A greedy approximation for minimum connected dominating sets
Given a graph, a connected dominating set is a subset of vertices such that every vertex is either in the subset or adjacent to a vertex in the subset and the subgraph induced by the subset is connected. A minimum connected dominating set is such a vertex subset with minimum cardinality. In this paper, we present a new one-step greedy approximation with performance ratio ln δ + 2 where δ is the maximum degree in the input graph. The interesting aspect is that the greedy potential function of this algorithm is not supmodular while all previously known one-step greedy algorithms with similar performance have supmodular potential functions.

title: A Special Case for Subset Interconnection Designs
abstract: Given a set X and subsets X1, …, Xm, we consider the problem of finding a graph G with vertex set X and the minimum number of edges such that for i = 1,…,m, the subgraph G, induced by Xi is connected. We show that in the special case that every point in X appears in at most three Xi's, the problem is still MAX SNP-complete; however, there exists a polynomial-time approximation within a factor of 5 3 from optimal. As an intermediate result, we also show that the vertex cover problem in cubic graphs is MAX SNP-completc.

title: Performance evaluation of some noise reduction methods
abstract: In this paper, a quantitative evaluation of some noise reduction methods is presented. It first classifies the existing local noise reduction techniques by the use of neighbors and statistics and proposes some new filtering techniques. Subsequently, five performance evaluation criteria including processing time, mean distortion, mean busyness, stability, and correct processing ratio are discussed. By using these performance evaluation criteria, an experiment that involves four factors (test image, noise, mask size, and iteration) was designed to evaluate the performance of the local noise reduction methods. The results indicate that some new local noise reduction techniques have performance better than that of the standard existing techniques under certain criteria.

title: An adaptive method for detecting dominant points
abstract: In this paper, we propose an adaptive method for the polygonal approximation of a digitized curve. Instead of setting a fixed length of support region in advance, the new method will compute the suitable length of support region for each point to find the best approximated curvature. The dominant points are identified as the points with local maximum curvatures. In addition, the break point detection is conducted to reduce the computations. The experimental results show that the proposed method can approximate the curves effectively.

title: Industrial Strength Ontology Management
abstract: Ontologies are becoming increasingly prevalent and important in a wide range of e-commerce applications. E-commerce applications are using ontologies to support parametric searches, enhanced navigation and browsing, interoperable heterogeneous information systems, supplier enablement, configuration management, and transaction discovery. Applications such as information and service discovery and autonomous agents that are built on top of the emerging Semantic Web for the WWW also require extensive use of ontologies. Ontology-enhanced commercial applications, such as these and others require ontology management that is scalable (supporting thousands of simultaneous distributed users), available (running 365×24×7), fast, and reliable. This level of ontology management is necessary not only for the initial development and maintenance of ontologies, but is essential during deployment, when scalability, availability, reliability and performance are absolutely critical. VerticalNet's Ontology Builder and Ontology Server products are specifically designed to provide the ontology management infrastructure needed for e-commerce applications. These tools bring the best ontology and knowledge representation practices together with the best enterprise solutions architecture to provide a robust and scalable ontology management solution.

title: Detecting the Dominant Points by the Curvature-Based Polygonal Approximation
abstract: Detecting dominant points is an important step for object recognition. Corner detection and polygonal approximation are two major approaches for dominant point detection. In this paper, we propose the curvature-based polygonal approximation method which combines the corner detection and polygonal approximation techniques to detect the dominant points. This detection method consists of three procedures: (1) extract the break points that do not lie on a straight line, (2) detect the potential corners, and (3) perform polygonal approximation by partitioning the curves between two consecutive potential corners. Both quantitative and qualitative evaluations have been conducted. Experimental results show that the combined methods are superior to the conventional methods, and the dominant points can be properly detected by the combined methods.

title: Collision Attack and Pseudorandomness of Reduced-Round Camellia