The login functionality, being the gateway to app usage, plays a critical role in both user experience and application security. As Android apps increasingly incorporate login functionalities, they support a variety of authentication methods with complicated login processes, catering to personalized user experiences. However, the complexities in managing different operations in login processes make it difficult for developers to handle them correctly. In this paper, we present the first empirical study of login issues in Android apps. We analyze 361 issues from 44 popular open-source Android repositories, examining the root causes, symptoms, and trigger conditions of these issues. Our findings indicate that the vast majority of the login issues are induced by the improper handling of complex state transitions during the login process, which can prevent users from logging in or misdirect them to incorrect subsequent actions. Additionally, we observed that issues related to this cause typically require the convergence of multiple trigger conditions to manifest. These findings can help developers to model the login processes which can help them to identify the causes of issues and design targeted test cases and precise test oracles. Our dataset has been made openly available to facilitate future research in this area.

To foster trust in machine learning models, explanations must be faithful and stable for consistent insights. Existing relevant works rely on the $\ell_p$ distance for stability assessment, which diverges from human perception. Besides, existing adversarial training (AT) associated with intensive computations may lead to an arms race. To address these challenges, we introduce a novel metric to assess the stability of top-$k$ salient features. We introduce R2ET which trains for stable explanation by efficient and effective regularizer, and analyze R2ET by multi-objective optimization to prove numerical and statistical stability of explanations. Moreover, theoretical connections between R2ET and certified robustness justify R2ET's stability in all attacks. Extensive experiments across various data modalities and model architectures show that R2ET achieves superior stability against stealthy attacks, and generalizes effectively across different explanation methods. The code can be found at https://github.com/ccha005/R2ET.

Reconstructing a High Dynamic Range (HDR) image from several Low Dynamic Range (LDR) images with different exposures is a challenging task, especially in the presence of camera and object motion. Though existing models using convolutional neural networks (CNNs) have made great progress, challenges still exist, e.g., ghosting artifacts. Transformers, originating from the field of natural language processing, have shown success in computer vision tasks, due to their ability to address a large receptive field even within a single layer. In this paper, we propose a transformer model for HDR imaging. Our pipeline includes three steps: alignment, fusion, and reconstruction. The key component is the HDR transformer module. Through experiments and ablation studies, we demonstrate that our model outperforms the state-of-the-art by large margins on several popular public datasets.