Interorganizational collaborative capacity: development of a database to refine instrumentation and explore patterns

Interorganizational collaborative capacity (ICC) is the capability of organizations (or a set of organizations) to enter into, develop, and sustain interorganizational systems in pursuit of collective outcomes. The objectives of the ICC research program are (1) to understand the success factors that lead to and the barriers that interfere with ICC; (2) to construct diagnostic methods and tools to assess these factors; and (3) to develop methods that contribute to the development of ICC in and among agencies and organizations. The research literature indicates that a major barrier blocking progress in understanding ICC is the absence of reliable, valid measures for the construct. This study addresses this problem. It presents the results of ICC scale development using samples of public sector, defense and security professionals from two areas: Homeland Defense and Security and Defense Acquisition and Contracting. The research presents scales that have very good to excellent internal consistency reliability and convergent validity. The report then applies the survey to create a profile and do a summary assessment of a major DoD Acquisition and Contracting organization's ICC. The survey factors are integrated into our ICC open systems model. The value of survey results in developing an organization's (or an organizational set's) current ICC is discussed, as are future research directions.Approved for public release; distribution is unlimited

A diagnostic approach to building collaborative capacity in an interagency context

Federal Acquisition Reform has consistently called for more and better collaboration among participating organizations. Experience shows, however, that inter-organizational collaboration can be difficult at best. Our research focuses on imperatives of successful collaboration and aims to assist organizations in diagnosing their collaborative capacity. Based on prior research with homeland security organizations, we offer a model of inter-organizational collaborative capacity grounded in a systems perspective. We then identify enablers and barriers that contribute to collaborative capacity. A diagnostic process based on the established practices of organization development is offered to guide the design of tailored assessments of collaborative capacity. We present a comprehensive set of both interview and survey questions, based on our model, which can be used in creating a collaborative capacity audit. The ability to diagnose collaborative capacity encourages literacy around collaboration and assists leaders in determining mechanisms for developing their organization's collaborative capacity. Finally, we describe the future plans for validating these assessment tools.-- p. iv.Approved for public release; distribution is unlimited

Energy Academic Group Compilation of Abstracts 2012-2016

This report highlights the breadth of energy-related student research at NPS and reinforces the importance of energy as an integral aspect of today's Naval enterprise. The abstracts provided are from theses and a capstone project report completed by December 2012-March 2016 graduates.http://archive.org/details/energyacademicgr109454991

Constructing Prediction Intervals with Neural Networks: An Empirical Evaluation of Bootstrapping and Conformal Inference Methods

Artificial neural networks (ANNs) are popular tools for accomplishing many machine learning tasks, including predicting continuous outcomes. However, the general lack of confidence measures provided with ANN predictions limit their applicability, especially in military settings where accuracy is paramount. Supplementing point predictions with prediction intervals (PIs) is common for other learning algorithms, but the complex structure and training of ANNs renders constructing PIs difficult. This work provides the network design choices and inferential methods for creating better performing PIs with ANNs to enable their adaptation for military use. A two-step experiment is executed across 11 datasets, including an imaged-based dataset. Two non-parametric methods for constructing PIs, bootstrapping and conformal inference, are considered. The results of the first experimental step reveal that the choices inherent to building an ANN affect PI performance. Guidance is provided for optimizing PI performance with respect to each network feature and PI method. In the second step, 20 algorithms for constructing PIs—each using the principles of bootstrapping or conformal inference—are implemented to determine which provides the best performance while maintaining reasonable computational burden. In general, this trade-off is optimized when implementing the cross-conformal method, which maintained interval coverage and efficiency with decreased computational burden

Optimizing Workforce Performance: Perceived Differences of Army Officer Critical Thinking Talent Across Level of Education

The U.S. Army’s operating environment continues to become increasingly complex and unpredictable, where U.S. technological advantage continues to erode. The complexities stem from the Army’s doctrinal assumption that the future operating environment is unknown and constantly changing (Department of the Army [DA], 2014a). Diminishing technological advantage results in more reliance on soldiers’ cognitive capability, and less on high technology weapons systems (McMaster, 2015). A review of military literature shows extensive research on the importance of Army leaders to be talented critical thinkers (Fischer, Spiker, & Riedel, 2008, 2009; Gerras, 2008; Thomas & Gentzler, 2013). Human capital literature reveals many college graduates do not possess the critical thinking skills required of the workforce (Laird, Seifert, Pascarella, Mayhew, & Blaich, 2014; Liu, Frankel & Roohr, 2014). Senior Army leaders identify critical thinking and problem solving as the most important outcomes of officer education, but also identify graduates of Army education institutions often lack these competencies (Hatfield, Steele, Riley, Keller-Glaze, & Fallesen, 2011). Human capital theory (Becker, 1993) and human resource development theory (Swanson, 2001) form the theoretical framework of this study to measure the perceived level of critical thinking talent of junior Army officers with different levels of education, and determine if differences exist between groups. The two groups in the sample consist of junior Army officers with (n = 50) and without (n = 50) a 4-year college degree. Both groups were administered the CCTDI and CCTST critical thinking instruments, and one-way MANOVAs calculated the effect of a 4-year degree on perceived level of critical thinking talent. No significant effect was indicated between groups on either CCTDI scores or CCTST scores. This non-experimental, cross-sectional, explanatory study finds 4-year degrees may not produce the critical thinking outcomes the Army expects. The Army can mitigate this through developing a critical thinking framework across the professional military education continuum, as well as evaluating leader critical thinking talent during Army training events. Future considerations include larger samples across multiple Army installations and multiple branches

Use of Graph Neural Networks in Aiding Defensive Cyber Operations

In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information. Additionally, cyber-attacks differ depending on the objective and evolve rapidly to disguise defensive systems. However, a typical cyber-attack demonstrates a series of stages from attack initiation to final resolution, called an attack life cycle. These diverse characteristics and the relentless evolution of cyber attacks have led cyber defense to adopt modern approaches like Machine Learning to bolster defensive measures and break the attack life cycle. Among the adopted ML approaches, Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures due to their ability to process and learn from heterogeneous cyber threat data. In this paper, we look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain. We address each phase of CKC and discuss how GNNs contribute to preparing and preventing an attack from a defensive standpoint. Furthermore, We also discuss open research areas and further improvement scopes.Comment: 35 pages, 9 figures, 8 table