Mine countermeasures (MCM) missions entail planning and operations in very dynamic and uncertain operating environments, which pose considerable risk to personnel and equipment. Frequent schedule repairs are needed that consider the latest operating conditions to keep mission on target. Presently no decision support tools are available for the challenging task of MCM mission rescheduling. To address this capability gap, we have developed the CARPE system to assist operation planners. CARPE constantly monitors the operational environment for changes and recommends alternative repaired schedules in response. It includes a novel schedule repair algorithm called Case-Based Local Schedule Repair (CLOSR) that automatically repairs broken schedules while satisfying the requirement of minimal operational disruption. It uses a case-based approach to represent repair strategies and apply them to new situations. Evaluation of CLOSR on simulated MCM operations demonstrates the effectiveness of case-based strategy. Schedule repairs are generated rapidly, ensure the elimination of all mines, and achieve required levels of clearance.
A variety of anomaly detection algorithms have been applied to surveillance tasks for detecting threats with some
success. However, it is not clear which anomaly detection algorithms should be used for domains such as ground-based
maritime video surveillance. For example, recently introduced algorithms that use local density techniques have
performed well for some tasks, but they have not been applied to ground-based maritime video surveillance. Also, the
reasons for the performance differences of anomaly detection algorithms on problems of varying difficulty are not well
understood. We address these two issues by comparing families of global and local anomaly detection algorithms on
tracks extracted from ground-based maritime surveillance videos. Obtaining maritime anomaly data can be difficult or
even impractical. Therefore, we use a generative approach to vary and control the difficulty of anomaly detection tasks
and to focus on borderline and difficult situations in our empirical comparison studies. We report that global algorithms
outperform local algorithms when tracks have large and unstructured variations, while they perform equally well when
the tracks have only minor variations.
Maritime assets such as ports, harbors, and vessels are vulnerable to a variety of near-shore threats such as small-boat attacks. Currently, such vulnerabilities are addressed predominantly by watchstanders and manual video surveillance, which is manpower intensive. Automatic maritime video surveillance techniques are being introduced to reduce manpower costs, but they have limited functionality and performance. For example, they only detect simple events such as perimeter breaches and cannot predict emerging threats. They also generate too many false alerts and cannot explain their reasoning. To overcome these limitations, we are developing the Maritime Activity Analysis Workbench (MAAW), which will be a mixed-initiative real-time maritime video surveillance tool that uses an integrated supervised machine learning approach to label independent and coordinated maritime activities. It uses the same information to predict anomalous behavior and explain its reasoning; this is an important capability for watchstander training and for collecting performance feedback. In this paper, we describe MAAW's functional architecture, which includes the following pipeline of components: (1) a video acquisition and preprocessing component that detects and tracks vessels in video images, (2) a vessel categorization and activity labeling component that uses standard and relational supervised machine learning methods to label maritime activities, and (3) an ontology-guided vessel and maritime activity annotator to enable subject matter experts (e.g., watchstanders) to provide feedback and supervision to the system. We report our findings from a preliminary system evaluation on river traffic video.
In this paper we evaluate the use of case-based classification to resolve a number of questions related to information
sharing in the context of an Integrated Web services Brokering System (IWB). We are developing the IWB to
independently decompose and analyze ad hoc Web services interface descriptions in order to identify Web services of
interest. Our approach is to have the IWB cache information about each service in order to support an autonomous
mediation process. In this mediation process, the IWB independently matches the user's data request to the correct
method within the appropriate Web service, translates the user's request to the correct syntax and structure of the Web
service request, dynamically invokes the method on the service, and translates the Web service response. We use casebased
classification as a means of automating the IWB's analysis of relevant services and operations. Case-based
classification retrieves and reuses decisions based on training data. We use sample Web Service Description Language
(WSDL) files and schema from actual Web services as training data in our approach and do not require the service to
pre-deploy an OWL-S ontology. We present our evaluation of this approach and performance ratings in the context of
meteorological and oceanographic (MetOc) Web services as it relates to the IWB.
Web Services are becoming the standard technology used to share data for many Navy and other DoD operations. Since Web Services technologies provide for discoverable, self-describing services that conform to common standards, this paradigm holds the promise of an automated capability to obtain and integrate data. However, automated integration of applications to access and retrieve data from heterogeneous sources in a distributed system such as the Internet poses many difficulties. Assimilation of data from Web-based sources means that differences in schema and terminology
prevent simple querying and retrieval of data. Thus, machine understanding of the Web Services interface is necessary
for automated selection and invocation of the correct service. Service availability is also an issue that needs to be
resolved. There have been many advances on ontologies to help resolve these difficulties to support the goal of sharing
knowledge for various domains of interest.
In this paper we examine the use of case-based classification as an alternative/supplement to using ontologies for
resolving several questions related to knowledge sharing. While ontologies encompass a formal definition of a domain of
interest, case-based reasoning is a problem solving methodology that retrieves and reuses decisions from stored cases to
solve new problems, and case-based classification involves applying this methodology to classification tasks. Our
approach generalizes well in sparse data, which characterizes our Web Services application. We present our study as it
relates to our work on development of the Advanced MetOc Broker, whose objective is the automated application
integration of meteorological and oceanographic (MetOc) Web Services.
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