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.
17777200
A similarity-aware approach to testing based fault localization
Debugging is a time-consuming task in software development and maintenance. To accelerate this task, several approaches have been proposed to automate fault localization. In particular, testing based fault localization (TBFL), which utilizes the testing information to localize the faults, seem to be very promising. However, the similarity between test cases in the test suite has been ignored in the research on TBFL. In this paper, we investigate this similarity issue and propose a novel approach named similarity-aware fault localization (SAFL), which can calculate the suspicion probability of each statement with little impact by the similarity issue. To address and deal with the similarity between test cases, SAFL applies the theory of fuzzy sets to remove the uneven distribution of the test cases. We also performed an experimental study for two real-world programs at different size levels to evaluate SAFL together with another two approaches to TBFL. Experimental results show that SAFL is more effective than the other two approaches when the test suites contain injected redundancy, and SAFL can achieve a competitive result with normal test suites. SAFL can also be more effective than applying test suite reduction to current approaches to TBFL.
14328389
Alternative scalable algorithms for lattice-based feature location
Considering the scalability of using formal concept analysis to locate features in source code, we present a set of alternative straightforward algorithms to achieve the same objectives. A preliminary experiment indicates that the alternative algorithms are more scalable to deal with the large numbers of data to some extent.
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.
1882847
User Feedback-Based Refinement for Web Services Retrieval using Multiple Instance Learning
A critical step in the process of reusing existing WSDL-specified components is the discovery of potentially relevant Web services. Traditional category based Web service retrieval usually can achieve good recall but worse precision because some semantically relevant Web services are not actually relevant as they cannot provide suitable interfaces. In this paper, we present an interactive Web services retrieval mechanism to refine the coarse retrieval results set in category based retrieval. In the refinement, the signature matching of Web services that concerning the structure of operation specifications is investigated from a multi-instances view. In detail, each Web service is represented as a bag in multiple instance learning, while each operation in this Web service is regarded as an instance. This representation lies in that a user regards a service as useful if at least one operation provided by this Web service is useful. Experimental results show that our approach can improve the retrieval performance significantly: It can gain 83% precision in average after two rounds of user relevance feedback
1111153
Towards the UML evaluation using taxonomic patterns on meta-classes
In order to evaluate the design quality of the UML, understanding meta-classes is a key activity as they are the primary weapons by which the UML specifies the application domains. The paper introduces taxonomic patterns for clustering the UML meta-classes based on the observation of their evolution and fitness. The result sets of the patterns assist in finding the evidence of the concerns about the UML design and quality. It not only helps to find out problematic meta-classes, possible design defects of the UML and the inconsistency between the UML meta-models and the application domains; but also provides valuable information for guiding the development and evaluation of the UML. The work can be the basis of further quality analysis of the UML meta-models.
41094552
The Statistical Inference Method in Heuristic Search Techniques
In this paper we present a new heuristic searching algorithm by introducing statistical inference method on the basic of algorithm A*. It's called algorithm SA*. The following results have been proved. (1) Algorithm SA* in superior to algorithm A*. (2) The mean complexity of SA* is CN2, but in some case A* exhibits exponential complexity (eN). (3) In a (N,d,F)- game tree, the mean complexity of SA* is CN2, but the complexity of other known game-searching algoritham (α-β , SSS* etc.) is at least dN. (4) The maximal storage-space required by SA* is C,N. 
 
This shows that under a given significance level SA* is superior to other known algorithm (e.g. A*, B*, α-β, SSS* etc.).
12937715
Improvement of Color Video Demosaicking in Temporal Domain
Color demosaicking is critical to the image quality of digital still and video cameras that use a single-sensor array. Limited by the mosaic sampling pattern of the color filter array (CFA), color artifacts may occur in a demosaicked image in areas of high-frequency and/or sharp color transition structures. However, a color digital video camera captures a sequence of mosaic images and the temporal dimension of the color signals provides a rich source of information about the scene via camera and object motions. This paper proposes an inter-frame demosaicking approach to take advantage of all three forms of pixel correlations: spatial, spectral, and temporal. By motion estimation and statistical data fusion between adjacent mosaic frames, the new approach can remove much of the color artifacts that survive intra-frame demosaicking and also improve tone reproduction accuracy. Empirical results show that the proposed inter-frame demosaicking approach consistently outperforms its intra-frame counterparts both in peak signal-to-noise measure and subjective visual quality
214819
Scalable search-based image annotation of personal images
With the prevalence of digital cameras, more and more people have considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation (SBIA) algorithm that is analogous to Web page search. First, content-based image retrieval (CBIR) technology is used to retrieve a set of visually similar images from a large-scale Web image set. Then, a text-based keyword search (TBKS) technique is used to obtain a ranked list of candidate annotations for each retrieved image. Finally, a fusion algorithm is used to combine the ranked lists into the final annotation list. The application of both efficient search technologies and Web-scale image set guarantees the scalability of the proposed algorithm. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.
636109
VizStruct: exploratory visualization for gene expression profiling
MOTIVATION
DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples.


RESULTS
The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy.


AVAILABILITY
Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.
32965761
The Next Big Thing: Web Services Collaboration
In this paper, we introduce the lifecycle of a Web services solution that consists of Web services modeling, development, deployment, publishing, as well as discovery, composition and collaboration, plus monitoring and analytical control. Then some research and development challenges of building a Web services solution in the context of Web services solution lifecycle are described. Especially, we present an emerging Web services research direction, namely, Web services collaboration, followed by a few major research issues associated with facilitating extended business collaboration using Web services and semantic annotation.