Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Alexandra. I. Cristea"'
Publikováno v:
IEEE Access, Vol 12, Pp 77536-77554 (2024)
Real-world applications of word embeddings to downstream clustering tasks may experience limitations to performance, due to the high degree of dimensionality of the embeddings. In particular, clustering algorithms do not scale well when applied to hi
Externí odkaz:
https://doaj.org/article/d4511aa25f7a4e57bf52f255a03761a5
Publikováno v:
Frontiers in Human Neuroscience, Vol 18 (2024)
Externí odkaz:
https://doaj.org/article/58f1a803de744b728d5d3b07810e1117
Publikováno v:
AI Open, Vol 4, Iss , Pp 165-174 (2023)
Stock price prediction is challenging in financial investment, with the AI boom leading to increased interest from researchers. Despite these recent advances, many studies are limited to capturing the time series characteristics of price movement via
Externí odkaz:
https://doaj.org/article/5bbb1c10abbd42838611a4557ab1a0be
Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation
Autor:
Jialin Yu, Alexandra I. Cristea, Anoushka Harit, Zhongtian Sun, Olanrewaju Tahir Aduragba, Lei Shi, Noura Al Moubayed
Publikováno v:
AI Open, Vol 4, Iss , Pp 19-32 (2023)
This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence
Externí odkaz:
https://doaj.org/article/7405011d2af64562ae5861bfc0674a5f
Autor:
Filipe Dwan Pereira, Samuel C. Fonseca, Sandra Wiktor, David B. F. Oliveira, Alexandra I. Cristea, Aileen Benedict, Mohammadali Fallahian, Mohsen Dorodchi, Leandro S. G. Carvalho, Rafael Ferreira Mello, Elaine H. T. Oliveira
Publikováno v:
IEEE Access, Vol 11, Pp 22513-22525 (2023)
Online judges (OJ) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This
Externí odkaz:
https://doaj.org/article/337ae35acc464aada5e99898ed8b59cb
Autor:
Monira Abdulrahman Almeqren, Latifah Almuqren, Fatimah Alhayan, Alexandra I. Cristea, Diane Pennington
Publikováno v:
Frontiers in Psychology, Vol 14 (2023)
ProblemSentiment Analysis (SA) automates the classification of the sentiment of people’s attitudes, feelings or reviews employing natural language processing (NLP) and computational approaches. Deep learning has recently demonstrated remarkable suc
Externí odkaz:
https://doaj.org/article/e3d5abcca19a4e7b979fbfbfe589b5f5
Autor:
Luiz Rodrigues, Filipe D. Pereira, Armando M. Toda, Paula T. Palomino, Marcela Pessoa, Leandro Silva Galvão Carvalho, David Fernandes, Elaine H. T. Oliveira, Alexandra I. Cristea, Seiji Isotani
Publikováno v:
International Journal of Educational Technology in Higher Education, Vol 19, Iss 1, Pp 1-25 (2022)
Abstract There are many claims that gamification (i.e., using game elements outside games) impact decreases over time (i.e., the novelty effect). Most studies analyzing this effect focused on extrinsic game elements, while fictional and collaborative
Externí odkaz:
https://doaj.org/article/9c8f0881f5014cc0984fa08ecc7c451f
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
Externí odkaz:
https://doaj.org/article/4ec5c16bf4b345e4b7c2081de4cc6cf4
Autor:
Filipe Dwan Pereira, Samuel C. Fonseca, Elaine H. T. Oliveira, Alexandra I. Cristea, Henrik Bellhauser, Luiz Rodrigues, David B. F. Oliveira, Seiji Isotani, Leandro S. G. Carvalho
Publikováno v:
IEEE Access, Vol 9, Pp 117097-117119 (2021)
Predicting student performance as early as possible and analysing to which extent initial student behaviour could lead to failure or success is critical in introductory programming (CS1) courses, for allowing prompt intervention in a move towards all
Externí odkaz:
https://doaj.org/article/7618ab10e2ba4630a140e36ea8cc49b9
Autor:
Tahani Aljohani, Alexandra I. Cristea
Publikováno v:
Frontiers in Research Metrics and Analytics, Vol 6 (2021)
Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic charac
Externí odkaz:
https://doaj.org/article/d65d5a67716b4e49a131c1e3afd6844d