检索结果-内蒙古大学图书馆

Efficient Registered Attribute Based Access Control with Same Sub-policies in Mobile Cloud Computing

IEEE Transactions on Mobile Computing 2025年

作者： Weng, Wuwei Li, Jiguo Zhang, Yichen Lu, Yang Shen, Jian Han, Jinguang Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Nanjing Normal University School of Computer and Electronic Information Nanjing210023 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China Southeast University School of Cyber Science and Engineering Nanjing210096 China

Ciphertext-policy attribute-based encryption (CP-ABE) has long been considered as a promising access control technology for cloud storage. However, CP-ABE depends on a central trusted authority to generate and distribute decryption keys, resulting in the key escrow issue. Most existing solutions only mitigate this problem but fail to resolve it entirely. Registered attribute-based encryption (RABE), a new cryptographic primitive, fundamentally addresses the key escrow problem by modifying the trust model, but its high computational overhead limits its practical application. Inspired by this challenge, we present an efficient registered attribute-based access control scheme designed for data encrypted with access policies containing the same sub-policy. In our scheme, users generate their own keys, while a key manager, who does not hold keys, replaces the central authority in managing users. Additionally, for data encrypted with the same sub-policy, the user's initial decryption stores the relevant parameters, which can be used for subsequent decryptions to reduce computational overhead. The proposed scheme is proven to achieve semantic security. Performance analysis demonstrates that our scheme enhances decryption efficiency by roughly 41.4% compared to existing RABE scheme, with a minimal storage trade-off, making it more practical for cloud storage application. © 2002-2012 IEEE.

关键词： Ciphertext

来源：评论

学校读者我要写书评

暂无评论

A Secure Lightweight RFID Mutual Authentication Protocol Without Explicit Challenge-Response Pairs 2nd

A Secure Lightweight RFID Mutual Authentication Protocol Wit...

引用

2nd EAI International Conference on Applied Cryptography in computer and Communications, AC3 2022

作者： Huang, Keke Lin, Changlu Liu, Yali College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Lab of Network Security and Cryptology Fujian Fuzhou350007 China School of Mathematics and Statistics Fujian Normal University Fujian Fuzhou350117 China College of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China

ISBN: (纸本)9783031170805

Radio Frequency Identification (RFID) has been widely deployed to various scenarios, but its security and privacy issues need to be concerned due to the tag’s limited computing and storage resources. While benefiting from the great convenience and advantages of RFID systems, security is still a considerable threat to their applications, such as desynchronization attacks and cloning attacks. In this paper, we propose a secure lightweight mutual authentication protocol based on the configurable tristate physical unclonable functions and the cryptographic primitive of verifiable secret sharing to solve these issues. More specifically, the tag equipped with the configurable tristate physical unclonable functions structure can enhance the tag’s security and effectively resist machine learning modeling attacks. Verifiable secret sharing plays the role of decentralized storage of secrets, and ensures the verifiability of the correctness of the each shares. The verifiability provided by verifiable secret sharing is effective against tag impersonation attacks, and the validity of the each share provided by the tag has to be verified before it is adopted. Finally, the correctness of our protocol is analyzed formally using BAN-logic and the its security is verified informally by the Scyther. In addition, we analyze security properties including data integrity, data confidentiality, anonymity, mutual authentication, forward security and resistance to various malicious attacks. The results show that the proposed protocol satisfies various security properties and resistance to diverse malicious attacks. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

MDFG: Multi-Dimensional Fine-Grained Modeling for Fatigue Detection 39

MDFG: Multi-Dimensional Fine-Grained Modeling for Fatigue De...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Mei Zhu, Xiaojie Hu, Ruimin Zhu, Dongliang Liao, Liang Ye, Mang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Cyber Science and Engineering Wuhan University Whuhan China Cyberspace Security Laboratory School of Network and Information Security Xidian University Xi'an China The School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Fatigue is a critical factor contributing to accidents in industries such as safety monitoring and engineering construction. Fatigue exhibits dynamic complexity and non-stationary characteristics, so there are many intermediate states of short-term variation between alert and fatigue. Capturing and learning the signs of these intermediate states is essential for accurate fatigue assessment. However, current fatigue detection methods primarily rely on coarse-grained labels, typically spanning minutes to hours, and commonly treat alert and fatigue as two distinctly separate distributions, overlooking the expression of intermediate states and oversimplifying the rich distribution information of fatigue types and levels, thereby limiting detection effectiveness. To address these, this paper explores a refined representation of fatigue in terms of three dimensions: time, type, and level, and proposes a MultiDimensional Fine-Grained Modeling for Fatigue Detection (MDFG). This introduces the SmallLoss to extract trustworthy samples, utilizes clustering to identify diverse subtypes under alert and fatigued states, and establishes base class sets in each state. Subsequently, a complete base class set containing intermediate state bases is constructed using the base class synthesis method, which achieves the expression of intermediate fatigue states from absence to presence. Finally, fatigue levels are quantified based on the matching between samples and the complete base class set. Moreover, to cope with the complex variability of fatigue states, MDFG employs meta-learning for training. MDFG achieves an Average accuracy improvement of 10.0% and 12.1% on two real datasets compared to methods that do not consider fine-grained information. Extensive experiments demonstrate that the MDFG exhibits superior robustness and stability among current fatigue detection methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserv

