Journal of Computer Science and Technology <p>The Journal of Computer Science and Technology (JCS&amp;T) is semiannual, open access, and peer-reviewed International Journal promoting the dissemination of original research and technological implementation experiences in the areas of Computer Science, Engineering, and Information Systems. JCS&amp;T is aimed at the general public interested in research, development and applications in the previous mentioned areas, providing a common place to encourage interaction among these community members.</p> <p>Specific topics of interest include: Intelligent Systems; Artificial Intelligence; Semantic Web; Algorithms; Cluster, Grid, Cloud &amp; Accelerator Computing; Fault-Tolerant System; Parallel Architectures; Computer Graphics; Virtual Reality; Human-Computer Interfaces; Image Processing; Technology &amp; Education; E-Learning; M-Learning; Software Engineering; Quality and Software Metrics; Real-Time Systems; Signal Processing; Data Bases; Data Mining; Big Data; Operating Systems; Network Architecture and Configuration; Security; Industrial Systems; Robotics; E-Government; Modelling &amp; Simulation and Computer Science Applications.</p> Postgraduate Office, School of Computer Science, UNLP en-US Journal of Computer Science and Technology 1666-6046 <header class="entry-header"></header> <div class="entry-content">&nbsp; <h4><strong>Copyright and Licensing</strong></h4> <p>Articles accepted for publication will be licensed under the&nbsp;<a href="" target="_blank" rel="noopener">Creative Commons BY-NC</a>. Authors must sign a non-exclusive distribution&nbsp;<a href="">agreement</a>&nbsp;after article acceptance.</p> </div> A T-cell algorithm for solving dynamic economic power dispatch problems <pre>This paper presents the artificial immune system IA_DED (Immune Algorithm Dynamic Economic Dispatch)</pre> <pre>to solve the Dynamic Economic Dispatch (DED) problem and the Dynamic Economic Emission Dispatch (DEED) problem.</pre> <pre>Our approach considers these as dynamic problems whose constraints change over time. </pre> <pre>IA\DED is inspired on the activation process that T cells suffer in order to find partial solutions.</pre> <pre>The proposed approach is validated using several DED problems taken from specialized literature and </pre> <pre>one DEED problem. The latter is addressed by transforming a multi-objective</pre> <pre>problem into a single-objective problem by using a linear aggregating function that combines the</pre> <pre>(weighted) values of the objectives into a single scalar value.</pre> <pre>Our results are compared with respect to those obtained by </pre> <pre>other approaches taken from the specialized literature. We also provide some </pre> <pre>statistical analysis in order to determine the sensitivity</pre> <pre>of the performance of our proposed approach to its parameters. Part of this work was presented at the XXV </pre> <pre>Argentine Congress of Computer Science (CACIC), 2019.</pre> <pre>&nbsp;</pre> <p>&nbsp;</p> Victoria Aragón Carlos A. Coello Coello Mario A. Leguizamón Copyright (c) 2020 Victoria Aragón 2020-05-26 2020-05-26 20 1 e01 e01 10.24215/16666038.20.e01 Green High Performance Simulation for AMB models of Aedes aegypti <p>The increase in temperature caused by the climate change has resulted in the rapid dissemination of infectious diseases. Given the alert for the current situation, the World Health Organization (WHO) has declared a state of health emergency, highlighting the severity of the situation in some countries. For this reason, coming up with knowledge and tools that can help control and eradicate the vectors propagating these diseases is of the utmost importance. High-performance modeling and simulation can be used to produce knowledge and strategies that allow predicting infections, guiding actions and/or training health/civil protection agents. The model developed as part of this research work is aimed at assisting the decision-making process for disease prevention and control, as well as evaluating the reproduction and predicting the evolution of the Aedes aegypti mosquito, which is the transmitting vector of the <strong>dengue</strong>, <strong>Zika </strong>and <strong>chikungunya</strong> diseases. Since a large number of simulation runs are required to achieve results with statistical variability, GPU has been used. This platform has enough computational power to reduce execution time while maintaining a lower energy consumption. Different scenarios and experiments are proposed to corroborate the benefits of the architecture proposed.</p> Erica Soledad Montes de Oca Remo Suppi Laura Cristina De Gisuti Marcelo Naiouf Copyright (c) 2020 Erica Soledad Montes de Oca, Remo Suppi, Laura Cristina De Gisuti, Marcelo Naiouf 2020-05-26 2020-05-26 20 1 e02 e02 10.24215/16666038.20.e02 Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design <p>Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We are researching on the adjustments of its control parameters to obtain the best HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.