Papers by Eduardo Ortiz Holguin
El gran impacto de las aplicaciones delivery durante la pandemia COVID-19 fue reflejado en el inc... more El gran impacto de las aplicaciones delivery durante la pandemia COVID-19 fue reflejado en el incremento de los comentarios de sus clientes mediante la red social Twitter. En los tweets los clientes manifiestan su agrado y desagrado respecto a la calidad de servicio recibida por parte de las empresas y colaboradores encargados de brindarlo. El experimento inicia con una la recopilación de tweets, de los cuales obtuvimos 2214. Estos fueron sometidos a una limpieza, quedándonos así 1171 tweets, que finalmente fueron estudiados mediante una red neuronal para determinar la polaridad de los mismo. Es así como con una exactitud del 0.7650 de parte de la red neuronal se obtuvieron 493 tweets de polaridad positiva y 677 tweets de polaridad negativa, los cuales manifiestan la falta de trabajo por parte de las empresas para mejorar su servicio ante la adaptación de las medidas sanitarias para el COVID-19.
Companion of the ACM/SPEC International Conference on Performance Engineering, 2021
We analyze a dataset of 51 current (2019-2020) Distributed Systems syllabi from top Computer Scie... more We analyze a dataset of 51 current (2019-2020) Distributed Systems syllabi from top Computer Science programs, focusing on finding the prevalence and context in which topics related to performance are being taught in these courses. We also study the scale of the infrastructure mentioned in DS courses, from small client-server systems to cloud-scale, peer-to-peer, global-scale systems. We make eight main findings, covering goals such as performance, and scalability and its variant elasticity; activities such as performance benchmarking and monitoring; eight selected performance-enhancing techniques (replication, caching, sharding, load balancing, scheduling, streaming, migrating, and offloading); and control issues such as trade-offs that include performance and performance variability.
Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 2021
Correctly applying distributed systems concepts is important for software that seeks to be scalab... more Correctly applying distributed systems concepts is important for software that seeks to be scalable, reliable and fast. For this reason, Distributed Systems is a course included in many Computer Science programs. To both describe current trends in teaching distributed systems and as a reference for educators that seek to improve the quality of their syllabi, we present a review of 51 syllabi of distributed systems courses from top Computer Science programs around the world. We manually curated the syllabi and extracted data that allowed us to identify approaches used in teaching this subject, including choice of topics, book, and paper reading list. We present our results and a discussion on whether what is being taught matches the guidelines of two important curriculum initiatives. CCS CONCEPTS • Social and professional topics → Computer science education; Model curricula.
SIGCSE-2021-DistributedSystemsSyllabiDataset
<strong>Dataset Repository for "Have We Reached Consensus? An Analysis of Distributed ... more <strong>Dataset Repository for "Have We Reached Consensus? An Analysis of Distributed Systems Syllabi"</strong> This repository contains a curated dataset of 51 syllabi of Distributed Systems courses across the world. The dataset can be used to analyze current trends in teaching distributed systems (as of 11/2020). For a detailed description of our compilation and curation process, please refer to our SIGCSE paper (see reference below). The preferred way to cite this dataset if for others to cite our SIGCSE paper and optionally include a footnote with a link to this Zenodo repository. <strong>Paper</strong> Cristina L. Abad, Eduardo Ortiz-Holguin, and Edwin F. Boza. 2021. Have We Reached Consensus?: An Analysis of Distributed Systems Syllabi. In Proceedings of the 52nd ACM Technical Symposium on Computer ScienceEducation (SIGCSE '21), March 13–20, 2021, Virtual Event, USA.ACM, NewYork, NY, USA, 7 pages.
Wind Turbine Main Bearing Failure Prediction using a Hybrid Neural Network
Journal of Physics: Conference Series
Energy is necessary for economic growth and improved well-being, but it poses a great challenge t... more Energy is necessary for economic growth and improved well-being, but it poses a great challenge to be generated without increasing costs and avoiding pollution. A viable option is wind energy because it is a clean and renewable. However, continuous monitoring and maintenance of wind turbines is required for the further development of wind farms. Main bearing failures were identified by the European Academy of Wind Energy as a critical issue in terms of increasing the availability and reliability of wind turbines. In this work, it is proposed a hybrid neural network for main bearing failure prognosis. This network consists of a two-dimensional convolutional neural network (to extract spatial-temporal characteristics from the data) sequentially connected with a long short-term memory network (to learn sequence patterns) to predict the slow-speed shaft temperature (the closest temperature to the main bearing). The mean square error between its real measurement and its prediction gives ...
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Papers by Eduardo Ortiz Holguin