A general software defect-proneness prediction framework

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.BACKGROUND - Predicting defect-prone software components is an economically important activity and so has received a good deal of attention. However, making sense of the many, and sometimes seemingly inconsistent, results is difficult. OBJECTIVE - We propose and evaluate a general framework for software defect prediction that supports 1) unbiased and 2) comprehensive comparison between competing prediction systems. METHOD - The framework is comprised of 1) scheme evaluation and 2) defect prediction components. The scheme evaluation analyzes the prediction performance of competing learning schemes for given historical data sets. The defect predictor builds models according to the evaluated learning scheme and predicts software defects with new data according to the constructed model. In order to demonstrate the performance of the proposed framework, we use both simulation and publicly available software defect data sets. RESULTS - The results show that we should choose different learning schemes for different data sets (i.e., no scheme dominates), that small details in conducting how evaluations are conducted can completely reverse findings, and last, that our proposed framework is more effective and less prone to bias than previous approaches. CONCLUSIONS - Failure to properly or fully evaluate a learning scheme can be misleading; however, these problems may be overcome by our proposed framework.National Natural Science Foundation of Chin

Vacuum induced Berry phases in single-mode Jaynes-Cummings models

Motivated by the work [Phys. Rev. Lett. 89, 220404 (2002)] for detecting the vacuum-induced Berry phases with two-mode Jaynes-Cummings models (JCMs), we show here that, for a parameter-dependent single-mode JCM, certain atom-field states also acquire the photon-number-dependent Berry phases after the parameter slowly changed and eventually returned to its initial value. This geometric effect related to the field quantization still exists, even the filed is kept in its vacuum state. Specifically, a feasible Ramsey interference experiment with cavity quantum electrodynamics (QED) system is designed to detect the vacuum-induced Berry phase.Comment: 10 pages, 4 figures

Relaxed 2-D Principal Component Analysis by LpL_p Norm for Face Recognition

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure

On the Convergence of Ritz Pairs and Refined Ritz Vectors for Quadratic Eigenvalue Problems

For a given subspace, the Rayleigh-Ritz method projects the large quadratic eigenvalue problem (QEP) onto it and produces a small sized dense QEP. Similar to the Rayleigh-Ritz method for the linear eigenvalue problem, the Rayleigh-Ritz method defines the Ritz values and the Ritz vectors of the QEP with respect to the projection subspace. We analyze the convergence of the method when the angle between the subspace and the desired eigenvector converges to zero. We prove that there is a Ritz value that converges to the desired eigenvalue unconditionally but the Ritz vector converges conditionally and may fail to converge. To remedy the drawback of possible non-convergence of the Ritz vector, we propose a refined Ritz vector that is mathematically different from the Ritz vector and is proved to converge unconditionally. We construct examples to illustrate our theory.Comment: 20 page

Effect of finishing rolling reduction on microstructures and textures of grain oriented silicon steel

The effect of finishing rolling reduction on microstructures and textures of grain oriented silicon steel was researched by optical microscopy, zeiss ultra 55 Scanning Electron Microscope (SEM) and Electron Backscatter Diffraction (EBSD) technique severally. The results show that the grain size of hot rolled sheets and decarburized strips decreases, while the center grain size of the normalized sheet increases with the increase of the finishing rolling reduction. The pearlite content increases with the increase of the finishing rolling reduction after normalization. Compare with the previous research, the effect of finishing rolling reduction on the grain size of primary recrystallization is greater than that of roughing rolling reduction, and large rolling reduction is beneficial to the formation of {110} texture

Effect of hot rolling reduction on microstructures and textures of grain oriented silicon steel

The effect of hot rolling reduction on microstructures and textures of grain oriented silicon steel was studied by optical microscopy (OM), zeiss ultra 55 Scanning electron microscope (SEM) and Electron backscatter diffraction (EBSD) technique respectively. The results indicate that the effect of hot rolling reduction on grain size of hot rolled and normalized sheets in surface layer is great, while the effect on grain size of primary recrystallized grain is little. The shear zone thickens with the finishing reduction decreases, moreover strong {111} and {110} textures can be obtained in hot rolled sheets. Combined with the previous research conclusions, it can be found that the rolling process of oriented silicon steel is contributed to the formation of texture, while the recrystallization process reduces the sharpness of the texture

Polyoxometallates@zeolitic-imidazolate-framework derived bimetallic tungsten-cobalt sulfide/porous carbon nanocomposites as efficient bifunctional electrocatalysts for hydrogen and oxygen evolution

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Hydrogen is one of the most promising sustainable energy among numerous new energy resources. Electrocatalytic water splitting for H2 generation is a clean and sustainable approach due to the use of widely existed water as resource. The searching for efficient and low-cost non-precious metal based electrocatalysts for water splitting, including both cathodic hydrogen evolution reaction (HER) and anodic oxygen evolution reaction (OER), still remains a great challenge. In this work we report a simple method that utilizes the one-pot in-situ synthesized POMs@ZIFs (POMs = Polyoxometallates, ZIFs = Zeolitic imidazolate frameworks) as precursor for the production of WS2/Co1-xS/N, S co-doped porous carbon nanocomposite as efficient electrocatalysts. These precursors POMs@ZIFs can effectively prevent the agglomeration of metal compound particles during heat treatment and leads to homogeneous dispersion of metal active sites within carbon matrix. The resulting bimetallic Co–W sulfide/heteroatom doped porous carbon composites show significant improvement in electrocatalytic activity towards both OER (Tafel slop of 53 mV dec−1 with overpotential of 0.365 V @10 mA cm−2 current density in 1 M KOH media) and HER (Tafel slop of 64 mV dec−1 with overpotential of 0.250 V @10 mA cm−2 current density in 0.5 M H2SO4 solution). This work opens up a new way to obtain low cost bifunctional electrocatalysts towards both OER and HER in water splitting.European CommissionEngineering and Physical Sciences Research Council (EPSRC