In today's globalized world, bridging the cultural divide is more critical
than ever for forging meaningful connections. The Socially-Aware Dialogue
Assistant System (SADAS) is our answer to this global challenge, and it's
designed to ensure that conversations between individuals from diverse cultural
backgrounds unfold with respect and understanding. Our system's novel
architecture includes: (1) identifying the categories of norms present in the
dialogue, (2) detecting potential norm violations, (3) evaluating the severity
of these violations, (4) implementing targeted remedies to rectify the
breaches, and (5) articulates the rationale behind these corrective actions. We
employ a series of State-Of-The-Art (SOTA) techniques to build different
modules, and conduct numerous experiments to select the most suitable backbone
model for each of the modules. We also design a human preference experiment to
validate the overall performance of the system. We will open-source our system
(including source code, tools and applications), hoping to advance future
research. A demo video of our system can be found
at:~\url{this https URL}. We have released our code and software
at:~\url{this https URL}.

社会意识对话助手系统（SADAS）旨在跨越文化鸿沟，确保来自不同文化背景的个体之间的对话以尊重和理解展开。该系统的新颖架构包括：(1) 识别对话中存在的规范类别，(2) 检测潜在的规范违反，(3) 评估这些违反的严重性，(4) 实施有针对性的修复措施以纠正违规行为，(5) 解释这些修复措施的原理。我们使用一系列先进技术来构建不同的模块，并进行了多次实验以选择每个模块最合适的骨干模型。我们还设计了一项人类偏好实验来验证系统的整体性能。我们将开源我们的系统（包括源代码、工具和应用），希望推进未来的研究。我们的系统演示视频可以在此网址找到：[演示视频网址]。我们已经在此网址上发布了我们的代码和软件。

SADAS: 面向双语社会文化对话中纠正规范违反的对话助手系统

SADAS: A Dialogue Assistant System Towards Remediating Norm Violations  in Bilingual Socio-Cultural Conversations

Cell segmentation for multi-modal microscopy images remains a challenge due
to the complex textures, patterns, and cell shapes in these images. To tackle
the problem, we first develop an automatic cell classification pipeline to
label the microscopy images based on their low-level image characteristics, and
then train a classification model based on the category labels. Afterward, we
train a separate segmentation model for each category using the images in the
corresponding category. Besides, we further deploy two types of segmentation
models to segment cells with roundish and irregular shapes respectively.
Moreover, an efficient and powerful backbone model is utilized to enhance the
efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS
2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and
the running time for all cases is within the time tolerance.

多模态显微图像的细胞分割仍然是一个挑战，为了解决这个问题，我们首先开发了一个基于细胞的低级图像特征的自动分类流程，然后训练了一个基于类别标签的分类模型。然后，我们针对每个类别训练了一个单独的分割模型，使用对应类别中的图像。此外，我们还部署了两种类型的分割模型，分别用于分割具有圆形和不规则形状的细胞。此外，为了增强分割模型的效率，我们采用了一种高效且强大的主干模型。在 NeurIPS 2022 Cell Segmentation Challenge 的调优集上评估，我们的方法达到了 F1 得分 0.8795，所有情况的运行时间都在时间容限范围内。