5,081 research outputs found
Saliency Prediction for Mobile User Interfaces
We introduce models for saliency prediction for mobile user interfaces. A
mobile interface may include elements like buttons, text, etc. in addition to
natural images which enable performing a variety of tasks. Saliency in natural
images is a well studied area. However, given the difference in what
constitutes a mobile interface, and the usage context of these devices, we
postulate that saliency prediction for mobile interface images requires a fresh
approach. Mobile interface design involves operating on elements, the building
blocks of the interface. We first collected eye-gaze data from mobile devices
for free viewing task. Using this data, we develop a novel autoencoder based
multi-scale deep learning model that provides saliency prediction at the mobile
interface element level. Compared to saliency prediction approaches developed
for natural images, we show that our approach performs significantly better on
a range of established metrics.Comment: Paper accepted at WACV 201
Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development
Mobile devices and platforms have become an established target for modern
software developers due to performant hardware and a large and growing user
base numbering in the billions. Despite their popularity, the software
development process for mobile apps comes with a set of unique, domain-specific
challenges rooted in program comprehension. Many of these challenges stem from
developer difficulties in reasoning about different representations of a
program, a phenomenon we define as a "language dichotomy". In this paper, we
reflect upon the various language dichotomies that contribute to open problems
in program comprehension and development for mobile apps. Furthermore, to help
guide the research community towards effective solutions for these problems, we
provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference
on Program Comprehension (ICPC'18
AI Dining Suggestion App
Trying to decide what to eat can sometimes be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as Application Program Interfaces (APIs) for using them. This restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an app that eases the process of finding a restaurant to eat. This app has a Tinder-like user friendly User Interface (UI) design to change the common way that lists of restaurants are presented to users on mobile apps. It also uses the help of Artificial Intelligence (AI) with neural networks to train both supervised and unsupervised learning models that can learn from one\u27s dining pattern over time to make better suggestions at any time
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