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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Modeling, Designing, and Implementing an Ad-hoc M-Learning Platform that Integrates Sensory Data to Support Ubiquitous Learning

Nguyen, Hien M. 18 September 2015 (has links)
Learning at any-time, at anywhere, using any mobile computing platform learning (which we refer to as “education in your palm”) empowers informal and formal education. It supports the continued creation of knowledge outside a classroom, after-school programs, community-based organizations, museums, libraries, and shopping malls with under-resourced settings. In doing so, it fosters the continued creation of a cumulative body of knowledge in informal and formal education. Anytime, anywhere, using any device computing platform learning means that students are not required to attend traditional classroom settings in order to learn. Instead, students will be able to access and share learning resources from any mobile computing platform, such as smart phones, tablets using highly dynamic mobile and wireless ad-hoc networks. There has been little research on how to facilitate the integrated use of the service description, discovery and integration resources available in mobile and wireless ad-hoc networks including description schemas and mobile learning objects, and in particular as it relates to the consistency, availability, security and privacy of spatio-temporal and trajectory information. Another challenge is finding, combining and creating suitable learning modules to handle the inherent constraints of mobile learning, resource-poor mobile devices and ad-hoc networks. The aim of this research is to design, develop and implement the cutting edge context-aware and ubiquitous self-directed learning methodologies using ad-hoc and sensor networks. The emphasis of our work is on defining an appropriate mobile learning object and the service adaptation descriptions as well as providing mechanisms for ad-hoc service discovery and developing concepts for the seamless integration of the learning objects and their contents with a particular focus on preserving data and privacy. The research involves a combination of modeling, designing, and developing a mobile learning system in the absence of a networking infrastructure that integrates sensory data to support ubiquitous learning. The system includes mechanisms to allow content exchange among the mobile ad-hoc nodes to ensure consistency and availability of information. It also provides an on-the-fly content service discovery, query request, and retrieving data from mobile nodes and sensors.

Target Sequence Clustering

Shih, Benjamin 01 December 2011 (has links)
Researchers have discovered many successful algorithms and methodologies for solving problems at the intersection of machine learning and education research. This umbrella category, “educational data mining,” has enjoyed a series of successes that span the research process, from post-hoc data analysis that generates models to the use of those models in successful educational interventions. However, most of these successes have arisen from the use of pre-existing psychological and educational constructs (e.g., guessing) and thus from the use of semi-supervised or fully-supervised machine learning algorithms. Algorithms for novel discovery, also known as unsupervised clustering, have enjoyed significantly fewer successes in this domain, partially because education data exhibit unique, complex structure. This thesis is a mixture of algorithm development, simulation, and experimentation on real-world data, all designed to define and test a novel paradigm for clustering in education (and a range of other domains). This paradigm, target clustering, revolves around the inclusion of high-level targets, such as student learning from pre-test to post-test. This approach differs from other existing machine learning approaches in that it is designed completely, from the initial concept to the final execution, for solving educational research problems, taking advantage of the structural complexities that are problematic for other algorithms. This thesis includes a range of data sets drawn from a variety of research domains, but does not include new data from experiments in the psychological sense.1 However, the thesis includes analysis of methodology, results, and implications from an educational research perspective and relies entirely on education data and research problems.

Learning Large-Scale Conditional Random Fields

Bradley, Joseph K. 01 January 2013 (has links)
Conditional Random Fields (CRFs) [Lafferty et al., 2001] can offer computational and statistical advantages over generative models, yet traditional CRF parameter and structure learning methods are often too expensive to scale up to large problems. This thesis develops methods capable of learning CRFs for much larger problems. We do so by decomposing learning problems into smaller, simpler subproblems. These decompositions allow us to trade off sample complexity, computational complexity, and potential for parallelization, and we can often optimize these trade-offs in model- or data-specific ways. The resulting methods are theoretically motivated, are often accompanied by strong guarantees, and are effective and highly scalable in practice. In the first part of our work, we develop core methods for CRF parameter and structure learning. For parameter learning, we analyze several methods and produce PAC learnability results for certain classes of CRFs. Structured composite likelihood estimation proves particularly successful in both theory and practice, and our results offer guidance for optimizing estimator structure. For structure learning, we develop a maximum-weight spanning tree-based method which outperforms other methods for recovering tree CRFs. In the second part of our work, we take advantage of the growing availability of parallel platforms to speed up regression, a key component of our CRF learning methods. Our Shotgun algorithm for parallel regression can achieve near-linear speedups, and extensive experiments show it to be one of the fastest methods for sparse regression.

