检索结果-内蒙古大学图书馆

A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Aspect-Based Capsule Network With Mutual Attention for Recommendations

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2021年第3期2卷 228-237页

作者： Yang, Zhenyu Wang, Xuesong Cheng, Yuhu Liu, GuoJing The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Qilu University of Technology Shandong Academy of Sciences Software Integration Institute Jinan250353 China The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. Recently, mainstream recommendation approaches have been using deep learning techniques to utilize review text. These efforts employ an affinity matrix to model the correlations between users and items and in turn aggregate the contextual features of users and items to form latent representations with some interpretability. However, constructing this simple interaction does not capture the complex correlations between users and items. Furthermore, a single representation of latent features is not sufficient to express user preferences and item attributes in reviews with multiple granularities. To this end, we propose an aspect-based capsule network and mutual attention for recommendation systems, named Acapnet. We design a novel contextual mutual attention mechanism for modeling the fine-grained correlations in the contextual features of users and items. We employ reverse dynamic routing to aggregate user and item context features into aspect features (capsules) for rating predictions;the vector length of a capsule can be used as a basis for estimating aspect importance. Extensive experiments are conducted on five real-world datasets with different features. The results show that the proposed Acapnet is superior to recent state-of-the-art models in terms of performance. Further analysis reveals that our model effectively mitigates the homogeneity among various aspect capsules with strong interpretability. © 2021 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

引用

arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

来源：评论

学校读者我要写书评

暂无评论

Context-Aware Program Simplification to Improve Information Retrieval-Based Bug Localization

Context-Aware Program Simplification to Improve Information ...

引用

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Yilin Yang Ziyuan Wang Zhenyu Chen Baowen Xu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China

Information Retrieval-based Bug localization (IRBL) techniques have become a hot research topic in bug localization due to their few external dependencies and low execution cost. However, existing IRBL techniques have many challenges regarding localization granularity and applicability. First, existing IRBL techniques have not yet achieved statement-level bug localization. Second, almost all studies are limited to Java-based projects, and the effectiveness of these techniques for other widely used programming languages (e.g., Python) is still unknown. The reason for these deficiencies is that existing IRBL techniques mainly employ conventional NLP techniques to analyze the bug reports and have not yet fully exploited the stack trace attached to the bug reports. To improve IRBL techniques in terms of localization granularity and adaptability, we propose a context-aware program simplification technique—COPS—that is able to localize defective statements in suspicious files by analyzing the stack trace in bug reports, which enables statement-level bug localization for Python-based projects. Experiments using 948 bug reports show that our technique can localize the buggy statements with 102.6% higher Top@10, 56.2% higher MAP@10, and 95.6% higher MRR@10 than the baseline. Compared with the state-of-the-art techniques, COPS can improve 19.1% in MAP@10 and achieve 92% buggy statement coverage with a full scope search. Experimental results show that COPS has higher bug localization effectiveness than existing IRBL techniques; and that COPS achieves the same effectiveness with higher execution efficiency than state-of-the-art statement-level defect techniques.

关键词： Location awareness Costs computer bugs software algorithms software quality Syntactics Reliability engineering

来源：评论

学校读者我要写书评

暂无评论

Qoe-Aware Edge Server Placement in Mobile Edge Computing Using an Enhanced Genetic Algorithm

SSRN

引用

SSRN 2025年

作者： Sha, Jinxiang Wu, Jintao Wang, Mingliang Pu, Yonglin Lu, Sheng Bilal, Muhammad School of Computer Science and Technology Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Yunnan Kunming650221 China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom

Mobile Edge Computing (MEC) enhances service quality by decentralizing computational resources to network edges, thereby reducing latency and improving Quality of Service (QoS). However, the spatial distribution of edge servers critically impacts network transmission efficiency, while heterogeneous user perceptions of QoS metrics frequently lead to suboptimal Quality of Experience (QoE). Current research on Edge Server Placement (ESP) predominantly focuses on localized optimization of QoS metrics, yet fails to adequately incorporate systematic QoE modeling and coordinated optimization frameworks, leading to significant discrepancies between actual user experience and satisfaction with resource allocation. To address this gap, this study establishes a formalized QoE-aware Edge Server Placement (EESP) framework by rigorously characterizing the interdependence between QoE and QoS. We first prove the NP-completeness of the EESP problem through computational complexity analysis. Subsequently, we develop an Integer Linear Programming-based exact solver (EESP-O) for small-scale scenarios and propose an Enhanced Genetic Algorithm (EESP-EGA) for large-scale deployments. The EESP-EGA integrates adaptive crossover probability mechanisms and elite retention strategies to achieve near-optimal solutions for complex real-world configurations. Experimental evaluations conducted on a broad range of real-world datasets demonstrate that the proposed method outperforms several existing representative approaches in terms of QoE. © 2025, The Authors. All rights reserved.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Deployment and Application of Deep Learning Models under Computational Constraints

Deployment and Application of Deep Learning Models under Com...

