다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining - Concepts and Techniques.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.

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Image Matching from Handcrafted to Deep Features: A Survey

斜向妥当化(Cross-Validation)の必要と方法

Context: Previous studies have shown that a transfer learning model, TCA+ proposed by Nam et al., can significantly improve the performance of cross-project defect prediction (CPDP). TCA+ achieves the improvement by reducing data distribution difference between source (training data) and target (testing data) projects. However, TCA+ is unstable, i.e., its performance varies largely when using different source projects to build prediction models. In practice, it is hard to choose a suitable source project to build the prediction model. Objective: To address the limitation of TCA+, we propose a two-phase transfer learning model (TPTL) for CPDP. Method: In the first phase, we propose a source project estimator (SPE) to automatically choose two source projects with the highest distribution similarity to a target project from candidates. Next, two source projects that are estimated to achieve the highest values of F1-score and cost-effectiveness are selected. In the second phase, we leverage TCA+ to build two prediction models based on the two selected projects and combine their prediction results to further improve the prediction performance. Results: We evaluate TPTL on 42 defect datasets from PROMISE repository, and compare it with two versions of TCA+ (TCA+_Rnd, randomly selecting one source project; TCA+_All, using all alternative source projects), a related source project selection model TDS proposed by Herbold, a state-of-the-art CPDP model leveraging a log transformation (LT) method, and a transfer learning model Dycom with better form of TCA. Experiment results show that, on average across 42 datasets, TPTL respectively improves these baseline models by 19%, 5%, 36%, 27%, and 11% in terms of F1-score; by 64%, 92%, 71%, 11%, and 66% in terms of cost-effectiveness. Conclusion: The proposed TPTL model can solve the instability problem of TCA+, showing substantial improvements over the state-of-the-art and related CPDP models.

A two-phase transfer learning model for cross-project defect prediction

Multi-scale curvature product for robust image corner detection in curvature scale space

Corner detection based on gradient correlation matrices of planar curves

Context Topic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have demonstrated success in mining software repository tasks. Understanding software change messages described by the unstructured nature-language text is one of the fundamental challenges in mining these messages in repositories. Objective We seek to present a novel automatic change message classification method characterized by semi-supervised topic semantic analysis. Method In this work, we present a semi-supervised LDA based approach to automatically classify change messages. We use domain knowledge of software changes to make labeled samples which are added to build the semi-supervised LDA model. Next, we verify the cross-project analysis application of our method on three open-source projects. Our method has two advantages over existing software change classification methods: First of all, it mitigates the issue of how to set the appropriate number of latent topics. We do not have to choose the number of latent topics in our method, because it corresponds to the number of class labels. Second, this approach utilizes the information provided by the label samples in the training set. Results Our method automatically classified about 85% of the change messages in our experiment and our validation survey showed that 70.56% of the time our automatic classification results were in agreement with developer opinions. Conclusion Our approach automatically classifies most of the change messages which record the cause of the software change and the method is applicable to cross-project analysis of software change messages.

Xiaohong Zhang

Papers

A two-phase transfer learning model for cross-project defect prediction

Multi-scale curvature product for robust image corner detection in curvature scale space

Corner detection based on gradient correlation matrices of planar curves

Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation

Robust image corner detection based on scale evolution difference of planar curves