基于SDN的蜂窝移动网络优化研究

现有的蜂窝网络不灵活、设备昂贵、控制平面协议复杂,配置界面由供应商定制。因此,本文考虑利用软件定义网络（SDN）的思想,简化蜂窝数据网络的设计和管理,并提供新业务。然而,要支持大量的用户频繁地流动、要对其行为进行细粒度的测量和控制并实10时跟进,这些都是对未来的SDN架构应该解决的新的可扩展性的挑战。所以本文提出了一种软件定义的蜂窝网络架构：一、允许控制器应用基于用户的属性来执行政策,而不是基于网络地址和位置;二、通过每台交换机上的本地代理,实现实时、细粒度的控制;三、将交换机进行扩展来支持像网络资源的深度包检测和头压缩,以满足移动数据服务的需求;四、支持基于用户的属性,而不是分组报头字段,进行网络资源的灵活切片,并通过15设置控制器进行无线资源的管理、准入控制和移动性控制,对基站和无线资源的进行灵活的“切片”。     

User demographics prediction based on mobile data

Demographics prediction is an important component of user profile modeling. The accurate prediction of users’ demographics can help promote many applications, ranging from web search, personalization to behavior targeting. In this paper, we focus on how to predict users’ demographics, including “gender”, “job type”, “marital status”, “age” and “number of family members”, based on mobile data, such as users’ usage logs, physical activities and environmental contexts. The core idea is to build a supervised learning framework, where each user is represented as a feature vector and users’ demographics are considered as prediction targets. The most important component is to construct features from raw data and then supervised learning models can be applied. We propose a feature construction framework, CFC (contextual feature construction), where each feature is defined as the conditional probability of one user activity under the given contexts. Consequently, besides employing standard supervised learning models, we propose a regularized multi-task learning framework to model different kinds of demographics predictions collectively. We also propose a cost-sensitive classification framework for regression tasks, in order to benefit from the existing dimension reduction methods. Finally, due to the limited training instances, we employ ensemble to avoid overfitting. The experimental results show that the framework achieves classification accuracies on “gender”, “job” and “marital status” as high as 96%, 83% and 86%, respectively, and achieves Root Mean Square Error (RMSE) on “age” and “number of family members” as low as 0.69 and 0.66 respectively, under the leave-one-out evaluation.     

Analysis and design of Web-based information systems

We have developed a method for analysis and design of web-based information systems (WBISs), and tools to support the method, WebArchitect and PilotBoat. The method and the tools focus on architectures and functions of web sites, rather than on appearance of each web resource (page), such as graphics and layouts. Our goal is to efficiently develop WBISs that best support particular business processes at least maintenance cost. Our method consists of two approaches, static and dynamic. We use the entity relation (E-R) approach for the statis aspects of WBISs, and use scenario approach for the dynamic aspects. The E-R analysis and design, based on relationship management methodology (RMM) developed by Isakowitz et al., defines what are entities and how they are related. The scenario analysis defines how web resources are accessed, used, and changed by whom. The method also defines attributes of each web resource, which are used in maintaining the resource. WebArchitect enables designers and maintainers to directly manipulate meta-level links between web resources that are represented in a hierarchical manner. PilotBoat is a web client that navigates and lets users collaborate through web sites. We have applied our approaches to the WWW6 proceedings site.     

基于虚拟组织的网格文件资源共享模型

网格计算主要关注大规模的资源共享,且这种共享是高度可控的。为解决网格环境下文件资源共享与管理的问题,提出了一个网格文件资源共享模型FsvGrid。该模型引入注册通知机制,并采用确定性算法与非确定性算法相结合的消息传递机制,使得网格中的各个节点之间能够高效协作;采用分层结构,屏蔽了文件资源的多样性;增加了共享的安全性,可以对共享进行控制;提出了一种依靠虚拟组织来对文件资源进行管理的方式,解决分布式资源难以管理的问题。     

ContextGrid: A contextual mashup-based collaborative browsing system

Due to a large amount of resources (i.e., information and knowledge) available on world wide web, it has been more difficult for users to effectively find relevant web resources. Most of the current web browsing methods and systems have been investigated to apply adaptive approaches which can extract personal contexts (e.g., interests and preferences) of the users. In this paper, we propose a contextual mashup-based collaborative browsing (co-browsing) platform, called ContextGrid, for providing online users with various knowledge sharing services. Particularly, the proposed mashup scheme can integrate heterogeneous pieces of information collected by various Open APIs, and assist the users to decide which partners should be selected for mutual collaborations. In order to evaluate the proposed mashup-based method, we have implemented a co-browsing platform which can exchange bookmarks, and measured whether the contextual mashup scheme makes a meaningful influence on improving the performance of the co-browsing process with multiple users.     

