RCIPL
Repositório Institucional do Politécnico de Lisboa
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Advanced function composition in serverless platforms
Publication . Silva, Tiago Luís Lima da; Freitas, Filipe Bastos de; Simão, José Manuel de Campos Lages Garcia
Abstract
Serverless computing, particularly Function-as-a-Service (FaaS) platforms, allows developers to focus on the software engineering aspects of their services without managing the underlying infrastructure. These platforms rely on stateless functions that are triggered by events, making them a common choice for workflows and function composition. However, despite their advantages, serverless workflows often require developers to meet provider-specific requirements, leading to portability challenges and vendor lock-in. Previous work has attempted to address these limitations. The QuickFaaS project demonstrated the importance of standardizing function definitions across platforms to create a uniform programming model. Building on this, the OmniFlow project introduced a Domain-Specific Language (DSL) that enables developers to define serverless workflows in a provider-agnostic manner, allowing them to be reused across different cloud environments without modification. This work extends OmniFlow by introducing additional capabilities that enhance serverless workflow execution and function composition. The proposed enhancements include
control flow-based workflow execution for repetitive tasks, enabling the definition of iterations within their workflows without relying on provider-specific construct. In addition, it also introduces support for parallel execution, allowing workflows to scale efficiently. By leveraging parallel processing, serverless applications can execute independent tasks concurrently, improving performance and reducing execution time. Additionally, this research explores cross-cloud function composition, ensuring that workflows can seamlessly integrate functions across multiple cloud providers, to mitigate vendor lock-in and allow developers to optimize performance by leveraging the strengths of different platforms while maintaining a unified workflow definition. The proposed enhancements provide a more flexible, scalable, and portable approach to serverless workflow orchestration, enabling developers to build complex workflows that are not constrained by the limitations of individual cloud providers.
Previsão de crescimento de fendas de fadiga utilizando elementos finitos e inteligência artificial
Publication . Ferra, Tiago Henrique da Cruz; Baptista, Ricardo Miguel Gomes Simões
A análise da trajetória de propagação de fendas de fadiga constitui um desafio relevante na área da integridade estrutural, devido à complexidade associada à simulação numérica ou à experimentação. Em particular, o estudo de componentes sujeitos a carregamentos biaxiais revela-se ainda mais exigente, uma vez que envolve a aplicação simultânea e independente de forças em direções perpendiculares, aumentando significativamente a variabilidade dos cenários possíveis. Neste trabalho, foi estudada a propagação de fissuras em provetes cruciformes sob carregamentos cíclicos biaxiais em fadiga, combinando metodologias numéricas baseadas no Método dos Elementos Finitos (FEM) com a aplicação de técnicas de Inteligência Artificial (AI), nomeadamente redes neuronais artificiais (ANN). Para a geração dos dados de treino da rede, recorreu-se a simulações numéricas que permitiram determinar os fatores de intensidade de tensão para fendas com diferentes geometrias, comprimentos e orientações. Uma vez treinada, a rede mostrou-se capaz de prever, de forma quase imediata, os fatores de intensidade de tensão necessários ao cálculo da direção e da taxa de crescimento da fenda. Os resultados obtidos pela ANN foram comparados com as trajetórias de propagação calculadas exclusivamente por FEM. A análise dos desvios entre coordenadas, calculados através da raiz quadrada do erro médio quadrático (RMSE), revelaram um erro mínimo de 0,0006 mm e um erro máximo de 2,1890 mm, sendo o erro médio de 0,3613 mm. Assim, a abordagem híbrida FEM–IA revelou-se vantajosa pela sua elevada eficiência computacional, mantendo um nível de precisão adequado à previsão do comportamento em fadiga. Assim, os modelos baseados em redes neuronais demonstraram ser uma ferramenta promissora de apoio à análise estrutural, contribuindo para reduzir significativamente o tempo e os recursos necessários em estudos de propagação de fendas em regimes de carregamento complexo.
