检索结果-内蒙古大学图书馆

Corrigendum to "DSTF: A diversified spatio-temporal feature extraction model for traffic flow prediction" [Neurocomputing 621 (2025) 129280] (Neurocomputing (2025) 621, (S0925231224020514), (10.1016/***.2024.129280))

引用

Neurocomputing 2025年 638卷

作者： Wang, Xing Wang, Xiaojun Huang, Faliang Zou, Fumin Liao, Lyuchao Zeng, Ruihao Fujian Normal University College of Computer and Cyber Security China Fujian Normal University Digital Fujian Institute of Big Data Security Technology China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision China Fujian University of Technology Fujian Key Laboratory of Automotive Electronic and Electrical Drive Technology China The University of Sydney School of Civil Engineering Sydney Australia

The authors regret to inform that: (1) During the resubmission stage of the revised manuscript, in compliance with reviewers' recommendations to show standard deviations in the experimental results (In Section 5. Experiment implementation), a batch replacement operation (intended to replace "+" with "±" for data formatting) inadvertently replaced the "+" symbols in the mathematical formulas and the explanatory text with "±" symbols (In Section 3. Problem statement and Section 4. Methodology). (2) In proof stage, we sincerely apologize for failing to identify this issue during the final proofreading stage. Correction statement and details: As this error may affect readers' understanding, we request a corrigendum to rectify this error. In Section 3. Problem statement and Section 4. Methodology, all instances of "±" symbols within mathematical formulas and their explanatory text should be reverted to the original "+" symbols. The authors would like to apologize for any inconvenience caused. © 2025 The Authors

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

arXiv

引用

arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

来源：评论

学校读者我要写书评

暂无评论

Paradigm shift in psychiatric neuroscience:Multidimensional integrative theory

Medicine Plus

引用

Medicine Plus 2024年第2期1卷 1-5页

作者： Zhu-Qing Gong Bharat B.Biswal Xi-Nian Zuo State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain ScienceBeijing Normal UniversityBeijing 100875China National Basic Science Data Center Beijing 100190China

Abnormal structure and function of the human brain cause various mental and neurological *** search for neurobiological mechanisms and biomarkers associated with psychiatric disorders has always been a focal point and a challenging issue in *** magnetic resonance imaging(fMRI)allows the noninvasive re-cording of high spatial-temporal resolution brain activity in patients,making it one of the primary research methods in integrative neuroscience.

关键词： dimension shift siona multi integrative neuroscience paradigm psychiatric theory

来源：评论

学校读者我要写书评

暂无评论

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

引用

arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Cooperative Sensing Via Matrix Factorization of the Partially Received Sample Covariance Matrix

Cooperative Sensing Via Matrix Factorization of the Partiall...

引用

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

作者： Rui Zhou Wenqiang Pu Licheng Zhao Ming-Yi You Qingjiang Shi Sergios Theodoridis Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China School of Software Engineering Tongji University Shanghai China Department of Electronics Systems Aalborg University Denmark Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece

A fundamental problem in cognitive radio is spectrum sensing, which detects the presence of the primary users in a licensed spectrum. To boost the detection performance and robustness, the multiantenna detector has been investigated and various related methods have been developed, e.g., the energy detector, the eigenvalue arithmetic-to-geometric mean detector, and the generalized likelihood ratio test detector. Cooperative sensing, which makes use of multiple receivers distributed in different locations, has the advantage of being able to make full use of the distributed antennas and enjoy a high spatial diversity gain. However, the successful employment of cooperative sensing depends on the reliable information exchange among the cooperating receivers over a long range, which may be impractical for real-world scenarios. In this paper, we consider the scenario where each receiving node can only broadcast its received raw data in a short-range communication fashion. We propose a novel cooperative sensing scheme by allowing each node to send to the fusion center only local correlation coefficients, computed within a neighborhood. A detection algorithm, based on matrix factorization of the partially received sample covariance matrix, i.e., with missing entries, is proposed. The performance of our proposed cooperative scheme is verified via numerical experiments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

I-mRMR: Incremental max-relevance, and min-redundancy feature selection 3rd

I-mRMR: Incremental max-relevance, and min-redundancy featur...

引用

3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Xiu, Yeliang Zhao, Suyun Chen, Hong Li, Cuiping Information of School Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China

ISBN: (纸本)9783030260743

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

arXiv

引用

arXiv 2023年

作者： Zeng, Guangyang Mu, Biqiang Shi, Ling Chen, Jiming Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen China The Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

High Sensitivity Surrounding Refractive Index Sensor Based on Helical Long-Period Fiber Gratings Inscribed in Tapered Double-Cladding Fiber

High Sensitivity Surrounding Refractive Index Sensor Based o...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Yanping He Yuehui Ma Chen Jiang Peng Wei Yunqi Liu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Advanced Photonic Technology Lab College of Electronics and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing China

We demonstrate a surrounding refractive index (SRI) sensor based on helical long-period fiber grating inscribed in the tapered double-cladding fiber. The maximum SRI sensitivity is 5483.33 nm/RIU.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A dual-polarized zhe-shaped conformal patch antenna for 5G millimeter-wave applications 4

A dual-polarized zhe-shaped conformal patch antenna for 5G m...

引用

4th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2021

作者： Yang, Baifa Huang, Haixuan Feng, Botao Deng, Li College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781665432030

We propose a dual-polarized conformal Zhe-shaped patch antenna that is suitable for future millimeter wave (mmW) applications. The proposed antenna is composed of 3 different layers. A Zhe-shaped (喆) patch is printed on the upper surface of the top dielectric layer. In order to excite the Zhe-shaped radiating patch, two feeding networks are designed in the other two layers. The simulation result shows that a frequency bandwidth of 10.5% (24.75-27.5 GHz) and a good isolation performance ( © 2021 IEEE

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised classification on data streams with recurring concept drift and concept evolution

引用

knowledge-Based Systems 2021年 215卷 106749-106749页

作者： Zheng, Xiulin Li, Peipei Hu, Xuegang Yu, Kui Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601 Anhui China

Mining non-stationary stream is a challenging task due to its unique property of infinite length and dynamic characteristics let alone the issues of concept drift, concept evolution and limited labeled data. Although more attention has been attracted on the issues of concept drift and evolution in data streams, however, most of existing methods are supervised in nature, which probably result in a worse classification performance and lower efficiency in the case with scarcity of labeled data. Thus, in this paper, we proposed a semi-supervised framework with recurring concept drift and novel class detection called ESCR, which aims to detect recurring concept drift and concept evolution in data streams with partially labeled data. It is firstly built on an ensemble model consisted of several clustering-based classifiers. In terms of this framework, we adopt Jensen–Shannon divergence based change detection technique on classifier confidence score instead of classification error rate to detect recurring concept drifts. Meanwhile, we take concept evolution into consideration by monitoring the outliers with strong cohesion. Moreover, we further improve the execution efficiency of our framework by exploiting the recursive function and dynamic programming. Finally, extensive experiments conducted on both benchmark and synthetic data sets demonstrate the effectiveness and efficiency of our proposed semi-supervised framework in the handling of data streams with recurring concept drifts and concept evolution, as compared to several well-known semi-supervised data stream classification methods. © 2021 Elsevier B.V.

关键词： Concept evolution data stream classification Recurring concept drift

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

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

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：