New similarity measure for mining time series

针对时间序列的全序列聚类展开,提出一种新的相似性度量——全局特征,即从时间序列的统计分布特征、非线性和Fourier频谱转换等3个方面提取11个全局特征构建特征向量。利用特征向量来描述原时间序列,不仅保留了大部分原有的信息,还能加快聚类计算的速度。经过大量的实验验证表明,基于全局特征提取的相似性度量能得到合理的聚类结果,特别是对经济领域的时间序列效果更为明显。例举了2个数据进行实验,并从主观和客观两个角度对聚类结果进行评估。Proposes a new similarity measure-global characters for whole clustering of time series,that replaces the raw data with 11 global characteristics,from the aspects of statistical distribution,non-linear and Fourier transformation,thus can get a characteristic vector,which can hold most information of the original time seiries and reduce the calculating complexity.Experimentally compares the four similarity measures on three database under group-ward hierarchical clustering,evaluates the results objectively and subjecttively respectively,and is shown to yield useful and reasonable clustering,especially for economic time series.厦门大学985二期信息创新平台项目(No0000-X07204

Attribute reduction algorithm of rough set combined fuzzy set theory

结合模糊关系的理论,对粗糙集理论的属性约简算法进行研究,提出了一个新的属性约简算法,并给出了一个应用实例。This paper discussed the attribute reduction in rough set combined fuzzy relation theory,and then proposed a new attribute reduction algorithm and gave an illustrative example.国家自然科学基金资助项目(60275023);; 厦门大学科学研究基金资助项目(Y07002

A k-means-based Algorithm for Soft Subspace Clustering

软子空间聚类是聚类研究领域的一个重要分支和研究热点。高维空间聚类以数据分布稀疏和“维度效应“现象等问题而成为难点。在分析现有软子空间聚类算法不足的基础上,引入子空间差异的概念;在此基础上,结合簇内紧凑度的信息来设计新的目标优化函数;提出了一种新的k-MEAnS型软子空间聚类算法,该算法在聚类过程中无需设置额外的参数。理论分析与实验结果表明,相对于其他的软子空间算法,该算法具有更好的聚类精度。Soft subspace clustering is an important part and research hotspot in clustering research.Clustering in high dimensional space is especially difficult due to the sparse distribution of the data and the curse of dimensionality.By analyzing limitations of the existing algorithms,the concept of subspace difference is proposed.Based on these,a new objective function is given by taking into account the compactness of the subspace clusters and subspace difference of the clusters.And a subspace clustering algorithm based on k-means is presented.The additional parameter is not necessary in the novel algorithm.Theoretical analysis and experimental results demonstrate that the proposed algorithm significantly improves the accuracy.国家自然科学基金No.10771176---

Malware Identification Technique and its Applications

随着互联网技术的发展和安全形势的变化,恶意软件的数量呈指数级增长,恶意软件的变种更是层出不穷,传统的鉴别方法已经不能及时有效的处理这种海量数据,这使得以客户端为战场的传统查杀与防御模式不能适应新的安全需求,各大安全厂商开始构建各自的“云安全“计划。在这种大背景下,研究恶意软件检测关键技术是非常必要的。针对恶意软件数量大、变化快、维度高与干扰多的问题,我们研究云计算环境下的软件行为鉴别技术,探讨海量软件样本数据挖掘新方法、事件序列簇类模式挖掘新模型和算法及在恶意软件鉴别中的应用,并构建面向云安全的恶意软件智能鉴别系统原型以及中文钓鱼网站检测系统架构。With the development of the Internet technology and the changes of the situation of Internet security,we witness exponential increase of the number of malicious software and their endless variants.Traditional detection methods cannot effectively and timely deal with such mass of malicious software data,making traditional anti-virus platform running on PC client cannot satisfy current security requirements any more,thus some major Internet security venders have been launching their 'cloud security' program.Under such background,it is urgent to develop some new effective and efficient techniques for malware detection.In this paper,we investigate malware detection techniques based on cloud computing,including mining massive software samples,and applying new clustering models/algorithms for event sequences into malware detection,to deal with the critical issues of malware as being of large amount,fast change,highdimension and noise-laden.Furthermore,we propose a prototype of intelligent malware detection system for cloud security.国家自然科学基金(面向软件行为鉴别的事件序列挖掘方法研究;NO.61175123);深圳市生物、互联网、新能源产业发展专项资金(NO.CXB201005250021A

