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Publicações

Publicações por HumanISE

2021

Immersive Authoring of Virtual Reality Training

Autores
Cassola, F; Pinto, M; Mendes, D; Morgado, L; Coelho, A; Paredes, H;

Publicação
2021 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES ABSTRACTS AND WORKSHOPS (VRW 2021)

Abstract
The use of VR in industrial training contributes to reduce costs and risks, supporting more frequent and diversified use of experiential learning activities, an approach with proven results. In this work, we present an innovative immersive authoring tool for experiential learning in VR-based training. It enables a trainer to structure an entire VR training course in an immersive environment, defining its sub-components, models, tools, and settings, as well as specifying by demonstration the actions to be performed by trainees. The trainees performing the immersive training course have their actions recorded and matched to the ones specified by the trainer.

2021

Integration of CAD Models into Game Engines

Autores
Santos, B; Rodrigues, N; Costa, P; Coelho, A;

Publicação
GRAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 1: GRAPP

Abstract
Computer-aided design (CAD) and 3D modeling are similar, but they have different functionalities and applications. CAD is a fundamental tool to create object models, design parts, and create 2D schematics from 3D designed objects that can later be used in manufacturing. Meanwhile, 3D modeling is mostly used in entertainment, to create meshes for animation and games. When there is the necessity of using real-life object models in game engines, a conversion process is required to go from CAD to 3D meshes. Converting from the continuous domain of CAD to the discrete domain of 3D models represents a trade-off between processing cost and visual accuracy, in order to obtain the best user experience. This work explores different methods for the creation of meshes and the reduction of the number of polygons used to represent them. Based on these concepts, an interactive application was created to allow the users to control how the model looks in the game engine, in a simple way, while also optimizing and simplifying the mapping of textures for the generated meshes. This application (CADto3D) generates accurate 3D models based on CAD surfaces while giving the user more control over the final result than other current solutions.

2021

An experience of using Kahoot! while going online

Autores
Cruz, S; Urbano, D; Coelho, A; Pego, JP;

Publicação
2021 4TH INTERNATIONAL CONFERENCE OF THE PORTUGUESE SOCIETY FOR ENGINEERING EDUCATION (CISPEE)

Abstract
This report describes a preliminary study that took place during the second semester of the school year 2019-2020, where suddenly classes had to be held online due to COVID 19 pandemic. Kahoot! a gamified application was used in some of the problem-solving classes of an undergraduate physics course of the integrated masters of the Electrical and Computers Engineering program. The quizzes applied covered rigid body dynamics and thermodynamics, both contents included in the syllabus of the course. The study was planned prior to the pandemic and the necessary adjustments of teaching online altered the goals. A simple analysis of the data obtained with the Kahoot! quizzes is performed and the results are discussed in the context of the positive and negative effects of going online.

2021

InventiveTr@ining – Inven!RA architecture Activity Provider modules for online tracking of microelectronics student

Autores
Cota, Duarte; Cruzeiro, Tiago; Beck, Dennis; Coelho, António; Morgado, Leonel;

Publicação
Revista de Ciências da Computação

Abstract
The Inven!RA architecture is an approach for online tracking of progression towards learning objectives, from analytics of distributed learning activities, provided by multiple third parties. However, there are few examples on how to implement such third-party learning activities, known as Activity Provider modules. We followed the Inven!RA architecture interfacing specification to create and implement two sample learning activities: a technical documentation analysis activity and an Arduino microelectronics programming activity. Integration tests with an Inven!RA architecture prototype confirmed the adequacy of this implementation. Thus, these samples provide clarification on how to design and develop Inven!RA Activity Provider modules.;A arquitetura Inven!RA é uma abordagem para o acompanhamento online da evolução face a objetivos de aprendizagem, através de dados analíticos de atividades de aprendizagem distribuídas, proporcionadas por um leque variado de entidades externas. Como são escassos os exemplos de implementação destas atividades de aprendizagem externas, designadas por módulos de Prestadores de Atividades,seguimos a especificação de interfaces da arquitetura para criar e implementar dois exemplos de atividades de aprendizagem: uma atividade de análise de documentação técnica e uma de programação de microeletrónica com Arduino. Testes de integração com um protótipo da Inven!RA confirmaram a adequação destas implementações. Consequentemente, proporcionam clarificação quanto à forma de conceber e desenvolver módulos de Prestadores de Atividades para a arquitetura Inven!RA.

2021

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

Autores
Pavlovic, M; Scheffer, L; Motwani, K; Kanduri, C; Kompova, R; Vazov, N; Waagan, K; Bernal, FLM; Costa, AA; Corrie, B; Akbar, R; Al Hajj, GS; Balaban, G; Brusko, TM; Chernigovskaya, M; Christley, S; Cowell, LG; Frank, R; Grytten, I; Gundersen, S; Haff, IH; Hovig, E; Hsieh, PH; Klambauer, G; Kuijjer, ML; Lund Andersen, C; Martini, A; Minotto, T; Pensar, J; Rand, K; Riccardi, E; Robert, PA; Rocha, A; Slabodkin, A; Snapkov, I; Sollid, LM; Titov, D; Weber, CR; Widrich, M; Yaari, G; Greiff, V; Sandve, GK;

Publicação
NATURE MACHINE INTELLIGENCE

Abstract
Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

2021

A Comparative Study on the Performance of the IB+ Tree and the I2B+ Tree

Autores
Carneiro, E; de Carvalho, AV; Oliveira, MA;

Publicação
Journal of Information Systems Engineering and Management

Abstract
Index structures were often used to optimise fetch operations to external storage devices (secondary memory). Nowadays, this also holds for increasingly large amounts of data residing in main-memory (primary memory). Within this scope, this work focuses on index structures that efficiently insert, query and delete valid-time data from very large datasets. This work performs a comparative study on the performance of the Interval B+ tree (IB+ tree) and the Improved Interval B+ tree (I2B+ tree): a variant that improves the time-efficiency of the deletion operation by reducing the number of traversed nodes to access siblings. We performed an extensive analysis of the performance of two operations: insertions and deletions, on both index structures, using multiple datasets with growing volumes of data, distinct temporal distributions and tree parameters (time-split alpha and node order). Results confirm that the I2B+ tree globally outperforms the IB+ tree, since, on average, deletion operations are 7% faster, despite insertions requiring 2% more time. Furthermore, results also allowed to determine the key factors that augment the performance difference on deletions between both trees. Copyright © 2021 by Author/s and Licensed by Veritas Publications Ltd., UK.

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