检索结果-内蒙古大学图书馆

46th IEEE/ACM International Conference on software engineering, ICSE 2024

作者： Zhong, Wenkang Xu, Tongtong Li, Chuanyi Ge, Jidong Liu, Kui Bissyandé, Tegawendé F. Luo, Bin Ng, Vincent National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Huawei Software Engineering Application Technology Lab Hangzhou China University of Luxembourg Luxembourg Human Language Technology Research Institute University of Texas at Dallas RichardsonTX United States

ISBN: (纸本)9798400702174

To date, over 40 Automated Program Repair (APR) tools have been designed with varying bug-fixing strategies, which have been demonstrated to have complementary performance in terms of being effective for different bug classes. Intuitively, it should be feasible to improve the overall bug-fixing performance of APR via assembling existing tools. Unfortunately, simply invoking all available APR tools for a given bug can result in unacceptable costs on APR execution as well as on patch validation (via expensive testing). Therefore, while assembling existing tools is appealing, it requires an efficient strategy to reconcile the need to fix more bugs and the requirements for practicality. In light of this problem, we propose a Preference-based Ensemble Program Repair framework (P-EPR), which seeks to effectively rank APR tools for repairing different bugs. P-EPR is the first non-learning-based APR ensemble method that is novel in its exploitation of repair patterns as a major source of knowledge for ranking APR tools and its reliance on a dynamic update strategy that enables it to immediately exploit and benefit from newly derived repair results. Experimental results show that P-EPR outperforms existing strategies significantly both in flexibility and effectiveness. © 2024 IEEE Computer Society. All rights reserved.

关键词： Well testing

来源：评论

学校读者我要写书评

暂无评论

Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Minyang Tang, Xiangyun Zhang, Tao Liu, Guangyuan Weng, Yu Minzu University of China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance China Beijing Jiaotong University School of Software Engineering China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Cross-Lingual Word Alignment for ASEAN languages with Contrastive Learning

Cross-Lingual Word Alignment for ASEAN Languages with Contra...

引用

International Conference on Asian language Processing (IALP)

作者： Jingshen Zhang Xinying Qiu Teng Shen Wenyu Wang Kailin Zhang Wenhe Feng School of Information Science and Technolgy College of Computer and Software Engineering Hohai University Nanjing China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (数字)9798331540852

ISBN: (纸本)9798331540869

Cross-lingual word alignment plays a crucial role in various natural language processing tasks, particularly for low-resource languages. Recent study proposes a BiLSTM-based encoder-decoder model that outperforms pre-trained language models in low-resource settings. However, their model only considers the similarity of word embedding spaces and does not explicitly model the differences between word embeddings. To address this limitation, we propose incorporating contrastive learning into the BiLSTM-based encoder-decoder framework. Our approach introduces a multi-view negative sampling strategy to learn the differences between word pairs in the shared cross-lingual embedding space. We evaluate our model on five bilingual aligned datasets spanning four ASEAN languages: Lao, Vietnamese, Thai, and Indonesian. Experimental results demonstrate that integrating contrastive learning consistently improves word alignment accuracy across all datasets, confirming the effectiveness of the proposed method in low-resource scenarios. We will release our data set and code to support future research on ASEAN or more low-resource word alignment.

关键词： Codes Accuracy Contrastive learning Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Towards an Understanding of Large language Models in software engineering Tasks

arXiv

引用

arXiv 2023年

作者： Zheng, Zibin Ning, Kaiwen Zhong, Qingyuan Chen, Jiachi Chen, Wenqing Guo, Lianghong Wang, Weicheng Wang, Yanlin School of Software Engineering Sun Yat-sen University China Zhuhai Key Laboratory of Trusted Large Language Models China Pengcheng Laboratory China

Large language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks. Derivative products, like ChatGPT, have been extensively deployed and highly sought after. Meanwhile, the evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. However, there is still a lack of systematic research on applying and evaluating LLMs in software engineering. Therefore, this paper comprehensively investigate and collate the research and products combining LLMs with software engineering, aiming to answer two questions: (1) What are the current integrations of LLMs with software engineering? (2) Can LLMs effectively handle software engineering tasks? To find the answers, we have collected related literature as extensively as possible from seven mainstream databases and selected 123 timely papers published starting from 2022 for analysis. We have categorized these papers in detail and reviewed the current research status of LLMs from the perspective of seven major software engineering tasks, hoping this will help researchers better grasp the research trends and address the issues when applying LLMs. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of LLMs in various software engineering tasks, guiding researchers and developers to optimize. Copyright © 2023, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CancersQA: Federated Learning with Pre-trained Models for Intelligent Medical Diagnosis 8th

CancersQA: Federated Learning with Pre-trained Models for In...

