Social Propagation: Boosting Social Annotations for Web Mining

This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Social annotations are novel resources and valuable in many web applications, including web search and browsing. Although they are developing fast, social annotations of web pages cover only a small proportion (<0.1%) of the World Wide Web. To alleviate the low coverage of annotations, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included, namely basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. The model is evaluated on a dataset of 40,422 web pages randomly sampled from 100 most popular English sites and ten famous academic sites. Each page’s annotations are obtained by querying the history interface of Del.icio.us. Experimental results show that the proposed model is very effective in increasing the coverage of annotations while still preserving novel properties of social annotations. Applications of propagated annotations on web search and classification further verify the effectiveness of the model.     

Fundamental calculus on generalized stochastically bounded bursty traffic for communication networks

Since many applications and networks do not require or provide deterministic service guarantees, stochastic service guarantee analysis is becoming increasingly important and has attracted a lot of research attention in recent years. For this, several stochastic versions of deterministic traffic models have been proposed in the literature. Unlike previous stochastic models that are based on the traffic amount property of an input process, we present another stochastic model, generalized Stochastically Bounded Burstiness (gSBB), which is based on the virtual backlog property of the input process. We show the advantages of this approach. We study the superposition of gSBB traffic, and set up the input–output relation. Under various service disciplines, we characterize the output process for each source and investigate probabilistic upper bound on delay. Finally, we introduce a stochastic ordering monotonicity property of gSBB. With this property, we show that many well-known traffic models can be readily represented using the proposed gSBB model. These results set up the basis for a network calculus for gSBB traffic.     

Understanding design activities through computer simulation

In this paper, a new experimental approach – computer simulation – is introduced for understanding design activities and for validating design theories. Following the generic framework of computer simulation, three main components in simulating design activities are introduced: mathematical model, simulation model, and statistical analysis. The mathematical model consists of the design governing equation and Environment-Based Design (EBD), based on which three routes are introduced to look for new design solutions: (1) formulating the design problem differently at the beginning of a design process may get quite different solutions, in which creative design could emerge; (2) extending designer’s knowledge and experience can help generate more candidate solutions, and so increasing the probability of generating a good concept; (3) changing the sequence of design problem decomposition may change product requirements, and thus change the generated design concepts. By viewing mesh generation algorithms as design agents, a computer simulation environment is used to study design activities. Statistical analysis is conducted to validate quantitatively the three routes to new design solutions. The results show that computer simulation is an effective approach to studying design activities.     

Semplore: A scalable IR approach to search the Web of Data

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.     

一种新的基于Zernike正交矩亚像素边缘定位的直径测量方法

提出了一种新的基于亚像素边缘定位技术的棒状物体直径测量方法;该方法利用两个CCD摄像头,分别获得两张棒状物体横截面的椭圆特征图像,利用仿射变换将灰度椭圆特征图像变换成圆形特征图像,然后利用亚像素中的Zernike正交矩对边缘进行精确定位,进而分别计算出棒状物体的直径,再将两个测量数据加以平均融合,完成对直径的测量;实验结果表明,与像素级方法相比较,该方法定位精度高,测量误差小,能够达到提高测量精度的目的。     

信息与计算科学专业实践教学体系的探索与优化

信息与计算科学专业是联系数学与信息科学的一门新兴专业,目前很多学校开设了这个专业,很多学校已具有较完善的理论课程,但实践教学体系尚不完善,对学生的创新能力培养非常不利。我们经过多年的积淀,并结合专业培养目标,对信息与计算科学专业的实践教学体系进行探索与优化。     

地铁空间中装饰的导向作用

快速发展的地铁是我国城市解决交通困难的一个重要方式。而地铁的有效的导向设计又是保证地铁快捷的一个重要方面。传统的导向功能主要依靠标识系统来实现。但是,通过调研发现,很多经常乘坐地铁的乘客并不很依赖标识系统,而是凭借以往的认知记忆来识别空间导向自己。本文提出是否可以通过非标识的方式来解决这类乘客的困扰。通过实验和分析,本文探寻了一些设计导则,说明如何利用设计地铁站的装饰来辅助地铁的导向。     

基于AUML的Agent建模方案研究

AUML(Agent UML)是Agent建模普遍采用的方法,其通过对UML的扩展来支持Agent的建模.通过借鉴面向对象的系统分析方法,给出了AUML建模方案,并详细描述了AUML的建模机制和各类图的扩展.以编队指挥系统为例,从分析系统用例,分析和识别最初的Agent类型,责任识别,提取Agent之间的关系及精化Agent,确定交互直至Agent信息的配置,建立了一整套基于AUML的Agent建模方案.     

一种RBAC改进模型及其在军事Web信息系统中的应用

军事Web信息系统要求用户权限必须严格控制、统一管理、维护简便.基于RBAC的访问控制模型能有效弥补DAC和MAC的不足.针对著名的RBAC96模型的缺陷,提出数据资源以业务功能表示、预设角色与用户临时权限的改进角色权限控制模型.该改进方法已经在某军事Web信息系统中得到了应用.