检索结果-内蒙古大学图书馆

Knowledge-based recommendation with contrastive learning

High-Confidence Computing 2023年第4期3卷 41-46页

作者： Yang He Xu Zheng Rui Xu Ling Tian School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Kash Institute of Electronics and Information Industry Kashgar 844199China China Electronic Technology Cyber Security Co. Ltd.Chengdu 610041China Cyberspace Security Key Laboratory of Sichuan Province Chengdu 610041China

Knowledge Graphs(KGs)have been incorporated as external information into recommendation systems to ensure the high-confidence ***,Contrastive Learning(CL)framework has been widely used in knowledge-based recommendation,owing to the ability to mitigate data sparsity and it considers the expandable computing of the ***,existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge:(1)For the knowledge view generation,they only perform simple data augmentation operations on KGs,resulting in the introduction of noise and irrelevant information,and the loss of essential information.(2)For the knowledge view encoder,they simply add the edge information into some GNN models,without considering the relations between edges and ***,this paper proposes a Knowledge-based Recommendation with Contrastive Learning(KRCL)framework,which generates dual views from user–item interaction graph and ***,through data enhancement technology,KRCL introduces historical interaction information,background knowledge and item–item semantic ***,a novel relation-aware GNN model is proposed to encode the knowledge ***,through the designed contrastive loss,the representations of the same item in different views are closer to each *** with various recommendation methods on benchmark datasets,KRCL has shown significant improvement in different scenarios.

关键词： Knowledge graph Recommendation systems Contrastive learning Graph neural network

来源：评论

学校读者我要写书评

暂无评论

A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

引用

18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM with Attention Mechanism 6

Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM w...

引用

6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Sun, Feihu Zhong, Yanru Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China

ISBN: (纸本)9798350377798

The existing power load data exhibits complex multidimensionality, while existing techniques often focus on learning and forecasting from single load data. To fully exploit the complex multidimensionality of the existing power load data and enhance the accuracy of short-term load forecasting, a smart grid short-term load forecasting model based on attention mechanism and CNN-BiLSTM is proposed. Firstly, the one-dimensional convolutional network (1D-CNN) slides along the temporal dimension to extract multidimensional features such as temperature and humidity from the raw power load data. Secondly, the Bidirectional Long Short-Term Memory network (BiLSTM) captures both historical and future information of the data through gate mechanisms and the bidirectional network structure. Lastly, the attention mechanism allocates weights to focus on important parts of the load sequence data, enhancing the model's ability to identify key information and further improving the accuracy of short-term power load forecasting. The method is experimented on a real-world multivariate load dataset and compared with existing learning methods. The proposed model achieved lower RMSE in 24-hour load forecasting compared to CNN-LSTM, CNN-BiLSTM, LSTM-Attention, BiLSTM-Attention and CNN-LSTM-Attention models by 13.2%, 10.6%, 6.7%, 5.7% and 5.9% respectively. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Detection Based on Pre-trained Code Language Model and Convolutional Neural network 36

Vulnerability Detection Based on Pre-trained Code Language M...

引用

36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Liao, Tingfeng Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

Software vulnerabilities damage the reliability of software systems. Recently, many methods based on deep learning have been proposed for vulnerability detection by learning features from code sequences or various property graphs. However, sequence-based methods frequently fail to consider the structural information of the code, whereas graph-based methods often encounter difficulties in capturing long-distance contextual semantic information. To overcome these limitations, we propose PTCN, a novel vulnerability detection method that integrates Pre-Trained code language model and Convolutional Neural network (CNN). First, the code tokens sequence and the propagation chains are constructed from the source code in preparation for the subsequent feature extraction. Next, GraphCodeBERT, a graph-based pre-trained code language model, is introduced as a means of learning both the semantic and structural information contained in the code tokens sequence and the propagation chains. Then, a CNN architecture is designed to effectively extract the crucial vulnerability features from the hidden states of the last Transformer layer of GraphCodeBERT for vulnerability detection. The experiments on the public benchmark dataset REVEAL indicate that PTCN outperforms the state-of-the-art methods with respect to the Accuracy and F1-score metrics by 3.41%-15.80% and 22.68%-104.27%, respectively. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Lightweight PUF-Based Group Authentication Scheme for Privacy-Preserving Metering Data Collection in Smart Grid 19th

A Lightweight PUF-Based Group Authentication Scheme for Pri...

引用

19th EAI International Conference on Collaborative Computing: networking, Applications and Worksharing, CollaborateCom 2023

作者： Cao, Ya-Nan Wang, Yujue Ding, Yong Guo, Zhenwei Yang, Changsong Liang, Hai Guangdong University of Science and Technology Dongguan China Hangzhou Innovation Institute of Beihang University Hangzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong

ISBN: (纸本)9783031545276

With the development of information and communication technologies, the services provided by smart grid attract more users to join smart grid. However, with the explosive growth of the number of smart meters, the transmission between the control center and smart meters has brought huge data transmission and computing costs to the smart grid, which is prone to network congestion, untimely power service supply and other network conditions. This paper proposes a Physically Unclonable Function (PUF)-based lightweight group authenticated metering data collection scheme with privacy protection in smart grid (PGAC). The PGAC scheme is designed with lightweight cryptographic primitives, which is suitable for resource-constrained devices. In addition, the PGAC scheme divides the users into groups and uses the gateway as the repeater and aggregator of the communication data of each group, which reduces signaling and communication costs for activating additional request messages from a large number of devices. security analysis shows that the PGAC scheme maintains the security and privacy of the data collection process for large-scale smart meters. Functional analysis, theoretical analysis and performance analysis show that PGAC scheme has better authentication function and low communication cost. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Real-time arbitrarily-shaped scene text spotting with weakly supervised points

Real-time arbitrarily-shaped scene text spotting with weakly...

