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University of Porto

Academic institutioneurope · pt
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Research library189linked papers
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Selected work

Representative Papers

Multilanguage Detection of Design Pattern Instances

Feb 01, 2025Journal of Software: Evolution and Process

Existing design pattern detection tools are predominantly language-specific, hindering consistent identification of pattern instances across multi-language codebases. To address this, we propose DP-LARA—a cross-language design pattern detection approach built upon the LARA framework and a novel virtual Abstract Syntax Tree (vAST). DP-LARA establishes the first unified, language-agnostic pattern matching paradigm by mapping Java and C/C++ source code to semantically equivalent vAST representations, then applying static analysis augmented with an extensible rule engine for precise and consistent pattern instance identification. Experimental evaluation demonstrates that DP-LARA achieves detection accuracy on par with state-of-the-art single-language tools for both Java and C/C++, while significantly improving cross-language result consistency and reducing language adaptation effort by over 60%. This work introduces a scalable, maintainable paradigm for multi-language software architecture analysis.

20 citations1 influentialRead paper

CitiLink-Minutes: A Multilayer Annotated Dataset of Municipal Meeting Minutes

Feb 12, 2026

This study addresses the scarcity of high-quality annotated data in municipal council minutes, which has hindered the application of natural language processing (NLP) to research on local governance transparency. To bridge this gap, the authors present a multilayer-annotated dataset comprising 120 Portuguese-language municipal meeting records, structured across three dimensions: metadata, agenda items, and voting outcomes. This resource is the first to offer structured interlinking and dual manual annotation, rigorously validated by linguists and anonymized to ensure privacy. Adhering strictly to FAIR principles, the dataset encompasses over one million tokens and more than 38,000 annotations. Accompanied by baseline model performance across multiple NLP tasks, it fills a critical void in local governance text resources and supports the development of downstream applications.

2 citationsRead paper

CitiLink: Enhancing Municipal Transparency and Citizen Engagement through Searchable Meeting Minutes

Jan 26, 2026

This study addresses the challenge posed by lengthy and unstructured municipal meeting minutes, which hinder efficient public access to critical information. To overcome this, the authors propose a novel framework that integrates large language models—such as Gemini—with traditional retrieval techniques. The approach leverages the language model to automatically extract metadata and agenda items, which are then combined with BM25-based full-text search and faceted filtering to create an interactive, searchable, and structured system. Evaluated on 120 meeting minutes from six Portuguese municipalities, user studies demonstrate that the system significantly enhances usability and information retrieval efficiency, with Gemini excelling in the information extraction task. This work offers a scalable technical pathway to improve government transparency and civic engagement.

1 citationsRead paper

The Role of Deep Learning in Financial Asset Management: A Systematic Review

Mar 03, 2025

This study addresses the lack of systematic analysis on the evolution of deep learning (DL) in financial asset management. We conduct a bibliometric and systematic literature review of 612 high-quality empirical studies (2018–2023) indexed in Scopus, focusing on three emerging trends: the integration of eXplainable AI (XAI) with Deep Reinforcement Learning (DRL), Transformer-based hybrid architectures, and fusion of alternative data—including ESG metrics and sentiment signals. Our method identifies critical gaps and synthesizes methodological advances across domains. Key contributions include: (i) proposing the first XAI-DRL joint framework, enhancing both transparency and dynamic adaptability of investment decisions; (ii) empirically validating that alternative-data-driven models achieve simultaneous improvements in interpretability and robustness. Results show DL adoption improves portfolio Sharpe ratios by an average of 22% and reduces mean absolute error in price forecasting by 17–34%.

1 citationsRead paper
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