arXiv subjects

Nuno Fachada

Publications and source records attributed to Nuno Fachada.

At least 19 recordsLinked to original sources

arXiv2026

Can Large Language Models Implement Agent-Based Models? An ODD-based Replication Study

Large language models (LLMs) can now synthesize non-trivial executable code from textual descriptions, raising an important question: can LLMs reliably implement agent-based models from standardized specifications in a way that supports replication, verification, and validation? We address this question by evaluating 17 contemporary LLMs on a controlled ODD-to-code translation task, using the PPHPC predator-prey model as a fully specified reference. Generated Python implementations are assessed through staged executability checks, model-independent statistical comparison against a validated NetLogo baseline, and quantitative measures of runtime efficiency and maintainability. Results show that behaviorally faithful implementations are achievable but not guaranteed, and that executability alone is insufficient for scientific use. GPT-4.1 consistently produces statistically valid and efficient implementations, with Claude 3.7 Sonnet performing well but less reliably. Overall, the findings clarify both the promise and current limitations of LLMs as model engineering tools, with implications for reproducible agent-based and ecological modeling.

cs.SE↗

arXiv2025

Structural Tree Extraction from 3D Surfaces

This paper introduces a method to extract a hierarchical tree representation from 3D unorganized polygonal data. The proposed approach first extracts a graph representation of the surface, which serves as the foundation for structural analysis. A Steiner tree is then generated to establish an optimized connection between key terminal points, defined according to application-specific criteria. The structure can be further refined by leveraging line-of-sight constraints, reducing redundancy while preserving essential connectivity. Unlike traditional skeletonization techniques, which often assume volumetric interpretations, this method operates directly on the surface, ensuring that the resulting representation remains relevant for navigation-aware geometric analysis. The method is validated through two use cases: extracting structural representations from tile-based elements for procedural content generation, and identifying key points and structural metrics for automated level analysis. Results demonstrate its ability to produce simplified, coherent representations, supporting applications in procedural generation, spatial reasoning, and map analysis.

cs.GR↗

arXiv2025

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries

Large Language Models (LLMs) have advanced rapidly as tools for automating code generation in scientific research, yet their ability to interpret and use unfamiliar Python APIs for complex computational experiments remains poorly characterized. This study systematically benchmarks a selection of state-of-the-art LLMs in generating functional Python code for two increasingly challenging scenarios: conversational data analysis with the \textit{ParShift} library, and synthetic data generation and clustering using \textit{pyclugen} and \textit{scikit-learn}. Both experiments use structured, zero-shot prompts specifying detailed requirements but omitting in-context examples. Model outputs are evaluated quantitatively for functional correctness and prompt compliance over multiple runs, and qualitatively by analyzing the errors produced when code execution fails. Results show that only a small subset of models consistently generate correct, executable code. GPT-4.1 achieved a 100\% success rate across all runs in both experimental tasks, whereas most other models succeeded in fewer than half of the runs, with only Grok-3 and Mistral-Large approaching comparable performance. In addition to benchmarking LLM performance, this approach helps identify shortcomings in third-party libraries, such as unclear documentation or obscure implementation bugs. Overall, these findings highlight current limitations of LLMs for end-to-end scientific automation and emphasize the need for careful prompt design, comprehensive library documentation, and continued advances in language model capabilities.

cs.SE↗

arXiv2025

Closing the Loop in Affect-Driven Game Adaptation: A Systematic Review

Recognizing player state is only one component of affective game adaptation; inferred experience must also be translated into adaptive interventions that modify gameplay or game content. Although player experience modeling and content adaptation are established research areas, fewer studies examine how sensing, modeling, and adaptation are integrated into complete, empirically evaluated gameplay systems. This PRISMA-guided systematic review analyzes 23 empirical studies published from January 1, 2015, to December 31, 2025, that implement a complete experience-driven loop defined here as the combination of player data acquisition, player experience modeling, and adaptive game content. Complete-loop systems were relatively uncommon in the retrieved corpus, and the selected systems were predominantly oriented toward dynamic difficulty adjustment, engagement, rehabilitation, or performance-related goals. Game telemetry was the dominant input modality, while non-invasive sources with affective relevance, such as facial expression analysis and peripheral interaction data, were less common. Knowledge-based methods, including rule-based systems and heuristics, dominated both modeling and adaptation because of their interpretability and low deployment requirements, whereas machine learning approaches were less frequent and remained constrained by data availability, transparency, and runtime integration challenges. Most importantly, affective information was often used to support challenge calibration or related adaptation objectives, while stress, anxiety, horror, and related affective states were rarely addressed as explicit adaptation targets. These findings identify a gap within this review scope: affective information may enter an adaptive loop without making affective state the objective of adaptation.

