检索结果-内蒙古大学图书馆

IEEE Open Journal of the Communications Society 2025年 6卷 3389-3403页

作者： Wei, Zhiwei Mao, Jingxin Li, Bing Zhang, Rongqing School of Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Tongji University School of Software Engineering Shanghai200092 China

Vehicular Fog Computing (VFC) is a promising paradigm in intelligent transportation systems (ITS), which offloads computation-intensive tasks to mobile fog nodes for real-time and low-latency services. In the forthcoming era of low-altitude economy, Unmanned Aerial Vehicles (UAVs) are being integrated as task-carrying entities into the ITS, and the novel low-altitude VFC is witnessing new challenges, introduced by dynamic UAV missions, high mobility, and privacy concerns. To preserve the offloading privacy and enhance the offloading performance in the dynamic low-altitude VFC, in this paper, we facilitate the learning-based methods and propose a hierarchical federated reinforcement learning framework. The framework consists of two levels: the local level provides Deep Reinforcement Learning (DRL) models for task vehicles and UAVs, and the cross-regional contextual level for coordinating the local experiences. At the local DRL level, we design an Attention-enhanced Federated Proximal Policy Optimization (AFedPPO) algorithm to enable decentralized training and execution (DTDE) for task offloading, which is privacy-preserving, effective, and scalable for the low-altitude VFC systems. At the cross-regional level, we introduce a contextual clustering and personalized (CCP) federated learning (FL) mechanism, which adaptively aggregates the local experiences according to the regional features. Extensive simulation results validate an average 35% improvement of the proposed framework compared to the state-of-the-art FL schemes, and in some cases, even outperform the centralized training (CTDE) baseline. To the best of our knowledge, this is the first work to theoretically discuss how contextual information can enhance the performance of the DRL-based offloading strategy under FL settings. © 2020 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Decentralized Federated Learning for Wireless Traffic Prediction

引用

IEEE Communications Letters 2025年第5期29卷 1057-1061页

作者： Zhang, Haochang Huang, Sirui Zhou, Xiaotian Zhang, Chuanting Jia, Junrong Shandong University School of Control Science and Engineering China Shandong University Shandong Key Lab. of Intelligent Communications and Sensing-Computation Integration China Shandong University Institute of Intelligent Communication Technologies China Shandong University School of Software China

Wireless traffic prediction is indispensable for future intelligent cellular networks, as it can guide the resource allocation smartly to boost the usage efficiency. While the deep learning based methods have been reported to have promising performance, they encounter issues such as data privacy and data heterogeneity. To overcome these, in this letter we design a decentralized federated learning based network (DFLNet) for wireless traffic prediction, where a two layered federated learning framework is proposed. In the proposed algorithm, the base stations are divided into clusters, where the intra-cluster parameter aggregation is achieved through attention mechanism and that of inter-cluster is realized by reinforcement learning. The proposed approach enables the collaborative model updates to be carried out among the most spatial correlated clients, without involving the adversarial information provided by the geometrical remote clients. Simulations confirm the improved accuracy of the proposed algorithm compared to the benchmark schemes. © 1997-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Comparing Puncture-Detection Approaches for Manual Needle Insertions Through the Parietal Pleura

IEEE Transactions on Medical Robotics and Bionics

引用

IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 455-468页

作者： L'Orsa, Rachael Zareinia, Kourosh Sutherland, Garnette R. Westwick, David Kuchenbecker, Katherine J. University of Calgary Schulich School of Engineering’s Department of Electrical and Software Engineering Project neuroArm’s Surgical Performance Laboratory Calgary Canada Max Planck Institute for Intelligent Systems Haptic Intelligence Department Stuttgart Germany Toronto Metropolitan University Department of Mechanical Industrial and Mechatronics Engineering Toronto Canada University of Calgary Project neuroArm Cumming School of Medicine Calgary Canada University of Calgary Schulich School of Engineering’s Department of Electrical and Software Engineering Calgary Canada

