Search Results for author: Longxiang Gao

Found 16 papers, 2 papers with code

An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

no code implementations15 Aug 2022 Chenhao Xu, Youyang Qu, Tom H. Luan, Peter W. Eklund, Yong Xiang, Longxiang Gao

Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models.

Federated Learning

Temporal Knowledge Graph Completion: A Survey

no code implementations16 Jan 2022 Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, JianXin Li

KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.

Temporal Knowledge Graph Completion

Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles

no code implementations28 Dec 2021 Jinkai Zheng, Tom H. Luan, Longxiang Gao, Yao Zhang, Yuan Wu

In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT.

Autonomous Vehicles Edge-computing

Semantic Code Search for Smart Contracts

no code implementations28 Nov 2021 Chaochen Shi, Yong Xiang, Jiangshan Yu, Longxiang Gao

To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features.

Code Search

Prototypes-Guided Memory Replay for Continual Learning

no code implementations28 Aug 2021 Stella Ho, Ming Liu, Lan Du, Longxiang Gao, Yong Xiang

The experimental results testify the superiority of our method in alleviating catastrophic forgetting and enabling efficient knowledge transfer.

Continual Learning Meta-Learning +3

Federated Learning Meets Natural Language Processing: A Survey

no code implementations27 Jul 2021 Ming Liu, Stella Ho, Mengqi Wang, Longxiang Gao, Yuan Jin, He Zhang

Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.

Federated Learning

A Bytecode-based Approach for Smart Contract Classification

no code implementations31 May 2021 Chaochen Shi, Yong Xiang, Robin Ram Mohan Doss, Jiangshan Yu, Keshav Sood, Longxiang Gao

Our experimental studies on over 3, 300 real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models.

Classification Ensemble Learning

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

no code implementations12 Mar 2021 Chenhao Xu, Yong Li, Yao Deng, Jiaqi Ge, Longxiang Gao, Mengshi Zhang, Yong Xiang, Xi Zheng

Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster.

Edge-computing Federated Learning +1

Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images

no code implementations18 Jan 2021 Uno Fang, JianXin Li, Xuequan Lu, Mumtaz Ali, Longxiang Gao, Yong Xiang

Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive.

SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline

no code implementations19 Oct 2020 Jiaxin Ju, Ming Liu, Longxiang Gao, Shirui Pan

The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.

Graph Clustering graph construction +4

SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression

1 code implementation17 Jul 2020 Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari

Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.

Document Summarization Multi-Document Summarization

Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data

no code implementations12 Oct 2019 Yuan Jin, Ming Liu, Yunfeng Li, Ruohua Xu, Lan Du, Longxiang Gao, Yong Xiang

Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values.

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