In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.

Android applications that using WebView can load and display web pages. Interaction with web pages allows JavaScript code within the web pages to access resources on the Android device by using the Java object, which is registered into WebView. If this WebView feature were exploited by an attacker, JavaScript code could be used to launch attacks, such as stealing from or tampering personal information in the device. To address these threats, we propose an access control on the security-sensitive APIs at the Java object level. The proposed access control uses static analysis to identify these security-sensitive APIs, detects threats at runtime, and notifies the user if threats are detected, thereby preventing attacks from web pages.