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- research-articleJuly 2024
A Dual Robust Graph Neural Network Against Graph Adversarial Attacks
AbstractGraph Neural Networks (GNNs) have gained widespread usage and achieved remarkable success in various real-world applications. Nevertheless, recent studies reveal the vulnerability of GNNs to graph adversarial attacks that fool them by modifying ...
- research-articleApril 2024
Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 158, Pages 1–24https://doi.org/10.1145/3653976Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only ...
- ArticleFebruary 2024
Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI
Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge PapersOct 2023, Pages 77–87https://doi.org/10.1007/978-3-031-52448-6_8AbstractQuantitative cardiac magnetic resonance imaging (MRI) is an increasingly important diagnostic tool for cardiovascular diseases. Yet, co-registration of all baseline images within the quantitative MRI sequence is essential for the accuracy and ...
- ArticleFebruary 2024
Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge PapersOct 2023, Pages 349–358https://doi.org/10.1007/978-3-031-52448-6_33AbstractDeep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks to learn the image prior as ...
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RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing
Proceedings of the VLDB Endowment (PVLDB), Volume 17, Issue 3Pages 264–277https://doi.org/10.14778/3632093.3632094In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with ...
- research-articleNovember 2023
Mixed artificial intelligence models for compressive strength prediction and analysis of fly ash concrete
- Wei Liang,
- Wei Yin,
- Yu Zhong,
- Qian Tao,
- Kunpeng Li,
- Zhanyuan Zhu,
- Zuyin Zou,
- Yusheng Zeng,
- Shucheng Yuan,
- Han Chen
Advances in Engineering Software (ADES), Volume 185, Issue CNov 2023https://doi.org/10.1016/j.advengsoft.2023.103532Highlights- Solves the problem of time-consuming and costly traditional laboratory procedures for analyzing the compressive strength of coagulation.
The construction industry is facing challenges from the hazardous nature of Ordinary Portland Cement (OPC) production as one of the main contributors to global warming and CO2 emission. Given its increasing demand, the need to replace ...
Graphical AbstractDisplay Omitted
- research-articleAugust 2023
A Novel Unsupervised Approach for Cross-Lingual Word Alignment in Low Isomorphic Embedding Spaces
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 31Pages 3027–3041https://doi.org/10.1109/TASLP.2023.3301208Cross-lingual word alignment is the task for word translation between monolingual word embedding spaces of two different languages. Recent work is mostly based on supervised approaches, while their success relies on bilingual seed dictionaries derived ...
- ArticleSeptember 2022
Efficient Bayesian Uncertainty Estimation for nnU-Net
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022Sep 2022, Pages 535–544https://doi.org/10.1007/978-3-031-16452-1_51AbstractThe self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its ...
- ArticleSeptember 2022
- short-paperJuly 2022
DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2022, Pages 2190–2194https://doi.org/10.1145/3477495.3531828Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit ...
- research-articleApril 2022
Automatic Arabic Grammatical Error Correction based on Expectation-Maximization routing and target-bidirectional agreement
Knowledge-Based Systems (KNBS), Volume 241, Issue CApr 2022https://doi.org/10.1016/j.knosys.2022.108180AbstractAutomatic Grammar Error Correction (GEC) detects and corrects various types of syntax, spelling, and grammatical errors. Different approaches such as rule-based, Statistical Machine Translation (SMT), and Neural Machine Translation (...
Highlights- We proposed an automatic Arabic grammar error correction.
- we proposed a semi-...
- research-articleFebruary 2022
Deep Recursive Embedding for High-Dimensional Data
IEEE Transactions on Visualization and Computer Graphics (ITVC), Volume 28, Issue 2Feb. 2022, Pages 1237–1248https://doi.org/10.1109/TVCG.2021.3122388Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We ...
- ArticleNovember 2021
ALGNN: Auto-Designed Lightweight Graph Neural Network
PRICAI 2021: Trends in Artificial IntelligenceNov 2021, Pages 500–512https://doi.org/10.1007/978-3-030-89188-6_37AbstractGraph neural networks (GNNs) are widely used on graph-structured data, and its research has made substantial progress in recent years. However, given the various number of choices and combinations of components such as aggregator and activation ...
- doctoral_thesisJanuary 2021
Integral Representations and Reproducing Kernels for Weighted Function Spaces on Tubular Domains
AbstractIn this thesis, we consider generalizations of some well known topics, including the Paley–Wiener theorem and Bergman reproducing kernels, that have far-reaching applications in various areas of complex analysis and signal analysis. One intention ...
- research-articleNovember 2020
Comparative Evaluation of Multiport DC Power Router for DC Distribution Grid
2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia)Nov 2020, Pages 3263–3268https://doi.org/10.1109/IPEMC-ECCEAsia48364.2020.9367692DC power router serves as the key node in DC distribution grid, with the multiple operation aims of voltage regulation, power dispatching and fault protection. In this paper, three series of DC power router topologies have been developed in duality of ...
- research-articleOctober 2020
Learning Motion Based Auxiliary Task for Cardiomyopathy Recognition with Cardiac Magnetic Resonance Images
CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application EngineeringOctober 2020, Article No.: 137, Pages 1–5https://doi.org/10.1145/3424978.3425122Accurate analysis of the patient's heart function, and early diagnosis of myocardial disease can improve the treatment effect and reduce the medical cost significantly. Among the different medical imaging techniques, cardiac magnetic resonance (CMR) has ...
- research-articleDecember 2019
A Novel Deep DPCA-SVM Method for Fault Detection in Industrial Processes
2019 IEEE 58th Conference on Decision and Control (CDC)Pages 2916–2921https://doi.org/10.1109/CDC40024.2019.9029388Fault detection is an important step to ensure safe and reliable production in industrial processes. Data-driven technology is one of the most widely studied fault detection methods. This paper proposed a data-driven fault detection method named deep ...
- review-articleNovember 2019
Self-adaptive weighted level set evolution based on local intensity difference for parotid ducts segmentation
Computers in Biology and Medicine (CBIM), Volume 114, Issue CNov 2019https://doi.org/10.1016/j.compbiomed.2019.103432Abstract BackgroundParotid ducts (PDs) play an important role in the diagnosis and treatment of parotid lesions. Segmentation of PDs from Cone beam computed tomography (CBCT) images has a significant impact to the pathological ...
Highlights- Proposed method performs better in segmenting parotid duct images with noise, intensity inhomogeneity, and blurred border.
- ArticleOctober 2019
The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019Oct 2019, Pages 623–631https://doi.org/10.1007/978-3-030-32245-8_69AbstractConvolutional neural network (CNN), in particular the Unet, is a powerful method for medical image segmentation. To date Unet has demonstrated state-of-art performance in many complex medical image segmentation tasks, especially under the ...