Recently, indoor scene synthesis has gathered significant attention, leading to the development of numerous indoor datasets. However, existing datasets only address static furniture and scenes, ignoring the need for dynamic interior design scenarios that emphasize flexible functionalities. Addressing this gap, we present DIFF (Dataset for Indoor Flexible Furniture), featuring expertly crafted and labeled furniture modules capable of inter-transforming between different states, e.g., a cabinet can be inter-transformed to a desk. Each module exhibits flexibility in shifting to multiple shapes and functionalities. Additionally, we propose a method that adapts our dataset to generate flexible layouts. By matching our flexible objects to objects from existing datasets, we use a graph-based approach to migrate the spatial relation priors for optimizing a layout; subsequent layouts are then generated by minimizing a transition-cost function. Analyses and user studies validate the quality of our modules and demonstrate the plausibility of the proposed method.

Rust is an emerging, strongly-typed programming language focusing on efficiency and memory safety. With increasing projects adopting Rust, knowing how to use Unsafe Rust is crucial for Rust security. We observed that the description of safety requirements needs to be unified in Unsafe Rust programming. Current unsafe API documents in the standard library exhibited variations, including inconsistency and insufficiency. To enhance Rust security, we suggest unsafe API documents to list systematic descriptions of safety requirements for users to follow.

Open-domain question answering (OpenQA) is an essential but challenging task in natural language processing that aims to answer questions in natural language formats on the basis of large-scale unstructured passages. Recent research has taken the performance of benchmark datasets to new heights, especially when these datasets are combined with techniques for machine reading comprehension based on Transformer models. However, as identified through our ongoing collaboration with domain experts and our review of literature, three key challenges limit their further improvement: (i) complex data with multiple long texts, (ii) complex model architecture with multiple modules, and (iii) semantically complex decision process. In this paper, we present VEQA, a visual analytics system that helps experts understand the decision reasons of OpenQA and provides insights into model improvement. The system summarizes the data flow within and between modules in the OpenQA model as the decision process takes place at the summary, instance and candidate levels. Specifically, it guides users through a summary visualization of dataset and module response to explore individual instances with a ranking visualization that incorporates context. Furthermore, VEQA supports fine-grained exploration of the decision flow within a single module through a comparative tree visualization. We demonstrate the effectiveness of VEQA in promoting interpretability and providing insights into model enhancement through a case study and expert evaluation.