Asistente Virtual para sitios web

[Resumen] El objetivo de este trabajo fin de grado es el desarrollo e implementación de un asistente virtual para el sitio web de la Facultad de Informática de la Universidad de A Coruña (UDC). Los usuarios serán capaces de interactuar con el asistente, tanto por entrada de texto como a través de voz, y le podrán pedir información sobre cualquier apartado de la web. Para promover el uso generalizado del asistente, se encontrará disponible tanto en gallego como en español. El proyecto seguirá una metodología que incluye todos los ciclos que corresponden a un proyecto software, desde un análisis previo para conocer los objetivos principales del proyecto, planificación, pasando por el diseño, implementación y pruebas. Con este trabajo se ha conseguido un sistema que implementa y demuestra las funcionalidades de un sistema conversacional.[Abstract] The objective of this end-of-degree project is to develop and implement a virtual assistant for the website of the College of Informatic Engineers of the University of A Coruña (UDC). Users will be able to interact with the assistant, both by keyboard inputs and voice, so they will be able to ask for any information of the different sections of the website. In accordance with the promotion of the generalized use of the assistant, its use will be available for both Galician and Spanish. The project will follow a methodology that includes all the software steps that correspond to a project with this characteristics, from a analysis that allows to know the main objectives of the projet, planning, through the design, implementation and finally testing. Most of the development was carried out following a framework that was planned in the initial phase of the project. With this work, it was achieved a system that implements and demonstrated the functionalities of a question answering process with an virtual assistant as its interface.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2018/201

Health and wellbeing among retired elite athletes: Empirical evidence

Objectives: The implications that sport retirement generates among high-level athletes has led to an increase in scientific investigations which describe the quality of life associated to the athletes´ health. The main objective of the study was to determinate the primary problems faced by retired athletes. Study design: The search was carried out in 2 databases: Scopus and WOS. 47 articles were found, 34 and 13 respectively. Method: The design of this study is a descriptive, non-experimental path cast post ex facto retrospection. Results: The research shows three dominant content trends about the quality of life of retired athletes: First mental health, where depression, stress and identity problems were the most notable variables. Second, physical health related to injuries and pain threshold. Last, the quality of life associated to physical activity and healthy habits. Conclusions: Mental health is a key factor to consider before, during and after the sporting career but also physical health is also another determining concept to consider. Physical activity is also a key concept in the lives of retired athletes

Designing an Open Source Virtual Assistant

[Abstract] A chatbot is a type of agent that allows people to interact with an information repository using natural language. Nowadays, chatbots have been incorporated in the form of conversational assistants on the most important mobile and desktop platforms. In this article, we present our design of an assistant developed with open-source and widely used components. Our proposal covers the process end-to-end, from information gathering and processing to visual and speech-based interaction. We have deployed a proof of concept over the website of our Computer Science Faculty.This work was supported by projects RTI2018-093336-B-C22 (MCIU & ERDF) and GPC ED431B 2019/03 (Xunta de Galicia & ERDF). Also, this work has received financial support from CITIC, Centro de Investigación del Sistema universitario de Galicia, which is financial supported by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the ERDF (80%) and Secretaría Xeral de Universidades (20%), (Ref ED431G 2019/01)Xunta de Galicia; ED431B 2019/03Xunta de Galicia; ED431G2019/0

Explainable Depression Symptom Detection in Social Media

Users of social platforms often perceive these sites as supportive spaces to post about their mental health issues. Those conversations contain important traces about individuals' health risks. Recently, researchers have exploited this online information to construct mental health detection models, which aim to identify users at risk on platforms like Twitter, Reddit or Facebook. Most of these models are centred on achieving good classification results, ignoring the explainability and interpretability of the decisions. Recent research has pointed out the importance of using clinical markers, such as the use of symptoms, to improve trust in the computational models by health professionals. In this paper, we propose using transformer-based architectures to detect and explain the appearance of depressive symptom markers in the users' writings. We present two approaches: i) train a model to classify, and another one to explain the classifier's decision separately and ii) unify the two tasks simultaneously using a single model. Additionally, for this latter manner, we also investigated the performance of recent conversational LLMs when using in-context learning. Our natural language explanations enable clinicians to interpret the models' decisions based on validated symptoms, enhancing trust in the automated process. We evaluate our approach using recent symptom-based datasets, employing both offline and expert-in-the-loop metrics to assess the quality of the explanations generated by our models. The experimental results show that it is possible to achieve good classification results while generating interpretable symptom-based explanations

Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] Depression is one of the most prevalent mental health diseases. Although there are effective treatments, the main problem relies on providing early and effective risk detection. Medical experts use self-reporting questionnaires to elaborate their diagnosis, but these questionnaires have some limitations. Social stigmas and the lack of awareness often negatively affect the success of these self-report questionnaires. This article aims to describe techniques to automatically estimate the depression severity from users on social media. We explored the use of pre-trained language models over the subject’s writings. We addressed the task “Measuring the Severity of the Signs of Depression” of eRisk 2020, an initiative in the CLEF Conference. In this task, participants have to fill the Beck Depression Questionnaire (BDI-II). Our proposal explores the application of pre-trained Multiple-Choice Question Answering (MCQA) models to predict user’s answers to the BDI-II questionnaire using their posts on social media. These MCQA models are built over the BERT (Bidirectional Encoder Representations from Transformers) architecture. Our results showed that multiple-choice question answering models could be a suitable alternative for estimating the depression degree, even when small amounts of training data are available (20 users).This work was supported by projects RTI2018-093336-B-C22 (MCIU & ERDF), GPC ED431B 2019/03 (Xunta de Galicia & ERDF) and CITIC, which is financial supported by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the ERDF (80%) and Secretaría Xeral de Universidades (20%), (Ref ED431G 2019/01).Xunta de Galicia; ED431B 2019/03Xunta de Galicia; ED431G 2019/0

BDI-Sen: a sentence dataset for clinical symptoms of depression

[Abstract] People tend to consider social platforms as convenient media for expressing their concerns and emotional struggles. With their widespread use, researchers could access and analyze user-generated content related to mental states. Computational models that exploit that data show promising results in detecting at-risk users based on engineered features or deep learning models. However, recent works revealed that these approaches have a limited capacity for generalization and interpretation when considering clinical settings. Grounding the models' decisions on clinical and recognized symptoms can help to overcome these limitations. In this paper, we introduce BDI-Sen, a symptom-annotated sentence dataset for depressive disorder. BDI-Sen covers all the symptoms present in the Beck Depression Inventory-II (BDI-II), a reliable questionnaire used for detecting and measuring depression. The annotations in the collection reflect whether a statement about the specific symptom is informative (i.e., exposes traces about the individual's state regarding that symptom). We thoroughly analyze this resource and explore linguistic style, emotional attribution, and other psycholinguistic markers. Additionally, we conduct a series of experiments investigating the utility of BDI-Sen for various tasks, including the detection and severity classification of symptoms. We also examine their generalization when considering symptoms from other mental diseases. BDI-Sen may aid the development of future models that consider trustworthy and valuable depression markers

Semantic Similarity Models for Depression Severity Estimation

Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting users' symptom severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\% improvement over state of the art in terms of measuring depression severity.Comment: Accepted at the EMNLP 2023 conferenc

The Influence of Economic Barriers and Drivers on Energy Efficiency Investments in Maritime Shipping, From the Perspective of the Principal-Agent Problem

[Abstract] Maritime transport stands out as a strategic sector; the increasing trend in maritime traffic makes it essential to reduce energy consumption and emissions through investment in energy efficiency. However, investments can be hindered by barriers, and drivers are necessary to reduce or overcome them and promote investment. Consequently, the purpose of this study is to analyze what factors influence investment decisions—and how they do so—when there are principal-agent problems in the shipowner–charterer relationship. The methodology is based on the following process: model and hypotheses formulation, variable definition, the creation of a study sample and statistical treatment through a descriptive analysis of variables and a binomial logistic regression model, all based on a state-of-the-art application. The results corroborate the hypotheses and indicate that principal-agent problems and split incentives, especially in time charter contracts, and a lack of verified information make the shipowners less likely to invest. Moreover, energy efficiency measures are less likely to be implemented in older vessels, possibly due to the difficulty associated with recovering the investment; they are more likely in larger and newer vessels, and regulation encourage their adoption. Furthermore, investment is more likely in vessels with verified information and high levels of both activity and harmful emissions. Improved knowledge in this field could help businesses and governments to act in a more sustainable manner, without detriment to an innovative and competitive sector.Xunta de Galicia; ED481A-2015/224This research was supported by Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia, grant number Ref. ED481A-2015/224 “Axudas á etapa predoutoral” Galician Plan of Research, Innovation and Growth 2011–2015 (Plan I2C) and Agencia Estatal de Investigación (Ministerio de Ciencia, Innovación y Universidades) under research project with reference RTI2018-100702-B-I00, co-funded by the European Regional Development Fund (ERDF/FEDER)