Search Results for author: Longxiang Gao

Found 21 papers, 3 papers with code

Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey

no code implementations14 Dec 2023 Yichen Wan, Youyang Qu, Wei Ni, Yong Xiang, Longxiang Gao, Ekram Hossain

Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server.

Data Poisoning Federated Learning +1

Learning to Learn for Few-shot Continual Active Learning

no code implementations7 Nov 2023 Stella Ho, Ming Liu, Shang Gao, Longxiang Gao

Recent advances in CL are mostly confined to a supervised learning setting, especially in NLP domain.

Active Learning Continual Learning +3

Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System

no code implementations30 May 2023 Jiwei Guan, Lei Pan, Chen Wang, Shui Yu, Longxiang Gao, Xi Zheng

As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios.

Autonomous Driving Open-Ended Question Answering +1

From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding

no code implementations22 Mar 2023 Borui Cai, Yong Xiang, Longxiang Gao, Di wu, He Zhang, Jiong Jin, Tom Luan

To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures.

Knowledge Distillation Knowledge Graph Embedding +2

Hybrid Variational Autoencoder for Time Series Forecasting

no code implementations13 Mar 2023 Borui Cai, Shuiqiao Yang, Longxiang Gao, Yong Xiang

Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables.

Time Series Time Series Forecasting +1

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

1 code implementation15 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 World Knowledge

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 Scheduling

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

Prototype-Guided Memory Replay for Continual Learning

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

Continual learning (CL) refers to a machine learning paradigm that learns continuously without forgetting previously acquired knowledge.

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 +1

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

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

Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy.

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.

Clustering Graph Clustering +6

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.

Clustering Document Summarization +2

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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