Qualia-based Exploitation of Sensing Technology (QuEST) is an approach to create a cognitive exoskeleton to improve human-machine decision quality. In this paper, the authors present QuEST-motivated man-machine information fusion with an example for multimedia narratives. User-based situation awareness includes both elements of external sensory perception and internal cognitive explanation. The authors outline QuEST elements and tenets towards a reasoning approach that achieves human intelligence amplification (IA) in relation to data aggregation from machine artificial intelligence (AI). In a use case example for multimedia exploitation, they showcase the need for enhanced understanding of the man (mind-body cognition) and the machine (sensor-based reasoning) for establishing a cohesive narrative of situational activities. QuEST tenets of structurally coherent, situated conceptualization, and simulated experience are utilized in organizing multimedia reports of Video Event Segmentation by Text (VEST).
Effective exploitation of the historical record of meteorological data has long been impeded by an inability to rapidly identify relevant data, and by the difficulties of managing the huge volume of data involved. We present a new approach to archiving meteorological data that addresses these problems.Our approach is implemented in MetVUW Workbench, a system for intelligent retrieval and display of historical meteorological data. The system is intended to serve as a "memory amplifier" for meteorologists that allows them to rapidly locate historical situations of interest.In developing MetVUW Workbench, we have addressed challenging issues of representation and retrieval of spatial information. This paper outlines our approach to some of these issues.
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