Anúncios
Prova de CAT da aluna Patrícia Isabel Figueira da Piedade
30 agosto 2024, 14:54 • Sandra Espírito Santo
Prova de CAT da aluna Patrícia Isabel Figueira da Piedade
Título da Tese : Neuroqueer(ing) Public Space: A Participatory Approach to Designing Playful Urban Technologies with and for Neurodiversity
Serão realizadas no dia 23 de janeiro, às 10.00 Horas
Sala V0.15
Thesis Abstract
Access to public spaces is of the utmost importance for social cohesion, inclusion, and civic engagement, yet these environments often present significant accessibility and comfort challenges for neurodivergent individuals. Moreover, while technology is becoming evermore present within them, the research topic of public space technologies for neurodivergent individuals remains underexplored and tied to techno-abelist constructs. Grounded in Neuroqueer Technosience, this proposal examines how playful, interactive technologies might promote joyful and meaningful engagement with public spaces for neurodivergent individuals. Through a Participatory Action Research approach, we will collaborate with neurodiverse (i.e., neurodivergent and neurotypical) stakeholders to collectively investigate current experiences, co-design novel technologies, and evaluate their impact on comfort, enjoyment, and accessibility. The research objectives are to (1) deepen understanding of neurodivergent experiences in public space, (2) innovate new forms of technology-enhanced playful engagement with public spaces, and (3) assess the impact of these technologies across neurodiverse user groups. Ultimately, the project aims to drive sustainable positive change, demonstrating the potential of co-designed playful urban technologies to foster neuro-inclusive public spaces.
Orientação: Hugo Miguel Aleixo Albuquerque Nicolau
Co-orientação:
Co-orientação; Rui Filipe Fernandes Prada
Anna Carter
Prova de CAT aluno André Alves Augusto
26 julho 2024, 15:55 • Sandra Espírito Santo
Prova de CAT aluno André Alves Augusto
Título da Tese : Safeguarding Blockchain Interoperability Mechanisms
Local: reserva da sala José Tribolet, (020) no Pavilhão de Informática II, para a realização de provas de CAT do aluno André Alves Augusto
no dia 16 de setembro, às 11.00.
De forma a contemplar solicitação de membro do júri, iremos passar a provas de CAT do André Augusto para Zoom.
Serão realizadas em https://videoconf-colibri.zoom.us/j/94893092762?pwd=qRhsSTcXBEKajgaS9lHlyYgbVySWOy.1
Thesis Abstract
The widespread adoption of blockchain technology has resulted in a transformative era in financial services and computing systems, facilitating the emergence of groundbreaking use cases such as decentralized finance (DeFi). As blockchain ecosystems expand, the need for interoperability across chains becomes increasingly evident, driving the development of cross-chain solutions to bridge these networks. However, the interoperability between disparate blockchain networks remains a pressing concern, with vulnerabilities in cross-chain protocols exposing systems to potential breaches and attacks. Since June 2021, cross-chain protocols have lost more than 3.2 billion USD. This highlights the relevance and timeliness of this work. In this context, this thesis proposal addresses the critical challenge of securing cross-chain transactions against malicious actors. The first contribution is a Systematization of Knowledge (SoK), which dissects existing literature and synthesizes knowledge on the security and privacy of blockchain interoperability solutions. Through a systematic literature review, we identify key properties and approaches for secure and private cross-chain transactions. It identifies vulnerabilities and attack vectors prevalent in cross-chain bridges, laying the groundwork for robust defense mechanisms. This proposal introduces innovative approaches to enhance the resiliency of cross-chain protocols against the risks associated with hacking attempts. One such approach involves leveraging Maximal Extractable Value (MEV) strategies as a defense mechanism, wherein deviations from a cross-chain model trigger proactive measures to destructively front-run attacker transactions. Furthermore, this document proposes the use of machine learning models fueled by historical data to predict and prevent cross-chain hacks. By analyzing behavioral patterns exhibited by attackers before engaging with protocols, these models can identify and flag potentially malicious transactions in real-time, bolstering the security of cross-chain protocols. Additionally, the research explores novel methods for streamlining cross-chain hack response using program repair techniques. By delving into the source code of compromised protocols and compiling datasets of vulnerabilities, bridge operators can expedite the resolution process and minimize downtime following an attack, thus enhancing the resilience of cross-chain ecosystems.
Prova de CAT Daniel Rosa Ramos
8 maio 2024, 15:23 • Sandra Espírito Santo
PROVA DE CAT
Título da Tese : Towards Automated API Refactoring for Evolving Codebases
Local: Sala 336 do INESC-ID ou via zoom no link de ZOOM: https://cmu.zoom.us/j/91880772800?pwd=K2RZQS9kR0FndDdUODJhekM3RFZVUT09
Data : 10 de MAIO de 2024
THESIS ABSTRACT :
Modern software development heavily relies on third-party libraries and frameworks, which yield significant productivity gains. Libraries expose functionality through Application Programming Interfaces (APIs). Although stable API selection is desirable, it is often not possible, as software must adapt to new technical requirements or shifts in stakeholder or market demands. Therefore, as libraries evolve, clients may need to migrate APIs to adapt to these changes. The task of adapting APIs to accommodate non-functional changes is a form of software refactoring, a crucial practice in software engineering. Refactoring entails modifying code to improve its quality and reduce complexity. However, refactoring is typically labor-intensive and prone to errors. The complexity of API refactoring has spurred numerous research efforts towards automating this task. A widely used method for automating API refactoring is to generate match-replace rules by mining vast amounts of data from client projects of the libraries sourced from collaborative coding platforms like GitHub. However, a significant challenge with mining approaches is their limited effectiveness due to reliance on data from clients that have already undergone refactoring, which is often scarce. In this thesis proposal, we explore novel methods for automated API refactoring that do not rely on extensive training data or specific refactoring examples from client projects. In particular, we explore three alternative data sources. First, we use API documentation to discover API mappings, which we use to both generate migration rules and as a heuristic to guide the migration process. Specifically, the API mappings are used as a heuristic to guide a program synthesis approach to migrate client code effectively and reliably. Second, we use the API development process, particularly library pull requests, to learn API migration rules for addressing breaking changes. Our core idea is that if a library changes functionality, its tests, and internal usages will likely change as well, providing a rich data source for generating migration rules. Third, we exploit natural language, as software is enriched with an abundance of natural language data, including commit messages, issue reports, and comments. We use this unstructured data to test equivalence between API usages by synthesizing pairs of code examples in the source and target libraries. Our goal is to then abstract these code examples to generate broadly applicable migration scripts. So far, we have implemented our ideas as proof of concepts in two automated refactoring tools, as well as a language and toolset for expressing API refactorings. Our proof-of-concept tools leverage state-of-the-art program synthesis and machine learning techniques, which are crucial for establishing API mappings, synthesizing migration scripts, and migrating client code directly. We evaluated the two tools on real datasets by migrating client programs found on collaborative coding platforms. Our ongoing research aims to automatically generate training examples from natural language and documentation, which we will then use to generate migration scripts for libraries where migration data is scarce.