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Publications

Publications by CESE

2021

Estratégias dos estudos métricos da informação para o mapeamento de inovação

Authors
Rebouças Nascimento, M; Clara Cândido, A; Augusto Zimmermann, R; Wielewicki, P;

Publication
Comunicação & Inovação

Abstract
O presente artigo objetiva identificar estratégias metodológicas existentes no âmbito dos estudos métricos da informação que contribuem para o mapeamento de inovação. Em termos metodológicos, este estudo qualitativo de caráter exploratório e descritivo parte do levantamento da produção de conhecimento relacionada às tipologias dos estudos métricos na base de dados Web of Science, por meio da relação do termo inovação nas palavras-chave e keywords plus dos artigos. Os resultados destacam a aplicação da inovação nas metrias da informação no âmbito da altmetria, bibliometria, cientometria, patentometria e webometria.

2021

Forecasting of Urban Public Transport Demand Based on Weather Conditions

Authors
Correia, R; Fontes, T; Borges, JL;

Publication
Advances in Intelligent Systems and Computing

Abstract
Weather conditions have a major impact on citizens’ daily mobility. Depending on weather conditions trips may be delayed, demand may be changed as well as the modal shift. These variations have a major impact on the use and operation of public transport, particularly in transport systems that operate close to capacity. However, the influence of weather conditions on transport demand is difficult to predict and quantify. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a regression model to estimate the demand of urban public transport buses based on weather conditions. Transit bus ridership and weather conditions were collected along a year from a medium-size European metropolitan area (Oporto, Portugal) and linked under the assumption that individuals choose the travel mode based on the weather conditions that are observed during the departure hour, the hour before and two hours before. The transit ridership data were also labelled according to the hour, day of the week, month, and whether there was a strike and/or holiday or not. The results demonstrate that it is possible to predict the demand of public transport buses using the weather conditions observed two hours before with low error for the entire network (MAE = 143 and RMSE = 322). The use of weather conditions allow to decreases the error of the prediction by ~8% for the entire network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

2021

Are BERT embeddings able to infer travel patterns from Twitter efficiently using a unigram approach?

Authors
Murços, F; Fontes, T; Rossetti, RJF;

Publication
IEEE International Smart Cities Conference, ISC2 2021, Manchester, United Kingdom, September 7-10, 2021

Abstract
Public opinion is nowadays a valuable data source for many sectors. In this study, we analysed the transportation sector using messages extracted from Twitter. Contrasting with the traditional surveying methods that are high-cost and inefficient used in transportation sector, social media are popular sources of crowdsensing. This work used BERT embeddings, an unsupervised pre-trained model released in 2018, to classify travel-related terms using tweets collected from three distinct cities: New York, London, and Melbourne. In order to understand if a simple model can have a good performance, we used unigrams. A list of 24 travel-related words was used to classify the messages. Popular words are train, walk, car, station, street, and avenue. Between 3% to 5% of all messages are classified as traffic-related, while along the typical working hours of the day the values is around 5-6%. A high model performance was obtained, with precision and accuracy higher than 0.80 and 0.90, respectively. The results are consistent for all the three cities assessed. © 2021 IEEE.

2021

Collaborative Engineering definition: Distinguishing it from Concurrent Engineering through the complexity and semiotics lenses

Authors
Putnik, GD; Putnik, Z; Shah, V; Varela, L; Ferreira, L; Castro, H; Catia, A; Pinheiro, P;

Publication
IOP Conference Series: Materials Science and Engineering

Abstract

2021

Collaborative Engineering: A Review of Organisational Forms for Implementation and Operation

Authors
Putnik, GD; Putnik, Z; Shah, V; Varela, L; Ferreira, L; Castro, H; Catia, A; Pinheiro, P;

Publication
IOP Conference Series: Materials Science and Engineering

Abstract

2021

Using Video Games to Improve Capabilities in Decision Making and Cognitive Skill: A Literature Review

Authors
Castro, H; Pinto, N; Pereira, F; Ferreira, L; Ávila, P; Bastos, J; Putnik, GD; Cruz Cunha, M;

Publication
5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND COMPUTATIONAL INTELLIGENCE 2020

Abstract
Video games provide a means to improve a human's cognitive skills. There are several genres of games that affect different cognitive subcategory. The purpose of this paper is to determine whether video games could really improve cognitive skills and decision-making; and which video games genre affect which cognitive skills. The authors assess previous experiments related to video games and cognitive skills. The paper reviewed 27 experimental and literature review studies. The results of the review proved that video games do improve cognitive skills and decision-making. Cognitive skills such as perception, attentional control, and decision-making improves when subjects were trained with video games. In relation to video games genre, Real-time strategy (RTS) players outperforms First-person shooter (FPS) players on cognitive flexibility while FPS players tend to have lower switching cost in work. People with profession such as nurses and doctors showed improved decision-making and risk assessment when trained with serious simulation games. High school and undergraduate students who played video games exhibit better result when given tasks related to cognitive abilities compared to students who do not played video games. We encourage further studies to conduct a much bigger experiment to correlate with our findings. (C) 2021 The Authors. Published by Elsevier B.V.

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