Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM). In the second stage, the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure (SICM). Experimental results show that the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.     

改进的基于局部模块度的社团划分算法

针对大多复杂网络社团划分算法不能快速发现最优节点加入社团的问题,提出一种利用节点亲密度的局部社团划分算法。引入节点亲密度的概念量化社团与邻居节点的关系,按照节点亲密度由大到小选择节点加入社团,最后以局部模块度为指标终止局部社团扩展。在真实网络和人工仿真网络进行实验,并与基于信息压缩的随机游走算法等4种典型社团划分算法相比较,所提算法划分结果的综合评价指标(F1score)和标准化互信息(NMI)均好于比较算法。实验研究表明,所提算法具有较好的时间效率和准确度,适用于大规模网络社团划分。     

The Interpersonal Process Model of Intimacy in Marriage: A Daily-Diary and Multilevel Modeling Approach.

This study used daily reports of interactions in marriage to examine predictions from the conceptualization of intimacy as the outcome of an interpersonal process. Both partners of 96 married couples completed daily diaries assessing self-disclosure, partner disclosure, perceived partner responsiveness, and intimacy on each of 42 consecutive days. Multivariate multilevel modeling revealed that self-disclosure and partner disclosure both significantly and uniquely contributed to the contemporaneous prediction of intimacy. Perceived partner responsiveness partially mediated the effects of self-disclosure and partner disclosure on intimacy. Global marital satisfaction, relationship intimacy, and demand-withdraw communication were related to daily levels of intimacy. Implications for the importance of perceived partner responsiveness in the intimacy process for married partners are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Interpersonal Process Model of Intimacy: The Role of Self-Disclosure, Partner Disclosure, and Partner Responsiveness in Interactions Between Breast Cancer Patients and Their Partners.

This study evaluated H. Reis and P. Shaver's (1988) interpersonal process model of intimacy in a sample of 98 women with breast cancer and their partners. Couples engaged in two discussions and rated self- and partner disclosure, perceived partner responsiveness, and intimacy experienced. A mediational model was tested in which partner responsiveness mediated the association between disclosure and intimacy. For patients, perceived responsiveness partially mediated the association between partner disclosure and intimacy, but self-disclosure was not significantly associated with responsiveness or intimacy. For partners, perceived responsiveness mediated the association between self-disclosure and perceived partner disclosure and intimacy. For breast cancer patients, partner disclosure predicted patient feelings of intimacy, because this type of disclosure was associated with greater feelings of acceptance, understanding, and caring. These findings may have implications for interventions to improve relationship closeness among couples coping with breast cancer. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于多特征分类的微博好友推荐

现有微博好友推荐算法使用的用户信息比较单一,不能充分利用微博用户信息来刻画用户特征,导致推荐效果不理想。为解决该问题,在综合分析用户标签信息、内容信息、交互信息以及社交拓扑信息的基础上,通过计算主题相关度、兴趣相关度、用户亲密度进行特征挖掘,并采用K最近邻分类算法为目标用户进行微博好友推荐。在新浪微博真实用户数据集上的实验结果表明,该算法的准确率、召回率、F1度量值分别为16.5%,26.8%,19.2%,推荐效果优于基于内容的推荐算法和基于社会过滤的推荐算法。     

融合位置和社交属性的热点轨迹聚类算法

提出一种新的热点轨迹聚类算法（LSHT）,结合社交网络中的位置属性和社交特征,利用基于位置的社交网络（LBSN）用户中的好友关系特征,引入用户亲密度和用户活跃度,对用户之间的联系进行分析;提出对用户轨迹进行评分的评分函数,计算用户轨迹的影响力。结合用户亲密度和轨迹评分值,挖掘用户好友中的热点轨迹。该算法能够将热门轨迹中的不真实轨迹进行过滤。实验表明,该算法能更快更准确地挖掘出用户好友中的热点轨迹。     

基于SET的在线支付持卡人隐私保护研究

本文从持卡人隐私保护角度出发,通过分析持卡人、商家、收单银行和发卡银行之间的数据交换过程,指出了基于SET(Secure Electronic Transaction)的在线支付存在持卡人隐私保护缺陷,并且提出了改进方案。     

一种基于相对关系亲密度的局部社团发现算法

在复杂网络研究中,社会网络中的社团发现,对商业营销、疾病传播控制等具有实际意义.目前许多研究针对于全局网络进行社团挖掘.挖掘算法因其较高的复杂度往往不适用于动态、大型网络.针对某个点或者某个区域的局部社团挖掘成为了近期的研究热点.为此提出了一种新的相对关系亲密度的计算方法,并与已有算法思想结合,形成了一种新的局部社团发现算法,提高了算法性能.基于已知社会网络、随机测试网络的实验证明了算法的有效性.     

基于团结构亲密度的移动社交网络数据转发算法

由于移动社交网络中不存在稳定的端到端连接,因此移动社交网络中的数据转发是一个重要问题.从节点的友好性角度出发,利用节点间的友好性,构造了节点间的团结构并利用团与节点、社区之间的亲密度,提出了一种基于团结构亲密度的数据转发算法(DFAIG).基本思想是,数据包携带节点只有在本社区AP或者相遇节点与以目的节点为中心的团结构的亲密度达到一定要求时,才转发数据包给相遇节点.仿真结果显示:与著名的Epidemic,Label和SGBR相比,提出的算法在降低网络开销上具有明显优势,且有效地提高数据包传递率.     

以空间亲密感为前提的剧院观众厅设计

从现场表演中演员和观众二者之间的关系切入,通过对传统优秀剧院、全球新建剧院的案例分析以及国内外剧院观众厅视线设计的比较,探讨实现观众厅空间“亲密感”的设计手段.指出营造观众厅空间的“亲密感”,激发演员和观众的互动和交流,才是剧院建造的最终目标.