Thesis (D. Min.)--Gordon-Conwell Theological Seminary, 2006. / Abstract and vita. Includes bibliographical references (leaves 213-222).

In today's world, desktop computers have become such an integral part of our lives that it is practically impossible to imagine anything being done without the aid of computers. As the world becomes more and more fast paced and users feel a need to have computers on the go, desktop computers have reduced in size without compromising on performance. The late 90s saw the desktop segment make room for the laptop and the small screen devices (SSD) segment, which demonstrated faster growth rates than the desktop segment. The SSD segment, however, had a growth rate that was nowhere near the combined growth rate of desktop and laptop computers. Portability of SSD was one factor that stood out among many others to account for the unprecedented growth rate of the SSD segment that the computer industry had witnessed. One of the most important, albeit under-represented and neglected, factors of a product is its usability. Usability, or the ease with which a product can be used, can be considered to be one of the most important factors in the success or failure of product. Determining the usability of small screen devices presents a bigger challenge, primarily because of the screen size of the SSD. The process of usability engineering aims to solve some/most of the problems that the SSD has. To make up for the drawbacks of usability engineering, systems engineering was used in this thesis, since both disciplines have considerable overlap in their processes. A growing number of SSD users use the Internet in one form or the other. The Internet has grown rapidly in the last decade, and nearly everyone using the Internet has come across a search engine sometime or other. Although research has been limited to the area of desktop search engines, there has not been enough research done in the area of search engines for small screen devices. This thesis compares two different search engines on small screen devices to find the better between the two. To do so, it takes a close look at the usability engineering approach from a system engineering perspective revealing several deficiencies, which may have hitherto gone unnoticed. It also shows a method to integrate several key Systems Engineering components into the usability engineering approach. / Graduation date: 2006

Many search problems are commonly solved with combinatoric algorithms that unnecessarily duplicate and serialize work at considerable computational expense. There are techniques available that can eliminate redundant computations and perform remaining operations concurrently, effectively reducing the branching factors of these algorithms. This thesis applies these techniques to the problem of parsing natural language. The result is an efficient programming language that can reduce some of the expense associated with principle-based parsing and other search problems. The language is used to implement various natural language parsers, and the improvements are compared to those that result from implementing more deterministic theories of language processing.

In this thesis, we study theoretically and empirically the additive abstraction-based heuristics. First we present formal general definitions for abstractions that extend to general additive abstractions. We show that the general definition makes proofs of admissibility, consistency, and additivity easier, by proving that several previous methods for defining abstractions and additivity satisfy three imple conditions. Then we investigate two general methods for defining additive abstractions and run experiments to determine the effectiveness of these methods for two benchmark state spaces: TopSpin and the Pancake puzzle. Third, we propose that the accuracy of the heuristics generated by abstraction can be improved by checking for infeasibility. The theory and experiments demonstrate the approach to detect infeasibility and the application of this technique to different domains. Finally, we explore the applications of additive abstraction-based heuristics in two state spaces with nonuniform edge costs: the Sequential Ordering Problem (SOP) and the weighted Pancake puzzle. We formalize a novel way of generating additive and non-additive heuristics for these state spaces. Furthermore, we investigate the key concepts to generate good additive and non-additive abstractions. Experiments show that compared to some simple alternative heuristics, well chosen abstractions can enhance the quality of suboptimal solutions for large SOP instances and reduce search time for the weighted Pancake problems.

In a document retrieval system where data is stored and compared with a specific query and then compared with other documents, we need to find the document that is most similar to the query. The most similar document will have the weight higher than other documents. When more than one document are proposed to the user, these documents have to be sorted according to their weights. Once the result is presented to the user by a recommender system, the user may check any document of interest. If there are two different documents' lists, as two proposed results presented by different recommender systems, then, there is a need to find which list is more efficient. To do so, the measuring tool "Search Engine Ranking Efficiency Evaluation Tool [SEREET]" came to existence. This tool assesses the efficiency of each documents list and assigns a numerical value to the list. The value will be closer to 100% if the ranking list efficiency is high which means more relevance documents exist in the list and documents are sorted according to their relevance to the user. The value will be closer to 0% when the ranking list efficiency is poor and all of the presented documents are uninteresting documents to the user. A model to evaluate ranking efficiency is proposed in the dissertation, then it is proved it mathematically. Many mechanisms of search engine have been proposed in order to assess the relevance of a web page. They have focused on keyword frequency, page usage, link analysis and various combinations of them. These methods have been tested and used to provide the user with the most interesting web pages, according to his or her preferences. The collaborative filtering is a new approach, which was developed in this dissertation to retrieve the most interesting documents to the user according to his or her interests. Building a user profile is a very important issue in finding the user interest and categorizes each user in a suitable category. This is a requirement in collaborative filtering implementation. The inference tools such as time spent in a web page, mouse movement, page scrolling, mouse clicks and other tools were investigated. Then the dissertation shows that the most efficient and sufficient tool is the time a user spent on a web page. To eliminate errors, the system introduces a low threshold and high threshold for each user. Once the time spent on a web page breaks this threshold, an error is reported. SEREET tool is one of the contributions to the scientific society, which measures the efficiency of a search engine ranking list. Considerable work were carried, then the conclusion was that the amount of time spent on a web page is the most important factor in determining a user interest of a web page and also it is a sufficient tool which does not require collaborations from other tools such as mouse movements or a page scrolling. The results show that implicit rating is a satisfactory measure and can replace explicit rating. New filtering technique was introduced to design a fully functional recommender system. The linear vector algorithm which was introduced improves the vector space algorithm (VSA) in time complexity and efficiency. The use of machine learning enhances the retrieved list efficiency. Machine learning algorithm uses positive and negative examples for the training, these examples are mandatory to improve the error rate of the system. The result shows that the amount of these examples increases proportionally with the error rate of the system.