Tube thoracostomy (chest tube insertion) is a surgical procedure that treats pneumothorax, a potentially life threatening condition where air accumulates between the chest wall and the lungs. The literature reports high complication rates for this procedure, including accidental fatality due to poor manual depth control during tool insertion. We hypothesize that an instrumented needle-holder could help operators recognize pleural puncture and improve depth control, and we present a puncture-detection experiment that contributes toward this goal. An operator manually inserted a bevel-tip needle into ex vivo porcine ribs and through the parietal pleura via a sensorized percutaneous device that records position, force, and videos. We use this rich dataset of 63 insertions to thoroughly test four previously published data-driven puncture-detection (DDPD) algorithms against two new real-time algorithms: a custom recursive digital filter with coefficients optimized for our application, and a difference equation that compares standard deviations between adjacent sliding windows. Our algorithms achieve a precision (true positives over total identified punctures) of 23% and 22%, respectively, while the precision of existing DDPD algorithms ranges from 0% to 21%. Despite these performance improvements, our results show the limitations of DDPD algorithms and motivate new methods for detecting pleural membrane punctures in thoracostomy. © 2018 IEEE.

关键词： Needles

来源：评论

学校读者我要写书评

暂无评论

Cross-graph Knowledge Exchange for Personalized Response Generation in Dialogue Systems

引用

IEEE Internet of Things Journal 2025年第10期12卷 13769-13789页

作者： Dong, Yuezhou Qin, Ke Ke, Pei Liang, Shuang Luo, Guangchun National Key Laboratory of Intelligent Collaborative Computing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China National Key Laboratory of Intelligent Collaborative Computing Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

Recent advancements in language models have greatly improved dialogue systems, but they still face challenges in generating personalized responses that are consistent with the user's persona and dialogue context. Existing approaches typically model dialogue context and persona information together in a unified manner, but they lack fine-grained differentiation between the two, leading to inaccurate user modeling. This misalignment hinders the ability of dialogue systems to produce personalized responses. In this work, we propose CKE (Cross-Graph Knowledge Exchange), a novel algorithm designed to enhance personalized response generation in dialogue systems. CKE constructs separate dialogue user graphs for each party in the dialogue, representing both their dialogue context and persona information. These graphs are then utilized to perform cross-graph structured knowledge aggregation, where the aggregation is under supervised by both its own and the other party's persona and dialogue context, providing richer, more accurate representations. Furthermore, CKE introduces a hybrid prompt template that combines both discrete and continuous elements, improving the language model's ability to leverage graph-structured information. The experimental results demonstrate that CKE significantly outperforms existing baseline methods in generating more coherent, contextually appropriate, and personalized responses in dialogue systems. © 2014 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

MULTI-TARGET VITAL SIGN DETECTION BY FUSION OF BIOLOGICAL RADAR AND CONVOLUTIONAL NEURAL NETWORK

引用

Scalable Computing 2025年第3期26卷 1496-1503页

作者： Yuan, Hongbin Yuan†, Chenyao Cao, Huiqun School of Telecommunications and Intelligent Manufacturing Sias University Henan Zhengzhou451150 China School of Computer and Software Engineering Sias University Zhengzhou451150 China Academic Affairs Office of Kaifeng Modern Technology Secondary Vocational School Henan Kaifeng475000 China

In order to address the increasing demand for vital sign detection, the author proposes a multi-target vital sign detection research that combines biological radar and convolutional neural network. Based on the fundamental architecture of convolutional neural networks (CNNs), the author combines classification-based CNN object detection techniques to develop a biological radar multi-target vital sign detection platform. The feasibility of this approach is confirmed through experiments, demonstrating the integration of biological radar and CNNs for multi-target vital sign detection. The experimental results indicate that the biological radar achieves a recognition accuracy of 96.1%, proving the effectiveness of the biological radar detection algorithm. The research on multi-target vital sign detection based on the fusion of biological radar and convolutional neural network is an effective auxiliary method that can provide reference for relevant researchers. © 2025 SCPE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

