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Young People, Social Media, And Impacts On Well-Being, Andreana Nop 2020 Clark University

Young People, Social Media, And Impacts On Well-Being, Andreana Nop

School of Professional Studies

Millennials and Generation Z were born into an age where social media and digital technology have been integrated in nearly all aspects of their lives. While social media has proven to be a valuable communication tool in connecting with each other and sharing information, the long-term psychosocial effects are beginning to become more apparent as social media matures. This study analyzes what these effects are and how communication is impacted for these young people. It questions how young people can leverage social media and decrease harm. The study will be conducted through a literature review and analysis. Its goal is ...


Dallas, Tx: The Staggering Wealth Gap, Macy Golman 2020 Clark University

Dallas, Tx: The Staggering Wealth Gap, Macy Golman

School of Professional Studies

In the summer of 2019, I had the privilege of serving as an AmeriCorps member with an organization called Equal Heart. One of Equal Heart’s main initiatives was to provide meals to children in underserved populations, to make sure that without school in session, these children would still be receiving food. Unfortunately, in many instances, without these meals, many of the children we served would likely not know when they would be eating their next fulfilling meal. There were certain pockets of Dallas that we would travel to everyday, places that were fifteen minutes maximum from my house, but ...


The Mental Health Of Black Men: Stabilizing Trauma With Emotional Intelligence, Davis Brandford 2020 Clark University

The Mental Health Of Black Men: Stabilizing Trauma With Emotional Intelligence, Davis Brandford

School of Professional Studies

The purpose of this study is to explore the relationship between the impact of historical trauma and barriers on African-American males and the effects of emotional intelligence in reducing traumatic experiences. This research study is based on previous research and studies that explores the historical review of African- American oppression, trauma in black males, and mental health in the African American community. This study will utilize the historical trauma and emotional intelligence theories to explore barriers that African Americans have experienced over time and the role emotional intelligence can play to reduce trauma. It also explores the relevance of historical ...


Elder Isolation In Immigrant Communities, Jessica Da Silva 2020 Clark University

Elder Isolation In Immigrant Communities, Jessica Da Silva

School of Professional Studies

This paper examined loneliness, as a measurement of perceived social isolation, in older immigrant adults. Previous research shows that older adults are more likely to experience social isolation and loneliness. Both of which have a direct correlation with their overall health (Wilson & Molton, 2010, Cacioppo et al., 2002) and mortality rates (Holt-Lunstad et al, 2015). Another international study found that immigrants in particular are at a higher risk for experiencing loneliness (Government of Canada, 2018). In this study, 35 immigrants and non-immigrants participants answered a survey which included 20 questions from the UCLA Loneliness Scale Version 3 (Russel, 1996). Participants ...


Straggler-Resistant Distributed Matrix Computation Via Coding Theory: Removing A Bottleneck In Large-Scale Data Processing, Aditya Ramamoorthy, Anindya Bijoy Das, Li Tang 2020 Iowa State University

Straggler-Resistant Distributed Matrix Computation Via Coding Theory: Removing A Bottleneck In Large-Scale Data Processing, Aditya Ramamoorthy, Anindya Bijoy Das, Li Tang

Electrical and Computer Engineering Publications

The current BigData era routinely requires the processing of large scale data on massive distributed computing clusters. Such large scale clusters often suffer from the problem of "stragglers", which are defined as slow or failed nodes. The overall speed of a computational job on these clusters is typically dominated by stragglers in the absence of a sophisticated assignment of tasks to the worker nodes. In recent years, approaches based on coding theory (referred to as "coded computation") have been effectively used for straggler mitigation. Coded computation offers significant benefits for specific classes of problems such as distributed matrix computations (which ...


Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao ZHANG, Yuan FANG, Zemin LIU, Min WU, Xinming ZHANG 2020 Singapore Management University

Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang

Research Collection School Of Information Systems

Given that heterogeneous information networks (HIN) encompass nodes and edges belonging to different semantic types, they can model complex data in real-world scenarios. Thus, HIN embedding has received increasing attention, which aims to learn node representations in a low-dimensional space, in order to preserve the structural and semantic information on the HIN. In this regard, metagraphs, which model common and recurring patterns on HINs, emerge as a powerful tool to capture semantic-rich and often latent relationships on HINs. Although metagraphs have been employed to address several specific data mining tasks, they have not been thoroughly explored for the more general ...


Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt 2020 East Tennessee State University

Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt

Electronic Theses and Dissertations

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its ...


