检索结果-内蒙古大学图书馆

2nd International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2019

作者： Li, Yanrong Li, Xiaoyong Hou, Liyang Kong, Wenping Ma, Wenxue Key Laboratory of Trustworthy Distributed Computing and Servic Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Integrated Command and Dispatch Technology Beijing China

ISBN: (纸本)9781450372619

After unconventional events occur, rapid emergency response is often needed in order to reduce losses. When there are more than one disaster points and more than one supply points, how to coordinate the local dispatch of emergency resources among these points is a problem worth studying. In this paper, we design a multi-objective, multi-point to multi-point and multi-resource emergency scheduling model that consider resources arrive time and transportation cost. Then a new emergency resource scheduling algorithm based on multi objective evolutionary algorithm based on decomposition (MOEA/D) is proposed, a new mutation operator was also designed to solve this problem. The experimental results show that this method can get a feasible scheduling scheme, which is faster than NSGA-II and has better scheduling results. © 2019 ACM.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A Multi-Objective Emergency Resource Scheduling Method Based on MOEA/D 19

A Multi-Objective Emergency Resource Scheduling Method Based...

引用

Proceedings of the 2019 2nd International Conference on Algorithms, computing and Artificial Intelligence

作者： Yanrong Li Xiaoyong Li Liyang Hou Wenping Kong Wenxue Ma Key Laboratory of Trustworthy Distributed Computing and Servic Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory For Integrated Command And Dispatch Technology Beijing China

ISBN: (纸本)9781450372619

关键词： MOEA/D

来源：评论

学校读者我要写书评

暂无评论

Auto-GAN: GAN-Based Self-Supervised Collaborative Learning for Robust Spatio-Temporal Trajectory Classification in IoT

引用

IEEE Internet of Things Journal 2025年

作者： Jia, Jia Li, Linghui Li, Ximing Wang, Ning Cai, Binsi Zhang, Xu Qiu, Pengfei Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Research Institute of China Mobile Communications Corporation Beijing100032 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China China Academy of Information and Communications Technology Beijing China Nanjing University School of Electronic Science and Engineering Nanjing210023 China

With the rapid proliferation of crowd mobility data produced by ubiquitous mobile devices equipped with spatial positioning modules, deep neural networks (DNNs) have become widely applied in spatio-temporal trajectory modeling. However, recent studies have shown that DNNs are vulnerable to adversarial examples with strong transferability, which are crafted by introducing small perturbations to original examples but can cause catastrophic mistakes. To mitigate this vulnerability and enhance model robustness, we propose a novel self-supervised collaborative learning framework named Auto-GAN that consists of a generator for automatically learning robust latent features and a discriminator for providing comprehensive guidance to the generator. By leveraging the collaboration between the generator and discriminator, our proposed method significantly improves the denoising performance. Moreover, we combine point-level and feature-level constraints into training processes between original example reconstruction and adversarial example denoising, thereby effectively suppressing the potential "error amplification effect". Extensive experiments conducted on two representative real-world mobility datasets show that our proposed method can significantly enhance the model’s robustness against various adversarial attacks, while preserving the model’s prediction accuracy on original examples. © 2014 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Joint resource management for MC-NOMA: A deep reinforcement learning approach

arXiv

引用

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 Hybrid Routing Algorithm based on Destruction Resistance Cost and Reliability in Flying Ad-Hoc Networks

A Hybrid Routing Algorithm based on Destruction Resistance C...

引用

International Conference on Computer and Communications (ICCC)

作者： Kun Guo Dongbin Wang Hui Zhi Yibo Gao Tao Zhang Tiejun Lv Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing University of Posts and Telecommunications Beijing China TravelSky Technology Limited Beijing China Cyberspace Security Research Center Peng Cheng Laboratory Beijing University of Posts and Telecommunications Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9781665409513

Existing routing algorithms in Flying Ad-Hoc Net- works (FANETs) lack the optimization consideration of destruction resistance cost caused by routing recovery when some nodes fail suddenly. In this paper, we propose a hybrid routing algorithm based on destruction resistance cost and reliability. Based on the real-time network topology and network state, the algorithm can provide a multi-parameters-based routing path with low destruction resistance cost for communication transmission. It can ensure that the main routing path can be quickly recovered after interruption and reduce the process of discovering new routing paths. We have constructed a variety of simulation scenarios to analyze the proposed algorithm and the existing algorithms. The simulation results show that the pro- posed algorithm has good performance in reducing destruction resistance cost.

