A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs

As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.

A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.

Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment

Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.

MULTIPLE-SCATTERING STUDIES OF ADSORPTION STRUCTURE OF C_2D_2/Si(111)7×7

The multiple scattering cluster (MSC) method has been employed to perform a theoretical analysis on carbon is near edge X-ray absorption fine structure of the deuteron acetylene (C2 D2) adsorbed on Si(111)7× 7 at room temperature. From the MSC study. it is confirmed that the (22D2 molecule is bonded to a pair of adjacent Si adatom and Si restatom with C-Si bond length about 0.18nm. The carbon-deuteron bond is bent away front the surface and the CCD bond angle is about 120°. The molecule plane tilt slightly away from the surface normal. Compared with C2D2 in gas phase, the C-C bond and C-D bond are elongated by about 0.03nm and 0.02nm respectively when acetylene was adsorbed on the subtrate. Keyowrds: adsorption of deuteron acetylene on Si(111)7×7. near edge X- ray absorption fine structure. multiple scattering cluster method

Multiple Hypergraph Clustering of Web Images by Mining Word2Image Correlations

In this paper, we consider the problem of clustering Web images by mining correlations between images and their corresponding words. Since Web images always come with associated text, the corresponding textual tags of Web images are used as a source to enhance the description of Web images. However, each word has different contribution for the interpretation of image semantics. Therefore, in order to evaluate the importance of each corresponding word of Web images, we propose a novel visibility model to compute the extent to which a word can be perceived visually in images, and then infer the correlation of word to image by the integration of visibility with tf-idf. Furthermore, Latent Dirichlet Allocation （LDA） is used to discover topic information inherent in surrounding text and topic correlations of images could be defined for image clustering. For integrating visibility and latent topic information into an image clustering framework, we first represent textual correlated and latent-topic correlated images by two hypergraph views, and then the proposed Spectral Multiple Hypergraph Clustering （SMHC） algorithm is used to cluster images into categories. The SMHC could be regarded as a new unsupervised learning process with two hypergraphs to classify Web images. Experimental results show that the SMHC algorithm has better clustering performance and the proposed SMHC-based image clustering framework is effective.

Identifying the skeptics and the undecided through visual cluster analysis of local network geometry

By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or at their boundaries(the skeptics).Identifying these nodes is relevant in marketing applications like voter targeting,because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters.So far this identification task is not as well studied as other network analysis tasks like clustering,identifying central nodes,and detecting motifs.We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.