检索结果-内蒙古大学图书馆

Dynamic trust model for Vehicular cyber-Physical Systems

International Journal of Network Security 2018年第1期20卷 157-167页

作者： Zhao, Hongzhuan Sun, Dihua Yue, Hang Zhao, Min Cheng, Senlin Key Laboratory of Cyber Physical Social Dependable Service Computation Chongqing University Area A No.174 Shazheng street Shapingba district Chongqing China School Architecture and Transportation Engineering Guilin University of electronic Technology No.1 Jingji road Qixing district Guilin Guangxi China Johns Hopkins Healthcare LLC Glen Burnie Johns Hopkins HealthCare LLC 6704 Curtis Court Glen BurnieMD21060 United States

Trust is a useful model for the interactions of Vehicular Ad Hoc Network (VANET) in Vehicular cyber-Physical System (VCPS). Given a dynamic nature in transportation, traditional static trust models cannot effectively create the trust relationship among moving vehicles, and cannot handle quickly and dynamically the frequent vehicular interactions in a network topology. A novel trust model of VANET in VCPS is proposed to theorize the trust relationship in the dynamic traffic environment and perform a verification through an improved trust chain and some trusted computing theories. This trust model developed can improve the vehicular interaction security and the driving safety and resist malicious attacks and deceptions. Another, the vehicular trustworthiness is evaluated for the trust model development. The simulation experimental results show that the proposed trust model has a better performance for transportation applications than traditional models. © The Japan Society for Analytical Chemistry.

关键词： cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

Defending against the advanced persistent threat: An optimal control approach

arXiv

引用

arXiv 2017年

作者： Li, Pengdeng Yang, Xiaofan Xiong, Qingyu Wen, Junhao Tang, Yuan Yan Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 School of Software Engineering Chongqing University Chongqing400044 Department of Computer and Information Science University of Macau

The new cyber attack pattern of advanced persistent threat (APT) has posed a serious threat to modern society. This paper addresses the APT defense problem, i.e., the problem of how to effectively defend against an APT campaign. Based on a novel APT attack-defense model, the effectiveness of an APT defense strategy is quantified. Thereby, the APT defense problem is modeled as an optimal control problem, in which an optimal control stands for a most effective APT defense strategy. The existence of an optimal control is proved, and an optimality system is derived. Consequently, an optimal control can be figured out by solving the optimality system. Some examples of the optimal control are given. Finally, the influence of some factors on the effectiveness of an optimal control is examined through computer experiments. These findings help organizations to work out policies of defending against APTs. Copyright © 2017, The Authors. All rights reserved.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Streaming Graph Partitioning for Large Graphs with Limited Memory

Streaming Graph Partitioning for Large Graphs with Limited M...

引用

IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Qi Li Jiang Zhong Linjiang Zheng Xue Li College of Computer Science Chongqing university Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing China School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia

ISBN: (纸本)9781538637913

With the graph data scale constantly expanding, the personal computer has brought in severe challenge for the traditional graph partitioning because of its limited memory capacity. The streaming model has been applied in graph partitioning in recent years because it is more efficient than offline partitioning. However, the cache storage structure of original streaming method is inefficient for searching operations on the one hand, on the other hand, the efficiency gradually is reduced with the number of vertices which have been allocated increased because there is no space left for storing more in memory. A caching strategy for streaming algorithm is put forward in this paper including efficient cache storage structure, which uses the vertex and its neighbors' subset information as basic entry. The cache management module manages cache content. Our method is effective on the condition whether it has limitation to cache capacity or not. By using our cache strategy, it only takes about 25 minutes for partitioning twitter-2010 that have 1.4 billion edges while the original streaming method needs 42 minutes, As can prove the effectiveness and superiority of our method.

