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Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Description: Article presenting a review, comparison, and critical assessment of published approaches for predicting RNA-binding residues in proteins using non-redundant databases.
Date: May 10, 2012
Creator: Walia, Rasna R.; Caragea, Cornelia; Lewis, Benjamin A.; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser et al.
Partner: UNT College of Engineering

Automated Classification of Emotions Using Song Lyrics

Description: This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics. I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the social network Twitter. I use the Twitter API to collect a large corpus of tweets manually labeled by their authors for one of the six emotions of interest. I then compare the classification of emotions obtained when training on data automatically collected from Twitter versus data obtained through crowd sourced annotations.
Date: December 2012
Creator: Schellenberg, Rajitha
Partner: UNT Libraries

Modeling Alcohol Consumption Using Blog Data

Description: How do the content and writing style of people who drink alcohol beverages stand out from non-drinkers? How much information can we learn about a person's alcohol consumption behavior by reading text that they have authored? This thesis attempts to extend the methods deployed in authorship attribution and authorship profiling research into the domain of automatically identifying the human action of drinking alcohol beverages. I examine how a psycholinguistics dictionary (the Linguistics Inquiry and Word Count lexicon, developed by James Pennebaker), together with Kenneth Burke's concept of words as symbols of human action, and James Wertsch's concept of mediated action provide a framework for analyzing meaningful data patterns from the content of blogs written by consumers of alcohol beverages. The contributions of this thesis to the research field are twofold. First, I show that it is possible to automatically identify blog posts that have content related to the consumption of alcohol beverages. And second, I provide a framework and tools to model human behavior through text analysis of blog data.
Date: May 2013
Creator: Koh, Kok Chuan
Partner: UNT Libraries

Ddos Defense Against Botnets in the Mobile Cloud

Description: Mobile phone advancements and ubiquitous internet connectivity are resulting in ever expanding possibilities in the application of smart phones. Users of mobile phones are now capable of hosting server applications from their personal devices. Whether providing services individually or in an ad hoc network setting the devices are currently not configured for defending against distributed denial of service (DDoS) attacks. These attacks, often launched from a botnet, have existed in the space of personal computing for decades but recently have begun showing up on mobile devices. Research is done first into the required steps to develop a potential botnet on the Android platform. This includes testing for the amount of malicious traffic an Android phone would be capable of generating for a DDoS attack. On the other end of the spectrum is the need of mobile devices running networked applications to develop security against DDoS attacks. For this mobile, phones are setup, with web servers running Apache to simulate users running internet connected applications for either local ad hoc networks or serving to the internet. Testing is done for the viability of using commonly available modules developed for Apache and intended for servers as well as finding baseline capabilities of mobiles to handle higher traffic volumes. Given the unique challenge of the limited resources a mobile phone can dedicate to Apache when compared to a dedicated hosting server a new method was needed. A proposed defense algorithm is developed for mitigating DDoS attacks against the mobile server that takes into account the limited resources available on the mobile device. The algorithm is tested against TCP socket flooding for effectiveness and shown to perform better than the common Apache module installations on a mobile device.
Date: May 2014
Creator: Jensen, David
Partner: UNT Libraries

Monitoring Dengue Outbreaks Using Online Data

Description: Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.
Date: May 2014
Creator: Chartree, Jedsada
Partner: UNT Libraries

Toward a Grounded Theory of Community Networking

Description: This dissertation presents a preliminary grounded theory of community networking based on 63 evaluations of community networking projects funded by the National Telecommunications and Information Administration’s Technology Opportunities Program (TOP) between 1994 and 2007. The substantive grounded theory developed is that TOP projects differed in their contribution to positive outcomes for intended disadvantaged community beneficiaries based on the extent and manner in which they involved the disadvantaged community during four grant process phases: partnership building, project execution, evaluation, and close-out. Positive outcomes for the community were facilitated by using existing communication channels, such as schools, to connect with intended beneficiaries; local financial institutions to provide infrastructure to support local trade; and training to connect community members to jobs. Theoretical contributions include situating outcomes for disadvantaged communities within the context of the grant process; introducing the “vulnerable community” concept; and identifying other concepts and properties that may be useful in further theoretical explorations. Methodological contributions include demonstrating grounded theory as a viable method for exploring large text-based datasets; paving the way for machine learning approaches to analyzing qualitative data; and illustrating how project evaluations can be used in a similar fashion as interview data. Practical contributions include providing information to guide community networking-related policies and initiatives from the perspectives of stakeholders at all levels, including establishing funded projects as local employment opportunities and re-conceptualizing sustainability in terms of human networks rather than technological networks.
Date: May 2014
Creator: Masten-Cain, Kathryn
Partner: UNT Libraries

[Dataset of Web Archiving Research Articles]

Description: Datasets used in the presentation, "Towards Building a Collection of Web Archiving Research Articles." The files included here were used to conduct several Machine Learning classification experiments that result in a corpus of scholarly research articles on the topic of web archiving.
Date: August 2014
Creator: Reyes Ayala, Brenda & Caragea, Cornelia
Partner: UNT College of Information