Linking Research

Intelligent user interfaces 2017 (IUI2017)

Posted by dgarijov on March 30, 2017

I have just returned from an amazing IUI2017 in Limassol, Cyprus and, as I have done with other conferences, I think it would be useful to share a summary of my notes in this post. This was my first time attending the IUI conference, and I am gladly surprised with both the quality of the event and friendliness of the community. As a Semantic Web researcher, it was also very positive to learn how problems are tackled from a human-computer interaction perspective. I have to admit that this is often overlooked in many semantic web applications.IMG_20170312_131824098

What was I doing in IUI2017?

My role in the conference was to present our paper towards the generation of data narratives, or, in a more ambitious manner, our attempt to write the “methods” section of a paper automatically (see some examples here). The idea is simple: in computational experiments, the inputs, methods (i.e., scientific workflows), intermediate results, outputs and provenance are explicit in the experiment. However, scientists have to process all these data by themselves and summarize it in the paper. By doing so, they may omit important details that are critical for reusing or reproducing the work. Instead, our approach aims to use all the resources that are explicit in the experiment to generate accurate textual descriptions in an automated way.

I wanted to attend the conference in part to receive feedback on our current approach. Although our work was well received, I learned that the problem can get complex really quickly. In fact, I think it can become a whole area of research itself! I hope to see more approaches in the future in this direction. But that is the topic for another post. Let’s continue with the rest of the conference:


The conference lasted three days, with one main keynote opening each of them. The conference opened with Shumin Zhain, from Google, who described their work on modern touchscreen keyboard interfaces. This will ring a bell to anyone reading this post, as the result of their work can be seen on any Android phone nowadays. I am sure they will not have problems finding users to evaluate their approaches.

In particular, the speaker introduced the system to capture gestures to recognize words, as if you were drawing a line. Apparently, before 2004 they had been playing around with different keyboard configurations that helped users write in a more efficient manner. However, people have different finger sizes, and adapting the keyboard to them is still a challenge. Current systems have several user models, and combine them to adapt to different situations. It was in 2004 when they came with the first prototype of SHARK, a shape writer that used neural networks to decode keyboard movements. They refined their prototype until achieving the result that we see today on every phone.

However, there are still many challenges remaining. Smart watches have a screen that is too small for writing. And new formats without screen such as wearable devices or virtual reality don’t use standard keyboards. Eye tracking solutions have not made significant progress, and while speech recognition has evolved a lot, it is not likely to replace traditional writers any time soon.

The second speakers was George Samaras, who described their work to personalize interfaces based on the emotions shown by the users of a system. The motivation for this need is that currently an 80% of the errors of automated systems are due to human mistakes rather than mechanical ones, especially when the interfaces are complex, such as in aviation or nuclear plants. Here cognitive systems are crucial, and adapting the content and navigation to the humans using them becomes a priority.

The speaker presented their framework to classify users based on the relevant factors in interfaces. For example, the verbals prefer textual explanations, while imagers like image explanations for e.g., browsing results. Another example is how users prefer to explore the results: we have the wholist, who prefer a top down exploration, versus the analysit, who would rather go for bottom up search. This is can become an issue in collaborations, as users that prefer to perceive the information in the same way may collaborate more efficiently together. A study performed over 10 years with more than 1500 shows that personalized interfaces lead to a faster task completion.

Finally, the speaker presented their work for tackling the emotions of users. Recognizing them is important, as depending on their mood, users may be keen to see the interface in one way or the other. They have developed a set of cognitive agents, which aim to personalize services and persuade users to complete certain tasks. Persuasion is more efficient when taking into account emotions as well.


The final keynote was presented by Panos Markopoulos, who introduced their work on hci design for patient rehabilitation. Having a proper interaction with patients (in exercises for kids and elderly people, arm training for stroke survivors, etc.) is critical for their recovery. However, this interaction has to be meaningful or patients will get bored and not complete their recovery exercises. The speaker described their work with therapists to track patient recovery in exercises such as pouring wine, cleaning windows, etc. The talk ended with a summary of some of the current challenges in this area, such as adapting feedback from patient behavior, sustaining engagement on the long run or personalization of exercises.


  • Recommendation is still a major topic in HCI. Peter Brusilovsky gave a nice overview of their work on personalization in the context of relevance-based visualization, as part of the ESIDA workshop. Personalized visualizations are now gaining more relevance in recommendation, but picking the right visualization for users is still a challenge. In addition, users are starting to demand why certain recommendations are more relevant, so non-symbolic approaches like topic modeling present issues.
  • Semantic web as a means to address curiosity in recommendations. SIRUP uses LOD paths with cosine similarity to find potential connections relevant for users.
  • Most influential paper award: Trust in recommender systems (O’Donovan and Smyth), where they developed a trust model for users, taking into account provenance too. Congrats!



IUI 2017 had 193 participants this year, almost half of them students (86); and an acceptance rate of 23% (27% for full papers). You can check the program for more details. I usually prefer this kind of conferences because they are relatively small, you can see most of the presented work without having to choose and you can talk to everyone very easily. If I can, I will definitely come back.

I also hope to see more influence of Semantic Web techniques to facilitate some of the challenges in HCI, as I think it there is a lot of potential to help in explanation, trust or personalization. I look forward to attending next year in Tokyo!


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