Unidade de Investigação - CIIC - Computer Science and Communication Research Centre
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Percorrer Unidade de Investigação - CIIC - Computer Science and Communication Research Centre por Domínios Científicos e Tecnológicos (FOS) "Ciências Médicas::Outras Ciências Médicas"
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- Internet-based identification of anxiety in university students using text and facial emotion analysisPublication . Guerrero, Graciela; Avila, Daniel; Silva, Fernando José Mateus da; Pereira, António; Fernández-Caballero, AntonioBackground: Anxiety in university students can lead to poor academic performance and even dropout. The Adult Manifest Anxiety Scale (AMAS-C) is a validated measure designed to assess the level and nature of anxiety in college students. Objective: The aim of this study is to provide internet-based alternatives to the AMAS-C in the automated identification and prediction of anxiety in young university students. Two anxiety prediction methods, one based on facial emotion recognition and the other on text emotion recognition, are described and validated using the AMAS-C Test Anxiety, Lie and Total Anxiety scales as ground truth data. Methods: The first method analyses facial expressions, identifying the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the neutral expression, while the students complete a technical skills test. The second method examines emotions in posts classified as positive, negative and neutral in the students' profile on the social network Facebook. Both approaches aim to predict the presence of anxiety. Results: Both methods achieved a high level of precision in predicting anxiety and proved to be effective in identifying anxiety disorders in relation to the AMAS-C validation tool. Text analysis-based prediction showed a slight advantage in terms of precision (86.84 %) in predicting anxiety compared to face analysis-based prediction (84.21 %). Conclusions: The applications developed can help educators, psychologists or relevant institutions to identify at an early stage those students who are likely to fail academically at university due to an anxiety disorder.
- Microsoft's Your Phone environment from a digital forensic perspectivePublication . Domingues, Patricio; Andrade, Luis Miguel; Frade, MiguelYour Phone is a Microsoft dual mobile/desktop application that links a Windows 10 environment to a smartphone. The Android version provides the smartphone's user with the ability to control the mobile device from Windows 10, allowing to place/receive calls, send/receive text messages such as SMS, MMS and RCS, access up to the last 2000 photos/screenshots of the device and to receive notifications from applications, all through the Windows 10 Your Phone application and, if configured to do so, within Windows 10 notification center. This work analyzes the Your Phone environment, that is, Your Phone Companion for Android and Your Phone for Windows 10. The paper studies the digital forensic artifacts that can be found in a post mortem analysis, focusing on the SQLite3 databases used by both the Android and Windows 10 applications. We also compare the examined version with a previous version of Your Phone, showing that Your Phone newest functionalities bring new valuable artifacts for forensic examiners. The study shows that Your Phone data left on a Windows 10 device can be useful to access a copy of messages, photos, and document interactions, especially when the Android device is inaccessible or even physically unavailable. To ease the task for digital forensic examiners, we have updated our open-source YPA software that collects and analyzes Your Phone data from a Windows 10 system. YPA runs as a module within the digital forensic Autopsy software.
- Using text mining to diagnose and classify epilepsy in childrenPublication . Luis Pereira; Rijo, Rui; Silva, Catarina; Agostinho, MargaridaEpilepsy diagnosis can be an extremely complex process, demanding considerable time and effort from physicians and healthcare infrastructures. Physicians need to classify each specific type of epilepsy based on different data, e.g., types of seizures, events and exams' results. This work presents a text mining approach to support medical decisions relating to epilepsy diagnosis and classification in children. We propose a text mining process that, using patient medical records, applies ontologies and named entities recognition as preprocessing steps, then applying K-Nearest Neighbors as a white-box lazy method to classify each instance. Results on real medical records suggest that the proposed framework shows good performance and clear interpretations, albeit the reduced volume of available training data.