We investigate how non-native English speakers (NNESs) interact with diverse information aids to assess and select AI-generated paraphrases.We develop ParaScope, an AI paraphrasing assistant that integrates diverse information aids, such as back-translation, explanations, and usage examples, and logs user interaction data.Our in-lab study with 22 NNESs reveals that user preferences for information aids vary by language proficiency, with workflows progressing from global to more detailed information.While backtranslation was the most frequently used aid, it was not a decisive factor in suggestion acceptance; users combined multiple information aids to make informed decisions.Our findings demonstrate the potential of explainable AI paraphrasing tools to enhance NNESs' confidence, autonomy, and writing efficiency, while also emphasizing the importance of thoughtful design to prevent information overload.Based on these findings, we offer design implications for explainable AI paraphrasing tools that support NNESs in making informed decisions when using AI writing systems.

We present Rushmore, a system that securely displays static or animated images using TrustZone. The core functionality of Rushmore is to securely decrypt and display encrypted images (sent by a trusted party) on a mobile device. Although previous approaches have shown that it is possible to securely display encrypted images using TrustZone, they exhibit a critical limitation that significantly hampers the applicability of using TrustZone for display security. The limitation is that, when the trusted domain of TrustZone (the secure world) takes control of the display, the untrusted domain (the normal world) cannot display anything simultaneously. This limitation comes from the fact that previous approaches give the secure world exclusive access to the display hardware to preserve security. With Rushmore, we overcome this limitation by leveraging a well-known, yet overlooked hardware feature called an IPU (Image Processing Unit) that provides multiple display channels. By partitioning these channels across the normal world and the secure world, we enable the two worlds to simultaneously display pixels on the screen without sacrificing security. Furthermore, we show that with the right type of cryptographic method, we can decrypt and display encrypted animated images at 30 FPS or higher for medium-to-small images and at around 30 FPS for large images. One notable cryptographic method we adapt for Rushmore is visual cryptography, and we demonstrate that it is a light-weight alternative to other cryptographic methods for certain use cases. Our evaluation shows that in addition to providing usable frame rates, Rushmore incurs less than 5% overhead to the applications running in the normal world.

Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit on an app. However, today's social media apps offer a mix of quasiessential and addictive features in an app (e.g., Instagram has following feeds, recommended feeds, stories, and direct messaging features), which makes it hard to apply a uniform logic for all uses of an app without a nuanced understanding of feature-level usage behaviors. We study when and why people regret using different features of social media apps on smartphones. We examine regretful feature uses in four smartphone social media apps (Facebook, Instagram, YouTube, and KakaoTalk) by utilizing feature usage logs, ESM surveys on regretful use collected for a week, and retrospective interviews from 29 Android users. In determining whether a feature use is regretful, users considered different types of rewards they obtained from using a certain feature (i.e., social, informational, personal interests, and entertainment) as well as alternative rewards they could have gained had they not used the smartphone (e.g., productivity). Depending on the types of rewards and the way rewards are presented to users, probabilities to regret vary across features of the same app. We highlight three patterns of features with different characteristics that lead to regretful use. First, "following"-based features (e.g., Facebook's News Feed and Instagram's Following Posts and Stories) induce habitual checking and quickly deplete rewards from app use. Second, recommendation-based features situated close to actively used features (e.g., Instagram's Suggested Posts adjacent to Search) cause habitual feature tour and sidetracking from the original intention of app use. Third, recommendation-based features with bite-sized contents (e.g., Facebook's Watch Videos) induce using "just a bit more," making people fall into prolonged use. We discuss implications of our findings for how social media apps and intervention tools can be designed to reduce regretful use and how feature-level usage information can strengthen self-reflection and behavior changes.

We present ProDroid, a framework that provides Android app developers an ability to quickly produce functional prototypes. With ProDroid, developers can create a new app that imports various kinds of functionality provided by other existing Android apps. Our evaluation shows that with the help of ProDroid, a developer was able to import a function from an existing Android app into a new prototype with only 55 lines of Java code, while the function itself requires 10,334 lines of Java code to implement.

