P-集合(packet sets)是一个动态模型,P-集合是由内P-集合X^F(internal packet set X^F)与外P-集合(outer packet set X^F)构成的元素集合对;或者(X^F,X^F)是P-集合.利用内P-集合与信息融合交叉,给出内P-信息融合概念,给出一些基本理论结果,并利用这些特性进行信息过滤-辨识.最后利用这些结果给出应用.
为进一步提高水印算法的抗攻击性能,提出了基于支持向量机(Support Vector Machine,SVM)与奇异值分解(Singular Value Decomposition,SVD)的盲水印算法。首先对宿主图像进行DWT变换,将低频子带分成互不重叠的子块;然后利用SVM建立子块的局部相关性模型,根据模型预测结果与对应位置的低频系数值的大小关系产生特征序列,该序列与水印进行异或运算产生特征水印序列,将特征水印序列通过奇偶量化规则嵌入原始图像小波低频子带对应子块的最大奇异值。实验结果表明,该算法不仅具有较好的不可感知性,而且具有较强的抗攻击能力。
Social tagging systems are widely applied in Web 2.0.Many users use these systems to create,organize,manage,and share Internet resources freely.However,many ambiguous and uncontrolled tags produced by social tagging systems not only worsen users' experience,but also restrict resources' retrieval efficiency.Tag clustering can aggregate tags with similar semantics together,and help mitigate the above problems.In this paper,we first present a common co-occurrence group similarity based approach,which employs the ternary relation among users,resources,and tags to measure the semantic relevance between tags.Then we propose a spectral clustering method to address the high dimensionality and sparsity of the annotating data.Finally,experimental results show that the proposed method is useful and efficient.
Hui-zong LIXue-gang HUYao-jin LINWei HEJian-han PAN
S-粗集(Singular rough sets)具有动态特征,近似特征。S-粗集是改进Z.Pawlak粗集得到的,在一定条件下S-粗集被还原成Z.Pawlak粗集。利用单向S-粗集(one direction singular rough sets)与单向S-粗集对偶(dual of one direction singular rough sets),给出内-生成知识,外-生成知识与内-外生成知识概念,给出知识动态发现,发现原理与发现定理,给出知识发现具有的属性合取范式扩张-萎缩特征,给出应用。S-粗集是研究知识动态发现的新理论与新方法。
