Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures

The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters.

A Revised Short Form of the Extended Class Play Among Italian Early Adolescents

Among the measures assessing peer reputation, the Extended Class Play (ECP) is now used extensively in North American settings. Little information is available in other cultural contexts. Furthermore, practical considerations suggest developing a shorter form of the ECP to be used in socio-educational environments. This study examined the ECP dimensions of peer assessment in Italy as well as developed a shorter form of the measure. We revised the ECP using factor analyses according to an explorative-confirmatory approach. The original 37-item ECP was shortened to a 22-item version and, subsequently, the properties of the revised measure were evaluated. We performed two studies comprising, respectively, 643 (55% male; 97% Italian; Mage = 12.20, SD = 0.60) and 652 (58% male; 94% Italian; Mage = 12.26, SD = 0.66) seventh-grade young adolescents living in southern and central Italy. Results revealed a clear and consistent seven-factor structure and acceptable levels of reliability and validity. Factors included constructs such as Shyness-Withdrawal, Prosociality-Leadership, Aggression, Popularity-Sociability, Victimization, Rejection-Exclusion, and Boastfulness. Correlations between the dimensions of the 22-item ECP and teacher ratings of young adolescents’ behaviors further confirmed the validity of the solution. Findings suggest the importance of culturally revised measures of social reputation and indicate that the short form of the ECP has considerable promise to be considered a valuable measure to assess the multidimensional aspects of peer relationships.

IMPROVING DETECTION PERFORMANCE OF DUPLICATE BUG REPORTS USING EXTENDED CLASS CENTROID INFORMATION

In software maintenance, bug reports play an important role in the correctness of software packages. Unfortunately, the duplicatebug report problem arises because there are too many duplicate bug reports in various software projects. Handling with duplicate bug reports is thus time-consuming and has high cost of software maintenance. Therefore, this research introduces a detection scheme based on the extended class centroid information (ECCI) to enhance thedetection performance. This method is extended from the previous one, which used only centroid method without considering the effects of both inner and inter class. Besides, this method also improved the previous use of normalized cosine in identifying the similarity between two bug reports by denormalized cosine. The effectiveness of ECCI is proved through the empirical study with three open-source projects: SVN, Argo UML and Apache. The experimental results show thatECCI outperforms other detection schemes by about 10% in all cases.