检索结果-内蒙古大学图书馆

2020 IEEE International Conference on Power, Intelligent computing and Systems, ICPICS 2020

作者： Xu, Chengqi Hong, Xuehai Yao, Yuanzhou Shen, Hengheng Ma, Qian Jiang, Hui Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Big Data Research Institute Cloud Computing Center Chinese Academy of Sciences Shangrao China College of Information Engineering Sichuan Agricultural University Ya'an City Sichuan Province China

ISBN: (纸本)9781728198736

A multi-scale R-FCN detection algorithm is presented to solve the problem of region-based full convolution network (R-FCN) in multi-scale object detection. Firstly, in order to solve the problem that R-FCN algorithm has limited receptive field and semantic information in single feature map detection, a multi-scale proposal box (proposal) is obtained by using multi-scale feature maps in the main network. At the same time, in order to make the feature maps of different layers have rich semantic information at the same time during the two-stage detection, the feature maps of different layers are fused from top to bottom. Finally, in order to make the two-stage position-sensitive score map also have good multiscale representation ability, a multi-layer shared convolution method is used to generate the position-sensitive score map. The experimental results on PASCAL VOC dataset show that the multiscale R-FCN algorithm in this paper is better than the original R-FCN algorithm in detection accuracy. At the same time, the detection map shows that the algorithm performs better in multi-scale object detection. © 2020 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Auction Theory and Game Theory Based Pricing of Edge computing Resources: A Survey

引用

IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Neighbor Importance-Aware Graph Collaborative Filtering for Item Recommendation

SSRN

引用

SSRN 2022年

作者： Wang, Qingxian Liu, Xinyu Bai, Yanan Chen, Bo Shi, Xiaoyu Chengdu610054 China Chongqing University of Technology Chongqing401135 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Intelligent Terminal Key Laboratory of SiChuan Province Yibin644000 China

The emerging topic of Graph Neural Network (GNN) has attracted increasing attention and achieved state-of-the-art (SOTA) performance in many recommendation problems, due to its strong ability in node representation with exploring high-order information. To learn a node’s representation, most previous methods usually linearly combine the embeddings of node features, amusing the equal importance of neighbors. However, due to the intrinsic differences (i.e., degree, create time) over neighbors, we argue that these differences carry important signals for node representation. Ignoring them will lead to a sub-optimal in node representation and thus weaken the effectiveness of the follow-up graphbased operations. To address it, we propose big-SAGE@ for item recommendation with rating prediction task, which is a neighbor importance-aware graph neural network. Specifically, its main idea is two-fold: 1) A rating-confidencebased neighborhood sampling method is introduced, making the sampling process biased to those more valuable nodes. 2) An attention network is integrated to achieve the rating prediction task, by flexibly incorporating information from user and item embedding features. Finally, we verified the effectiveness of the proposed model on six public data sets. Extensive experimental results demonstrate the superior performance of big-SAGE@ in the rating prediction task, compared to the SOTA methods. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units

引用

数学研究 2020年第2期53卷 159-191页

作者： Bo Li Shanshan Tang Haijun Yu NCMIS&LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China China Justice Big Data Institute Beijing 100043 China

Deep neural network with rectified linear units (ReLU) is getting more and more popular recently. However, the derivatives of the function represented by a ReLU network are not continuous, which limit the usage of ReLU network to situations only when smoothness is not required. In this paper, we construct deep neural networks with rectified power units (RePU), which can give better approximations for smooth functions. Optimal algorithms are proposed to explicitly build neural networks with sparsely connected RePUs, which we call PowerNets, to represent polynomials with no approximation error. For general smooth functions, we first project the function to their polynomial approximations, then use the proposed algorithms to construct cor-responding PowerNets. Thus, the error of best polynomial approximation provides an upper bound of the best RePU network approximation error. For smooth functions in higher dimensional Sobolev spaces, we use fast spectral transforms for tensor-product grid and sparse grid discretization to get polynomial approximations. Our construc-tive algorithms show clearly a close connection between spectral methods and deep neural networks: PowerNets with n hidden layers can exactly represent polynomials up to degree sn, where s is the power of RePUs. The proposed PowerNets have po-tential applications in the situations where high-accuracy is desired or smoothness is required.

