Abstract: An unsupervised region-growing network (RGN) is trained to perform object segmentation on video data degraded by atmospheric turbulence. The method includes obtaining input data containing turbulence-degraded video, extracting a video frame sequence, and training the RGN using a selected algorithm incorporating a region-growing algorithm and a grouping loss function. A bidirectional optical flow sequence is computed for multiple reference frames within the video sequence. Pixel-level masks are generated for detected moving objects, followed by applying the region-growing algorithm to create coarse masks. A grouping loss function refines these masks to ensure consistency across consecutive frames. The trained RGN outputs refined masks as object segmentation data for the received video, improving segmentation accuracy in turbulent environments.

Abstract: According to an aspect of an embodiment, a method may include identifying a fault at a fault location in a software program using a test suite. The method may also include determining multiple textual similarity scores by determining a textual similarity score with respect to each of multiple repair candidates for the fault. In addition, the method may include sorting the repair candidates based on the textual similarity scores. The method may also include selecting a particular repair candidate from the repair candidates based on the sorting. Moreover, the method may include implementing the particular repair candidate at the fault location based on the selection of the particular repair candidate.

Abstract: According to an aspect of an embodiment, a method may include identifying a fault at a fault location in a software program using a test suite. The method may also include determining multiple textual similarity scores by determining a textual similarity score with respect to each of multiple repair candidates for the fault. In addition, the method may include sorting the repair candidates based on the textual similarity scores. The method may also include selecting a particular repair candidate from the repair candidates based on the sorting. Moreover, the method may include implementing the particular repair candidate at the fault location based on the selection of the particular repair candidate.