检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Yang, Xiaoyi Zhao, Yanqi Zhou, Sufang Wang, Lianhai Department of Computing The Hong Kong Polytechnic University Hong Kong School of Cyberspace Security Xi’an University of Posts and Telecommunications Xi’an710121 China School of Computer and Information Engineering Henan University Kaifeng475004 China Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology Shandong Academy of Sciences Jinan250014 China

Secure multiparty computation (MPC) is an important means to realize privacy computing (PC). Private set intersection cardinality (PSI-CA) is a variant of an important problem in MPC research, that is private set intersection. PSI-CA allows multiple participants jointly compute the size of the common intersection of their private sets through interaction without revealing any other information about their respective sets. Delegated PSI-CA delegates a large number of computing in the protocol to some untrusted cloud servers, which makes the protocol efficient. Delegated PSI-CA is considered to be of great significance for the construction of privacy-preserving contact tracing systems recently. However, the existing work is still not ideal for the resource-limited mobile terminal of clients. In this paper, we propose a lightweight delegated PSI-CA protocol based on multi-point oblivious pseudorandom function and collision-resistant hash function. Our protocol does not need to do additional pre-operations, so the computation complexity and the communication complexity on the client side are further reduced compare to the existing works. In addition, we build a privacy-preserving contact tracing system by utilizing our protocol, which can be publicly checked, named PC-CONTrace. The experimental results show that our system is more practical and advantageous for densely populated areas. © 2023, The Authors. All rights reserved.

关键词： Hash functions

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On a Scalable Path for Multimode MIMO-DSP

arXiv

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arXiv 2022年

作者： Barbosa, Fabio A. Rothe, Stefan Pohle, Dennis Czarske, Jürgen W. Ferreira, Filipe M. Optical Networks Dept Electronic & Electrical Eng University College London United Kingdom Laboratory of Measurement and Sensor System Technique Faculty of Electrical and Computer Engineering TU Dresden Dresden01062 Germany

A novel MIMO-DSP for space-division multiplexing over multimode fibres is proposed. A principal modes approach is shown to provide two-fold benefits: over 13 times channel memory reduction while minimising the number ... 详细信息

关键词： Space division multiple access

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Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

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FFH-ABE: Fast File-Hierarchy Attribute-Based Encryption Scheme for the Internet of Things

TechRxiv

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TechRxiv 2023年

作者： Wang, Haiyan Li, Yuan Wang, Shulan Hang, Lianguan Luo, Fucai Department of New Networks Peng Cheng Laboratory Shenzhen China College of Information Science and Technology Jinan University Guangzhou China College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou China

With the rapid deployment of cloud computing and the exponential growth of data, it is a trend for users with limited resources to outsource the tasks of data sharing and platform building to proxy cloud service providers (PSCPs). However, the openness and uncontrollability of the computing environment pose a great threat to users’ sensitive data and personal privacy. The highly functional encryption mechanisms based on ciphertext-policy attribute-based encryption (CP-ABE) were proposed to address these problems of secure data sharing in the IoT. In this paper, we propose an efficient and flexible file-hierarchy CP-ABE scheme based on the linear secret sharing scheme (LSSS) matrix. First, we design a hierarchical access control matrix using LSSS that can encrypt and decrypt multiple files simultaneously with a single access policy. Second, we employ multiple access control matrices to make access control more flexible. Third, we prove that our scheme is secure against the chosen-plaintext attack (CPA) under the decisional bilinear Diffie-Hellman (DBDH) assumption. Furthermore, we optimize the access control structure, and conduct experimental comparisons with the existing schemes by theoretical analysis and experimental simulation, showing that our scheme achieves significant improvements in both computation and storage costs. © 2023, CC BY.

关键词： Access control

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Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer

arXiv

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arXiv 2023年

作者： Zhang, Zhihao Chen, Yiwei Zhang, Weizhan Yan, Caixia Zheng, Qinghua Wang, Qi Chen, Wangdu School of Computer Science and Technology Xi'an Jiaotong University Key Laboratory of Intelligent Networks and Network Security Ministry of Education Xi'an Jiaotong University Xi’an China School of Computer Science and Technology Xi'an Jiaotong University & National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi’an China MIGU Video Shanghai China

Viewport prediction is a crucial aspect of tile-based 360◦ video streaming system. However, existing trajectory based methods lack of robustness, also oversimplify the process of information construction and fusion between different modality inputs, leading to the error accumulation problem. In this paper, we propose a tile classification based viewport prediction method with Multi-modal Fusion Transformer, namely MFTR. Specifically, MFTR utilizes transformer-based networks to extract the long-range dependencies within each modality, then mine intra- and inter-modality relations to capture the combined impact of user historical inputs and video contents on future viewport selection. In addition, MFTR categorizes future tiles into two categories: user interested or not, and selects future viewport as the region that contains most user interested tiles. Comparing with predicting head trajectories, choosing future viewport based on tile's binary classification results exhibits better robustness and interpretability. To evaluate our proposed MFTR, we conduct extensive experiments on two widely used PVS-HM and Xu-Gaze dataset. MFTR shows superior performance over state-of-the-art methods in terms of average prediction accuracy and overlap ratio, also presents competitive computation efficiency. © 2023, CC BY.