关键词： Deep neural network rectified linear unit rectified power unit sparse grid Power-Net

来源：评论

学校读者我要写书评

暂无评论

data-Driven Modeling to Facilitate Policymaking in Fighting to Contain the COVID-19 Pandemic

引用

Procedia Computer Science 2021年 185卷 320-329页

作者： Robin G. Qiu Ethan Wang Iris Gong Big Data Lab Division of Engineering and Info Sci The Pennsylvania State University Malvern PA 19355 USA College of Computing Georgia Institute of Technology Atlanta GA 30332 USA Methacton School District Eagleville PA 19403 USA

This study shows how data-driven modeling can be applied to facilitating policymaking at the geographical hierarchy in terms of the administrative structure of regions and communities when a public health crisis arises. Specifically, rich data and machine learning based models are explored for public health policies, exploring the timing and restrictive levels of intervention measures, such as school/workplace closure and lifting, gathering ban, or travel restrictions, needed for a community, at the region and community level as time goes. This study articulates that rich data and machine learning work well in reducing policy discrepancies. Real world data of COVID-19 cases at the state level in the U.S. are used first in this study to show the consequence of different policy responses in 2020. To demonstrate what different policy responses could result, an agent-based simulation model using a small-scale school setting will be then presented. The simulation model could be further developed, scaled up, and customarily adopted across any geographical hierarchy, facilitating policymaking in public health.

关键词： COVID-19 Public Health Social Intervention Mitigation Measure SEIR Modeling Policy Response Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Hiding Scrambling Text Message in Speech Signal Based on Lightwight Hyperchaotic Map and Conditional Lsb Mechanisms

SSRN

引用

SSRN 2023年

作者： Al Sibahee, Mustafa A. Abduljaleel, Iman Qays Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Ma, Junchao National Engineering Laboratory for Big Data System Computing Technology Shenzhen Technology University 518060 China Department of Computer Science College of Computer Science and Information Technology University of Basrah 61004 Iraq College of Computer Science and Software Engineering Shenzhen University 518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

This study proposed a lightweight and secure audio steganography system for hiding text messages during transmission over the Internet to address the computational cost exaggeration, and Insufficient levels of security in earlier studies. The paper proposes a two-phase functioning mechanism. Text characters are transformed into ASCII code and then stored in a vector, which is then divided into three sub-vectors. The sub-vectors are scrambled using a two low complexity operation, namely a forwardbackward reading technique and an odd-even index. Two scrambling circuits can be performed: the first on tiny sub-vectors and another on the vector as a whole. In the hiding phase, the speech signal samples were divided into 256 blocks using only 200 values per block, and low complexity Quadratic and the Hénon maps to hide the speech signal in a random manner. This method utilizes the conditional LSB as low complexity algorithm to identify hidden bits and a special hyperchaotic map algorithm for randomly choosing locations. The proposed approach provides good security for a scrambled text message with high SNR and PSNR, small MSE error and PESQ, SSIM near one, BER close to zero, and MOS near five as well as the lowest computational hiding cost. © 2023, The Authors. All rights reserved.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Penetration trajectory optimization for the hypersonic gliding vehicle encountering two interceptors

arXiv

引用

arXiv 2022年

作者： Shen, Zhipeng Yu, Jianglong Dong, Xiwang Hua, Yongzhao Ren, Zhang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The penetration trajectory optimization problem for the hypersonic gliding vehicle (HGV) encountering two interceptors is investigated. The HGV penetration trajectory optimization problem considering the terminal target area is formulated as a nonconvex optimal control problem. The nonconvex optimal control problem is transformed into a second-order cone programming (SOCP) problem, which can be solved by state-of-the-art interior-point methods. In addition, a penetration strategy that only requires the initial line-of-sight angle information of the interceptors is proposed. The convergent trajectory obtained by the proposed method allows the HGV to evade two interceptors and reach the target area successfully. Furthermore, a successive SOCP method with a variable trust region is presented, which is critical to balancing the trade-off between time consumption and optimality. Finally, the effectiveness and performance of the proposed method are verified by numerical simulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Impact of vegetation albedo on the habitability of Earth-like exoplanets

