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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yu, Xiaozong Dong, Youran Wang, Jialin Xu, Guanjun Chen, Jiajia Liu, Xiaolong Song, Zhaohui School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710126 China Space Information Research Institute Hangzhou Dianzi University Hangzhou310018 China Lishui Institute of Hangzhou Dianzi University Lishui323050 China China Mobile System Integration Co. Ltd. Shijiazhuang050011 China

ISBN: (纸本)9798350325959

The next-generation satellite-to-ground communication urgently needs a higher transmission rate. Due to the limited spectrum resources of traditional radio frequency links, free space optical (FSO) communication, as a promising alternative, is gaining increasing attention in the satellite community. However, atmospheric turbulence, fog intensity, and pointing error impairments seriously affect the performance of the FSO communication system. To effectively compensate for the influence of the above factors, the dual-hop FSO communication for the space-air-ground integrated network using the unmanned aerial vehicle as a relay is proposed in this paper. In particular, the statistical expressions of the proposed system, namely the cumulative distribution function and probability density function, are derived to evaluate the performance of the proposed system. Based on the above statistical results, performance indicators for the proposed system are derived, such as the outage probability, average bit error rate, and ergodic capacity. Finally, the performance indicators mentioned above are verified by simulation. © 2023 IEEE.

关键词： atmospheric turbulence fog free space optical communication space-air-ground integrated network

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Gradient-YOLO: Exploring the integration of gradient architecture into the YOLO network

Gradient-YOLO: Exploring the integration of gradient archite...

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International Joint Conference on Neural networks (IJCNN)

作者： Gang Li Cheng Zhang Delong Han Letian Gao Mingle Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection is an essential task in the field of computer vision. The one-stage object detection model directly completes object detection through a single forward propagation and has fast real-time object detection capabilities, making it widely used—especially the model of the You Only Look Once (YOLO) series. Improving the detection accuracy of the YOLO model has always been a research topic. The gradient architecture cascades feature information extraction and aggregates all features at the end, which can improve the feature fusion ability of the network. Combining the idea of gradient architecture, this paper proposes a YOLO based on gradient architecture: Gradient-YOLO. Specifically, this paper integrates gradient architecture into the backbone, neck, and bottleneck structures in the YOLO network, obtaining gradient backbone, gradient neck, and gradient bottleneck, respectively. By combining gradient architecture, various network parts in Gradient-YOLO can fully aggregate multi-layer features, reduce feature loss, and thus improve detection accuracy. This paper takes the mature YOLOv5 model and the latest YOLOv8 model as the baseline and combines gradient thinking to obtain Gradient-YOLOv5 and GradientYOLOv8. Moreover, conduct experimental testing on the MS COCO dataset. Regarding the mAP@.5 detection indicator, compared to YOLOv5s, Gradient-YOLOv5s increased by 5.07%. Compared to YOLOv8n, Gradient-YOLOv8n has increased by 4.63%. Therefore, the combination of gradient architecture can improve the detection accuracy of YOLO networks.

关键词： YOLO Accuracy Aggregates Computational modeling computer architecture Feature extraction Real-time systems

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A High-Dimensional Temporal Data Publishing Method Based on Dynamic Bayesian networks and Differential Privacy

A High-Dimensional Temporal Data Publishing Method Based on ...

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International Joint Conference on Neural networks (IJCNN)

作者： Yaxin Wang Zhen Zhang Heng Qian Yongchao Gao Qiuyue Wang Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Massive high-dimensional data generated by the Internet typically contains sensitive privacy information. Protecting data privacy while maintaining utility has become a pressing challenge. We propose a novel high-dimensional temporal data publishing method leveraging dynamic Bayesian networks and differential privacy. Initially, a dynamic Bayesian network is constructed, utilizing mutual information filtering of data. Subsequently, we calculate the Coherent Neighborhood Propinquity for each node within the network to determine edge sensitivity and establish a privacy budget. Noise is then strategically added to attribute data in accordance with the sensitivity and privacy budget requirements, ensuring the dataset complies with ε-differential privacy standards. Experimental results demonstrate that the data availability performance of the SMAP dataset (Soil Moisture Active Passive) surpasses that of competing algorithms while providing an equivalent level of privacy protection. Hence, our method significantly enhances data availability without compromising differential privacy protection.

