Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLDL) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLDL can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLDL. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLDL (MVSLDL) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.

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Software defect prediction is one of the most popular research topics in software engineering. It aims to predict defect-prone software modules before defects are discovered, therefore it can be used to better prioritise software quality assurance effort. In recent years, especially for recent 3 years, many new defect prediction studies have been proposed. The goal of this study is to comprehensively review, analyse and discuss the state-of-the-art of defect prediction. The authors survey almost 70 representative defect prediction papers in recent years (January 2014-April 2017), most of which are published in the prominent software engineering journals and top conferences. The selected defect prediction papers are summarised to four aspects: machine learning-based prediction algorithms, manipulating the data, effort-aware prediction and empirical studies. The research community is still facing a number of challenges for building methods and many research opportunities exist. The identified challenges can give some practical guidelines for both software engineering researchers and practitioners in future software defect prediction.