Error Detection with Memory Tags

Gumpertz, Richard H. 01 December 1981 (has links)
The ability to achieve and maintain system reliability is an important problem that has become more critical as the use of computers has become more common. Fundamental to improved reliability is the ability to detect errors promptly, before their effects can be propagated. This dissertation proposes methods for using storage tags to detect a broad class of hardware and software errors that might otherwise go undetected. Moreover, the suggested schemes require minimal extensions to the hardware of typical computers. In fact, it is shown that in many situations tags can be added to words of storage without using any extra bits at all. Although tagging is central to the discussion, the methods used differ radically from those used in traditional tagged architectures. Most notably, no attempt is made to use the tags to control what computations are performed. Instead, the tags are used only to check the consistency of those operations that would be performed anyway in the absence of tagging. By so doing, redundancy already present in typical programs can be harnessed for detecting errors. Furthermore, it becomes possible to check an arbitrary number of assertions using only a small tag of fixed size. The dissertation examines various strategies for exploiting the proposed tagging mechanisms; both the positive and negative aspects of each application are considered. Finally, an example is described, showing how tagging might be implemented in a real machine.

The implementation of a graphics package in ADA

Walker, Reginald Louis 01 July 1986 (has links)
The motivation for this thesis was the need for an inexpensive graphics package that could be used to support courses in computer graphics and computer vision in the Mathematical and Computer Sciences Department of Atlanta University. The implemented graphics package used a portion of the CORE Graphics System and the hardware used consisted of Zenith Z-100 micro-computers in the Micro-computer Laboratory of Atlanta University. This graphics system was initially implemented in the Microsoft Pascal programming language. Due to limitations inherent in Pascal, the initial graphics package did not represent the best design practices. The graphics package was converted and expanded using the Ada programming language. The Ada programming language had the ability to satisfy all of the objectives of this project which were: to create a graphics package that was portable, expandable, represented the best software design practices, and able to support computer courses at Atlanta University. Discussed in this thesis are the basic features of the extended graphics system in Ada, the general principles, an operation guide, and problems encountered using the CORE Graphics System.

Precedence grammars for compiler construction

Tseng, Jennifer Fan-Yuan 01 December 1981 (has links)
The basic objective of this thesis is to study the theory of precedence grammars. In particular, this paper deals with results concerning LR(k), SPG, UIEPG, and UIWPG and their hierarchical structures. Various theorems are proved to show different relationships. A brief theory of precedence parsing is also presented.

Outlier detection in spatial data using the m-SNN algorithm

Parana-Liyanage, Krishani 01 July 2013 (has links)
Outlier detection is an important topic in data analysis because of its applications to numerous domains. Its application to spatial data, and in particular spatial distribution in path distributions, has recently attracted much interest. This recent trend can be seen as a reflection of the massive amounts of spatial data being gathered through mobile devices, sensors and social networks. In this thesis we propose a nearest neighbor distance based method the Modified-Shared Nearest Neighbor outlier detection (m-SNN) developed for outlier detection in spatial domains. We modify the SNN technique for use in outlier detection, and compare our approach with the widely used outlier detection technique, the LOF Algorithm and a base Gaussian approach. It is seen that the m-SNN compares well with the LOF in simple spatial data distributions and outperforms it in more complex distributions. Experimental results of using buoy data to track the path of a hurricane are also shown.

Asynchronous instant messaging using service-oriented architectures (aimsoa)

Thomas, Jamar 01 August 2005 (has links)
Instant messengers suffer from poor scalability, flexibility, security, and interoperability. This study attempts to solve these problems using the strengths of Service-Oriented Architectures. The key components to achieve these improvements include several Java related technologies such as JAX-RPC, JAXM, SOAP, WSDL, J2EE servlets and Enterprise Java Beans. SOAP provides a universal messaging protocol that heterogeneous parties can understand. JAX-RPC provides synchronous SOAP messaging, as well as a loosely coupled design that allows for a very flexible distributed architecture. JAXM provides asynchronous SOAP messaging. When used together, applications can implement robust instant messaging functionality. Registration, login, and other instant messaging configuration operations can be fulfilled through the use of JAX-RPC while JAXM can be used to fulfill requirements such as send and receive. Servlets and Enterprise Java Beans augment the benefits of Service-Oriented Architectures with the former being extremely scalable, portable, and modular. AIMSOA encapsulates these components to provide an instant messaging architecture solution that will augment the weaknesses of current instant messaging architectures by providing a solution for better scalability, flexibility, and interoperability.

Measuring the influence of mainstream media on twitter users

Eltayeby, Omar 01 May 2014 (has links)
No description available.

Detecting image manipulations without the original

Priester, Marquita B. 01 May 2008 (has links)
This study examines whether image manipulations can be detected without the original image present. The idea for the study was based on the premise that there is currently no existing benchmark for determining if an image was manipulated. A case study analysis approach was used to analyze data gathered in order to determine if noticeable differences could be recognized between the original image and the altered images based on the defmed tests. The researcher found that there are significant differences between the test images and based on the defmed tests, there possibly exists criteria to defme an altered image. The conclusions drawn from the findings suggest that there exists specific tests that can indicate an altered image.

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