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Wasm is gaining popularity outside the Web as a well-specifed low-level binary format with ISA portability, low memory footprint and polyglot targetability, enabling efficient in- process sandboxing of untrusted code. Despite these advantages, Wasm adoption for new domains is often hindered by the lack of many standard system interfaces which precludes reusability of existing software and slows ecosystem growth. This paper proposes thin kernel interfaces for Wasm, which directly expose OS userspace syscalls without breaking intra- process sandboxing, enabling a new class of virtualization with Wasm as a universal binary format. By virtualizing the bottom layer of userspace, kernel interfaces enable effortless application ISA portability, compiler backend reusability, and armor programs with Wasm’s built-in control flow integrity and arbitrary code execution protection. Furthermore, existing capability-based APIs for Wasm, such as WASI, can be implemented as a Wasm module over kernel interfaces, improving reuse, robustness, and portability through better layering. We present an implementation of this concept for two kernels – Linux and Zephyr – by extending a modern Wasm engine and evaluate our system’s performance on a number of sophisticated applications which can run for the first time on Wasm. 

Heisenbugs, notorious for their ability to change behavior and elude reproducibility under observation, are among the toughest challenges in debugging programs. They often evade static detection tools, making them especially prevalent in cyber-physical edge systems characterized by complex dynamics and unpredictable interactions with physical environments. Although dynamic detection tools work much better, most still struggle to meet low enough jitter and overhead performance requirements, impeding their adoption. More importantly however, dynamic tools currently lack metrics to determine an observed bug's difficulty or heisen-ness undermining their ability to make any claims regarding their effectiveness against heisenbugs. This paper proposes a methodology for detecting and identifying heisenbugs with low overheads at scale, actualized through the lens of dynamic data-race detection. In particular, we establish the critical impact of execution diversity across both instrumentation density and hardware platforms for detecting heisenbugs; the benefits of which outweigh any reduction in efficiency from limited instrumentation or weaker devices. We develop an experimental WebAssembly-backed dynamic data-race detection framework, Beanstalk, which exploits this diversity to show superior bug detection capability compared to any homogeneous instrumentation strategy on a fixed compute budget. Beanstalk's approach also gains power with scale, making it suitable for low-overhead deployments across numerous compute nodes. Finally, based on a rigorous statistical treatment of bugs observed by Beanstalk, we propose a novel metric, the heisen factor, that similar detectors can utilize to categorize heisenbugs and measure effectiveness. We reflect on our analysis of Beanstalk to provide insight on effective debugging strategies for both in-house and in deployment settings. 

Virtual Reality (VR) telepresence platforms are being challenged to support live performances, sporting events, and conferences with thousands of users across seamless virtual worlds. Current systems have struggled to meet these demands which has led to high-profile performance events with groups of users isolated in parallel sessions. The core difference in scaling VR environments compared to classic 2D video content delivery comes from the dynamic peer-to-peer spatial dependence on communication. Users have many pair-wise interactions that grow and shrink as they explore spaces. In this paper, we discuss the challenges of VR scaling and present an architecture that supports hundreds of users with spatial audio and video in a single virtual environment. We leverage the property of \textit{spatial locality} with two key optimizations: (1) a Quality of Service (QoS) scheme to prioritize audio and video traffic based on users' locality, and (2) a resource manager that allocates client connections across multiple servers based on user proximity within the virtual world. Through real-world deployments and extensive evaluations under real and simulated environments, we demonstrate the scalability of our platform while showing improved QoS compared with existing approaches. 

Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter. 

Public spaces like concert stadiums and sporting arenas are ideal venues for AR content delivery to crowds of mobile phone users. Unfortunately, these environments tend to be some of the most challenging in terms of lighting and dynamic staging for vision-based relocalization. In this paper, we introduce FLASH 1 , a system for delivering AR content within challenging lighting environments that uses active tags (i.e., blinking) with detectable features from passive tags (quads) for marking regions of interest and determining pose. This combination allows the tags to be detectable from long distances with significantly less computational overhead per frame, making it possible to embed tags in existing video displays like large jumbotrons. To aid in pose acquisition, we implement a gravity-assisted pose solver that removes the ambiguous solutions that are often encountered when trying to localize using standard passive tags. We show that our technique outperforms similarly sized passive tags in terms of range by 20-30% and is fast enough to run at 30 FPS even within a mobile web browser on a smartphone.