35345915
Ranking Component Retrieval Results by Leveraging User History Information
6538157
Threshold Tuning for Improved Classification Association Rule Mining
One application of Association Rule Mining (ARM) is to identify Classification Association Rules (CARs) that can be used to classify future instances from the same population as the data being mined. Most CARM methods first mine the data for candidate rules, then prune these using coverage analysis of the training data. In this paper we describe a CARM algorithm that avoids the need for coverage analysis, and a technique for tuning its threshold parameters to obtain more accurate classification. We present results to show this approach can achieve better accuracy than comparable alternatives at lower cost.
15561253
An experimental comparison of four test suite reduction techniques
As a test suite usually contains redundancy, a subset of the test suite (representative set) may still satisfy all the test objectives. As the redundancy increases the cost of executing the test suite, many test suite reduction techniques have been brought out in spite of the NP-completeness of the general problem of finding the optimal representative set of the test suite. In the literature, some experimental studies of test suite reduction techniques have already been reported, but there are still shortcomings of the studies of these techniques. This paper presents an experimental comparison of the four typical test suite reduction techniques: heuristic H, heuristic GRE, genetic algorithm-based approach and ILP-based approach. The aim of the study is to provide a guideline for choosing the appropriate test suite reduction techniques.
13211852
Requirements guided dynamic software clustering
In this paper, we propose a requirements guided dynamic approach to address software clustering -which aims at providing the logically meaningful and high-level decompositions of large and complex systems. In our approach, the hierarchical structure of functional requirements are constructed by a text document clustering technique named hierarchical agglomerative clustering (HAC) as a high-level skeleton to facilitate the further decomposition of source code through dynamic analysis. We also perform an experimental study based on a GNU system and present the quantitative and qualitative analysis of the experimental results.
22235359
Towards an Optimization-Based Method for Consolidating Domain Variabilities in Domain-Specific Web Services Composition
In this paper, we put forward an automatic method of acquiring the specific system composition model from a domain composition model and requirements for the specific system in domain-specific Web services composition. This is referred to as the variability consolidation problem in this paper. To achieve this goal, we designed a language to describe domain properties for Web services composition. The basis of our approach is to transform the domain composition model and the requirements for the specific system into a mathematical optimization problem, which can be solved by existing algorithms. Thus, this method is fully automatic and not prone to human errors. Our preliminary experimental results show that our method is quite feasible for solving problems with real world sizes.
19304521
Applying OO Metrics to Assess UML Meta-models
UML has been coming of age through more than seven years development, in which there are not only minor revision like from UML 1.1 to UML 1.2, but also significant improvement like final adoption of UML 2.0 submissions. However there is so far lack of an objective assessment to UML meta-models, which can be used to control and predict the evolution of the UML. In this paper we regard UML meta-models as the equivalent of Object-Oriented (OO) design models. Therefore, we can adapt OO design metrics and criteria as a method to assess UML meta-models. Our method conducts the assessment of stability and design quality to UML meta-models in versions of 1.1, 1.3, 1.4 (with Action Semantics), 1.5, and 2.0. Based on the results we analyze the evolution of the UML versions and provide the applicability suggestions to the method.
9731248
A Novel Gabor-LDA Based Face Recognition Method
In this paper, a novel face recognition method based on Gabor-wavelet and linear discriminant analysis (LDA) is proposed. Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images onto discriminant vectors. Second, discriminant vectors are used to select discriminant pixels, the number of which is much less than that of a whole image. Gabor features are extracted only on these discriminant pixels. Then, applying LDA on the Gabor features, one can obtain the Gabor-LDA features. Finally, a combined classifier is formed based on these two types of LDA features. Experimental results show that the proposed method performs better than traditional approaches in terms of both efficiency and accuracy.
7654369
Extension of a list coloring problem
Abstract For a graph H , f ( H ) is the smallest integer k such that the join of H with an empty graph E k of order k is not | V ( H ) | -choosable. It was conjectured that for a triangle-free graph G , f ( G ) = n 2 μ ( G ) n n − 2 μ ( G ) , where n = | V ( G ) | and μ ( G ) is the cardinality of a maximum matching of graph G [S. Gravier, F. Maffray, B. Mohar, On a list-coloring problem, Discrete Math. 268 (2003) 303–308]. We verify this conjecture in the case of forests, and propose some related problems.
6035916
Ranking web objects from multiple communities
Vertical search is a promising direction as it leverages domain-specific knowledge and can provide more precise information for users. In this paper, we study the Web object-ranking problem, one of the key issues in building a vertical search engine. More specifically, we focus on this problem in cases when objects lack relationships between different Web communities, and take high-quality photo search as the test bed for this investigation. We proposed two score fusion methods that can automatically integrate as many Web communities (Web forums) with rating information as possible. The proposed fusion methods leverage the hidden links discovered by a duplicate photo detection algorithm, and aims at minimizing score differences of duplicate photos in different forums. Both intermediate results and user studies show the proposed fusion methods are practical and efficient solutions to Web object ranking in cases we have described. Though the experiments were conducted on high-quality photo ranking, the proposed algorithms are also applicable to other ranking problems, such as movie ranking and music ranking.
9035359
Visibility Queries in Simple Polygons and Applications
In this paper we explore some novel aspects of visibility for stationary and moving points inside a simple polygon P. We provide a mechanism for expressing the visibility polygon from a point as the disjoint union of logarithmically many canonical pieces using a quadraticspace data structure. This allows us to report visibility polygons in time proportional to their size, but without the cubic space overhead of earlier methods. The same canonical decomposition can be used to determine visibility within a frustum, or to compute various attributes of the visibility polygon efficiently. By exploring the connection between visibility polygons and shortest path trees, we obtain a kinetic algorithm that can track the visibility polygon as the viewpoint moves along polygonal paths inside P, at a polylogarithmic cost per combinatorial change in the visibility. The combination of the static and kinetic algorithms leads to a space query-time tradeoff for the visibility from a point problem and an output-sensitive algorithm for the weak visibility from a segment problem.
18096926
Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method
Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The main idea of NPP is to approximate the classical locally linear embedding (i.e. LLE) by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Preliminary experimental results on known manifold data show the effectiveness of the proposed method.