Multi‐level reranking approach for bug localization

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi‐level re‐ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi‐level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets.     

Defects in liquid crystals (II)

Defects in the disordered (uniaxial) liquid crystals, nematic, smecticA and cholesteric, and the use of topological analysis in classifying them, are discussed. While the latter is very successful in classifying defects in nematics, it fails to do so in the case of smecticA and cholesteric liquid crystals because of geometrical constraints. However, topological arguments have been partially successful in predicting some of the defects in cholesterics. The known features of the isotropic (cubic and amorphous) cholesteric blue phases are summarised and the various theoretical models picturing them as defect lattices, are also discussed briefly.     

Two approaches for solving the buffer allocation problem in unreliable production lines

This paper presents an integrated approach to solve the buffer allocation problem in unreliable production lines so as to maximize the throughput rate of the line with minimum total buffer size. The proposed integrated approach has two control loops; the inner loop and the outer loop. While the inner loop control includes an adaptive tabu search algorithm proposed by Demir et al. [8], binary search and tabu search are proposed for the outer loop. These nested loops aim at minimizing the total buffer size to achieve the desired throughput level. To improve the efficiency of the proposed tabu search, alternative neighborhood generation mechanisms are developed. The performances of the proposed algorithms are evaluated by extensive computational experimentation, and the results are reported.     

Classification of semantic relations between nominals

The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of semantic relations in text. We present the development and evaluation of a semantic analysis task: automatic recognition of relations between pairs of nominals in a sentence. The task was part of SemEval-2007, the fourth edition of the semantic evaluation event previously known as SensEval. Apart from the observations we have made, the long-lasting effect of this task may be a framework for comparing approaches to the task. We introduce the problem of recognizing relations between nominals, and in particular the process of drafting and refining the definitions of the semantic relations. We show how we created the training and test data, list and briefly describe the 15 participating systems, discuss the results, and conclude with the lessons learned in the course of this exercise.     

Saliency detection based on hybrid artificial bee colony and firefly optimization

Pattern Analysis and Applications - Saliency detection is one of the challenging problems still tackled by image processing and computer vision research communities. Although not very numerous,...     

Minimax mutual information approach for independent component analysis

Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information-theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estimate into the mutual information expression, and in the latter we incorporate the source pdf assumption in the algorithm through the use of nonlinearities matched to the corresponding cumulative density functions (cdf). Alternative solutions to ICA use higher-order cumulant-based optimization criteria, which are related to either one of these approaches through truncated series approximations for densities. In this article, we propose a new ICA algorithm motivated by the maximum entropy principle (for estimating signal distributions). The optimality criterion is the minimum output mutual information, where the estimated pdfs are from the exponential family and are approximate solutions to a constrained entropy maximization problem. This approach yields an upper bound for the actual mutual information of the output signals - hence, the name minimax mutual information ICA algorithm. In addition, we demonstrate that for a specific selection of the constraint functions in the maximum entropy density estimation procedure, the algorithm relates strongly to ICA methods using higher-order cumulants.     

The Effect of Uncertainties in the Kuhn-Mark-Houwink-Sakurada Constants on the Quantitative Analysis by Gel Permeation Chromatography

The effect of uncertainties in Kuhn-Mark-Houwink-Sakurada (KMHS) constants on weight average and number average molecular weights and molecular weight distributions of some poly(ethyl methacrylate) samples have been investigated. Parameters were computed by the Weiss and Cohn-Ginsberg method. Whereas errors introduced in M_w, M_n and HI values due to uncertainty in the K value are insignificant, uncertainties in the KMHS exponent present a serious problem. Deviations of ± 3% around a particular a value cause a ± 20% error in M_w and ± 15% error in M_n, values. The effect of deviations in the KMHS exponent on the HI values is dependent on the polydisperity of the sample investigated.     

Investigation of breakage behavior of different mineralogical and morphological characteristic pumices

The objective of this study is to analyze dry grinding behavior of four different pumices in terms of Bond grindability value, selection and breakage parameters values. For this purpose, firstly, Bond grindability test were made for four pumices. Then, eight different mono-size fractions for each of pumices were carried out between 1.7 and 0.106 mm formed by a \({\surd 2}\) sieve series, and ground batch wise in a laboratory ball mill for determination of breakage parameters. Finally, S _i and B _i,j equations were determined from the size distributions at different grinding times, and the model parameters (S _i , a _T , α, γ, β and \({\phi_{j}}\)) were compared for four different pumice samples.     

Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

Principal components analysis is an important and well-studied subject in statistics and signal processing. Several algorithms for solving this problem exist, and could be mostly grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this study, we propose an alternate approach that avoids deflation and gradient-search techniques. The proposed method is an on-line procedure based on recursively updating the eigenvector and eigenvalue matrices with every new sample such that the estimates approximately track their true values as would be calculated analytically from the current sample estimate of the data covariance matrix. The perturbation technique is theoretically shown to be applicable for recursive canonical correlation analysis, as well. The performance of this algorithm is compared with that of a structurally similar matrix perturbation-based method and also with a few other traditional methods like Sanger’s rule and APEX.

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