高校学科导航资源的知识组织模式及优化

就目前国内外学科资源导航建设现状 ,CALIS学科导航项目建设进展 ,导航库知识组织模式 ,Web站点信息资源的维护方案 ,信息技术对组织模式的影响 ,网络信息资源评价体系的建设方法、指标 ,建立信息质量控制机制和体系 ,信息的优化方案等问题做了探讨 ,分析了网络信息组织的发展趋势 .

传统电信大客户光纤接入方案探讨

通信大客户业务是电信运营商努力寻找新的业务增长点中的最热点,如何优化网络结构、规范大客户光纤的组网方式,是一个需要研究的课题,本文通过对大客户业务特点的分析和通信大客户光纤接入技术的研究,提出了大客户光纤通信接入网络的各种建设模式。希望本文对传统电信运营商的大客户光纤建设具有实际的指导意义。

Service-Oriented Network Virtualization Architecture for Internet of Things

As a key technology to realize smart services of Internet of Things（IoT）, network virtualization technology can support the network diversification and ubiquity, and improve the utilization rate of network resources. This paper studies the service-ori- ented network virtualization architecture for loT services. Firstly the semantic description method for loT services is proposed, then the resource representation model and resource management model in the environment of network virtualization are presented. Based on the above models, the service-oriented virtual network architecture for loT is established. Finally, a smart campus system is designed and deployed based on the service-oriented virtual network architecture. Moreover, the proposed architecture and models are verified in experiments.

A Game Theoretic Approach for Energy-Efficient In-Network Caching in Content-Centric Networks

Recently, content-centric networking （CCN） has become a hot research topic for the diffusion of contents over the Internet. Most existing works on CCN focus on the improvement of network resource utilization. Consequently, the energy consumption aspect of CCN is largely ignored. In this paper, we propose a distributed energyefficient in-network caching scheme for CCN, where each content router only needs locally available information to make caching decisions considering both caching energy consumption and transport energy consumption. We formulate the in-network caching problem as a non-cooperative game. Through rigorous mathematical analysis, we prove that pure strategy Nash equilibria exist in the proposed scheme, and it always has a strategy profile that implements the socially optimal configuration, even if the touters are self-interested in nature. Simulation results are presented to show that the distributed solution is competitive to the centralized scheme, and has superior performance compared to other popular caching schemes in CCN. Besides, it exhibits a fast convergence speed when the capacity of content routers varies.

STUDY ON STRATEGY OF DYNAMIC JOINT ROUTING AND RESOURCE ALLOCATION IN LAYERED OPTICAL TRANSPORT NETWORKS

A layered network model for optical transport networks is proposed in this paper,which involves Internet Protocol(IP) ,Synchronous Digital Hierarchy(SDH) and Wavelength Division Mul-tiplexing(WDM) layers. The strategy of Dynamic Joint Routing and Resource Allocation(DJRRA) and its algorithm description are also presented for the proposed layered network model. DJRRA op-timizes the bandwidth usage of interface links between different layers and the logic links inside all layers. The simulation results show that DJRRA can reduce the blocking probability and increase network throughput effectively,which is in contrast to the classical separate sequential routing and resource allocation solutions.

Green Networking Based Energy Efficient Communications

The mainstream approaches to green networking are discussed first from the view of engineering,including resource consolidation,server virtualization,selective connectedness,and proportional computing.A brief introduction to network virtualization techniques is given then and a virtual node embedding approach is provided.Finally,three kinds of enhanced green networking schemes by network virtualization are proposed,that is enhancement to sever virtualization,resource consolidation and Adaptive Link Rate(ALR).Examples are included to show the virtue of network virtualization to green networking in terms of energy efficient communications.

Degree-Based Probabilistic Caching in Content-Centric Networking

Content-Centric Networking is a novel future network architecture that attracts increasing research interests in recent years. In-network caching has been regarded as a prominent feature of Content-Centric Networking since it is able to reduce the network traffic, alleviate the server bottleneck and decrease the user access latency. However, the CCN default caching scheme results in a high caching redundancy, causing an urgent need for an efficient caching scheme. To address this issue, we propose a novel implicit cooperative caching scheme to efficiently reduce the caching redundancy and improve the cache resources utilization. The simulation results show that our design achieves a higher hit ratio and a shorter cache hit distance in comparison with the other typical caching schemes.

Probabilistic Top-k Query:Model and Application on Web Traffic Analysis

Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network(CDN).

Approach in High Precision Topic-Specific Resource Discovery on the Web

The Internet presents numerous sources of useful information nowadays. However, these resources are drowning under the dynamic Web, so accurate finding user-specific information is very difficult. In this paper we discuss a Semantic Graph Web Search (SGWS) algorithm in topic-specific resource discovery on the Web. This method combines the use of hyperlinks, characteristics of Web graph and semantic term weights. We implement the algorithm to find Chinese medical information from the Internet. Our study showed that it has better precision than traditional IR (Information Retrieval) methods and traditional search engines. Key words HITS - evolution web graph - power law distribution - context analysis CLC number TP 391 - TP 393 Foundation item: Supported by the National High-Performance Computation Fund (00303)Biography: Ye Wei-guo (1970-), male, Ph. D candidate, research direction: Web information mining, network security, artificial intelligence.

