检索结果-内蒙古大学图书馆

International Conference on Communications Workshops

作者： Qian Liu Fangqing Tan Tiejun Lv Hui Gao Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications China 100876

ISBN: (纸本)9781509015269

Non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for the fifth generation (5G) mobile communication due to its high spectral efficiency (SE). Green radio (GR) which focuses on energy efficiency (EE) is becoming an inevitable trend. In this paper, we study the tradeoff between EE and SE in downlink NOMA systems. The tradeoff problem is formulated as an optimization problem in which the basic idea is to maximize EE under a satisfying minimum SE requirement. It is proved that the system EE is a strictly quasi-concave function of transmission power. By formulating the optimization problem as a bilayer optimization problem, the optimal solution to the optimization problem is obtained, including optimal power allocation coefficients among multiplexed users and optimal transmission power value. Simulation results are provided to further confirm our theoretical findings and the effectiveness of our proposed power allocation scheme for achieving a flexible tradeoff between EE and SE.

关键词： Downlink NOMA Systems

来源：评论

学校读者我要写书评

暂无评论

Improving stock market prediction via heterogeneous information fusion

arXiv

引用

arXiv 2018年

作者： Zhang, Xi Zhang, Yunjia Wang, Senzhang Yao, Yuntao Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Computer Science University of Illinois at ChicagoIL60607 United States Institute for Data Science Tsinghua University Beijing100084 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan Guangdong523808 China

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsicrelations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in HBase

Implementing an Inference Engine for RDFS/OWL Constructs and...

引用

International Conference on Semantics, Knowledge and Grid (SKG)

作者： Zhengbo Liu Wenbin Yao Dongbin Wang National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ministry of Education for Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

The Inference Engine is an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for HBase Semantic Data Store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, and ii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of the HBase Database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using LUBM semantic datasets demonstrates the effectiveness of our approach.

关键词： Engines Ontologies Semantics Cognition Resource description framework OWL Databases

来源：评论

学校读者我要写书评

暂无评论

A Recommendation System Based on Multi-attribute 9

A Recommendation System Based on Multi-attribute

引用

9th International Conference on service Science, ICSS 2016

作者： Dong, Jiali Li, Xiaoyong Fang, Binxing Beijing University of Post and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9781509027279

In recent years, more and more internet companies trend to fuse their products with personal recommendation functions. Although the idea of this business model satisfies characteristic of Internet industry felicitously, scientists and engineers gradually find out that the effect is worse than they expected. The quality of recommendation is limited by cold start, data sparse, lack of learning contextual aware, and so on. One of the most important drawbacks of existing recommendation systems is that they usually use only rating matrix as useful information and not fully consider item attributes. In this paper, we proposed an optimized recommendation system, which based on modeling of items in a multidimensional space of item's attribute. The presented approach takes item attributes, user preferences and user context into consideration, and is mainly composed of two modules: (1) attribute-latent factor model, which is used to show user latent preference for each attribute, (2) multi-attribute model, which is used to show user preference between all attributes. The research is conducted in a practical application environment, and the preliminary experimental results show the validity of the recommendation. © 2016 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

引用

The Journal of China Universities of Posts and Telecommunications 2016年第2期23卷 56-65,96页

作者： Kang Kai Wang Cong Luo Tao School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts Telecommunications

Vehicular Ad-hoc networks（VANETs） are kinds of mobile Ad-hoc networks（MANETs）, which consist of mobile vehicles with on-board units（OBUs） and roadside units（RSUs）. With the rapid development of computation and communication technologies, peripheral or incremental changes in VANETs evolve into a revolution in process. Cloud computing as a solution has been deployed to satisfy vehicles in VANETs which are expected to require resources（such as computing, storage and networking）. Recently, with special requirements of mobility, location awareness, and low latency, there has been growing interest in research into the role of fog computing in VANETs. The merging of fog computing with VANETs opens an area of possibilities for applications and services on the edge of the cloud computing. Fog computing deploys highly virtualized computing and communication facilities at the proximity of mobile vehicles in VANET. Mobile vehicles in VANET can also demand services of low-latency and short-distance local connections via fog computing. This paper presents the current state of the research and future perspectives of fog computing in VANETs. Moreover, we discuss the characteristics of fog computing and services based on fog computing platform provided for VANETs. In this paper, some opportunities for challenges and issues are mentioned, related techniques that need to be considered have been discussed in the context of fog computing in VANETs. Finally, we discuss about research directions of potential future work for fog computing in VANETs. Within this article, readers can have a more thorough understanding of fog computing for VANETs and the trends in this domain.

关键词： VANET fog computing cloud computing application vehicular cloud computing

来源：评论

学校读者我要写书评

暂无评论

APPLIED RESEARCH ON VISUAL MINING TECHNOLOGY IN MEDICAL DATA

APPLIED RESEARCH ON VISUAL MINING TECHNOLOGY IN MEDICAL DATA

引用

2016 4th IEEE International Conference on Cloud computing and Intelligence Systems（IEEE CCIS2016）

作者： Chunshan Wu Yongqiang Weng Qiaowei Jiang Wenming Guo Cong Wang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Software Engineering Beijing University of Post and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of AutomationBeijing University of Post and Telecommunications

ISBN: (纸本)9781509012572

Data mining is a process of discovering information from large datasets by using automated algorithms, which makes it difficult for users to intuitively understand, explore and optimize the datasets and algorithms process. Recently with the development of the visual field, researchers have been exploring how to use the visualization methods to make users intuitively understand datasets and algorithms. In this paper we introduce the concept of visual data mining and dissect the visual data mining technology, mainly focusing on three related aspects: source data, data mining processes and data mining results;then design and implement a visual mining system based on decision tree algorithm, which achieves the encapsulation of data mining algorithms, and presents to the user a simple interactive interface, so that users can carry out visual data mining;finally apply the visual data mining technology to the survey data of elderly anxiety patients, and analyze the decision rules of anxiety disorders, which has important significance for clinical research and decision-making.

