Creating an Agent Based Framework to Maximize Information Utility

With increased reliance on communications to conduct military operations, information centric network management becomes vital. A Defense department study of information management for net-centric operations lists the need for tools for information triage (based on relevance, priority, and quality) to counter information overload, semi-automated mechanisms for assessment of quality and relevance of information, and advances to enhance cognition and information understanding in the context of missions [30]. Maximizing information utility to match mission objectives is a complex problem that requires a comprehensive solution in information classification, in scheduling, in resource allocation, and in QoS support. Of these research areas, the resource allocation mechanism provides a framework to build the entire solution. Through an agent based mindset, the lessons of robot control architecture are applied to the network domain. The task of managing information flows is achieved with a hybrid reactive architecture. By demonstration, the reactive agent responds to the observed state of the network through the Unified Behavior Framework (UBF). As information flows relay through the network, agents in the network nodes limit resource contention to improve average utility and create a network with smarter bandwidth utilization. While this is an important result for information maximization, the agent based framework may have broader applications for managing communication networks

Improving Optimization of Convolutional Neural Networks through Parameter Fine-tuning

In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter fine-tuning over random initialization. We find that training should not be reduced after transferring weights, larger, more similar networks tend to be the best source task, and parameter fine-tuning can often outperform randomly initialized ensembles. The experimental framework and findings will help to train models with improved accuracy

System-Agnostic Security Domains for Understanding and Prioritizing Systems Security Engineering Efforts

As modern systems continue to increase in size and complexity, current systems security practices lack an effective approach to prioritize and tailor systems security efforts to successfully develop and field systems in challenging operational environments. This paper uniquely proposes seven system-agnostic security domains, which assist in understanding and prioritizing systems security engineering (SSE) efforts. To familiarize the reader with the state-of-the-art in SSE practices, we first provide a comprehensive discussion of foundational SSE concepts, methodologies, and frameworks. Next, the seven system-agnostic security domains are presented for consideration by researchers and practitioners. The domains are intended to be representative of a holistic SSE approach, which is universally applicable to multiple systems classes and not just a single-system implementation. Finally, three examples are explored to illustrate the utility of the system-agnostic domains for understanding and prioritizing SSE efforts in information technology systems, Department of Defense weapon systems, and cyber-physical systems

A Customizable Framework for Prioritizing Systems Security Engineering Processes, Activities, and Tasks

As modern systems become increasingly complex, current security practices lack effective methodologies to adequately address the system security. This paper proposes a repeatable and tailorable framework to assist in the application of systems security engineering (SSE) processes, activities, and tasks as defined in the recently released National Institute of Standards and Technology (NIST) Special Publication 800-160. First, a brief survey of systems-oriented security methodologies is provided. Next, an examination of the relationships between the NIST-defined SSE processes is conducted to provide context for the engineering problem space. These findings inform a mapping of the NIST SSE processes to seven system-agnostic security domains which enable prioritization for three types of systems (conventional IT, cyber-physical, and defense). These concrete examples provide further understanding for applying and prioritizing the SSE effort. The goal of this paper is assist practitioners by informing the efficient application of the 30 processes, 111 activities, and 428 tasks defined in NIST SP 800-160. The customizable framework tool is available online for developers to employ, modify, and tailor to meet their needs

Analysis of Simulated Imagery for Real-Time Vision-Based Automated Aerial Refueling

This Paper presents a three-dimensional graphical simulation (virtual world) that replicates a complete aerial refueling scenario including the tanker, the tanker’s stereo vision system, and an approaching receiver aircraft. This virtual world generates real-time imagery of a geometrically accurate receiver flying a physically realistic aerial refueling approach. The approach is recorded from the perspective of the tanker’s virtual stereo cameras. The stereo imagery is processed via a novel stereo vision pipeline that dynamically computes the position and orientation (pose) of the receiver relative to the tanker in near real time. Additionally, to increase the visual fidelity, emphasis is given to quantifying the negative aliasing effects caused by the rasterization of the virtual world. An antialiasing solution that mitigates these artifacts and allows simulated imagery to better approximate physically captured imagery is proposed. Results show antialiased imagery reduces epipolar error by a factor of 4. This reduction more than doubles the accuracy of our stereo vision algorithm at the refueling contact point. As simulated imagery is increasingly used to test and develop aeronautical vision pipelines, this aliasing must be mitigated to minimize artifacts intrinsic to rasterized synthetic imagery that negatively affect vision processing algorithms. Abstract (c) AIA