Scholarly Works @ SHSU

Scholarly Works @ SHSU is a digital service that collects, preserves, and distributes digital material. Repositories are important tools for preserving an organization's legacy; they facilitate digital preservation and scholarly communication.


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Recent Submissions

A Crowded Sky: New Threats and Opportunities for Homeland Security in the Cislunar Economy
(Institute for Homeland Security, 2023-10-15) Reese, Nick
Homeland security has not traditionally been thought of as a mission area supporting space activities. Homeland security organizations, however, have been long time consumers of space data and services. Today, the space domain has opened for commercial activity and geopolitical competition alike. The security of the homeland is closely tied to the security of the space domain across multiple risk factors. The homeland security field faces an opportunity to pivot to be more involved in government and commercial space activities by bringing its unique capabilities and authorities to bear against challenges that did not exist a decade ago. This paper will study the evolution of the space economy and the role it now plays in the security of the homeland.
Convergence of Mission and Moment: Imagining the Emerging Technology Analyst
(Institute for Homeland Security, 2023-10-15) Reese, Nick
The Department of Homeland Security (DHS) was built to prevent terror attacks in the homeland and its culture and structure reflect its birth in 2002. Unlike the world changing event that created DHS, the gradual fading of the terror threat has left it misaligned to respond to new nation-state sponsored threats. The homeland security mission is at a true inflection point as it looks for new ways to use its capabilities and authorities while the central force driving global competition is being established. Just as the field of cyber was being established in the late 1990s and early 2000s in response to new threats, so too must the field of emerging technology be developed today. Examining the realities of the world today, we see the need for professionals who specialize in how emerging technologies create risks and opportunities in a way that is distinct from how cyber professionals do the same for the cyber domain. This work examines the geopolitical reality and how it reflects on the homeland. It goes a step further by conducting a comparative analysis between current cyber analyst requirements and skills and what would be required for an equivalent emerging technology analyst. This analysis informs governments, academia, and industry by creating a baseline from which emerging technology professionals can be created and evaluated with direct application on practitioners in critical infrastructure.
Examining Use Cases for Drones (UAS/RPAS) at the Texas Medical Center
(Institute for Homeland Security, 2023-10-15) Allen, Bryce S
The Texas Medical Center (TMC) is the largest medical center in the world, with over 50 million square feet of developed land. With that size and notoriety come unique threats and challenges. As the TMC continues growth in size, and in technological advancement, an emphasis should be placed on how to utilize technologies already being integrated effectively in other critical sectors to support the growth of the TMC. One area of potential is the use of unmanned/un-crewed aircraft systems (UAS), more commonly known as drones, in supporting critical infrastructure inspection, testing, and preventative maintenance. Further, drone use for security of facilities, people, and high-risk areas is examined. This paper focuses on expanding on these potential use cases by exploring drone use in other industries that support the TMC (i.e., energy), and how to effectively integrate drone technologies while mitigating common concerns for safety and privacy.
Comparative Analysis of NLP Model for Detecting Depression on Twitter
(Institute for Homeland Security, 2023-10-15) Gupta, Khushi; Jinad, Razaq; Liu, Qingzhong
Depression is a serious mental health issue affecting a significant portion of the world’s population. With the widespread use of social media platforms, researchers have explored the possibility of utilizing natural language processing (NLP) techniques to detect signs of depression in users’ posts. In this paper, we present a comparative analysis of six different NLP models, namely BERT, RoBERTa, DistilBERT, ALBERT, Electra, and XLNet, for depression detection on Twitter data. The experiments compare the performance of different models, and the results reveal that the highest-performing models include XLNet, DistilBERT, and RoBERTa with accuracies of over 99%.
Countering Workplace Violence in Healthcare: Voices from the Field
(Institute for Homeland Security, 2023-10-15) Denham, Magdalena A; Denham, Mark V
Overall, the U.S. healthcare system has the highest workplace violence (WPV) rates of any occupational setting in the United States. Specifically, among 25,000 incidents of WPV reported annually, 75% percent occur in the healthcare system. Workers in healthcare are four times more likely to be victimized than workers in other private industries.