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Instance Reweighting Adversarial Training Based on Confused Label

intelligent Automation & Soft computing 2023年第8期37卷 1243-1256页

作者： Zhicong Qiu Xianmin Wang Huawei Ma Songcao Hou Jing Li Zuoyong Li Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou511442China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger *** probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision ***,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our *** tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during ***-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial *** of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth ***-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger *** demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.

关键词： Reweighting adversarial training adversarial example boundary closeness confused label

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A Utility-Adjustable Reverse Auction Mechanism for UAV Data Collection Task Allocation 20th

A Utility-Adjustable Reverse Auction Mechanism for UAV Data...

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20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Chen, Peng Zhang, Jixian School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9789819628636

Due to their low cost, easy deployment, and high flexibility, unmanned aerial vehicles(UAV) have become widely utilized for various data collection tasks across different fields. In particular, virtual service providers(VSP) recruit UAVs to collect data in their regions of interest(ROI), and leverage this data to offer a range of services, such as metaverse, traffic management, etc. To attract UAV owners to participate in the recruitment activities, VSP need to provide participating users with payments as rewards to motivate their participation. However, the recruitment demands of different VSPs for UAVs can vary significantly. For instance, time-sensitive tasks require timely recruitment of sufficient UAVs to ensure prompt completion of data collection tasks, necessitating higher payments to increase user enthusiasm. Conversely, in other types of tasks, the VSP’s objective might be to maximize its own utility. However, designing a general incentive mechanism for VSPs with different tasks that satisfies incentive compatibility(IC) and individual rationality(IR) to address the issues of UAV task allocation and payment is challenging. If VSP offer insufficient payments to users, it will be difficult to attract enough users to share their UAVs;conversely, offering excessive payments to users will harm the VSP’ profits. To address this problem, we propose a utility-adjustable reverse auction mechanism based on reverse Vickrey-Clarke-Groves(VCG) auction and affine maximizer auction(AMA) theory to cope with different task scenarios. This incentive mechanism can maximize social welfare while flexibly adjusting the utility allocation between VSP and users, ensuring non-negative utility for both parties. Experimental results show that the mechanism has excellent performance. © IFIP International Federation for information processing 2025.

关键词： Unmanned aerial vehicles (UAV)

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different *** work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： Deep learning Metaheuristics Reinforcement learning Benchmark testing Search problems Robustness Behavioral sciences

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ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks 18th

ABCD-HN: An Artificial Network Benchmark for Community Dete...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Liu, Junjie Guo, Kun Wu, Ling Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial Network Benchmark for Community Detection on Heterogeneous Networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heterogeneous networks

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Regional People Counting Approach Based on Multi-frames Fusion Features Using MIMO Radar

Regional People Counting Approach Based on Multi-frames Fusi...

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International Conference on Internet of Things, Communication and intelligent Technology, IoTCIT 2022

作者： Huang, Xiaoze Yang, Zhaocheng Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China

ISBN: (纸本)9789819904150

Considering the diversity and temporal persistence of motion states and the limitation of data samples, we propose an approach based on multi-frames fusion features (MFFF-based) to count regional people employing multi-input multi-output (MIMO) radar. Random forest classifiers are used in this paper to classify motion states with the multi-frames fusion features including the average value features, the energy block proportion features and the adjacent energy block correlation coefficient features, and then cascade random forest classifiers with the modified two-dimensional (2D) spatial attribute features under clutter suppression with two different update coefficients, trained with data of people with different motion states to classify number of persons in the test field. The experimental results show that the proposed approach's average classification accuracy of zero person, one person and multiple persons is above 98.50% and significantly better than that of the existing regional people counting methods in the metal ramp. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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DE-Unet: Dual-encoder U-Net for Ultra-high Resolution Remote Sensing Image Segmentation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12290-12302页

作者： Liu, Ye Song, Shitao Wang, Miaohui Gao, Hao Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications United Kingdom

In recent years, there has been a growing demand for remote sensing image semantic segmentation in various applications. The key to semantic segmentation lies in the ability to globally comprehend the input image. While recent transformer-based methods can effectively capture global contextual information, they suffer from high computational complexity, particularly when it comes to ultra-high resolution (UHR) remote sensing images, it is even more challenging for these methods to achieve a satisfactory balance between accuracy and computation speed. To address these issues, we propose in this paper a CNN-based dual-encoder U-Net for effective and efficient UHR image segmentation. Our method incorporates dual encoders into the symmetrical framework of U-Net. The dual encoders endow the network with strong global and local perception capabilities simultaneously, while the U-Net's symmetrical structure guarantees the network's robust decoding ability. Additionally, multi-path skip connections ensure ample information exchange between the dual encoders, as well as between the encoders and decoders. Furthermore, we proposes a context-aware modulation fusion module that guides the encoder-encoder and encoder-decoder data fusion through global receptive fields. Experiments conducted on public UHR remote sensing datasets such as the Inria Aerial and DeepGlobe have demonstrated the effectiveness of proposed method. Specifically on the Inria Aerial dataset, our method achieves a 77.42% mIoU which outperforms the baseline [1] by 3.14% while maintaining comparable inference speed. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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A Generalized Non-convex Surrogated Framework for Anomaly Detection on Blurred Hyperspectral Images

