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Publications

Publications by CRIIS

2022

Websites Usability Evaluation of the Terras De Tras-Os-Montes Hotels

Authors
Morais, EP; Cunha, CR; Santos, A;

Publication
MARKETING AND SMART TECHNOLOGIES, VOL 1

Abstract
Website is a bridge between users and online information. It is extremely important in terms of marketing and must be designed according to the rules of usability, especially in hotel industry. Websites with high usability value will be accessed by more users. Therefore, building a useful website is important. This study aims to evaluate, from the point of view of usability, the websites of hotel establishments in Terras de Tras-os-Montes, a region located in the north of Portugal.

2022

A Highly Customizable Information Visualization Framework

Authors
Spínola, L; Silva, DC; Reis, LP;

Publication
Computational Science - ICCS 2022 - 22nd International Conference, London, UK, June 21-23, 2022, Proceedings, Part II

Abstract

2022

A Highly Customizable Information Visualization Framework

Authors
Spínola, L; Silva, DC; Reis, LP;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
The human brain can quickly become overwhelmed by the amounts of data computers can process. Consequently, data abstraction is necessary for a user to grasp information and identify valuable patterns. Data is usually abstracted in a pictorial or graphical format. Nowadays, users demand more personalization from the systems they use. This work proposes a user-centered framework that aims to ease creating visualizations for the developers of a platform while offering the end-user a highly customizable experience. The conceptualized solution was prototyped and tested to ensure the information about the data is transmitted to the user in a quick and effective manner. The results of a user study showed that users are pleased with the usability of the prototype and prove that they desire control over the configuration of their visualizations. This work not only confirmed the usefulness of previously explored personalization options for visual representations, but also explored promising new personalization options. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Reinforcement Learning for Multi-Agent Competitive Scenarios

Authors
Coutinho M.; Reis L.P.;

Publication
2022 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022

Abstract
Reinforcement Learning techniques allow learning complex behaviors to deal with a variety of situations in a matter of hours. This complexity is even more prominent in multi-agent continuous 3D environments. This paper compares how the actions taken by two agents independently trained via a self-play approach differ from the ones taken when they are controlled by the same policy. It also explores the emergence of competitive or collaborative behaviors in a natural game setting. By implementing a 3D simulated version of the Dance Dance Revolution, the acquisition of more specific abilities like equilibrium, balance, and dexterity was tested. The approach achieved very good results learning a predefined sequence of buttons (7 arrows correctly pressed in 20M timesteps), revealing a similar learning behavior to human beings (improving with training and performing better in this kind of sequence than in random ones). The self-play approach also produced some interesting effects by developing cooperative behaviors in theoretically competitive scenarios.

2022

Reinforcement Learning for Multi-Agent Competitive Scenarios

Authors
Coutinho, M; Reis, LP;

Publication
IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022, Santa Maria da Feira, Portugal, April 29-30, 2022

Abstract

2022

Forecasting Student s Dropout: A UTAD University Study

Authors
Da Silva, DEM; Pires, EJS; Reis, A; Oliveira, PBD; Barroso, J;

Publication
FUTURE INTERNET

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.

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