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Fun as a strategic advantage: applying lessons in engagement from commercial games to military logistics training

Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players and hold their attention. But do those engagement-improving methods have a place in instructional environments with a captive and motivated audience? Our experience building a logistics supply chain training game for the Marine Corps University suggests that yes; applying lessons in engagement from commercial games can both help improve player experience with the learning environment, and potentially improve learning outcomes.
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Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players...

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Modeling probability of alert of Bluetooth low energy-based automatic exposure notifications

Published in:
MIT Lincoln Laboratory Report ACTA-4

Summary

BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy (BLE) signal attenuation domains. However, many public health definitions of "exposure" to a disease are based upon the distance between an index case and another person. To bridge the conceptual gap for public health authorities (PHAs) from the familiar distance-and-duration space to the signal attenuation-and-duration space, BLEMUR uses BLE signal attenuation as a proxy for distance between people, albeit an imprecise one. This paper will discuss the EN settings that can be manipulated, the BLE data collected, how data support a model of the relationship between measured attenuation and distance between phones, and how BLEMUR calculates the probability of alert for a distance and duration based on the settings and data.
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Summary

BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy...

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System analysis for responsible design of modern AI/ML systems

Summary

The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective is needed when designing and implementing ML algorithms and systems. Such perspective can provide a formal way for evaluating and enabling responsible ML practices. In this paper, we review components of the System Analysis methodology and highlight how they connect and enable responsible practices of ML design.
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Summary

The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective is needed when designing and implementing ML algorithms and systems. Such perspective can provide a formal way for evaluating and enabling responsible...

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COVID-19 exposure notification in simulated real-world environments

Summary

Privacy-preserving contact tracing mobile applications, such as those that use the Google-Apple Exposure Notification (GAEN) service, have the potential to limit the spread of COVID-19 in communities, but the privacy-preserving aspects of the protocol make it difficult to assess the performance of the apps in real-world populations. To address this gap, we exercised the CovidWatch app on both Android and iOS phones in a variety of scripted realworld scenarios, relevant to the lives of university students and employees. We collected exposure data from the app and from the lower-level Android service, and compared it to the phones' actual distances and durations of exposure, to assess the sensitivity and specificity of the GAEN service configuration as of February 2021. Based on the app's reported ExposureWindows and alerting thresholds for Low and High alerts, our assessment is that the chosen configuration is highly sensitive under a range of realistic scenarios and conditions. With this configuration, the app is likely to capture many long-duration encounters, even at distances greater than six feet, which may be desirable under conditions with increased risk of airborne transmission.
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Summary

Privacy-preserving contact tracing mobile applications, such as those that use the Google-Apple Exposure Notification (GAEN) service, have the potential to limit the spread of COVID-19 in communities, but the privacy-preserving aspects of the protocol make it difficult to assess the performance of the apps in real-world populations. To address this...

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The Simulation of Automated Exposure Notification (SimAEN) Model

Summary

Automated Exposure Notication (AEN) was implemented in 2020 to supplement traditional contact tracing for COVID-19 by estimating "too close for too long" proximities of people using the service. AEN uses Bluetooth messages to privately label and recall proximity events, so that persons who were likely exposed to SARS-CoV-2 can take the appropriate steps recommended by their health care authority. This paper describes an agent-based model that estimates the effects of AEN deployment on COVID-19 caseloads and public health workloads in the context of other critical public health measures available during the COVID-19 pandemic. We selected simulation variables pertinent to AEN deployment options, varied them in accord with the system dynamics available in 2020-2021, and calculated the outcomes of key metrics across repeated runs of the stochastic multi-week simulation. SimAEN's parameters were set to ranges of observed values in consultation with public health professionals and the rapidly accumulating literature on COVID-19 transmission; the model was validated against available population-level disease metrics. Estimates from SimAEN can help public health officials determine what AEN deployment decisions (e.g., configuration, workflow integration, and targeted adoption levels) can be most effective in their jurisdiction, in combination with other COVID-19 interventions (e.g., mask use, vaccination, quarantine and isolation periods).
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Summary

Automated Exposure Notication (AEN) was implemented in 2020 to supplement traditional contact tracing for COVID-19 by estimating "too close for too long" proximities of people using the service. AEN uses Bluetooth messages to privately label and recall proximity events, so that persons who were likely exposed to SARS-CoV-2 can take...

