Peachy Parallel Assignments (EduHPC 2018)

Peachy Parallel Assignments are a resource for instructors teaching parallel and distributed programming. These are high-quality assignments, previously tested in class, that are readily adoptable. This collection of assignments includes implementing a subset of OpenMP using pthreads, creating an animated fractal, image processing using histogram equalization, simulating a storm of high-energy particles, and solving the wave equation in a variety of settings. All of these come with sample assignment sheets and the necessary starter code.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Facilitar la inclusión de ejercicios prácticos de programación paralela en cursos de Computación Paralela o de alto rendimiento (HPC)Comunicación en congreso: Descripción de ejercicios prácticos con acceso a material ya desarrollado y probado

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksDuring the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well as sensitive content throughout the network has fostered applied researh to automatically measure the reliability of social networks accounts via Artificial Intelligence (AI). In this paper, we present a multilingual approach for addressing the bot identification task in Twitter via Deep learning (DL) approaches to support end-users when checking the credibility of a certain Twitter account. To do so, several experiments were conducted using state-of-the-art Multilingual Language Models to generate an encoding of the text-based features of the user account that are later on concatenated with the rest of the metadata to build a potential input vector on top of a Dense Network denoted as Bot-DenseNet. Consequently, this paper assesses the language constraint from previous studies where the encoding of the user account only considered either the metadatainformation or the metadata information together with some basic semantic text features. Moreover, the Bot-DenseNet produces a low-dimensional representation of the user account which can be used for any application within the Information Retrieval (IR) framewor

Intervención cognitivo conductual en parejas maritales

La psicología cognitivo conductual en relaciones de pareja analiza los conflictos que llevan a poner fin a estas relaciones, tomando en cuenta cómo es que aparecen los problemas y cómo se mantienen en el tiempo. Cuando hablamos de intervención cognitivo conductual en relaciones de pareja, hacemos referencia a la utilización de técnicas de esta corriente psicológica aplicadas al ámbito marital y que han demostrado probada eficacia para manejar los conflictos que surgen en las relaciones conyugales. El presente trabajo de investigación pretende dar a conocer y brindar un análisis crítico acerca de este tipo de intervención, además de proporcionar un acercamiento a la terapia marital cognitivo conductual.Cognitive behavioral psychology in couple relationships analyzes the conflicts that lead to an end to these relationships, taking into account how problems appear and how they are maintained over time. When we talk about cognitive behavioral intervention in relationships, we refer to the use of this psychological techniques applied to marital current field and have demonstrated proven to handle conflicts that arise in conjugal relations. This research work aims to make known and provide a critical analysis about this type of intervention, in addition to providing an approach to cognitive behavioral marital therapy.Trabajo de investigació

DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera

In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

To meet the demands of a rising population greenhouses must face the challenge of producing more in a more efficient and sustainable way. Innovative mobile robotic solutions with flexible navigation and manipulation strategies can help monitor the field in real-time. Guided by Integrated Pest Management strategies, robots can perform early pest detection and selective treatment tasks autonomously. However, combining the different robotic skills is an error prone work that requires experience in many robotic fields, usually deriving on ad-hoc solutions that are not reusable in other contexts. This work presents Robotframework, a generic ROS-based architecture which can easily integrate different navigation, manipulation, perception, and high-decision modules leading to a faster and simplified development of new robotic applications. The architecture includes generic real-time data collection tools, diagnosis and error handling modules, and user-friendly interfaces. To demonstrate the benefits of combining and easily integrating different robotic skills using the architecture, two flexible manipulation strategies have been developed to enhance the pest detection in its early state and to perform targeted spraying in simulated and field commercial greenhouses. Besides, an additional use-case has been included to demonstrate the applicability of the architecture in other industrial contexts.This work was supported in part by the GreenPatrol European Project through the European GNSS Agency by the European Union's (EU) Horizon 2020 Research and Innovation Program under Grant 776324 [11]. Documen

Use of transgenic GFP reporter strains of the nematode Caenorhabditis elegans to investigate the patterns of stress responses induced by pesticides and by organic extracts from agricultural soils

