FabHacks with Everyday Objects

Yuxuan Mei, Benjamin T. Jones, Dan Cascaval, Jennifer Mankoff, Etienne Vouga, Adriana Schulz: FabHacks: Transform Everyday Objects into Home Hacks Leveraging a Solver-aided DSL. SCF 2024: 4

Storing, organizing, and decorating are key parts of making a home nice. Buying new things for these tasks can be expensive, and reuse is better for the planet. One way to do this is with a “home hack.” This is when you use things you already have at home to solve a problem. But creating these hacks can be hard, especially if they are big, need to be nailed or screwed to the wall.

We have a system called FabHacks to help make these home hacks easier to create. It uses a new, hack-specific language we made called FabHaL to help you build these hacks. We looked at home hacks people share online and found ways to connect household items using specific methods. We also have a simple app to help you design such hacks. FabHacks, is based on a solver-aided domain-specific language (S-DSL). It leverages a physics-based solver that finds the expected physical configuration of a hack. We tested FabHacks by having people use our system, and they could easily make and try different designs.

Notably Inaccessible

Venkatesh Potluri, Sudheesh Singanamalla, Nussara Tieanklin, Jennifer Mankoff: Notably Inaccessible – Data Driven Understanding of Data Science Notebook (In)Accessibility. ASSETS 2023: 13:1-13:19

Computational notebooks are tools that help people explore, analyze data, and create stories about that data. They are the most popular choice for data scientists. People use software like Jupyter, Datalore, and Google Colab to work with these notebooks in universities and companies.

There is a lot of research on how data scientists use these notebooks and how to help them work together better. But there is not much information about the problems faced by blind and visually impaired (BVI) users. BVI users have difficulty using these notebooks because:

  • The interfaces are not accessible.
  • The way data is shown is not user-friendly for them.
  • Popular libraries do not provide outputs they can use.

We analyzed 100,000 Jupyter notebooks to find accessibility problems. We looked for issues that affect how these notebooks are created and read. From our study, we give advice on how to make notebooks more accessible. We suggest ways for people to write better notebooks and changes to make the notebook software work better for everyone.

Touchpad Mapper

Ather Sharif, Venkatesh Potluri, Jazz Rui Xia Ang, Jacob O. Wobbrock, Jennifer Mankoff: Touchpad Mapper: Examining Information Consumption From 2D Digital Content Using Touchpads by Screen-Reader Users: ASSETS ’24 (best poster!) and W4A ’24 (open access)

Touchpads are common, but they are not very useful for people who use screen readers. We created and tested a tool called Touchpad Mapper to let Blind and visually impaired people make better use of touchpads. Touchpad Mapper lets screen-reader users use touchpads to interact with digital content like images and videos.

Touchpad mapping could be used in many apps. We built two examples:

  1. Users can use the touchpad to identify where things are in an image.
  2. Users can control a video’s progress with the touchpad, including rewinding and fast-forwarding.

We tested Touchpad Mapper with three people who use screen readers. They said they got information more quickly with our tool than with a regular keyboard.

Bespoke Slides for Fluctuating Access Needs

Kelly Avery Mack, Kate S. Glazko, Jamil Islam, Megan Hofmann, Jennifer Mankoff: “It’s like Goldilocks: ” Bespoke Slides for Fluctuating Audience Access Needs. ASSETS 2024: 71:1-71:15

Slide deck accessibility is usually thought to mainly impact people who are blind or visually impaired. However, many other people might need modifications to access a slide deck.

We talked with 17 people who have disabilities and use slide decks and learned their needs did not always overlap. For some people, their own needs even changed at times. For example, some needed lower contrast colors at night.

Next, we explored how a tool could help change a presentation to fit their needs. We tested this tool with 14 of the people we talked to earlier. Then, we interviewed four people who make and present slide decks to get their thoughts on this tool.

Finally, we tried to make a working version of this tool. It has some of the features that the people we talked to wanted, but we learned that when apps are not designed for access, and not open source, they make full access hard to add.

Stacy Hsueh

Stacy is an Asian woman with black hair, wearing a black turtle neck, smiling with head slightly turned to the side.

Stacy Hsueh is a postdoctoral researcher at UW’s Center for Research and Education on Accessible Technology and Experiences (CREATE), working with Jen Mankoff and Anat Caspi.

Her work uses critical theories to interrogate computing norms and employs design methods to explore disability-led design. Her current research focuses on understanding experiences of precarity in underserved communities and examining the role of technology plays in challenging or reinforcing structural inequities. 

Miles Stanley

A young white, cisgender man standing outdoors, smiling. He has medium-length brown hair and wears a blue North Face jacket with a hood. The background is blurred and features a cityscape taken from an elevated viewpoint.

Miles is a third-year computer science undergraduate at the University of Washington. He is interested in new applications of generative AI, HCI, and programming languages. In his free time, he enjoys running and brunch with friends. Miles is currently working on the Accessible Flowcharts Project.

Runxin Shi

A young man with short black hair smiles at the camera while seated at a wooden table in a café. He is wearing a bright green button-up shirt and holding a fork in one hand and a spoon in the other. On the table in front of him is a tray holding a plate of spaghetti and a small bowl of fresh salad. Behind him, there are wooden shelves, and a window.

Runxin Shi is a third-year undergraduate studying Computer Science at the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He is passionate about using modern technologies, such as wearable devices, to enhance accessibility and unlock new possibilities for people with disabilities. With experience in backend development, qualitative research, user research, and interaction design, Runxin takes a multidisciplinary approach to problem-solving. Beyond academics, he has a deep appreciation for art and enjoys visiting museums and creating interactive art installations in his free time.

Varun Narayanswamy

[An Indian male] wearing a gray hoodie, sitting in a striped multicolor chair. In his arms are two kittens, both orange and white, the one on the left pressing its face into his arm while the other looks to the side. The boy is looking down at the cat on the right.

Varun Narayanswamy is a student in the Master’s from Human Computer Interaction and Design (MHCI+D). His research interests include HCI, data visualization, frontend development, mobile development, and education technology.

Yusuf Mohammed

Yusuf is a second-year undergraduate at the University of Washington majoring in Computer Science. He has prior experience with full stack web development and databases. He is interested in how generative AI can be used to improve accessibility. He is also interested in Machine Learning and Systems Programming. In his free time, he enjoys playing spikeball and watching football. Currently, he is working on the Text Simplification Project in the Make4All lab.

KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting

Knitting machines can fabricate complex fabric structures using robust industrial fabrication machines. However, machine knitting’s full capabilities are only available through low-level programming languages that operate on individual machine operations. We present KnitScript, a domain-specific machine knitting scripting language that supports computationally driven knitting designs. KnitScript provides a comprehensive virtual model of knitting machines, giving access to machine-level capabilities as they are needed while automating a variety of tedious and error-prone details. Programmers can extend KnitScript with Python programs to create more complex programs and user interfaces. We evaluate the expressivity of KnitScript through a user study where nine machine knitters used KnitScript code to modify knitting patterns. We demonstrate the capabilities of KnitScript through three demonstrations where we create: a program for generating knitted figures of randomized trees, a parameterized hat template that can be modified with accessibility features, and a pattern for a parametric mixed-material lampshade. KnitScript advances the state of machine-knitting research by providing a platform to develop and share complex knitting algorithms, design tools, and patterns.

Megan Hofmann, Lea Albaugh, Tongyan Wang, Jennifer Mankoff, Scott E. Hudson: KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting. UIST 2023: 21:1-21:21

https://youtube.com/watch?v=emV3xNVlg-0%3Fsi%3DLOM1EWRCmhcnzQJY