Creación de corpus de palabras embebidas de tweets generados en Argentina

El procesamiento de textos de cualquier índole es una tarea de gran interés en la comunidad científica. Una de las redes sociales donde frecuentemente las personas se expresan libremente es Twitter, y por lo tanto, es una de las principales fuentes para obtener datos textuales. Para poder realizar cualquier tipo de análisis, como primer paso se debe representar los textos de manera adecuada para que, luego, puedan ser usados por un algoritmo. En este artículo se describe la creación de un corpus de representaciones de palabras obtenidas de Twitter, utilizando Word2Vec. Si bien los conjuntos de tweets utilizados no son masivos, se consideran suficientes para dar el primer paso en la creación de un corpus. Un aporte importante de este trabajo es el entrenamiento de un modelo que captura los modismos y expresiones coloquiales de Argentina, y que incluye emojis y hashtags dentro del espacio vectorial.Text processing of any kind is a task of great interest in the scientific community. One of the social networks where people frequently express themselves freely is Twitter, and therefore, it is one of the main sources for obtaining textual data. In order to perform any type of analysis, the first step is to represent the texts in a suitable way so that they can then be used by an algorithm. This paper describes the creation of a corpus of word representations obtained from Twitter using Word2Vec. Although the sets of tweets used are not massive, they are considered sufficient to take the first step in the creation of a corpus. An important contribution of this work is the training of a model that captures the idioms and colloquial expressions of Argentina, and includes emojis and hashtags within the vector space

Identificación de emociones en textos de una red social

Las redes sociales se suelen utilizar para expresar opiniones sobrediferentes aspectos de la sociedad, como productos, servicios, política, celebridades, etc. Empresas, organizaciones y gobiernos hanmostrado su interés en conocer las opiniones que los usuarios tienensobre sus actividades o productos. Además de determinar si una opinión es positiva o negativa, resulta interesante establecer cuál es elsentimiento o emoción manifestada en la opinión. Identificar la emoción que un usuario expresa en un mensaje textual puede entendersecomo clasificar o categorizar el mensaje según sus características.En este trabajo, se desarrolló un método para clasificar textos breves uopiniones de la red social Twitter según la emoción que expresan. Enprimer lugar, fue necesario estructurar los textos descartando las partesirrelevantes y tratando de mantener la mayor cantidad de informaciónposible. Luego se utilizaron técnicas de aprendizaje automático para lageneración de un corpus de opiniones etiquetadas. Por último, se aplicó un método de clasificación por ponderación con diccionarios léxicosasociados a tres valores emocionales: valencia, activación y dominancia.   ARK: http://id.caicyt.gov.ar/ark:/s25457012/8h70ot6fnSocial networks are often used to express opinions on different aspectsof society, products, services, politics, celebrities, etc. Companies,organizations and governments have shown interest in knowing whatusers think about their activities or products. In addition to determining whether an opinion is positive or negative, itis interesting to determine what the feeling oremotion expressed in the opinion is. Identifying theemotion that a user expresses in a textual messagecan be understood as classifying or categorizing themessage according to its characteristics.In this work, a method was developed to classifyshort texts or opinions of the social networkTwitter, according to the emotion they express.First, it was necessary to structure the texts bydiscarding irrelevant parts, but trying to keep asmuch information as possible. Then, automaticlearning techniques were used to generate acorpus of tagged opinions. Finally, a method ofclassification by weighting was applied with lexicaldictionaries associated with three emotionalvalues: valence, activation and dominance

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Creación de corpus de palabras embebidas de tweets generados en Argentina ; Creation of a corpus of embedded words from tweets generated in Argentina

p. 7-24El procesamiento de textos de cualquier índole es una tarea de gran interés en la comunidad científica. Una de las redes sociales donde las personas se expresan con frecuencia y libremente es Twitter y, por lo tanto, es una de las principales fuentes para obtener datos textuales. Para poder realizar cualquier tipo de análisis, como primer paso se debe representar los textos de manera adecuada para que, luego, puedan ser usados por un algoritmo. En este artículo se describe la creación de un corpus de representaciones de palabras obtenidas de Twitter, utilizando Word2Vec. Si bien los conjuntos de tweets utilizados no son masivos, se consideran suficientes para dar el primer paso en la creación de un corpus. Un aporte importante de este trabajo es el entrenamiento de un modelo que captura los modismos y expresiones coloquiales de Argentina, y que incluye emojis y hashtags dentro del espacio vectorial. Text processing of any kind is a task of great interest in the scientific community. One of the social networks where people express themselves frequently and freely is Twitter, and therefore, it is one of the main sources for obtaining textual data. In order to perform any type of analysis, the first step is to represent texts in a suitable way so that they can afterwards be used by an algorithm. This paper describes the creation of a corpus of word representations obtained from Twitter applying Word2Vec. Although the sets of tweets used are not massive, they are considered sufficient to take the first step in the creation of a corpus. An important contribution of this work is the training of a model that captures the idioms and colloquial expressions of Argentina, and includes emojis and hashtags within the vector space.Fil: Cardoso, Carolina A.. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Amor Lisardo, Matias Nicolas. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Monge, Agustina. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Talamé, María Lorena. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina

