Java implementation of a MathML rendering engine

Current approaches to presentation of mathematics (on paper or in electronic format have usability drawbacks that make learning and appreciation of mathematics challenging and often frustrating. In a framework of a big research project of identifying new approaches to communicating mathematical ideas in a highly usable and effective manner, we are building prototype software toolkits displaying documents with math content in various comprehensive ways. For the encoding of mathematics we use MathML, a standard XML-based markup language allowing specification of both facade and underlying semantic content of mathematical presentations, as well as providing certain possibili­ties for dynamics and interactivity in communication of math. The Java implementation of a MathML rendering en­gine which we use in our prototypes is the topic of the current project.Keywords: Math Visualization, User Interface, MathML, XML, Java.2002 best estimate for issue date and commencement year based on available information

ScreenTrack: Using a Visual History of a Computer Screen to Retrieve Documents and Web Pages

Computers are used for various purposes, so frequent context switching is inevitable. In this setting, retrieving the documents, files, and web pages that have been used for a task can be a challenge. While modern applications provide a history of recent documents for users to resume work, this is not sufficient to retrieve all the digital resources relevant to a given primary document. The histories currently available do not take into account the complex dependencies among resources across applications. To address this problem, we tested the idea of using a visual history of a computer screen to retrieve digital resources within a few days of their use through the development of ScreenTrack. ScreenTrack is software that captures screenshots of a computer at regular intervals. It then generates a time-lapse video from the captured screenshots and lets users retrieve a recently opened document or web page from a screenshot after recognizing the resource by its appearance. A controlled user study found that participants were able to retrieve requested information more quickly with ScreenTrack than under the baseline condition with existing tools. A follow-up study showed that the participants used ScreenTrack to retrieve previously used resources and to recover the context for task resumption.Comment: CHI 2020, 10 pages, 7 figure

Tasktracer: a desktop environment to support multi-tasking knowledge workers

This paper reports on TaskTracer — a software system being designed to help highly multitasking knowledge workers rapidly locate, discover, and reuse past processes they used to successfully complete tasks. The system monitors users ’ interaction with a computer, collects detailed records of users ’ activities and resources accessed, associates (automatically or with users ’ assistance) each interaction event with a particular task, enables users to access records of past activities and quickly restore task contexts. We present a novel Publisher-Subscriber architecture for collecting and processing users ’ activity data, describe several different user interfaces tried with TaskTracer, and discuss the possibility of applying machine learning techniques to recognize/predict users ’ tasks

Supporting Software Developers’ Focused Work on Window-Based Desktops

Software developers, like other information workers, continuously switch tasks and applications to complete their work on their computer. Given the high fragmentation and complexity of their work, staying focused on the relevant pieces of information can become quite challenging in today’s windowbased environments, especially with the ever increasing monitor screen-size. To support developers in staying focused, we conducted a formative study with 18 professionals in which we examined their computer based and eye-gaze interaction with the window environment and devised a relevance model of open windows. Based on the results, we developed a prototype to dim irrelevant windows and reduce distractions, and evaluated it in a user study. Our results indicate that our model was able to predict relevant open windows with high accuracy and participants felt that integrating visual prominence into the desktop environment reduces clutter and distraction, which results in reduced window switching and an increase in focus