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Publicações

Publicações por CRAS

2024

Raising Awareness to Waste Collection and Recycling in Urban Spaces – An EPS@ISEP 2023 Project

Autores
Bohon, N; Durand, O; Emmelot, C; Hellemans, K; Jasny, L; Reisinger, K; Duarte, J; Malheiro, B; Ribeiro, C; Justo, J; Silva, F; Ferreira, P; Guedes, P;

Publicação
Lecture Notes in Educational Technology

Abstract
The European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) is a capstone engineering design programme in which students, organised in multidisciplinary and multicultural teams, develop a solution for a proposed problem, taking into account sustainability, ethical and market concerns. This paper describes a research project aimed at raising awareness and changing behaviour in relation to waste disposal, carried out by a team of EPS@ISEP students during spring 2023. BinIt, as the project is named, targets young adults who want to live in a cleaner city. Unlike other campaigns, it simplifies and stimulates proper waste disposal and recycling, tackling the root of the problem and creating a new social norm. BinIt includes a campaign, a web app and the Garbage Gladiator bin. The app consists of a city map where users can pin and check bin locations, and an educational platform with information on waste disposal and recycling issues. Gamification is incorporated through a ranking system. The Garbage Gladiator is a physical container for urban public spaces specially designed to encourage people to dispose of their waste correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

2024

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

Autores
De Arriba-Pérez, F; García-Méndez, S; Leal, F; Malheiro, B; Burguillo, JC;

Publicação
INFORMATICA

Abstract
Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87% spam F-measure.

2024

Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly

Autores
Méndez, SG; Leal, F; Malheiro, B; Burguillo Rial, JC; Veloso, B; Chis, AE; Vélez, HG;

Publicação
CoRR

Abstract

2024

Online detection and infographic explanation of spam reviews with data drift adaptation

Autores
Arriba Pérez, Fd; Méndez, SG; Leal, F; Malheiro, B; Burguillo, JC;

Publicação
CoRR

Abstract

2024

Exposing and Explaining Fake News On-the-Fly

Autores
Arriba Pérez, Fd; Méndez, SG; Leal, F; Malheiro, B; Burguillo, JC;

Publicação
CoRR

Abstract

2024

Interpretable classification of wiki-review streams

Autores
Méndez, SG; Leal, F; Malheiro, B; Burguillo Rial, JC;

Publicação
CoRR

Abstract

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