GPS and smartphones enable users to place location-based annotations, capturing rich environmental context. Previous research demonstrates that blind and low vision (BLV) people can use annotations to explore unfamiliar areas. However, current commercial systems allowing BLV users to create annotations have never been evaluated, and current GPS-based systems can deviate several meters. Motivated by high-accuracy visual positioning technology, we first conducted a formative study with 24 BLV participants to envision a more accurate and inclusive annotation system. Surprisingly, many participants viewed the high-accuracy technology not just as an annotation system but also as a tool for precise last-few-meters navigation. Guided by participant feedback, we developed NaviNote, which combines vision-based high-precision localization with an agentic architecture to enable voice-based annotation authoring and navigation. Evaluating NaviNote with 18 BLV participants showed that it significantly improved navigation performance and supported users in understanding and annotating their surroundings. Based on these findings, we discuss design considerations for future accessible annotation authoring systems.

intent taxonomy with five visual intents characterized by participants' gaze behaviors.We demonstrated the difference between low vision and sighted participants' gaze behaviors and how visual ability affected low vision participants' gaze patterns across visual intents.Our findings underscore the importance of combining visual ability information, visual context, and eye tracking data in visual intent recognition, setting up a foundation for intent-aware assistive technologies for low vision people.

While videos have become increasingly prevalent in delivering information across different educational and professional contexts, individuals with ADHD often face attention challenges when watching informational videos due to the dynamic, multimodal, yet potentially distracting video elements. To understand and address this critical challenge, we designed FocusView, a video customization interface that allows viewers with ADHD to customize informational videos from different aspects. We evaluated FocusView with 12 participants with ADHD and found that FocusView significantly improved the viewability of videos by reducing distractions. Through the study, we uncovered participants' diverse perceptions of video distractions (e.g., background music as a distraction vs. stimulation boost) and their customization preferences, highlighting unique ADHD-relevant needs in designing video customization interfaces (e.g., reducing the number of options to avoid distraction caused by customization itself). We further derived design considerations for future video customization systems for the ADHD community.

, an AR interface that supports landmark perception for PLV by providing both overviews of space structures and in-situ landmark augmentations. We evaluated VisiMark with 16 PLV and found that VisiMark enabled PLV to perceive landmarks they preferred but could not easily perceive before, and changed PLV's landmark selection from only visually-salient objects to cognitive landmarks that are more important and meaningful. We further derive design considerations for AR-based landmark augmentation systems for PLV.

Abstract In cloud computing data centers, containerized tasks are regularly scheduled from one physical host to another due to resource management requirements such as handling machine failures, rebalancing server resources, and upgrading/scaling applications. After the container running in the source host is scheduled to the target host, it suffers from I/O performance degradation until the DRAM buffer is fully rebuilt. However, migrating the DRAM buffer from the source host to the target host could also introduce intolerable downtime of containerized tasks. Especially, as the DRAM buffer capacity of the application already increases to about dozens or hundreds of GB, the cost of downtime due to container migration becomes unacceptable. Many researchers have devoted themselves to developing an effective DRAM buffer warm-up scheme to avoid the cold bootstrap issue after container migration, such as pre-copy and post-copy schemes. However, the cold bootstrap and large-capacity buffer migration issues of container scheduling are still an open research problem. In this paper, motivated by the observation that the DRAM buffer is always flushed to the storage backend before starting the container in the target host, we proposed a scheme named ZeroCopy to utilize the file system to assist the DRAM buffer migration. ZeroCopy traverses the files in the DRAM buffer and flags these files when these files are flushed into the file system, and reloads these files into DRAM after starting the container in the target host. By this scheme, the container migration procedure does not require migrating data buffers and can start within an acceptable time. We conduct a series of experiments with public cloud traces to measure several key metrics on container migration. The results show that ZeroCopy outperforms these existing schemes. The average data transmission volume is reduced by about 6.25 times compared with state-of-the-art, and the downtime of container migration is also reduced by 31.8%.

LiDAR segmentation is crucial for autonomous driving perception. Range view methods have been widely adopted for these applications due to their intuitiveness and ease of implementation. However, the inherent shortcomings of the range view approach (e.g., assuming that point clouds within the same pixel of a range image have the same semantic class) make it difficult to perform accurate fine-grained segmentation tasks, thus limiting its potential in practical applications. To address these issues, we propose RangeFusion, an end-to-end framework that greatly improves the ability to learn and process LiDAR point clouds from range views by employing a multispatial learning model. A novel range-scan space (RSS) is proposed to address the inability of existing range view methods to accurately aggregate features of neighboring points. This space achieves accurate and efficient neighboring point feature aggregation with linear time complexity. In addition, a supervised label smoothing method called multilevel feature selection heads (MFSHs) is designed, which achieves more fine-grained semantic prediction by subdividing the full point cloud into multisemantic hierarchical subclouds and adaptively fusing the features with confidence filtering. The performance of the proposed method was evaluated on several benchmarks, including SemanticKITTI and nuScenes. On these two datasets, mean intersection over union (mIoU) scores of 67.9% and 80.2% were achieved, respectively. This demonstrates that the proposed approach outperforms existing range view- and multiview-based approaches while maintaining efficient performance at 26.5 FPS. In addition, real road data were collected for testing. The code is available athttps://github.com/Wansit99/RangeFusion.

First responders (FRs) navigate hazardous, unfamiliar environments in the field (e.g., mass-casualty incidents), making life-changing decisions in a split second. AR head-mounted displays (HMDs) have shown promise in supporting them due to its capability of recognizing and augmenting the challenging environments in a hands-free manner. However, the design space have not been thoroughly explored by involving various FRs who serve different roles (e.g., firefighters, law enforcement) but collaborate closely in the field. We interviewed 26 first responders in the field who experienced a state-of-the-art optical-see-through AR HMD, as well as its interaction techniques and four types of AR cues (i.e., overview cues, directional cues, highlighting cues, and labeling cues), soliciting their first-hand experiences, design ideas, and concerns. Our study revealed both generic and role-specific preferences and needs for AR hardware, interactions, and feedback, as well as identifying desired AR designs tailored to urgent, risky scenarios (e.g., affordance augmentation to facilitate fast and safe action). While acknowledging the value of AR HMDs, concerns were also raised around trust, privacy, and proper integration with other equipment. Finally, we derived comprehensive and actionable design guidelines to inform future AR systems for in-field FRs.