When Crypto Fails: Demystifying Cryptographic Defects in Ethereum Smart Contracts

引用

IEEE Transactions on software engineering 2025年第05期51卷 1381-1398页

作者： Zhang, Jiashuo Chen, Jiachi Shen, Yiming Zhang, Tao Wang, Yanlin Chen, Ting Gao, Jianbo Chen, Zhong Peking University School of Computer Science China Sun Yat-sen University School of Software Engineering China Macau University of Science and Technology School of Computer Science and Engineering China University of Electronic Science and Technology of China School of Computer Science and Engineering China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation China

Ethereum has officially provided a set of system-level cryptographic APIs to enhance smart contracts with cryptographic capabilities. These APIs have been utilized in over 13.8% of Ethereum transactions, motivating developers to implement various on-chain cryptographic tasks, such as digital signatures. However, since developers may not always be cryptographic experts, their ad-hoc and potentially defective implementations could compromise the theoretical guarantees of cryptography, leading to real-world security issues. To mitigate this threat, we conducted a comprehensive study aimed at demystifying and detecting cryptographic defects in smart contracts. Through the analysis of 3,762 real-world security reports, we defined 12 types of cryptographic defects in smart contracts with detailed descriptions and practical detection patterns. Based on this categorization, we proposed CryptoScan, the first static analyzer to automate the pre-deployment detection of cryptographic defects in smart contracts. CryptoScan utilizes cross-contract and inter-procedure static analysis to identify crypto-related execution paths and employs taint analysis to extract fine-grained crypto-specific semantics for defect detection. Furthermore, we collected a large-scale dataset containing 79,598 real-world crypto-related smart contracts and evaluated CRYPTOSCAN’s effectiveness on it. The results demonstrated that CryptoScan achieves an overall precision of 96.1% and a recall of 93.3%. Notably, CryptoScan revealed that 19,707 (24.8%) out of 79,598 smart contracts contain at least one cryptographic defect. Although not all defects directly cause financial losses, they indicate prevalent non-standard cryptographic implementations that should be addressed in real-world practices. © 2025 IEEE. All rights reserved.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Fairness-Aware Budgeted Edge Server Placement for Connected Autonomous Vehicles

引用

IEEE Transactions on Mobile Computing 2025年第6期24卷 4762-4776页

作者： Wu, Jintao Xu, Xiaolong Cui, Guangming Zhang, Yiwen Qi, Lianyong Dou, Wanchun Cai, Zhipeng Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software Nanjing210044 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China Anhui University School of Computer Science and Technology Anhui230031 China College of Computer Science and Technology Qingdao266580 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Georgia State University Department of Computer Science United States

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency. © 2002-2012 IEEE.

关键词： Integer linear programming

来源：评论

学校读者我要写书评

暂无评论

NumScout: Unveiling Numerical Defects in Smart Contracts using LLM-Pruning Symbolic Execution

引用

IEEE Transactions on software engineering 2025年第05期51卷 1538-1553页

作者： Chen, Jiachi Shao, Zhenzhe Yang, Shuo Shen, Yiming Wang, Yanlin Chen, Ting Shan, Zhenyu Zheng, Zibin Hangzhou Normal University Intelligent Transportation and Information Security Laboratory Hangzhou311121 China Sun Yat-sen University School of Software Engineering Zhuhai519082 China University of Electronic Science and Technology of China School of Computer Science and Engineering School of Cyber Security Chengdu611731 China Kashi Institute of Electronics and Information Industry Kashi844000 China