关键词： Partitioning algorithms Silicon Cache storage Liquid crystal on silicon Conferences Computer science Microcomputers

来源：评论

学校读者我要写书评

暂无评论

System Identification and Prediction of Dynamic System and Microwave Thermal Process Using a Recurrent Fuzzy Quantum Neural Network

引用

IOP Conference Series: Materials Science and Engineering 2018年第1期466卷

作者： Chaoyin Li Qingyu Xiong Kai Wang Yang Yu Tong Liu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Automation Chongqing University Chongqing 400044 China School of Big Data & Software Chongqing University Chongqing 401331 China

Microwave heating is a time-varying, non-linear process. Mechanism modeling of the microwave thermal process is extremely difficult because of the complex microwave heating environment. This paper presents a recurrent fuzzy quantum neural network with full feedbacks (RFQNN) for prediction and identification of dynamic systems and the actual microwave heating process. In the RFQNN, a quantum neural network is introduced to the consequent part of the fuzzy rules to improve the mapping ability and the identification precision. All of the rules are generated and learned online through a simultaneous structure and parameter learning. During the structure learning, an online clustering algorithm combined with Mahalanobis distance elimination algorithm perform effectively in generating or removing fuzzy rules. And then a gradient descent algorithm is introduced to update the parameters during the parameter learning process. And finally, we test the RFQNN by dynamic plants and the microwave thermal process. The results show that it performs well in dynamic system processing compared with other recurrent fuzzy neural networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Data Driven Development Trend Analysis of Mainstream Information Technologies 9

Data Driven Development Trend Analysis of Mainstream Informa...

引用

9th International Conference on service Science, ICSS 2016

作者： Wen, Junhao Sun, Guanghui Luo, Fengji Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China Faculty of Engineering and Information Technology University of Sydney SydneyNSW Australia

ISBN: (纸本)9781509027279

Software developers often find answers to their programming issues on the Internet. Q&A (Question & Answer) websites has been becoming more and more popular. Among the available technical Q&A sites, the most prevalent one is the Stack Overflow, it has been becoming one of the invaluable knowledge repositories. The development trends of language and mainstream operation systems are discussed by using the SVD (Singular Value Decomposition) and K-means algorithm in this paper. Some key findings include: Traditional programming languages such as C#, C++, C develop slowly and the light script languages such as Php, JavaScript and Python develop rapidly, traditional operation systems such as Windows and Linux develop slowly and the mobile devices operation systems such as IOS and Android develop rapidly. Through studying and analyzing the development of mainstream technology, people can grasp the dynamic situation of the software field, which has important and far-reaching significance to guide the work of software engineering. © 2016 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

EDAHT: An expertise degree analysis model for mass comments in the e-commerce system 1

引用

12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Zhong, Jiang Y., Xiong W., Guo J., Xie Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400030 China College of Computer Science Chongqing University Chongqing400030 China

ISBN: (数字)9783319495866

ISBN: (纸本)9783319495859

In order to help consumers to retrieve the most valuable information from amount of comments quickly, we present a method of evaluating the expertise degree of comments. Firstly, we propose an algorithm to construct automatically an attribute-word hierarchy tree from the massive comments data. Secondly, we develop an expertise degree analysis based on attribute-word hierarchy tree (EDAHT) to estimate the expertise degree of comments. The experiments results on 8,000 manual scoring data show that EDAHT model is high consistent with the manual scoring data, and this novel model is effective. © Springer International Publishing AG 2016.

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

Discovering trip hot routes using large scale taxi trajectory data 1

引用

12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Zheng, Linjiang Feng, Qisen Liu, Weining Zhao, Xin Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing400030 China College of Computer Science Chongqing University Chongqing400030 China

ISBN: (数字)9783319495866

ISBN: (纸本)9783319495859

Discovering trip hot routes is very meaningful for drivers to pick up a passenger, as well as for managers to plan urban public transport. Riding by taxis is one of the important means of transportation. Large scale taxi trajectory data from taxi GPS device implicates residents’ trip behavior. In this paper, we present a method to discover trip hot routes using large scale taxi trajectory data. Firstly, we measure taxi trajectory similarity with longest common subsequence (LCS). LCS-based DBSCAN trajectory clustering algorithm was proposed. Then hot routes were extracted using large scale taxi trajectory data. Our experiment shows that the trajectory clustering algorithm and hot route extraction method are effective. © Springer International Publishing AG 2016.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Improving temporal recommendation accuracy and diversity via long and short-term preference transfer and fusion models

Improving temporal recommendation accuracy and diversity via...

