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IEEE International Conference on Communications (ICC)

作者： Yibo Lian Tao Zhang Changqiao Xu Wei Dong Minrui Xu Zhenyu Xiahou Jiawen Kang Jiqiang Liu Dusit Niyato School of Computer Science Beijing University of Posts and Telecommunications School of Software Engineering Beijing Jiaotong University School of Computer Science and Engineering Nanyang Technological University Jiangxi Academy of Cyber Security School of Automation Guangdong University of Technology

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The development of LEO satellite networks (LSN) makes them a potential solution to deliver broadcast/multicast traffic to deploy and upgrade massive amounts of Internet of Things (IoT) devices in future 6G networks. However, inherent resource constraints of LSN leave them vulnerable to a multitude of security threats, most notably distributed denial-of-service (DDoS) attacks. Existing solutions are primarily based on machine learning detection methods which are incapable of defending against unknown zero-day attacks. This paper presents an innovative solution leveraging deep reinforcement learning (DRL) to create a dynamic multicast tree based on moving target defense (MTD), aimed at enhancing the security of multicast services in LSN. The proposed solution adopts an adaptive orbital tree mutation (AOTM) scheme that dynamically adjusts multicast tree configurations considering quality of service (QoS) constraints to avoid attacks on vulnerable nodes. Simulations demonstrate the effectiveness of the AOTM scheme, showcasing its superior defense success rates compared to existing state-of-the-art algorithms.

关键词： Space vehicles Multicast algorithms Satellites Heuristic algorithms Satellite broadcasting software algorithms Quality of service

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PointDKT: Dual-Key Transformer for Point Cloud

PointDKT: Dual-Key Transformer for Point Cloud

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International Symposium on Autonomous Systems (ISAS)

作者： Jifeng Yang Guang-Guan Zhou Yang Chen School of Software Southeast University Nanjing China School of Biological Science and Medical Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Benefiting from the development of transformer-based networks in natural language processing and computer vision, researches on 3D point cloud understanding have made great progress in recent years. However, existing works often focus solely on aggregating local features or construct global dependencies through methods that significantly increase memory and computation complexity. Addressing these challenges, we present Point Dual-Key Transformer(PDKT), a novel and straightforward end-to-end network architecture. To expand the receptive fields and preserve the local geometric structural information, we introduce an efficient dual-key cross attention mechanism, which builds upon standard cross attention to facilitate global feature capture across the network without neglecting local detail. Furthermore, we adopt a learnable operator to automatically integrate local and global branches. Extensive experiment results demonstrate the effectiveness and superiority of our method on point cloud understanding tasks

关键词： Point cloud compression computer vision Three-dimensional displays computer architecture Network architecture Transformers Natural language processing

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Ensemble Distillation for Out-of-distribution Detection 29

Ensemble Distillation for Out-of-distribution Detection

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Dongdong Xu, Jingyao Lu, Siyang Wei, Xiang Wang, Liqiang University of Central Florida Department of Civil Environmental and Construction Engineering OrlandoFL United States Beijing Jiaotong University School of Computer and Information Technolog Beijing China Beijing Jiaotong University School of Software Engineering Beijing China University of Central Florida Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798350330717

Out-of-distribution detection is critical to a reliable application of deep neural networks. To reduce model uncertainty, we propose a simple yet effective approach of ensemble knowledge distillation. We blend ensemble model and knowledge distillation to improve model generalization on indomain recognition, thereby yielding accurate and robust out-of-distribution detection. The former effectively expands data recognition feature space, while the latter further regularizes the model through knowledge distillation, enhancing in-domain feature recognition. This effective integration successfully yields lower model uncertainty on in-domain feature recognition and improves anomaly detection in a more scalable manner. We justify our approach through extensive experiments on various benchmarks, demonstrating its significant improvement in out- of-distribution detection. We validate our approach with a variety of up-to-date DNNs, like Vision Transformer. © 2023 IEEE.

关键词： Data Augmentation Ensemble Learning Knowledge Distillation Out-of-distribution Detection

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Covert Communication in Underlay Multi-Antenna Cognitive Radio Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1493-1507页

作者： Fan, Hongbin Yang, Weiwei Shi, Hui Pan, Gaofeng Si, Jiangbo Wang, Yutao Rong, Chuanzhen Army Engineering University of PLA College of Communications Engineering Nanjing210007 China Xiangnan University College of Computer and Artificial Intelligence Chenzhou423000 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710100 China

In this paper, we investigated a multi-antenna covert communication in underlay cognitive radio networks (UCRNs), by considering both cases that Willie has the perfect and statistical channel state information (CSI) of the link from the secondary transmitter (ST) to the primary receiver (PR), respectively. Specifically, the optimal antenna of the secondary receiver (SR) is selected to receive the private information from ST, and then one of the remaining antennas is randomly selected to transmit jamming signals. The closed-form expressions of Willie’s detection error probability, the average minimum detection error probability, the transmission outage probability and the maximum effective covert rate under both cases are derived. The maximum effective covert rate is obtained by optimising the maximum jamming power of SR. The numerical results show that the combined effect of the maximum interference tolerance of PR and the maximum interference power of SR provides the positive effective covert rate between ST and SR. It is further found that the maximum effective covert rate between ST and SR when Willie knows the perfect CSI of the ST-SR link is greater than that when Willie only knows the statistical CSI of the ST-SR link. © 2015 IEEE.