In recent years, the Ethereum platform has witnessed a proliferation of smart contracts, accompanied by exponential growth in total value locked (TVL). High-TVL smart contracts often require complex numerical computations, particularly in mathematical financial models used by many decentralized applications (DApps). Improper calculations can introduce numerical defects, posing potential security risks. Existing research primarily focuses on traditional numerical defects like integer overflow, and there is currently a lack of systematic research and effective detection methods targeting new types of numerical defects. In this paper, we identify five new types of numerical defects through the analysis of 1,199 audit reports by utilizing the open card method. Each defect is defined and illustrated with a code example to highlight its features and potential consequences. We also propose NumScout, a symbolic execution-based tool designed to detect these five defects. Specifically, the tool combines information from source code and bytecode, analyzing key operations such as comparisons and transfers, to effectively locate defects and report them based on predefined detection patterns. Furthermore, NumScout uses a large language model (LLM) to prune functions which are unrelated to numerical operations. This step allows symbolic execution to quickly enter the target function and improve runtime speed by 28.4%. We run NumScout on 6,617 real-world contracts and evaluated its performance based on manually labeled results. We find that 1,774 contracts contained at least one of the five defects, and the tool achieved an overall precision of 89.7%. © 2025 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Energy Consumption Minimization Algorithm in IoT Network Integrating Data and Energy Transfer

引用

IEEE Access 2025年 13卷 75924-75934页

作者： Wang, Yang Yu, Qin Hu, Jie Yang, Kun University of Electronic Science and Technology of China Huzhou313001 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China

Nowadays, Internet of Things (IoT) devices are widely deployed and how to power such massive IoT devices has become a significant challenge. Especially for the battery-powered IoT devices, limited energy storage and continuous energy consumption result in frequent battery replacement and potential environmental pollution. Because radio frequency (RF) signals carry not only information but also energy, Wireless Power Transfer (WPT) in RF domain has been proved to be a reliable and controllable technology to deal with this problem. A typical Data and Energy Integrated Network (DEIN) commonly conceives one DEIN base station and numerable DEIN devices. The DEIN base station is capable of transmitting data and energy integrated signals to the DEIN devices with Simultaneous Wireless Information and Power Transfer (SWIPT) technology, and the DEIN devices are capable of harvesting energy from the downlink RF signals for uplink data transmission. In order to coordinate the wireless data and energy transmission and avoid conflicts, a transmission protocol based on the Time-Division Multiple Access (TDMA) scheme is designed. Furthermore, an optimization problem of minimizing the energy consumption of the DEIN base station is formulated. In order to address this problem, this paper proposes an energy-efficient algorithm by jointly optimizing of the transmission power of the DEIN base station, the uplink and downlink time slots and power splitting factors of the IoT devices. Both the effectiveness and efficiency of the proposed joint optimization algorithm have been validated with the simulation. © 2013 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

A Lightweight and High-Fidelity Model for Generalized Audio-Driven 3D Talking Face Synthesis 27th

A Lightweight and High-Fidelity Model for Generalized Audi...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Shunce Zhong, Yuwei Wang, Huixuan Peng, Jingliang Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China School of Information Science and Engineering University of Jinan Jinan China School of Software and Microelectronics Peking University Beijing China School of Software Shandong University Jinan China

ISBN: (纸本)9783031781247

We present LW-GeneFace, a lightweight and high-fidelity model for generalized audio-driven facial animation, in this paper. We develop this model by reducing the size while maintaining the synthetic quality of GeneFace, an audio-driven facial animation model known for its high fidelity and generalization capabilities. Specifically, we compress the first and the third stages of GeneFace as they dominate the model size. In the first stage, we propose a lightweight version of the WaveNet-based network inspired by MobileNetV3 and DP-block. It utilizes depthwise separable convolution and dual-path feature extraction to compress the network while maintaining effective feature extraction. The shared network structure in the dual-path feature extraction further reduces model complexity and improves training efficiency. In the third stage, we generate realistic 3D renderings at reduced model size by introducing novelties in RAD-NeRF. Technically, we reduce the hash table sizes in the grid-based encoding modules, as well as present a lightweight bottleneck MLP architecture to increase the non-linearity of the model. Experimental results demonstrate that LW-GeneFace achieves state-of-the-art performance with both model size and synthetic quality considered. The source code of LW-GeneFace will be released after acceptance of this paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Animation

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：