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Description
Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this

Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this work has integrated context-aware search principles with applications of preference based re-ranking and query modifications. This research investigates several aspects of context-aware search principles, specifically context-sensitive and preference based re-ranking of results which take user inputs as to their preferred content, and combines this with search query modifications which automatically search for a variety of modified terms based on the given search query, integrating these results into the overall re-ranking for the context. The result of this work is a novel web search algorithm which could be applied to any online learning environment attempting to collect relevant resources for learning about a given topic. The algorithm has been evaluated through user studies comparing traditional search results to the context-aware results returned through the algorithm for a given topic. These studies explore how this integration of methods could provide improved relevance in the search results returned when compared against other modern search engines.
ContributorsVan Egmond, Eric (Author) / Burleson, Winslow (Thesis advisor) / Syrotiuk, Violet (Thesis advisor) / Nelson, Brian (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Corporations invest considerable resources to create, preserve and analyze

their data; yet while organizations are interested in protecting against

unauthorized data transfer, there lacks a comprehensive metric to discriminate

what data are at risk of leaking.

This thesis motivates the need for a quantitative leakage risk metric, and

provides a risk assessment system,

Corporations invest considerable resources to create, preserve and analyze

their data; yet while organizations are interested in protecting against

unauthorized data transfer, there lacks a comprehensive metric to discriminate

what data are at risk of leaking.

This thesis motivates the need for a quantitative leakage risk metric, and

provides a risk assessment system, called Whispers, for computing it. Using

unsupervised machine learning techniques, Whispers uncovers themes in an

organization's document corpus, including previously unknown or unclassified

data. Then, by correlating the document with its authors, Whispers can

identify which data are easier to contain, and conversely which are at risk.

Using the Enron email database, Whispers constructs a social network segmented

by topic themes. This graph uncovers communication channels within the

organization. Using this social network, Whispers determines the risk of each

topic by measuring the rate at which simulated leaks are not detected. For the

Enron set, Whispers identified 18 separate topic themes between January 1999

and December 2000. The highest risk data emanated from the legal department

with a leakage risk as high as 60%.
ContributorsWright, Jeremy (Author) / Syrotiuk, Violet (Thesis advisor) / Davulcu, Hasan (Committee member) / Yau, Stephen (Committee member) / Arizona State University (Publisher)
Created2014
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Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate

This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011