Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

Abstract As an important tool of multiway/tensor data analysis tool, Tucker decomposition has been applied widely in various fields. But traditional sequential Tucker algorithms have been outdated because tensor data is growing rapidly in term of size. To address this problem, in this article, we focus on parallel Tucker decomposition of dense tensors on distributed‐memory systems. The proposed method uses hierarchical SVD to accelerate the SVD step in traditional sequential algorithms, which usually takes up most computation time. The data distribution strategy is designed to follow the implementation of hierarchical SVD. We also find that compared with the state‐of‐the‐art method, the proposed method has lower communication cost in large‐scale parallel cases under the assumption of the α–β model.

Software effort estimation (SEE) is a crucial step in software development. Effort data missing usually occurs in real-world data collection. Focusing on the missing data problem, existing SEE methods employ the deletion, ignoring, or imputation strategy to address the problem, where the imputation strategy was found to be more helpful for improving the estimation performance. Current imputation methods in SEE use classical imputation techniques for missing data imputation, yet these imputation techniques have their respective disadvantages and might not be appropriate for effort data. In this paper, we aim to provide an effective solution for the effort data missing problem. Incompletion includes the drive factor missing case and effort label missing case. We introduce the low-rank recovery technique for addressing the drive factor missing case. And we employ the semi-supervised regression technique to perform imputation in the case of effort label missing. We then propose a novel effort data imputation approach, named low-rank recovery and semi-supervised regression imputation (LRSRI). Experiments on 7 widely used software effort datasets indicate that: (1) the proposed approach can obtain better effort data imputation effects than other methods; (2) the imputed data using our approach can apply to multiple estimators well.

When a company lacks local data in hand, engineers can build an effort model for the effort estimation of a new project by utilizing the training data shared by other companies. However, one of the most important obstacles for data sharing is the privacy concerns of software development organizations. In software engineering, most of existing privacy-preserving works mainly focus on the defect prediction, or debugging and testing, yet the privacy-preserving data sharing problem has not been well studied in effort estimation. In this paper, we aim to provide data owners with an effective approach of privatizing their data before release. We firstly design an Interval Covering based Subclass Division (ICSD) strategy. ICSD can divide the target data into several subclasses by digging a new attribute (i.e., class label) from the effort data. And the obtained class label is beneficial to maintaining the distribution of the target data after obfuscation. Then, we propose a manifold learning based bi-directional data obfuscation (MLBDO) algorithm, which uses two nearest neighbors, which are selected respectively from the previous and next subclasses by utilizing the manifold learning based nearest neighbor selector, as the disturbances to obfuscate the target sample. We call the entire approach as ICSD&MLBDO. Experimental results on seven public effort datasets show that: 1) ICSD&MLBDO can guarantee the privacy and maintain the utility of obfuscated data. 2) ICSD&MLBDO can achieve better privacy and utility than the compared privacy-preserving methods.

Cross-company defect prediction (CCDP) learns a prediction model by using training data from one or multiple projects of a source company and then applies the model to the target company data. Existing CCDP methods are based on the assumption that the data of source and target companies should have the same software metrics. However, for CCDP, the source and target company data is usually heterogeneous, namely the metrics used and the size of metric set are different in the data of two companies. We call CCDP in this scenario as heterogeneous CCDP (HCCDP) task. In this paper, we aim to provide an effective solution for HCCDP. We propose a unified metric representation (UMR) for the data of source and target companies. The UMR consists of three types of metrics, i.e., the common metrics of the source and target companies, source-company specific metrics and target-company specific metrics. To construct UMR for source company data, the target-company specific metrics are set as zeros, while for UMR of the target company data, the source-company specific metrics are set as zeros. Based on the unified metric representation, we for the first time introduce canonical correlation analysis (CCA), an effective transfer learning method, into CCDP to make the data distributions of source and target companies similar. Experiments on 14 public heterogeneous datasets from four companies indicate that: 1) for HCCDP with partially different metrics, our approach significantly outperforms state-of-the-art CCDP methods; 2) for HCCDP with totally different metrics, our approach obtains comparable prediction performances in contrast with within-project prediction results. The proposed approach is effective for HCCDP.