Identificación de emociones en textos de una red social ; Identification of emotions in texts of a social network

p. 7-20Las redes sociales se suelen utilizar para expresar opiniones sobre diferentes aspectos de la sociedad, como productos, servicios, política, celebridades, etc. Empresas, organizaciones y gobiernos han mostrado su interés en conocer las opiniones que los usuarios tienen sobre sus actividades o productos. Además de determinar si una opinión es positiva o negativa, resulta interesante establecer cuál es el sentimiento o emoción manifestada en la opinión. Identificar la emoción que un usuario expresa en un mensaje textual puede entenderse como clasificar o categorizar el mensaje según sus características. En este trabajo, se desarrolló un método para clasificar textos breves u opiniones de la red social Twitter según la emoción que expresan. En primer lugar, fue necesario estructurar los textos descartando las partes irrelevantes y tratando de mantener la mayor cantidad de información posible. Luego se utilizaron técnicas de aprendizaje automático para la generación de un corpus de opiniones etiquetadas. Por último, se aplicó un método de clasificación por ponderación con diccionarios léxicos asociados a tres valores emocionales: valencia, activación y dominancia. Social networks are often used to express opinions on different aspects of society, products, services, politics, celebrities, etc. Companies, organizations and governments have shown interest in knowing what users think about their activities or products. In addition to determining whether an opinion is positive or negative, it is interesting to determine what the feeling or emotion expressed in the opinion is. Identifying the emotion that a user expresses in a textual message can be understood as classifying or categorizing the message according to its characteristics. In this work, a method was developed to classify short texts or opinions of the social network Twitter, according to the emotion they express. First, it was necessary to structure the texts by discarding irrelevant parts, but trying to keep as much information as possible. Then, automatic learning techniques were used to generate a corpus of tagged opinions. Finally, a method of classification by weighting was applied with lexical dictionaries associated with three emotional values: valence, activation and dominance.Fil: Monge, Agustina. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Amor Lisardo, Matias Nicolas. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Talamé, María Lorena. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Cardoso, Alejandra Carolina. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina

Análisis de discurso negativo en redes sociales

Las redes sociales brindan un gran espacio de difusión de contenidos, denuncias sociales, noticias y eventos, pero también suelen contener un lado negativo como la circulación de contenido gráfico o textual discriminatorio, amenazante, con insultos o que expresen odio hacia la diversidad o hacia la diferencia...Fil: Cardoso, Alejandra Carolina. Universidad Católica de Salta. Facultad de Ingeniería; Argentina.Fil: Talamé, María Lorena. Universidad Católica de Salta. Facultad de Ingeniería; Argentina.Fil: Amor, Matias Nicolas Lisardo. Universidad Católica de Salta. Facultad de Ingeniería; Argentina.Fil: Sandoval, Pablo Mateo. Universidad Católica de Salta. Facultad de Ingeniería; Argentina

Ares Galaxy: análisis del comportamiento y del código fuente ; Ares Galaxy: analysis of the behavior and the source code

p. 7-20Ares Galaxy es uno de los programas más conocidos utilizados para la descarga de archivos de música y videos, entre otros. Estos tipos de programas se basan en la arquitectura de comunicación Peer to Peer que permite el intercambio de información entre las computadoras de la red. Los programas de descarga de archivos son muy populares y fáciles de usar. Sin embargo, muchas veces los usuarios desconocen que este tipo de intercambio tiene riesgos, como, por ejemplo, favorecer el tráfico de pornografía infantil. Por ello, resulta importante investigar si Ares Galaxy realiza modificaciones en el sistema operativo sin el consentimiento del usuario. Para detectar posibles modificaciones en el registro del sistema operativo y su configuración, se siguieron tres caminos. Se observaron los archivos generados y modificados durante la instalación y uso de Ares Galaxy, y luego, se analizaron con software de forensia informática para comprender el contenido de archivos encriptados. También se examinó el comportamiento de Ares Galaxy desde su código fuente. En este proyecto de la cátedra Compiladores, se utilizaron herramientas como la gramática de Delphi, expresiones regulares para la detección de elementos de interés dentro del programa y otras herramientas relacionadas con compiladores y traductores. Ares Galaxy is one of the most popular programs used to download music files and videos, among others. These types of programs are based on the architecture of communication Peer to Peer that allows the exchange of information among the computers of the network. File download programs are very popular and easy to use. However, many times users are unaware that this type of information exchange has risks, such as favoring the trafficking of child pornography. Therefore, it is important to investigate whether Ares Galaxy makes modifications to the operating system without the user's consent. To detect possible modifications to the operating system registry and its configuration, three paths were followed. The files generated and modified during the installation and use of Ares Galaxy were observed, and then, analyzed with forensics software to understand the content of encrypted files. The behavior of Ares Galaxy was also examined from its source code.Fil: Amor Lisardo, Matias Nicolas. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Maccio, Iván. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Occhipinti, Ignacio. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Talamé, María Lorena. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina.Fil: Cardoso, Alejandra. Universidad Católica de Salta. Facultad de Ingeniería e Informática; Argentina

