The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus’s spread while maintaining economic and social activities. The computational epidemiology problems of DCT often involve parameter optimization through learning processes, making it crucial to understand how to apply machine learning techniques for effective DCT optimization. While numerous research studies on DCT have emerged recently, most existing reviews primarily focus on DCT application design and implementation. This paper offers a comprehensive overview of privacy-preserving machine learning-based DCT in preparation for future pandemics. We propose a new taxonomy to classify existing DCT strategies into forward, backward, and proactive contact tracing. We then categorize several DCT apps developed during the COVID-19 pandemic based on their tracing strategies. Furthermore, we derive three research questions related to computational epidemiology for DCT and provide a detailed description of machine learning techniques to address these problems. We discuss the challenges of learning-based DCT and suggest potential solutions. Additionally, we include a case study demonstrating the review’s insights into the pandemic response. Finally, we summarize the study’s limitations and highlight promising future research directions in DCT.

World-scale augmented reality (AR) is a form of AR where users move around the real world, viewing and interacting with 3D models at specific locations. However, given the geographical scale of world-scale AR, pre-fetching and storing numerous high-quality 3D models locally on the device is infeasible. For example, it would be impossible to download and store 3D ads from all the storefronts in a city onto a single device. A key challenge is thus deciding which remotely-stored 3D models should be fetched onto the AR device from an edge server, in order to render them in a timely fashion - yet with high visual quality - on the display. In this work, we propose a 3D model retrieval framework that makes intelligent decisions of which quality of 3D models to fetch, and when. The optimization decision is based on quality-compression tradeoffs, network bandwidth, and predictions of which 3D models the AR user is likely to view next. To support our framework, we collect real-world traces of AR users playing a world-scale AR game, and use this to drive our simulation and prediction modules. Our results show that the proposed framework can achieve higher visual quality of the 3D models while missing fewer display deadlines (by 20%) and wasting fewer bytes (by 10x), compared to a baseline approach of pre-fetching models within a fixed distance of the user.

Augmented reality (AR) seamlessly overlays virtual objects onto the real world, enabling an exciting new range of applications. Multiple users view and interact with virtual objects, which are replicated and shown on each user's display. A key requirement of AR is that the replicas should be quickly updated and converge to the same state; otherwise, users may have laggy or inconsistent views of the virtual object, which negatively affects their experience. A second key requirement is that the movements of virtual objects in space should preserve certain integrity properties either due to physical boundaries in the real world, or privacy and safety preferences of the user. For example, a virtual cup should not sink into a table, or a private virtual whiteboard should stay within an office. The challenge tackled in this paper is the coordination of virtual objects with low latency, spatial integrity properties and convergence. We introduce “well-organized” replicated data types that guarantee these two properties. Importantly, they capture a local notion of conflict that supports more concurrency and lower latency. To implement well-organized virtual objects, we introduce a credit scheme and replication protocol that further facilitate local execution, and prove the protocol's correctness. Given an AR environment, we automatically derive conflicting actions through constraint solving, and statically instantiate the protocol to synthesize custom coordination. We evaluate our implementation, HAMBAZI, on off-the-shelf Android AR devices and show a latency reduction of 30.5-88.4% and a location staleness reduction of 35.6-75.6%, compared to three baselines, for varying numbers of devices, AR environments, request loads, and network conditions.