User continuance intention to use cloud storage service

Cloud computing is a huge and important change in the field of network application in recent years to provide users with a completely different IT service and delivery mode. Among various cloud services, cloud storage is a service most closely related to web users’ need because it involves the storage of users’ all important data and backup files. In this study, a sample survey was conducted in Taiwan, and key factors influencing individual users’ adoption of the cloud storage service were analyzed and discussed based on Task-Technology Fit theory. The research results indicate that “cloud storage service”, “unstructured task”, “cloud storage self-efficacy” and “opinion of reference groups” all have significant positive influences on the “perceived usefulness”, which further has influence on users’ continuance intention to use the cloud storage service. The findings also support that the privacy protection risk and the lack of privacy-policy risk in the cloud storage service produce negative moderating effects on the perceived usefulness and the continuance intention.     

Adaptive resource scheduling mechanism in P2P file sharing system

Due to the inefficient resource adjustment, the current P2P file sharing systems cannot achieve the balanced relationship between supplements and demands over the resources. In this case, the uploading bandwidth of the system node cannot be utilized efficiently and the overall system QoS is degraded. In this paper, an adaptive resource scheduling mechanism called Push mechanism, is proposed, in which “proactive” strategies are provided to handle the unbalance supplement-demand relationship of some resource. Specifically, the system firstly forecasts which resource will becoming insufficient, then it pre-increase the uploaders over such resource so that the system performance is improved. Through numerical practical experiment in download platform of Tencent, it is proved that the proposed mechanism increases the downloading rate, saves the traffic on the server and optimizes the system performance.     

Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.     

Maximization of users experience in websites: estimating the optimum size of the multimedia content

In this paper, an extended experimental research is performed in order to calculate the mean opinions score (MOS) of several users considering quality of experience variables such as “information perception” and “multimedia type of preference”. The experimental campaign resulted in a great archive of measurements of the MOS of users over several multimedia files. It was found that multimedia type affects information perception whereas users prefer information to be presented by certain multimedia types. A quality factor $q_{i}$ is introduced for the first time for each multimedia type at the best of the authors’ knowledge. This quality factor connects MOS (i.e. user quality experience) and file size (i.e. network resources) with multimedia type. These quality factors express a measure of how MOS is affected by each individual multimedia type and it is inherent to the multimedia type. Approximating equations incorporating MOS and file size as well as quality factors are extracted and are used to construct an optimization problem. The optimization problem has as a target to optimize (i.e. maximize) the overall MOS of “information perception” and “multimedia type of preference” of a website that present information using several multimedia types, constrained by the total size of the website in bytes. Finally, an upper bound for the MOS is extracted based on the total file size of the website in terms of bytes.     

Integrating perceived playfulness into expectation-confirmation model for web portal context

This paper investigated the value of including “playfulness” in expectation-confirmation theory (ECT) when studying continued use of a web site. Original models examined cognitive beliefs and effects that influence a person’s intention to continue to use an information system. Here, an extended ECT model (with an additional relationship between perceived playfulness and satisfaction) was shown to provide a better fit than a simple path from perceived usefulness to satisfaction. The results indicated that perceived playfulness, confirmation to satisfaction, and perceived usefulness all contributed significantly to the users’ intent to reuse a web site. Thus, we believe that the extended ECT model is an appropriate tool for the study of web site effects.     

A novel feature selection framework for automatic web page classification

The number of Internet users and the number of web pages being added to WWW increase dramatically every day.It is therefore required to automatically and e?ciently classify web pages into web directories.This helps the search engines to provide users with relevant and quick retrieval results.As web pages are represented by thousands of features,feature selection helps the web page classifiers to resolve this large scale dimensionality problem.This paper proposes a new feature selection method using Ward s minimum variance measure.This measure is first used to identify clusters of redundant features in a web page.In each cluster,the best representative features are retained and the others are eliminated.Removing such redundant features helps in minimizing the resource utilization during classification.The proposed method of feature selection is compared with other common feature selection methods.Experiments done on a benchmark data set,namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.     

Unsupervised word sense disambiguation with N-gram features

The present paper concentrates on the issue of feature selection for unsupervised word sense disambiguation (WSD) performed with an underlying Naïve Bayes model. It introduces web N-gram features which, to our knowledge, are used for the first time in unsupervised WSD. While creating features from unlabeled data, we are “helping” a simple, basic knowledge-lean disambiguation algorithm to significantly increase its accuracy as a result of receiving easily obtainable knowledge. The performance of this method is compared to that of others that rely on completely different feature sets. Test results concerning nouns, adjectives and verbs show that web N-gram feature selection is a reliable alternative to previously existing approaches, provided that a “quality list” of features, adapted to the part of speech, is used.     

网格区域元知识管理模型

网格资源的发现和管理,对计算网格中的作业调度、资源共享、资源定位和获取有着至关重要的作用。网格全局的资源的描述通常被看作是元知识,这些元知识包括MDS所提供软硬件资源信息、文件资源信息、数据库管理系统元数据资源信息、和Html静态页面上的超链接信息。本文给出一个网格资源元知识的发现、管理模型。实验表明,该模型可以有效的发现、管理网格区域上的资源信息。     

A case study-based investigation of students’ experiences with social software tools

The term “social software” covers a range of tools which allow users to interact and share data with other users, primarily via the web. Blogs, wikis, podcasts and social networking websites are some of the tools that are being used in educational, social and business contexts. We have examined the use of social software in the UK further and higher education to collect evidence of the effective use of social software in student learning and engagement. We applied case study methodology involving educators and students from 26 initiatives. In this paper, we focus on the student experience: educational goals of using social software; benefits to the students; and the challenges they experience. Our investigations have shown that social software supports a variety of ways of learning: sharing of resources; collaborative learning; problem-based and inquiry-based learning; and reflective learning. Students gain transferable skills of team working, negotiation, communication and managing digital identities. Although these tools enhance a student's sense of community, the need to share and collaborate brings in additional responsibility and workload, which some students find inflexible and “forced”. Our findings show that students have concerns about usability, privacy and the public nature of social software tools for academic activities.     