引用

Lecture Notes in Computer Science

作者： Zhang, Bei Feng, Yong College of Computer Science Chongqing University Chongqing400030 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400030 China

ISBN: (纸本)9783319458168

Temporal factor plays an important role in products and services recommended process. It is necessary to combine temporal factors with effective methods to improve recommendation performance. In this paper, we present a novel approach to improve personalized recommendation performance with changing user preferences based on temporal dataset. In the approach, we take consideration of different influence of long and short-term user preferences and construct a preference transfer model based on our enhanced Hidden Markov Model. Then we accomplish preference fusion by adopting our Long and Short Term Graph, a graph model modified from Session-based Temporal Graph, to recommend unknown items. Finally, the experimental results show that our approach achieves important improvements compared to some existing approaches in performance. © Springer International Publishing Switzerland 2016.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Distributed lazy association classification algorithm based on spark 1

引用

12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Li, Xueming Zhang, Chaoyang Chen, Guangwei Sun, Xiaoteng Zhang, Qi Yang, Haomin Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400030 China College of Computer Science Chongqing University Chongqing400030 China

ISBN: (数字)9783319495866

ISBN: (纸本)9783319495859

The lazy association classification algorithms are inefficient when classifying multiple unclassified samples at the same time. The existing lazy association classification algorithms are sequential which can’t deal with the big data problems. To solve these problems, we propose a distributed lazy association classification algorithm based on Spark, named as SDLAC. Firstly, it clusters the unclassified samples by K-Means algorithm. Secondly, it executes distributed projections according to clustered results, and mines classification association rules by a distributed mining algorithm based on spark. Then it constructs classifier to classify unclassified samples. The experiments are conducted on the 5 UCI datasets and a big dataset from the first national college competition on cloud computing(China). The results show that SDLAC algorithm is more accurate than the CBA algorithm. Besides, its efficiency is far more than the typical distributed lazy association classification algorithm. In other words, the SDLAC algorithm can adapt big data environment. © Springer International Publishing AG 2016.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Dropout Prediction in MOOCs Using Behavior Features and Multi-view Semi-supervised Learning

Dropout Prediction in MOOCs Using Behavior Features and Mult...

引用

International Joint Conference on Neural Networks

作者： Wentao Li Min Gao Hua Li Qingyu Xiong Junhao Wen Zhongfu Wu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University)

ISBN: (纸本)9781509006212

While massive open online courses (MOOCs) have gained increasing popularity in recent years, dropout prediction has been an important task to solve due to the high rates of dropout students found in MOOCs. Current methods normally apply supervised learning methods to dropout prediction, using general features extracted from behavior records without the concern of behavior types. However, student learning behavior is diverse and there are no sufficient labeled data to train a model because it is a time-costing task to label enormous data in practice. To solve these problems, in this work, we proposed a novel multi-view semi-supervised learning model based on behavior features for the dropout prediction task. Specifically, we derived features from each type of learning behavior to form multi-view behavior features. In addition, based on these features, we proposed a new multi-view semi-supervised learning method to make use of a large number of unlabeled data to assist insufficient labeled data for improving prediction performance. We conducted experiments on KDD Cup 2015 dataset, and the results show that our proposed method achieves better prediction of student dropouts as compared to state-of-the-art approaches.

关键词： Semi-supervised Learning Student Dropouts Learning behavior Online Courses Popularity

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：