检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Wang, Shaoyang Lv, Tiejun Ni, Wei Beaulieu, Norman C. Guo, Y. Jay Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Sydney2122 Australia Beijing Key Laboratory for Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users. © 2021, CC BY.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

A more compact object detector head network with feature enhancement and relational reasoning

arXiv

引用

arXiv 2021年

作者： Zhang, Wenchao Fu, Chong Chang, Xiangshi Zhao, Tengfei Li, Xiang Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Baidu Smart Life Group NO. 10 Xibeiwang East Street Ke Ji Yuan Haidian District Beijing100193 China School of Computer Science University of Auckland New Zealand

Modeling implicit feature interaction patterns is of significant importance to object detection tasks. However, in the two-stage detectors, due to the excessive use of hand-crafted components, it is very difficult to reason about the implicit relationship of the instance features. To tackle this problem, we analyze three different levels of feature interaction relationships, namely, the dependency relationship between the cropped local features and global features, the feature autocorrelation within the instance, and the cross-correlation relationship between the instances. To this end, we propose a more compact object detector head network (CODH), which can not only preserve global context information and condense the information density, but also allows instance-wise feature enhancement and relational reasoning in a larger matrix space. Without bells and whistles, our method can effectively improve the detection performance while significantly reducing the parameters of the model, e.g., with our method, the parameters of the head network is 0.6× smaller than the state-of-the-art Cascade R-CNN, yet the performance boost is 1.3% on COCO test-dev. Without losing generality, we can also build a more lighter head network for other multi-stage detectors by assembling our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Dual-Band Tunable Electromagnetically Induced Transparency (EIT) Metamaterial Based on Vanadium Dioxide

引用

Photonics 2025年第5期12卷

作者： Zhu, Lei Wang, Shujie Wang, Yun Dong, Liang Li, Hailong Wang, Yiya Ding, Xumin School of Computer and Control Engineering Qiqihar University Qiqihar161006 China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar161006 China Communication and Electronics Engineering Institute Qiqihar University Qiqihar161006 China Advanced Microscopy and Instrumentation Research Center Harbin Institute of Technology Harbin150080 China

A dual-band tunable terahertz electromagnetically induced transparency (EIT) metamaterial is introduced. The EIT metamaterial consists of two rectangular split rings, two metal strips, and a patterned vanadium dioxide (VO2) located at the back. The rectangular split rings serve as the bright resonator to generate two resonance valleys at distinct frequencies. The metal strips act as the dark resonator and are indirectly activated via the coupling influence of the bright resonator. The EIT metamaterial’s response mechanism is analyzed via the field effect and the two-particle model, with theoretical fitting results showing strong agreement with the simulation results. Moreover, VO2’s conductivity is altered to dynamically control the EIT effect in both frequency bands. Two transparency windows, with modulation depths of 70% and 75%, are observed as the conductivity of VO2 decreases. Simultaneously, the simulation results reveal a favorable slow light effect, with group delays reaching 51 ps and 74 ps at the transparency windows. The proposed metamaterial holds considerable promise for future modulator, filter, and slow light device applications. © 2025 by the authors.

关键词： Luminescent devices

来源：评论

学校读者我要写书评

暂无评论

Identification of important nodes on large-scale Internet based on unsupervised learning 2020

Identification of important nodes on large-scale Internet ba...

引用

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Fuyang Fang Daojuan Zhang Chonghua Wang De Zhang Yuanfei Zhang Chao Li Xi Luo Ye Fu Information Science Academy of China Electronics Technology Group Corporation Beijing China State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute co. Ltd Beijing China China Industrial Control Systems Cyber Emergency Response Team Beijing China Cyber space Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9781450387828

In recent years, scholars have conducted in-depth researches on the robustness, structural vulnerability, and detection and identification of devices in cyberspace from different perspectives such as complex networks and cyberspace resource mapping. Aiming at the problem of identifying important nodes on a large-scale Internet, a CETCRank algorithm for identifying important Internet nodes based on unsupervised learning is proposed. When the algorithm analyzes the attributes of each cyberspace equipment, it not only considers the graph structure characteristics based on the network topology, but also integrates the threat metric of cyberspace equipment. Based on the hypothesis of the cyber attack model, the effective identification of important nodes in the Internet can be realized by integrating the node attributes into the constructed Markov chain model. Experiments show that the time and space complexity of the CETCRank algorithm is suitable for analyzing large-scale Internet, and the recognition performance of important nodes is better than the PageRank algorithm.

