Modern workloads are demanding increasingly larger memory capacity. Compute Express Link (CXL)-based memory tiering has emerged as a promising solution for addressing this problem by utilizing traditional DRAM alongside slow-tier CXL memory devices. We analyze prior tiering systems and observe two challenges for high-performance memory tiering: adapting to skewed but dynamically varying data hotness distributions while minimizing memory and cache overhead due to tiering. To address these challenges, we propose HybridTier, an adaptive and lightweight tiering system for CXL memory. HybridTier tracks both long-term data access frequency and short-term access momentum simultaneously to accurately capture and adapt to shifting hotness distributions. HybridTier reduces the metadata memory overhead by tracking data accesses probabilistically, obtaining higher memory efficiency by trading off a small amount of tracking inaccuracy that has a negligible impact on application performance. To reduce cache overhead, HybridTier uses lightweight data structures that optimize for data locality to track data hotness. Our evaluations show that HybridTier outperforms prior systems by up to 91% (19% geomean), incurring 2.0-7.8x less memory overhead and 1.7-3.5x less cache misses.

The growing use of technology in K-8 classrooms highlights a parallel need for formal learning opportunities aimed at helping children use technology safely and protect their personal information. Even the youngest students are now using tablets, laptops, and apps to support their learning; however, there are limited curricular materials available for elementary and middle school children on digital privacy and security topics. To bridge this gap, we developed a series of micro-lessons to help K-8 children learn about digital privacy and security at school. We first conducted a formative study by interviewing elementary school teachers to identify the design needs for digital privacy and security lessons. We then developed micro-lessons--multiple 15-20 minute activities designed to be easily inserted into the existing curriculum--using a co-design approach with multiple rounds of developing and revising the micro-lessons in collaboration with teachers. Throughout the process, we conducted evaluation sessions where teachers implemented or reviewed the micro-lessons. Our study identifies strengths, challenges, and teachers' tailoring strategies when incorporating micro-lessons for K-8 digital privacy and security topics, providing design implications for facilitating learning about these topics in school classrooms.

Increased usage of additive manufacturing (AM) in various industries has solidified its role as an advanced manufacturing technique. However, there is an inherent lack of reliability in AM processes, particularly common in extrusion or deposition-based methods due to the stochastic nature of ma-terial deposition. This necessitates an intelligent manufacturing solution to address the drawbacks of AM. Thus, we propose a novel layer-wise approach toward closed-loop AM, which is capable of in-situ monitoring and repairing geometric defects. In this paper, we present a system that uses a robotic AM experimental platform that mimics a conventional open-loop fabrication setup, which we augment into a closed-loop system using two add-ons: in-situ inspection subsystem and online process correction subsystem. The in-situ inspection subsystem collects 3D point cloud scans and compares them against a reference CAD model, categorizing geometric deviations as positive or negative defects. Then the subsequent online process correction subsystem uses a re-plan and/or repair strategy to address the positive and/or negative defects, respectively. To evaluate this idea, we conducted three experiments on parts with manually induced defects to investigate the system's ability to repair those parts, thereby reducing defects, improving part accuracy, and enhancing mechanical properties. Comparing the defective and repaired parts, we observe a reduction in defect percent by volume from 10.7% to 1.3%, an improvement in geometric tolerance from 3.86% error to 0.08% error, and an increase in the part's breaking load from 4.77 kN to 6.31 kN. These experiments prove that our layer-wise closed-loop additive manufacturing approach improves the quality, tolerance, and reliability of plastic 3D printed parts, with the potential to extend to other extrusion/deposition-based AM processes, or even subtractive manufacturing and hybrid manufacturing methods.

Vulnerability discovery is an essential aspect of software security. Currently, the demand for security experts significantly exceeds the available vulnerability discovery workforce. Further, the existing vulnerability discovery workforce is highly homogeneous, dominated by white and Asian men. As such, one promising avenue for increasing the capacity of the vulnerability discovery community is through recruitment and retention from a broader population. Although significant prior research has explored the challenges of equity and inclusion in computing broadly, the competitive and frequently self-taught nature of vulnerability discovery work may create new variations on these challenges. This paper reports on a semi-structured interview study (N = 16) investigating how people from marginalized populations come to participate in vulnerability discovery, whether they feel welcomed by the vulnerability discovery community, and what challenges they face when joining the vulnerability discovery community. We find that members of marginalized populations face some unique challenges, while other challenges common in vulnerability discovery are exacerbated by marginalization.

With the rapid shift to remote learning in the early days of the COVID-19 pandemic, parents, teachers, and students had to quickly adapt to what scholars have called "emergency remote learning" (ERL). This transition required increased reliance on digital tools, exacerbating privacy and security threats associated with expanded data collection and new vulnerabilities. In this study, we adopt a sociotechnical and infrastructural perspective to understand how these threats emerged through breakdowns and tensions in elementary school ERL. Through interviews with 29 US-based teachers and parents of elementary school students (grades PreK-6), we identify two core findings related to privacy and security. First, we detail three breakdowns in the ERL sociotechnical infrastructure: (1) reduced attention to privacy and security issues as parents and teachers cobbled together a patchwork of tools needed to make ERL work; (2) privacy and security risks that emerged from ambiguous and shifting school policies; and (3) the failure to adapt standard authentication mechanisms (e.g., passwords) to be usable by young children. Second, we identify tensions between parents' and teachers' desire to help children advance in their education and their desire for children's privacy and security in ERL, as well as tensions resulting from the collapse of home and school contexts. These findings collectively suggest that ERL exacerbated existing--and created new--privacy and security challenges for young students, and we argue these challenges will carry beyond the pandemic due to the increasing use of technology to supplement traditional education. In light of these findings, we recommend researchers and educators use a framework of care to develop social and technical approaches to improving remote learning in order to protect children's privacy and security.