检索结果-内蒙古大学图书馆

Reinforcement learning based edge computing in B5G

Digital Communications and Networks 2024年第1期10卷 1-6页

作者： Jiachen Yang Yiwen Sun Yutian Lei Zhuo Zhang Yang Li Yongjun Bao Zhihan Lv School of Electrical and Information Engineering Tianjin UniversityTianjin300072China Technology and Data Center ***Beijing100176China School of Data Science and Software Engineering Qingdao UniversityQingdao266101China

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology *** the same time,Beyond 5G will become one of the important supports for the development of edge computing *** paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge *** trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and *** results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

关键词： Reinforcement learning Edge computing Beyond 5G Vehicle-to-pedestrian

来源：评论

学校读者我要写书评

暂无评论

MotionUVRNN: A Motion Capture Recurrent Neural Network for Wind Field Forecast Correction via U and V Components Modeling

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2024年第7期51卷 791-800页

作者： Gu, Zhan-Xin Wu, Jie Jin, Wei Han, Guo-Jing School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Anshan Meteorological Service Anshan114004 China

Wind field forecasting is crucial for human activities, but numerical weather prediction still has room to improve accuracy. In this paper, we formalize wind field forecast correction as a spatiotemporal sequence prediction task and propose MotionUVRNN, a recurrent neural network model suitable for wind field correction. The model consists of three elements: a feature decoupling module, an encoder, and a corrector. The feature decoupling module decouples the wind field into U-Wind (Wind component in x/longitude-direction) feature, V-Wind (Wind component in y/latitude direction) feature, and spatiotemporal feature. The encoder unifiedly models the motion trend and transient variation of the wind field. A novel long short-term memory dynamic capture unit (RS-LSTM) is proposed as the core part of the encoder to capture the different dynamic patterns of U-Wind and V-Wind. The corrector extracts and integrates multi-scale wind field information to capture regional features. By correcting the 3-12 hour wind field forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF;EC for short), the proposed model reduced the wind speed prediction error by 53.37% to 46.52%, and lowered the wind direction error by 21.99% to 13.26%. Moreover, this model surpasses the existing leading spatiotemporal sequence prediction models in wind field correction. © (2024) International Association of Engineers. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

AsyCo: an asymmetric dual-task co-training model for partial-label learning

引用

Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

来源：评论

学校读者我要写书评

暂无评论

Central Attention Mechanism for Convolutional Neural Networks

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2024年第10期51卷 1642-1648页

作者： Geng, Y.X. Wang, L. Wang, Z.Y. Wang, Y.G. School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Automation Design Institute Metallurgical Engineering Technology Co. Ltd. Dalian116000 China

Model performance has been significantly enhanced by channel attention. The average pooling procedure creates skewness, lowering the performance of the network architecture. In the channel attention approach, average pooling is used to collect feature information to provide representative values. By leveraging the central limit theorem, we hypothesize that the strip-shaped average pooling operation will generate a one-dimensional tensor by considering the spatial position information of the feature map. The resulting tensor, obtained through average pooling, serves as the representative value for the features, mitigating skewness during the process. By incorporating the concept of the central limit theorem into the channel attention operation process, this study introduces a novel attention mechanism known as the"Central Attention Mechanism (CAM)." Instead of directly using average pooling to generate channel representative values, the central attention approach employs star-stripe average pooling to normalize multiple feature representative values into a single representative value. In this way, strip-shaped average pooling can be utilized to collect data and generate a one-dimensional tensor, while star-stripe average pooling can provide feature representative values based on different spatial directions. To generate channel attention for the complementary input features, the activation of the feature representation value is performed for each channel. Our attention approach is flexible and can be seamlessly incorporated into various traditional network structures. Through rigorous testing, we demonstrate the effectiveness of our attention strategy, which can be applied to a wide range of computer vision applications and outperforms previous attention techniques. © (2024), (International Association of Engineers). All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Modeling contextual goals and detecting context conflicts from BDD-based user stories

引用

International Journal of Computers and Applications 2024年第12期46卷 1057-1068页

作者： Zheng, Liwei Wang, Yan Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： BDD-based user stories contextual goal model context conflict detection

来源：评论

学校读者我要写书评

暂无评论

Ensemble of Two-Path Capsule Networks for Diagnosis of Turner Syndrome Using Global-Local Facial Images

引用

Journal of Shanghai Jiaotong university(Science) 2023年第4期28卷 459-467页

作者：刘璐 School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China

Turner syndrome(TS)is a chromosomal disorder disease that only affects the growth of female *** diagnosis is of high significance for the ***,clinical screening methods are time-consuming and *** researchers used machine learning-based methods to detect TS,the performance of which needed to be ***,we propose an ensemble method of two-path capsule networks(CapsNets)for detecting TS based on global-local facial ***,the TS facial images are preprocessed and segmented into eight local parts under the direction of physicians;then,nine two-path CapsNets are respectively trained using the complete TS facial images and eight local images,in which the few-shot learning is utilized to solve the problem of limited data;finally,a probability-based ensemble method is exploited to combine nine classifiers for the classification of *** studying base classifiers,we find two meaningful facial areas are more related to TS patients,i.e.,the parts of eyes and *** results demonstrate that the proposed model is effective for the TS classification task,which achieves the highest accuracy of 0.9241.

