Social and Behavioral Sciences Commons^™

Domain Restriction Zones: An Evolution Of The Military Exclusion Zone, Cole M. Mooty, Robert A. Bettinger, Mark G. Reith

Faculty Publications

Since the early part of the twenty-first century, US adversaries have expanded their military capabilities within and their access to new warfighting domains. When faced with the growth of adversaries’ asymmetric capabilities, the means, tactics, and strategies previously used by the US military lose their proportional effectiveness. To avoid such degradation of capability, the operational concept of the military exclusion zone (MEZ) should be revised to suit the modern battlespace while also addressing the shifts in national policy that encourage diplomacy over military force. The concept and development of domain restriction zones (DRZs) increase the relevancy of traditional MEZs in …

A Hierarchical Cluster Approach Toward Understanding The Regional Variable In Country Conflict Modeling, Benjamin D. Leiby, Darryl K. Ahner

Faculty Publications

Purpose: This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

Design/methodology/approach: This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

Findings: This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

Originality/value: This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before …

Incentivizing Innovation: Promoting Technical Competency To Win Future Wars., James E. Bevins

Faculty Publications

Despite numerous studies and initiatives, most current Air Force efforts to add science and technology talent have been insufficient. This begs the question: How does the Air Force incentivize and promote the necessary technical competence required to win future competition, conflicts, and wars? Several key initiatives, grounded in behavioral economics, can incentivize innovation and pursue science and technology expertise. Developed in the context of peer adversaries’ actions; global trends in technology, competition, and conflict; and the global competition for science and technology talent, these recommendations have the potential to reform institutional culture and unleash the creativity and talent of the …

Beyond The High Ground: A Taxonomy For Earth-Moon System Operations, Adam P. Wilmer, Robert A. Bettinger

Faculty Publications

Situational and space domain awareness in the space domain can no longer be confined to that which is found in geosynchronous orbit. International activities—commercial and military—and threats to the planet itself exist and are increasing across the entire Earth-Moon system. This reality requires a new taxonomy to accurately classify space domain awareness missions and better apply resources to and development of the same. This work presents such a taxonomy for the classification of space domain awareness regions.

Forecasting Country Conflict Using Statistical Learning Methods, Sarah Neumann, Darryl K. Ahner, Raymond R. Hill

Faculty Publications

Purpose — This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict. Design/methodology/approach — In this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction. Findings — In this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict. Research limitations/implications — The study is based on actual historical data and is purely data driven. …

Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

Multimodal hyperspectral and lidar data sets provide complementary spectral and structural data. Joint processing and exploitation to produce semantically labeled pixel maps through semantic segmentation has proven useful for a variety of decision tasks. In this work, we identify two areas of improvement over previous approaches and present a proof of concept network implementing these improvements. First, rather than using a late fusion style architecture as in prior work, our approach implements a composite style fusion architecture to allow for the simultaneous generation of multimodal features and the learning of fused features during encoding. Second, our approach processes the higher …

Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam

Faculty Publications

Lack of instrument sensitivity to low electron density (N_e) concentration makes it difficult to measure sharp N_e vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region N_e from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the …

A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

Sporadic-E (E_s) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting E_s formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate E_s occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

A Simulation–Optimization Framework For Post-Disaster Allocation Of Mental Health Resources, Stephen Cunningham, Steven J. Schuldt, Christopher M. Chini, Justin D. Delorit

Faculty Publications

Extreme events, such as natural or human-caused disasters, cause mental health stress in affected communities. While the severity of these outcomes varies based on socioeconomic standing, age group, and degree of exposure, disaster planners can mitigate potential stress-induced mental health outcomes by assessing the capacity and scalability of early, intermediate, and long-term treatment interventions by social workers and psychologists. However, local and state authorities are typically underfunded, understaffed, and have ongoing health and social service obligations that constrain mitigation and response activities. In this research, a resource assignment framework is developed as a coupled-state transition and linear optimization model that …

Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers

Faculty Publications

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …

Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …

Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals

Faculty Publications

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

Black Space Versus Blue Space: A Proposed Dichotomy Of Future Space Operations, Robert A. Bettinger, Carl A. Poole [*]

Faculty Publications

This article will examine the proposed space operations structure by first outlining the historical foundations for differences in maritime and air domain military capabilities, specifically brown-w­ater versus blue-w­ater navies, and “local/ regional” versus “global” airpower. Next, the article will present the concept of black space and blue space in terms of an environment-­specific definition, as well as an examination of the technical capability requirements, mission types, and national prestige and geopolitical considerations underpinning the proposed operation types. Finally, the article will explore how the USSF might support future space exploration within the black-­space and blue-s­pace operations structure.

