Software diffing (a.k.a., code alignment) is a fundamental technique to differentiate similar and dissimilar code pieces between two given software products. It can enable various kinds of critical security analysis, e.g., n-day bug localization, software plagiarism detection, etc. To date, many diffing tools have been proposed dedicated to aligning binaries. However, few research efforts have elaborated on cross-version Android app diffing, largely hindering the security assessment of wild apps. To sum up, existing diffing works usually establish scalability-oriented alignment algorithms, and suffer from significant alignment errors when handling the large codebases of modern apps. To fill this gap, we propose A pk D iffer , a method-level (i.e., function-level) diffing tool dedicated to aligning versions of the same closed-source Android app. A pk D iffer achieves a good balance between scalability and effectiveness, by featuring a two-stage decomposition-based alignment solution. It first decomposes the codebase of each app version, respectively, into multiple functionality units; then tries to precisely align methods that serve equivalent app functionalities across versions. In evaluation, the results show that A pk D iffer noticeably outperforms existing alignment algorithms in precision and recall, while still having a satisfactory time cost. In addition, we used A pk D iffer to track the one-year evolution of 100 popular Google Play apps. By pinpointing the detailed code locations where app versions deviate in privacy collection, we convincingly revealed that app updates may pose ever-evolving privacy threats to end-users.

Three-dimensional ultrasound (US) aims to provide sonographers with the spatial relationships of anatomical structures, playing a crucial role in clinical diagnosis. Recently, deep-learning-based freehand 3-D US has made significant advancements. It reconstructs volumes by estimating transformations between images without external tracking. However, image-only reconstruction poses difficulties in reducing cumulative drift and further improving reconstruction accuracy, particularly in scenarios involving complex motion trajectories. In this context, we propose an enhanced motion network (MoNetV2) to enhance the accuracy and generalizability of reconstruction under diverse scanning velocities and tactics. First, we propose a sensor-based temporal and multibranch structure (TMS) that fuses image and motion information from a velocity perspective to improve image-only reconstruction accuracy. Second, we devise an online multilevel consistency constraint (MCC) that exploits the inherent consistency of scans to handle various scanning velocities and tactics. This constraint exploits scan-level velocity consistency (SVC), path-level appearance consistency (PAC), and patch-level motion consistency (PMC) to supervise interframe transformation estimation. Third, we distill an online multimodal self-supervised strategy (MSS) that leverages the correlation between network estimation and motion information to further reduce cumulative errors. Extensive experiments clearly demonstrate that MoNetV2 surpasses existing methods in both reconstruction quality and generalizability performance across three large datasets.

For the safety assessment of autonomous driving systems (ADS), simulation testing has become an important complementary technique to physical road testing. In essence, simulation testing is a scenario-driven approach, whose effectiveness is highly dependent on the quality of given simulation scenarios. Moreover, simulation scenarios should be encoded into well-formatted files, otherwise, ADS simulation platforms cannot take them as inputs. Without large public datasets of simulation scenario files, both industry and academic applications of ADS simulation testing are hindered.

Adequate medical images are often indispensable in contemporary deep learning-based medical imaging studies, although the acquisition of certain image modalities may be limited due to several issues including high costs and patients issues. However, thanks to recent advances in deep learning techniques, the above tough problem can be substantially alleviated by medical images synthesis, by which various modalities including T1/T2/DTI MRI images, PET images, cardiac ultrasound images, retinal images, and so on, have already been synthesized. Unfortunately, the arterial spin labeling (ASL) image, which is an important fMRI indicator in dementia diseases diagnosis nowadays, has never been comprehensively investigated for the synthesis purpose yet. In this paper, ASL images have been successfully synthesized from structural magnetic resonance images for the first time. Technically, a novel unbalanced deep discriminant learning-based model equipped with new ResNet sub-structures is proposed to realize the synthesis of ASL images from structural magnetic resonance images. The extensive experiments have been conducted. Comprehensive statistical analyses reveal that: 1) this newly introduced model is capable to synthesize ASL images that are similar towards real ones acquired by actual scanning; 2) synthesized ASL images obtained by the new model have demonstrated outstanding performance when undergoing rigorous tests of region-based and voxel-based corrections of partial volume effects, which are essential in ASL images processing; and 3) it is also promising that the diagnosis performance of dementia diseases can be significantly improved with the help of synthesized ASL images obtained by the new model, based on a multi-modal MRI dataset containing 355 demented patients in this paper.