Human instructors must monitor and react to multiple, simultaneous sources of information when training and assessing complex behaviors and maneuvers. The difficulty of this task requires the instructor to make mental inferences and approximations, which may result in less than optimal training outcomes. We present a novel performance monitoring and evaluation system that automatically analyzes and contextualizes flight control and system data streams from high-fidelity aircraft simulators to support, validate, and augment an instructor’s evaluative judgments during pilot training. We present initial results from the CAMBIO system, which leverages machine learning to assess a pilot trainee’s performance in executing flight procedures. CAMBIO’s machine learning approach currently achieves 80% accuracy in performance categorization.

We consider the problem of how to improve automatic target recognition by fusing the naive sensor-level classification decisions with intuition, or context, in a mathematically principled way. This is a general approach that is compatible with many definitions of context, but for specificity, we consider context as co-occurrence in imagery. In particular, we consider images that contain multiple objects identified at various confidence levels. We learn the patterns of co-occurrence in each context, then use these patterns as hyper-parameters for a Hierarchical Bayesian Model. The result is that low-confidence sensor classification decisions can be dramatically improved by fusing those readings with context. We further use hyperpriors to address the case where multiple contexts may be appropriate. We also consider the Bayesian Network, an alternative to the Hierarchical Bayesian Model, which is computationally more efficient but assumes that context and sensor readings are uncorrelated.

Currently, obtaining reliable situational awareness of the social landscape is an arduous, lengthy process involving manual analyses by social scientists. These traditional methods do not scale to the speed and diversity required by DoD operations or the high-speed, international business model in today's corporate environment. Conversely, "big data" easily scales to meet these challenges but lacks the rigor of social science theory. We present Big Open-Source Social Science (BOSSS), a research and development project that leverages the strengths of social- and computer-science technology to address the operational need for rapid and reliable human-landscape situational-awareness. BOSSS iteratively filters, navigates, and summarizes diverse open-source data to characterize a local population's social structure, conflicts, cleavages, affinities, and animosities. BOSSS automatically scrapes open-access data from the web and performs natural language processing to populate a knowledge graph with a custom schema. BOSSS then mines the graph to extract key, theory-agnostic social-science principles of human inter-relations and dynamics . homophily, stratification, sentiment, and conflict. Automated quantitative social-network analysis provides up-to-date indicators of trends or anomalies within the local population's social landscape. BOSSS' s emerging technology will provide a dramatic reduction in the cognitive workload for the next generation of analysts and will facilitate more rapid situational awareness both for deployed soldiers and private companies conducting operations abroad.

Personalized search provides a potentially powerful tool, however, it is limited due to the large number of roles that a person has: parent, employee, consumer, etc. We present the role-relevance algorithm: a search technique that favors search results relevant to the user’s current role. The role-relevance algorithm uses three factors to score documents: (1) the number of keywords each document contains; (2) each document’s geographic relevance to the user’s role (if applicable); and (3) each document’s topical relevance to the user’s role (if applicable). Results on a pre-labeled corpus show an average improvement in search precision of approximately 20% compared to keyword search alone. We further consider several extensions to this algorithm.

An increasing portion of modern socializing takes place via online social networks. Members of these communities often play distinct roles that can be deduced from observations of users' online activities. One such activity is the sharing of multimedia, the popularity of which can vary dramatically. Here we discuss our initial analysis of anonymized, scraped data from consenting Facebook users, together with associated demographic and psychological profiles. We present five clusters of users with common observed online behaviors, where these users also show correlated profile characteristics. Finally, we identify some common properties of the most popular multimedia content.

CADRE (continuous analysis and discovery from relational evidence) is a link detection system that takes in a threat pattern and partial evidence about threat cases and outputs threat hypotheses with inferred actors and events. CADRE uses a Prolog-based frame system to represent threat patterns and enforce temporal and equality constraints among pattern slots. Based on rules involving uniquely identifying slots in the pattern, CADRE triggers an initial set of threat hypotheses, and then refines these hypotheses by generating queries for unknown slots from constraints involving known slots. To evaluate hypotheses, CADRE scores each local hypothesis using a probabilistic model in order to create a consistent, high-value global hypothesis by pruning conflicting lower scoring hypotheses. In a program-wide first year evaluation using simulated threats, CADRE performed best overall among five participating link detection systems.

ALPHA/Sim is a general-purpose, discrete-event simulation tool. ALPHA/Sim allows a user to graphically build a simulation model, enter input data via integrated forms, execute the simulation model, and view the simulation results, within a single graphical environment. In this paper, we introduce ALPHA/Sim and describe how to use ALPHA/Sim to build, simulate, and analyze a simple manufacturing system. In addition, we briefly describe some advanced features and list some sample applications.