Software components expose Application Programming Interfaces (APIs) as a means to access their functionality, and facilitate reuse. Developers use APIs supplied by programming languages to access the core data structures ...

The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like ...

Regression testing of an evolving software system may involve significant challenges. While, there would be a requirement of maximising the probability of finding out if the latest changes to the system has broken some ...

Time management is one of the critical modules of safety-critical systems. Applications need strong assurance from the operating system that their hard real-time requirements are met. Partitioned system has recently evolved ...

This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with ...

Two standard graph representations for linear codes are the Tanner graph and the tailbiting trellis. Such graph representations allow the decoding problem for a code to be phrased as a computational problem on the corresponding ...

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised classification tasks. In this thesis, we propose new algorithms for solving ...

Data Mining deals with extracting valid, novel, easily understood by humans, potentially useful and general abstractions from large data. A data is large when number of patterns, number of features per pattern or both are ...

Over the past two decades, microprocessor manufacturers have typically relied on wider issue widths and deeper pipelines to obtain performance improvements for single threaded applications. However, in the recent years, ...

Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data ...