cs.HC↗

arXiv2025

DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks

This paper investigates the performance of 16 Large Language Models (LLMs) in automating LoRaWAN-related engineering tasks involving optimal placement of drones and received power calculation under progressively complex zero-shot, natural language prompts. The primary research question is whether lightweight, locally executed LLMs can generate correct Python code for these tasks. To assess this, we compared locally run models against state-of-the-art alternatives, such as GPT-4 and DeepSeek-V3, which served as reference points. By extracting and executing the Python functions generated by each model, we evaluated their outputs on a zero-to-five scale. Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models -- particularly Phi-4 and LLaMA-3.3 -- also demonstrated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues. These findings illustrate the potential of LLM-based approaches for specialized engineering applications while highlighting the need for careful model selection, rigorous prompt design, and targeted domain fine-tuning to achieve reliable outcomes.

cs.SE↗

arXiv2025

Games! What are they good for? The Struggle of Serious Game Adoption for Rehabilitation

The field of serious games for health has grown significantly, demonstrating effectiveness in various clinical contexts such as stroke, spinal cord injury, and degenerative neurological diseases. Despite their potential benefits, therapists face barriers to adopting serious games in rehabilitation, including limited training and game literacy, concerns about cost and equipment availability, and a lack of evidence-based research on game effectiveness. Serious games for rehabilitation often involve repetitive exercises, which can be tedious and reduce motivation for continued rehabilitation, treating clients as passive recipients of clinical outcomes rather than players. This study identifies gaps and provides essential insights for advancing serious games in rehabilitation, aiming to enhance their engagement for clients and effectiveness as a therapeutic tool. Addressing these challenges requires a paradigm shift towards developing and co-creating serious games for rehabilitation with therapists, researchers, and stakeholders. Furthermore, future research is crucial to advance the development of serious games, ensuring they adhere to evidence-based principles and engage both clients and therapists. This endeavor will identify gaps in the field, inspire new directions, and support the creation of practical guidelines for serious games research.

cs.HC↗

arXiv2024

Generating 3D Terrain with 2D Cellular Automata

This paper explores the use of 2D cellular automata (CA) to generate 3D terrains through a simple additive approach. Experimenting with multiple CA transition rules produced aesthetically interesting, navigable landscapes, suggesting applicability for terrain generation in games.

nlin.CG↗

arXiv2024

Raster Forge: Interactive Raster Manipulation Library and GUI for Python

Raster Forge is a Python library and graphical user interface for raster data manipulation and analysis. The tool is focused on remote sensing applications, particularly in wildfire management. It allows users to import, visualize, and process raster layers for tasks such as image compositing or topographical analysis. For wildfire management, it generates fuel maps using predefined models. Its impact extends from disaster management to hydrological modeling, agriculture, and environmental monitoring. Raster Forge can be a valuable asset for geoscientists and researchers who rely on raster data analysis, enhancing geospatial data processing and visualization across various disciplines.

eess.IV↗

arXiv2024

Data Science for Geographic Information Systems

The integration of data science into Geographic Information Systems (GIS) has facilitated the evolution of these tools into complete spatial analysis platforms. The adoption of machine learning and big data techniques has equipped these platforms with the capacity to handle larger amounts of increasingly complex data, transcending the limitations of more traditional approaches. This work traces the historical and technical evolution of data science and GIS as fields of study, highlighting the critical points of convergence between domains, and underlining the many sectors that rely on this integration. A GIS application is presented as a case study in the disaster management sector where we utilize aerial data from Tr\'oia, Portugal, to emphasize the process of insight extraction from raw data. We conclude by outlining prospects for future research in integration of these fields in general, and the developed application in particular.

eess.IV↗

arXiv2024

Text Clustering with Large Language Model Embeddings

Text clustering is an important method for organising the increasing volume of digital content, aiding in the structuring and discovery of hidden patterns in uncategorised data. The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. This study argues that recent advancements in large language models (LLMs) have the potential to enhance this task. The research investigates how different textual embeddings, particularly those utilised in LLMs, and various clustering algorithms influence the clustering of text datasets. A series of experiments were conducted to evaluate the impact of embeddings on clustering results, the role of dimensionality reduction through summarisation, and the adjustment of model size. The findings indicate that LLM embeddings are superior at capturing subtleties in structured language. OpenAI's GPT-3.5 Turbo model yields better results in three out of five clustering metrics across most tested datasets. Most LLM embeddings show improvements in cluster purity and provide a more informative silhouette score, reflecting a refined structural understanding of text data compared to traditional methods. Among the more lightweight models, BERT demonstrates leading performance. Additionally, it was observed that increasing model dimensionality and employing summarisation techniques do not consistently enhance clustering efficiency, suggesting that these strategies require careful consideration for practical application. These results highlight a complex balance between the need for refined text representation and computational feasibility in text clustering applications. This study extends traditional text clustering frameworks by integrating embeddings from LLMs, offering improved methodologies and suggesting new avenues for future research in various types of textual analysis.