关键词： Differential privacy Privacy Sensitivity Publishing Time series analysis Dynamic scheduling Bayes methods

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Improving User QoE via Joint Trajectory and Resource Optimization in Multi-UAV Assisted MEC

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IEEE Transactions on Services Computing 2025年第03期18卷 1472-1486页

作者： Gao, Yang Tao, Jun Xu, Yifan Wang, Zuyan Gao, Yu Wang, Meiling Hainan University School of Cyberspace Security Haikou570228 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Southeast University School of Cyber Science and Engineering Nanjing211189 China ZTE Communications Co. Ltd Nanjing210012 China

As a promising network architecture, Mobile Edge Computing (MEC), has been proven that can effectively reduce the end-to-end latency and the energy consumption. The Unmanned Aerial Vehicle (UAV) assisted MEC network, where the UAV can provide the computation offloading services for the mobile users, can further alleviate the huge deployment cost of static edge servers. However, it remains unsolved how multiple cooperative flying UAVs serve the ground users, especially considering that these UAVs may share the same wireless channel and can communicate with the users while flying. In this paper, we first propose the Age of Task (AoT) metric to measure the quality of experience, and then formulate the joint optimization problem to minimize the worst AoT among all the users. Based on the block coordinate descent (BCD) method, this problem is transformed into three non-convex programming sub-problems (i.e., the UAV-user association sub-problem, the UAV trajectory planning sub-problem and the transmit power optimization sub-problem). Specifically, the successive convex approximation (SCA) technique is exploited iteratively to deal with the non-convexity in the UAV trajectory and transmit power optimization. Numerical results show that the proposed scheme outperforms the benchmark offloading schemes in terms of AoT. © 2008-2012 IEEE.

关键词： Drones

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Imperceptible and Reliable Adversarial Attack 1

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4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a "sparse" or "global" way. Since the number of pixels perturbed by the "sparse" method and the perturbation intensity of each pixel added by the "global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an "pixel-wise" method which is somewhere in between the "sparse" or "global" way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Multi-Classification Segmentation Method of Gastric Cancer Pathological Images Based on Deep Learning

Multi-Classification Segmentation Method of Gastric Cancer P...

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Advanced information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Hehu Zhou Jingshan Pan Li Na Qingyan Ding Chengjun Zhou Wantong Du Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Pathology The Second Hospital of Shandong University Jinan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Gastric cancer is a serious health threat, and pathological imaging is important in detecting it. These images can assist doctors in accurately determining the location of the cancer, thereby providing an important reference for clinical decision-making. In the field of image processing, deep learning technology is leading to more and more excellent segmentation models. The Trans-Unet model has achieved success in the image segmentation. However, when applying this model to gastric cancer pathological section data, the segmentation boundary appears jagged. We propose three possible solutions. First, we designed an attention connection module to replace the skip connections in the model to enhance the prediction accuracy of the model. Second, we designed a prediction processing unit that takes the model’s prediction results as input and uses a Conditional Random Field (CRF) for further prediction calculations. To enable the model to meet the requirements of multi-classification of images, we designed a dynamic weight selection module to enhance the model’s accuracy for multi-classification tasks. After our optimization, the optimized model improved by 8% on the DSC evaluation index and 39% on the HD evaluation index. In addition, the jagged boundary problem in the prediction results has also been effectively improved. Through comparative experiments and erosion experiments, we found that the improved method enhances the accuracy of model prediction and reduces the jagged results at the boundary.

关键词： Training Image segmentation Pathology Adaptation models Accuracy Computational modeling Predictive models Data models Conditional random fields Cancer

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A Supervision Game-Theoretic Incentive Mechanism for Cooperative Sensing in Connected and Autonomous Vehicles

A Supervision Game-Theoretic Incentive Mechanism for Coopera...

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Future Communications and networks (FCN), International Conference on

作者： Pincan Zhao Changle Li Xinrui Zhang Yuchuan Fu Mengqiu Tian State Key Laboratory of Integrated Services Networks Xidian University Xi’an Shaanxi China School of Information Technology Carleton University Ottawa ON Canada Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

Cooperative sensing has emerged as a promising approach to enhance the sensing capabilities of connected and autonomous vehicles (CAVs) by enabling them to share sensory data through vehicle-to-vehicle communication. However, the lack of motivation for honest participation and the presence of malicious vehicles hinder the widespread adoption of cooperative sensing. To address these challenges, we propose a novel incentive mechanism based on a supervision-oriented game-theoretic approach. Our framework models the interactions between sensing providers (SPs) and sensing requesters (SRs) as a strategic game, where a supervision mechanism is introduced to monitor and penalize misbehavior. We analyze the Nash equilibrium of the game and derive optimal strategies for both SPs and SRs. Extensive simulations demonstrate the effectiveness of our approach in promoting sensing cooperation, ensuring the utility of participants, and improving the overall performance of cooperative sensing in CAVs compared to existing methods.

