The objective of this research is to identify, develop, adapt, prototype, integrate and demonstrate open access force protection and security technologies and processes. The goal is to provide more open public access to recreational and other non-restricted facilities on military bases and to improve the overall base safety and security utilizing advanced video and signal based surveillance. A testbed was created at the Pacific Missile Range Facility (PMRF), Kauai, Hawaii to demonstrate novel and innovative security solutions that serve these objectives. The testbed consists of (1) novel sensors (video cameras, radio frequency identification tags, and seismic, lidar, microwave, and infrared sensors), (2) a computer, data storage, and network infrastructure, and (3) behavior analysis software. The behavior analysis software identifies patterns of behavior and discriminates "normal" and "anomalous" behavior in order to anticipate and predict threats so that they can be interdicted before they impact mission critical operations or cause harm to people and infrastructure.

The large size and high-percentage of domain-specific code in most legacy systems makes it unlikely that automated tools will be able to extract a complete underlying design. Yet automated tools can clearly recognize portions of the design. This suggests exploring environments in which programmer and system work together to understand legacy software. DECODE is such an environment. It supports programmer and system co-operation to extract design information from legacy software systems. DECODE's automated program understanding component recognizes standard implementations of domain independent plans to produce an initial knowledge base of object-orientated design elements. DECODE's structured notebook component provides the user with a graphical view of the initial understanding, which the user can extend by linking arbitrary source code fragments to either existing or new design elements, and then uses this design information to support conceptual queries about the program's code and design.

MERA (Meta Entity-Relation-Attribute) is a versatile graphical language which can be used for modeling and analysis of the user requirements, for building a prototype, for representing designer's knowledge for controlling design process, etc. The current version of MERA has capacity for animation, view operation, user adaptability, and some semantic level processing. It also contains dozens of modeling formalisms which were created to support various tasks in the software engineering practice. The authors have created many tools to support the MERA language including a syntax-driven graph editor, user interface simulator, requirements analysis and prototyping environments, methodology adaptable design environment, requirements interview system and program understanding system. This paper presents the important features of the MERA language and MERA based software engineering tools.>

PAU is an all‐paths chart‐based unification parser that uses the same uniform representation for regular syntax, irregular syntax such as idioms, and semantics. PAU's representation has very little redundancy, simplifying the task of adding new semantics and syntax fo PAU's knowledge base. PAU uses relations between the syntax and semantics to avoid the proliferation of rules found in semantic grammars. By encoding semantics at the same level of representation as syntax, PAU is able to use semantic constraints early in the parse to eliminate semantically anomalous syntactic interpretations. Examples are given to show how PAU can handle the many eccentricities of different idioms using the same mechanisms as are used to handle regular syntax and semantics. These include the ability of some idioms, but not other idioms of the same syntactic form to undergo passivization, particle movement, action nominalization, indirect object movement, modification by adjectives, gerundive nominalization, prepositional phrase preposing, and topicalization. PAU's representation is bidirectional and is also used by a companion generator. PAU is designed to be efficient, runs in real time on typical workstations, and is being used in a number of natural language systems.

UC is a natural language computer consultant system for the UNIX operating system. The user model in UC encodes the user's knowledge state and allows UC to tailor its responses to the user. The model encodes apriori knowledge in a double stereotype system that is extremely efficient. Models of individual users are updated dynamically and build on top of the user's stereotype. The model deals with uncertainty in apriori information and attempts to deduce the user's level during the course of a session.

The knowledge structures implemented in UC, the UNIX Consultant are sufficient for UC to reply to a large range of user queries in the domain of the UNIX operating system. This paper describes how these knowledge structures are used in the natural language tasks of parsing, inference, planning, goal detection, and generation, and how they are organized to enable efficient access even with the large database of an expert system. The structuring of knowledge to provide direct answers to common queries and the high usability and efficiency of knowledge structures allow UC to hold an interactive conversation with a user.