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Bluetooth Low Energy (BLE) Data Collection for COVID-19 Exposure Notification

Summary

Privacy-preserving contact tracing mobile applications, such as those that use the Google-Apple Exposure Notification (GAEN) service, have the potential to limit the spread of COVID-19 in communities; however, the privacy-preserving aspects of the protocol make it difficult to assess the performance of the Bluetooth proximity detector in real-world populations. The GAEN service configuration of weights and thresholds enables hundreds of thousands of potential configurations, and it is not well known how the detector performance of candidate GAEN configurations maps to the actual "too close for too long" standard used by public health contact tracing staff. To address this gap, we exercised a GAEN app on Android phones at a range of distances, orientations, and placement configurations (e.g., shirt pocket, bag, in hand), using RF-analogous robotic substitutes for human participants. We recorded exposure data from the app and from the lower-level Android service, along with the phones' actual distances and durations of exposure.
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Summary

Privacy-preserving contact tracing mobile applications, such as those that use the Google-Apple Exposure Notification (GAEN) service, have the potential to limit the spread of COVID-19 in communities; however, the privacy-preserving aspects of the protocol make it difficult to assess the performance of the Bluetooth proximity detector in real-world populations. The...

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Energy resilience: exercises for Marine Corps installations

Published in:
Marine Corps Gazette, Vol. 106, No. 2, February 2022, p. 20-24.
Topic:
R&D group:

Summary

Microgrids are areas that are self-sufficient for power that can controllably disconnect from the incoming utility feed and control generation assets in conjunction with changing load requirements. They are increasingly being touted as a way to improve installations energy resilience because they allow installations to decouple from the larger electric grid if it fails and continue to provide power in the face of growing natural and man-made threats to Marine Corps installations. However, before commanders can put resources toward upgrading infrastructure, they need to identify and understand their vulnerabilities. A key way to do this is by holding exercises designed to simulate grid failures and outages either in a tabletop manner or in realtime. These exercises also help personnel train for disruptions, understand their impact on operations, and identify unknown interdependencies that can be just as important as investing in resilient technology and the physical electric grid. In order for the equipment to work, personnel have to know how to employ it and commands need to understand how outages will affect their installations. These types of exercises are as important as the physical infrastructure or ensuring the energy resilience of Marine Corps installations and the missions that depend on them in the future.
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Summary

Microgrids are areas that are self-sufficient for power that can controllably disconnect from the incoming utility feed and control generation assets in conjunction with changing load requirements. They are increasingly being touted as a way to improve installations energy resilience because they allow installations to decouple from the larger electric...

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Radar-optimized wind turbine siting

Author:
Published in:
IEEE Trans. Sustain. Energy, Vol. 13, No. 1, January 2022, pp. 403-13.

Summary

A method for analyzing wind turbine-radar interference is presented. A model is used to derive layouts for siting wind turbines that reduces their impact on radar systems, potentially allowing for increased wind turbine development near radar sites. By choosing a specific wind turbine grid stagger based on a wind farm's orientation relative to a radar site, the impacts on that radar can be minimized. The proposed changes to wind farm siting are relatively minor and do not have a significant effect on wind turbine density. With proper optimization of radar clutter mitigation, radar tracking performance above such wind farms can be significantly increased. Both present-day and potential future or upgraded radar systems are analyzed. The reduction in radar performance due to wind turbine clutter is approximately halved using this method. The developed method is robust with respect to controlled variations in wind turbine placement caused by potential obstructions.
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Summary

A method for analyzing wind turbine-radar interference is presented. A model is used to derive layouts for siting wind turbines that reduces their impact on radar systems, potentially allowing for increased wind turbine development near radar sites. By choosing a specific wind turbine grid stagger based on a wind farm's...

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A hybrid algorithm for parameter estimation (HAPE) for dynamic constant power loads

Published in:
IEEE Trans. Ind. Electron., Vol. 68, No. 11, November 2021, pp. 10326-35.
Topic:
R&D group:

Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced. In order to verify the load model and the HAPE, two experiments are conducted with different DCPLs using a Power-Hardwarein-the-Loop (PHiL) testbed. The PHiL testbed consists of a real-time computer working with a programmable power amplifier in order to perturb the input voltage's amplitude and frequency. Each connected DCPL in two separate experiments serves as the device under test (DUT). Using the captured experimental data as a reference, the HAPE is then invoked. The resulting parameter estimates are used to define simulation models. Both resulting DCPL models are simulated to produce waveforms that closely resemble experimental waveforms. Finally, the HAPE's resulting parameter estimates are presented, and the performance of the HAPE is discussed.
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Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced...

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Utility of inter-subject transfer learning for wearable-sensor-based joint torque prediction models

Published in:
43rd Annual Intl. Conf. of the IEEE Engineering in Medicine & Biology, 31 October 2021-4 November 2021.

Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single- and multi-subject based parameter transfer on LSTM models trained for "sensor-to-joint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single- and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.
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Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning...

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