关键词： Unsupervised Learning CETCRank Algorithm Important Nodes Large-scale Internet

来源：评论

学校读者我要写书评

暂无评论

An effective verifiable symmetric searchable encryption scheme in cloud computing 7

An effective verifiable symmetric searchable encryption sche...

引用

7th International Conference on Information technology: IoT and Smart City, ICIT 2019

作者： Wang, Kangle Dong, Xiaolei Shen, Jiachen Cao, Zhenfu Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781450376631

With the booming Internet industry, users' demand for resources is also increasing. In order to meet the needs of users, cloud computing came into being. In today's era, cloud storage is already the most popular application in cloud computing, and users are increasingly inclined to store important and private information in the cloud. One of the most common methods is to encrypt the information before uploading it to the cloud, but searching the information it needs in the cloud server becomes an obstacle, and symmetric searchable encryption can help users achieve this. In this paper, we design a verifiable method based on an efficient symmetric searchable encryption scheme to verify the correctness of search results returned by the cloud server. Through safety analysis and performance evaluation tests, we demonstrate that our solutions are safe and effective. © 2019 Association for computing Machinery.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection

Interactive Two-Stream Decoder for Accurate and Fast Salienc...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Huajun Zhou Xiaohua Xie Jian-Huang Lai Zixuan Chen Lingxiao Yang School of Data and Computer Science Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recently, contour information largely improves the performance of saliency detection. However, the discussion on the correlation between saliency and contour remains scarce. In this paper, we first analyze such correlation and then propose an interactive two-stream decoder to explore multiple cues, including saliency, contour and their correlation. Specifically, our decoder consists of two branches, a saliency branch and a contour branch. Each branch is assigned to learn distinctive features for predicting the corresponding map. Meanwhile, the intermediate connections are forced to learn the correlation by interactively transmitting the features from each branch to the other one. In addition, we develop an adaptive contour loss to automatically discriminate hard examples during learning process. Extensive experiments on six benchmarks well demonstrate that our network achieves competitive performance with a fast speed around 50 FPS. Moreover, our VGG-based model only contains 17.08 million parameters, which is significantly smaller than other VGG-based approaches. Code has been made available at: https://***/moothes/ITSD-pytorch.

关键词： Correlation Saliency detection Task analysis Decoding Linear programming Silicon Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Slimmable generative adversarial networks

arXiv

引用

arXiv 2020年

作者： Hou, Liang Yuan, Zehuan Huang, Lei Shen, Huawei Cheng, Xueqi Wang, Changhu CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences ByteDance AI Lab SKLSDE Institute of Artificial Intelligence Beihang University

Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models makes them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power. In this paper, we introduce slimmable GANs (SlimGANs), which can flexibly switch the width of the generator to accommodate various quality-efficiency trade-offs at runtime. Specifically, we leverage multiple discriminators that share partial parameters to train the slimmable generator. To facilitate the consistency between generators of different widths, we present a stepwise inplace distillation technique that encourages narrow generators to learn from wide ones. As for class-conditional generation, we propose a sliceable conditional batch normalization that incorporates the label information into different widths. Our methods are validated, both quantitatively and qualitatively, by extensive experiments and a detailed ablation study. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

DMU-ABSE: Dynamic multi-user attribute-based searchable encryption with file deletion and user revocation 1

引用

5th International Symposium on security and Privacy in Social networks and Big Data, SocialSec 2019

作者： Liu, Jiming Cao, Zhenfu Dong, Xiaolei Shen, Jiachen Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (数字)9789811507588

ISBN: (纸本)9789811507571

Searchable encryption (SE) is a new cryptographic technique that allows data users searching for the files of their interests over huge amounts of encrypted files on the cloud. When it comes to multi-user setting, more issues should be addressed comparing to single-user setting, including key distribution, search privilege control and access control. In this paper, we propose DMU-ABSE, a dynamic multi-user ciphertext-policy attribute-based searchable encryption scheme with file deletion and user revocation. We manipulate an attribute-based encryption to achieve fine-grained search privilege control and hidden policy in multi-user setting while searching time of the proposed scheme is constant (O(1)). With the help of proxy re-encryption, we build one searchable index matrix by different owners in order to improve the searching efficiency. Furthermore, our scheme implements access control by embedding decryption keys into the index matrix. The proposed scheme is proved IND-CKA and IND-CPA semantically secure and experimental results shows that our scheme is efficient. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Access control

来源：评论

学校读者我要写书评

暂无评论

Poisoning attack against estimating from pairwise comparisons

arXiv

引用

arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

来源：评论

学校读者我要写书评

暂无评论

GSLB: The Graph Structure Learning Benchmark

arXiv

引用

arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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：