Early Detection Of Mild Cognitive Impairment With In-Home Sensors To Monitor Behavior Patterns In Community-Dwelling Senior Citizens In Singapore: Cross-Sectional Feasibility Study, Iris Rawtaer, Rathi Mahendran, Ee Heok Kua, Hwee-pink TAN, Hwee Xian TAN, Tih-Shih Lee, Tze Pin Ng 2020 Singapore Management University

Early Detection Of Mild Cognitive Impairment With In-Home Sensors To Monitor Behavior Patterns In Community-Dwelling Senior Citizens In Singapore: Cross-Sectional Feasibility Study, Iris Rawtaer, Rathi Mahendran, Ee Heok Kua, Hwee-Pink Tan, Hwee Xian Tan, Tih-Shih Lee, Tze Pin Ng

Research Collection School Of Information Systems

Background: Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection.Objective: The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively.Methods: We recruited 59 community-dwelling seniors (aged ...


Symbolic Verification Of Message Passing Interface Programs, Hengbiao YU, Zhenbang CHEN, Xianjin FU, Ji WANG, Zhendong SU, Jun SUN, Chun HUANG, Wei DONG 2020 National University of Defense Technology

Symbolic Verification Of Message Passing Interface Programs, Hengbiao Yu, Zhenbang Chen, Xianjin Fu, Ji Wang, Zhendong Su, Jun Sun, Chun Huang, Wei Dong

Research Collection School Of Information Systems

Message passing is the standard paradigm of programming in high-performance computing. However, verifying Message Passing Interface (MPI) programs is challenging, due to the complex program features (such as non-determinism and non-blocking operations). In this work, we present MPI symbolic verifier (MPI-SV), the first symbolic execution based tool for automatically verifying MPI programs with non-blocking operations. MPI-SV combines symbolic execution and model checking in a synergistic way to tackle the challenges in MPI program verification. The synergy improves the scalability and enlarges the scope of verifiable properties. We have implemented MPI-SV and evaluated it with 111 real-world MPI verification tasks. The ...


On The Explanation And Implementation Of Three Open-Source Fully Homomorphic Encryption Libraries, Alycia Carey 2020 University of Arkansas, Fayetteville

On The Explanation And Implementation Of Three Open-Source Fully Homomorphic Encryption Libraries, Alycia Carey

Computer Science and Computer Engineering Undergraduate Honors Theses

While fully homomorphic encryption (FHE) is a fairly new realm of cryptography, it has shown to be a promising mode of information protection as it allows arbitrary computations on encrypted data. The development of a practical FHE scheme would enable the development of secure cloud computation over sensitive data, which is a much-needed technology in today's trend of outsourced computation and storage. The first FHE scheme was proposed by Craig Gentry in 2009, and although it was not a practical implementation, his scheme laid the groundwork for many schemes that exist today. One main focus in FHE research is ...


A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz 2020 University of Arkansas, Fayetteville

A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz

Computer Science and Computer Engineering Undergraduate Honors Theses

Effective monitoring of adherence to at-home exercise programs as prescribed by physiotherapy protocols is essential to promoting effective rehabilitation and therapeutic interventions. Currently physical therapists and other health professionals have no reliable means of tracking patients' progress in or adherence to a prescribed regimen. This project aims to develop a low-cost, privacy-conserving means of monitoring at-home exercise activity using a gym mat equipped with an array of capacitive sensors. The ability of the mat to classify different types of exercises was evaluated using several machine learning models trained on an existing dataset of physiotherapy exercises.


Investigating Machine Learning Techniques For Gesture Recognition With Low-Cost Capacitive Sensing Arrays, Michael Fahr Jr. 2020 University of Arkansas, Fayetteville

Investigating Machine Learning Techniques For Gesture Recognition With Low-Cost Capacitive Sensing Arrays, Michael Fahr Jr.

Computer Science and Computer Engineering Undergraduate Honors Theses

Machine learning has proven to be an effective tool for forming models to make predictions based on sample data. Supervised learning, a subset of machine learning, can be used to map input data to output labels based on pre-existing paired data. Datasets for machine learning can be created from many different sources and vary in complexity, with popular datasets including the MNIST handwritten dataset and CIFAR10 image dataset. The focus of this thesis is to test and validate multiple machine learning models for accurately classifying gestures performed on a low-cost capacitive sensing array. Multiple neural networks are trained using gesture ...


Applying Imitation And Reinforcement Learning To Sparse Reward Environments, Haven Brown 2020 University of Arkansas, Fayetteville

Applying Imitation And Reinforcement Learning To Sparse Reward Environments, Haven Brown

Computer Science and Computer Engineering Undergraduate Honors Theses

The focus of this project was to shorten the time it takes to train reinforcement learning agents to perform better than humans in a sparse reward environment. Finding a general purpose solution to this problem is essential to creating agents in the future capable of managing large systems or performing a series of tasks before receiving feedback. The goal of this project was to create a transition function between an imitation learning algorithm (also referred to as a behavioral cloning algorithm) and a reinforcement learning algorithm. The goal of this approach was to allow an agent to first learn to ...