关键词： Jamming

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A Study on the Construction and Practical Application of the Smart Education Data Platform at Guizhou Traffic Technician and Transportation College 14

A Study on the Construction and Practical Application of the...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Li, Hong Zhang, Hong Deng, Shaoxun Li, Bo Guizhou Bit Software Co. Ltd. Research and Development Department Guiyang China Guizhou Traffic Technician and Transportation College Department of Management Engineering Guiyang China Guizhou University School of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350356670

This paper investigates the digital transformation at Guizhou Traffic Technician and Transportation College, aligned with the 'China Education Modernization 2035' agenda. It introduces a microservices-based data platform aimed at upgrading educational IT systems, enhancing instructional quality, and informing decisions through smarter data use. The approach integrates microservices, cloud, big data, and AI to achieve efficient data handling and analytics, leading to improved management and decision-making processes. The study documents the platform's architecture, security, challenges, and solutions, along with successes like data visualization and automated reporting. Findings emphasize the project's relevance for vocational education's digital transition, pointing out future challenges, including data security and technology updates. The research concludes with key takeaways for institutions undergoing digitalization, underscoring the paramountcy of data security and tech optimization. © 2024 IEEE.

关键词： Big data

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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

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computer Systems science & engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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Safeguarding Privacy in Real-Time Trajectory Releases for AI-Driven Consumer Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Cai, Sujin Tian, Wei Lv, Taizhi Wang, Xiangchen Li, Xin Jiangsu Maritime Institute School of Information and Engineering Nanjing211106 China Guizhou University of Commerce College of Computer and Information engineering Guiyang550014 China Southern University of Science and Technology College of Computer Science Shenzhen518055 China Hohai University College of Computer Science and Software Engineering Nanjing211106 China

Intelligent consumer applications optimize product recommendations by analyzing users' real-time trajectory information and mobility patterns to gain insights into their consumption preferences. However, privacy concerns often cause individuals to hesitate when sharing their locations. This paper introduces a real-time trajectory release scheme called Dynamic Trajectory Releasing based on Geo-indistinguishability (DTRG) that ensures client-side data protection. A significant contribution of this work is the introduction of a novel concept, w-event Geo-indistinguishability, which extends the measurement of privacy for real-time trajectories. Building upon the theoretical foundation of w-event Geo-indistinguishability, we design a dynamic privacy budget allocation strategy that doesn't require pre-setting trajectory length. This strategy recycles unused privacy budgets and progressively reallocating them in a backward cumulative manner, enhancing the utilization efficiency of the privacy budget. By incorporating users' travel information for prediction, DTRG adaptively perturbs real locations based on prediction results. Through the collaborative operation of the Prediction Mechanism (PM), Detection Mechanism (DM) and Noisy Mechanism (NM), this approach further optimizes the utility of the budget. Experimental results demonstrate that DTRG significantly improves data availability, achieving up to a 36% increase compared to some competing algorithms while maintaining the desired level of privacy. © 1975-2011 IEEE.

关键词： Differential privacy

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Haodi Wang, Yichi Jian, Yifan Jiang, Jiahui Bai, Zhaohai Ma, Lin College of Computer Science and Software Engineering Shenzhen University China Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University China

ISBN: (纸本)9798350368741

Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State-Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-ofthe-art performance and provides strong support for climate downscaling and the planning of climate-related strategies. © 2025 IEEE.

关键词： Climate downscaling Deep Learning Multimodal fusion Operator learning Time series forecasting

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Ilayaraja V S.P. Rajamohana Jagadeesan S V. Aishwarya M. Rakesh School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science School of Engineering & Technology Pondicherry University Karaikal Puducherry India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability.

关键词： Deep learning Surveys Accuracy Tropical cyclones Disasters Sea measurements Estimation Transformers Satellite images Reliability

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State-of-the-Art Approach: Boosting IoT Sensor Network Security with Visual Secret Sharing

State-of-the-Art Approach: Boosting IoT Sensor Network Secur...

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Smart World Congress (SWC), IEEE

作者： Sijjad Ali Jia Wang Victor Chung Ming Leung Uzair Aslam Bhatti Mughair Aslam Bhatti College of Computer Science and Software Engineering Shenzhen University Guangdong China School of Information and Communication Engineering Hainan University China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

This research paper is based on the issue of protecting the sensor networks in the era of IoT because the data is very sensitive and huge, and is collected in resource-constrained environments. We put forward a new approach to the enhancement of the security of IoT sensor networks by applying VSS on visual data. Our approach is based on the implementation of the VSS approach which implies that no single node will contain full information about the visual input which will be divided among the nodes. Furthermore, this approach also increases the security of the data, and at the same time is appropriate for the IoT environments since it does not require a large amount of computational power and network bandwidth. In addition, we explain how this method can be used, discuss its features, and based on the data from the experiments, prove the effectiveness of the suggested approach. The results of the research show that data security and the ability to counteract attacks can be resist if VSS is used in systems of IoT sensor networks.

关键词： Visualization Resists Bandwidth Network security Boosting Internet of Things Cryptography

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