关键词： Resistance Analytical models Costs Computer network reliability Simulation Routing Real-time systems

来源：评论

学校读者我要写书评

暂无评论

AutoDE: Automated vulnerability discovery and exploitation 4

AutoDE: Automated vulnerability discovery and exploitation

引用

4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Zhang, Yuntao Liu, Tong Wang, Zhongru Ruan, Qiang Fang, Binxing Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China Zhejiang Lab Hangzhou China Beijing DigApis Technology Co. Ltd Beijing China

ISBN: (纸本)9781728145280

Recently, automated testing for software vulnerability finding and exploitation has attracted more and more attention, due to the high scalability and efficiency. However, it is non-trivial and challenging since the existing methods have limited effectiveness to discover vulnerabilities, that are exploitable to hack the software. To address these challenges, we propose an Automated Vulnerability Discovery and Exploitation framework, AutoDE, which aims to improve the effectiveness of the vulnerability discovery and exploitation. In the vulnerability discovery stage, we propose Anti-Driller to alleviate the 'path explosion' problem. It first generates a specific input through the symbolic execution by using the control ?ow graph (CFG), then leverages a mutation-based fuzzer to mutate this case to find vulnerabilities which can avoid unqualified mutation. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits that produce a shell based on the detected vulnerabilities with several attack techniques. We have conducted extensive experiments on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset. The experimental results clearly demonstrate the effectiveness and scalability of the proposed framework. © 2019 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Threshold-Assisted Soft-Output Direct Detection FTN Algorithm for Bandwidth-Limited Systems

Threshold-Assisted Soft-Output Direct Detection FTN Algorith...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shuangyue Liu Ji Zhou Mengqi Guo Xizi Tang Zhongliang Sun Han Cui Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China Jinan University Guangzhou China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China

A threshold-assisted soft-output direct detection faster than Nyquist (SO-DD-FTN) is proposed to compensate bandwidth-limitation impairments in 56-Gb/s PAM-4 systems using 10G-class optics. Experimental results show t... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Degradation Asia Optics Optical fiber communication Computational complexity Photonics

来源：评论

学校读者我要写书评

暂无评论

Sentence segmentation for classical Chinese based on LSTM with radical embedding

引用

The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 1-8页

作者： Han Xu Wang Hongsu Zhang Sanqian Fu Qunchao Liu Jun School of software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)Ministry of EducationBeijing 100876China The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Institute of Scientific and Technical Information of ChinaBeijing 100038China Insitute of Quantitative Social Science Harvard UniversityCambridgeMAUSA Department of statistics Harvard UniversityCambridgeMAUSA

A low-than character feature embedding called radical embedding is proposed,and applied on a long-short term memory(LSTM) model for sentence segmentation of pre-modern Chinese *** dataset includes over 150 classical Chinese books from 3 different dynasties and contains different literary ***-conditional random fields(LSTM-CRF) model is a state-of-the-art method for the sequence labeling *** model adds a component of radical embedding,which leads to improved *** results based on the aforementioned Chinese books demonstrate better accuracy than earlier methods on sentence segmentation,especial in Tang’s epitaph texts(achieving an F1-score of 81.34%).

关键词： LSTM radical embedding sentence segmentation

来源：评论

学校读者我要写书评

暂无评论

SWRR:The Link Scheduling Algorithm Based on Weighted Round-Robin

引用

Computers, Materials & Continua 2019年第6期59卷 965-982页

作者： Tianbo Lu Ru Yan Chao Li Lihua Yin Hao Chen Xieyu Zou School of Software Engineering Beijing University of Posts and TelecommunicationsBeijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(Beijing University of Posts and Telecommunications) Ministry of EducationBeijing100876China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Department Computer Science and Engineering The University of Texas at ArlingtonArlington76091USA

With the rapid development of the Internet,people pay more and more attention to the protection of *** second-generation onion routing system Tor is the most commonly used among anonymous communication systems,which can be used to protect user privacy *** recent years,Tor’s congestion problem has become the focus of attention,and it can affect Tor’s performance even user ***,we investigate the causes of Tor network congestion and summarize some link scheduling algorithms proposed in recent *** we propose the link scheduling algorithm SWRR based on WRR(Weighted Round Robin).In this process,we design multiple weight functions and compare the performance of these weight functions under different congestion conditions,and the appropriate weight function is selected to be used in our algorithms based on the experiment ***,we also compare the performance of SWRR with other link scheduling algorithms under different congestion conditions by experiments,and verify the effectiveness of the algorithm SWRR.

关键词： Weighted Round-Robin Tor congestion control link scheduling anonymous communication

来源：评论

学校读者我要写书评

暂无评论

QPSK-Assisted MIMO Equalization for 800-Gb/s/λ DP-256QAM Systems

QPSK-Assisted MIMO Equalization for 800-Gb/s/λ DP-256QAM Sy...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Siqi Liu Shuangyue Liu Xizi Tang Mengqi Guo Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication EngineeringBeijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace SecurityBeijing University of Posts and Telecommunications Beijing China

A QPSK-assisted MIMO equalization is investigated to compensate bandwidth limitation for 800-Gb/s/λ DP-256QAM systems with only 25G-class optics. Compared with conventional MIMO equalization, the proposed equalizatio... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Bandwidth Optics Optical fiber communication Optical noise MIMO communication Signal to noise ratio Photonics

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：