This paper seriously investigates the two typical datasets in CAD sequence learning. The DeepCAD (with 178k samples) paper indicates that the limited size of the Fusion 360 dataset (less than 10k samples) is insufficient for training a well-generalized model. Thus, almost all of the top conference and journal papers adopt DeepCAD dataset for training. However, this investigation reveals that, although there is a huge gap in data size between Fusion 360 Reconstruction dataset and DeepCAD dataset, they have almost the same ability in CAD sequence learning. We devise reasonable experiments and a data augmentation method to demonstrate that Fusion 360 Reconstruction dataset and DeepCAD dataset are essentially indistinguishable, exhibiting equivalent capabilities in CAD sequence learning for simple sketch and extrusion commands. Therefore, to advance the development of CAD sequence learning, we need more complex and advanced CAD datasets, which is a more challenging task for our community in the future.

Virtual reality (VR) technologies have emerged as valuable tools for medical and emergency training, providing safe, immersive, and repeatable environments where complex procedures can be practiced effectively. This article presents an immersive VR system designed to train workplace first-aid responders, with a particular focus on cardiopulmonary resuscitation (CPR). The platform integrates a physical CPR manikin with virtual patient overlays through mixed-reality calibration, incorporates realistic emergency scenarios with environmental hazards, and enables synchronous multiuser interaction between trainees and instructors. To assess its potential, we provide a detailed description of the system's architecture and functionalities, introducing the results of an extensive user study employing validated questionnaires on usability, performance, and user experience. The proposed framework contributes to the advancement of VR-based medical education, highlighting its benefits, current limitations, and future research opportunities.

While it is necessary for most (if not all) visualization and visual analytics (VIS) publication venues to use peer review processes to assure the quality of the papers to be published, it is also necessary for the VIS community to appraise and improve the quality of peer review processes from time to time. In recent years, rejecting a VIS paper seems to have become rather easy, as many rejection reasons are available to criticize a given paper. In this article, we analyze possible causes of this phenomenon and recommend possible remedies. In particular, over the past decades, the visualization field has rapidly grown to include many types of contributions and specialized research areas. Given this large landscape of topics, we need to ensure that good contributions within each area are reviewed properly, published, and built upon to make significant advancement in the area concerned. Therefore, it is crucial that our review process applies specific criteria for each area and does not expect individual publications to satisfy many review criteria designed for other areas. In this way, we hope VIS review processes will enable more VIS research with X factors (original, innovative, significant, impactful, rigorous, insightful, or inspirational) to be published promptly, allowing VIS researchers and practitioners to make even more impactful contributions to data sciences.

Large language models (LLMs) show potential in understanding visualizations and may capture design knowledge. However, their ability to predict human feedback remains unclear. To explore this, we conduct three studies evaluating the alignment between LLM-based agents and human ratings in visualization tasks. The first study replicates a human-subject study, showing promising agent performance in human-like reasoning and rating, and informing further experiments. The second study simulates six prior studies using agents and finds that alignment correlates with experts' pre-experiment confidence. The third study tests enhancement techniques, such as input preprocessing and knowledge injection, revealing limitations in robustness and potential bias. These findings suggest that LLM-based agents can simulate human ratings when guided by high-confidence hypotheses from expert evaluators. We also demonstrate the usage scenario in rapid prototyping study designs and discuss future directions. We note that simulation may only serve as complements and cannot replace user studies.