Percorrer por autor "Fachada, Nuno"

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    Automated Generation of Map Pieces for Snappable Meshes
    (ACM, 2023-04-12) Andrade, Diogo de; Fachada, Nuno
    Snappable Meshes is an algorithm that procedurally generates 3D environments by iteratively selecting and linking pre-built map pieces. These pieces are triangular meshes annotated by designers with connectors marking potential links, and bounding volumes indicating where overlaps should be avoided. In this article, we present a method for automatically generating connectors and bounding volumes from generic non-manifold triangular meshes for use with the Snappable Meshes algorithm, minimizing artist/designer work, while encouraging iteration of map piece design, an essential part of successful environment generation.
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    Biofeedback Game Design
    (2018) Luz, Filipe Costa; Fachada, Nuno; Junior, Roberto; ECATI - School of Communication, Architecture, Arts and Information Technologies
    This project aims to explore the psychological effect of the player during gaming and how reciprocally game designers can manipulate the sensations of the player through biofeedback information (physiological sensors) and implementation of certain game mechanics.
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    cf4ocl: A C framework for OpenCL
    (Elsevier B.V., 2017) Fachada, Nuno; Lopes, Vitor V.; Martins, Rui C.; Rosa, Agostinho C.; ECATI - School of Communication, Architecture, Arts and Information Technologies
    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.
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    Closing the loop : a systematic review of experience-driven game adaptation
    (2025-05-02) Lopes, Phil; Fachada, Nuno; Fonseca, Maria; ECATI - School of Communication, Architecture, Arts and Information Technologies; HEI-LAB - Human Environment Interaction Lab; COPELABS - Cognitive and People-centric Computing
    Adaptive game systems aim to enrich player experiences by dynamically adjusting game content in response to user data. While extensive research has addressed content personalization and player experience modeling, the integration of these components into fully operational adaptive gameplay systems remains limited. This systematic review, conducted in accordance with PRISMA guidelines, analyzes 17 empirical studies published between January 2015 and May 2024, identifying and analyzing approaches that implement the complete experience-driven loop -- including player sensing, modeling, and content adaptation. Game telemetry remains the most prevalent sensing modality, although other non-invasive methods suitable for affective modeling -- such as facial expression analysis (FEA) and peripheral interaction data -- remain underutilized despite their potential for real-time emotional inference. Knowledge-based methods, such as rule-based systems and heuristics, dominate modeling and adaptation due to their interpretability and low resource demands, whereas machine learning approaches face challenges related to data availability and transparency. Despite their relevance to immersive and therapeutic experiences, affective states such as stress and anxiety remain largely ignored, as systems continue to favor performance over emotion-sensitive adaptation. These findings highlight a crucial research direction: advancing emotionally responsive game systems that move beyond performance optimization by incorporating underutilized sensing modalities -- such as FEA and peripheral interaction -- to enable real-time affect-driven personalization. Advancing in this direction holds strong potential to increase immersion, personalize gameplay, and support affect regulation across entertainment and therapeutic contexts.
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    ColorShapeLinks: A board game AI competition for educators and students
    (Elsevier, 2021-02-23) Fachada, Nuno; ECATI - School of Communication, Architecture, Arts and Information Technologies
    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.
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    A computational pipeline for modeling and predicting wildfire behavior
    (Science and Technology Publications, Lda, 2022) Fachada, Nuno; Xie, Min; Behringer, Reinhold; Chang, Victor; COPELABS - Cognitive and People-centric Computing
    Wildfires constitute a major socioeconomic burden. While a number of scientific and technological methods have been used for predicting and mitigating wildfires, this is still an open problem. In turn, agent-based modeling is a modeling approach where each entity of the system being modeled is represented as an independent decision-making agent. It is a useful technique for studying systems that can be modeled in terms of interactions between individual components. Consequently, it is an interesting methodology for modeling wildfire behavior. In this position paper, we propose a complete computational pipeline for modeling and predicting wildfire behavior by leveraging agent-based modeling, among other techniques. This project is to be developed in collaboration with scientific and civil stakeholders, and should produce an open decision support system easily extendable by stakeholders and other interested parties. Keywords: Agent-based Modeling, High-performance Computing, Computational Intelligence, Verification and Validation, Wildfires.
