Thesis offers

Desenvolupament d'una app web per la integració d'un simulador d'Edge Computing (EC). El funcionament del simulador és el següent: pren com entrada un model que consisteix en nombre de servidors amb uns recursos i un nombre de treballs amb requeriments de recursos els quals han d'estar processats abans d'un temps donat, a més s'introdueix el temps de simulació de dies expressat en minuts. Després de la simulació s'ha de visualitzar les mètriques de jobs completats i els recursos utilitzats. Aquest TFG contempla les següents tasques:

- Determinar quina és l'arquitectura del sistema més adient per la integració.

- Requisits no funcionals:

- Usabilitat.
- Suportabilitat.
- Seguretat.

- Requisits funcionals:

- Accés via Web.
- Gestió d'usuaris.
     Alta/baixa usuaris del simulador.
    Control sessions i d'historial de resultats.

   Usuari administrador desplegament de versions del simulador.
   Control del simulador: carga els models, introduir paràmetres simulador, visualitzar resultats, control simulació.

Virtual reality is becoming a tool for helping patients with several illnesses (strokes, frozen shoulder...) to engage in the rehabilitation exercises.

The purpose of this project is the creation of a serious game that helps patients to engage in rehabilitation exercises for the frozen shoulder.

 Los Transformers han revolucionado el procesamiento del lenguaje natural al modelar eficazmente datos secuenciales. Sin embargo, están limitados por una ventana de contexto fija, lo que provoca que los tokens más antiguos se descarten a medida que se procesan nuevos. La arquitectura de Memorizing Transformers aborda esta limitación introduciendo un mecanismo de caché que retiene los tokens más relevantes más allá de la ventana de contexto. Esto permite que la información crítica persista a lo largo de documentos extensos, mejorando así la precisión del modelo.

Este proyecto tiene como objetivos:

1- implementar el modelo de Memorizing Transformers utilizando PyTorch.

2- Comparar el rendimiento de la implementación propia con la implementación oficial.

3- Probar el modelo en conjuntos de datos con características variadas para ver cómo se desempeña en diferentes contextos.

GIS technology gives a wide-ranging support to environmental modeling, especially to groundwater modeling. Numerous GIS applications have been developed to define the hydrogeological conceptual model as well as the numerical model. However, there is a scarcity of tools to implement the conceptual model in numerical modeling platforms. This transition needs of specific methodologies to adapt the geometries and alphanumerical data from the conceptual model to the numerical model to get optimal numerical results. Although most necessities can be satisfied with inherent GIS tools, there are particular steps in the implementation of hydrogeological conceptual model into the numerical modeling software that have not been automatized and put together yet. They are mostly related to finite element mesh generation.

To address this gap, the project aims to enhance and expand a set of computational tools called ArcArAz, which focus on the preprocessing of geometric configurations and parameterization for groundwater numerical models. These tools can also be applied in other environmental sciences. The new version of ArcArAz is expected to be compatible with modern GIS systems, data formats, and computational architectures, optimizing performance to its fullest potential.

This project will be conducted in collaboration with the Universitat Politècnica de Catalunya (UPC), the Institute of Environmental Assessment and Water Research (IDAEA), and the Barcelona Supercomputing Center (BSC).

The application of Artificial Intelligence (AI) and Machine Learning (ML) to network security (AI4SEC) is paramount against cybercrime. While AI/ML is mainstream in domains such as computer vision and natural language processing, traditional AI/ML has produced below-par results in AI4SEC. Solutions do not properly generalize, are ineffective in real deployments, and are vulnerable to adversarial attacks. A fundamental limitation is the lack of AI/ML technology specific to network security. Due to their unique ability to learn and generalize over graph-structured information, graph-learning approaches, and in particular Graph Neural Networks (GNNs), have recently enabled groundbreaking applications in multiple fields where data are generally represented as graphs. Network security data are intrinsically relational, and initial research suggests that graph-structured representations and GNNs have the potential to become foundational to AI4SEC, in the way convolutional and recursive networks were to computer vision and natural language processing.
The goal of GRAPHS4SEC is to leverage graph data representations and modern GNN technology to conceive a new breed of robust GNN-based network security methods which could radically advance the AI4SEC practice. The objectives of GRAPHS4SEC are: (a) to investigate algorithmic methods that facilitate modeling and learning from graph-based network security data; (b) to compare the benefits and overheads of GNN-based AI4SEC to traditional AI/ML in terms of detection performance, generalization, scalability, and robustness against adversarial attacks; (c) to showcase the benefits and improvements of GRAPHS4SEC technology in four critical, real-world network security applications with significant impact for society, considering (in particular) the detection and early mitigation of phishing and fake/malicious websites, a threat among the most popular and society-wide harmful in today's Internet.

La integración de RINA con dispositivos basados en el estándar IEEE 802.15.4 permitirá una gestión más eficiente y segura de las redes de sensores, optimizando el rendimiento y la escalabilidad en aplicaciones de Internet de las Cosas. Este proyecto abordará la adaptación de tramas, gestión de recursos, seguridad y calidad de servicio, proporcionando una solución innovadora que mejorará la interoperabilidad y la funcionalidad de los sistemas IoT. La investigación incluirá el diseño, implementación, pruebas y optimización de la shim, con el objetivo de crear una plataforma robusta que potencie las capacidades de RINA en la gestión de redes de sensores y dispositivos inteligentes. La shim 802.15.4 se integrará con la arquitectura RINAsense, el cual es una librería para el rápido prototipado de sensores RINA.