Kari Sentz

The foundational principles of warfare have always required a combatant to "know the enemy and know yourself", and to consider the likelihood that methods of deception will be employed on both sides. Further, the need to estimate adversarial situations, threats, and intentions imputes a need for forecasting these states to future times to be relevant to decision-making. These combined requirements lead to a very challenging context for the design, development, and evaluation of multisensor information fusion processes and systems. This paper reviews the nature of these requirements, points out that there is a wide range of related research across varied communities on these topics, and that there is relatively little work in the situation management and information fusion communities currently addressing these challenges. This paper is a call for new and more directly applicable research involving multidisciplinary efforts to advance Level 3 situation and threat estimation capabilities with human and sensor-based fusion systems that is essential for comprehensive situational awareness and assessment in adversarial environments.

Knowledge Acquisition, Representation, Processing, & Presentation (KARPP) is a new system level approach to the acquisition and aggregation of mission-valued and qualified situation knowledge for use by a decision maker. KARPP is decomposed into a service oriented architecture (SOA) of knowledge aggregation services (KAS) with well-defined inputs and outputs which are implemented in hidden processes. The transfer of knowledge from one KAS to another is through a qualified knowledge request (QKR) which results in an aggregated knowledge response (AKR). The notion of qualified knowledge is introduced to specify the knowledge that is needed by a requesting KAS as well as the knowledge quality attributes and the magnitude of those qualities which are required to meet mission needs. The goals of this approach are to provide an architectural framework for realizing managed, adaptive fusion processing capabilities, and to integrate the sensor/database control and data acquisition with the fusion-based data processing which will maximize the expected knowledge value rate (EKVR) provided to mission managers.

The research described in this paper is addressing the problem of designing a comprehensive knowledge ontology of information quality combining ontology of quality characteristics and ontology of models for computing their values and incorporating the designed Knowledge Ontology in the architecture of information fusion systems. A use case illustrating the utility of the designed ontology in an earthquake scenario is also presented.

Situation management involves the acquisition and dissemination of knowledge to effect situation awareness and assessment. In a companion paper, the concept of Knowledge Acquisition, Representation, Processing, & Presentation (KARPP) system has been introduced. In that paper, it was proposed to manage an adaptive data fusion process including the decomposition of a sensor and database knowledge acquisition and aggregation system into a service oriented architecture (SOA) of knowledge aggregation services (KAS) with standards-based inputs and outputs which are implemented in hidden processes. This paper reviews current approaches to SOA and expands the KARPP architecture into a taxonomy of required and support services as well as discusses particulars of a self-organizing implementation of KARPP in a dynamic SOA of microservices. In addition to a taxonomy of services, a self-documenting approach is introduced to allow for preplanned product improvement (PPI) and incremental development as well as the concept of a Knowledge Aggregation System Service broker.

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. This review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.

We propose a tensor train based data structure to accelerate the calculation of Dempster-Shafer operations such as belief and Dempster's rule of combination. This approach relies on the fact that the matrix representation of these operators possess rank-1 tensor network decompositions, allowing for far more efficient calculations in tensor train format. Numerical experiments demonstrate the superior performance of the proposed method in computing Dempster-Shafer quantities.

Dempster-Shafer Theory (DST) is a mathematical framework to handle imprecision and uncertainty in reasoning and decision making. One assumption of DST is that of a closed-world, or the assumption that all propositions are known a priori. In this work, we explore an alternative formulation of Dempster-Shafer that allows for the dynamic inclusion of new propositions. Specifically, we expand the framework to include the complement of every set of propositions. This adjustment enables an open-world interpretation that can support unspecified and dynamic propositions as we learn about the problem space. Including complementary sets distinguishes this from previous work in DST where the open world is attributed to the empty set. We demonstrate our open world Dempster-Shafer Theory on a variety of synthetic and real datasets.

A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture ( "morphology ") signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling's T2), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.