arXiv

引用

arXiv 2024年

作者： Bisesi, Erica Murante, Giuseppe Provenzale, Antonello Biasiotti, Lorenzo von Hardenberg, Jost Ivanovski, Stavro Maris, Michele Monai, Sergio Silva, Laura Simonetti, Paolo Vladilo, Giovanni INAF − Astronomical Observatory of Trieste Via G. Tiepolo 11 Trieste34143 Italy CNR − Institute of Geosciences and Earth Resources via G. Moruzzi 1 Pisa56124 Italy Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34151 Italy ICSC − National Research Center in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 BO Casalecchio di Reno40033 Italy University of Trieste Piazzale Europa 1 Trieste34127 Italy Polytechnic University of Turin − DIATI Corso Duca degli Abruzzi 24 Turin10129 Italy CNR − Institute of Atmospheric Science and Climate Corso Fiume 4 Turin10133 Italy

Vegetation can modify the planetary surface albedo via the Charney mechanism, as plants are usually darker than the bare surface of the continents. We updated ESTM (Earth-like Surface Temperature Model) to incorporate the presence, distribution and evolution of two dynamically competing vegetation types that resemble grasslands and trees (the latter in the double stages of life: adults and seedlings). The newly developed model was applied to estimate how the climate-vegetation system reaches equilibrium across different rocky planetary configurations, and to assess its impact on temperature and habitability. With respect to a world with bare granite continents, the effect of vegetation-albedo feedback is to increase the average surface temperature. Since grasses and trees exhibit different albedos, they affect temperature to different degrees. The ultimate impact on climate depends on the outcome of the competition between these vegetation types. The change in albedo due to vegetation extends the habitable zone and enhances the overall planetary habitability beyond its traditional outer edge. This effect is especially relevant for planets that have a larger extension of continents than Earth. For Earth, the semi-major axis d = 1.04 UA represents the turning point where vegetation enhances habitability from h = 0.0 to h = 0.485 (in the grass-dominance case), to h = 0.584 (in the case of coexistence between grasses and trees), and to h = 0.612 (in the tree-dominance case). This illustrates the transition from a snowball state to a planet with intermediate habitability at the outer edge of the circumstellar habitability zone. Copyright © 2024, The Authors. All rights reserved.

关键词： Vegetation

来源：评论

学校读者我要写书评

暂无评论

Overview of the Tenth Dialog System Technology Challenge: DSTC10

引用

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

来源：评论

学校读者我要写书评

暂无评论

Shallow feature matters for weakly supervised object localization

arXiv

引用

arXiv 2021年

作者： Wei, Jun Wang, Qin Li, Zhen Wang, Sheng Zhou, S. Kevin Cui, Shuguang School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data University of Science and Technology of China Institute of Computing Technology Chinese Academy of Sciences CryoEM Center SUSTech

Weakly supervised object localization (WSOL) aims to localize objects by only utilizing image-level labels. Class activation maps (CAMs) are the commonly used features to achieve WSOL. However, previous CAM-based methods did not take full advantage of the shallow features, despite their importance for WSOL. Because shallow features are easily buried in background noise through conventional fusion. In this paper, we propose a simple but effective Shallow feature-aware Pseudo supervised Object Localization (SPOL) model for accurate WSOL, which makes the utmost of low-level features embedded in shallow layers. In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly. Besides, we further propose a general class-agnostic segmentation model to achieve the accurate object mask, by only using the initial CAMs as the pseudo label without any extra annotation. Eventually, a bounding box extractor is applied to the object mask to locate the target. Experiments verify that our SPOL outperforms the state-of-the-art on both CUB-200 and ImageNet-1K benchmarks, achieving 93.44% and 67.15% (i.e., 3.93% and 2.13% improvement) Top-5 localization accuracy, respectively. © 2021, CC BY.

关键词： Object recognition

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：