COPELABS – Associação para Investigação e Desenvolvimento em Cognição e Computação Centrada nas Pessoas

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Percorrer COPELABS – Associação para Investigação e Desenvolvimento em Cognição e Computação Centrada nas Pessoas por título

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    Algorithms for estimating the location of remote nodes using Smartphones
    (Institute of Electrical and Electronics Engineers Inc., 2019) Pedro, Dario; Tomic, Slavisa; Bernardo, Luís; Beko, Marko; Oliveira, Rodolfo; Dinis, Rui; Pinto, Paulo; Amaral, Pedro; COPELABS (FCT) - Centro de Investigação em Computação Centrada nas Pessoas e Cognição (CTS)
    Locating the position of a remote node on a wireless network is becoming more relevant, as we move forward in the Internet of things and in autonomous vehicles. This paper proposes a new system to implement the location of remote nodes. A new prototype Android application has been developed to collect real measurements and to study the performance of several smartphone's sensors and location algorithms, including an innovative one, based on the second order cone programming (SOCP) relaxation. The application collects theWiFi access points information and the terminal location. An internal odometry module developed for the prototype is used when Android's service is unavailable. This paper compares the performance of existing location estimators given in closed form, an existing SOCP one, and the new SOCP location estimator proposed, which has reduced complexity. An algorithm to merge measurements from non-identical terminals is also proposed. Cooperative and terminal stand-alone operations are compared, showing a higher performance for SOCP-based ones, that are capable of estimating the path loss exponent and the transmission power. The heterogeneous terminals were also used in the tests. Our results show that the accurate positioning of static remote entities can be achieved using a single smartphone. On the other hand, the accurate real-time positioning of the mobile terminal is provided when three or more scattered terminal nodes cooperate sharing the samples taken synchronously.
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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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    Energy-based acoustic localization by improved elephant herding optimization
    (Institute of Electrical and Electronics Engineers Inc., 2020) Correia, Sergio D.; Beko, Marko; Tomic, Slavisa; Da Silva Cruz, Luis A.; COPELABS (FCT) - Centro de Investigação em Computação Centrada nas Pessoas e Cognição (CTS)
    The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.
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    Estimating directional data from Network Topology for improving tracking performance
    (Multidisciplinary Digital Publishing Institute (MDPI), 2019) Tomic, Slavisa; Beko, Marko; Dinis, Rui; Montezuma, Paulo; COPELABS (FCT) - Centro de Investigação em Computação Centrada nas Pessoas e Cognição (CTS)
    This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.
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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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    Procedural generation of 3D maps with snappable meshes
    (Institute of Electrical and Electronics Engineers Inc., 2022-04-20) Silva, Rafael Castro e; Fachada, Nuno; Andrade, Diogo de; Códices, Nélio; COPELABS - Cognitive and People-centric Computing
    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. INDEX TERMS : 3D maps, computer games, designer-centric methods, layout, procedural content generation (PCG).
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    Target localization via integrated and segregated ranging based on RSS and TOA measurements
    (MDPI, 2019) Tomic, Slavisa; Beko, Marko; COPELABS - Cognitive and People-centric Computing
    This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.
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    Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-02-22) Freitas, Cristina; Eleutério, João; Soares, Gabriela; Enea, Maria; Nunes, Daniela; Fortunato, Elvira; Martins, Rodrigo; Águas, Hugo; Pereira, Eulália; Vieira, Helena L.A.; Ferreira, Lúcio Studer; Franco, Ricardo; COPELABS - Cognitive and People-centric Computing
    Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the development of low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS–plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay’s potential for immediate clinical decision-making.