• Digital Humanities and Cultural Heritage
• Publications close
• Research data close
• VTechWorks close

The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions (CBAR-tpd), the Integrated Digital Event Archiving and Library (IDEAL), and the Global Event Trend Archive Research (GETAR) projects. The tweet collection topics include heart attack, solar eclipse, terrorism, etc. There are several collections on naturally occurring events such as hurricanes, floods, and solar eclipses. Such naturally occurring events are distributed across space and time. It would be beneficial to researchers if we can perform a spatial-temporal analysis to test some hypotheses, and to find any trends that tweets would reveal for such events. I apply an existing algorithm to detect locations from tweets by modifying it to work better with the type of datasets I work with. I use the time captured in tweets and also identify the tense of the sentences in tweets to perform the temporal analysis. I build a rule-based model for obtaining the tense of a tweet. The results from these two algorithms are merged to analyze naturally occurring moving events such as solar eclipses and hurricanes. Using the spatial-temporal information from tweets, I study if tweets can be a relevant source of information in understanding the movement of the event. I create visualizations to compare the actual path of the event with the information extracted by my algorithms. After examining the results from the analysis, I noted that Twitter can be a reliable source to identify places affected by moving events almost immediately. The locations obtained are at a more detailed level than in news-wires. We can also identify the time that an event affected a particular region by date. News now travels faster on social media than through news channels. Information from social media can help retrieve minute details that might not be emphasized in news. People tend to describe their actions or sentiments in tweets. I aim at studying if such collections of tweets are dependable sources for identifying paths of moving events. In events like hurricanes, using Twitter can help in analyzing people’s reaction to such moving events. These may include actions such as dislocation or emotions during different phases of the event. The results obtained in the experiments concur with the actual path of the events with respect to the regions affected and time. The frequency of tweets increases during event peaks. The number of locations affected that are identified are significantly more than in news wires. Master of Science

A vivid account of 18th century frontier life in southwest Virginia and Kentucky.

We describe our approach to generating summaries of a shooting event from a large collection of webpages. We work with two separate events - a shooting at a school in Newtown, Connecticut and another at a mall in Tucson, Arizona. Our corpora of webpages are inherently noisy and contain a large amount of irrelevant information. In our approach, we attempt to clean up our webpage collection by removing all irrelevant content. For this, we utilize natural language processing techniques such as word frequency analysis, part of speech tagging and named entity recognition to identify key words about our news events. Using these key words as features, we employ classification techniques to categorize each document as relevant or irrelevant. We discard the documents classified as irrelevant. We observe that to generate a summary, we require some specific information that enables us to answer important questions such as "Who was the killer?", "Where did the shooting happen?", "How many casualties were there?" and so on. To enable extraction of these essential details from news articles, we design a template of the event summary with slots that pertain to information we would like to extract. We designed regular expressions to identify a number of 'candidate' values for the template slots. Using a combination of word frequency analysis and specific validation techniques, we choose the top candidate for each slot of our template. We use a grammar based on our template to generate a human readable summary of each event. We utilize the Hadoop MapReduce framework to parallelize our workflow, along with the NLTK language processing library to simplify and speed our development. We learned that a variety of different methods and techniques are necessary in order to provide an accurate summary for any collection. It is seen that cleaning poses an incredibly difficult yet necessary task when attempting to semantically interpret data. We found that our attempts to extract relevant topics and sentences using the topic extraction method Latent Dirichlet Allocation and k-means clustering did not result in topics and sentences that were indicative of our corpus. We demonstrate an effective way of summarizing a shooting event that extracts relevant information by using regular expressions and generates a comprehensive human-readable summary utilizing a regular grammar. Our solution generates a summary that includes key information needed in understanding a shooting event such as: the shooter(s), date of the shooting, location of the shooting, number of people injured and wounded, and the weapon used. This solution is shown to work effectively for two different types of shootings: a mass murder, and an assassination attempt. Filename and description of all included files: ShootingsReportPdf.pdf - PDF version of our project report. ShootingsReportDoc.docx - MS Word version of our project report. ShootingsPresentationPpt.pptx - MS PowerPoint version of our project presentation. ShootingsPresentationPdf.pdf - PDF version of our project presentation. Source code is included in folder "shooting_summary_code": shooting_summary_code/mapper.py - The map script used on the Hadoop cluster. This file contains the regular expressions which are used to extract the data from the collections. shooting_summary_code/parse.py - Standard frequency based reducer on the cluster. Takes sorted input from the mapper, and reduces based on frequency. shooting_summary_code/reducer.py - Python which parses the output from the mapper and reducer. Does not run on Hadoop cluster. Relies on data being sorted by frequency (most frequent at top of file). Contains the implementation of the regular grammar, along with filtering techniques. shooting_summary_code/TrigramTagger.pkl - A Python pickled version of our trigram tagger, which is used in parse.py. NSF DUE-1141209 and IIS-1319578

A number of problems in Computer Vision, Natural Language Processing, and Machine Learning produce structured outputs in high-dimensional space, which makes searching for the global optimal solution extremely expensive. Thus, greedy algorithms, making trade-offs between precision and efficiency, are widely used. Unfortunately, they in general lack theoretical guarantees. In this thesis, we prove that greedy algorithms are effective and efficient to search for multiple top-scoring hypotheses from structured (neural) models: 1) Entropy estimation. We aim to find deterministic samples that are representative of Gibbs distribution via a greedy strategy. 2) Searching for a set of diverse and high-quality bounding boxes. We formulate this problem as the constrained maximization of a monotonic sub-modular function such that there exists a greedy algorithm having near-optimal guarantee. 3) Fill-in-the-blank. The goal is to generate missing words conditioned on context given an image. We extend Beam Search, a greedy algorithm applicable on unidirectional expansion, to bidirectional neural models when both past and future information have to be considered. We test our proposed approaches on a series of Computer Vision and Natural Language Processing benchmarks and show that they are effective and efficient. Ph. D.

The table of contents and information about The Smithfield Review.

The table of contents and information about The Smithfield Review.

This article covers conditions leading to the establishment of the tuberculosis sanatorium by the Commonwealth of Virginia, its early operations, different medical personnel, and factors affecting its successes—and failures.

The dehumanization of African people in the United States began with their capture and the exploitation of their labor and bodies, which white people justified through the conviction of their inherent superiority. Collective religious passion was an effective defensive measure, but it did not prevent slaveholders from exploiting black labor. This paper will use testimonies and narratives to argue that religion in the South from 1840 – 1865 offered a social sphere within enslaved communities that relieved experiences of dehumanization under slavery. Although enslaved people did not always intend to challenge the institution of slavery, their actions demonstrated resistance to the objectives of slavery, especially their own dehumanization.