We present X-Droid, a framework that provides Android app developers an ability to quickly and easily produce functional prototypes. Our work is motivated by the need for such ability and the lack of tools that provide it. Developers want to produce a functional prototype rapidly to test out potential features in real-life situations. However, current prototyping tools for mobile apps are limited to creating non-functional UI mockups that do not demonstrate actual features. With X-Droid, developers can create a new app that imports various kinds of functionality provided by other existing Android apps. In doing so, developers do not need to understand how other Android apps are implemented or need access to their source code. X-Droid provides a developer tool that enables developers to use the UIs of other Android apps and import desired functions into their prototypes. X-Droid also provides a run-time system that executes other apps' functionality in the background on off-the-shelf Android devices for seamless integration. Our evaluation shows that with the help of X-Droid, a developer imported a function from an existing Android app into a new prototype with only 51 lines of Java code, while the function itself requires 10,334 lines of Java code to implement (i.e., 200× improvement).

While a certain sound serves a purpose to someone, for others, the same sound could be noise. Specifically, an alarm sound would be necessary for someone to wake up and start the day, while it would be an unwanted sound for people sharing the same room, who need not wake up as early. Noise cancellation is useful in this scenario, but most existing techniques require costly equipments (e.g., high quality speakers or microphones) or devices that are uncomfortable to wear during sleep. We thus explore the possibility of using only commodity smartphones to achieve active noise control and present Virtual Earplugs. As most people own a smartphone that include a microphone and speakers, and has an alarm clock feature, we believe our system could be easily used in practice. We highlight the technical challenges in realizing our vision and demonstrate the feasibility of our approach using our preliminary prototype. Our results indicate that we can reduce the alarm sound by up to 19~dB in only 2.1~seconds of processing delay.

Delay-tolerant Wi-Fi offloading is known to improve overall mobile network bandwidth at low delay and low cost. Yet, in reality, we rarely find mobile apps that fully support opportunistic Wi-Fi access. This is mainly because it is still challenging to develop delay-tolerant mobile apps due to the complexity of handling network disruptions and delays. In this paper, we present Cedos, a practical delay-tolerant mobile network access architecture in which one can easily build a mobile app. Cedos consists of three components. First, it provides a familiar socket API whose semantics conforms to TCP, while the underlying protocol, D2TP, transparently handles network disruptions and delays in mobility. Second, Cedos allows the developers to explicitly exploit delays in mobile apps. App developers can express maximum user-specified delays in content download or use the API for real-time buffer management at opportunistic Wi-Fi usage. Third, for backward compatibility to existing TCP-based servers, Cedos provides D2Prox, a protocol-translation Web proxy. D2Prox allows intermittent connections on the mobile device side, but correctly translates Web transactions with traditional TCP servers. We demonstrate the practicality of Cedos by porting mobile Firefox and VLC video streaming client to using the API. We also implement delay/disruption-tolerant podcast client and run a field study with 50 people for eight weeks. We find that up to 92.4% of the podcast traffic is offloaded to Wi-Fi, and one can watch a streaming video in a moving train while offloading 48% of the content to Wi-Fi without a single pause.

Delay-tolerant Wi-Fi offloading is known to improve overall mobile network bandwidth at low delay and low cost. Yet, in reality, we rarely find mobile apps that fully support opportunistic Wi-Fi access. This is mainly because it is still challenging to develop delay-tolerant mobile apps due to the complexity of handling network disruptions and delays.

No abstract available.

For recording of neural signals from large population of neurons, stringent constraints are imposed on the design of neural amplifiers. We have designed neural amplifier in FD-SOI technology in order to achieve lower power consumption, smaller area, and better noise efficiency factor compared to the standard bulk processes. A symmetric pseudo resistor was realized with resistances on the order of 1015Ω, enabling a low cut-off frequency of 0.6mHz. The designed neural amplifier occupies an area of 0.004mm2, with simulated performance demonstrating an input-referred noise of 3.07μVrmsand a power consumption of 6μW.