To enhance the efficiency of crowd evacuation and inform collaborative design strategies, it is essential to investigate the effects of exit features on human exit choices. This study explores how exit distance, crowd density near exits, and exit location settings influence individual exit selection. We conducted a virtual reality experiment, revealing that all targeted features significantly impact exit choices, with density exerting the most substantial influence, followed by distance and exit location being the least impactful. Additionally, the interaction between distance and location significantly affected exit decisions. By integrating our findings into machine learning models, we demonstrate the potential of these exit features for informing collaborative evacuation strategies and designing systems that support effective decision-making in crowd dynamics. This research contributes to understanding human behavior in evacuation scenarios, emphasizing the importance of collaborative approaches in optimizing crowd management.

Federated learning (FL) enables collaborative model training while preserving data privacy through decentralized data storage. However, the frequent transmission of high-dimensional model updates between FL clients and the central server incurs substantial communication overhead. Although prior studies compress model updates to reduce transmission overhead, fixed-rate schemes retain two major limitations: insensitivity to client-level Non-IID and uniform layer-wise compression, resulting in undercompression or over-compression of different layers. To overcome these issues, we propose a Layer-wise Adaptive Compression in Non-IID Situation (LWACN) algorithm, which applies global-local parameter similarity and client label entropy to measure the degree of client non-IID in compression. Moreover, we introduce window loss fluctuation and layer importance to mitigate the mismatching problem caused by the constant compression rate. Extensive experiments demonstrate that LWACN exhibits a better convergence rate and generalization ability than fixed compression. Specifically, compared to the state-of-the-art method, LWACN reduces transmission cost by up to 19.4%, and improves the final model accuracy by 5.5%.

Compilers play a central role in translating high-level code into executable programs, making their correctness essential for ensuring code safety and reliability. While extensive research has focused on verifying the correctness of compilers for single-language compilation, the correctness of cross-language compilation — which involves the interaction between two languages and their respective compilers — remains largely unexplored. To fill this research gap, we propose CrossLangFuzzer , a novel framework that introduces a universal intermediate representation (IR) for JVM-based languages and automatically generates cross-language test programs with diverse type parameters and complex inheritance structures. After generating the initial IR, CrossLangFuzzer applies three mutation techniques — LangShuffler, FunctionRemoval , and TypeChanger — to enhance program diversity. By evaluating both the original and mutated programs across multiple compiler versions, CrossLangFuzzer successfully uncovered 10 confirmed bugs in the Kotlin compiler, 4 confirmed bugs in the Groovy compiler, 7 confirmed bugs in the Scala 3 compiler, 2 confirmed bugs in the Scala 2 compiler, and 1 confirmed bug in the Java compiler. Among all mutators, TypeChanger is the most effective, detecting 11 of the 24 compiler bugs. Furthermore, we analyze the symptoms and root causes of cross-compilation bugs, examining the respective responsibilities of language compilers when incorrect behavior occurs during cross-language compilation. To the best of our knowledge, this is the first work specifically focused on identifying and diagnosing compiler bugs in cross-language compilation scenarios. Our research helps to understand these challenges and contributes to improving compiler correctness in multi-language environments.

Limited by the size of the dataset, deep learning models for medical image analysis are usually difficult to train well, and the complex deep learning model with large amount of trainable parameters can not achieve good results. At the same time, due to the lack of clear boundaries, especially in the root tips and roots, as well as the huge differences in shape and texture between images from different patients, an overly simple model cannot accurately segment organs. In order to improve the accuracy of organ segmentation for prostate region detection, in this paper we propose an attention based U-Net framework, which includes an attention mechanism and residual feature extraction network. In addition, we also design an improved loss function to improve the training effect for organ segmentation. We conduct several batches of experiments with the prostate dataset PROMISE12 and the pneumothorax dataset SIIM, the experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.