When a reader encounters a word in English, they split the word into smaller orthographic units in the process of recognizing its meaning. For example, "rough", when split according to phonemes, is decomposed as r-ou-gh (not as r-o-ugh or r-ough), where each group of letters corresponds to a sound. Since there are many ways to segment a group of letters, this constitutes a computational operation that has to be solved by the reading brain, many times per minute, in order to achieve the recognition of words in text necessary for reading. In English, the irregular relationships between groups of letters and sounds, and the wide variety of possible groupings make this operation harder than in more regular languages such as Italian. If this segmentation takes a significant amount of time in the process of recognizing a word, it is conceivable that providing segmentation information in the text itself could help the reading process by reducing its computational cost. In this paper we explore whether and how different visual interventions from the visualization literature could communicate segmentation information for reading and word recognition. We ran a series of pre-registered lexical decision experiments with 192 participants that tested five main types of visual segmentations: outlines, spacing, connections, underlines and color. The evidence indicates that, even with a moderate amount of training, these visual interventions always slow down word identification, but each to a different extent (between 32.7ms-color technique-and 70.7ms-connection technique). These findings are important because they indicate that, at least for typical adult readers with a moderate amount of specific training in these visual interventions, accelerating the lexical decision task is unlikely. Importantly, the results also offer an empirical measurement of the cost of a common set of visual manipulations of text, which can be useful for practitioners seeking to visualize alongside or within text without impacting reading performance. Finally, the interaction between typographically encoded information and visual variables presented unique patterns that deviate from existing theories, suggesting new directions for future inquiry.

Reading academic papers is a fundamental part of higher education and research, but navigating these information-dense texts can be challenging. In particular, low-vision readers using magnification encounter additional barriers to quickly skimming and visually locating information. In this work, we explored the design of interfaces to enable readers to: 1) navigate papers more easily, and 2) input the required navigation hooks that AI cannot currently automate. To explore this design space, we ran two exploratory studies. The first focused on current practices of low-vision paper readers, the challenges they encounter, and the interfaces they desire. During this study, low-vision participants were interviewed, and tried out four new paper navigation prototypes. Results from this study grounded the design of our end-to-end system prototype Ocean, which provides an accessible front-end for low-vision readers, and enables all readers to contribute to the backend by leaving traces of their reading paths for others to leverage. Our second study used this exploratory interface in a field study with groups of low-vision and sighted readers to probe the user experience of reading and creating traces. Our findings suggest that it may be possible for readers of all abilities to organically leave traces in papers, and that these traces can be used to facilitate navigation tasks, in particular for low-vision readers. Based on our findings, we present design considerations for creating future paper-reading tools that improve access, and organically source the required data from readers.

Web search is a key digital literacy skill that can be particularly challenging for people with dyslexia, a common learning disability that affects reading and spelling skills in about 15% of the English-speaking population. In this paper, we collected and analyzed eye-tracking, search log, and self-report data from 27 participants (14 with dyslexia) to confirm that searchers with dyslexia struggle with all stages of the search process and have markedly different gaze patterns and search behavior that reflect the strategies used and challenges faced. Based on these findings, we discuss design implications to improve the cognitive accessibility of web search.

As reading increasingly shifts from paper to online media, many web browsers now provide a "Reader View,'' which modifies web page layout and design for better readability. However, research has yet to establish whether Reader Views are effective in improving readability and how they might change the user experience. We characterize how Mozilla Firefox's Reader View significantly reduces the visual complexity of websites by excluding menus, images, and content. We then conducted an online study with 391 participants (including 42 who self-reported having been diagnosed with dyslexia), showing that compared to standard websites the Reader View increased reading speed by 5% for readers on average, and significantly improved perceived readability and visual appeal. We suggest guidelines for the design of websites and browsers that better support people with varying reading skills.

The emergence of personal computing devices offers both a challenge and opportunity for displaying text: small screens can be hard to read, but also support higher resolution. To fit content on a small screen, text must be small. This small text size can make computing devices unusable, in particular to low-vision users, whose vision is not correctable with glasses. Usability is also decreased for sighted users straining to read the small letters, especially without glasses at hand. We propose animated scripts called livefonts for displaying English with improved legibility for all users. Because paper does not support animation, traditional text is static. However, modern screens support animation, and livefonts capitalize on this capability. We evaluate our livefont variations' legibility through a controlled lab study with low-vision and sighted participants, and find our animated scripts to be legible across vision types at approximately half the size (area) of traditional letters, while previous smartfonts (static alternate scripts) did not show a significant legibility advantage for low-vision users. We evaluate the learnability of our livefont with low-vision and sighted participants, and find it to be comparably learnable to static smartfonts after two thousand practice sentences.