The Traded Water Footprint Of Global Energy From 2010 To 2018, Christopher M. Chini, Rebecca A. M. Peer

Faculty Publications

The energy-water nexus describes the requirement of water-for-energy and energy-for-water. The consumption of water in the production and generation of energy resources is also deemed virtual water. Pairing the virtual water estimates for energy with international trade data creates a virtual water trade network, facilitating analysis of global water resources management. In this database, we identify the virtual water footprints for the trade of eleven different energy commodities including fossil fuels, biomass, and electricity. Additionally, we provide the necessary scripts for downloading and pairing trade data with the virtual water footprints to create a virtual water trade network. The resulting …

A Review Of Energy-For-Water Data In Energy-Water Nexus Publications, Christopher M. Chini, Lauren E. Excell, Ashlynn S. Stillwell

Faculty Publications

Published literature on the energy-water nexus continues to increase, yet much of the supporting data, particularly regarding energy-for-water, remains obscure or inaccessible. We perform a systematic review of literature that describes the primary energy and electricity demands for drinking water and wastewater systems in urban environments. This review provides an analysis of the underlying data and other properties of over 170 published studies by systematically creating metadata on each study. Over 45% of the evaluated studies utilized primary data sources (data collected directly from utilities), potentially enabling large-scale data sharing and a more comprehensive understanding of global water-related energy demand. …

Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing [*], Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola

Faculty Publications

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed …

An Argument Against Satellite Resiliency: Simplicity In The Face Of Modern Satellite Design, Dax Linville [*], Robert A. Bettinger

Faculty Publications

The US Air Force and the wider US government rely heavily on space-based capabilities in various orbital regimes to project national security and sovereignty. However, these capabilities are enabled by the design, launch, and operation of satellites produced with a design methodology that favors large, monolithic, and technologically exquisite space systems. Despite the ability for these satellites to provide enduring and resilient capabilities, they suffer from a woefully long acquisition process that debilitates any prospect of rapid satellite reconstitution in the event of a space war.

The Nuclear Network: Multiplex Network Analysis For Interconnected Systems, Bethany L. Goldblum, Andrew W. Reddie, Thomas C. Hickey, James E. Bevins, Sarah Laderman, Nathaniel Mahowald, Austin P. Wright, Elie Katzenson, Yara Mubarak

Faculty Publications

States facing the decision to develop a nuclear weapons program do so within a broader context of their relationships with other countries. How these diplomatic, economic, and strategic relationships impact proliferation decisions, however, remains under-specified. Adding to the existing empirical literature that attempts to model state proliferation decisions, this article introduces the first quantitative heterogeneous network analysis of how networks of conflict, alliances, trade, and nuclear cooperation interact to spur or deter nuclear proliferation. Using a multiplex network model, we conceptualize states as nodes linked by different modes of interaction represented on individual network layers. Node strength is used to …

Finding The Fuel Of The Arab Spring Fire: A Historical Data Analysis, Darryl K. Ahner, Luke M. Brantley

Faculty Publications

Purpose: This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition occurred than normally observed in previous studies for certain Middle Eastern and North African countries.

Design/Methodology/Approach: Previous prediction models decrease in accuracy during times of volatile conflict transition. Also, proper strategies for handling the Arab Spring have been highly debated. This paper identifies which countries were affected by the Arab Spring and then applies data analysis techniques to predict a country’s tendency to suffer from …

Non-Gnss Smartphone Pedestrian Navigation Using Barometric Elevation And Digital Map-Matching, Daniel Broyles, Kyle J. Kauffman, John F. Raquet, Piotr Smagowski

Faculty Publications

Pedestrian navigation in outdoor environments where global navigation satellite systems (GNSS) are unavailable is a challenging problem. Existing technologies that have attempted to address this problemoften require external reference signals or specialized hardware, the extra size,weight, power, and cost of which are unsuitable for many applications. This article presents a real-time, self-contained outdoor navigation application that uses only the existing sensors on a smartphone in conjunction with a preloaded digital elevation map. The core algorithm implements a particle filter, which fuses sensor data with a stochastic pedestrian motion model to predict the user’s position. The smartphone’s barometric elevation is then …

Predicting Public Bicycle Adoption Using The Technology Acceptance Model, Benjamin T. Hazen, Robert E. Overstreet, Yacan Wang

