检索结果-内蒙古大学图书馆

Engineering Reports 2025年第5期7卷

作者： Aiping Zhou Jin Qian School of Information Engineering Taizhou University Taizhou China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China

Significant cardinality change is an important sign of the beginning of network attacks. Hosts associated with significant cardinality changes usually exhibit abnormal behavior. Identifying abnormal hosts is meaningful for many applications such as anomaly detection. High-speed data streams remain a great challenge to accurately estimate cardinality changes and detect abnormal hosts in real-time. Sketches are a type of probability data structure, which are widely used to compress high-rate data streams and estimate their statistics. However, most existing studies cannot simultaneously measure two kinds of cardinality changes in a distributed manner and efficiently reconstruct addresses of abnormal hosts in a centralized manner because of high calculation and memory overhead. In this paper, we propose reversible sketch-based abnormal host identification. It constructs a reversible data structure and estimates cardinality changes using a probabilistic counting approach, so that abnormal sources and destinations are simultaneously identified based on their cardinality changes between consecutive measurement periods. Moreover, addresses of abnormal hosts can be reconstructed by only simple inverse calculation to find out attackers and victims. The experimental results illustrate that the proposed approach obtains superior performance for cardinality change estimation and addresses of abnormal host reconstruction in accuracy and performance compared with the existing approaches.

关键词： anomaly detection cardinality change estimation data streams reversible sketch

来源：评论

学校读者我要写书评

暂无评论

Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

引用

IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing 210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing 210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing 210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

来源：评论

学校读者我要写书评

暂无评论

Parallel Sketch Based Super Node Detection with Traceability

引用

Chinese Journal of Electronics 2018年第6期27卷 1133-1140页

作者： ZHOU Aiping LIU Lijun ZHU Huisheng ZHU Chen'gang CHEN Lin School of Computer Science and Technology Taizhou UniversityTaizhou 225300 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

Traffic measurement and monitoring is crucial for network applications, such as network security, network management and so on. One central problem is to detect super nodes, which have significant change of connection degree between consecutive measurement *** to weakness in massive network traffic processing for the centralized algorithm and low detection accuracy, space efficiency for super node detection algorithm based on flow sampling, we propose Parallel sketch based super node detection with traceability(PSD). It constructs parallel sketch and estimates connection degree of nodes by probabilistic counting approach, so that super nodes are identified using connection degree change between consecutive measurement periods. Moreover, IP addresses of super nodes are reconstructed by simple computing to trace attacker or victims. The experimental results illustrate that the proposed method outperforms the Compact spread estimator(CSE) and Data streaming and sampling(DSS) in terms of detection accuracy and storage utilization.

关键词： network measurement Super node detection Parallel sketch network data stream

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

Universal and General Quantum Simultaneous Secret Distribution with Dense Coding by Using One-Dimensional High-Level Cluster States

引用

Journal of computer Science & Technology 2021年第1期36卷 221-230页

作者： Zhi-Hao Liu Han-Wu Chen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Nanjing 211189China

A universal and general quantum simultaneous secret distribution(QSSD)protocol is put forward based on the properties of the one-dimensional high-level cluster states,in which one sender dispatches different high-level classical secret messages to many users at the same *** to the idea of quantum dense coding,the sender can send different two-dit classical messages(two d-level classical numbers)to different receivers simultaneously by using a one-dimensional d-level cluster state,which means that the information capacity is up to the *** estimate the security of quantum channels,a new eavesdropping check strategy is put ***,a new attack model,the general individual attack is proposed and *** is shown that the new eavesdropping check strategy can effectively prevent the traditional attacks including the general individual *** addition,multiparty quantum secret report(MQSR,the same as quantum simultaneous secret submission(QSSS))in which different users submit their different messages to one user simultaneously can be gotten if the QSSD protocol is changed a little.

关键词： quantum simultaneous secret distribution(QSSD) multiparty quantum secret report(MQSR) quantum simultaneous secret submission(QSSS) quantum secret sharing quantum broadcast communication

来源：评论

学校读者我要写书评

暂无评论

Community-Preserving Social Graph Release with Node Differential Privacy

引用

Journal of computer Science & Technology 2023年第6期38卷 1369-1386页

作者：张森倪巍伟付楠 School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 211189China

The goal of privacy-preserving social graph release is to protect individual privacy while preserving data *** structure,which is an important global pattern of nodes,is a crucial data utility as it is fundamental to many graph analysis ***,most existing methods with differential privacy(DP)commonly fall into edge-DP to sacri-fice security in exchange for ***,they reconstruct graphs from the local feature-extraction of nodes,resulting in poor community *** by this,we develop PrivCom,a strict node-DP graph release algorithm to maximize the utility on the community structure while maintaining a higher level of *** this algorithm,to reduce the huge sensitivity,we devise a Katz index based private graph feature extraction method,which can capture global graph structure features while greatly reducing the global sensitivity via a sensitivity regulation ***,under the condition that the sensitivity is fixed,the feature captured by the Katz index,which is presented in matrix form,requires privacy budget *** a result,plenty of noise is injected,mitigating global structural *** bridge this gap,we de-sign a private eigenvector estimation method,which yields noisy eigenvectors from extracted low-dimensional ***,a dynamic privacy budget allocation method with provable utility guarantees is developed to preserve the inherent relationship between eigenvalues and eigenvectors,so that the utility of the generated noise Katz matrix is well ***,we reconstruct the synthetic graph via calculating its Laplacian with the noisy Katz *** results confirm our theoretical findings and the efficacy of PrivCom.

关键词： differential privacy social graph community structure private eigenvector

来源：评论

学校读者我要写书评

暂无评论

CGNet: Classification-Guided Multi-Task Interactive network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun 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 Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia university datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion information exchange Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering

引用

Big Data Mining and Analytics 2018年第3期1卷 211-221页

作者： Yu Liu Shuai Wang M.Shahrukh Khan Jieyu He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information Integration Southeast University

Due to the widespread availability of implicit feedback(e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback,implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems,some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders(DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.

关键词： hybrid recommender system neural collaborative filtering auto-encoder implicit feedback

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

YOLO-CORE: Contour Regression for Efficient Instance Segmentation

引用

Machine Intelligence Research 2023年第5期20卷 716-728页

作者： Haoliang Liu Wei Xiong Yu Zhang School of Computer Science and Engineering and the Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 211189China

Instance segmentation has drawn mounting attention due to its significant ***,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level *** this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance *** mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector *** proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and *** achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO *** superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding ***,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).

关键词： computer vision instance segmentation object shape prediction contour regression polar distance.

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：