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Unsolicited bulk telephone calls — termed "robocalls" — nearly outnumber legitimate calls, overwhelming telephone users. While the vast majority of these calls are illegal, they are also ephemeral. Although telephone service providers, regulators, and researchers have ready access to call metadata, they do not have tools to investigate call content at the vast scale required. This paper presents SnorCall, a framework that scalably and efficiently extracts content from robocalls. SnorCall leverages the Snorkel framework that allows a domain expert to write simple labeling functions to classify text with high accuracy. We apply SnorCall to a corpus of transcripts covering 232,723 robocalls collected over a 23-month period. Among many other findings, SnorCall enables us to obtain first estimates on how prevalent different scam and legitimate robocall topics are, determine which organizations are referenced in these calls, estimate the average amounts solicited in scam calls, identify shared infrastructure between campaigns, and monitor the rise and fall of election-related political calls. As a result, we demonstrate how regulators, carriers, anti-robocall product vendors, and researchers can use SnorCall to obtain powerful and accurate analyses of robocall content and trends that can lead to better defenses. 

Battery-free sensing devices harvest energy from their surrounding environment to perform sensing, computation, and communication. A core challenge for these devices is maintaining usefulness despite erratic, random, or irregular energy availability, which causes inconsistent execution, loss of service, and power failures. Adapting execution (degrading or upgrading) based on available or predicted power/energy seems promising to stave off power failures, meet deadlines, or increase throughput. However, due to constrained resources and limited local information, deciding what and when exactly to adapt is challenging. This article explores the fundamentals of energy-aware adaptation for intermittently powered computers and proposes heuristic adaptation mechanisms to dynamically modulate the program complexity at run-time to enable higher sensor coverage and throughput. While we target battery-free, intermittently powered, resource-constrained sensors, we see a general application to all energy harvesting devices. 

Battery-free sensing devices harvest energy from their surrounding environment to perform sensing, computation, and communication. This enables previously impossible applications in the Internet-of-Things. A core challenge for these devices is maintaining usefulness despite erratic, random or irregular energy availability; which causes inconsistent execution, loss of service and power failures. Adapting execution (degrading or upgrading) seems promising as a way to stave off power failures, meet deadlines, or increase throughput. However, because of constrained resources and limited local information, it is a challenge to decide when would be the best time to adapt, and how exactly to adapt execution. In this paper, we systematically explore the fundamental mechanisms of energy-aware adaptation, and propose heuristic adaptation as a method for modulating the performance of tasks to enable higher sensor coverage, completion rates, or throughput, depending on the application. We build a task based adaptive runtime system for intermittently powered sensors embodying this concept. We complement this runtime with a user facing simulator that enables programmers to conceptualize the tradeoffs they make when choosing what tasks to adapt, and how, relative to real world energy harvesting environment traces. While we target battery-free, intermittently powered sensors, we see general application to all energy harvesting devices. We explore heuristic adaptation with varied energy harvesting modalities and diverse applications: machine learning, activity recognition, and greenhouse monitoring, and find that the adaptive version of our ML app performs up to 46% more classifications with only a 5% drop in accuracy; the activity recognition app captures 76% more classifications with only nominal down-sampling; and find that heuristic adaptation leads to higher throughput versus non-adaptive in all cases. 

The relationship between angler catch rates and fish abundance can contribute to or hinder sustainable exploitation of fisheries depending on whether catch rates are proportional to fish abundance or are hyperstable. We performed a whole-ecosystem experiment in which fish abundance was manipulated and paired with weekly angler catch rate estimates from controlled experimental fishing. Catch rates were hyperstable (β = 0.47) in response to changes in fish abundance. By excluding effort sorting (i.e., catch rates remaining high because less skilled anglers leave the fishery as abundance declines), our experiment isolated the influence of fish aggregation as a driver of hyperstability. Spatial analysis of catch locations did not identify clustering around specific points, suggesting that loose aggregation to preferred habitat at the scale of the entire littoral zone was enough to maintain stable catch rates. In our study, general, non-spawning, habitat preferences created loose aggregations for anglers to target, which was sufficient to generate hyperstability. Habitat preferences are common to nearly all fishes and widely known to anglers, suggesting that many harvest-oriented recreational fisheries can be expected to exhibit hyperstability.