cs.CL↗

arXiv2023

Multispectral Indices for Wildfire Management

The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional ground-based techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This study investigates the use of multispectral aerial and satellite imagery for wildfire management through an assessment of current literature and two practical case studies. We evaluate several multispectral indices for their ability to extract environmental features critical for analyzing wildfire behavior, including vegetation, water bodies, and artificial structures. Our results highlight NVDI for vegetation, MNDWI for water features, and MSR for artificial structures as particularly effective for segmentation and feature extraction. The application of these indices enhances wildfire data processing and supports improved monitoring, risk assessment, and response strategies, demonstrating the potential of multispectral imagery to complement traditional wildfire monitoring and management approaches.

eess.IV↗

arXiv2023

MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification

This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.

cs.CL↗

arXiv2023

Generating Multidimensional Clusters With Support Lines

Synthetic data is essential for assessing clustering techniques, complementing and extending real data, and allowing for more complete coverage of a given problem's space. In turn, synthetic data generators have the potential of creating vast amounts of data -- a crucial activity when real-world data is at premium -- while providing a well-understood generation procedure and an interpretable instrument for methodically investigating cluster analysis algorithms. Here, we present Clugen, a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions. Clugen is open source, comprehensively unit tested and documented, and is available for the Python, R, Julia, and MATLAB/Octave ecosystems. We demonstrate that our proposal can produce rich and varied results in various dimensions, is fit for use in the assessment of clustering algorithms, and has the potential to be a widely used framework in diverse clustering-related research tasks.

cs.LG↗

arXiv2022

Procedural Generation of 3D Maps with Snappable Meshes

In this paper we present a technique for procedurally generating 3D maps using a set of premade meshes which snap together based on designer-specified visual constraints. The proposed approach avoids size and layout limitations, offering the designer control over the look and feel of the generated maps, as well as immediate feedback on a given map's navigability. A prototype implementation of the method, developed in the Unity game engine, is discussed, and a number of case studies are analyzed. These include a multiplayer game where the method was used, together with a number of illustrative examples which highlight various parameterizations and piece selection methods. The technique can be used as a designer-centric map composition method and/or as a prototyping system in 3D level design, opening the door for quality map and level creation in a fraction of the time of a fully human-based approach.

cs.AI↗

arXiv2021

ColorShapeLinks: A board game AI competition for educators and students

ColorShapeLinks is an AI board game competition framework specially designed for students and educators in videogame development, with openness and accessibility in mind. The competition is based on an arbitrarily-sized version of the Simplexity board game, the motto of which, "simple to learn, complex to master", is curiously also applicable to AI agents. ColorShapeLinks offers graphical and text-based frontends and a completely open and documented development framework built using industry standard tools and following software engineering best practices. ColorShapeLinks is not only a competition, but both a game and a framework which educators and students can extend and use to host their own competitions. It has been successfully used for running internal competitions in AI classes, as well as for hosting an international AI competition at the IEEE Conference on Games.

cs.CY↗

arXiv2017

cf4ocl: a C framework for OpenCL

OpenCL is an open standard for parallel programming of heterogeneous compute devices, such as GPUs, CPUs, DSPs or FPGAs. However, the verbosity of its C host API can hinder application development. In this paper we present cf4ocl, a software library for rapid development of OpenCL programs in pure C. It aims to reduce the verbosity of the OpenCL API, offering straightforward memory management, integrated profiling of events (e.g., kernel execution and data transfers), simple but extensible device selection mechanism and user-friendly error management. We compare two versions of a conceptual application example, one based on cf4ocl, the other developed directly with the OpenCL host API. Results show that the former is simpler to implement and offers more features, at the cost of an effectively negligible computational overhead. Additionally, the tools provided with cf4ocl allowed for a quick analysis on how to optimize the application.

cs.DC↗

arXiv2017

Parallelization Strategies for Spatial Agent-Based Models

Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. Large scale emergent behavior in ABMs is population sensitive. As such, the number of agents in a simulation should be able to reflect the reality of the system being modeled, which can be in the order of millions or billions of individuals in certain domains. A natural solution to reach acceptable scalability in commodity multi-core processors consists of decomposing models such that each component can be independently processed by a different thread in a concurrent manner. In this paper we present a multithreaded Java implementation of the PPHPC ABM, with two goals in mind: 1) compare the performance of this implementation with an existing NetLogo implementation; and, 2) study how different parallelization strategies impact simulation performance on a shared memory architecture. Results show that: 1) model parallelization can yield considerable performance gains; 2) distinct parallelization strategies offer specific trade-offs in terms of performance and simulation reproducibility; and, 3) PPHPC is a valid reference model for comparing distinct implementations or parallelization strategies, from both performance and statistical accuracy perspectives.

cs.DC↗

arXiv2017

micompr: An R Package for Multivariate Independent Comparison of Observations

The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations.

cs.MS↗