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2021 2nd International Conference on Modeling, Big Data Analytics and Simulation, MBDAS 2021

作者： Wang, Yiteng Bao, Jinhong Li, Zehua Science and Technology on Communication Information Security Control Laboratory China No.36 Research Institute of China Electronics Technology Group Corporation China College of Telecommunication Engineering Hangzhou Dianzi University Hangzhou310018 China

As satellite positioning has shortcomings such as susceptibility to multipath interference, we look forward to using a new algorithm to solve these problems. The method of derivative dynamic time regularization is used as the matching method of geomagnetic navigation. Due to the factors of the inertial device itself, a certain cumulative error will occur in the positioning solution of the inertial navigation (INS). Therefore, an outdoor error model needs to be established, and the positioning error is corrected by filtering technology. To solve this problem, the Unscented Kalman Filter is used to fuse the positioning results of the two to obtain a more accurate positioning, with an average accuracy within 1.5 meters. © Published under licence by IOP Publishing Ltd.

关键词： Kalman filters

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Prompt and Contrastive Learning for Few-shot Sentiment Classification 5

Prompt and Contrastive Learning for Few-shot Sentiment Class...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Wang, Fei Chen, Long Huang, Xiaohua Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive Learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. Thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings. © 2022 ACM.

关键词： Computational linguistics

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News-driven financial warning based on label information attention 5

News-driven financial warning based on label information att...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Zhilin, Ren Long, Chen Xiaohua, Huang Wenjing, Wang Cai, Xu Wei, Zhao Ziyu, Guan Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Most existing news-driven stock market prediction methods ignore the potential relationship between financial news and stocks. The complex relationship can help us to improve the accuracy of algorithmic trading systems. Therefore, we propose a deep learning method for financial warning by fusing Stock Label information (SLI). We extract events from news texts and fuse stock information together as feature vectors, using neural networks to model the underlying relationship between news and stocks. Experimental results show that our method outperforms other baseline methods in experiments on TPX500 and TPX100 datasets. CCS CONCEPTS •Computing methodologies •Artificial intelligence •Natural language processing © 2022 ACM.

关键词： Deep learning

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Rotational random extraction of Toeplitz matrix for ghost imaging 4

Rotational random extraction of Toeplitz matrix for ghost im...

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4th International Conference on Optoelectronic science and Materials, ICOSM 2022

作者： Cheng, Zhengdong Sun, Maosheng Chen, Yi Chang, Haikuan Liu, Yanyan State Key Laboratory of Pulsed Power Laser Technology National University of Defense Technology Huangshan Road No.460 HeFei230037 China Science and Technology on Electro-Optical Information Security Control Laboratory Weiwu Road No.9 Tianjin300450 China

ISBN: (纸本)9781510662209

If Toeplitz matrix is used for compression-aware ghost imaging, the imaging quality will be very low. In order to solve the problem, a new experimental scheme for ghost imaging is proposed in this paper. The scheme first extracts the Toeplitz matrix elements randomly and sparsely using a revolving matrix, and then modulates the illumination light field. The sampling is performed by using the Toeplitz matrix as a fixed matrix and another measurement matrix as a revolving matrix. The revolving matrix rotates around its center at a constant angular velocity. The fixed Toeplitz matrix is superimposed with the revolving matrix to form the optical field modulation matrix. The results of simulation experiments show that light field modulation scheme of the rotational random extraction of Toeplitz matrix can modulate the light field with more randomness. The use of this scheme in ghost imaging experiments results in high quality images with low distortion. © 2023 SPIE. All rights reserved.

关键词： Matrix algebra

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Direct Position Determination in the Presence of Direction-Dependent Mutual Coupling Using a Moving Array 34

Direct Position Determination in the Presence of Direction-D...

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34th General Assembly and Scientific Symposium of the International Union of Radio science, URSI GASS 2021

作者： Ma, Fei Wang, Yuexian Wang, Ling Gong, Yanyun School of Electronics and Information Northwestern Polytechnical University Xi'an China The Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9789463968027

In this paper, a new approach without iteration is proposed for direct position determination (DPD) with Doppler shifts in the presence of unknown direction-dependent mutual coupling (DDMC). The special banded symmetric Toeplitz structure of mutual coupling matrix and Doppler shift containing location information can be exploited for constructing a space-time transformation matrix, and the spectral function which is utilized to obtain the locations of sources is established via the orthogonality between space-time transformation matrix and noise subspace. Simulation results show that our solution can accurately determine the emitter position in a single-step without a priori information of the DDMC. © 2021 URSI.

