BackgroundWearable technology is an exciting new field in humans and animals. In dogs activity monitors have helped to provide objective measurement tools where pet owner observation had been the only source of information. Previous research has focused on measuring overall activity versus rest. This has been relatively useful in determining changes in activity in orthopedic disease or post-surgical cases [Malek et al., BMC Vet Res 8:185, 2012, Yashari et al., BMC Vet Res 11:146, 2015]. Assessment of pruritus via changes in activity, however, requires an assumption that increased activity is due to scratching or other pruritic behaviors. This is an inaccurate method with obvious flaws as other behaviors may also register as greater activity. The objective of this study was to validate the ability of a multidimensional high frequency sensor and advanced computer analysis system, (Vetrax®, AgLogica Holdings, Inc., Norcross, GA, USA) to specifically identify pruritic behaviors (scratching and head shaking). To establish differences between behaviors, sensor and time stamped video data were collected from 361 normal and pruritic dogs. Video annotations were made by two observers independently, while blinded to sensor data, and then evaluated for agreement. Annotations that agreed between the two were used for further analysis. The annotations specified behaviors at specific times in order to compare with sensor data. A computer algorithm was developed to interpret and differentiate between these behaviors. Test subject data was then utilized to test and score the system’s ability to accurately predict behaviors.ResultsResults for prediction of head shaking behavior included sensitivity and specificity of 72.16% and 99.78% respectively. Analysis of scratching produced sensitivity and specificity of 76.85% and 99.73% respectively. These results illustrate the ability of the system to accurately report both scratching and head shaking with an overall accuracy of 99.24% and 99.56% respectively.ConclusionsThis study validates the use of this system to accurately and objectively report scratching and head shaking in dogs. While a small portion of scratching or head shaking behaviors may be missed, as indicated by the sensitivity, when detected, the confidence that these behaviors occurred is extremely high. These factors make this system a very useful tool for objective assessment of pruritus in clinical and research settings.

Traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective Evolutionary Programming) framework builds upon this by also handling feature vectors, allowing the use of signal processing and machine learning functions as primitives, in addition to the more conventional operators. GTMOEP is a novel method for automated, data-driven algorithm creation, capable of outperforming human derived solutions.As an example, GTMOEP was applied to the problem of predicting how long an emergency responder can remain in a hazmat suit before the effects of heat stress cause the user to become unsafe. An existing third-party physics model was leveraged for predicting core temperature from various situational parameters. However, a sustained high heart rate also means that a user is unsafe. To improve performance, GTMOEP was evaluated to predict an expected pull time, computed from both thresholds during human trials.GTMOEP produced dominant solutions in multiple objective space to the performance of predictions made by the physics model alone, resulting in a safer algorithm for emergency responders to determine operating times in harsh environments. The program generated by GTMOEP will be deployed to a mobile application for their use.

We present the Test Matrix Tool (TMT) framework, a simulationagnostic framework providing end-to-end support for robust analysis of complex systems. The need to execute a large number of simulations is common to many problem environments, even those already reduced by Design of Experiments or similar methodologies. TMT addresses key end-user needs in easing the specification, execution and analysis of simulation workloads in ways that are consistent between specific applications of the framework. The TMT design contributes modular specifications for key data communicated between and within the specification, execution and analysis components. Our TMT implementation is an instantiation of those formats freely available for general use. TMT's data analysis component provides a variety of featuresdata filtering, comparison, transformation and visualization-for analytic tasks on any TMT-embedded model. We provide a brief case study as an example of its use in a real-world application.

We are interested in data fusion strategies for Intelligence, Surveillance, and Reconnaissance (ISR) missions. Advances in theory, algorithms, and computational power have made it possible to extract rich semantic information from a wide variety of sensors, but these advances have raised new challenges in fusing the data. For example, in developing fusion algorithms for moving target identification (MTI) applications, what is the best way to combine image data having different temporal frequencies, and how should we introduce contextual information acquired from monitoring cell phones or from human intelligence? In addressing these questions we have found that existing data fusion models do not readily facilitate comparison of fusion algorithms performing such complex information extraction, so we developed a new model that does. Here, we present the Spatial, Temporal, Algorithm, and Cognition (STAC) model. STAC allows for describing the progression of multi-sensor raw data through increasing levels of abstraction, and provides a way to easily compare fusion strategies. It provides for unambiguous description of how multi-sensor data are combined, the computational algorithms being used, and how scene understanding is ultimately achieved. In this paper, we describe and illustrate the STAC model, and compare it to other existing models. INTRODUCTIONSensors, algorithms and computational power continue to evolve rapidly. Beyond physical sensors such as cameras, lidars, and spectrometers, cell phones and social media generate contextual information of growing importance in surveillance applications. However, the sensor fusion models used to describe and compare processing architectures have been comparatively stagnant. Important models such as the JDL (Joint Directors of Laboratories) model [1, 2] have been revised and updated over time, but have not been sufficiently generalized to reflect the current state of the art in sensors or algorithms. The limitations of existing fusion models become especially clear when working in scene characterization and moving target identification (MTI) applications. These applications require fine-grained information about tracked objects including detailed attributes about them. These attributes may be inferred from multi-sensor sensor data, relationships between entities, behavioral patterns recurring through time, and inference of activities and intents of these entities.In this paper we introduce a new fusion model with the goal of addressing these limitations: the Spatial, Temporal, Algorithm, and Cognition (STAC) model. Like other models, STAC provides an organizational basis for describing the extraction of multiple information streams from multi-source data, and the multi-level fusion of these information streams leading to cognition. However, STAC is novel in that it provides for the development of cognition over time.We believe this temporal dimension to be increasingly important for change detection and MTI applications because the source data are often acquired over diffe...

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