5643714
On Some Characterisation Problems of Subdomain Testing
Subdomain testing is a very general approach to the selection of test cases. It captures the characteristics of testing strategies that require the test suite to cover some predefined testing requirements. This paper attempts to characterise precisely the failure distributions for the best and worst case of any given subdomain testing strategy. Our analysis has revealed some crucial factors and principles that affect the effectiveness of subdomain testing strategies.
33188963
Improving the quality of classification trees via restructuring
The classification-hierarchy table developed by Chen and Poon (1996) provides a systematic approach to construct classification trees from given sets of classifications and their associated classes. The paper enhances their study by defining a metric to measure the "quality" of a classification tree, and providing an algorithm to improve this quality.
43120543
A simulation study on some heuristics for test suite reduction
In order to test a program, software testers must first define a testing objective. From this a test suite is then constructed. In fact, some subsets of the constructed test suite may still satisfy the same testing objective. Such subsets are referred to as representative sets of the test suite. Finding the optimal representative sets of the test suite reduction problem is known to be NP-complete. Different heuristics, including G, GE, grE and H, have been proposed by different researchers. However, it has been demonstrated that none of these four heuristics are guaranteed to deliver smaller sized representative sets. This paper presents the result of a simulation study of these four heuristics. The aim of the study is to provide application guidelines for choosing the most appropriate heuristic for test suite reduction.
14782445
On the effectiveness of test case allocation schemes in partition testing
In partition testing, the input domain is divided into two or more disjoint subdomains according to some criteria and then test data are selected from each subdomain. Hence we must design a partitioning scheme that governs how to divide the input domain, and a test case allocation scheme that controls how to allocate test cases to the subdomains. Given any partitioning scheme, the test case allocation scheme plays a crucial part in the effectiveness of testing. This paper develops guidelines for comparing different test case allocation schemes. This provides solutions to the class of problems where various test case allocation schemes under the same partitioning scheme are compared for the effectiveness of testing. We illustrate with examples several potential applications of our findings.
12141135
Construction of classification trees via the classification-hierarchy table
Abstract The classification-tree method developed by Grochtmann and coworkers is a black box testing technique to assist test engineers to construct test cases systematically from the functional specifications, via the construction of classification trees. This paper supplements their studies by proposing a methodology to construct classification trees systematically from given sets of classifications and their associated classes, via the notion of the classification-hierarchy table. The intuition of the classification-hierarchy table is to capture the hierarchical relationship for every pair of distinct classifications from which classification trees can be constructed.
21381089
Dividing Strategies for the Optimization of a Test Suite
Use of the divide-and-conquer approach to optimize a test suite does not guarantee that the optimal solutions could be re-constructed from those of the sub-problems. This paper investigates how the re-construction could be achieved with two dividing strategies. Results of a simulation study are also reported.
33918362
Approximation Algorithms for the Selection of Robust Tag SNPs
Recent studies have shown that the chromosomal recombination only takes places at some narrow hotspots. Within the chromosomal region between these hotspots (called haplotype block), little or even no recombination occurs, and a small subset of SNPs (called tag SNPs) is sufficient to capture the haplotype pattern of the block. In reality, the tag SNPs may be genotyped as missing data, and we may fail to distinguish two distinct haplotypes due to the ambiguity caused by missing data. In this paper, we formulate this problem as finding a set of SNPs (called robust tag SNPs) which is able to tolerate missing data. To find robust tag SNPs, we propose two greedy and one LP-relaxation algorithms which give solutions of \((m+1)\ln\frac{K(K-1)}{2}\), \(\ln((m+1)\frac{K(K-1)}{2})\), and O(mln K) approximation respectively, where m is the number of SNPs allowed for missing data and K is the number of patterns in the block.
15282622
Total variation models for variable lighting face recognition
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions
45055182
Mining frequent tree-like patterns in large datasets
Sequential pattern mining is crucial to data mining domains. This paper proposes a novel data mining approach for exploring hierarchical tree structures, named tree-like patterns, representing the relationships for a pair of items in a sequence. Using tree-like patterns, the relationships for a pair of items can be identified in terms of the cause and effect. A novel technique that efficiently counts support values for tree-like patterns using a queue structure is proposed. In addition, this paper addresses an efficient scheme for determining the frequency of a tree-like pattern in a sequence using a dynamic programming approach. Each tree-like pattern embedded in a sequence is considered to have a certain valuable meaning or the degree of importance used in different applications. Two addressed formulas are applied to determine the degree of significance for a specific sequence, which denotes the degree of consecutive items in a tree-like pattern for a sequence. The larger the degree of significance a tree-like pattern has, the more the tree-like pattern is compacted in the sequence. The characteristics differentiating the explored patterns from those obtained with other schemes are discussed. A simulation analysis of the proposed data mining approach is utilized to demonstrate its efficacy. Finally, the proposed approach is designed and implemented in a data mining system integrated into a novel e-learning platform.
6145819
HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms
UNLABELLED
Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed with regions of low LD. Such LD patterns make it possible to select a set of single nucleotide polymorphism (SNPs; tag SNPs) for genome-wide association studies. We have developed a suite of computer programs to analyze the block-like LD patterns and to select the corresponding tag SNPs. Compared to other programs for haplotype block partitioning and tag SNP selection, our program has several notable features. First, the dynamic programming algorithms implemented are guaranteed to find the block partition with minimum number of tag SNPs for the given criteria of blocks and tag SNPs. Second, both haplotype data and genotype data from unrelated individuals and/or from general pedigrees can be analyzed. Third, several existing measures/criteria for haplotype block partitioning and tag SNP selection have been implemented in the program. Finally, the programs provide flexibility to include specific SNPs (e.g. non-synonymous SNPs) as tag SNPs.


AVAILABILITY
The HapBlock program and its supplemental documents can be downloaded from the website http://www.cmb.usc.edu/~msms/HapBlock.
7383373
Scale-Driven Iterative Optimization for Brain Extraction and Registration
We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.