关键词： Electric fields

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Analysis of Attack Effectiveness Evaluation of AD hoc Networks based on Rough Set Theory 17

Analysis of Attack Effectiveness Evaluation of AD hoc Networ...

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17th International Conference on Computational Intelligence and security, CIS 2021

作者： Dai, Jiahao Chen, Yongqun China Electronic Corporation NO.36 Institute Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665494892

This paper mainly studies an attack effectiveness evaluation method for AD hoc networks based on rough set theory. Firstly, we use OPNET to build AD hoc network simulation scenario, design and develop attack module, and obtain network performance parameters before and after the attack. Then the rough set theory is used to evaluate the attack effectiveness. The results show that this method can effectively evaluate the performance of AD hoc networks before and after attacks. © 2021 IEEE.

关键词： Rough set theory

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The differential fault analysis on block cipher FeW

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Cybersecurity 2023年第2期6卷 62-74页

作者： Haiyan Xiao Lifang Wang Jinyong Chang School of Computer Science Northwestern Polytechnical UniversityXiAn 710055People's Republic of China Engineering University of PAP XiAn 710055People's Republic of China School of Information and Control Engineering XiAn University of Architecture and TechnologyXi'An 710055People's Republic of China State Key Laboratory of Information Security(SKLOIS) Institute of Information EngineeringChinese Academy of SciencesBeijing 100089People's Republic of China

Feather weight(FeW)cipher is a lightweight block cipher proposed by Kumar et *** 2019,which takes 64 bits plaintext as input and produces 64 bits *** Kumar et ***,FeW is a software oriented design with the aim of achieving high efficiency in software based *** seems that FeW is immune to many cryptographic attacks,like linear,impossible differential,differential and zero correlation ***,in recent work,Xie et *** the security of *** precisely,they proved that under the differential fault analysis(DFA)on the encryption states,an attacker can completely recover the master secret *** this paper,we revisit the block cipher FeW and consider the DFA on its key schedule algorithm,which is rather popular cryptanalysis for kinds of block *** particular,by respectively injected faults into the 30th and 29th round subkeys,one can recover about 55/80~69%bits of master *** the brute force searching remaining bits,one can obtain the full master secret *** simulations and experiment results show that our analysis is practical.

关键词： Differential fault analysis Block cipher FeW Side channel attack

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An analysis of network traffic identification based on decision tree 2

An analysis of network traffic identification based on decis...

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2nd International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2021

作者： Dai, Jiahao Chen, Yongqun Chen, Yucheng Meng, Aodong China Electronic Corporation NO.36 Institute Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665432658

With the strong development momentum of the Internet, network not only brings convenience to our life, but also poses threats to our network security. Users can hide their IP and identity information through encryption algorithm, and carry out various illegal activities and transactions in the network. Therefore, it will be of great value to identify special traffic which impact on network security and exploring abnormal situations in the network. Special traffic is important to the security of the network space. In this paper, we establish a decision tree model based on C4.5 and random forest to compare the accuracy of the two decision tree algorithms in traffic classification. Experimental results show that these two methods can identify traffic effectively. © 2021 IEEE.

关键词： Decision trees

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Energy Minimization Oriented Resource Allocation for Relay Assisted NOMA-MEC Networks

Energy Minimization Oriented Resource Allocation for Relay A...

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IEEE Conference on Global communications (GLOBECOM)

作者： Qianru Wang Liping Qian Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the growing demand for image transmission, there is a need for solutions that offer low energy consumption and low latency. In this paper, we present a novel relay-assisted system based on non-orthogonal multiple access (NOMA) and mobile edge computing (MEC). Our proposed system compresses images at the device end, decompresses them at either a relay or a cloud server (CS). The primary objective is to minimize system energy consumption under given task delay constraints. Considering that this is a non-convex optimization problem, we solve it by decomposing it into a continuous subproblem and a discrete subproblem. To solve the continuous subproblem, we convexify it by introducing new parameters and change variables to get the optimal the computing power of devices, relay and CS, sampling rate of devices, transmission power of devices and relay. To solve the discrete subproblem, we propose a cross-entropy (CE) algorithm to obtain the optimal decompression decision and subcarrier allocation decision. Simulation results demonstrate the accuracy and effectiveness of our algorithm in optimizing total energy consumption compared to the Linear Interactive and General Optimizer (LINGO) and frequency division multiple access (FDMA) methods.

关键词： Performance evaluation NOMA Energy consumption Image coding Multi-access edge computing Simulation Optimization methods Resource management Servers Relays

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