We introduce CREPE (Rapid Chest Xray Report Evaluation by Predicting Multicategory Error Counts), a rapid, interpretable, and clinically grounded metric for automated chest X-ray report generation.CREPE uses a domain-specific BERT model fine-tuned with a multi-head regression architecture to predict error counts across six clinically meaningful categories.Trained on a large-scale synthetic dataset of 32,000 annotated report pairs, CREPE demonstrates strong generalization and interpretability.On the expert-annotated ReX-Val dataset, CREPE achieves a Kendall's τ correlation of 0.786 with radiologist error counts, outperforming traditional and recent metrics.CREPE achieves these results with an inference speed approximately 280 times faster than large language model (LLM)-based approaches, enabling rapid and fine-grained evaluation for scalable development of chest X-ray report generation models.

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.

This paper proposes Mimic, an automated UI compatibility testing system for Android apps. Mimic is designed specifically for comparing the UI behavior of an app across different devices, different Android versions, and different app versions. This design choice stems from a common problem that Android developers and researchers face-how to test whether or not an app behaves consistently across different environments or internal changes. Mimic allows Android app developers to easily perform backward and forward compatibility testing for their apps. It also enables a clear comparison between a stable version of app and a newer version of app. In doing so, Mimic allows multiple testing strategies to be used, such as randomized or sequential testing. Finally, Mimic programming model allows such tests to be scripted with much less developer effort than other comparable systems. Additionally, Mimic allows parallel testing with multiple testing devices and thereby speeds up testing time. To demonstrate these capabilities, we perform extensive tests for each of the scenarios described above. Our results show that Mimic is effective in detecting forward and backward compatibility issues, and verify runtime behavior of apps. Our evaluation also shows that Mimic significantly reduces the development burden for developers.

Trusted Applications (TAs) written for Trusted Execution Environments (TEEs) using ARM TrustZone are currently written in C; there is limited support for higher-level languages. This leads to common manual memory management problems like buffer overflow and use-after-free. Higher-level languages, which have managed runtimes, allow for automated memory management, the benefits of which are widely accepted. To allow for automated memory management of TAs, we need to have a runtime that handles allocation and garbage collection (GC). However, having the entire allocator and GC in the secure world would increase the Trusted Computing Base (TCB) of the secure world. We propose TrustGC, a mechanism to partition garbage collection and allocation between the secure world and the normal world. TrustGC allows for automated memory management of TAs by leveraging the help of a GC partly running in the normal world.

Gesto is a system that enables task automation for Android apps using gestures and voice commands. Using Gesto, a user can record a UI action sequence for an app, choose a gesture or a voice command to activate the UI action sequence, and later trigger the UI action sequence by the corresponding gesture/voice command. Gesto enables this for existing Android apps without requiring their source code or any help from their developers. In order to make such capability possible, Gesto combines bytecode instrumentation and UI action record-and-replay. To show the applicability of Gesto, we develop four use cases using real apps downloaded from Google Play-Bing, Yelp, AVG Cleaner, and Spotify. For each of these apps, we map a gesture or a voice command to a sequence of UI actions. According to our measurement, Gesto incurs modest overhead for these apps in terms of memory usage, energy usage, and code size increase. We evaluate our instrumentation capability and overhead using 1,000 popular apps downloaded from Google Play. Our result shows that Gesto is able to instrument 94.9% of the apps without any significant overhead. In addition, since our prototype currently supports 6 main UI elements of Android, we evaluate our coverage and measure what percentage of UI element uses we can cover. Our result shows that our 6 UI elements can cover 96.4% of all statically-declared UI element uses in the 1,000 Google Play apps.

This paper proposes a new technique that enables open innovation in mobile platforms. Our technique allows third-party developers to modify, instrument, or extend platform API calls and deploy their modifications seamlessly. The uniqueness of our technique is that it enables modifications completely at the app layer without requiring any platform-level changes. This allows practical openness---third parties can easily distribute their modifications for a platform without the need to update the entire platform. To demonstrate the benefits of our technique, we have developed a prototype on Android called Reptor and used it to instrument real-world apps with novel functionality. Our evaluation in realistic scenarios shows that Reptor has little overhead in performance and energy, and only modest overhead in memory usage that ranges from 0.6% to 10% for the observed worst cases.

We demonstrate AutoClicker, a fully automated UI testing system for large-scale Android apps using multiple devices. It provides a way to quickly and easily verify that a large number of Android apps behave correctly at runtime in a repeatable manner.

We demonstrate Reptor, a bytecode instrumentation tool enabling API virtualization on Android. It provides a general way to alter functionality of platform APIs on Android. With Reptor, third-party developers can modify the behavior of platform APIs according to their needs. All modifications are completely at the app layer without modifying the underlying platform. This allows practical openness---third-party developers can easily distribute their modifications for a platform without the need to update the entire platform.

We demonstrate Gesto, a dynamic gesture mapping tool. It provides users to map any gesture to a certain UI event that the users need. Also, the mapping can be easily changed by users.

Carrier aggregation (CA) technique in LTE-Advanced has been proposed to aggregate two or more component carriers (CC) for supporting high data rate transmission over a wider bandwidth. In this paper, we propose to apply per-CC based different antenna tilting to improve cell edge throughput, where antenna electrical tilting angles of CCs among eNB are set differently. We also propose a cell planning for efficient per-CC different tilting and a cross-CC scheduling algorithm to maximize the achievable performance gain. Through intensive LTE-Advanced system level simulations, it is shown that the cell edge user throughput increases significantly in linear and multi cells, by using the proposed scheme.