Attention-Based Recurrent Neural Networks (RNNs) for Short Text Classification: An Application in Public Health Monitoring

In this paper, we propose an attention-based approach to short text classification, which we have created for the practical application of Twitter mining for public health monitoring. Our goal is to automatically filter Tweets which are relevant to the syndrome of asthma/difficulty breathing. We describe a bi-directional Recurrent Neural Network architecture with an attention layer (termed ABRNN) which allows the network to weigh words in a Tweet differently based on their perceived importance. We further distinguish between two variants of the ABRNN based on the Long Short Term Memory and Gated Recurrent Unit architectures respectively, termed the ABLSTM and ABGRU. We apply the ABLSTM and ABGRU, along with popular deep learning text classification models, to a Tweet relevance classification problem and compare their performances. We find that the ABLSTM outperforms the other models, achieving an accuracy of 0.906 and an F1-score of 0.710. The attention vectors computed as a by-product of our models were also found to be meaningful representations of the input Tweets. As such, the described models have the added utility of computing document embeddings which could be used for other tasks besides classification. To further validate the approach, we demonstrate the ABLSTM’s performance in the real world application of public health surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE). A strong positive correlation was observed between the ABLSTM surveillance signal and the real-world asthma/difficulty breathing syndromic surveillance data. The ABLSTM is a useful tool for the task of public health surveillance

Abandono escolar temprano en la minoría gitana

El objetivo de este trabajo es analizar el impacto del abandono escolar temprano en el alumnado gitano. Este colectivo posee unas peculiaridades culturales que inciden en su tendencia al abandono y fracaso escolar, por lo que centraré todo el ámbito teórico y de intervención en este tipo de alumnado. Para ello, delimitaré primero el marco teórico en el que nos encontramos, destacando aspectos relacionados con todo el marco legal que se rige en la actualidad, después, me acercaré al tema que me ocupa estudiando el fenómeno del abandono escolar temprano en España, por un lado y la relación que existe entre la educación y los gitanos por otro, para poder posteriormente relacionar el colectivo gitano y el porqué de su abandono escolar temprano. Una vez estudiado el tema, es importante, analizar el ámbito desde el que se va a realizar esta intervención, Educación Social, y finalmente, diseñar un plan de intervención para paliar este fenómeno a través de una propuesta en un contexto concreto.Grado en Educación Socia

Paradoxical personality scale: Its development and construct validity analysis

Se presenta el proceso de construcción y validación de la Escala de Personalidad Paradójica, diseñada a partir de la propuesta de Csikszentmihalyi (1996), quien describiera el concepto evaluado en relación a los individuos creativos. Se redactaron 150 reactivos que fueron sometidos a juicio experto y a examen de validez aparente en un estudio piloto. La versión resultante fue usada en un estudio factorial exploratorio (473 estudiantes; 50.5% varones, 49.5% mujeres; 18 a 35 años; = 21.82; DT= 3.14). La estructura resultante, de 6 dimensiones y 30 ítems, fue confirmada mediante un análisis factorial confirmatorio (800 estudiantes universitarios; 44.4% varones, 55.6% mujeres; 18 a 35 años; = 23.47; DT= 3.30). Ambas muestras provenían de la población de estudiantes universitarios de Buenos Aires, Argentina. También se analizó la consistencia interna y la estabilidad temporal de las puntuaciones, obteniéndose en ambos casos coeficientes aceptables, dada la composición de las dimensiones subyacentes al constructo analizado. Se discuten los resultados a la luz de los modelos teóricos propuestos, las ventajas de la brevedad y sencillez de aplicación y según nuevas líneas de investigación.The development and construct validation process of the Paradoxical Personality Scale is presented in this paper. The concept assessed has been posed by Csikszentmihalyi (1996) and was described as related to creative individuals. Following his guidelines, 150 items were designed and judged by five experts, and later analysed from a facies standpoint. The resulting version was used in a sample of college students (n=473; 50.5% males, 49.5% females) from 18 to 35 years (M = 21.82; DT= 3.14), to explore underlying dimensions. A 30item/6-factor solution was firstly isolated and after confirmed by a confirmatory factor analysis developed with 800 college students (44.4% males, 55.6% females), between18 and 35 years (M = 23.47; DT= 3.30). Both samples were selected from the population of college students from Buenos Aires, Argentina. Internal consistency and temporal stability of scores were also tested, obtaining adequate coefficients in both cases, in view of the composition of the dimensions underlying the construct analysed. Results show acceptable psychometric properties as well as shortness and simplicity for data gathering, which are discussed taking into account theoretical models and new research lines.Fil: Freiberg Hoffmann, Agustín. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de la Iglesia, Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Psicología; ArgentinaFil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Psicología; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires; Argentin

Perceived Parenting Styles and Parental Inconsistency Scale: Construct Validity in Young Adults

