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Full-Text Articles in Engineering
Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt
Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt
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Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a …
Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang
Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang
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Sickle cell disease (SCD) is an inherited red blood cell disorder that can cause a multitude of complications throughout a patient's life. Pain is the most common complication and a significant cause of morbidity. Since pain is a highly subjective experience, both medical providers and patients express difficulty in determining ideal treatment and management strategies for pain. Therefore, the development of objective pain assessment and pain forecasting methods is critical to pain management in SCD. On the other hand, the rapidly increasing use of mobile health (mHealth) technology and wearable devices gives the ability to build a remote health intervention …
Extracting Information From Subroutines Using Static Analysis Semantics, Luke A. Burnett
Extracting Information From Subroutines Using Static Analysis Semantics, Luke A. Burnett
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Understanding how a system component can interact with other services can take an immeasurable amount of time. Reverse engineering embedded and large systems can rely on understanding how components interact with one another. This process is time consuming and can sometimes be generalized through certain behavior.We will be explaining two such complicated systems and highlighting similarities between them. We will show that through static analysis you can capture compiler behavior and apply it to the understanding of a function, reducing the total time required to understand a component of whichever system you are learning.
Finding Data Races In Software Binaries With Symbolic Execution, Nathan D. Jackson
Finding Data Races In Software Binaries With Symbolic Execution, Nathan D. Jackson
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Modern software applications frequently make use of multithreading to utilize hardware resources better and promote application responsiveness. In these applications, threads share the program state, and synchronization mechanisms ensure proper ordering of accesses to the program state. When a developer fails to implement synchronization mechanisms, data races may occur. Finding data races in an automated way is an already challenging problem, but often impractical without source code or understanding how to execute the program under analysis. In this thesis, we propose a solution for finding data races on software binaries and present our prototype implementation BINRELAY. Our solution makes use …
Hierarchical Anomaly Detection For Time Series Data, Ryan E. Sperl
Hierarchical Anomaly Detection For Time Series Data, Ryan E. Sperl
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With the rise of Big Data and the Internet of Things, there is an increasing availability of large volumes of real-time streaming data. Unusual occurrences in the underlying system will be reflected in these streams, but any human analysis will quickly become out of date. There is a need for automatic analysis of streaming data capable of identifying these anomalous behaviors as they occur, to give ample time to react. In order to handle many high-velocity data streams, detectors must minimize the processing requirements per value. In this thesis, we have developed a novel anomaly detection method which makes use …
Predicting Subjective Sleep Quality Using Objective Measurements In Older Adults, Reza Sadeghi
Predicting Subjective Sleep Quality Using Objective Measurements In Older Adults, Reza Sadeghi
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Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support …
An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies, Swetha Kondapalli
An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies, Swetha Kondapalli
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Emergency Medical Service (EMS) providers are the first responders for an injured patient on the field. Their assessment of patient injuries and determination of an appropriate hospital play a critical role in patient outcomes. A majority of states in the US have established a state-level governing body (e.g., EMS Division) that is responsible for developing and maintaining a robust EMS system throughout the state. Such divisions develop standards, accredit EMS agencies, oversee the trauma system, and support new initiatives through grants and training. But to do so, these divisions require data to enable them to first understand the similarities between …
Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman
Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman
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As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases is also increasing. The rate at which individuals in the United States of America and worldwide that succumb to Cardiovascular Disease (CVD) is rising as well. Approximately 2,303 Americans die to some form of CVD per day according to the American Heart Association. Furthermore, the Center for Disease Control and Prevention states that 647,000 Americans die yearly due to some form of CVD, which equates to one person every 37 seconds. Finally, the World Health Organization reports that the number one cause of …
Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman
Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman
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In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that …
An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow
An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow
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This paper presents a framework for the generation of 3D models. This is an important problem for many reasons. For example, 3D models are important for systems that are involved in target recognition. These systems use 3D models to train up accuracy on identifying real world object. Traditional means of gathering 3D models have limitations that the generation of 3D models can help overcome. The framework uses a novel generative adversarial network (GAN) that learns latent representations of two dimensional views of a model to bootstrap the network’s ability to learn to generate three dimensional objects. The novel architecture is …
Geoaware - A Simulation-Based Framework For Synthetic Trajectory Generation From Mobility Patterns, Jameson D. Morgan
Geoaware - A Simulation-Based Framework For Synthetic Trajectory Generation From Mobility Patterns, Jameson D. Morgan
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Recent advances in location acquisition services have resulted in vast amounts of trajectory data; providing valuable insight into human mobility. The field of trajectory data mining has exploded as a result, with literature detailing algorithms for (pre)processing, map matching, pattern mining, and the like. Unfortunately, obtaining trajectory data for the design and evaluation of such algorithms is problematic due to privacy, ethical, dataset size, researcher access, and sampling frequency concerns. Synthetic trajectories provide a solution to such a problem as they are cheap to produce and are derived from a fully controllable generation procedure. Citing deficiencies in modern synthetic trajectory …
Topological Analysis Of Averaged Sentence Embeddings, Wesley J. Holmes
Topological Analysis Of Averaged Sentence Embeddings, Wesley J. Holmes
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Sentence embeddings are frequently generated by using complex, pretrained models that were trained on a very general corpus of data. This thesis explores a potential alternative method for generating high-quality sentence embeddings for highly specialized corpora in an efficient manner. A framework for visualizing and analyzing sentence embeddings is developed to help assess the quality of sentence embeddings for a highly specialized corpus of documents related to the 2019 coronavirus epidemic. A Topological Data Analysis (TDA) technique is explored as an alternative method for grouping embeddings for document clustering and topic modeling tasks and is compared to a simple clustering …
Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi
Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi
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Quantitative susceptibility mapping (QSM) is a powerful technique that reveals changes in the underlying tissue susceptibility distribution. It can be used to measure the concentrations of iron and calcium in the brain both of which are linked with numerous neurodegenerative diseases. However, reconstructing the QSM image from the MRI phase data is an ill-posed inverse problem. Different methods have been proposed to overcome this difficulty. Still, the reconstructed QSM images suffer from streaking artifacts and underestimate the measured susceptibility of deep gray matter, veins, and other high susceptibility regions. This thesis proposes a structurally constrained Susceptibility Weighted Imaging and Mapping …
Enabling Static Program Analysis Using A Graph Database, Jialun Liu
Enabling Static Program Analysis Using A Graph Database, Jialun Liu
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This thesis presents the design, the implementation, and the evaluation of a database-oriented static program analysis engine for the PHP programming language. This engine analyzes PHP programs by representing their semantics using a graph-based data structure, which will be subsequently stored into a graph database. Such scheme will fundamentally facilitate various program analysis tasks such as static taint analysis, visualization, and data mining. Specifically, these complex program analysis tasks can now be translated into built-in declarative graph database operations with rich features. Our engine fundamentally differs from other existing static program analysis systems that mainly leverage intermediate representation (IRs) to …
Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty
Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty
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Real-time distributed systems constitute computing nodes that are connected by a network and coordinate with one another to accomplish a cooperative task, combining the responsiveness, fault-tolerance and geographic independence to support time-constrained collaborative applications, including distributed Human-Machine Teaming. In this thesis research the viability of real-time distributed collaborative technologies is demonstrated through the design, development and validation of prototype systems that support two human-machine teaming scenarios namely, ACE-IMS (Affirmation Cue based Interruption Management Systems) and ReadMI (Real-time Assessment of Dialogue in Motivational Interview). ACE-IMS demonstrates how a combination of AI capabilities and the cloud and mobile computing infrastructure can be …
Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman
Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman
Browse all Theses and Dissertations
In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that …