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    Desafios no Ensino de Programação a Alunos de Videojogos
    (Sociedade Portuguesa de Ciências dos Videojogos, 2020-05) Fachada, Nuno; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
    As unidades curriculares (UCs) de programação num curso de Videojogos devem ter dois objetivos: 1) um objetivo mais geral, que consiste em fornecer aos alunos as bases que lhes permitam assimilar conceitos gerais de programação, matemática e física, bem como desenvolver o seu pensamento lógico e algorítmico; e, 2) um objetivo mais específico, que consiste na aprendizagem das ferramentas e conceitos concretos que permitam aos alunos trabalhar de forma fluída no game engine de eleição do curso. Este último objetivo enquadra as UCs de programação numa lógica top-down, pois a seleção do game engine guia a forma como os respetivos programas são preparados. Dentro desta perspetiva, as UCs de Programação devem alimentar e ter em vista possíveis colaborações com as restantes UCs, em especial as de game development puro. De forma transversal, os exemplos de aula e projetos de avaliação devem estar devidamente adaptados aos alunos em questão, de modo a tornar a exposição das matérias o mais apelativa possível. Neste documento discutiremos a forma como estes desafios estão a ser abordados na Licenciatura em Videojogos da Universidade Lusófona de Humanidades e Tecnologias.
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    Drop Project : an automatic assessment tool for programming assignments
    (Elsevier B.V., 2022-06-01) Cipriano, Bruno Pereira; Fachada, Nuno; Alves, Pedro; COPELABS - Cognitive and People-centric Computing
    Automated assessment tools (AATs) are software systems used in teaching environments to automate the evaluation of computer programs implemented by students. These tools can be used to stimulate the interest of computer science students in programming courses by providing quick feedback on their work and highlighting their mistakes. Despite the abundance of such tools, most of them are developed for a specific course and are not production-ready. Others lack advanced features that are required for certain pedagogical goals (e.g. Git integration) and/or are not flexible enough to be used with students having different computer literacy levels, such as first year and second year students. In this paper we present Drop Project (DP), an automated assessment tool built on top of the Maven build automation software. We have been using DP in our teaching activity since 2018, having received more than fifty thousand submissions between projects, classroom exercises, tests and homework assignments. The tool’s automated feedback has allowed us to raise the difficulty level of the course’s projects, while the grading process has become more efficient and consistent between different teachers. DP is an extensively tested, production-ready tool. The software’s code and documentation are available in GitHub under an open-source software license. Keywords: Automated assessment ;Computer science education; Programming education ; Unit testing
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    Enlarged PLIN5-uncoated lipid droplets in inner regions of skeletal muscle type II fibers associate with type 2 diabetes
    (Elsevier GmbH, 2022-04) Fachada, Vasco; Rahkila, Paavo; Fachada, Nuno; Turpeinen, Tuomas; Kujala, Urho M.; Kainulainen, Heikki; COPELABS - Cognitive and People-centric Computing
    Skeletal muscle physiology remains of paramount importance in understanding insulin resistance. Due to its high lipid turnover rates, regulation of intramyocellular lipid droplets (LDs) is a key factor. Perilipin 5 (PLIN5) is one of the most critical agents in such regulation, being often referred as a protector against lipotoxicity and consequent skeletal muscle insulin resistance. We examined area fraction, size, subcellular localization and PLIN5 association of LDs in two fiber types of type 2 diabetic (T2D), obese (OB) and healthy (HC) individuals by means of fluorescence microscopy and image analysis. We found that T2D type II fibers have a significant sub-population of large and internalized LDs, uncoated by PLIN5. Based on this novel result, additional hypotheses for the pathophysiology of skeletal muscle insulin resistance are formulated, together with future research directions.
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    Fun Maths for All Game Development Students
    (Association for Computing Machinery, 2020-06-15) Fachada, Nuno; Andrade, Diogo Nuno Dias Mesquita Gomes de; COPELABS - Cognitive and People-centric Computing
    We present an approach for teaching maths to game development undergraduates from a wide variety of backgrounds. We discuss the concepts behind an introductory maths course, its assessment and put forward some preliminary results on a practical implementation.
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    Games! What are they good for? : the struggle of serious game adoption for rehabilitation
    (2025-01-12) Fonseca, Maria Micaela; Fachada, Nuno; Sousa, Micael; Oliveira, Jorge; Rodrigues, Pedro; Sousa, Sara; Quaresma, Claudia; Lopes, Phil; HEI-LAB - Human Environment Interaction Lab; ECATI - School of Communication, Architecture, Arts and Information Technologies; COPELABS - Cognitive and People-centric Computing
    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.
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    generateData—A 2D data generator
    (Elsevier B.V., 2020-05) Fachada, Nuno; Rosa, Agostinho C.; ECATI - School of Communication, Architecture, Arts and Information Technologies
    generateData is a MATLAB/Octave function for generating 2D data clusters. Data is created along straight lines, which can be more or less parallel depending on the selected input parameters. The function also allows to fine-tune the generated data with respect to number of clusters, total data points, average cluster separation and several other distributional properties.