Performance characterization of applications' hardware behavior is essential for making the best use of available hardware resources. Modern architectures offer access to many hardware events that are capable of providing information to reveal architectural performance bottlenecks throughout the core and memory hierarchy. These events can provide programmers with unique and powerful insights into the causes of the resource bottlenecks in their applications. However, interpreting these events has been a significant challenge. We present an automated system that uses machine learning to identify an application's performance problems. Our system provides programmers with insights about the performance of their applications while shielding them from the onerous task of digesting hardware events. It uses a decision tree algorithm, random forests on our micro-benchmarks to fingerprint the performance problems. Our system divides a profiled application into functions and automatically classifies each function by the dominant hardware resource bottlenecks. Using the classifications from the hotspot functions, we were able to achieve an average speedup of 1.73 from three applications in the PARSEC benchmark suite. Our system provides programmers with a guideline of where, what, and how to fix the detected performance problems in applications, which would have otherwise required considerable architectural knowledge.

Abstract Font‐rendering technologies play a critical role in presenting clear and aesthetic fonts to enhance the experience of reading from computer screens. This article presents three studies investigating visual and psychological correlates of people's preferences toward different onscreen text enhancements such as ClearType developed by Microsoft. Findings suggested that (a) people's acuity and hue sensitivity were two major factors that affect their preferences to ClearType's color filtering of subpixels on fonts, and (b) specific personality traits such as disagreeableness also could correlate with people's impressions of different onscreen text enhancements that were used. These empirical data would inform digital typographers and human–computer interaction scientists who aim to develop better systems of onscreen reading.

HIPs, or Human Interactive Proofs, are challenges meant to be easily solved by humans, while remaining too hard to be economically solved by computers. HIPs are increasingly used to protect services against automatic script attacks. To be effective, a HIP must be difficult enough to discourage script attacks by raising the computation and/or development cost of breaking the HIP to an unprofitable level. At the same time, the HIP must be easy enough to solve in order to not discourage humans from using the service. Early HIP designs have successfully met these criteria [1]. However, the growing sophistication of attackers and correspondingly increasing profit incentives have rendered most of the currently deployed HIPs vulnerable to attack [2,7,12]. Yet, most companies have been reluctant to increase the difficulty of their HIPs for fear of making them too complex or unappealing to humans. The purpose of this study is to find the visual distortions that are most effective at foiling computer attacks without hindering humans. The contribution of this research is that we discovered that 1) automatically generating HIPs by varying particular distortion parameters renders HIPs that are too easy for computer hackers to break, yet humans still have difficulty recognizing them, and 2) it is possible to build segmentation-based HIPs that are extremely difficult and expensive for computers to solve, while remaining relatively easy for humans.

ABSTNACTMuch is known about depth and breadth tradeoff issues in graphical user interface menu design.We describe an experiment to see if large breadth and decreased depth is preferable, both subjectively and via performance data, while attempting to design for optimal scent throughout different structures of a website.A study is reported which modified previous procedures for investigating depth/breadth tradeoffs in content design for the web.Results showed that, while increased depth did harm search performance on the web, a medium condition of depth and breadth outperformed the broadesf shallow web structure overall.

Effective management of documents on computers has been a central user interface problem for many years.One common approach involves using 2D spatial layouts of icons representing the documents, particularly for information workspace tasks.This approach takes advantage of human 2D spatial cognition.More recently, several 3D spatial layouts have engaged 3D spatial cognition capabilities.Some have attempted to use spatial memory in 3D virtual environments.However, there has been no proof to date that spatial memory works the same way in 3D virtual environments as it does in the real world.We describe a new technique for document management called the Data Mountain, which allows users to place documents at arbitrary positions on an inclined plane in a 3D desktop virtual environment using a simple 2D interaction technique.We discuss how the design evolved in response to user feedback.We also describe a user study that shows that the Data Mountain does take advantage of spatial memory.Our study shows that the Data Mountain has statistically reliable advantages over the Microsoft Internet Explorer Favorites mechanism for managing documents of interest in an information workspace.