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About Computer Science and Engineering

Computer Science and Engineering

Computers, ranging from ever so scaleddown programmable devices, the ubiquitous smartphones to supercomputers currently capable of performing more than a trillion operations per second, have become a central, and increasingly indispensable, component of everyday life. Computer science and engineering are the linchpins to the unstoppable evolution of computing and enable its application to an ever-growing plethora of computerbased solutions.

Additionally, computer systems in crucial sectors such as utilities, healthcare, transportation, and finance present new, often unanticipated, risks that defy our knowledge and present hard and intricate challenges associated with interoperability, scalability, security, and criticality. Worldwide, computing systems in organisations account for over 10% of all global energy consumption and approximately 2% of global CO2 emissions, making the sustainability of much of our innovation also a significant challenge.

news
Computer Science and Engineering

INESC TEC researchers propose innovative cryptography solution for potential quantum computers threats

The solution proposed by Manuel Barbosa and João Barbosa, researchers at INESC TEC, features a hybrid key-encapsulation mechanism (KEM), capable of addressing the demands of the dynamics of hybrid models that combine pre-quantum and post-quantum algorithms.

21st May 2024

Computer Science and Engineering

INESC TEC joins new collaboration project to bring Europe and Japan closer together in supercomputing

Medicine, climate, quantum physics or materials science. These are just few of the areas where supercomputing and modelling can play a major role in scientific, industrial, and social development. To strategically improve cooperation between Europe and Japan in this area of research, the HANAMI - HPC Alliance for Applications and Supercomputing Innovation: the Europe-Japan Collaboration project was born.

17th May 2024

Computer Science and Engineering

INESC TEC research on the application of Boolean functions in quantum computing integrates major publication

INESC TEC researchers developed a study to test the ability to perform any Boolean function on quantum computers designed according to a measurement-based quantum computation model.  

17th May 2024

Computer Science and Engineering

INESC TEC researchers strengthen partnership with CENTRA network

Since its establishment, the CENTRA network has been working to facilitate collaborative endeavours that allow the adoption of transnational cyber infrastructures - relying on several members from different countries: Indonesia, the United States of America, Vietnam, or Japan. Hence, and to strengthen the relationships with said partners, INESC TEC researchers travelled to Tokyo to attend the latest event of this initiative.

19th March 2024

Computer Science and Engineering

INESC TEC part of project to support users of European supercomputers, including the Portuguese Deucalion

EPICURE (High-level specialised application support service in High-Performance Computing) is the project that brings together the supercomputers of the European EuroHPC Joint Undertaking (EuroHPC JU) network to support its users.

28th February 2024

Publications

2024

Assessment of Multiple Fiducial Marker Trackers on Hololens 2

Authors
Costa, GM; Petry, MR; Martins, JG; Moreira, APGM;

Publication
IEEE ACCESS

Abstract
Fiducial markers play a fundamental role in various fields in which precise localization and tracking are paramount. In Augmented Reality, they provide a known reference point in the physical world so that AR systems can accurately identify, track, and overlay virtual objects. This accuracy is essential for creating a seamless and immersive AR experience, particularly when prompted to cope with the sub-millimeter requirements of medical and industrial applications. This research article presents a comparative analysis of four fiducial marker tracking algorithms, aiming to assess and benchmark their accuracy and precision. The proposed methodology compares the pose estimated by four algorithms running on Hololens 2 with those provided by a highly accurate ground truth system. Each fiducial marker was positioned in 25 sampling points with different distances and orientations. The proposed evaluation method is not influenced by human error, relying only on a high-frequency and accurate motion tracking system as ground truth. This research shows that it is possible to track the fiducial markers with translation and rotation errors as low as 1.36 mm and 0.015 degrees using ArUco and Vuforia, respectively.

2024

Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection

Authors
Rufino, J; Ramírez, JM; Aguilar, J; Baquero, C; Champati, J; Frey, D; Lillo, RE; Fernández-Anta, A;

Publication
HELIYON

Abstract
In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

2024

Inspection of Part Placement Within Containers Using Point Cloud Overlap Analysis for an Automotive Production Line

Authors
Costa, M; Dias, J; Nascimento, R; Rocha, C; Veiga, G; Sousa, A; Thomas, U; Rocha, L;

Publication
Lecture Notes in Mechanical Engineering

Abstract
Reliable operation of production lines without unscheduled disruptions is of paramount importance for ensuring the proper operation of automated working cells involving robotic systems. This article addresses the issue of preventing disruptions to an automotive production line that can arise from incorrect placement of aluminum car parts by a human operator in a feeding container with 4 indexing pins for each part. The detection of the misplaced parts is critical for avoiding collisions between the containers and a high pressure washing machine and also to avoid collisions between the parts and a robotic arm that is feeding parts to a air leakage inspection machine. The proposed inspection system relies on a 3D sensor for scanning the parts inside a container and then estimates the 6 DoF pose of the container followed by an analysis of the overlap percentage between each part reference point cloud and the 3D sensor data. When the overlap percentage is below a given threshold, the part is considered as misplaced and the operator is alerted to fix the part placement in the container. The deployment of the inspection system on an automotive production line for 22 weeks has shown promising results by avoiding 18 hours of disruptions, since it detected 407 containers having misplaced parts in 4524 inspections, from which 12 were false negatives, while no false positives were reported, which allowed the elimination of disruptions to the production line at the cost of manual reinspection of 0.27% of false negative containers by the operator. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

Towards MRAM Byte-Addressable Persistent Memory in Edge Database Systems

Authors
Ferreira, LM; Coelho, F; Pereira, JO;

Publication
Joint Proceedings of Workshops at the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, August 28 - September 1, 2023.

Abstract
There is a growing demand for persistent data in IoT, edge and similar resource-constrained devices. However, standard FLASH memory-based solutions present performance, energy, and reliability limitations in these applications. We propose MRAM persistent memory as an alternative to FLASH based storage. Preliminary experimental results show that its performance, power consumption, and reliability in typical database workloads is competitive for resource-constrained devices. This opens up new opportunities, as well as challenges, for small-scale database systems. MRAM is tested for its raw performance and applicability to key-value and relational database systems on resource-constrained devices. Improvements of as much as three orders of magnitude in write performance for key-value systems were observed in comparison to an alternative NAND FLASH based device. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

2023

Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk

Authors
Correia, A; Paulino, D; Paredes, H; Guimarães, D; Schneider, D; Fonseca, B;

Publication
26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023, Rio de Janeiro, Brazil, May 24-26, 2023

Abstract

2023

Policy gradients using variational quantum circuits

Authors
Sequeira, A; Santos, LP; Barbosa, LS;

Publication
QUANTUM MACHINE INTELLIGENCE

Abstract
Variational quantum circuits are being used as versatile quantum machine learning models. Some empirical results exhibit an advantage in supervised and generative learning tasks. However, when applied to reinforcement learning, less is known. In this work, we considered a variational quantum circuit composed of a low-depth hardware-efficient ansatz as the parameterized policy of a reinforcement learning agent. We show that an epsilon-approximation of the policy gradient can be obtained using a logarithmic number of samples concerning the total number of parameters. We empirically verify that such quantum models behave similarly to typical classical neural networks used in standard benchmarking environments and quantum control, using only a fraction of the parameters. Moreover, we study the barren plateau phenomenon in quantum policy gradients using the Fisher information matrix spectrum.