引用

8th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2023

作者： Yang, Kunyu Peng, Luyao Liu, Zheng Wang, Chaomurilige Dai, Yihang Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China School of Information Engineering Minzu University of China Beijing100081 China School of Chinese Ethnic Minority Languages and Literatures Minzu University of China Beijing100081 China School of Software Engineering JiangXi Agricultural University Nanchang330000 China

ISBN: (纸本)9783031717154

Pre-trained Models (PTM) have demonstrated extraordinary efficacy in the domain of natural language processing (NLP), particularly in addressing critical challenges within question-answering (QA) systems. However, the medical QA field grapples with issues such as the low robustness of generation models and a shortage of available medical data, which is further complicated by stringent privacy requirements. Leveraging the synergy between Federated Learning (FL) and PTM, we propose an innovative CancersQA method for intelligent cancer diagnosis and effective aggregating algorithm WeiPro. This method employs the GPT-2 model in an FL framework and fine-tunes it using sparse and unevenly distributed medical consultation data. Our novel method is designed to perform optimally on small sample datasets, markedly reducing training time. Our WeiPro aggregation strategy consolidates the parameters of the FL clients by taking into account various factors. Finally, we conducted rigorous experiments to evaluate CancersQA’s performance. Our experimental results indicate that our approach significantly surpasses other models in medical QA tasks underscoring its superior potential for adapting large language models to healthcare’s critical domain. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

引用

2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Li, Yangding Fu, Shaobin Feng, Hao Zeng, Yangyang Wang, Jinghao Jiang, Zhihao Zhang, Lvyun Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University School of Information Science and Engineering Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China Hechi University School of Big Data and Computer Science Guangxi China

ISBN: (纸本)9798350312492

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive. © 2023 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Cross-Lingual Word Alignment for ASEAN languages with Contrastive Learning

arXiv

引用

arXiv 2024年

作者： Zhang, Jingshen Qiu, Xinying Shen, Teng Wang, Wenyu Zhang, Kailin Feng, Wenhe School of Information Science and Technolgy Guangdong University of Foreign Studies Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China College of Computer and Software Engineering Hohai University Nanjing China

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Practical Program Repair via Preference-based Ensemble Strategy

Practical Program Repair via Preference-based Ensemble Strat...

引用

International Conference on software engineering (ICSE)

作者： Wenkang Zhong Chuanyi Li Kui Liu Tongtong Xu Jidong Ge Tegawendé F. Bissyandé Bin Luo Vincent Ng National Key Laboratory for Novel Software Technology Nanjing University China Nanjing China Huawei Software Engineering Application Technology Lab Hangzhou China University of Luxembourg Luxembourg Human Language Technology Research Institute University of Texas at Dallas Richardson Texas USA

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

关键词： Measurement Costs Computer bugs Maintenance engineering software Ensemble learning Testing

来源：评论

学校读者我要写书评

暂无评论

Enabling Trustable Financing: A Verifiable Privacy-Preserving Cross-Chain Protocol

Enabling Trustable Financing: A Verifiable Privacy-Preservin...

引用

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Lidu Lou Wenbin He Xiangyun Tang Yiying Yang Tao Zhang Yanyu Cheng Cai Xu Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China China School of Software University of South China School of Software Engineering Beijing Jiaotong University China School of Cyberspace Hangzhou Dianzi University China State Key Laboratory of Media Convergence Production Technology and Systems China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Generative AI-enabled wireless communications enhance efficiency and offer new tools for supply chain finance (SCF) innovation. SCF optimizes capital efficiency and reduces risk, but information asymmetry hinders risk assessment. Blockchain technology addresses these challenges by solving information asymmetry and regulatory issues. However, issues of privacy protection, reliability, and traceability remain. To address this, we design a verifiable privacy protection cross-chain protocol based on threshold homomorphic encryption. This protocol ensures confidential sharing and verifiable access to enterprise information through homomorphic encryption and distributed private key technology, enhancing data security and controllability in SCF. Our simulation experiments demonstrate the protocol's feasibility and effectiveness in cross-chain SCF scenarios, supporting the application of blockchain technology in supply chain finance.

关键词： Wireless communication Privacy Technological innovation Protocols Supply chains Finance Blockchains Reliability Homomorphic encryption Protection

来源：评论

学校读者我要写书评

暂无评论

Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Minyang Li Xiangyun Tang Tao Zhang Guangyuan Liu Yu Weng Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Minzu University of China China School of Software Engineering Beijing Jiaotong University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

关键词： Training Accuracy Federated learning Distributed databases Robustness Data models Servers Global communication Standards Faces

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：