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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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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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Near-term regional climate change over Bangladesh

Published in:
Clim. Dyn., Vol. 57, July 2021, pp. 3055-73.

Summary

Bangladesh stands out as a climate change hot spot due to its unique geography, climate, high population density, and limited adaptation capacity. Mounting evidence suggests that the country is already suffering from the effects of climate change which may get worse without aggressive action. Here, we use an ensemble of high-resolution (10 km) regional climate model simulations to project near-term change in climate extremes, mainly heat waves and intense rainfall, for the period (2021–2050). Near-term climate projections represent a valuable input for designing sound adaptation policies. Our climate projections suggest that heatwaves will become more frequent and severe in Bangladesh under the business-as-usual scenario (RCP8.5). In particular, extremes of wet-bulb temperature (a temperature and humidity metric important in evaluating humid heat stress) in the western part of Bangladesh including Bogra, Ishurdi, and Jessore are likely to exceed the extreme danger threshold (according to U.S. National Weather Service criterion), which has rarely been observed in the current climate. The return periods of extreme heat waves are also significantly shortened across the country. In addition, country-averaged rainfall is projected to increase by about 6% during the summer months, with the largest increases (above 10%) in the eastern mountainous areas, such as Sylhet and Chittagong. Meanwhile, insignificant changes in extreme rainfall are simulated. Our results suggest that Bangladesh is particularly susceptible
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Summary

Bangladesh stands out as a climate change hot spot due to its unique geography, climate, high population density, and limited adaptation capacity. Mounting evidence suggests that the country is already suffering from the effects of climate change which may get worse without aggressive action. Here, we use an ensemble of...

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Geographic source estimation using airborne plant environmental DNA in dust

Summary

Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis, we developed a pipeline that utilizes the environmental DNA (eDNA) from plants in dust samples to estimate previous sample location(s). The species of plant-derived eDNA within dust samples were identified using metabarcoding and their geographic distributions were then derived from occurrence records in the USGS Biodiversity in Service of Our Nation (BISON) database. The distributions for all plant species identified in a sample were used to generate a probabilistic estimate of the sample source. With settled dust collected at four U.S. sites over a 15-month period, we demonstrated positive regional geolocation (within 600 km2 of the collection point) with 47.6% (20 of 42) of the samples analyzed. Attribution accuracy and resolution was dependent on the number of plant species identified in a dust sample, which was greatly affected by the season of collection. In dust samples that yielded a minimum of 20 identified plant species, positive regional attribution improved to 66.7% (16 of 24 samples). Using dust samples collected from 31 different U.S. sites, trace plant eDNA provided relevant regional attribution information on provenance in 32.2%. This demonstrated that analysis of plant eDNA in dust can provide an accurate estimate regional provenance within the U.S., and relevant forensic information, for a substantial fraction of samples analyzed.
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Summary

Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis, we developed a pipeline that utilizes the environmental DNA (eDNA) from plants in dust samples to estimate previous sample...

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The Human Trafficking Technology Roadmap: a targeted development strategy for the Department of Homeland Security

Summary

Human trafficking is a form of modern-day slavery that involves the use of force, fraud, or coercion for the purposes of involuntary labor and sexual exploitation. It affects tens of million of victims worldwide and generates tens of billions of dollars in illicit profits annually. While agencies across the U.S. Government employ a diverse range of resources to combat human trafficking in the U.S. and abroad, trafficking operations remain challenging to measure, investigate, and interdict. Within the Department of Homeland Security, the Science and Technology Directorate is addressing these challenges by incorporating computational social science research into their counter-human trafficking approach. As part of this approach, the Directorate tasked an interdisciplinary team of national security researchers at the Massachusetts Institute of Technology's Lincoln Laboratory, a federally funded research and development center, to undertake a detailed examination of the human trafficking response across the Homeland Security Enterprise. The first phase of this effort was a government-wide systems analysis of major counter-trafficking thrust areas, including law enforcement and prosecution; public health and emergency medicine; victim services; and policy and legislation. The second phase built on this systems analysis to develop a human trafficking technology roadmap and implementation strategy for the Science and Technology Directorate, which is presented in this document.
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Summary

Human trafficking is a form of modern-day slavery that involves the use of force, fraud, or coercion for the purposes of involuntary labor and sexual exploitation. It affects tens of million of victims worldwide and generates tens of billions of dollars in illicit profits annually. While agencies across the U.S...

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Detection and characterization of human trafficking networks using unsupervised scalable text template matching

Summary

Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex traffickers. This shift has allowed traffickers to list their victims in multiple geographic areas simultaneously, while also improving operational security by using multiple methods of electronic communication with buyers; complicating the ability of law enforcement to disrupt these illicit organizations. In this paper, we address this issue and present a novel unsupervised and scalable template matching algorithm for analyzing and detecting complex organizations operating on adult service websites. The algorithm uses only the advertisement content to uncover signature patterns in text that are indicative of organized activities and organizational structure. We apply this method to a large corpus of adult service advertisements retrieved from backpage.com, and show that the networks identified through the algorithm match well with surrogate truth data derived from phone number networks in the same corpus. Further exploration of the results show that the proposed method provides deeper insights into the complex structures of sex trafficking organizations, not possible through networks derived from phone numbers alone. This method provides a powerful new capability for law enforcement to more completely identify and gather evidence about trafficking networks and their operations.
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Summary

Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex...

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Trust and performance in human-AI systems for multi-domain command and control

Summary

Command and Control is one of the core tenants of joint military operations, however, the nature of modern security threats, the democratization of technology globally, and the speed and scope of information flows are stressing traditional operational paradigms, necessitating a fundamental shift to better concurrently integrate and operate across multiple physical and virtual domains. In this paper, we aim to address these challenges through the proposition of three concepts that will guide the creation of integrated human-AI Command and Control systems, inspired by recent advances and successes within the commercial sector and academia. The first concept is a framework for integration of AI capabilities into the enterprise that optimizes trust and performance within the workforce. The second is an approach for facilitating multi-domain operations though realtime creation of multi-organization multi-domain task teams by dynamic management of information abstraction, teaming, and risk control. The third is a new paradigm for multi-level data security and multi-organization data sharing that will be a key enabler of joint and coalition multi-domain operation in the future. Lastly, we propose a set of recommendations towards the research, development, and instantiation of these transformative advances in Command and Control capability.
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Summary

Command and Control is one of the core tenants of joint military operations, however, the nature of modern security threats, the democratization of technology globally, and the speed and scope of information flows are stressing traditional operational paradigms, necessitating a fundamental shift to better concurrently integrate and operate across multiple...

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Cloud computing in tactical environments

Summary

Ground personnel at the tactical edge often lack data and analytics that would increase their effectiveness. To address this problem, this work investigates methods to deploy cloud computing capabilities in tactical environments. Our approach is to identify representative applications and to design a system that spans the software/hardware stack to support such applications while optimizing the use of scarce resources. This paper presents our high-level design and the results of initial experiments that indicate the validity of our approach.
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Summary

Ground personnel at the tactical edge often lack data and analytics that would increase their effectiveness. To address this problem, this work investigates methods to deploy cloud computing capabilities in tactical environments. Our approach is to identify representative applications and to design a system that spans the software/hardware stack to...

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