PROSICSI: Propuesta de un sistema integrado de gestión de calidad y de seguridad de la información para el laboratorio de Forensia Digital

En el contexto judicial, la obtención de la evidencia digital debe realizarse respetando principios forenses primordiales: evitar la contaminación, actuar metodológicamente y mantener la cadena de custodia...Fil: Aráoz Fleming, José. Universidad Católica de Salta. Facultad de Ingeniería; Argentina.Fil: Juárez, Nahuel Alejandro. Universidad Católica de Salta. Facultad de Ingeniería; Argentina.Fil: Amor, Matias Nicolas Lisardo. Universidad Católica de Salta. Facultad de Ingeniería; Argentina.Fil: Parra de Gallo, Herminia Beatriz. Universidad Católica de Salta. Facultad de Ingeniería; Argentina

Immunocompromised patients with acute respiratory distress syndrome: Secondary analysis of the LUNG SAFE database

Background: The aim of this study was to describe data on epidemiology, ventilatory management, and outcome of acute respiratory distress syndrome (ARDS) in immunocompromised patients. Methods: We performed a post hoc analysis on the cohort of immunocompromised patients enrolled in the Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE) study. The LUNG SAFE study was an international, prospective study including hypoxemic patients in 459 ICUs from 50 countries across 5 continents. Results: Of 2813 patients with ARDS, 584 (20.8%) were immunocompromised, 38.9% of whom had an unspecified cause. Pneumonia, nonpulmonary sepsis, and noncardiogenic shock were their most common risk factors for ARDS. Hospital mortality was higher in immunocompromised than in immunocompetent patients (52.4% vs 36.2%; p &lt; 0.0001), despite similar severity of ARDS. Decisions regarding limiting life-sustaining measures were significantly more frequent in immunocompromised patients (27.1% vs 18.6%; p &lt; 0.0001). Use of noninvasive ventilation (NIV) as first-line treatment was higher in immunocompromised patients (20.9% vs 15.9%; p = 0.0048), and immunodeficiency remained independently associated with the use of NIV after adjustment for confounders. Forty-eight percent of the patients treated with NIV were intubated, and their mortality was not different from that of the patients invasively ventilated ab initio. Conclusions: Immunosuppression is frequent in patients with ARDS, and infections are the main risk factors for ARDS in these immunocompromised patients. Their management differs from that of immunocompetent patients, particularly the greater use of NIV as first-line ventilation strategy. Compared with immunocompetent subjects, they have higher mortality regardless of ARDS severity as well as a higher frequency of limitation of life-sustaining measures. Nonetheless, nearly half of these patients survive to hospital discharge. Trial registration: ClinicalTrials.gov, NCT02010073. Registered on 12 December 2013

Outcomes of Patients Presenting with Mild Acute Respiratory Distress Syndrome Insights from the LUNG SAFE Study

BACKGROUND: Patients with initial mild acute respiratory distress syndrome are often underrecognized and mistakenly considered to have low disease severity and favorable outcomes. They represent a relatively poorly characterized population that was only classified as having acute respiratory distress syndrome in the most recent definition. Our primary objective was to describe the natural course and the factors associated with worsening and mortality in this population. METHODS: This study analyzed patients from the international prospective Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE) who had initial mild acute respiratory distress syndrome in the first day of inclusion. This study defined three groups based on the evolution of severity in the first week: "worsening" if moderate or severe acute respiratory distress syndrome criteria were met, "persisting" if mild acute respiratory distress syndrome criteria were the most severe category, and "improving" if patients did not fulfill acute respiratory distress syndrome criteria any more from day 2. RESULTS: Among 580 patients with initial mild acute respiratory distress syndrome, 18% (103 of 580) continuously improved, 36% (210 of 580) had persisting mild acute respiratory distress syndrome, and 46% (267 of 580) worsened in the first week after acute respiratory distress syndrome onset. Global in-hospital mortality was 30% (172 of 576; specifically 10% [10 of 101], 30% [63 of 210], and 37% [99 of 265] for patients with improving, persisting, and worsening acute respiratory distress syndrome, respectively), and the median (interquartile range) duration of mechanical ventilation was 7 (4, 14) days (specifically 3 [2, 5], 7 [4, 14], and 11 [6, 18] days for patients with improving, persisting, and worsening acute respiratory distress syndrome, respectively). Admissions for trauma or pneumonia, higher nonpulmonary sequential organ failure assessment score, lower partial pressure of alveolar oxygen/fraction of inspired oxygen, and higher peak inspiratory pressure were independently associated with worsening. CONCLUSIONS: Most patients with initial mild acute respiratory distress syndrome continue to fulfill acute respiratory distress syndrome criteria in the first week, and nearly half worsen in severity. Their mortality is high, particularly in patients with worsening acute respiratory distress syndrome, emphasizing the need for close attention to this patient population.status: publishe