Information dissemination is the very reason for the existence of the Internet. Within the community of independent entities that make up the Internet, the quality of openness that has contributed to scalability and connectivity has also introduced numerous security and privacy challenges. This...

Social and computer networks permeate our lives. Large networks, such as the Internet, the World Wide Web (WWW), AND wireless smartphones, have indisputable economic and social importance. These networks have non-trivial topological features, i.e., features that do not occur in simple networks...

In this dissertation, I develop a new theoretical framework for the modeling, pricing analysis, and computation of solutions to electric power supply chains with power generators, suppliers, transmission service providers, and the inclusion of consumer demands. In particular, I advocate the...

Several models in econometrics and finance have been proven to be computationally intractable due to their complexity. In this dissertation, we propose an evolutionary-genetic-algorithm for solving these types of problems. We extend the models so that less restrictive assumptions are required...

The fact that all software has defects is one of the few things that are true across all software projects. Traditionally, few proprietary software project managers would risk cultivating a reputation for producing ``buggy' code by making details about the defects in their products public....

Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5855.

Source: Dissertation Abstracts International, Volume: 52-07, Section: B, page: 3718.

We investigate knowledge exchange among commercial organisations, the rationale behind it and its effects on the market. Knowledge exchange is known to be beneficial for industry, but in order to explain it, authors have used high level concepts like network effects, reputation and trust. We...

The problem of learning a latent model for sparse or low-dimensional representation of high-dimensional data has attracted significant attention for many years. This thesis focuses on learning latent models for sparse or low-dimensional representation of images, dynamic data, and documents with...

We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance...