检索结果-内蒙古大学图书馆

18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

引用

IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary information

引用

Advances in Atmospheric sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

来源：评论

学校读者我要写书评

暂无评论

Test Suite Generation Based on Context-Adapted Structural Coverage for Testing DNN 24

Test Suite Generation Based on Context-Adapted Structural Co...

引用

24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Kong, Wei Du, Qianjin Cao, Huayang Li, Hu Wang, Tong Tian, Jianwen Kuang, Xiaohui National Key Laboratory of Science and Technology on Information System Security Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9788995004395

Deep learning (DL) introduces a novel data-driven programming paradigm, where the system logic is constructed through data training. However, this approach poses challenges in terms of system analysis and defect detection. To address this issue, recent efforts have focused on testing deep learning systems using intuitive criteria known as Neuron Coverage (NC) and its variants. These criteria measure the activation proportion of neurons in a neural network. Unfortunately, recent DL applications have largely overlooked the importance of context during testing. To address this gap, this paper first incorporates the context of the DL pipeline deployed before test execution, such as medical diagnosis and Android Malware detection. Next, we formulate structural coverage criteria to guide test suite generation based on the properties of DL pipeline in different contexts. Furthermore, we proposed a coverage-guided search algorithm to efficiently generate test suites. Experimental results highlight the effectiveness of our approach in uncovering numerous erroneous behaviors in contexts such as medical image diagnosis and Android malware detection. This approach significantly enhances the robustness of DL models by considering the structural aspects of the DL pipeline. Copyright 2023 KICS.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Verifiable delay functions and delay encryptions from hyperelliptic curves

引用

Cybersecurity 2024年第4期7卷 110-121页

作者： Chao Chen Fangguo Zhang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Province Key Laboratory of Information Security Technology Guangzhou 510006China

Verifiable delay functions(VDFs)and delay encryptions(DEs)are two important primitives in decentralized systems,while existing constructions are mainly based on time-lock puzzles.A disparate framework has been established by applying isogenies and pairings on elliptic *** this line,we first employ Richelot isogenies and non-degenerate pairings from hyperelliptic curves for a new verifiable delay function,such that no auxiliary proof and interaction are needed for the ***,we demonstrate that our scheme satisfies all security requirements,in particular,our VDF can resist several attacks,including the latest attacks for ***,resorting to the same techniques,a secure delay encryption from hyperelliptic curves is constructed by modifying Boneh and Frankiln's IBE scheme,which shares the identical setup with our VDF *** far as we know,these schemes are the first cryptographic applications from high-genus isogenies apart from basic protocols,i.e.,hash functions and key exchange protocols.

关键词： Verifiable delay functions Delay encryptions Hyperelliptic curves Richelot isogenies Pairings

来源：评论

学校读者我要写书评

暂无评论

Explicit Constructions of Capacity-Achieving T-PIR Schemes over Small Fields via Generalized Minor Matrices

引用

IEEE Transactions on information Theory 2025年第7期71卷 5109-5129页

作者： Xu, Jingke Fang, Weijun Shandong Agricultural University School of Information Science and Engineering Tai’an271017 China Shandong University State Key Laboratory of Cryptography and Digital Economy Security Qingdao266237 China Ministry of Education Shandong University Key Laboratory of Cryptologic Technology and Information Security Qingdao266237 China Shandong University School of Cyber Science and Technology Qingdao266237 China

