Machine Learning in Compiler Optimization

In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity. In this article, we describe the relationship between machine learning and compiler optimisation and introduce the main concepts of features, models, training and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine learning based compilation and a detailed bibliography of its main achievements

Global and local feature-based transformations for fingerprint data protection

Due to its non-shareable characteristic, biometrics has been widely implemented for authenticating users. This characteristic asserts that biometrics meets the non-repudiation requirement which is one of the key factors in the authentication system. Among biometric modalities, fingerprints have the best capability for satisfying both technical and social aspects of an authentication system. Nevertheless, similar to other modalities, once the stored fingerprint template has been compromised, the effect will be forever since the fingerprint pattern is permanent. So, a mechanism which can protect this fingerprint pattern is desired. Common cryptographic approaches, however, do not work due to uncertainty in the captured fingerprint image caused by disturbing factors either in the scanner or in the finger itself. While authenticating fingerprints in a plain format is not secure, in a cipher format it is impractical because slightly different inputs result in completely different outputs. Therefore, a specific transformation mechanism is needed: one which is able to accept similar fingerprints and reject dissimilar fingerprints, while at the same time generating a relatively non-invertible fingerprint template. Most of the existing protection approaches, however, have high error rates which make them inappropriate to implement. The approaches proposed in this thesis are for addressing this problem, in particular. The proposed approaches comprise three modules: feature transformation, feature representation and feature comparison. The evaluation is to measure the accuracy, the capability for revoking the template and generating another template, and the capability for scrambling the fingerprint pattern. The first approach, which is a global feature-based transformation, is developed by exploring both the fingerprint singular point and minutiae points. The experimental results show that this approach is able to improve the existing performance, despite possible limitations (e.g., relying on the core point). In order to eliminate possible drawbacks of that global feature-based transformation, a local-based transformation is implemented by extracting only minutiae points. This has been able to eliminate the core-point dependency and at the same time produce only a slightly higher error rate than the previous proposed approach. To make further improvements, the third approach is designed in both Cartesian and polar coordinate spaces. This approach has been able to take advantages of being core point independent and at the same time generates higher performance than most of the existing approaches