GMU-CS-TR-2012-1

Localization and mapping has been an area of great importance and interest to the robotics and computer vision community. It has traditionally been accomplished with range sensors such as lasers and sonars. Recent improvements in processing power coupled with advancements in image matching and motion estimation has allowed development of vision based localization techniques. Despite much progress, there are disadvantages to both range sensing and vision techniques making localization and mapping that is inexpensive and robust hard to attain. With the advent of RGB-D cameras which provide synchronized range and video data, localization and mapping is now able to exploit both range data as well as RGB features. This thesis exploits the strengths of vision and range sensing localization and mapping strategies and proposes novel algorithms using RGB-D cameras. We show how to combine existing strategies and present through evaluation of the resulting algorithms against a dataset of RGB-D benchmarks. Lastly we demonstrate the proposed algorithm on a challenging indoor dataset and demonstrate improvements where either pure range sensing or vision techniques perform poorly.

GMU-CS-TR-2012-2

An important challenge in dynamic adaptation of a software system is to prevent inconsistencies (failures) and disruptions in its operations during and after change. Several prior techniques have solved this problem with various tradeoffs. All of them, however, assume the availability of detailed component dependency models. This paper presents a complementary technique that solves this problem in settings where such models are either not available, difficult to build, or outdated due to the evolution of the software. Our approach first mines the execution history of a software system to infer a "stochastic component dependency model", representing the probabilistic sequence of interactions among the system's components. We then demonstrate how this model could be used at runtime to infer the "best time" for adaptation of the system's components. We have thoroughly evaluated this research on a multi-user real world software system and under varying conditions.

GMU-CS-TR-2012-3

Co-clustering is a machine learning task where the goal is to simultaneously develop clusters of the data and of their respective features. We address the use of co-clustering ensembles to establish a consensus co-clustering over the data. As is obvious from its name, co-clustering is naturally multiobjective. Previous work tackled the problem using both rudimentary multiobjective optimization and expectation maximization, then later a gradient ascent approach which outperformed both of them. In this paper we develop a new preference-based multiobjective optimization algorithm to compete with the gradient ascent approach. Unlike this gradient ascent algorithm, our approach once again tackles the co-clustering problem with multiple heuristics, but also applies the gradient ascent algorithm's joint heuristic as a preference selection procedure. As a result, we are able to significantly outperform the gradient ascent algorithm on feature clustering and on problems with smaller datasets.