A collaborative filtering framework based on both local user similarity and global user similarity

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.     

球形全景图像的自动拼接

近年来,球形全景的虚拟现实方法开始发展起来.在已有的球形全景图像拼接方法中,基本上都是"半自动"的拼接方法,需要很多人工操作,效率低下、成本高昂.提出一种球面自寻匹配的拼接算法.该算法通过把多幅图像映射到一个合适的球面上,自动调整每幅图像的插入点,使得重合位置的差值图像灰度累积平均值最小,然后球面的图像反映射成平面形式,最终得到拼合图像.该算法可使程序实现完全自动化,使人工操作降至最低,从而降低全景图像的制作成本.     

Network traffic classification based on ensemble learning and co-training

Classification of network traffic is the essential step for many network researches. However,with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model,which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches,most of which only employed single classifier,multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings:limited flow accuracy rate,weak adaptability and huge demand of labeled training set. In this paper,statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set,then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.     

Cryptanalysis of RSA for a special case with d > e

In this paper, we study the RSA public key cryptosystem in a special case with the private exponent d larger than the public exponent e. When N ^0.258 ⩽ e ⩽ N ^0.854, d > e and satisfies the given conditions, we can perform cryptanalytic attacks based on the LLL lattice basis reduction algorithm. The idea is an extension of Boneh and Durfee’s researches on low private key RSA, and provides a new solution to finding weak keys in RSA cryptosystems. Supported partially by the National Basic Research Program of China (Grant No. 2003CB314805), the National Natural Science Foundation of China (Grant Nos. 90304014 and 60873249), and the Project funded by Basic Research Foundation of School of Information Science and Technology of Tsinghua     

MTK系统下的J2ME运行平台设计

在目前手机移动开发上,Java技术提供给用户多种应用服务,发展空间比较大。本文描述了J2ME运行平台在MTK系统下的设计和实现。首先,简要介绍了J2ME运行环境和MTK平台。然后,在分析此系统需要实现的功能基础上,根据现有资源和平台特性,提出了整个平台的框架,并且详细描述平台各个子功能模块的设计。最后,详细介绍了系统各个模块的实现。     

RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets.     

基于GPU的多分辨率体数据重构和渲染

基于小波变换的多分辨率压缩算法能够获得很高的压缩比,因而被广泛地用于压缩体数据.针对这种压缩策略,研究基于GPU的数据重构的方法,可以只从CPU向GPU传输少量的压缩数据,从而提高数据传输效率.因为好的数据结构是实现基于GPU的重构算法的关键,所以文中提出适合使用矩形纹理表示的数据结构--Nested Tileboard;然后给出基于该数据结构在GPU上实现多分辨率重构的方法,使用Nested Tileboard保存中间数据及重构结果;还提出了基于Nested Tileboard的多分辨率体绘制方法,直接对重构数据进行体绘制,从而实现数据重构和体绘制的无缝连接.     

概率引导的随机采样一致性算法

为了提高随机采样一致性算法的计算效率,提出一种概率引导的随机采样一致性算法.根据采样模型在原始数据上的检验结果调整每个样本点的采样概率,使得正确样本和正确模型被采样的概率得到提高.在首次获得正确模型之后,样本采样与模型更新构成了一个正反馈环节,经过若干次迭代后,正确样本被采样的概率远超过错误样本被采样的概率.理论分析和实验数据表明,该算法收敛需要的迭代次数较少,有效地提高了随机采样一致性算法的效率.     

三角网格的能量优化参数化方法

三角网格参数化是纹理映射、曲面拟合与曲面重构、网格编辑等工作的基础和环节,参数化变形的大小是衡量参数化好坏的标准.为此提出一种基于变形能量优化的三角网格参数化方法.采用区域增长算法逐层展平空间三角网格,得到空间三角网格曲面的自由边界的参数化结果,并利用保形变换将自由边界的参数化结果变换为规则边界的参数化结果;同时兼顾了参数化的角度变形和面积变形,使得参数化结果具有整体变形较小的特点,并能够避免三角形折叠的现象.将该方法应用于纹理映射中的数值实验表明,其比常见的几种参数化方法具有更好的纹理映射效果.     

光学透射头盔显示器标定综述

光学透射头盔显示器(OSTHMD)标定是增强现实技术研究中一个具有挑战性的关键技术问题.文中详细阐述了OSTHMD的标定技术,包括标定要求、标定计算模型、标定方法、标定评估、发展趋势和应用前景等,重点介绍了比较实用的基于单点主动对准法和基于图像的标定方法.