Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of programs. The problem of data redundancy exists in software defect data set, which has great influence on prediction effect. We propose a defect distribution prediction model (Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network (DBN) technology, which is an effective deep learning technique in image processing and natural language processing, whose features are similar to defects in source program. Experiment results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine (SVM), Locally linear embedding SVM (LLE-SVM), and Neighborhood preserving embedding SVM (NPE-SVM).

With the increased snapshot operations (creation or deletion), the performance of storage system will degrade severely whether in Copy on write (COW) or Redirect on write (ROW). This paper presents a novel snapshot scheme which could use of storage resources effectively and keep the historical data accurately for SSDHDD-hybrid storage system. SSD (source volume) stores all the active data and executes read/write operations without responding to snapshot operations. In contrast, HDDs (snapshot volume) store the writing data sequentially in the form of logs and are responsible for backup and snapshot operations. In this way, the hybrid storage system can get the high throughput power via SSD even the snapshot functions are active and HDDs can keep pace with the high Input/Output operations per second (IOPS) capability of the SSD. Logs are combined and kept in a separate image volume by the controller. So, fine-grained Recovery point objective (RPO) and good Recovery time objective (RTO) can be achieved. Experiment results show that the prototype can achieve excellent I/O performance while snapshot operations are active and consume a few extra storage space.

The robustness of adversarial examples to image scaling transformation is usually ignored when most existing adversarial attacks are proposed. In contrast, image scaling is often the first step of the model to transfer various sizes of input images into fixed ones. We evaluate the impact of image scaling on the robustness of adversarial examples applied to image classification tasks. We set up an image scaling system to provide a basis for robustness evaluation and conduct experiments in different situations to explore the relationship between image scaling and the robustness of adversarial examples. Experiment results show that various scaling algorithms have a similar impact on the robustness of adversarial examples, but the scaling ratio significantly impacts it.