Faculty Publications

Bicycle sharing programs provide a sustainable mode of urban transportation. Although cities across the globe have developed these systems for their citizens and visitors, usage rates are not as high as anticipated. This research uses the technology acceptance model as the basis to understand one’s intention to adopt bicycle sharing programs. Using survey data derived from 421 participants in Beijing, China, the proposed covariance-based structural equation model consisting of perceived quality, perceived convenience, and perceived value is found to predict 50.5% of the variance in adoption intention. The findings of this research contribute to theory and practice in the burgeoning …

Deployed Communications In An Austere Environment: A Delphi Study, Andrew Soine, James Harker, Alan R. Heminger

Faculty Publications

The information and communications technology (ICT) field is undergoing a period of tremendous change. The exponential growth rate of ICT capability in recent decades, which has had an undeniable effect on every aspect of our society, will likely have ramifications for military operations in austere environments. 1 The Air Force’s 689th Combat Communications Wing commissioned a study to forecast the future of mobile ICT in such environments. Researchers at the Air Force Institute of Technology chose to employ the Delphi technique as the methodology for executing this task. The following scenario, based on the results of that study, demonstrates how …

Driving Towards Success In The Air Force Cyber Mission: Leveraging Our Heritage To Shape Our Future, David Fadok, Richard Raines

Faculty Publications

Ongoing debates address what constitutes cyber warfare and whether or not we really are at war in cyberspace. This article does not enter into those issues; rather, it suggests how the Air Force and Air University should move forward to lead and support our nation’s cyber security needs. Thus, it focuses on analogous lessons learned from history, our position today and what it needs to be, and plans for getting there with respect to our cyberspace capabilities.

Cyber This, Cyber That...So What?, Eric D. Trias, Bryan Bell [*]

Faculty Publications

This article endorses the idea that cyber operations may be conducted in all war-fighting domains: air, space, cyberspace, land, and sea. In addition, despite the immaturity of cyberspace operational doctrines, the doctrines from air and space remain relevant and applicable to the cyberspace domain. Cyber operations are just another set of tools in the commander's toolbox. Although cyber operations have distinct ways of achieving effects, from an Air Force perspective they are similar too the air and space operations that support air and space (and cyberspace) functions. Known and established cyber operations provide war fighters with viable options to kinetic …

The Enhancement Of Graduate Digital Forensics Education Via The Dc3 Digital Forensics Challenge, Timothy H. Lacey, Gilbert L. Peterson, Robert F. Mills

Faculty Publications

No abstract provided.

Graduate Digital Forensics Education At The Air Force Institute Of Technology, Gilbert L. Peterson, Richard A. Raines, Rusty O. Baldwin

Faculty Publications

The Department of Electrical and Computer Engineering (AFIT/ENG) at the Air Force Institute of Technology (AFIT), currently offers a graduate-level introductory course in digital forensics. Students are introduced and exposed to several challenges and topics in the digital forensics course. The course addresses the ethical and legal procedures as well as basic forensic science principles in only the most general manner. A larger percentage of lecture and lab time is spent discussing the technical details of incident response and media analysis. The detail into the network forensics and digital device analysis topics start to breach technical details but not to …

A Multidiscipline Approach To Mitigating The Insider Threat, Jonathan W. Butts, Robert F. Mills, Gilbert L. Peterson

Faculty Publications

Preventing and detecting the malicious insider is an inherently difficult problem that expands across many areas of expertise such as social, behavioral and technical disciplines. Unfortunately, current methodologies to combat the insider threat have had limited success primarily because techniques have focused on these areas in isolation. The technology community is searching for technical solutions such as anomaly detection systems, data mining and honeypots. The law enforcement and counterintelligence communities, however, have tended to focus on human behavioral characteristics to identify suspicious activities. These independent methods have limited effectiveness because of the unique dynamics associated with the insider threat. The …

Defense Industrial Base Policy: Revisited, Michael E. Heberling

Faculty Publications

In an era of decreasing defense budgets and enemy threats, problems associated with maintaining a healthy defense industrial base have become pronounced. This article discussed defense industrial policy goals and argues that these goals may be collectively unobtainable.

Defense Contractor Buyer-Seller Relationships: Theoretical Approaches, Carl R. Templin

Faculty Publications

This article examines the applicability of three theoretical approaches to defining defense buyer-seller relationships. Economic Free-Market Theory explains the relative economic power of the participants but ignores the legal, political, and socioeconomic aspects so pervasive in defense acquisitions. Transaction Cost Economics provides a framework for determining the most cost-effective type of contract governance for each transaction. Systems theory explores the degree of interdependence between the buyers and sellers systems. Each theory contributes unique insights into defense buyer-seller relationships that can be used to judge the appropriateness of contracting laws, regulations, policies, and management approaches for specific acquisition environments.