Pattern Matching Method Based on Point Distribution for Multivariate Time Series

多元时间序列模式匹配的常用方法难以刻画序列的全局形状特征,比如,EuClId方法的鲁棒性不够强;而PCA方法不适合处理小规模多元时间序列.基于点的统计分布提出了一种能够有效刻画多元时间序列形状特征的模式匹配方法.首先,提取多元时间序列样本的局部重要点,作为模式描述的方式;然后,根据重要点的统计分布特点构建特征模式向量,并借助EuClId范数来度量两个特征模式向量之间的相似程度,进而进行多元时间序列模式匹配.采用该方法进行模式匹配,充分利用了序列的全局形状特征.实验结果表明,基于点分布特征的多元时间序列模式匹配能够有效地刻画序列的形状特征,且能处理多种规模的序列数据.Common methods for matching multivariate time series such as the Euclid method and PCA method have difficulties in taking advantage of the global shape of time series.The Euclid method is not robust, while the PCA method is not suitable to deal with the small-scale multivariate time series.This paper proposes a pattern matching method based on point distribution for multivariate time series, which is able to characterize the shape of series.Local important points of a multivariate time series and their distribution are used to construct the pattern vector.To match pattern of multivariate time series, the Euclid norm is used to measure the similarity between the pattern vectors.The global shape characteristic is used in the method to match patterns of series.The results of experiments show that it is easy to characterize the shape of multivariate time series with this method, with which various scales can be dealt with in series data.国家自然科学基金No.10771176;国家“九八五”工程二期基金No.0000-X07204---

干出和紫外辐射对坛紫菜光合作用的影响

以坛紫菜叶状体为材料,研究了干出和阳光紫外辐射(uVr)对其光合作用的影响.长时间干出和阳光uVr不能进一步诱导藻体合成紫外吸收物质,而且uVr对干出藻体的叶绿素A和类胡萝卜素合成有抑制作用.uVr显著抑制干出状态下藻体光系统II(PSII)的有效光化学效率和藻体的光合固碳速率,而且对光合活性的抑制作用随失水率的增大而增强.国家自然科学基金重点资助项目(批准号:90411018

The Relation between Child\u27s Motivation and Child\u27s Cognition of The Parents\u27 Attitude towards Bringing up The Child : Focus on "The Method of Control"

2003年に行われたOECDの調査において，学習時間や学習内容への興味が国際平均値よりも低いという結果が示されたことや，フリーターやニートが社会的に増加していることから，文部科学省は，学習意欲・勤労意欲の低い青少年が増えつつあると指摘している。この若者の「無気力」は昭和後期より世間一般で言われるようになり，深谷は，この「無気力」の傾向が児童期にも及んでいると指摘している。笠井は，子どもたちの無気力状態や無気力傾向は，「学業に対する選択的な無気力」ばかりでなく，友人関係や進路など生活全般に広がっていると考察している。船木は，学校で起こるいじめや不登校などの諸問題の影には，少なからず無気力が関連しているのではないかと述べている。このように児童期の意欲は，児童期を意義のあるものにするために，また，有能感を獲得するため，大変重要なものであり，児童が意欲を持って生活できるようにするために，児童期の意欲に関連する要因について明らかにしていくことが必要であろう。本研究では，「養育態度→意欲→意欲的行動」という連続した因果関係を全体的仮説とし，児童が認知している『親』の養育態度および，意欲，意欲的行動についての尺度作成を行い，本稿で述べるような具体的仮説について検討を行う

3D Face Modeling Method for a Certain Person

描述了一种特定人的人脸三维建模方法,该方法用该人的正面及侧面人脸两张照片,通过选择关键特征点,在基本人脸模型基础上,经过变形,得到特定人脸三维网格模型。再经纹理匹配,获得特定人的三维人脸模型。该方法已在微型计算机上进行了模拟,并成功地获得酷似真人的人脸三维模型。This paper describes a method to reconstruct 3D face from a front face photo and a side face photo.After selecting key feature points and metamorphosing the basic face mode,the 3D grid face of the special person is created.After texture mapping,the special person’s 3D face model is reconstructed.This method is implemented on a PC.The experiment is successful because the 3D face model and the true face are alike.福建省自然科学基金资助项目(A0410002