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Creative problem solving. Designing for your audience. Designing for contingencies. Designing feedback loops. Conducting meaningful user testing—even on the cheap. A primer on A/B testing. Testing accordion forms. Good "Help" content is hard to find. Quick and dirty remote user testing. Web standards for e-books.

Designing for Research

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Image quality may be about striking the balance between speed and quality, but there’s more to it than meets the eye. What if, despite having methods to develop better and better image experiences for the web, the user disagrees? In a quest to find answers, Jeremy Wagner takes us through an image quality study that he designs, develops, and iterates on with user feedback. Asking “Why?” is no easy undertaking in research. His lossy is your gain.

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It’s not about writing the most sophisticated code or finding the most clever solution to a problem; it’s about users and whether they’re able to use our products.” Manuel Matuzovic shares 11 lessons as a developer learning about incorporating accessibility into his work.

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A difficult user migration project led Aimee Gonzalez-Cameron to reevaluate how she approaches her work. She started to see herself not just as an expert on user experience, but as a teacher, a realization that transformed her work. She reflects on how developing a teaching mindset can improve any UX project.

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Incorporating accessibility from the beginning of a web design project is easier, more effective, and less expensive than making accessibility fixes after the fact. Yet most of us too often get stuck doing the latter. Fear not! ALA’s exclusive excerpt from Laura Kalbag’s Accessibility for Everyone is here to help. You’ll learn how to make the case for accessibility to reluctant coworkers, bosses, or clients. How to build your team, scope the project, and even budget the job.

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Color is a powerful tool that allows for an almost infinite array of design options. Yet when applying color to our work, we can have a “myopic” viewpoint that puts us, rather than our audience, front and center. Author Geri Coady discusses some solid color considerations we can make for our audiences in this excerpt from her new book, Color Accessibility Workflows, available from A Book Apart.

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So, clearly this is a very useful thing for W to do. But how does it learn to do this? It seems quite likely that there are lots of situations where it has seen a sentence like “the wall is blue” and know that it is valid before it sees a sentence like “the wall is red”. As such, shifting “red” a bit closer to “blue” makes the network perform better.

We still need to see examples of every word being used, but the analogies allow us to generalize to new combinations of words. You’ve seen all the words that you understand before, but you haven’t seen all the sentences that you understand before. So too with neural networks.

From Mikolov (2013a)

Word embeddings exhibit an even more remarkable property: analogies between words seem to be encoded in the difference vectors between words. For example, there seems to be a constant male-female difference vector:

W ( woman " ) W ( man " ) W ( aunt " ) W ( uncle " )

This may not seem too surprising. After all, gender pronouns mean that switching a word can make a sentence grammatically incorrect. You write, “ she is the aunt” but “ he is the uncle.” Similarly, “ he is the King” but “ she is the Queen.” If one sees “ she is the uncle ,” the most likely explanation is a grammatical error. If words are being randomly switched half the time, it seems pretty likely that happened here.

“Of course!” We say with hindsight, “the word embedding will learn to encode gender in a consistent way. In fact, there’s probably a gender dimension. Same thing for singular vs plural. It’s easy to find these trivial relationships!”

It turns out, though, that much more sophisticated relationships are also encoded in this way. It seems almost miraculous!

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It’s important to appreciate that all of these properties of W are side effects . We didn’t try to have similar words be close together. We didn’t try to have analogies encoded with difference vectors. All we tried to do was perform a simple task, like predicting whether a sentence was valid. These properties more or less popped out of the optimization process.

This seems to be a great strength of neural networks: they learn better ways to represent data, automatically. Representing data well, in turn, seems to be essential to success at many machine learning problems. Word embeddings are just a particularly striking example of learning a representation.

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