The paper investigates whether the methods chosen for representing uncertain geographic information aids or impairs decision-making in the context of wildfire hazard. Through a series of three human subject experiments, utilizing 180 subjects and employing increasingly di cult tasks, this research evaluates the e↵ect of five di↵erent visualizations and a text based representation on decisionmaking under uncertainty. Our quantitative experiments focus specifically on the task of decision-making under uncertainty, rather than the task of reading levels of uncertainty from the map. To guard against the potential for generosity and risk seeking in decision-making under uncertainty, the experimental design uses performance-based incentives. The experiments showed that the choice of representation makes little di↵erence to performance in cases where subjects are allowed the time and focus to consider their decisions. However, with the increasing di culty of time pressure, subjects performed best using a spectral color hue-based representation, rather than more carefully designed cartographic representations. Text-based and simplified boundary encodings were amongst the worst performers. The results have implications for the performance of decision-making under uncertainty using static maps, especially in the stressful environments surrounding an emergency.

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The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Abstract-Space-based positioning, navigation and timing (PNT) technologies, such as the Global Navigation Satellite Systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years PNT information has become increasingly critical to the security, safety and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure.Due to its vulnerabilities and line-of-sight requirements GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity and reliability. A multi-sensor navigation approach offers an effective augmentation in GNSSchallenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multi-sensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required.