Suppose a distributed storage system containing M files is replicated across N servers, and a user wants to privately retrieve one file by accessing the servers such that the identity of the retrieved file is kept secret from any subset of up to T servers, where each file can be viewed as a vector over the q-ary finite field Fq. A scheme designed for this purpose is called a T-private information retrieval (T-PIR) scheme. We consider the problem of explicitly constructing capacity-achieving T-PIR schemes over small finite fields. In this paper, we first provide a general framework for constructing explicit capacity-achieving T-PIR schemes for all parameters, which only relies on an MDS array matrix with a special information set. To construct such an MDS array matrix, we propose a new family of matrices over finite fields, called the generalized minor matrices of the Moore matrix, and establish a series of key identities. By combining favourable properties of generalized minor matrices with our framework, we construct an explicit capacityachieving T-PIR scheme with optimal sub-packetization over the field Fq, as small as possible, for three classes of parameters N, T,M ≥ 3. Specifically, the first class of construction works for all N = d(2t − 1), T = dt, and the field size q is the least prime power satisfying qt−1 ≥ N. Moreover, this construction generalizes the scheme proposed by Xu and Wang in 2022, which only considers the case of N = d(2t−1), T = dt with 2t−1≥ N. For all N = d(2t + 1), T = dt, our second T-PIR scheme is the first explicit construction, and the field size q is the least prime power satisfying qt ≥ N, which is the smallest field size among all known explicit capacity-achieving T-PIR schemes. Particularly, when 2t≥ N, the field size of such constructions can be reduced to 2. In the case of N = 4s and T = 2s + 1, our scheme is the first one to reduce the field size to q = 2. Compared with all known explicitly capacity-achieving T-PIR schemes, the

关键词： Parallel processing systems

来源：评论

学校读者我要写书评

暂无评论

Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

引用

Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling Data privacy Protocols Databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

来源：评论

学校读者我要写书评

暂无评论

Combining Local Electromagnetic Scattering and Global Structure Features for SAR Open Set Recognition

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12572-12587页

作者： Lin, Qinjie Sun, Hao Xu, Yanjie Wang, Junyu Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Automatic target recognition (ATR) is crucial for synthetic aperture radar (SAR) image interpretation. However, existing SAR ATR primarily rely on algorithms from the field of computer vision, many methods have not adequately considered the imaging sensitivity of SAR images. Moreover, SAR ATR typically follows the closed set assumption, but in dynamic and open real-world environments, SAR systems may encounter target classes that have not been seen during training. To address these issues, this article proposes an SAR open set recognition (OSR) method that combines local electromagnetic scattering and global structure features. The global structural features focus on the overall relationship between target and background, while the local electromagnetic scattering features can effectively capture the scattering characteristics of target. Hence, we construct a global and local feature guidance network, fuses these complementary features as prior knowledge to guide deep features. Then, a class-anchor-based loss function is constructed by anchor loss and tuple loss, which cluster the features of known classes targets around predefined class anchors. Finally, the distance ratio is calculated to identify known classes targets and reject unknown classes targets. The experimental results on the MSTAR and SAR-ACD datasets show that our method outperforms other OSR methods in recall, precision, F1, and accuracy, demonstrating a superior performance in OSR. © 2008-2012 IEEE.

关键词： Automatic target recognition

来源：评论

学校读者我要写书评

暂无评论

An Image Semantic Communication system Based on GAN and Relative Position-Encoding

引用

IEEE Communications Letters 2025年第6期29卷 1280-1284页

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Laboratory of Cryptography Application Technology Innovation and Testing Verification Ministry of Industry and Information Technology Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

In-memory computing based on phase change memory for high energy efficiency

引用

science China(information sciences) 2023年第10期66卷 20-41页

作者： Luchang HE Xi LI Chenchen XIE Zhitang SONG National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Micro-System and Information Technology Chinese Academy of Sciences School of Microelectronics University of Science and Technology of China

The energy efficiency issue caused by the memory wall in traditional von Neumann architecture is difficult to reconcile. In-memory computing(CIM) based on emerging nonvolatile memory(NVM) is a promising solution to avoid data movement between storage and processors and realize highly energy-efficient computing. Compared with other NVM technologies, phase change random access memory(PCM) exhibits comprehensive performance for analog computing. In this paper, we review advanced PCM techniques,including phase-change materials, mechanisms, and unique properties, as a foundation and inspiration for implementing CIM architecture. Meanwhile, state-of-the-art PCM-based CIM systems are well discussed for high energy efficiency in artificial neural networks, spiking neural networks, and other artificial intelligence(AI) applications. Finally, we present the remaining challenges and potential solutions of CIM for further investigation.

关键词： in-memory computing phase change memory energy efficiency neural network memory wall

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：