关键词： Turner syndrome(TS) two-path capsule network(CapsNet) ensemble method few-shot learning

来源：评论

学校读者我要写书评

暂无评论

Auto-3D-house Design from Structured User Requirements

引用

Machine Intelligence Research 2025年第2期22卷 368-385页

作者： Minkui Tan Qi Chen Zixiong Huang Qi Wu Yuanqing Li Jiaqiu Zhou School of Software Engineering South China University of TechnologyGuangzhou 510006China Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide SA 5005Australia School of Automation Science and Engineering South China University of TechnologyGuangzhou 510006China

We study the task of automated house design,which aims to automatically generate 3D houses from user ***,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)the understanding of user requirements,where the users can hardly provide high-quality requirements without any professional knowledge;2)the design of house plan,which mainly focuses on how to capture the effective information from user *** address the above issues,we propose an automatic house design framework,called auto-3D-house design(A3HD).Unlike the previous works that consider the user requirements in an unstructured way(e.g.,natural language),we carefully design a structured list that divides the requirements into three parts(i.e.,layout,outline,and style),which focus on the attributes of rooms,the outline of the building,and the style of decoration,*** the processing of architects,we construct a bubble diagram(i.e.,graph)that covers the rooms′attributes and relations under the constraint of *** addition,we take each outline as a combination of points and orders,ensuring that it can represent the outlines with arbitrary ***,we propose a graph feature generation module(GFGM)to capture layout features from the bubble diagrams and an outline feature generation module(OFGM)for outline ***,we render 3D houses according to the given style requirements in a rule-based *** on two benchmark datasets(i.e.,RPLAN and T3HM)demonstrate the effectiveness of our A3HD in terms of both quantitative and qualitative evaluation metrics.

关键词： Automated house design user requirements understanding outline processing layout generation graph feature generation.

来源：评论

学校读者我要写书评

暂无评论

Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network

引用

Journal of Genetics and Genomics 2023年第9期50卷 720-733页

作者： Yuying Huo Yilang Guo Jiakang Wang Huijie Xue Yujuan Feng Weizheng Chen Xiangyu Li School of Software Engineering Beijing Jiaotong UniversityBeijing 100044China School of Software Engineering Beijing University of TechnologyBeijing 100124China Baidu Inc. Beijing 100193China

Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in *** transcriptomics can provide multimodal and complementary information simultaneously,including gene expression profiles,spatial locations,and histology ***,most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology *** fully leverage the multi-modal information,we propose a SPAtially embedded Deep Attentional graph Clustering(SpaDAC)method to identify spatial domains while reconstructing denoised gene expression *** method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological *** results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics *** is a valuable tool for spatial domain detection,facilitating the comprehension of tissue architecture and cellular *** source code of SpaDAC is freely available at Github(https://***/huoyuying/***).

关键词： Spatialtranscriptomics Spatial domaindetection Multi-modal integration Graph attention network

来源：评论

学校读者我要写书评

暂无评论

Health Data Availability Protection:Delta-XOR-Relay Data Update in Erasure-Coded Cloud Storage Systems

引用

Computer Modeling in engineering & Sciences 2023年第4期135卷 169-185页

作者： Yifei Xiao Shijie Zhou School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China

To achieve the high availability of health data in erasure-coded cloud storage systems,the data update performance in erasure coding should be continuously ***,the data update performance is often bottlenecked by the constrained cross-rack *** techniques have been proposed in the literature to improve network bandwidth efficiency,including delta transmission,relay,and batch *** techniques were largely proposed individually previously,and in this work,we seek to use them *** mitigate the cross-rack update traffic,we propose DXR-DU which builds on four valuable techniques:(i)delta transmission,(ii)XOR-based data update,(iii)relay,and(iv)batch ***,we offer two selective update approaches:1)data-deltabased update,and 2)parity-delta-based *** proposed DXR-DU is evaluated via trace-driven local testbed *** experiments show that DXR-DU can significantly improve data update throughput while mitigating the cross-rack update traffic.

关键词： Data availability health data data update cloud storage IoT

来源：评论

学校读者我要写书评

暂无评论

A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

引用

Computers, Materials & Continua 2024年第3期78卷 3001-3019页

作者： Nannan Wu Xianyi Chen James Msughter Adeke Junjie Zhao School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information ***,these approaches have some *** example,a cover image lacks self-adaptability,information leakage,or weak *** address these issues,this study proposes a universal and adaptable image-hiding ***,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image ***,to improve perceived human similarity,perceptual loss is incorporated into the training *** experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality ***,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at ***,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

关键词： Deep image hiding attention mechanism privacy protection data security visual quality

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：