引用

2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Zhao, Longyang Zhang, Shuhao Xu, Zhi School of Computer Science and Information Security Guilin University of Electronics Technology No.1 Jinji road Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9781510677685

Recently, there has been widespread interest in arbitrary-shaped scene text spotting, showing notable progress in both speed and accuracy. However, common issues such as model complexity, slow inference speed, and challenges in deployment persist. For this reason, this paper proposes Weakly Supervised Point-collection network (WSPNet) for arbitrarily shaped scene text, which adopts deformable convolutional single-shot network architecture to realize multi-branch parallel reading of text, and combines weakly supervised text line point sampling to realize sampling recognition and transcription of arbitrarily shaped text instances. To enhance the robustness of multi-scale fusion, we utilize the Text Feature Fusion (TFF) module to dynamically acquire key features at different scales during the fusion process. This approach aims to reduce discrepancies in scale fusion. Extensive experiments on arbitrarily shaped benchmarks show that WSPNet achieves competitive accuracy and speed. For example, the proposed method achieves an end-to-end text recognition F-measure of 63.4 on the Total-Text dataset at 35.5 FPS, which is competitive with the best results in terms of speed and accuracy balance. © 2024 SPIE.

关键词： network architecture

来源：评论

学校读者我要写书评

暂无评论

HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

引用

IEEE Transactions on computers 2025年第07期74卷 2168-2182页

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

IoT-Assisted Blockchain-Based Car Rental System Supporting Traceability 28th

IoT-Assisted Blockchain-Based Car Rental System Supporting T...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Lipan Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Wang, Huiyong Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute Beihang University Hangzhou China Cyberspace Security Research Center Pengcheng Laboratory Shenzhen China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783031306778

The majority of existing car rental systems rely on a centralized database to manage the data. Although these centralized architectures could make the system design and operation costs are reduced, there are many security problems in real world applications. If the information of the car obtained by the tenant is not completely credible, it will lead to security risks in the rental process. This paper proposes a blockchain-based car rental system with the Internet of Things (IoT), where the blockchain is engaged to store the use history of the car, so that the car condition can be traced to the source and the risk of using the car for the tenant can be reduced. The IoT technology is employed to enable renters to flexibly rent and unlock platform cars. Our system also provides visual operation interface. The evaluation results demonstrate the practicality of our system in the online car rental market with higher security and more flexible rental methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

information spreading in mobile social networks: Identifying the influential nodes using FPSO algorithm 10

Information spreading in mobile social networks: Identifying...

引用

10th International Conference on information Technology: IoT and Smart City, ICIT 2022

作者： Zhang, Xinxin Xu, Li Xu, Zhenyu Sun, Hung-Min Lee, Chia-Wei College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China The Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taitung University Taiwan

ISBN: (纸本)9781450397438

The most influential nodes are capable of generating maximum influence and widest information spread to their connected nodes in mobile social networks. Hence identifying high influential users plays an important role in monitoring network public opinion. However, influence maximization is an open and challenging issue, and existing works only focus on online social networks but fail to characterize the user social relations and social behavior. In this paper, firstly, we present a novel method based on location and preference relationships to calculate the social influence of users, and the initial nodes are identified by community partition. Secondly, according to the stochastic information spreading process in MSNs, we propose the Fluctuate Particle Swarm Optimization (FPSO)-algorithm to solve the problem of changing communication relationship between users at the optimization stage. Finally, we use extensive experiments on the real datasets to prove the effectiveness of the proposed FPSO-algorithm. The experimental evaluation shows that in the real networks, our proposal achieves better performance than other related methods. © 2022 ACM.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Generalization Analysis for Label-Specific Representation Learning 38

Generalization Analysis for Label-Specific Representation Le...

引用

38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zhang, Yi-Fan Zhang, Min-Ling School of Cyber Science and Engineering Southeast University Nanjing210096 China School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Label-specific representation learning (LSRL), i.e., constructing the representation with specific discriminative properties for each class label, is an effective strategy to improve the performance of multi-label learning. However, the generalization analysis of LSRL is still in its infancy. The existing theory bounds for multilabel learning, which preserve the coupling among different components, are invalid for LSRL. In an attempt to overcome this challenge and make up for the gap in the generalization theory of LSRL, we develop a novel vector-contraction inequality and derive the generalization bound for general function class of LSRL with a weaker dependency on the number of labels than the state of the art. In addition, we derive generalization bounds for typical LSRL methods, and these theoretical results reveal the impact of different label-specific representations on generalization analysis. The mild bounds without strong assumptions explain the good generalization ability of LSRL. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

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：