Urban Search and Rescue (USAR) involves having to enter and explore partially collapsed buildings in search for victims trapped by the collapse. There are many hazards in doing this, because of the possibility of additional collapses, explosions, fires, or flooding of the area being searched. The use of robots for USAR would increase the safety of the operation for the humans involved, and make the operation faster, because the robots could penetrate areas inaccessible to human beings. Teleoperated robots have been deployed in USAR situations to explore confined spaces in the collapsed buildings and send back images of the interior to rescuers. These deployments have resulted in the identification of several problems found during the operation of these robots. This thesis addresses a problem that has been encountered repeatedly in these robots: the determination of the scale of unrecognizable objects in the camera views from the robot. A procedure that would allow the extraction of size using a laser pointer mounted on the robot's camera is described, and an experimental setup and results that verify this procedure have been shown. Finally, ways to extend the procedure have been explored

In this thesis, we discuss a new kind of polygon, which we call one-sided polygons. The shortest path between any pair of vertices of a one-sided polygon makes only left turns or right turns. We prove that the set of one-sided polygons is a superset of the star-shaped polygons and the spiral polygons. We also show that the set of one-sided polygons is a subset of the set of LR-visibility polygons. We present a linear time recognition algorithm for one-sided rectilinear polygons. We then discuss the searching of monotone and one-sided rectilinear polygons. We show that all one-sided polygons can be 1-searched and a search schedule can be given in linear time.

In two experiments we evaluated whether memory for item locations across trials can improve visual search performance. Measuring both response times and eye movements we examined how visual search performance is influenced by a progressive change in item locations across successive search trials. The positions of items in the search displays were slightly shifted across successive displays and the degree of shift (trial-to-trial stability) was varied across participants. In addition, at the beginning of a trial we either presented only the current target (no-preview) or current and the next target (preview). This allowed us to evaluate performance under preview and under load, as participants on some trials held a future target in working memory. We found that search performance improved with increased stability. In addition, we demonstrate direct links between the accuracy with which an item is observed on one trial and the facility of search for that item during later trials, implicating a strong influence of trial-to-trial memory. Finally, we found that previewing a target improved performance particularly under load. Overall, our results support a model of search in which relatively slow top-down information guides and constrains the deployment of fast perceptual processes.

In two experiments we evaluated whether memory for item locations across trials can improve visual search performance. Measuring both response times and eye movements we examined how visual search performance is influenced by a progressive change in item locations across successive search trials. The positions of items in the search displays were slightly shifted across successive displays and the degree of shift (trial-to-trial stability) was varied across participants. In addition, at the beginning of a trial we either presented only the current target (no-preview) or current and the next target (preview). This allowed us to evaluate performance under preview and under load, as participants on some trials held a future target in working memory. We found that search performance improved with increased stability. In addition, we demonstrate direct links between the accuracy with which an item is observed on one trial and the facility of search for that item during later trials, implicating a strong influence of trial-to-trial memory. Finally, we found that previewing a target improved performance particularly under load. Overall, our results support a model of search in which relatively slow top-down information guides and constrains the deployment of fast perceptual processes.

In this thesis, we discuss a new kind of polygon, which we call one-sided polygons. The shortest path between any pair of vertices of a one-sided polygon makes only left turns or right turns. We prove that the set of one-sided polygons is a superset of the star-shaped polygons and the spiral polygons. We also show that the set of one-sided polygons is a subset of the set of LR-visibility polygons. We present a linear time recognition algorithm for one-sided rectilinear polygons. We then discuss the searching of monotone and one-sided rectilinear polygons. We show that all one-sided polygons can be 1-searched and a search schedule can be given in linear time.