引用

IEEE International Conference on Big Data

作者： Jinwen Li Wendong Wang Beijing Univ. of Posts and Telecom Beijing China International School Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications

Deep learning is a computationally demanding field. So the GPU used has a very important impact on the effectiveness of deep learning. In addition, deep learning requires huge amounts of memory. High throughput means better performance, faster iterations, and more efficient completion of experiments. Therefore, the choice of GPU basically determines the success and experience of deep learning. This paper analyzes four commonly used computer deep learning model structures, VGG, ResNet50, MobileNet, InceptionV3, and their memory requirement. At the same time, this paper also analyzes the relationship between computing power and memory of several mainstream graphics cards produced by Nvidia which are often used for deep learning, including GeForce, Tesla, and etc. Therefore, in the case of limited medical conditions, such as the inability to purchase expensive graphics cards, or do not have enough memory graphics cards, an existing suitable computer model can be found to help medical personnel to perform gland image segmentation, so as to achieve a more efficient, fast and economical scheme.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Case study on dynamic scenario model for smart city 54

Case study on dynamic scenario model for smart city

引用

54th Annual Hawaii International Conference on System sciences, HICSS 2021

作者： Zhang, Lin Liu, Cheng Yan, Qiang Lu, Junqiang Qu, Li School of Economics and Management Beijing Information Science and Technology University China Institute of Emergency Management Systems China Academy of Electronics and Information Technology China School of Modern Post School of Automation Beijing University of Posts and Telecommunications China School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications China

ISBN: (纸本)9780998133140

Disaster resilience is important for a smart city. Analyzing disaster cases within a scenario-based analytical framework provides an effective way to acquire specific experience in disaster prevention and emergency management. This study proposes a dynamic scenario model, which consists of developing scenario representation and developing scenario sequence. Developing scenario representation provides a normalized representation framework for disaster scenarios. Meanwhile, how to make the representation contributes to severity evaluation is discussed. Developing scenario sequence establishes dependencies among disaster scenarios to show the whole disaster evolution of disaster cases. A disaster case of a crude oil tank is taken as an example to give a better understanding of the dynamic scenario model. The result shows that the dynamic scenario model can establish a more structurally and normalized representation for disaster scenarios. Furthermore, the model also provides an intuitive way for evaluation of disaster consequence severity, which helps in improving the situation awareness of the disaster. © 2021 IEEE computer Society. All rights reserved.

关键词： Disasters

来源：评论

学校读者我要写书评

暂无评论

Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph

arXiv

引用

arXiv 2024年

作者： Chen, Ziyang Zhang, Yongjun Li, Wenting Wang, Bingshu Zhao, Yong Chen, C.L. Philip College of Computer Science the State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Information Engineering Guizhou University of Commerce Guiyang550021 China School of Software Northwestern Polytechnical University Xi’an710129 China Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University Shenzhen518055 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inherently suffer from the loss of geometric structures in certain feature channels, creating a bottleneck in achieving precise detail matching. Additionally, these methods lack interpretability due to the black-box nature of deep learning. In this paper, we propose MoCha-V2, a novel learning-based paradigm for stereo matching. MoCha-V2 introduces the Motif Correlation Graph (MCG) to capture recurring textures, which are referred to as "motifs" within feature channels. These motifs reconstruct geometric structures and are learned in a more interpretable way. Subsequently, we integrate features from multiple frequency domains through wavelet inverse transformation. The resulting motif features are utilized to restore geometric structures in the stereo matching process. Experimental results demonstrate the effectiveness of MoCha-V2. MoCha-V2 achieved 1st place on the Middlebury benchmark at the time of its release. Code is available at here. Copyright © 2024, The Authors. All rights reserved.

关键词： Stereocenters

来源：评论

学校读者我要写书评

暂无评论

An Improved Deep Network with Fusion of Physical and Residual Information for Radar Echo Extrapolation

An Improved Deep Network with Fusion of Physical and Residua...

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Yang, Zhiyun Wu, Hao Liu, Qi Xu, Xiaolong School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science & Technology Nanjing China Michigan State University Michigan United States

ISBN: (纸本)9781665498142

Precipitation Nowcasting tasks have important social impact and economic significance in disaster warning, planning, etc. Currently it uses methods based on radar echo extrapolation. Some recent works have introduced deep neural networks into the field of radar echo extrapolation, where radar echo images are arranged in temporal order as inputs to the model. Previous works usually use convolutional neural networks(CNN), recurrent neural networks(RNN) or a combination of both, which do not fully exploit the partial differential equation relationships implied behind the radar echo motion. In this paper, a physical cell is incorporated into the original deep neural network, to add physical constraints to the data-driven model and collaborate with the LSTM variant cell to accurately model the trajectory and appearance of moving objects. The experiments achieve better predictions than the original model on the synthetic moving digital dataset and the realistic radar echo dataset. © 2021 IEEE.

关键词： deep nerual network precipitation nowcasting Radar Echo Extrapolation spatio-temporal prediction

来源：评论

学校读者我要写书评

暂无评论

Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplink NOMA IoT Systems

Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplin...

引用

IEEE International Conference on Communications (ICC)

作者： Tianheng Xu Yinjun Xu Jingyi Wu Haijun Zhang Ting Zhou Honglin Hu Victor C.M. Leung Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Shanghai Frontier lnnovation Research Institute Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Microelectronics Shanghai University Shanghai China Department of Electrical and Computer Engineering The University of British Columbia Canada College of Computer and Software Engineering Shenzhen University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The upcoming sixth-generation (6G) of ubiquitous connectivity communications systems is driven by the next-generation of Internet of Things (IoT) technology. Accordingly, the demand for spectrum resources is growing exponentially. Spectrum sensing, which dynamically explores spectrum holes, is expected to be crucial in the 6G era. In the meantime, Non-Orthogonal Multi-Access (NOMA), an efficient means of reusing resources, can enable multiple users to share the same frequency band stably. The combination of both technologies holds promise for more effective use of spectrum resources in future commu-nications. In this paper, we present a spectrum sensing method for the uplink communication scenario in NOMA with multi-user interference, which aims to make effective use of both static and dynamic gain on spectrum efficiency. Firstly, the sensing criterion and workflow are outlined. The closed-form solution for the sensing threshold configuration is derived thereafter. The simulation results demonstrate the feasibility of the proposed approach, which can increase the system throughput up to 19.7%, when compared to NOMA without spectrum sensing.

关键词： 6G mobile communication NOMA Closed-form solutions Simulation Throughput Orbits Sensors

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：