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IEEE Transactions on Image processing 2025年 34卷 3108-3122页

作者： Wang, Yinjian Li, Wei Gui, Yuanyuan Xie, Haijun Zhang, Lianbo National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Guangdong Zhuhai519088 China

Hyperspectral imaging is endowed with outstanding discriminability between different land types by its comprehensive sensing of the spectrum, thus favored applying to anomaly detection. However, blurring effect, as a critical cause for quality deterioration of hyperspectral imaging, has been omitted by previous hyperspectral anomaly detection models. On one hand, given that anomalies are sparsely distributed in nature, such blurring effect entangling neighboring pixels severely weighs those detection models down. On the other hand, abnormal objects jeopardize the low-dimensional structure of the image, thus deblurring those images with anomalies is more challenging than normal ones. Hence, it is of much significance to investigate anomaly detection using blurred hyperspectral images. To this end, this paper proposes a generalized nonconvex surrogated tensor framework that is able to perform anomaly detection robustly to blurring effects on hyperspectral images. The proposed framework is featured to be a unified paradigm which guarantees convergence for a broad class of non-convex surrogates. Through treating the spatial and spectral low-rankness adaptively via Block Term Decomposition, the unevenness in the multi-linear low-rankness of hyperspectral image is comprehensively considered, which together with the non-convex surrogates results in a tighter modeling of the low-dimensional prior of hyperspectral images. Extensive experiments demonstrate the superiority of the proposed method compared with the state-of-the-art methods on both hyperspectral image deblurring and anomaly detection. © 1992-2012 IEEE.

关键词： Principal component analysis

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The Adjacency Codes of the First Yellow Graphs

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Journal of Systems Science & Complexity 2023年第4期36卷 1757-1768页

作者： SHI Minjia LI Shitao KIM Jon-Lark SOLE Patrick Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationSchool of Mathematical SciencesAnhui UniversityHefei 230601China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing 100093China School of Mathematical Sciences Anhui UniversityHefei 230026China Department of Math Sogang UniversitySeoulSouth Korea Aix Marseille Univ CNRSCentrale MarseilleI2MMarseilleFrance

The authors study the binary codes spanned by the adjacency matrices of the strongly regular graphs(SRGs)on at most two hundred vertices whose existence is *** authors show that in length less than one hundred they cannot be cyclic,except for the exceptions of the SRGs of parameters(85,42,20,21)and(96,60,38,36).In particular,the adjacency code of a(85,42,20,21)is the zero-sum *** the range[100,200]the authors find 29 SRGs that could possibly have a cyclic adjacency code.

关键词： Cyclic codes strongly regular graphs adjacency codes self-orthogonal codes

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Complementary Dual-Branch Network for Space-Time Video Super-Resolution 27th

Complementary Dual-Branch Network for Space-Time Video Supe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Tian, Ming Li, Tianyi Luo, RongSheng Gao, Changxin Sang, Nong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031781247

Space-time video super-resolution aims to simultaneously increase the space-time resolution of low-resolution and low frame-rate videos. Existing deep learning-based methods have made notable strides, predominantly achieving space-time video super-resolution through the relatively simple integration of modules for video super-resolution and video frame interpolation sub-tasks. However, these methods typically do not fully exploit the inherent relationships between the two sub-tasks. To address this limitation, we propose a Complementary Dual-Branch Network designed to better explore the interdependence of the two sub-tasks. Specifically, our dual-branch architecture facilitates mutual enhancement between video super-resolution and video frame interpolation sub-tasks within each branch and provides mutual guidance between the two branches. Additionally, we introduce a simple yet effective strategy for the rough estimation of optical flow, incorporating Flow-Guided Deformable Alignment into space-time video super-resolution to achieve precise motion estimation. In addition, we use an RNN-based Backward and Forward Recurrent module to ensure that all frames can utilize the information of the whole sequence. It is more efficient and memory saving compared to the currently popular bidirectional LSTM module. Experimental results on several datasets show that our method achieves superior accuracy and requires fewer parameters compared to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical flows

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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