14423228
A New Median Graph Algorithm
Median graph is an important new concept introduced to represent a set of graphs by a representative graph. Computing the median graph is an NP-Complete problem. In this paper, we propose an approximate algorithm for computing the median graph. Our algorithm performs in two steps. It first carries out a node reduction process using a clustering method to extract a subset of most representative node labels. It then searches for the median graph candidates from the reduced subset of node labels according to a deterministic strategy to explore the candidate space. Comparison with the genetic search based algorithm will be reported. This algorithm can be used to build a graph clustering algorithm.
2622680
A direct approach to graph clustering
33546649
Distinguishing between overlapping components in mixture models
16049143
Two-Pass ID-Based Authenticated Key Agreement Protocol with Key Confirmation Using Pairings
In the area of secure communications, key agreement is one of the most important issues. In this paper, a practical two-party ID-based authenticated key agreement protocol is proposed, which uses pairings on certain elliptic curves. An important advantage of our protocol is that it provides unilateral key confirmation to the protocol initiator only in two passes. We first put forward a variant of the ID-based signature scheme due to Sakai et al., then we describe our protocol, which uses the variant as a building block. We show that the newly proposed key agreement protocol is fit for real-world applications, and at the same time, it satisfies every desired security requirements of key agreement protocols
18089723
Globally Optimal Grouping for Symmetric Boundaries
Many natural and man-made structures have a boundary that shows certain level of bilateral symmetry, a property that has been used to solve many computer-vision tasks. In this paper, we present a new grouping method for detecting closed boundaries with symmetry. We first construct a new type of grouping token in the form of a symmetric trapezoid, with which we can flexibly incorporate various boundary and region information into a unified grouping cost function. Particularly, this grouping cost function integrates Gestalt laws of proximity, closure, and continuity, besides the desirable boundary symmetry. We then develop a graph algorithm to find the boundary that minimizes this grouping cost function in a globally optimal fashion. Finally, we test this method by some experiments on a set of natural and medical images.
12791471
Dynamic scheduling methods for computational grid environments
In this paper, we propose an adaptive and dynamic scheduling method, called most fit task first (MFTF), for a class of computational grids, which are characterized by heterogeneous computing nodes and dynamic task arrivals. Some existing static scheduling methods assume that tasks arrive statically and may not perform well in the case of dynamic task arrivals. Our method can get stable task execution times whether tasks arrive statically or dynamically. We compare the task execution time with other methods to show the performance of the scheduling method.
36279845
Exploiting soft redundancy for error-resilient on-chip memory design
16758055
Clustering in Knowledge Embedded Space
Cluster analysis is a fundamental technique in pattern recognition. It is difficult to cluster data on complex data sets. This paper presents a new algorithm for clustering. There are three key ideas in the algorithm: using mutual neighborhood graphs to discover knowledge and cluster data; using eigenvalues of local covariance matrixes to express knowledge and form a knowledge embedded space; and using a denoising trick in knowledge embedded space to implement clustering. Essentially, it learns a new distance metric by knowledge embedding and makes clustering become easier under this distance metric. The experiment results show that the algorithm can construct a quality neighborhood graph from a complex and noisy data set and well solve clustering problems.