检索结果-内蒙古大学图书馆

An Attack on a Proposed Construction of Small-State Stream Ciphers and Proposals for New Constructions (Nov, bxac165, 2022)

引用

computer JOURNAL 2024年第2期67卷 806-806页

作者： Amin-Ghafari, Vahid Attari, Mahmoud Ahmadian School of Information and Control Engineering China University of Mining and Technology Jiangsu 221116 P. R. China Electrical and Computer Engineering School K. N. Toosi University of Technology Tehran *** Iran

Small-state stream ciphers (SSCs) idea is based on using key bits not only in the initialization but also continuously in the keystream generation phase. A time-memory-data tradeoff (TMDTO) distinguishing attack was successfully applied against all SSCs in 2017 by Hamann et al. They suggested using not only key bits but also initial value (IV) bits continuously in the keystream generation phase to strengthen SSCs against TMDTO attacks. Then, Hamann and Krause proposed a construction based on using only IV bits continuously in the packet mode. They suggested an instantiation of an SSC and claimed that it is resistant to TMDTO attacks. We point out that accessing IV bits imposes an overhead on cryptosystems that might be unacceptable in some applications. More importantly, we show that the proposed SSC remains vulnerable to TMDTO attacks 1. To resolve this security threat, the current paper proposes constructions based on storing key or IV bits that are the first to provide full security against TMDTO attacks. Five constructions are proposed for different applications by considering efficiency. Designers can obtain each construction’s minimum volatile state length according to the desirable keystream, key and IV lengths.

关键词： cryptography distinguishing attack IoT security lightweight encryption stream cipher time-memory-data tradeoff attack

来源：评论

学校读者我要写书评

暂无评论

Reconfiguration of vertex-disjoint shortest paths on graphs

引用

Journal of Graph Algorithms and Applications 2024年第3期28卷 87-101页

作者： Saito, Rin Eto, Hiroshi Ito, Takehiro Uehara, Ryuhei Graduate School of Information Sciences Tohoku University Sendai Japan School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

We introduce and study reconfiguration problems for (internally) vertex-disjoint shortest paths: Given two tuples of internally vertex-disjoint shortest paths for fixed terminal pairs in an unweighted graph, we are asked to determine whether one tuple can be transformed into the other by exchanging a single vertex of one shortest path in the tuple at a time, so that all intermediate results remain tuples of internally vertex-disjoint shortest paths. We also study the shortest variant of the problem, that is, we wish to minimize the number of vertex-exchange steps required for such a transformation, if exists. These problems generalize the well-studied Shortest Path Reconfiguration problem. In this paper, we analyze the complexity of these problems from the viewpoint of graph classes, and give some interesting contrast. © 2024, Brown University. All rights reserved.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

High Dynamic Range Imaging for Dynamic Scenes Based on Multi-Level Spike Camera

引用

IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5394-5406页

作者： Zhu, Zhenkun Xiong, Ruiqin Zhao, Jing Zhao, Rui Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking University School of Computer Science Beijing100871 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the current design is that each spike only represents the arrival of a fixed amount of photons. It can not deal with strong light areas in which the amount of accumulated photons reaches the pre-specified threshold multiple times within a single readout interval. In this paper, we propose a new spike camera model of high-speed imaging for high dynamic range scenarios. In this scheme, each pixel accumulates the incoming photons persistently and generates a new type of spike stream in which each spike symbol may be associated with different levels, indicating the arrival of different amounts of photons since the last readout. This enables the camera to support dynamic scenes with wider dynamic range. To achieve this, we propose a two-level buffer mechanism, one for photon accumulation and one for spike-firing encoding. We use a register to hold the number of spike-firings which has not been read out yet. At each readout time, the major part in the counter is read out via a carefully designed exponential encoding and the counter is updated. Such encoding and readout strategy enables a very efficient expansion of the dynamic range using a small number of encoding bits. Furthermore, we propose an image reconstruction scheme for the proposed camera, utilizing both spike intervals and spike levels to recover the light intensity. We incorporate Mamba and propose a temporal-spatial selective scan mechanism to extract temporal-spatial correlation within spike streams. We employ a pyramid adaptive filtering and alignment module to achieve coarse-to-fine feature alignment. Experimental results show that the proposed scheme can achieve better imaging quality and outperform the existing spike camera in high dynamic range scenarios. © 1991-2012 IEEE.

