This paper presents a novel method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is LSB matching. First, arguments are provided for modeling differences between adjacent pixels using first

Abstract. In this paper the properties of node-node adjacency matrix in acyclic digraphs are considered. It is shown that topological ordering and node-node adjacency matrix are closely related. In fact, first the one to one correspondence between upper triangularity of node-node adjacency matrix

The use of adjacency matrices to the chromosome coding in evolutionary circuits techniques is proposed. The approach is shown to overcome some of the drawbacks associated with other coding schemes simplifying the implementation of the evolution process. It is particularly shown that the proposed

Abstract not found

nodes are diseases connected if they have common genetic origin. Published as a supplement of the Proceedings of the National Academy of Sciences [1], the map was created to illustrate the genetic interconnectedness of apparently dis-tinct diseases. With time it crossed dis-ciplinary boundaries, taking up a life of its own. The New York Times created an interactive version of the map and the London-based Serpentine Gallery, one of the top contemporary art galleries in the world, have exhibited it part of their focus on networks and maps [2]. It is also featured in numerous books on design and maps [3, 4, 5]. Figure 2.0 (front cover)

There are several possibilities to represent a graph G = (V,E) in memory. Let the set of nodes be V = {1, 2,..., n} with edges E ⊆ V × V, and let m: = |E|.

This paper describes a spectral method for graph-matching. We adopt a graphical models viewpoint in which the graph adjacency matrix is taken to represent the transition probability matrix of a Markov chain. The nodeorder of the steady state random walk associated with this Markov chain

This paper presents an adjacent matrix based method for verifying and analyzing large scale workflows, which is used to find our three basic prosperities: sequential, parallel and simple mixing. Any workflow with the three characteristics can be grouped and deducted into smaller and even easier

This paper presents an adjacent matrix based method for verifying and analyzing large scale workflows, which is used to find our three basic prosperities: sequential, parallel and simple mixing. Any workflow with the three characteristics can be grouped and deducted into smaller and even easier

increasing interest in the recent decade. Over the years there have been several techniques being developed to achieve high success rate of accuracy in the identification and verification of individuals for authentication in security systems. This paper proposes an adjacency matrix based approach for face