Public availability of data about the supply and utilization of Dockless Electric Scooters (DESs) is needed to develop smart mobility applications to promote the use of sustainable, shared, and equitable transportation. Prior studies have analyzed some attributes of DES usage, but they are limited in three aspects. 1) Data collection methods: Prior studies relied on DES providers to provide usage data to authors. Unfortunately, convincing DES providers to provide complete usage data is extremely hard, if not impossible. Thus, this approach is not scalable and further limits the ability to study DES usage trends only in the regions and only at the times for which the DES providers provide the data. 2) Global DES usage analysis: Prior studies have not analyzed how DES usage varies in different parts of the world. 3) Aspects of the usage analysis: Prior studies have not analyzed important DES usage attributes such as the availability of DES in minority-concentrated areas, temporal trends of DES trips from different types of locations, etc. This paper fills the gap in prior work by addressing these three limitations. For data collection methods, we present novel techniques to continuously collect DES usage data in any city worldwide without relying on DES providers. For global DES usage analysis, we study the spatiotemporal DES usage trends of 12 DES providers in 11 cities worldwide. For the aspects of the usage analysis, we provide extensive analysis of various aspects of DES usage that have previously either never been studied or studied only briefly. These aspects include DES supply trends, competition among providers, supply and trip hotspots, equitable DES availability, and so on. The methods and findings that we present in this paper aid in understanding DES usage across the world and offer actionable insights to DES stakeholders.

Usage data on Dockless Electric Scooters (DES) is crucial for developing policies and mobility applications to support shared transportation by understanding their supply and utilization. However, such data is not publicly available and prior DES studies relied on DES providers for the provision of usage data, which is often incomplete and difficult to obtain. This limits the ability to audit these datasets and study DES usage trends globally and over time. This paper addresses these limitations by introducing scalable methods to continuously collect DES data globally without relying on providers. We collect DES usage trends from 12 providers across 11 cities worldwide and examine supply and utilization trends and provider competition in this study.

Deterministic MultiThreading (DMT) systems eliminate nondeterminism from the dynamic executions of multithreaded programs. They can greatly simplify multithreaded programming and ease the deployment of systems that rely on replication. We first categorize and compare existing DMT system designs along three axes, incorporating the most recent advances in DMT systems. From our study, we conclude that stable synchronization determinism is the most cost-effective design, and it is thus the focus of our work.

Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because: i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world; and ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries.

Server-side web applications are vulnerable to request races. While some previous studies of real-world request races exist, they primarily focus on the root cause of these bugs. To better combat request races in server-side web applications, we need a deep understanding of their characteristics. In this paper, we provide a complementary focus on race effects and fixes with an enlarged set of request races from web applications developed with Object-Relational Mapping (ORM) frameworks. We revisit characterization questions used in previous studies on newly included request races, distinguish the external and internal effects of request races, and relate request-race fixes with concurrency control mechanisms in languages and frameworks for developing server-side web applications.

Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant implications if SFAs treat emails of user interest as spam on a large scale. This is particularly important during national elections. To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. We analyzed the biases in the SFAs towards the left and the right candidates and further studied the impact of the interactions (such as reading or marking emails as spam) of email recipients on these biases. We observed that the SFAs of different email services indeed exhibit biases towards different political affiliations.

We propose incremental checkpointing techniques enabling transiently powered devices to retain computational state across multiple activation cycles. As opposed to the existing approaches, which checkpoint complete program state, the proposed techniques keep track of modified RAM locations to incrementally update the retained state in secondary memory, significantly reducing checkpointing overhead both in terms of time and energy.