关键词： visualization data mining medical data analysis

来源：评论

学校读者我要写书评

暂无评论

MapReduce-based efficient betweenness approximation pivot method for large graphs

引用

International Journal of Information Technology and Management 2016年第2期15卷 144-167页

作者： Deng, Xiao Long Li, Yu Xiao Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications 10 Xitucheng Road Beijing100876 China

The important person is vital for information spreading efficiency in social networks, which has always been the hottest research field in recent years. Nowadays, with the increased information spreading speed in our daily life such as cellphone or microblog, the government and enterprise have realised that it is necessary and important to manage and control the information spreading in social networks of people by recognising the important person or VIP customer. Meanwhile, with the rapid increase of information in society activities recently, the distributed information system engineering such as MapReduce-based computation system has become more and more popular in large-scale social network data analysis in emergency case management or VIP customer recognition. In this article, a novel effective and efficient node betweenness approximation pivot selection method for largescale graphs is proposed based on MapReduce-based system for social network analysis (SNA). Furthermore, it has been proved useful in four discrete datasets and one continuous dataset (six month cellphone call graph) in VIP customer discovery for large graphs from real telecom data in China to help deal with customer relationship management more efficiently. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Secure transmission with artificial noise in heterogeneous massive MIMO network 1st

Secure transmission with artificial noise in heterogeneous m...

引用

1st International Congress on Signal and Information Processing, Networking and Computers, ICSINC 2015

作者： Li, Chang Sun, Songlin Liu, Wei Huang, Hai School of Information and Communication Engineering Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Network and Platform M2M & Internet of Things Institute China Mobile Research Institute Beijing China

ISBN: (纸本)9781138028814

Heterogeneous Network (HetNet) is a promising technology to improve the capacity of future generations of cellular network, in which a mobile station can be served by multiple Base Stations (BSs) with different scales of coverage range, including short range Low Power Nodes (LPNs). In this paper, we study security provisioning in heterogeneous massive Multiple-Input–Multiple-Output (MIMO) systems. Specifically, we consider secure downlink transmission in a massive MIMO system with Matched-Filter (MF) precoding and Artificial Noise (AN) generation at the BS, and transmit the signal to the MT through the LPN in the presence of a passive multi-antenna eavesdropper. Thereby, we consider an AN shaping matrix, where the AN is transmitted in the null-space of the matrix formed by all MT channels. Our analytical and numerical results reveal that, in massive MIMO systems employing MF precoding and AN generation, the Signal-to- Interference-and Noise Ratio (SINR) of intended MTs in LPNs is higher than eavesdropper, we can also obtain the positive secrecy capacity which prove this method can guarantee the secure transmission. © 2016 Taylor & Francis Group, London.

关键词： Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

Community-Based Recommendations: A Solution to the Vulnerability Problem

Community-Based Recommendations: A Solution to the Vulnerabi...

引用

International Conference on Semantics, Knowledge and Grid (SKG)

作者： Jiali Dong Xiaoyong Li Binxing Fang Beijing University of Post and Telecommunications BUPT Beijing China Ministry of Education BUPT Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing China

ISBN: (纸本)9781509047963

Recommender systems assist users in the daunting task of sifting through large amounts of data in order to select relevant information or items. Unfortunately, such systems may be subject to attack by malicious users who want to manipulate the system's recommendations to suit their needs: to promote their own (or demote a competitor's) product/service, or to cause disruption in the recommender system. Attacks can cause the recommender system to become unreliable and untrustworthy, resulting in user dissatisfaction. In this paper, we proposed a community-based recommendation system. The presented approach the rating matrix to extract preferences for each user, and detect communities according to user preferences similarity. The predicted rating is calculated by user in the same community, and malicious users will be in one community for the similar behavior, therefore recommendation systems can reduce the impact of malicious users. Experimental results show that the community-based recommendations is more robust than traditional systems.

关键词： Prediction algorithms Robustness Recommender systems Matrix decomposition Collaboration Companies Motion pictures

来源：评论

学校读者我要写书评

暂无评论

TEXT SUMMARIZATION USING SENTENCE-LEVEL SEMANTIC GRAPH MODEL

TEXT SUMMARIZATION USING SENTENCE-LEVEL SEMANTIC GRAPH MODEL

引用

2016 4th IEEE International Conference on Cloud computing and Intelligence Systems（IEEE CCIS2016）

作者： Xu Han Tao Lv Qiaowei Jiang Xinyan Wang Cong Wang School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Air Force General Hospital

ISBN: (纸本)9781509012572

Text summarization is to generate a condensed version of the original document. The major issues for text summarization are eliminating redundant information,identifying important difference among documents and covering the informative content. In this paper, we propose a Sentence-Level Semantic Graph Model（SLSGM） which exploits the semantic information of sentence. SLSGM considers sentences as vertexes while the semantic relationship between sentences as the *** calculate the relevance values between sentences using semantic analysis and take the values as weights of edges while sentences’ values are scored by a variant of the traditional Page Rank graph ranking algorithm considering both internal and external information. The experimental results show that the applicability of the model to text summarization is feasible and effective.

关键词： Graph-based model Text summarization Semantic analysis

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：