20 citations · 49 across the 14 of their papers we have counts for
15 papers
The top coefficient of the Links-Gould invariant is Murasugi-sum multiplicative
Cristina Anghel, Daniel López Neumann, Roland van der Veen
We prove that the Laurent polynomial in that is the top coefficient of the Links-Gould invariant of the boundary of a Seifert surface is multiplicative unde…
Green Architectural Tactics in ML-enabled Systems: An LLM-based Repository Mining Study
Vincenzo De Martino, Silverio Martínez-Fernández, Fabio Palomba
Context: The increasing adoption of machine learning (ML) and artificial intelligence (AI) technologies raises growing concerns about their environmental sustainability. Developing…
A Tool for Automatically Cataloguing and Selecting Pre-Trained Models and Datasets for Software Engineering
Alexandra González, Oscar Cerezo, Xavier Franch +1
The rapid growth of machine learning assets has made it increasingly difficult for software engineers to identify models and datasets that match their specific needs. Browsing larg…
SEMODS: A Validated Dataset of Open-Source Software Engineering Models
Alexandra González, Xavier Franch, Silverio Martínez-Fernández
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face…
A Methodological Framework for LLM-Based Mining of Software Repositories
Vincenzo De Martino, Joel Castaño, Fabio Palomba +2
Large Language Models (LLMs) are increasingly used in software engineering research, offering new opportunities for automating repository mining tasks. However, despite their growi…
Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Luís Cruz, João Paulo Fernandes, Maja H. Kirkeby +22
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions.…
Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings
Alexandra González, Xavier Franch, David Lo +1
Context: Open-source Pre-Trained Models (PTMs) provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software E…
Exploring the Role of Women in Hugging Face Organizations
Maria Tubella Salinas, Alexandra González, Silverio Martínez-Fernández
Background: Despite its impact on innovation, gender diversity remains far from fully being achieved in open-source projects. Aims: We examine gender diversity in Hugging Face (HF)…
Insights into resource utilization of code small language models serving with runtime engines and execution providers
Francisco Durán, Matias Martinez, Patricia Lago +1
The rapid growth of language models, particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impa…
Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
Alexandra González, Xavier Franch, David Lo +1
Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailor…
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
Rafiullah Omar, Justus Bogner, Henry Muccini +3
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ense…
Innovating for Tomorrow: The Convergence of SE and Green AI
Luís Cruz, Xavier Franch Gutierrez, Silverio Martínez-Fernández
The latest advancements in machine learning, specifically in foundation models, are revolutionizing the frontiers of existing software engineering (SE) processes. This is a bi-dire…
Estimating Deep Learning energy consumption based on model architecture and training environment
Santiago del Rey, Luís Cruz, Xavier Franch +1
To raise awareness of the environmental impact of deep learning (DL), many studies estimate the energy use of DL systems. However, energy estimates during DL training often rely on…
Energy Efficiency of Training Neural Network Architectures: An Empirical Study
Yinlena Xu, Silverio Martínez-Fernández, Matias Martinez +1
The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational pow…
Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems
Silverio Martínez-Fernández, Xavier Franch, Andreas Jedlitschka +2
Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps bet…