John L. Delay

Matching social network biometrics using geo-analytical behavioral modeling

Social patterns and graphical representation of geospatial activity is important for describing a persons typical behavior. We discuss a framework using social media and GPS smart phone to track an individual and establish normal activity with a network biometric. An individuals daily routine may include visiting many locations - home, work, shopping, entertainment and other destinations. All of these activities pose a routine or status quo of expected behavior. What has always been difficult, however, is predicting a change to the status quo, or predicting unusual behavior. We propose taking the knowledge of location information over a relatively long period of time and marrying that with modern analytical capabilities. The result is a biometric that can be fused and correlated with anothers behavioral biometric to determine relationships. Our solution is based on the analytical environment to support the ingestion of many data sources and the integration of analytical algorithms such as feature extraction, crowd source analysis, open source data mining, trends, pattern analysis and linear game theory optimization. Our framework consists of a hierarchy of data, space, time, and knowledge entities. We exploit such statistics to predict behavior or activity based on past observations. We use multivariate mutual information as a measure to compare behavioral biometrics.

Optimal multi-dimensional fusion model for sensor allocation and accuracy assessment

I We describe a multi-dimensional model for fusion of activity based intelligence (ABI) hypothesis-driven evidence through optimal sensor management. We determine decision-making strategies based upon ability to perform data mining and pattern discovery utilizing open source, actionable information to prepare for specific events or situations from multiple information sources. Our solution is based on an analytical framework using game theory to support ingestion of data sources (evidence); integration of analytical algorithms for feature extraction, crowd source analysis, open source data mining, trends, and pattern analysis and linear game theory optimization to support multiple hypothesis analysis. This solution may also save money by offering a Pareto efficient, repeatable process for resource management. We combine operations research methods and remote sensing for decision-making with several possible actions, state of world, and a mixed pro bability metric. Our tool allows for calculating optimal strategies, provides greater knowledge about remote sensing access times and increases likelihood of a decision-maker making best decision. We fuse evidence using Dempsters Rule and Nash Equilibrium (NE) for allocation of demands by sensor modality. We discuss a method for calculating optimal detector to determine accuracy of resource allocation. By calculating all NE possibilities per period, optimization of sensor allocation is achieved for overall higher system efficiency. We model impact of decision-making on accuracy by adding more dimensions to decision-making process as sensitivity analysis. Future work is to implement the design on a distributed processing platform to support real-world-sized scenarios and simulations.

A cognitive hierarchical framework for evaluating emergency response activities

We describe a system model for determining decision making strategies based upon the ability to perform data mining and pattern discovery utilizing open source information to prepare for specific events or situations from multiple information sources. Within this paper, we discuss the development of a method for determining actionable information. We have integrated open source information linking to human sentiment and manipulated other user selectable interlinking relative probabilities for events based upon current knowledge. Probabilistic predictions are critical in practice on many decision making applications because optimizing the user experience requires being able to compute the expected utilities of mutually exclusive pieces of content. Hierarchy game theory for decision making is valuable where two or more agents seek their own goals, possibilities of conflicts, competition and cooperation. The quality of the knowledge extracted from the information available is restricted by complexity of the model. Hierarchy game theory framework enables complex modeling of data in probabilistic modeling. However, applicability to big data is complicated by the difficulties of inference in complex probabilistic models, and by computational constraints. We focus on applying probabilistic models to evaluating emergency response activities. We specifically evaluate adversarial competition to help decide and plan how much to give in our emergency response example to capture the position of highest donor nation using mixed probabilities from game theory. Our contribution in this paper is to combine linear programming, hierarchical game theory with uncertainty modeling as a tool in order to plan for activities based on open source intelligence.

Motion/imagery secure cloud enterprise architecture analysis

Cloud computing with storage virtualization and new service-oriented architectures brings a new perspective to the aspect of a distributed motion imagery and persistent surveillance enterprise. Our existing research is focused mainly on content management, distributed analytics, WAN distributed cloud networking performance issues of cloud based technologies. The potential of leveraging cloud based technologies for hosting motion imagery, imagery and analytics workflows for DOD and security applications is relatively unexplored. This paper will examine technologies for managing, storing, processing and disseminating motion imagery and imagery within a distributed network environment. Finally, we propose areas for future research in the area of distributed cloud content management enterprises.

Managing Video in a One Frame per Second Environment

There is a vast and growing need within government agencies to collect, manage, analyze/exploit and disseminate video assets for the purposes of national security, battlefield intelligence, briefings and public communications. However, the infrastructures, workflows and exploitation tools available for video still fall short of intelligence analysts needs to achieve optimum performance. Video at 30fps (Thirty Frames Per Second) provides a much greater challenge as apposed to the current paradigm of using or exploiting, 1fps (One Frame Per Second) images or photographs. When you add video to an already complex problem. sheer volume of data, number ofvideo formats to ingest and exploit, and a need to fuse imagery assets with video - the overall workflow processes must be improved. This paper examines the workflow processes and technologies that can merge these two worlds together allowing the intelligence community to leverage available assets and make better decisions.