As a free-living nematode, C. elegans is exposed to various pesticides used in agriculture, as well as to persistent organic residues which may contaminate the soil for long periods. Following on from our previous study of metal effects on 24 GFP-reporter strains representing four different stress-response pathways in C. elegans (Anbalagan et al. 2012), we now present parallel data on the responses of these same strains to several commonly used pesticides. Some of these, like dichlorvos, induced multiple stress genes in a concentration-dependent manner. Unusually, endosulfan induced only one gene (cyp-34A9) to very high levels (8-10-fold) even at the lowest test concentration, with a clear plateau at higher doses. Other pesticides, like diuron, did not alter reporter gene expression detectably even at the highest test concentration attainable, while others (such as glyphosate) did so only at very high concentrations. We have also used five responsive GFP reporters to investigate the toxicity of soil pore water from two agricultural sites in south-east Spain, designated P74 (used for cauliflower production, but significantly metal contaminated) and P73 (used for growing lettuce, but with only background levels of metals). Both soil pore water samples induced all five test genes to varying extents, yet artificial mixtures containing all major metals present had essentially no effect on these same transgenes. Soluble organic contaminants present in the pore water were extracted with acetone and dichloromethane, then after evaporation of the solvents, the organic residues were redissolved in ultrapure water to reconstitute the soluble organic components of the original soil pore water. These organic extracts induced transgene expression at similar or higher levels than the original pore water. Addition of the corresponding metal mixtures had either no effect, or reduced transgene expression towards the levels seen with soil pore water only. We conclude that the main toxicants present in these soil pore water samples are organic rather than metallic in nature. Organic extracts from a control standard soil (Lufa 2.2) had negligible effects on expression of these genes, and similarly several pesticides had little effect on the expression of a constitutive myo-3::GFP transgene. Both the P73 and P74 sites have been treated regularly with (undisclosed) pesticides, as permitted under EU regulations, though other (e.g. industrial) organic residues may also be present

CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-Scale Pattern Generation

Generative Adversarial Networks (GANs) are proving to be a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way of combining multiple GAN outputs into a cohesive whole, which would be useful in many areas, such as the generation of video game levels. Game levels often consist of several segments, sometimes repeated directly or with variation, organized into an engaging pattern. Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). Specifically, a CPPN can define latent vector GAN inputs as a function of geometry, which provides a way to organize level segments output by a GAN into a complete level. This new CPPN2GAN approach is validated in both Super Mario Bros. and The Legend of Zelda. Specifically, divergent search via MAP-Elites demonstrates that CPPN2GAN can better cover the space of possible levels. The layouts of the resulting levels are also more cohesive and aesthetically consistent.Comment: GECCO 2020. arXiv admin note: text overlap with arXiv:2004.0015

First results from the Very Small Array -- I. Observational methods

The Very Small Array (VSA) is a synthesis telescope designed to image faint structures in the cosmic microwave background on degree and sub-degree angular scales. The VSA has key differences from other CMB interferometers with the result that different systematic errors are expected. We have tested the operation of the VSA with a variety of blank-field and calibrator observations and cross-checked its calibration scale against independent measurements. We find that systematic effects can be suppressed below the thermal noise level in long observations; the overall calibration accuracy of the flux density scale is 3.5 percent and is limited by the external absolute calibration scale.Comment: 9 pages, 10 figures, MNRAS in press (Minor revisions

Uso de la plataforma Blackboard y de las herramientas digitales por los docentes, desde la perspectiva de los estudiantes del V ciclo de Electricidad Industrial de un instituto privado de Lima, durante el periodo académico 2021-II

Esta investigación tiene como objetivo conocer la percepción del uso de la plataforma Blackboard y de las herramientas digitales como: Padlet, Kahoot, Mentimeter y Genially por parte de los docentes en las clases virtuales, en los estudiantes del V ciclo de electricidad industrial de una institución particular de Lima, durante el año académico 2021 - II. La metodología de la investigación tiene un enfoque cualitativo, con un alcance descriptivo y un diseño fenomenológico. La técnica que se utilizó es la entrevista semiestructurada y el instrumento aplicado es la guía de entrevistas semiestructuradas con preguntas abiertas. La muestra está compuesta por 15 estudiantes del V ciclo de electricidad industrial de un instituto privado de Lima que corresponden al muestreo no probabilístico por conveniencia. La investigación finaliza que el uso de la plataforma Blackboard y de las herramientas digitales fue percibida por los entrevistados, como primordiales para la continuidad de sus estudios y del aprendizaje, valoran el esfuerzo empleado por los docentes en clases y la participación de sus compañeros para el logro de los objetivos en cada sesión, esto fue viable a través de la organización de los contenidos en la plataforma y de la utilidad de las herramientas en el desarrollo de las clases virtuales. La conectividad del internet fue fundamental para el uso de la plataforma Blackboard y de las herramientas digitales en el contexto virtual.Escuela de Postgrad