关键词： Safety factor

来源：评论

学校读者我要写书评

暂无评论

TSF-Net:Abnormal Video Behavior Detection Framework Based on Two-Stream Appearance Motion Semantic Fusion 23

TSF-Net:Abnormal Video Behavior Detection Framework Based on...

引用

Proceedings of the 2023 International Conference on information Education and Artificial Intelligence

作者： Bonan Li Rui Cao Xingang Wang Hong Zhang Jinyan Zhou China Radio and Television Shandong Network Co Ltd China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (纸本)9798400716157

Video anomalous behavior detection aims to detect rare and random anomalous events, which usually deviate from expectations. Existing detection methods do not consider the appearance semantics and motion semantics of the events in conjunction, so they lead to poor detection results. Since the appearance semantics and motion semantics of normal events are highly consistent and correspond, while the semantic consistency of abnormal events is low. Based on this, this paper proposes a two-stream fusion hybrid network for abnormal behavior detection. The appearance semantics and motion semantics of events are recorded separately using a multi-scale fused self-encoder to generate corresponding reconstructed appearance frames and reconstructed motion frames. Afterwards, the two frames are fused to generate the final prediction frame. We introduce temporal attention to enhance the model's ability to represent motion semantics and capture normal behavior with effective regular motion. Experiments on three standard public datasets show that the method proposed in this paper can accurately detect anomalous events with a performance comparable to state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Timo: In-memory temporal query processing for big temporal data

Timo: In-memory temporal query processing for big temporal d...

引用

作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

Frequency-aware Interaction network for Ultrasound Image Segmentation

引用

IEEE Transactions on Circuits and Systems for Video Technology 2024年

作者： Wang, Dongfang Zhou, Tao Zhang, Yizhe Gao, Shangbing Yang, Jian Nanjing University Of Science And Technology Pca Lab Key Lab Of Intelligent Percept. And Syst. For High-Dimensional Information Of Ministry Of Education Jiangsu Key Lab Of Image And Video Understanding For Social Security School Of Computer Science And Engineering Nanjing210094 China Huaiyin Institute Of Technology Faculty Of Computer And Software Engineering Huaian223003 China

Accurate segmentation of medical ultrasound images is crucial for guiding treatment decisions and assessing intervention effectiveness. The challenge of segmenting lesions in ultrasound images arises from factors such as low contrast, high speckle noise, artifacts, and blurred boundaries. Furthermore, this complexity varies significantly among lesions in different cases. While methods based on Convolutional Neural networks (CNNs) and Transformers have shown promising results in this field, each approach possesses distinct advantages and limitations. To address these challenges, we propose a novel Frequency-aware Interaction network (FINet). At the core of our FINet lies the proposed Multi-scale Frequency-aware Self-attention (MFS) module, which effectively captures multi-scale feature information within the self-attention layer. This enables our network to model both local and global features, capitalizing on the strengths of both CNNs and Transformers. Additionally, a frequency-aware network is introduced to learn the interactions between spatial locations in the frequency domain to enhance detailed feature representation such as edges. Furthermore, we present a collaborative interactive decoder network, in which a Selective Feature Interaction (SFI) module is proposed to facilitate the semantic and boundary feature interaction, resulting in more precise segmentation outcomes. Experimental results on four medical ultrasound image datasets show the superiority of our FINet over other state-of-the-art segmentation methods. More importantly, our model achieves an excellent trade-off between performance and computational efficiency. © 1991-2012 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

WaveLines:towards effective visualization and analysis of stability in power grid simulation