Centralized ledger system for document and process certification
Publication . Bartolomeu, Nuno António Oliveira; Leite, Nuno Miguel da Costa de Sousa; Pereira, João Miguel de Carvalho da Conceição
Abstract
Organizations increasingly require secure document management with integrity guarantees beyond traditional audit logs, particularly in regulated industries where external accountability is critical. While blockchain technologies provide strong tamper-detection, they present significant enterprise adoption challenges including cost volatility, low throughput, and unpredictable operational expenses. This thesis proposes a Centralized Ledger System (CLS) that provides blockchain-inspired integrity verification through self-hosted architecture without external dependencies. The system implements a three-phase entry lifecycle, signature collection and verification, supporting multi-party transactions with asynchronous workflows. A multi-ledger architecture enables organizational segregation of business domains while maintaining referential integrity. Key contributions include automated receipt generation for independent verification, selective payload erasure preserving cryptographic validation, entry linking for audit simplification, and integration of security services with two-factor authentication and key management. The modular design enables flexible deployment while maintaining cryptographic guarantees equivalent to blockchain systems. The solution addresses the gap between traditional audit systems and distributed ledgers by providing cost-predictable, vendor-independent functionality that integrates into existing workflows without specialized blockchain expertise.
Exam monitoring platform
Publication . Jorge, José Eduardo Leitão; Carvalho, Fernando Miguel Santos Gamboa Lopes de
Abstract
The increase in fraud in in-person exams, facilitated by Artificial Intelligence (AI) tools, represents a significant challenge to academic integrity. At the Instituto Superior de Engenharia de Lisboa (ISEL), as in other universities and schools, this problem has intensified in recent years. In response, this master’s thesis proposes the development of a secure and efficient web solution, the Exam Management Platform (EMP) and the Student Exam Plugin (SEP). The EMP is a web application for teachers, which allows for the management of exams and real time supervision. The SEP is an extension for Google Chrome, installed on the student’s computer, that monitors their actions during the exam. The solution uses students’ laptops and the Google Chrome browser, eliminating the need to install third-party software and reducing infrastructure costs. Its functionalities include user authentication, real-time monitoring of activities such as access to unauthorised Uniform Resource Locators (URLs), changes in window focus, browser resizing, right-clicks, and text selection. It also includes a notification system to alert supervisors to irregularities. All actions are logged for post-exam analysis. The system’s architecture integrates front-end and back-end technologies, communicating via RESTful Application Programming Interfaces (APIs) and Server-Sent Events (SSE) for real-time updates. Security is reinforced by Hypertext Transfer Protocol Secure (HTTPS), TwoFactor Authentication (2FA), authorisation tokens, and hash validation to detect plugin tampering. Tests, including scenarios in a controlled environment, demonstrated the system’s effectiveness in detecting and logging events, ensuring bidirectional communication, and verifying the plugin’s integrity. Although limited to Google Chrome and dependent on network connectivity, the system constitutes a practical response to ensure the integrity of in-person assessments.
A topic modelling-based recommender system for drugs using user experience reviews [TopicDrugRec]
Publication . Carvalho, Rafael Reis de; Pato, Matilde Pós-de-Mina; Datia, Nuno Miguel Soares
Abstract
The increasing volume of patient-reported data, alongside the rise of personalised medicine has made it challenging for healthcare professionals to incorporate patient experiences into their clinical decision-making due to information overload and demanding working shifts. Much of this data is available in the form of numerical drug ratings, which often fail to capture the complexity of user experiences by lacking contextual information and response bias. To address this, this dissertation proposes TopicDrugRec, a drug recommender system based on topic modelling and trained on the UCI ML Drug Review dataset, designed to support clinicians in providing safer and more personalised drug prescriptions. It follows a six step methodology: first, exploratory data analysis, data cleaning, followed by sentiment analysis to mitigate rating bias, topic modelling to extract latent themes from patient reports in the form of free text, integration of medical knowledge (drugdrug interactions, side effects and contraindications) to enhance patient safety, and the implementation of a web application and performance evaluation. The recommendation algorithm was designed to incorporate topic similarity, user sentiment, and perceived usefulness, allowing for tunable hyperparameters to generate the recommendations. Three topic modelling approaches were evaluated: Latent Dirichlet Allocation, Non-negative Matrix Factorization, and BERTopic. The evaluation showed semantic similarity, derived from topic modelling, to be the most influential factor in recommendation quality. Additionally, grouping medical conditions into ICD-11 categories mitigated dataset imbalanced and improved coverage, with the NMF-based model achieving the best performance on this setup, with a Precision@10 of 0.513 and Mean Reciprocal Rank @10 of 0.676. Despite being a proof-of-concept, these findings demonstrate TopicDrugRec’s potential in reducing information overload, enhancing medic-patient interaction and integrating patient feedback into data-driven decision-making. Additionally, it lays foundation for future work, including real world validation, curating more complex datasets with patient information, and providing explainable recommendations.