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Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
Poly-Gan: Regularizing Polygons With Generative Adversarial Networks, Lasith Niroshan, James Carswell
Poly-Gan: Regularizing Polygons With Generative Adversarial Networks, Lasith Niroshan, James Carswell
Conference Papers
Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features …
Detection Of Truthful, Semi-Truthful, False And Other News With Arbitrary Topics Using Bert-Based Models, Elena Shushkevich, John Cardiff, Anna Boldyreva
Detection Of Truthful, Semi-Truthful, False And Other News With Arbitrary Topics Using Bert-Based Models, Elena Shushkevich, John Cardiff, Anna Boldyreva
Conference Papers
Easy and uncontrolled access to the Internet provokes the wide propagation of false information, which freely circulates in the Internet. Researchers usually solve the problem of fake news detection (FND) in the framework of a known topic and binary classification. In this paper we study possibilities of BERT-based models to detect fake news in news flow with unknown topics and four categories: true, semi-true, false and other. The object of consideration is the dataset CheckThat! Lab proposed for the conference CLEF-2022. The subjects of consideration are the models SBERT, RoBERTa, and mBERT. To improve the quality of classification we use …
Cslinc - Development Of A National Outreach Vle, Keith Nolan, Keith Quille
Cslinc - Development Of A National Outreach Vle, Keith Nolan, Keith Quille
Conference Papers
Over the last year an online learning platform has been developed and piloted to the Irish second level education system allowing both students and teachers to participate in introductory computing modules. This poster will outline the development of the registration process of a system that is capable of managing potentially 728 schools, 1000+ classrooms and one million students (the entire Irish second level school system). CSLINC is an online student virtual learning environment for computing consisting of several modules built by academics and industry leaders and disseminated to schools through Moodle, our selected virtual learning environment. While Moodle has a …
Sgs: Mutant Reduction For Higher-Order Mutation-Based Fault Localization, Luxi Fan, Zheng Li, Hengyuan Liu, Paul Doyle, Haifeng Wang, Xiang Chen, Yong Liu
Sgs: Mutant Reduction For Higher-Order Mutation-Based Fault Localization, Luxi Fan, Zheng Li, Hengyuan Liu, Paul Doyle, Haifeng Wang, Xiang Chen, Yong Liu
Conference Papers
MBFL (Mutation-Based Fault Localization) is one of the most commonly studied fault localization techniques due to its promising fault localization effectiveness. However, MBFL incurs a high execution cost as it needs to execute the test suite on a large number of mutants. While previous studies have proposed mutant reduction methods for FOMs (First-Order Mutants) to help alleviate the cost of MBFL, the reduction of HOMs (Higher-Order Mutants) has not been thoroughly investigated. In this study, we propose SGS (Statement Granularity Sampling), a method which conducts HOMs reduction for HMBFL (Higher-Order Mutation-Based Fault Localization). Considering the relationship between HOMs and statements, …
Setransformer: A Transformer-Based Code Semantic Parser For Code Comment Generation, Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu, Paul Doyle
Setransformer: A Transformer-Based Code Semantic Parser For Code Comment Generation, Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu, Paul Doyle
Conference Papers
Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the …