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Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Image-Based Roadway Assessment Using Convolutional Neural Networks, Weilian Song
Image-Based Roadway Assessment Using Convolutional Neural Networks, Weilian Song
Theses and Dissertations--Computer Science
Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-flow speed. Inference speeds for our methods are mere milliseconds, enabling …
Enhance Nmf-Based Recommendation Systems With Auxiliary Information Imputation, Fatemah Alghamedy
Enhance Nmf-Based Recommendation Systems With Auxiliary Information Imputation, Fatemah Alghamedy
Theses and Dissertations--Computer Science
This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user's opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called "cold-start" issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating.
The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation …
Confprofitt: A Configuration-Aware Performance Profiling, Testing, And Tuning Framework, Xue Han
Confprofitt: A Configuration-Aware Performance Profiling, Testing, And Tuning Framework, Xue Han
Theses and Dissertations--Computer Science
Modern computer software systems are complicated. Developers can change the behavior of the software system through software configurations. The large number of configuration option and their interactions make the task of software tuning, testing, and debugging very challenging. Performance is one of the key aspects of non-functional qualities, where performance bugs can cause significant performance degradation and lead to poor user experience. However, performance bugs are difficult to expose, primarily because detecting them requires specific inputs, as well as specific configurations. While researchers have developed techniques to analyze, quantify, detect, and fix performance bugs, many of these techniques are not …
Application Of Boolean Logic To Natural Language Complexity In Political Discourse, Austin Taing
Application Of Boolean Logic To Natural Language Complexity In Political Discourse, Austin Taing
Theses and Dissertations--Computer Science
Press releases serve as a major influence on public opinion of a politician, since they are a primary means of communicating with the public and directing discussion. Thus, the public’s ability to digest them is an important factor for politicians to consider. This study employs several well-studied measures of linguistic complexity and proposes a new one to examine whether politicians change their language to become more or less difficult to parse in different situations. This study uses 27,500 press releases from the US Senate between 2004–2008 and examines election cycles and natural disasters, namely hurricanes, as situations where politicians’ language …
Learning To Map The Visual And Auditory World, Tawfiq Salem
Learning To Map The Visual And Auditory World, Tawfiq Salem
Theses and Dissertations--Computer Science
The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabilistic models and a weakly-supervised, multi-task training …
Ε-Superposition And Truncation Dimensions In Average And Probabilistic Settings For ∞-Variate Linear Problems, Jonathan M. Dingess
Ε-Superposition And Truncation Dimensions In Average And Probabilistic Settings For ∞-Variate Linear Problems, Jonathan M. Dingess
Theses and Dissertations--Computer Science
This thesis is a representation of my contribution to the paper of the same name I co-author with Dr. Wasilkowski. It deals with linear problems defined on γ-weighted normed spaces of functions with infinitely many variables. In particular, I describe methods and discuss results for ε-truncation and ε-superposition methods. I show through these results that the ε-truncation and ε-superposition dimensions are small under modest error demand ε. These positive results are derived for product weights and the so-called anchored decomposition.
Rule Mining And Sequential Pattern Based Predictive Modeling With Emr Data, Orhan Abar
Rule Mining And Sequential Pattern Based Predictive Modeling With Emr Data, Orhan Abar
Theses and Dissertations--Computer Science
Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of …
A Machine Learning Approach To Artificial Floorplan Generation, Genghis Goodman
A Machine Learning Approach To Artificial Floorplan Generation, Genghis Goodman
Theses and Dissertations--Computer Science
The process of designing a floorplan is highly iterative and requires extensive human labor. Currently, there are a number of computer programs that aid humans in floorplan design. These programs, however, are limited in their inability to fully automate the creative process. Such automation would allow a professional to quickly generate many possible floorplan solutions, greatly expediting the process. However, automating this creative process is very difficult because of the many implicit and explicit rules a model must learn in order create viable floorplans. In this paper, we propose a method of floorplan generation using two machine learning models: a …
Optimal Gateway Placement In Low-Cost Smart Cities, Oluwashina Madamori
Optimal Gateway Placement In Low-Cost Smart Cities, Oluwashina Madamori
Theses and Dissertations--Computer Science
Rapid urbanization burdens city infrastructure and creates the need for local governments to maximize the usage of resources to serve its citizens. Smart city projects aim to alleviate the urbanization problem by deploying a vast amount of Internet-of-things (IoT) devices to monitor and manage environmental conditions and infrastructure. However, smart city projects can be extremely expensive to deploy and manage partly due to the cost of providing Internet connectivity via 5G or WiFi to IoT devices. This thesis proposes the use of delay tolerant networks (DTNs) as a backbone for smart city communication; enabling developing communities to become smart cities …
Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal
Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal
Theses and Dissertations--Computer Science
Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task …