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SSRN 2024年

作者： Zhu, Chaoxi Zhao, Wei Zhao, Jianrong Zhou, Zhengchun National Key Laboratory of Security Communication Chengdu610041 China School of Economic Mathematics Southwestern University of Finance and Economics Chengdu610074 China School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China

Let L ∈ 2 be a lattice of 2-dimension. The conclusion "the shortest vector s1 (under the supremum norm) in L\{0} and the second shortest vector s2 (the shortest vector in L\{ns1|n ∈ }) form a base of L" is widely used. In this note, we find this conclusion is slightly flawed. Let c be a nonzero real number. We prove that the above conclusion is true except that L = {c(n1, n2)|n1, n2 ∈ } with {s1, s2} ⊆ {(c, c), (−c, −c), (c, −c), (−c, c)}, in which case s1 and s2 don’t form a base of L although s1 and s2 are successive minimal vectors of L under the supremum norm. © 2024, The Authors. All rights reserved.

关键词： Vectors

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Multi-view infrared small target recognition based on YOLOv5

Multi-view infrared small target recognition based on YOLOv5

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Chinese Automation Congress (CAC)

作者： Hua Gong Shasha Liu Song Guan Kuaikuai Yu Fang Liu College of Science Shengyang Ligong University Shenyang China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

In order to improve the recognition accuracy of small infrared targets in complex environment, a SyluInfrared dataset is built by taking multi-view infrared images with the Frock TiX600 infrared thermal imager. Seven image enhancement algorithms including image brightening, contrast adjustment, image blurring, image flipping and image scaling are designed to improve the image quality of small infrared targets. The YOLOv5 deep learning model is used to identify small infrared targets with multiple views to improve the recognition accuracy of small infrared targets. The experimental results show that the accuracy of multi-view infrared small target recognition model for YOLOv5 deep network is 97.7%, which is 6.6% higher than that of single-view infrared small target recognition model based on YOLOv5. The multi-view target recognition strategy significantly improves the infrared small target recognition performance.

关键词： Image quality Deep learning Image recognition Automation Target recognition Object detection Data models

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Joint Computation Offloading and Resource Allocation for LEO Satellite Networks Using Hierarchical Multi-Agent Reinforcement Learning

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IEEE Transactions on Cognitive communications and Networking 2024年

作者： Lai, Junyu Liu, Huashuo Xu, Guoyao Jiang, Weiwei Wang, Xiong Jiang, Dingde University of Electronic Science and Technology of China School of Aeronautics and Astronautics China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China Ministry of Education Key Laboratory of Universal Wireless Communications China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

The integration of edge computing with LEO satellite broadband networks (LSBNs) offers a transformative potential, yet remains underexplored in the optimization of joint computation offloading and resource allocation (JCORA). This problem is compounded by the issues of load imbalance and hybrid action spaces. To tackle them, we firstly propose a multi-level edge computing architecture that leverages the inter-satellite links to enable collaborative offloading among neighboring satellites, thereby enhancing global resource utilization and load balance in LSBNs. Moreover, existing studies demonstrate the benefits of deep reinforcement learning (DRL) for JCORA optimization but struggle with the complexities of hybrid action spaces. To address this, we elaborate a novel hierarchical multi-agent DRL (HMADRL) framework that decomposes the JCORA problem into two-layered subproblems, namely global computation offloading and local resource allocation. This decomposition effectively mitigates the challenge posed by hybrid action spaces. The computation offloading subproblem is formulated as a delayed-reward partially observable Markov decision process, optimized by using multi-agent deep Q-networks specialized in discrete action outputs. Meanwhile, the resource allocation subproblem is addressed through the deep deterministic policy gradient model, adept at handling continuous actions. Extensive experiments validate our approach, demonstrating improvements in delay reduction, outrage rate, and load balancing compared to baselines. © 2015 IEEE.

关键词： Markov processes

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors 39

NumbOD: A Spatial-Frequency Fusion Attack Against Object Det...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei 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 Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs’ attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Infrared Target Detection Based on the Fusion of Mask R-CNN and Image Enhancement Network

Infrared Target Detection Based on the Fusion of Mask R-CNN ...

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Chinese Automation Congress (CAC)

作者： Fang Liu Song Guan Kuaikuai Yu Hua Gong College of Science Shengyang Ligong University Shenyang China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

In this paper, we focus on the problem of the infrared image detection. Aiming at the problem of the low contrast of the infrared images, an adaptive gamma transformation algorithm (AGT) is established to intelligently improve the contrast of the infrared images. Aiming at the problem of the weak edge information of the infrared images, an improved Sobel operator edge enhancement algorithm (ISOEE) is designed to enrich the edge features of the infrared images. In order to solve the problem that the traditional infrared image detection algorithm relies on expert experience, the fusion infrared images detection model AIE-Mask R-CNN based on Mask R-CNN, AGT algorithm and ISOEE algorithm is proposed. This fusion model can realize accurate and intelligent detection for the infrared images. The experimental results show that the fusion model AIE-Mask R-CNN improves the detection accuracy of the infrared images.

