Parallel programming is the major stumbling block preventing the parallel processing industry from quickly satisfying the demand for parallel computer software. This research is aimed at solving some of the problems of software development for parallel computers. ELGDF is a graphical language for designing parallel programs. The goal of ELGDF...
Regression testing is an expensive software engineering activity intended to provide confidence that modifications to a software system have not introduced faults. Test case prioritization techniques help to reduce regression testing cost by ordering test cases in a way that better achieves testing objectives. In this thesis, we are interested...
This paper discusses opportunities for developments in spatial clustering methods to help leverage broad scale community science data for building species distribution models (SDMs). SDMs are critical tools that inform the science and policy needed to mitigate the impacts of climate change on biodiversity. Community science data span spatial and...
The gradient of a velocity vector field is an asymmetric tensor field which can provide critical insight that is difficult to infer from traditional trajectory-based vector field visualization techniques. I describe the structures in the eigenvalue and eigenvector fields of the gradient tensor and how these structures can be used...
We will describe two known strategies for static processor
allocation in an n-cube multiprocessor, namely the buddy system
strategy and the gray code strategy, and then propose a new strategy
that outperforms the first by (n-k+1) and the second by (n-k+1)/2 in
cube recognition. Furthermore, our strategy is suitable for...
How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation...
How can software practitioners assess whether their software supports diverse users? Although there are empirical processes that can be used to find “inclusivity bugs” piecemeal, what is often needed is a systematic inspection method to assess software’s support for diverse populations. To help fill this gap, this thesis introduces InclusiveMag,...
Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
The purpose of this study is to explore kernel machine learning methods for species distribution modeling. Previous studies have shown the success of Generalized Boosted Regression Models, however kernel methods have been unexplored for species distribution modeling. Using the eBird dataset, four machine learning methods were tested for accuracy and...