Thesis (Ph.D.)--University of Melbourne, Dept. of Electrical and Electronic Engineering, 2006. / Typescript. Includes bibliographical references (leaves 231-238).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references (leaves 77-81). Also available in electronic version. Access restricted to campus users.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, June 2010. / Thesis Advisor(s): Irvine, Cynthia E. ; Clark, Paul C. "June 2010." Description based on title screen as viewed on July 14, 2010. Author(s) subject terms: Trustworthy systems, separation kernels, secondary storage, storage hierarchy Includes bibliographical references (p. 143-144). Also available in print.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains vii, 79 p. Includes bibliographical references (p. 79). Available online via OhioLINK's ETD Center

Thesis (Ph. D.)--University of Wisconsin, 1966. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

No description available.

Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic modeling which can often be prone to overfitting and does not translate to improved discrimination. We propose a new paradigm centered on principles of structural risk minimization using a discriminative framework for speech recognition based on support vector machines (SVMs). SVMs have the ability to simultaneously optimize the representational and discriminative ability of the acoustic classifiers. We have developed the first SVM-based large vocabulary speech recognition system that improves performance over traditional HMM-based systems. This hybrid system achieves a state-of-the-art word error rate of 10.6% on a continuous alphadigit task ? a 10% improvement relative to an HMM system. On SWITCHBOARD, a large vocabulary task, the system improves performance over a traditional HMM system from 41.6% word error rate to 40.6%. This dissertation discusses several practical issues that arise when SVMs are incorporated into the hybrid system.

Safe kernel extensions are crucial for adding features like networking filters, security policies, and monitoring capabilities that organizations require in production environments. The Linux kernel traditionally lacked mechanisms for safe runtime extensions. BPF addressed this problem by enabling dynamic kernel extensions with safety guarantees enforced by an in-kernel verifier, ensuring kernel stability. The verifier verifies each BPF program without considering its interactions with other BPF programs, assuming these interactions will be safe. This assumption relies on both static limits enforced by the verifier and runtime checks in the kernel. However, this verification approach leaves the kernel vulnerable to safety issues when BPF programs nest within each other. This work identifies such safety issues, including stack overflows, deadlocks, performance issues, and missed events. To address these challenges, this research presents an approach for providing a global system view to the verifier to prevent uncontrolled nesting. We explored the first steps in this direction through a helper-rooted callgraph approach that provides a global view of BPF program interactions, enabling the prevention of these safety issues. / Master of Science / Linux is one of the most popular mainstream operating systems, running on over 96% of the world's servers. Extending the kernel is important because users need features like network filtering and system monitoring. Recently, BPF provided a safe way to extend Linux's capabilities by using an in-kernel safety checker called the verifier, which examines each program before it runs to prevent crashes and system stalls. Due to these safety guarantees, BPF has been widely adopted in industry for various use cases. In this work, we identified that when multiple BPF programs are nested, they can cause problems even though the programs are individually verified as safe. Our experiments showed three critical issues: stack overflows, deadlocks and performance problems (such as throughput loss). We identified that these crashes occur because the verifier lacks knowledge about how BPF programs interact with each other. To address this problem, we developed an approach called helper-rooted callgraphs that shows how different programs interact. This information can then be used by the verifier to prevent unsafe program interactions.

Lorsqu'une erreur survient dans le noyau Linux, celui-ci émet un rapport d’erreur appelé "kernel oops" contenant le contexte d’exécution de cette erreur. Les kernel oops décrivent des erreurs réelles de Linux, permettent de classer les efforts de débogage par ordre de priorité et de motiver la conception d’outils permettant d'améliorer la fiabilité du code de Linux. Néanmoins, les informations contenues dans un kernel oops n’ont de sens que si elles sont représentatives et qu'elles peuvent être interprétées correctement. Dans cette thèse, nous étudions une collection de kernel oops provenant d'un dépôt maintenu par Red Hat sur une période de huit mois. Nous considérons l’ensemble des caractéristiques de ces données, dans quelle mesure ces données reflètent d’autres informations à propos de Linux et l’interprétation des caractéristiques pouvant être pertinentes pour la fiabilité de Linux. Nous constatons que ces données sont bien corrélées à d’autres informations à propos de Linux, cependant, elles souffrent parfois de problèmes de duplication et de manque d’informations. Nous identifions également quelques pièges potentiels lors de l'étude des fonctionnalités, telles que les causes d'erreurs fréquentes et les causes d'applications défaillant fréquemment. En outre, un kernel oops fournit des informations précieuses et de première main pour un mainteneur du noyau Linux lui permettant d'effectuer le débogage post-mortem car il enregistre l’état du noyau Linux au moment du crash. Cependant, le débogage sur la seule base des informations contenues dans un kernel oops est difficile. Pour aider les développeurs avec le débogage, nous avons conçu une solution afin d'obtenir la ligne fautive à partir d’un kernel oops, i.e., la ligne du code source qui provoque l'erreur. Pour cela, nous proposons un nouvel algorithme basé sur la correspondance de séquences approximative utilisé dans le domaine de bioinformatique. Cet algorithme permet de localiser automatiquement la ligne fautive en se basant sur le code machine à proximité de celle-ci et inclus dans un kernel oops. Notre algorithme atteint 92% de précision comparé à 26 % pour l’approche traditionnelle utilisant le débogueur gdb. Nous avons intégré notre solution dans un outil nommé OOPSA qui peut ainsi alléger le fardeau pour les développeurs lors du débogage de kernel oops. / When a failure occurs in the Linux kernel, the kernel emits an error report called “kernel oops”, summarizing the execution context of the failure. Kernel oopses describe real Linux errors, and thus can help prioritize debugging efforts and motivate the design of tools to improve the reliability of Linux code. Nevertheless, the information is only meaningful if it is representative and can be interpreted correctly. In this thesis, we study a collection of kernel oopses over a period of 8 months from a repository that is maintained by Red Hat. We consider the overall features of the data, the degree to which the data reflects other information about Linux, and the interpretation of features that may be relevant to reliability. We find that the data correlates well with other information about Linux, but that it suffers from duplicate and missing information. We furthermore identify some potential pitfalls in studying features such as the sources of common faults and common failing applications. Furthermore, a kernel oops provides valuable first-hand information for a Linux kernel maintainer to conduct postmortem debugging, since it logs the status of the Linux kernel at the time of a crash. However, debugging based on only the information in a kernel oops is difficult. To help developers with debugging, we devised a solution to derive the offending line from a kernel oops, i.e., the line of source code that incurs the crash. For this, we propose a novel algorithm based on approximate sequence matching, as used in bioinformatics, to automatically pinpoint the offending line based on information about nearby machine-code instructions, as found in a kernel oops. Our algorithm achieves 92% accuracy compared to 26% for the traditional approach of using only the oops instruction pointer. We integrated the solution into a tool named OOPSA, which would relieve some burden for the developers with the kernel oops debugging.

In this thesis we investigate the convergence rate of gamma kernel estimators in recursive density estimation. Unlike the traditional symmetric and fixed function, the gamma kernel is a kernel function with bounded support and varying shapes. Gamma kernels have been used to address the boundary bias problem which occurs when a symmetric kernel is used to estimate a density which has support on [0, ?). The recursive density estimation is useful when an 'additional data' (on-line) comes from the population density which we want to estimate. We utilize the ideas and results from the adaptive kernel estimation to show that the L_2 convergence rate of the recursive kernel density estimators which use gamma kernels is n^(-4/5).