检索结果-内蒙古大学图书馆

IEEE Conference on Wireless communications and Networking

作者： Lizhao You Zimu Yuan Panlong Yang Guihai Chen State Key Laboratory of Novel Software Technology Nanjing University China Institute of Computing Technology Chinese Academy and Sciences China Institute of Communication Engineering People''s Liberation Army University of Science and Technology China

Neighbor discovery is an essential step for the self-organization of wireless sensor networks. Many algorithms have been proposed for efficient neighbor discovery. However, most of those algorithms need nodes to keep active during the process of neighbor discovery, which might be difficult for low-duty-cycle wireless sensor networks in many real deployments. In this paper, we investigate the problem of neighbor discovery in low-duty-cycle wireless sensor networks. We give an ALOHA-like algorithm and analyze the expected time to discover all n - 1 neighbors for each node. By reducing the analysis to the classical K Coupon Collector's Problem, we show that the upper bound is ne(log 2 n + (3 log 2 n - 1) log 2 log 2 n + c) with high probability, for some constant c, where e is the base of natural logarithm. Furthermore, not knowing number of neighbors leads to no more than a factor of two slowdown in the algorithm performance. Then, we validate our theoretical results by extensive simulations, and explore the performance of different algorithms in duty-cycle and non-duty-cycle networks. Finally, we apply our approach to analyze the scenario of unreliable links in low-duty-cycle wireless sensor networks.

关键词： Wireless sensor networks Algorithm design and analysis Upper bound Simulation Random variables Equations Synchronization

来源：评论

学校读者我要写书评

暂无评论

Cost-Optimized Microservice Deployment for IoT Application in Cloud-Edge Collaborative Environment

Cost-Optimized Microservice Deployment for IoT Application i...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyuan Zhang Bing Tang Qing Yang Wei Xu Feiyan Guo School of Computer Science and Engineering Hunan University of Science and Technology and Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China

With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires suitable strategies to ensure the quality of service for users. However, the existing container orchestration tools cannot flexibly select the best deployment location according to the user’s cost budget, and are insufficient in personalized deployment solutions. From the perspective of application providers, this paper considers the location distribution of users, application dependencies, and server price differences, and proposes a genetic algorithm-based Internet-of-Things (IoT) application deployment strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints. This problem is an NP-hard problem, and genetic algorithm is introduced to solve the optimization problem effectively and improve the deployment efficiency. The proposed algorithm is compared with four baseline algorithms, Time-Greedy, Cost-Greedy, Random and PSO, using real datasets and some synthetic datasets. The results show that the proposed algorithm outperforms other competing baseline algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

To be critical: Self-calibrated weakly supervised learning for salient object detection

arXiv

引用

arXiv 2021年

作者： Piao, Yongri Wang, Jian Zhang, Miao Ma, Zhengxuan Lu, Huchuan School of Information and Communication Engineering Dalian University of Technology China DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China

Weakly-supervised salient object detection (WSOD1) aims to develop saliency models using image-level annotations. Despite of the success of previous works, explorations on an effective training strategy for the saliency network and accurate matches between image-level annotations and salient objects are still inadequate. In this work, 1) we propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions, liberating the saliency network from error-prone propagation caused by pseudo labels. 2) we prove that even a much smaller dataset (merely 1.8% of ImageNet) with well-matched annotations can facilitate models to achieve better performance as well as generalizability. This sheds new light on the development of WSOD and encourages more contributions to the community. Comprehensive experiments demonstrate that our method outperforms all the existing WSOD methods by adopting the self-calibrated strategy only. Steady improvements are further achieved by training on the proposed dataset. Additionally, our method achieves 94.7% of the performance of fully-supervised methods on average. And what is more, the fully supervised models adopting our predicted results as"ground truths" achieve successful results (95.6% for BASNet and 97.3% for ITSD on F-measure), while costing only 0.32% of labeling time for pixel-level annotation. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Talking Head Generation for Media Interaction System with Feature Disentanglement

Talking Head Generation for Media Interaction System with Fe...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Lei Zhang Qingyang Chen Zhilei Liu College of Intelligence and Computing School of New Media and Communication Tianjin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun P.R. China State Key Laboratory for Novel Software Technology Nanjing University P.R. China School of New Media and Communication Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

The task of talking head generation for the media interaction system is to take images and audio clips of the target face as input, and generate a realistic video of the target synchronized with the audio. Most of the existing works directly take all the information in the image as input, which causes the problem of feature information redundancy. At the same time, in the stage of feature fusion, the image and audio information is directly spliced, and the relationship between the two modal information is ignored. To solve the problems, we initially implement the disentanglement of image by introducing a loss function to separate the image into identity features and content-related features. Besides, we introduce a multi-head selfattention mechanism to learn the relationship between the two modal information of image and audio, and implement the full fusion of multi-modal information. In addition, we validate the effectiveness of our model through extensive quantitative and qualitative analysis of two datasets. Extensive experiments show the superiority of the proposed model in all aspects.

关键词： Analytical models Redundancy Media Synchronization Task analysis Faces

来源：评论

学校读者我要写书评

暂无评论

Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based Distributed Storage System for Task Offloading in Vehicular Edge computing

Blockchain-based Distributed Storage System for Task Offload...