关键词： Adaptation models Automation Image edge detection Object detection Detection algorithms Image enhancement

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Semi-LDPC Convolutional Codes:Construction and Low-Latency Windowed List Decoding

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Journal of communications and information Networks 2021年第4期6卷 411-419页

作者： Qianfan Wang Suihua Cai Li Chen Xiao Ma School of Electronics and Communication Engineering Sun Yat-sen UniversityGuangzhou 510006China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China

This paper presents a new coding scheme called semi-low-density parity-check convolutional code(semi-LDPC-CC),whose parity-check matrix consists of both sparse and dense sub-matrices,a feature distinguished from the conventional *** propose sliding-window list(SWL)decoding algorithms with a fixed window size of two,resulting in a low decoding latency but a competitive error-correcting *** performance can be predicted by upper bounds derived from the first event error probability and by genie-aided(GA)lower bounds estimated from the underlying LDPC block codes(LDPC-BCs),while the complexity can be reduced by truncating the list with a threshold on the difference between the soft metrics in the serial decoding *** results are presented to validate our analysis and demonstrate the performance advantage of the semi-LDPC-CCs over the conventional LDPC-CCs.

关键词： low-density parity-check convolutional codes(LDPC-CCs) spatially coupled LDPC(SC-LDPC)codes sliding-window list(SWL)decoding

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IoT ETEI: End-to-End IoT Device Identification Method

IoT ETEI: End-to-End IoT Device Identification Method

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IEEE Conference on Dependable and Secure Computing

作者： Feihong Yin Li Yang Yuchen Wang Jiahao Dai Xidian University Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

The past decades have seen the rapid development of Internet of Things (IoT) in various domains. Identifying the IoT devices connected to the network is a crucial aspect of network security. However, existing work on identifying IoT devices based on manually extracted features and prior knowledge, leading to low efficiency and identification accuracy. In this paper, we propose an automatic end-to-end IoT device identification method (IoT ETEI) based on CNN+BiLSTM deep learning model, which outperforms traditional methods from the perspective of overhead and identify accuracy. We demonstrate the effectiveness and flexibility of the proposed method by deploying IoT ETEI in the face of identifying IoT devices on public datasets with the accuracy rate over 99 %, even for IoT devices that use encryption protocols.

关键词： Deep learning Telecommunication traffic Feature extraction Encryption Object recognition Internet of Things security

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Joint Optimization Task Offloading Strategy for Mobile Edge Computing 2

Joint Optimization Task Offloading Strategy for Mobile Edge ...

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2nd IEEE International Conference on information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Tang, Xue Wen, Zhan Chen, Jiali Li, Yiquan Li, Wenzao College of Communication Engineer Chengdu University of Information Technology Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu611730 China

ISBN: (纸本)9781665428767

With increase of Internet of Things (IoT) devices, the burden of cloud computing servers is gradually increasing. Mobile Edge Computing (MEC), as an emerging solution, can relieve the pressure of computing and improve the quality of user experience. One of the critical challenges in MEC is to make effective use of communication and computing resources and select the task processing mode reasonably. In this paper, based on the scenario of a single mobile device (MD) to one mobile edge server (MES), we propose a Joint optimization method base on Discrete Binary Particle Swarm Optimization Algorithm (JOBPSO) for system load. The JOBPSO can optimize MD's task offloading, reduce delay and energy consumption. Finally, the simulation results indicate that the performance of JOBPSO in reducing system load is better than the other two traditional offloading methods. © 2021 IEEE.

关键词： Mobile edge computing

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Outlier Detection Method based on Improved K-means Clustering Algorithm 21

Outlier Detection Method based on Improved K-means Clusterin...

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5th International Conference on Electronic information technology and Computer Engineering, EITCE 2021

作者： Liu, Wenfen Wang, Nan Huang, Yuehua Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin Guangxi China School of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin Guangxi China College of Computer Science and Engineering Guilin University of Aerospace Technology Guilin Guangxi China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (纸本)9781450384322

Outlier detection is one of the important methods of data mining and analysis. The previous K-means outlier detection methods are easy to be misled by outliers in the clustering process and cannot detect outliers well. To solve this problem, we propose a method of outlier detection based on an improved K-means clustering algorithm. This method adaptively detects edge points (including outliers and edge points of other clusters) through the local threshold obtained from the distance distribution of the data points in the cluster to the centroid. The edge points are ignored when the centroid is updated to avoid the situation that the edge points mislead the centroid update. After completing the clustering, the farthest point in each cluster is put into the outlier cluster iteratively. When the average distance of the data in the cluster reaches stability or reaches the maximum number of iterations, the iteration ends. This method retains the ease of use of traditional K-means which accelerates the speed of clustering iteration and finds outliers accurately in data sets. Experimental results show that the algorithm can effectively detect outliers and have good results on multiple data sets. © 2021 ACM.

关键词： Anomaly detection

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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Computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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