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A-Mash: Providing Single-App Illusion for Multi-App Use through User-centric UI Mashup

Title
A-Mash: Providing Single-App Illusion for Multi-App Use through User-centric UI Mashup
Author(s)
Lee, SunjaeKim, HoyoungKim, SijungLee, SangwookKim, HyosuSong, Jean YoungKo, Steven Y.Oh, SangeunShin, Insik
Issued Date
2022-10-20
Citation
ACM International Conference on Mobile Computing and Networking, pp.690 - 702
Type
Conference Paper
ISBN
9781450391818
Abstract
Mobile apps offer a variety of features that greatly enhance user experience. However, users still often find it difficult to use mobile apps in the way they want. For example, it is not easy to use multiple apps simultaneously on a small screen of a smartphone. In this paper, we present A-Mash, a mobile platform that aims to simplify the way of interacting with multiple apps concurrently to the level of using a single app only. A key feature of A-Mash is that users can mash up the UIs of different existing mobile apps on a single screen according to their preferences. To this end, A-Mash 1) extracts UIs from unmodified existing apps (dynamic UI extraction) and 2) embeds extracted UIs from different apps into a single wrapper app (cross-process UI embedding), while 3) making all these processes hidden from the users (transparent execution environment). To the best of our knowledge, A-Mash is the first work to enable UIs of different unmodified legacy apps to seamlessly integrate and synchronize on a single screen, providing an illusion as if they were developed as a single app. A-Mash offers great potential for a number of useful usage scenarios. For instance, a user can mashup UIs of different IoT administration apps to create an all-in-one IoT device controller or one can mashup today's headlines from different news and magazine apps to craft one's own news headline collection. In addition, A-Mash can be extended to an AR space, in which users can map UI elements of different mobile apps to physical objects inside their AR scenes. Our evaluation of the A-Mash prototype implemented in Android OS demonstrates that A-Mash successfully supports the mashup of various existing mobile apps with little or no performance bottleneck. We also conducted in-depth user studies to assess the effectiveness of the A-Mash in real-world use cases. © 2022 ACM.
URI
http://hdl.handle.net/20.500.11750/46803
DOI
10.1145/3495243.3560522
Publisher
Association for Computing Machinery
Related Researcher
  • 송진영 Song, Jean Young
  • Research Interests 인간-기계 상호작용; 인간-AI 상호작용; 크라우드소싱; 인공지능 데이터셋; 컴퓨터비전; Human-Computer Interaction; Artificial Intelligence; Human-AI Collaboration; Crowdsourcing; Computer Vision
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Appears in Collections:
Department of Electrical Engineering and Computer Science DGIST Intelligence Augmentation Group 2. Conference Papers

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