关键词： Adaptive filtering

来源：评论

学校读者我要写书评

暂无评论

Residual diverse ensemble for long-tailed multi-label text classification

引用

Science China(information Sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

来源：评论

学校读者我要写书评

暂无评论

DIEONet:Domain-Invariant information Extraction and Optimization Network for Visual Place Recognition

引用

computers, Materials & Continua 2025年第3期82卷 5019-5033页

作者： Shaoqi Hou Zebang Qin Chenyu Wu Guangqiang Yin Xinzhong Wang Zhiguo Wang School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Institute of Public Security Kash Institute of Electronics and Information IndustryKashi844000China

Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and *** is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR *** this end,this paper proposes a Domain-invariant information Extraction and Optimization Network(DIEONet)for *** core of the algorithm is a newly designed Domain-invariant information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object *** Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during *** demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative *** particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.

关键词： Visual place recognition domain-invariant information mining module multi-sample joint triplet loss

来源：评论

学校读者我要写书评

暂无评论

Soft transmission of 3D video for low power and low complexity scenario

引用

Digital Communications and Networks 2023年第3期9卷 769-778页

作者： Ao Li Taihai Yang Wenxin Wu Lei Luo School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology East China Normal UniversityShanghai200062China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Thanks to the rapid development of naked-eye 3D and wireless communication technology,3D video related applications on mobile devices have attracted a lot of ***,the time-varying characteristics of the wireless channel is very challenging for conventional source-channel coding based transmission ***,the high complexity of source-channel coding based transmission scheme is undesired for low power mobile *** advanced transmission scheme named Softcast was proposed to achieve efficient transmission performance for 2D image/***,it cannot be directly applied to wireless 3D video transmission with high *** paper proposes a more efficient soft transmission scheme for 3D video with a graceful quality adaptation within a wide range of channel Signal-to-Noise Ratio(SNR).The proposed method first extends the linear transform to 4 dimensions with additional view dimension to eliminate the view redundancy,and then metadata optimization and chunk interleaving are designed to further improve the transmission ***,a synthesis distortion based chunk discard strategy is developed to improve the overall 3D video quality under the condition of limited *** experimental results demonstrate that the proposed method significantly improves the 3D video transmission performance over the wireless channel for low power and low complexity scenarios.

关键词： Softcast Low power 3D video Wireless

来源：评论

学校读者我要写书评

暂无评论

A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optimization Algorithm for Handwritten Document Enhancement

引用

computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

来源：评论

学校读者我要写书评

暂无评论

Tensor-Based Factorial Hidden Markov Model for Cyber-Physical-Social Services

引用

IEEE Transactions on Services Computing 2025年第3期18卷 1825-1837页

作者： Lu, Zhixing Yang, Laurence T. Azman, Azreen Zhang, Shunli Zhou, Fang Hainan University School of Computer Science and Technology Hainan China Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang Malaysia Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China Huazhong University of Science and Technology School of Computer Science and Technology Huazhong China St. Francis Xavier University Department of Computer Science Canada

With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score. © 2008-2012 IEEE.

关键词： Viterbi algorithm

来源：评论

学校读者我要写书评

暂无评论

Ensemble Prototype Networks for Unsupervised Cross-Modal Hashing With Cross-Task Consistency

引用

IEEE Transactions on Multimedia 2025年 27卷 3476-3488页

作者： Liu, Xiaoqing Zeng, Huanqiang Shi, Yifan Zhu, Jianqing Yang, Kaixiang Yu, Zhiwen Huaqiao University School of Information Science and Engineering Xiamen361021 China Huaqiao University School of Engineering Quanzhou362021 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

In the swiftly advancing realm of information retrieval, unsupervised cross-modal hashing has emerged as a focal point of research, taking advantage of the inherent advantages of the multifaceted and dynamism inherent in multimedia data. Existing unsupervised cross-modal hashing methods rely mainly on initial pre-trained correlations among cross-modal features, and the inaccurate neighborhood correlations impacts the presentation of common semantics throughout the optimization. To address the aforementioned issues, we propose Ensemble Prototype Networks (EPNet), which delineates class attributes of cross-modal instances through an ensemble clustering methodology. EPNet seeks to extract correlation information between instances by leveraging local correlation aggregation and ensemble clustering from multiple perspectives, aiming to reduce initialization effects and enhance cross-modal representations. Specifically, the local correlation aggregation is first proposed within a batch of semantic affinity relationships to generate a precise and compact hash code among cross-modal instances. Secondly, the ensemble prototype module is employed to discern the class attributes of deep features, thereby aiding the model in extracting more universally applicable feature representations. Thirdly, an early attempt to constrict the representational congruity of local semantic affinity relationships and deep feature ensemble prototype correlations using cross-task consistency loss aims to enhance the representation of cross-modal common semantic features. Finally, EPNet outperforms several state-of-the-art cross-modal retrieval methods on three real-world image-text datasets in extensive experiments. © 1999-2012 IEEE.

关键词： Semantics

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：