引用

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yutian Zhang Bing Tang Qing Yang Xiaoyuan Zhang Jincheng Luo Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China

With the deepening requirements of development for the Internet of Vehicles technology, intelligent vehicles need to complete the complex task in a fast response time to meet the needs, which brings great challenges to reliable vehicle service provision. In order to solve this problem, we propose a task offloading scheme into the Internet of Vehicles, which can reduce the execution time of tasks. In this paper, we introduce a location-aware distributed storage system called LDSS to support task offloading. Data can be written, stored, computed and read out through different mobile edge servers. Firstly, the identity of the vehicle is stored on the blockchain, and then the vehicle needs to offload the data to the nearest mobile edge server. Then, the storage node and computing node are selected based on the time optimization strategy. Finally, by predicting the location of the vehicle, the mobile edge server nearest to the predicted vehicle location is selected as the access node for result downloading. After validating the identity of vehicle based on blockchain technology, the output result is returned to the vehicle. The experiments show that our proposed task offloading scheme based on blockchain can effectively achieve the effect of low latency and faster task response, as well as ensure data security.

关键词： Blockchains Servers Reliability Time factors Task analysis Low latency communication Optimization

来源：评论

学校读者我要写书评

暂无评论

Immune genetic programming based on register-stack structure

Immune genetic programming based on register-stack structure

引用

Congress on Evolutionary Computation

作者： Zeming Zhang Wenjian Luo Xufa Wang Nature Inspired Computation and Applications Laboratory Department of Computer Science and Technology University of Science and Technology Hefei China Nature Inspired Computation and Applications Laboratory Department of Computer Science and Technology and Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei China

Inspired by biological immune principles, a novel Immune Genetic Programming based on Register-Stack structure (rs-IGP) is proposed in this paper. In rs-IGP, an antigen represents a problem to be solved, and an antibody represents a candidate solution. A flexible and efficient antibody representation based on register-stack structure is designed for rs-IGP. Three populations are adopted in rs-IGP, i.e. the common population, the elitist population and the self set. The immune genetic operators are also developed, including clone operator, recombination operator, mutation operator, hypermutation operator, crossover operator and negative selection operator. The experimental results demonstrate that rs-IGP has better performance.

关键词： Genetic programming Evolutionary computation Genetic mutations Cloning Biological system modeling Problem-solving Genetic algorithms Gene expression

来源：评论

学校读者我要写书评

暂无评论

Learning the optimally coordinated routes from the statistical mechanics of polymers

arXiv

引用

arXiv 2019年

作者： Liao, Hao Wu, Xingtong Zhou, Mingyang Yeung, Chi Ho National Engineering Laboratory for big data computing systems Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Department of Science and Environmental Studies Education University of Hong Kong Hong Kong

Many major cities suffer from severe traffic congestion. Road expansion in the cites is usually infeasible, and an alternative way to alleviate traffic congestion is to coordinate the route of vehicles. Various path selection and planning algorithms are thus proposed, but most existing methods only plan paths separately and provide un-coordinated solutions. Recently, an analogy between the coordination of vehicular routes and the interaction of polymers is drawn;the spin glass theory in statistical physics is employed to optimally coordinate transportation routes. To further examine the advantages brought by path coordination, we incorporate the link congestion function developed by the Bureau of Public Roads (BPR) into the polymer routing algorithm. We then estimate in simulations the traveling time of all users saved by the polymer-BPR algorithm in randomly generated networks and real transportation networks in major cities including London, New York and Beijing. We found that a large amount of traveling time is saved in all studied networks, suggesting that the approach inspired by polymer physics is effective in minimizing the traveling time via path coordination, which is a promising tool for alleviating traffic congestions. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical mechanics

来源：评论

学校读者我要写书评

暂无评论

Detection of COVID-19 by GoogLeNet-COD 16th

Detection of COVID-19 by GoogLeNet-COD

引用

16th International Conference on Intelligent computing, ICIC 2020

作者： Yu, Xiang Wang, Shui-Hua Zhang, Xin Zhang, Yu-Dong School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Department of Medical Imaging The Fourth People’s Hospital of Huai’an Huai’anJiangsu Province223002 China Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9783030607982

The outbreak of COVID-19 has been striking the world for months and caused hundreds of thousands of mortality. Early and accurate detection turns out to be one of the most effective ways to slow the spreading of the virus. To help radiologists interpret images, we developed an automatic CT image-based detection system, which achieved high accuracy on the detection of COVID-19. The proposed model in the detection system is codenamed GoogLeNet-COD, which utilizes one of the state-of-the-art deep convolutional neural networks GooLeNet as the backbone. As GoogLeNet was initially trained on ImageNet, we first replaced the last top two layers with four new layers, which include the dropout layer, two fully-connected layers and the output layer. The dropout technique is utilized to prevent overfitting in the system by inserting a dropout layer in the network. The newly added fully-connected layer serves as a transitional layer that prevents significant information loss while the last fully-connected layer is used to generate possibilities for the final output layer. The hold-out validation method is used to evaluate the performance of the proposed system. The experiment on a private COVID-19 dataset showed a high accuracy of our system. © 2020, Springer Nature Switzerland AG.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

arXiv

引用

arXiv 2021年

作者： Liu, Lingbo Zhu, Yuying Li, Guanbin Wu, Ziyi Bai, Lei Lin, Liang School of Computer Science and Engineering Sun Yat-Sen University 510000 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Engineering Research Center for Advanced Computing Engineering Software of Ministry of Education China GuangDong Province Key Laboratory of Information Security Technology China School of Electrical and Information Engineering The University of Sydney 2000 Australia

Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD matrices of previous time intervals can not be obtained immediately in online metro systems, and conventional methods only used limited information to forecast the future OD and DO ridership separately. In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership. Specifically, an OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Moreover, a Dual Information Transformer is introduced to propagate the mutual information among OD features and DO features for modeling the OD-DO causality and correlation. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously. Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction. Our code is resealed at https://***/HCPLab-SYSU/HIAM. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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：