关键词： Connected vehicles Biological system modeling Ecosystems Vehicular ad hoc networks Games Nash equilibrium Data models Sensors Autonomous vehicles Monitoring

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Improving accuracy-robustness trade-off via pixel reweighted adversarial training 24

Improving accuracy-robustness trade-off via pixel reweighted...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jiacheng Zhang Feng Liu Dawei Zhou Jingfeng Zhang Tongliang Liu School of Computing and Information Systems The University of Melbourne State Key Laboratory of Integrated Services Networks Xidian University School of Computer Science The University of Auckland / RIKEN AIP Sydney AI Centre The University of Sydney

Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations are constrained by a predefined perturbation budget ε and are equally applied to each pixel within an image. However, in this paper, we discover that not all pixels contribute equally to the accuracy on AEs (i.e., robustness) and accuracy on natural images (i.e., accuracy). Motivated by this finding, we propose Pixel-reweighted AdveRsarial Training (PART), a new framework that partially reduces ε for less influential pixels, guiding the model to focus more on key regions that affect its outputs. Specifically, we first use class activation mapping (CAM) methods to identify important pixel regions, then we keep the perturbation budget for these regions while lowering it for the remaining regions when generating AEs. In the end, we use these pixel-reweighted AEs to train a model. PART achieves a notable improvement in accuracy without compromising robustness on CIFAR-10, SVHN and TinyImagenet-200, justifying the necessity to allocate distinct weights to different pixel regions in robust classification.

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DiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization

DiffForensics: Leveraging Diffusion Prior to Image Forgery D...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zeqin Yu Jiangqun Ni Yuzhen Lin Haoyi Deng Bin Li School of Computer Science and Engineering Sun Yat-sen University School of Cyber Science and Technology Sun Yat-sen University Department of New Networks Peng Cheng Laboratory Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

As manipulating images may lead to misinterpretation of the visual content, addressing the image forgery detection and localization (IFDL) problem has drawn serious public concerns. In this work, we propose a simple assumption that the effective forensic method should focus on the mesoscopic properties of images. Base on the assumption, a novel two-stage self-supervised framework leveraging the diffusion model for IFDL task, i.e., DiffForensics, is proposed in this paper. The DiffForensics begins with self-supervised denoising diffusion paradigm equipped with the module of encoder-decoder structure, by freezing the pre-trained encoder (e.g., in ADE-20K) to inherit macroscopic features for general image characteristics, while encour-aging the decoder to learn microscopic feature represen-tation of images, enforcing the whole model to focus the mesoscopic representations. The pre-trained model as a prior, is then further fine-tuned for IFDL task with the customized Edge Cue Enhancement Module (ECEM), which progressively highlights the boundary features within the manipulated regions, thereby refining tampered area local-ization with better precision. Extensive experiments on several public challenging datasets demonstrate the effectiveness of the proposed method compared with other state-of-the-art methods. The proposed DiffForensics could significantly improve the model's capabilities for both accurate tamper detection and precise tamper localization while con-currently elevating its generalization and robustness.

关键词： Location awareness Visualization Image edge detection Noise reduction Refining Multitasking Forgery

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Few-Shot Hyperspectral Image Classification with Spectral-Spatial Feature Fusion Based on Fuzzy Broad Learning System

Few-Shot Hyperspectral Image Classification with Spectral-Sp...

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IEEE International Conference on Image Processing

作者： Xiaopei Hu Guixin Zhao Aimei Dong Guohua Lv Yi Zhai Ying Guo Xiangjun Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In the few-shot hyperspectral image (HSI) classification, most current models don't fully utilize the advantage of spectral-spatial feature fusion, resulting in low classification accuracy. Therefore, we propose a few-shot HSI classification model with spectral-spatial feature fusion based on fuzzy broad learning system (FBLS) (FSFBLS). Firstly, we use a Gaussian filter to suppress noise while smoothing spectral features based on spatial information to achieve the first fusion of spectral-spatial features. Secondly, we use FBLS with fuzzy rules to fully model the complex mapping relationship between spectral-spatial features and HSI labels to complete HSI classification. The fuzzy processing can extract rich discriminative features to enhance the recognition of different categories. Finally, the guided filter corrects the misclassified samples of FBLS based on the guided image to achieve the second fusion of spectral-spatial features. Extensive experimental results on three public datasets demonstrate that FSFBLS achieves state-of-the-art classification performance compared to nine popular models.

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