</p> Gabriela F. Minetti José Hernandez Mercedes Carnero Carolina Salto Carlos Bermudez Mabel Sanchez Copyright (c) 2020 Gabriela F. Minetti 2020-05-26 2020-05-26 20 1 e03 e03 10.24215/16666038.20.e03 Parallelism and Hybridization in Differential Evolution to solve the Flexible Job Shop Scheduling Problem <p>Flexible Job Shop Scheduling Problem (FJSP) is one of the most challenging combinatorial optimization problems, with practical applicability in a real production environment. In this work, we propose a simple Differential Evolution (DE) algorithm to tackle this problem. To represent an FJSSP solution, a real value representation is adopted, which requires a very simple conversion mechanism to obtain a feasible schedule. Consequently, the DE algorithm still works on the continuous domain to explore the problem search space of the discrete FJSSP. Moreover, to enhance the local searchability and to balance the exploration and exploitation capabilities, a simple local search algorithm is embedded in the DE framework. Also, the parallelism of the DE operations is included to improve the efficiency of the whole algorithm. Experiment results confirm the significant improvement achieved by integrating the propositions introduced in this study. Additionally, test results show that our algorithm is competitive when compared with most existing approaches for the FJSSP.</p> Carolina Salto Franco Morero Carlos Bermúdez Copyright (c) 2020 Carolina Salto 2020-05-26 2020-05-26 20 1 e04 e04 10.24215/16666038.20.e04 Political Alignment Identification: a Study with Documents of Argentinian Journalists <p>Political alignment identification is an author profiling task that aims at identifying political bias/orientation in people’ writings. As usual in any automatic text analysis, a critical aspect here is having available adequate data sets so that the data mining and machine learning approaches can obtain reliable and informative results. <br>This article makes a contribution in this regard by presenting a new corpus for the study of political alignment in documents of Argentinian journalists. The<br>study also includes several kinds of analysis of documents of pro-government and opposition journalists such as the relevance of terms in each journalist class,<br>sentiment analysis, topic modelling and the analysis of psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count (LIWC) system. From the experimental results, interesting patterns could be observed such as the topics both types of journalists write about, how the sentiment polarities are distributed and how the writings of pro-government and opposition journalists differ in the distinct LIWC categories.</p> Viviana Mercado Andrea Villagra Marcelo Errecalde Copyright (c) 2020 Viviana Mercado, Andrea Villagra, Marcelo Errecalde 2020-05-26 2020-05-26 20 1 e05 e05 10.24215/16666038.20.e05 Thesis Overview Invariance and Same-Equivariance Measures for Convolutional Neural Networks <p>The main contributions of this thesis include:</p> <ul> <li>A comparative analysis of Neural Network based models for sign language handshape classification.</li> <li>An analysis of strategies to achieve equivariance to rotations in neural networks for: <ul> <li>Comparing the performance of strategies based on data augmentation and specially designed networks and layers.</li> <li>Determining strategies to retrain networks so that they acquire equivariance to rotations.</li> </ul> </li> <li>A set of measures to empirically analyze the equivariance of Neural Networks, as well as any other model based on latent representations, and the corresponding: <ul> <li>Validation of the measures to establish if they are indeed measuring the purported quantity. Analysis of the different variants of the proposed measures. Analysis of the properties of the measures, in terms of their variability to transformations, models and weight initialization.</li> <li>Analysis of the impact of several hyperparameters of the models on the structure of their equivariance, including Max Pooling layers, Batch Normalization, and kernel size. Analysis of the structure of the equivariance in several well known CNN models such as ResNet, All Convolutional and VGG. Analysis of the impact on the equivariance of using specialized models to obtain equivariance such as Transformational Invariance Pooling.</li> <li>Analysis of the class dependency of equivariance. Analysis of the effect of varying the complexity and diversity of the transformations on the measures.</li> </ul> </li> </ul> Facundo Quiroga Laura Lanzarini Copyright (c) 2020 Editorial Team 2020-05-26 2020-05-26 20 1 e06 e06 10.24215/16666038.20.e06