关键词： Forecasting

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Joint Task Scheduling and Container Image Caching in Edge Computing

Joint Task Scheduling and Container Image Caching in Edge Co...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Fangyi Mou Zhiqing Tang Jiong Lou Jianxiong Guo Wenhua Wang Tian Wang Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Institute of Artificial Intelligence and Future Networks Beijing Normal University China Yunnan Key Laboratory of Software Engineering Kunming Yunnan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

关键词： Processor scheduling Computational modeling Markov decision processes Image edge detection Containers Dynamic scheduling Delays

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A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition

arXiv

引用

arXiv 2023年

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo School of Computer Science and Technology The Ministry of Education Key Laboratory for Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong Shaanxi Xi’an710049 China School of Computer Science and Technology Xi’an Jiaotong Shaanxi Xi’an710049 China The BigKE Joint Innovation Center SHMEEA Shanghai China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Analysis of the Spatial Smoothing Influence on the Perfomance of 2D-MUSIC for Ultra-Dense networks

Analysis of the Spatial Smoothing Influence on the Perfomanc...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Ilya Grishin Grigoriy Fokin Vladimir Sevidov Darina Okuneva Department of Communication Networks The Bonch-Bruevich SPbSUT Saint Petersburg Russia Laboratory of Software Defined Radio The Bonch-Bruevich SPbSUT Saint Petersburg Russia Department of Radio Engineering S.M. Budyonny VAS Saint Petersburg Russia Department of Software Engineering and Computer Facilities The Bonch-Bruevich SPbSUT Saint Petersburg Russia

In this paper, we analyze the spatial smoothing as a method of signal processing, preceding the algorithm for determining the direction of arrival of 2D-MUSIC signals. The purpose of the method is to compensate the possible coherence of signals coming from different user devices to the uniform rectangular antenna array of the base station of the ultra-dense network operating in the millimeter wavelength range. The results of computational experiments allow to estimate the dependence of the performance of the 2D-MUSIC algorithm to the spatial smoothing procedure.

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5G Dual-mode Bandpass Filters Fed by Coaxial and ISGW 13

5G Dual-mode Bandpass Filters Fed by Coaxial and ISGW

引用

13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Chen, Jianpei Shen, Dongya Zhang, Xiupu Sa, Yuchao Wang, Lihui Yunnan University Yunnan Provincial Engineering Laboratory Of Cloud Wireless Access And Heterogeneous Networks Kunming650500 China IPhotonics Labs Department Of Electrical And Computer Engineering Concordia University MontrealQCH3G1M8 Canada School Of Electrics And Information Engineering Yunnan Minzu University Kunming650500 China

ISBN: (纸本)9781665434379

In the paper, two dual-model bandpass filters fed by coaxial and integrated substrate gap waveguide (ISGW) individually are proposed for 5G communication system. The 3dB operation band of the two filters are 25.6-26.7 GHz and 26.4-27.4 GHz. Two transmission zeros (TZs) are introduced by designing position of feeding structure. The simulated results show good filter characteristics of BPF. © 2021 IEEE.

关键词： Bandpass filters

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Detecting Fake-Normal Pornographic and Gambling Websites through one Multi-Attention HGNN

Detecting Fake-Normal Pornographic and Gambling Websites thr...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Xiaoqing Ma Chao Zheng Zhao Li Jiangyi Yin Qingyun Liu Xunxun Chen Institute of Information Engineering Chinese Academy of Sciences Beijing China National Engineering Laboratory for Information Security Technologies Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Geedge Networks Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

The rapid development of pornographic and gambling websites, fueled by the widespread abuse of information technology, has become a growing concern. They pose a serious threat to the physical and mental health of children and can also endanger personal property. Therefore, it is necessary to detect them. However, pornographic and gambling websites become more and more tricky, which shows fake-normal to evade censorship and challenges traditional content-based detection methods. Therefore, it is essential to rely on information about relationships between *** propose HMAN, one Multi-Attention Heterogeneous Graph Neural Network (HGNN) model to detect pornographic and gambling websites by integrating content features and structural information, even if they present fake-normal. By one multi-attention mechanism consisting of explicit weight, self-attention and attention mechanism, content features can be selectively utilized with the assistance of structural information. The experimental results show that our method achieves the best 95.1% Macro-Avg-F1 and outperforms all baselines. We also illustrate that all extracted metapaths do contribute to the detection, where the hyperlink, title/meta terms and IP address are relatively important.

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