NSI Journal | Conference Papers

This article utilises logistic regression analysis to determine the factors that influence people from Mexico, El Salvador, and Guatemala deciding to flee to other countries.

This article evaluates the factors impacting support for tough on crime policies in El Salvador. Examining theoretical and empirical scholarly work, we look at how fear, together with social and political contexts drive public appetite for punitive policies towards criminals.

This article was featured in Volume 10, Issue 1 of the US Army Training and Doctrine Command’s (TRADOC G2) journal, entitled Red Diamond.

During the Cold War era, the strategic threat against the US was clear. The country responded clearly with a policy toward the Soviet threat that centered on deterrence, containment, and mutually assured destruction. To enforce this policy, the US created a strategic triad composed of nuclear intercontinental ballistic missiles, Trident nuclear submarines, and long-range strategic bombers. Today, however, our security environment is profoundly different.

Recent statements by senior national security space leaders dubbing space a “warfighting domain” and calling for US space superiority are not a sufficient foundation for achieving a stable security environment in space.

This study examines direct, interactive, and indirect effects of racial identity and depression in a sample of 379 African American women

A web-based study of 393 undergraduates at a public university in the United States was conducted to examine the relationship between moral emotions (i.e., emotions that motivate prosocial tendencies) and support for political actions to assist Iraqi citizens after the Second Gulf War (2003–2004).

This book explores both counter-terrorism and enabling policy dimensions of emerging information technologies in national security.

Security and privacy aren’t dichotomous or conflicting concerns—the solution lies in developing and integrating advanced information technologies for counterterrorism along with privacy-protection technologies to safeguard civil liberties. Coordinated policies can help bind the two to their intended use.

September 11, 2001 might have been just another day if the U.S. intelligence agencies had been better equipped with information technology, according to the report of Congress’s Joint Inquiry into the events leading up to the Sept. 11 attacks.

In this paper we describe a novel data association algorithm, termed m-best S-D, that determines in O(mSkn^3) time (m assignments, S >=3 lists of size n, k relaxations) the (approximately) m-best solutions to an S-D assignment problem. The m-best S-D algorithm is applicable to tracking problems where either the sensors are synchronized or the sensors and/or the targets are very slow moving.

To date, there has been a lack of efficient and practical distributed- and shared-memory parallelizations of the data association problem for multitarget tracking.

In recent years, there has been considerable interest within the tracking community in an approach to data association based on the m-best two-dimensional (2-D) assignment algorithm. Much of the interest has been spurred by its ability to provide various efficient data association solutions, including joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT).

The focus of this paper is to present the results of our investigation and evaluation of various shared-memory parallelizations of the data association problem in multitarget tracking. The multitarget tracking algorithm developed was for a sparse air traffic surveillance problem, and is based on an Interacting Multiple Model (IMM) state estimator embedded into the (2D) assignment framework.

The interacting multiple model (IMM) estimator has been shown to be very effective when applied to air traffic surveillance problems. Hence, in an effort to design a real-time multiple model multi-target tracking algorithm that is independent of the number of modules used in the state estimator, we propose a “coarse-grained” (dynamic) parallelization that is superior, in terms of computational performance, to a “fine-grained” (static) parallelization of the state estimator, while not sacrificing tracking accuracy.