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    Generating 3D Terrain with 2D Cellular Automata
    (2024-06-01) Fachada, Nuno; Rodrigues, António R.; de Andrade, Diogo; Lopes, Phil; COPELABS - Cognitive and People-centric Computing; HEI-LAB - Human Environment Interaction Lab
    This paper presents an initial exploration on the use of 2D cellular automata (CA) for generating 3D terrains through a simple yet effective additive approach. By experimenting with multiple CA transition rules, this preliminary investigation yielded aesthetically interesting landscapes, hinting at the technique's potential applicability for real-time terrain generation in games.
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    Generating multidimensional clusters with support lines
    (Elsevier B.V., 2023) Fachada, Nuno; de Andrade, Diogo; COPELABS - Cognitive and People-centric Computing
    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. Keywords: Synthetic data, Clustering, Data generation, Multidimensional data
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    Generative art with swarm landscapes
    (Multidisciplinary Digital Publishing Institute (MDPI), 2020) Fachada, Nuno; Fernandes, Carlos M.; Rosa, Agostinho C.; Andrade, Diogo Nuno Dias Mesquita Gomes de; HEI-LAB - Human Environment Interaction Lab; ECATI - School of Communication, Architecture, Arts and Information Technologies
    We present a generative swarm art project that creates 3D animations by running a Particle Swarm Optimization algorithm over synthetic landscapes produced by an objective function. Different kinds of functions are explored, including mathematical expressions, Perlin noise-based terrain, and several image-based procedures. A method for displaying the particle swarm exploring the search space in aesthetically pleasing ways is described. Several experiments are detailed and analyzed and a number of interesting visual artifacts are highlighted.
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    GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Fachada, Nuno; Fernandes, Daniel; Fernandes, Carlos M.; Ferreira-Saraiva, Bruno D.; Matos-Carvalho, João P.; COPELABS - Cognitive and People-centric Computing; CICANT - Centre for Research in Applied Communication, Culture, and New Technologies; ECATI - School of Communication, Architecture, Arts and Information Technologies
    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, with GPT-4.1 standing out as the only model to always succeed in both tasks. 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.
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    Humans vs AI : an exploratory study with online and offline learners
    (Springer Nature, 2024-01-02) Inácio, João; Fachada, Nuno; Carvalho, João P. Matos; Fernandes, Carlos M.
    We present an exploratory study comparing human player performance against online and offline AI learning techniques—the Naive Bayes Classifier and Genetic Algorithms, respectively—using a simple turn-based game. Human player performance is also assessed according to gender, age, experience playing games, and boredom level during game sessions. Human players and AI techniques are shown to obtain statistically equivalent score distributions. No gender performance differences were found, although performance seems to decrease with age. To a lesser extent, performance appears to improve with self-assessed experience and boredom levels. This study offers a base for more comprehensive experiments, suggesting various directions for future research.
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    Improving the CS Curriculum of a Top-Down Videogames BA
    (Association for Computing Machinery, 2022-11-21) Fachada, Nuno; Serra, Pedro; Códices, Nélio; Luz, Filipe; Andrade, Diogo Nuno Dias Mesquita Gomes de; LOPES, LOUIS PHILIPPE SIMÕES CASTELO BRANCO; Fonseca, Maria Micaela; Neves, Pedro Pinto; Stikkolorum, Dave; Rahimi, Ebrahim; Stuurman, Sylvia; COPELABS - Cognitive and People-centric Computing; ECATI - School of Communication, Architecture, Arts and Information Technologies; HEI-LAB - Human Environment Interaction Lab
    We present a targeted curricular improvement on an interdisciplinary Videogames Bachelor of Arts, to be implemented in the 2022/23 academic year. The aim is to solve student adaptation issues with the program’s interdisciplinarity that were heightened by the COVID-19 pandemic.
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    micompm: A MATLAB/Octave toolbox for multivariate independent comparison of observations
    (JOSS, 2018) Fachada, Nuno; Rosa, Agostinho C.; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
    micompm is a MATLAB / GNU Octave port of the original micompr R package for comparing multivariate samples associated with different groups. Its purpose is to determine if the compared samples are significantly different from a statistical point of view. This method uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using statistical tests and score plots. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. It is aimed at researchers from all fields of science, although it requires some knowledge on design of experiments, statistical testing and multidimensional data analysis.
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    MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-04-23) Petukhova, Alina; Fachada, Nuno; FE - Faculty of Engineering; COPELABS - Cognitive and People-centric Computing
    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. Keywords: news dataset; text classification; NLP; media topic taxonomy