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Full-Text Articles in Physical Sciences and Mathematics
Exploring The Latent Space Of Image Captioning Networks, Mikian J. Musser
Exploring The Latent Space Of Image Captioning Networks, Mikian J. Musser
UNLV Theses, Dissertations, Professional Papers, and Capstones
State-of-the-art image captioning models can successfully produce a diverse set of accurate captions. Previous research has focused on improving caption diversity while maintaining a high level of fidelity. We shift the focus from accuracy and diversity to controllability. We use a modified version of the traditional encoder-decoder network that allows the model to produce a meaningful and structured latent space. We then explore the latent space using several latent cartographic methods: lerp, slerp, analogy completion, attribute vector rotation, and interpolation graphs. Additionally, we discuss different categories of latent space and provide modifications for each of the cartographic methods. Finally, we …
Machine Learning Analysis Of Single Nucleotide Polymorphism (Snp) Data To Predict Bone Mineral Density In African American Women, Erick Githua Wakayu
Machine Learning Analysis Of Single Nucleotide Polymorphism (Snp) Data To Predict Bone Mineral Density In African American Women, Erick Githua Wakayu
UNLV Theses, Dissertations, Professional Papers, and Capstones
Osteoporosis is a debilitating disease in which an individual’s bones weaken, making bones fragile and more susceptible to fracture. While commonly found amongst postmenopausal Caucasian and Asian women based on previous studies, those of African descent (African American/Black) have largely been ignored when it comes to osteoporotic studies, especially when it comes to Genome Wide Association Studies (GWAS). From GWA studies, we gain access to single nucleotide poly-morphisms (SNPs) that may contribute to certain illnesses, such as osteoporosis. With low Bone Mineral Density (BMD) being one of the primary markers of potential osteoporosis, it is prudent that proper research is …
Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed
Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed
UNLV Theses, Dissertations, Professional Papers, and Capstones
In this dissertation we develop different methods for forecasting pedestrian trajectories. Complete understanding of pedestrian motion is essential for autonomous agents and social robots to make realistic and safe decisions. Current trajectory prediction methods rely on incorporating historic motion, scene features and social interaction to model pedestrian behaviors. Our focus is to accurately understand scene semantics to better forecast trajectories. In order to do so, we leverage semantic segmentation to encode static scene features such as walkable paths, entry/exits, static obstacles etc. We further evaluate the effectiveness of using semantic maps on different datasets and compare its performance with already …
A Survey On Securing Iot Ecosystems And Adaptive Network Vision, Tejaswini Goli, Yoohwan Kim
A Survey On Securing Iot Ecosystems And Adaptive Network Vision, Tejaswini Goli, Yoohwan Kim
Computer Science Faculty Research
The rapid growth of Internet-of-Things (IoT) devices and the large network of interconnected devices pose new security challenges and privacy threats that would put those devices at high risk and cause harm to the affiliated users. This paper emphasizes such potential security challenges and proposes possible solutions in the field of IoT Security, mostly focusing on automated or adaptive networks. Considering the fact that IoT became widely adopted, the intricacies in the security field tend to grow expeditiously. Therefore, it is necessary for businesses to adopt new security protocols and to the notion of automated network security practices driven by …