http://smartech.gatech.edu/handle/1853/16123 Research Thesis Option for Computer Science Majors Search the Channel search http://smartech.gatech.edu/simple-search http://smartech.gatech.edu/handle/1853/21821 Title: Performance Information Sharing Middleware <br/> <br/>Authors: Reiss, Charles <br/> <br/>Abstract: This thesis presents a design for distributed monitoring system designed to enable monitoring-informed optimizationsin distributed applications. Microbenchmarks and an evaluation in a scientific-computing scenario are presented. The monitoring system is intended to assist when application requirements cannot be easily expressed in a form suitable for existing autonomic computing approaches. The design embeds awareness of the application's topology into the monitoring system so queries can reference a node's place in the application without embedding extra assumptions about the overall layout of the application. Through integration with dynamic code generation, users may make potentially application-specific metadata available and use such data within dynamically deployed filters and transformation functions. Evaluations demonstrate that this approach can provide timely and useful information with low overhead. http://smartech.gatech.edu/handle/1853/19947 Title: Evaluating the effectiveness of using touch sensor capacitors as an input device for a wrist watch computer <br/> <br/>Authors: Wilson, Gregory <br/> <br/>Abstract: On the go computing is becoming more important for users who wish to access information from anywhere. Wearable computers are an optimal solution to achieving this feat because it allows for easy accessibility and quick use. There are many challenges that arise with small computers worn on the body. One of the most common issues is the interaction between the computer and the user and more specifically how the user enters input. In this paper we research a potential effective way to interact with a wrist watch by mounting touch sensors on the watch band. http://smartech.gatech.edu/handle/1853/19946 Title: Learning Behaviors Through Demonstration: Artificial Intelligence for Non-Player Characters in an Interactive Drama <br/> <br/>Authors: Amundsen, Thomas Charles <br/> <br/>Abstract: In both the game industry and the academic community, there have not been many attempts to create complete interactive drama systems. I am working with a group that will be the first to undertake this daunting challenge, and one of the most important components in such a system is artificial intelligence (AI) to control the behaviors of non-player characters. Traditional approaches to designing the AI for any kind of interactive game involves designing characters who follow scripted behaviors. This method is cumbersome and amounts to gameplay that is repetitive and thus inhuman. This paper will describe my attempt to design a system that will allow an expert user to demonstrate behaviors to the system, which the system will use to learn how to behave on its own using case-based reasoning. The end result of this work will not only benefit interactive dramas but may help in the design of game AI for other genres of video games or simulations. http://smartech.gatech.edu/handle/1853/16121 Title: A Dynamic Approach to Statistical Debugging: Building Program Specific Models with Neural Networks <br/> <br/>Authors: Wood, Matthew <br/> <br/>Abstract: Computer software is constantly increasing in complexity; this requires more developer time, effort, and knowledge in order to correct bugs inevitably occurring in software production. Eventually, increases in complexity and size will make manually correcting programmatic errors impractical. Thus, there is a need for automated software-debugging tools that can reduce the time and effort required by the developer. The performance of previously developed debugging techniques can be greatly improved by combining them with machine-learning. Our research focuses on the application of neural networks within the domain of statistical debugging. Specifically, we develop methods to mine statistical debugging data that can then be used to train neural networks; these generated multi-layered neural networks can then be used to identify suspicious programmatic entities. Our developed networks are generated on a per program basis in order to leverage specific programmatic properties. In our empirical evaluation we compare our proposed approach with a state-of-the-art automated debugging technique. The results of the evaluation indicate that, for the cases considered, our approach is more effective than the considered technique.