
dataset and compared to recent state of the art methods using
standard metrics.1
Index Terms—Convolutional Neural Networks (CNN), Generative
Adversarial Network (GAN), Infrared Imagery colorization.
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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2016). Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles. Journal of Automation and Control Engineering (JOACE), Vol. 4, pp. 430–436.
Abstract: Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.
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Rafael E. Rivadeneira, A. D. S. and B. X. V. (2022). Multi-Image Super-Resolution for Thermal Images. In Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 (Vol. 4, pp. 635–642).
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Angel D. Sappa, Cristhian A. Aguilera, Juan A. Carvajal Ayala, Miguel Oliveira, Dennis Romero, Boris X. Vintimilla, et al. (2016). Monocular visual odometry: a cross-spectral image fusion based approach. Robotics and Autonomous Systems Journal, Vol. 86, pp. 26–36.
Abstract: This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Learning to Colorize Infrared Images. In 15th International Conference on Practical Applications of Agents and Multi-Agent Systems.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Learning Image Vegetation Index through a Conditional Generative Adversarial Network. In 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM).
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