Reflow: Automatically Improving Touch Interactions in Mobile Applications through Pixel-based Refinements

Touch is the primary way that users interact with smartphones. However, building mobile user interfaces where touch interactions work well for all users is a difficult problem, because users have different abilities and preferences. We propose a system, Reflow, which automatically applies small, personalized UI adaptations, called refinements -- to mobile app screens to improve touch efficiency. Reflow uses a pixel-based strategy to work with existing applications, and improves touch efficiency while minimally disrupting the design intent of the original application. Our system optimizes a UI by (i) extracting its layout from its screenshot, (ii) refining its layout, and (iii) re-rendering the UI to reflect these modifications. We conducted a user study with 10 participants and a heuristic evaluation with 6 experts and found that applications optimized by Reflow led to, on average, 9% faster selection time with minimal layout disruption. The results demonstrate that Reflow's refinements useful UI adaptations to improve touch interactions

Interactive Ambient Visualizations for Soft Advice

Some software packages offer the user soft advice: recommendations that are intended to help the user create high quality artifacts, but which may turn out to be bad advice. It is left to the user to determine whether the soft advice really will improve quality, and to decide whether or not to adopt it. Visualizations can help the user in making this decision, but we believe that conventional visualizations are less than ideal. In this paper, we describe an interactive ambient visualization to help users identify, understand and interpret soft advice. Our visualization was developed to help programmers interpret code smells, which are indications that a software project may be suffering from design problems. We describe a laboratory experiment with 12 programmers that tests several hypotheses about our visualization. The findings suggest that our tool helps programmers to identify smells more effectively, and to make more informed judgments about the design of the program under development. We then describe an application of our visualization technique in another domain: an English style and grammar advisor. This second application suggests that our technique can be applied to several domains, and also suggests how the technique must be varied to make it domain specific

OASIcs, Volume 67, PLATEAU\u2718, Complete Volume

OASIcs, Volume 67, PLATEAU\u2718, Complete Volum

Front Matter, Table of Contents, Preface, Conference Organization

Front Matter, Table of Contents, Preface, Conference Organizatio

fastread/src: FAST2

Core update with the techniques for 1. how to start and 2. when to stop starting review with either importing previous data or a keyword search is enabled and recommended; stopping review when there is few relevant papers left (according to the estimation)

Spatial Game Signatures for Bot Detection in Social Games

Bot detection is an emerging problem in social games that requires different approaches from those used in massively multi-player online games (MMOGs). We focus on mouse selections as a key element of bot detection. We hypothesize that certain interface elements result in predictable differences in mouse selections, which we call spatial game signatures, and that those signatures can be used to model player interactions that are specific to the game mechanics and game interface. We performed a study in which users played a game representative of social games. We collected in-game actions, from which we empirically identified these signatures, and show that these signatures result in a viable approach to bot detection. We make three contributions. First, we introduce the idea of spatial game signatures. Second, we show that the assumption that mouse clicks are normally distributed about the center of buttons is not true for every interface element. Finally, we provide methodologies for using spatial game signatures for bot detection