A Frameless Network Architecture for the Way Forward of C-RAN

The key technologies involved in the evolution of the Cloud-based Radio Access Network(C-RAN) are discussed in this paper. Taking the Frameless Network Architecture(FNA) as a starting point, a cell-lessbased network topology for a multi-tier Heterogeneous Network(Het Net) and ultra-dense network is proposed. The FNA network topology modeling is researched with centralized processing and distributed antenna deployments. The Antenna Element(AE) is released as a new dimensional radio resource that is included in the centralized Radio Resource Management(RRM) processes. This contributes to the on-demand user-centric serving-set associations with cell-edge effect elimination. The Control Plane(CP) and User Plane(UP) separation and adaptation are introduced for energy efficiency improvements. The centralized RRM and different optimization goals are discussed for fully exploring the merits from the centralized computing of C-RAN. Considering the complexity, near-optimal approaches for specific users' Quality-of-Service(Qo S) requirements are addressed. Finally, based on the research highlighted above, the way forward of C-RAN evolution is discussed.

FAIRNESS OF SHARING PROTECTION FOR MULTICAST IN WDM NETWORKS

In this paper,the sharing schemes of multicast in survivable Wavelength-Division Multi-plexed(WDM) networks are studied and the concept of Shared Risk Link Group(SRLG) is considered.While the network resources are shared by the backup paths,the sharing way is possible to make the backup paths selfish.This selfishness leads the redundant hops of the backup route and a large number of primary lightpaths to share one backup link.The sharing schemes,especially,the self-sharing and cross-sharing,are investigated to avoid the selfishness when computing the backup light-tree.In order to decrease the selfishness of the backup paths,it is important to make the sharing links fair to be used.There is a trade-off between the self-sharing and cross-sharing,which is adjusted through simulation to adapt the sharing degree of each sharing scheme and save the network resources.

Modeling and analysis of gradual hybrid anti-worm

The gradual hybrid anti-worm （GHAW） was presented. percentage of vulnerable hosts present in the network. For GHAW It changed its confrontation scheme in real time according to the its process of countering malicious internet worms was modeled. The performance of GHAW on two factors was also estimated： confronting validity against worms and consumption of network resources. Factors governing its performance, specifically the transformation threshold and the transformation rate, were analyzed. The simulation experiments show that GHAW has dynamical adaptability to changes of network conditions and offers the same level of effectiveness on confronting internet worms as the divide-and-rule hybrid anti-worm, with significantly less cost to network resources. The experiments also indicate that the transformation threshold is the key factor affecting the performance of GHAW.

Noisy Chaotic Neural Network for Resource Allocation in High-Speed Train OFDMA System

High-speed train communication system is a typical high-mobility wireless communication network. Resource allocation problem has a great impact on the system performance. However, conventional resource allocation approaches in cellular network cannot be directly applied to this kind of special communication environment. A multidomain resource allocation strategy was proposed in the orthogonal frequency-division multiple access(OFDMA) of high-speed. By analyzing the effect of Doppler shift, sub-channels, antennas, time slots and power were jointly considered to maximize the energy efficiency under the constraint of total transmission power. For the purpose of reducing the computational complexity, noisy chaotic neural network algorithm was used to solve the above optimization problem. Simulation results showed that the proposed resource allocation method had a better performance than the traditional strategy.

INTERNET ENRICHMENT IN AN ORAL INTERPRETATION CLASS

Exploitation of Internet potentialities offers a number of advantages in preparing students as oral interpreters. This paper describes how an oral interpretation class makes use of the Internet, suggests a rationale for the various procedures used, and presents case studies which explore the benefits of Internet aided interpretation practice for both students and teachers in pre-class preparation, in-class activity, and follow-up reinforcement. A questionnaire-based survey among 265 English majors also indicates students' enthusiasm and validates the teachers' experiment.

Recent advances in efficient computation of deep convolutional neural networks

Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems.At the same time,the computational complexity and resource consumption of these networks continue to increase.This poses a significant challenge to the deployment of such networks,especially in real-time applications or on resource-limited devices.Thus,network acceleration has become a hot topic within the deep learning community.As for hardware implementation of deep neural networks,a batch of accelerators based on a field-programmable gate array(FPGA) or an application-specific integrated circuit(ASIC)have been proposed in recent years.In this paper,we provide a comprehensive survey of recent advances in network acceleration,compression,and accelerator design from both algorithm and hardware points of view.Specifically,we provide a thorough analysis of each of the following topics:network pruning,low-rank approximation,network quantization,teacher–student networks,compact network design,and hardware accelerators.Finally,we introduce and discuss a few possible future directions.