Speech Processing In Computer Vision Applications, Nicholas Waterworth 2020 University of Arkansas, Fayetteville

Speech Processing In Computer Vision Applications, Nicholas Waterworth

Computer Science and Computer Engineering Undergraduate Honors Theses

Deep learning has been recently proven to be a viable asset in determining features in the field of Speech Analysis. Deep learning methods like Convolutional Neural Networks facilitate the expansion of specific feature information in waveforms, allowing networks to create more feature dense representations of data. Our work attempts to address the problem of re-creating a face given a speaker's voice and speaker identification using deep learning methods. In this work, we first review the fundamental background in speech processing and its related applications. Then we introduce novel deep learning-based methods to speech feature analysis. Finally, we will present ...


An Exploration Of Methods For Classifying Air-Written Letters From The Spanish Alphabet, Manuel Serna-Aguilera 2020 University of Arkansas, Fayetteville

An Exploration Of Methods For Classifying Air-Written Letters From The Spanish Alphabet, Manuel Serna-Aguilera

Computer Science and Computer Engineering Undergraduate Honors Theses

The ability to recognize human activity, especially air-writing, is an interesting challenge as one could identify any letter from many languages. I intend to investigate this problem of air-writing, but with the added twist of including the following letters from the Spanish alphabet: Á, É, Í, Ó, Ú, Ü, and Ñ. With this new alphabet, I set out to see what kinds of classifiers work best and on what kinds of data, since letters can be represented in multiple ways.

My tracking system will consist of a regular camera and a subject who will draw with a brightly colored marker ...


Identifying Privacy Policy In Service Terms Using Natural Language Processing, Ange-Thierry Ishimwe 2020 University of Arkansas, Fayetteville

Identifying Privacy Policy In Service Terms Using Natural Language Processing, Ange-Thierry Ishimwe

Computer Science and Computer Engineering Undergraduate Honors Theses

Ever since technology (tech) companies realized that people's usage data from their activities on mobile applications to the internet could be sold to advertisers for a profit, it began the Big Data era where tech companies collect as much data as possible from users. One of the benefits of this new era is the creation of new types of jobs such as data scientists, Big Data engineers, etc. However, this new era has also raised one of the hottest topics, which is data privacy. A myriad number of complaints have been raised on data privacy, such as how much ...


Dependency Mapping Software For Jira, Project Management Tool, Bentley Lager 2020 University of Arkansas, Fayetteville

Dependency Mapping Software For Jira, Project Management Tool, Bentley Lager

Computer Science and Computer Engineering Undergraduate Honors Theses

Efficiently managing a software development project is extremely important in industry and is often overlooked by the software developers on a project. Pieces of development work are identified by developers and are then handed off to project managers, who are left to organize this information. Project managers must organize this to set expectations for the client, and ensure the project stays on track and on budget. The main block in this process are dependency chains between tasks. Dependency chains can cause a project to take much longer than anticipated or result in the under utilization of developers on a project ...


Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim 2020 Washington University in St. Louis

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

Engineering and Applied Science Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is ...


Analyzing Flash X-Ray Machine Diagnostics, Sebastian Bustillo, Asaph Camillo, Jake Morris 2020 Ouachita Baptist University

Analyzing Flash X-Ray Machine Diagnostics, Sebastian Bustillo, Asaph Camillo, Jake Morris

Scholars Day Conference

The Cygnus Dual Beam Radiographic Facility consists of two flash x-ray machines, Cygnus 1 and 2. The seamless performance of these machines is critical to the maintenance of the United States stockpile of nuclear weapons. Since these SubCritical experiments cost about $104 million when something malfunctions millions of dollars are lost. As a result, it is imperative to assess the performance of the machine from diagnostics collected by the different sensors. Its performance is measured by the level of radiation dose a shot obtains. Utilizing exploratory data analysis, interesting trends were found and a 74.1% level of correctness was ...


Early Warning Solar Storm Prediction, Ian D. Lumsden, Marvin Joshi, Matthew Smalley, Aiden Rutter, Ben Klein 2020 University of Tennessee, Knoxville

Early Warning Solar Storm Prediction, Ian D. Lumsden, Marvin Joshi, Matthew Smalley, Aiden Rutter, Ben Klein

Chancellor’s Honors Program Projects

No abstract provided.


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