引用

Frontiers of computer Science 2021年第6期15卷 19-30页

作者： Tianye ZHANG Qi WANG Liwen LIN Jiazhi XIA Xiwang XU Yanhao HUANG Xiaonan LUO Wenting ZHENG Wei CHEN The state key lab of CAD&CG Zhejiang UniversityHangzhou 310058China School of Computer Science and Engineering Central South UniversityChangsha 410083China The State Key Lab of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Closely related to the safety and stability of power grids,stability analysis has long been a core topic in the electric *** approaches employ computational simulation to make the quantitative judgement of the grid stability under distinctive *** lack of in-depth data analysis tools has led to the difficulty in analytical tasks such as situation-aware analysis,instability reasoning and pattern *** facilitate visual exploration and reasoning on the simulation data,we introduce WaveLines,a visual analysis approach which supports the supervisory control of multivariate simulation time series of power *** design and implement an interactive system that supports a set of analytical tasks proposed by domain experts and experienced *** have been conducted with domain experts to illustrate the usability and effectiveness of WaveLines.

关键词： stability visual analysis power grid simulation data

来源：评论

学校读者我要写书评

暂无评论

Analysis of Interpretable Spiking Neural networks for Motor Imagery Recognition Tasks

SSRN

引用

SSRN 2023年

作者： Wang, Xiuqing Yang, Yunpeng Li, Qingru Li, Qiuting An, Yang Ye, Xiaoya Key Laboratory of Network & Information Security College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei Normal University Shijiazhuang China

Brain-computer interface (BCI) is an important aspect of human-computer interaction (HCI). Motor imagery (MI) EEG-based BCI has great application potential for assisting disabled people in motor function reconstruction and restoration. To fulfill the given tasks based on MI-EEG signals successfully, decoding MI-EEG signals accurately is important. Although deep learning (DL) can be used to analyze EEG signals, it still lacks interpretability. Compared with traditional artificial neural networks (ANNs), spiking neural networks (SNNs) can use spiking neurons which are bio-plausible for the neurons in the brain to communicate with each other, and analyze EEG signals by spike trains with better bio-interpretability. We propose a deep spiking convolutional neural network based on a self-attention (DSCNN-SA) mechanism for EEG-based MI recognition. The DSCNN-SA model is first used to extract the high-level spatio-temporal features of the EEG signal and transform the extracted features into spike trains as input to the deep spiking convolutional neural networks (DSCNNs), and analyze the EEG signal for MI according to the firing patterns of spiking neurons. The classification of 2-class motor imagery tasks is evaluated by the BCI Competition IV-2a and IV-2b datasets, and the average accuracy of the DSCNN-SA model is 85.53% and 81.52% respectively, which is better than that of the comparison models, such as KNN, MLP, DCNN-SA, ECCSP-TB2B, CNN-SAE, EEGNet, KLD and STNN, etc. The experimental results show that the DSCNN-SA model is effective for EEG-based MI recognition. © 2023, The Authors. All rights reserved.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

ICSGD-Momentum: SGD Momentum based on Inter-gradient Collision 22

ICSGD-Momentum: SGD Momentum based on Inter-gradient Collisi...

引用

22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zou, Weidong Cao, Weipeng Xia, Yuanqing Zhong, Bineng Li, Dachuan School of Automation Beijing Institute of Technology Beijing China Shenzhen China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798331527471

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, RAdam, Adabound, and AdaBelief. © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient and Accurate Encrypted Image Retrieval Scheme via Ball Tree

An Efficient and Accurate Encrypted Image Retrieval Scheme v...

引用

International Conference on Big Data Computing and Communications (BIGCOM)

作者： Xianxian Li Jie Lei Zhenkui Shi Feng Yu College of computer science Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network Information Integration Southeast University Nanjing China

With the rise and development of cloud computing, more and more companies try to outsource computing and storage to cloud in order to save storage and computing cost. Due to the rich information contained in images, the explosion of images is booming the image outsourcing. However, images may contain a lot of sensitive information and cloud servers are always not trusted. Directly outsourcing may lead to data breaches and incur privacy and security concerns. This has partly led to renewed interest in privacy-preserving encrypted image retrieval. However, there are still many challenges, such as low search accuracy and inefficiency due to the hundreds of high dimensional features extracted from a single image and the large scale of images. To address these challenges, in this paper, we propose an efficient, scalable and privacy-preserving image retrieval scheme via ball tree. First, the pre-trained Convolutional Neural network (CNN) model is employed to extract image feature vectors to improve search accuracy. Next, an encrypted ball tree is constructed by using Learning With Errors(LWE)-based secure k-Nearest Neighbor (kNN) algorithm. Finally, we conduct comprehensive experiments on real-world datasets and give a brief security analysis. The results show that our scheme is practical in terms of security, accuracy, and efficiency.

关键词： Cloud computing Costs Image retrieval Feature extraction Search problems Explosions Outsourcing

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：