The study examinesevidences of construct validity of the Perceived Parental Styles and Parental Inconsistency scale –EPIPP–, on a sample of 369 young adults. Individuals were asked about their father and their mother by means of 24 items. These conformsix subscales that constitutea first-order-model–Affection, Dialogue, Indifference, Verbal Coercion, Physical Coercion and Prohibition–that additionally group into two major scales –a second-order model–:Responsiveness and Demandingness.A confirmatory factor analysis was carried out on the first-orderand second-order factor structures,using maximum likelihood anda bootstrap procedure with 500 random samples. Resulting indexes showed an excellent fit in both modelsfor theFatherand Motherversions. Furthermore, adequate resultswere obtained in a cross-validation and afactorial invariance analysis. This way, solid evidences of construct validity were obtained for the EPIPP, suggesting it for the assessmentof perceived parenting in young adults.Fil: de la Iglesia, Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Freiberg Hoffmann, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentin

Predicting academic achievement: The role of Motivation and Learning Strategies

The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985). Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999). Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students). Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989) and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987). Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998). Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940). Results are discussed considering Self Determination Theory and previous research.Fil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Freiberg Hoffmann, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: de la Iglesia, Guadalupe. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Using data mining techniques to predict students at risk of poor performance

The achievement of good honours in Undergraduate degrees is important in the context of Higher Education (HE), both for students and for the institutions that host them. In this paper, we look at whether data mining can be used to highlight performance problems early on and propose remedial actions. Furthermore, some of the methods may also form the basis for recommender systems that may guide students towards their module choices to increase their chances of a good outcome. We use data collected through the admission process and through the students' degrees. In this paper, we predict good honours outcomes based on data at admission and on the first year module results. To validate the proposed results, we evaluate data relating to students with different characteristics from different schools. The analysis is achieved by using historical data from the Data Warehouse of a specific University. The methods used, however, are fairly general and can be used in any HE institution. Our results highlight groups of students at considerable risk of obtaining poor outcomes. For example, using admissions and first year module performance data we can isolate groups for one of the studied schools in which only 24% of students achieve good honour degrees. Over 67% of all low achievers in the school can be identified within this group

Análisis de las competencias TIC de los alumnos de Educación Secundaria y Bachillerato de Galicia

[Resumen] En el presente trabajo se lleva a cabo una valoración de las competencias en TIC de los alumnos de segundo ciclo de Educación Secundaria Obligatoria y Bachillerato en Galicia. Se ponen de manifiesto, a través de este estudio, las carencias existentes en este ámbito y el fracaso de la formación en TIC a nuestros alumnos. La mirada se centra ahora en las modificaciones realizadas en el currículo entre las que se contempla el “tratamiento de la información y la competencia digital” como una de las 8 competencias básicas para los alumnos.[Abstract] In the present study conducts an assessment of ICT skills of students in second cycle of Compulsory Secondary Education and Bachelor of Galicia. It highlights, through this study, the gaps in this area and the failure of ICT training to our students. The look is now focused on the changes made in the curriculum among which refers to “information processing and digital competence” as one of the 8 basic skills for students

Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing

In this paper, we investigate deep learning methods that may extract some word context for Twitter mining for syndromic surveillance. Most of the work on syndromic surveillance has been done on the flu or Influenza- Like Illnesses (ILIs). For this reason, we decided to look at a different but equally important syndrome, asthma/difficulty breathing, as this is quite topical given global concerns about the impact of air pollution. We also compare deep learning algorithms for the purpose of filtering Tweets relevant to our syndrome of interest, asthma/difficulty breathing. We make our comparisons using different variants of the F-measure as our evaluation metric because they allow us to emphasise recall over precision, which is important in the context of syndromic surveillance so that we do not lose relevant Tweets in the classification. We then apply our relevance filtering systems based on deep learning algorithms, to the task of syndromic surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE).We find that the RNN performs best at relevance filtering but can also be slower than other architectures which is important for consideration in real-time application. We also found that the correlation between Twitter and the real-world asthma syndromic surveillance data was positive and improved with the use of the deep- learning-powered relevance filtering. Finally, the deep learning methods enabled us to gather context and word similarity information which we can use to fine tune the vocabulary we employ to extract relevant Tweets in the first place

An Agricultural Precision Sprayer Deposit Identification System

Data-driven Artificial Intelligence systems are playing an increasingly significant role in the advancement of precision agriculture. Currently, precision sprayers lack fully automated methods to evaluate the effectiveness of their operation, e.g. whether spray has landed on target weeds. In this paper, using an agricultural spot spraying system images were collected from an RGB camera to locate spray deposits on weeds or lettuces. We present an interpretable deep learning pipeline to identify spray deposits on lettuces and weeds without using existing methods such as tracers or water-sensitive papers. We implement a novel stratification and sampling methodology to improve results from a baseline. Using a binary classification head after transfer learning networks, spray deposits are identified with over 90% Area Under the Receiver Operating Characteristic (AUROC). This work offers a data-driven approach for an automated evaluation methodology for the effectiveness of precision sprayers

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future