一种新型馈线窗在多系统基站中的应用

在3G及4G通信网络系统大规模建设中,对于2G站间距较小的区域,应充分利用已有2G站址资源,采取共建共享的建设模式。一方面,可以节省投资;另一方面,可加快无线接入网络建设速度,让用户更快、更好地享受高速网络体验。多系统共建共享时,光缆和电源线的数量会进一步增多,对馈线窗提出了更高的要求。本文总结了目前常见馈线窗产品的结构及缺点,在此基础上,介绍了一种新型馈线窗。分析表明,新型馈线窗不仅可以满足多系统共建共享需求,还可降低施工难度和工程造价,应在后期网络建设中推广使用。     

On oligopoly spectrum allocation game in cognitive radio networks with capacity constraints

Dynamic spectrum sharing is a promising technology to improve spectrum utilization in future wireless networks. The flexible spectrum management provides new opportunities for licensed primary user and unlicensed secondary users to reallocate the spectrum resource efficiently. In this paper, we present an oligopoly pricing framework for dynamic spectrum allocation in which the primary users sell excessive spectrum to the secondary users for monetary return. We present two approaches, the strict constraints (type-I) and the QoS penalty (type-II), to model the realistic situation that the primary users have limited capacities. In the oligopoly model with strict constraints, we propose a low-complexity searching method to obtain the Nash Equilibrium and prove its uniqueness. When reduced to a duopoly game, we analytically show the interesting gaps in the leader–follower pricing strategy. In the QoS penalty based oligopoly model, a novel variable transformation method is developed to derive the unique Nash Equilibrium. When the market information is limited, we provide three myopically optimal algorithms “StrictBEST”, “StrictBR” and “QoSBEST” that enable price adjustment for duopoly primary users based on the Best Response Function (BRF) and the bounded rationality (BR) principles. Numerical results validate the effectiveness of our analysis and demonstrate the convergence of “StrictBEST” as well as “QoSBEST” to the Nash Equilibrium. For the “StrictBR” algorithm, we reveal the chaotic behaviors of dynamic price adaptation in response to the learning rates.     

基于网格计算的非集中式资源发现的研究

网格计算为共享和访问大型且不同种类的远程资源集提供一种机制,例如电脑、联机装置、存储空间、数据和应用程序等资源。这些资源通过属性来标识。资源属性具有各种不同程度的动态性,从静态属性(比如操作系统版本)到高动态性(比如网络带宽或CPU负荷)。论文中,在P2P体系中进行大型的和动态的资源发现。在非集中式体系中,评估一套请求传递算法,该算法被设计成能适应不同资源成分(包括共享策略和资源类型)和动态性。为了达到这个目的,建立一个用来模拟两种应用特性的实验平台,这两种特性为:(1)资源按节点分布,而且在共享资源的数量和频率方面也不尽相同:(2)对资源的多种请求模式。     

An intelligent Internet information delivery system to evaluate site preferences

Due to the continual growth of the popularity of the Internet, commercial as well as industrial companies have been advertising their products and services via the Web, resulting in a drastic increase in the number of Web sites. With a huge amount of information available on various Web sites, it is important that the relevant and useful information favored by individual visitors is delivered to the destinations in a timely manner. The two traditional approaches for sorting web information including search engines and hierarchical indices require specific input by the visitors who may not have any specific favorite sites in mind. In most cases, site surfers are just “window-shopping” on the Internet, looking for “exciting” things. This paper proposes the development of an Intelligent Internet Information Delivery System (IIIDS) which is characterized by its machine learning capability based on the data of site spots “movements” by the users within the Web pages and then evaluates the site preferences of the relevant users by means of fuzzy logic principle. The development of IIIDS and the test of a prototype to evaluate its feasibility are covered in this paper.     

网页中商品“属性—值”关系的自动抽取方法研究

商品属性及其对应值的自动挖掘,对于基于Web的商品市场需求分析、商品推荐、售后服务等诸多领域有重要的应用价值。该文提出一种基于网页标题的模板构建方法,从结构化网页中抽取完整的商品“属性—值”关系。该方法包含四个关键技术 1)利用商品网页标题构建领域相关的属性词包;2)基于预设分隔符细化文本节点;3)结合领域商品属性词包获取种子“属性—值”关系;4)结合网页布局信息和字符信息来筛选与构建模板。该文的实验基于相机和手机两个领域展开,获得94.68%的准确率和90.57%的召回率。