perf(docs): polish the contents of README

README.md

@@ -38,14 +38,16 @@
 
 The _learnware_ paradigm, proposed by Professor Zhi-Hua Zhou in 2016 [1, 2], aims to build a vast model platform system, i.e., a _learnware dock system_, which systematically accommodates and organizes models shared by machine learning developers worldwide, and can efficiently identify and assemble existing helpful model(s) to solve future tasks in a unified way.
 
-Based on the _learnware_ paradigm, Beimingwu systematically implements the entire process of learnware from submission to deployment, helping users effectively search and reuse learnwares without the need to build machine learning models from scratch. A learnware is a well-performed trained model with a specification describing its capabilities. The specification includes a semantic specification in text and a statistical specification sketching the model's statistical information.
+Beimingwu is the first systematic implementation of the _learnware_ paradigm, offering a highly scalable framework as a powerful research platform for learnware-related studies. A learnware is a well-performed trained model with a specification that describes its capabilities, enabling it to be readily identified and reused in the future based on user requirements. The specification includes a semantic specification in text and a statistical specification sketching the model's statistical information.
+
+In the _learnware_ paradigm, global developers can share models with the learnware dock system, enabling efficient machine learning task-solving without starting from scratch.
 
 [1] Zhi-Hua Zhou. Learnware: on the future of machine learning. _Frontiers of Computer Science_, 2016, 10(4): 589–590 <br/>
 [2] Zhi-Hua Zhou. Machine Learning: Development and Future. _Communications of CCF_, 2017, vol.13, no.1 (2016 CNCC keynote)
 
 ## What features does Beimingwu have?
 
-As shown in the diagram below, Beimingwu systematically implements the core process of the learnware paradigm for the first time:
+As shown in the diagram below, the Beimingwu learnware dock system, serving as a research platform for the learnware paradigm, systematically implements the core processes of the paradigm for the first time:
 
 - **Submitting Stage**: The system includes multiple detection mechanisms to ensure the quality of uploaded learnwares. Additionally, the system trains a heterogeneous engine based on existing learnware specifications in the system to merge different specification islands and assign new specifications to learnwares. With more learnwares are submitted, the heterogeneous engine will continue to update, achieving continuous iteration of learnware specifications and building a more precise specification world.
 - **Deploying Stage**: After users upload task requirements, the system automatically selects whether to recommend a single learnware or multiple learnware combinations and provides efficient deployment methods. Whether it's a single learnware or a combination of multiple learnwares, the system offers convenient learnware reuse tools.
@@ -211,7 +213,7 @@ Please acknowledge the use of our project by citing these papers in your work. T
 
 ## How to Contribute
 
-Beimingwu is still young and may contain bugs and issues. We highly value and encourage contributions from the community. For detailed development guidelines, please consult our [Developer Guide](https://docs.bmwu.cloud/en/developer-guide/structure-and-guidelines.html). We kindly request that contributors adhere to the provided [Development Standards](https://docs.bmwu.cloud/en/developer-guide/structure-and-guidelines.html#development-standards) when participating in the project. Your valuable contributions are greatly appreciated.
+Building the learnware paradigm requires collective efforts from the community. As the first learnware dock system, Beimingwu is still in its early stages and may contain bugs and issues. We sincerely invite the community to upload models, collaborate in system development, and engage in research and enhancements in learnware algorithms. For detailed development guidelines, please consult our [Developer Guide](https://docs.bmwu.cloud/en/developer-guide/structure-and-guidelines.html). We kindly request that contributors adhere to the provided [Development Standards](https://docs.bmwu.cloud/en/developer-guide/structure-and-guidelines.html#development-standards) when participating in the project. Your valuable contributions are greatly appreciated.
 
 ## About Us
 

README_zh.md

@@ -38,14 +38,16 @@
 
 学件范式由周志华教授在2016年提出 [1, 2]，旨在构建一个巨大的模型平台系统：即学件基座系统，系统地组织管理世界各地的机器学习开发者分享的模型，并通过统一的方式识别、利用已有模型的能力快速解决新的机器学习任务。
 
-北冥坞基于学件范式，首次系统性地实现了学件从上传到部署的完整流程，帮助用户有效查搜和复用学件，而无需从零开始构建机器学习模型。学件由性能优良的机器学习模型和描述模型的规约构成。规约由两部分构成：语义规约通过文本描述模型的功能，而统计规约刻画模型所蕴含的统计信息。
+北冥坞是学件范式的首次系统性实现，其高度可扩展的架构为学件相关研究提供了强大的科研平台。学件由性能优良的机器学习模型和描述模型的规约组成。规约刻画了模型的能力，使得模型在未来能够根据用户需求被充分识别和复用。规约由两部分构成：语义规约通过文本描述模型的功能，而统计规约刻画模型所蕴含的统计信息。
+
+在学件范式下，世界各地的开发者可分享模型至学件基座系统，系统通过有效查搜和复用学件帮助用户高效解决机器学习任务，而无需从零开始构建机器学习模型。
 
 [1] Zhi-Hua Zhou. Learnware: on the future of machine learning. Frontiers of Computer Science, 2016, 10(4): 589–590 <br/>
 [2] 周志华. 机器学习: 发展与未来. 中国计算机学会通讯, 2017, vol.13, no.1 (2016 中国计算机大会 keynote)
 
 ## 北冥坞系统有哪些特性？
 
-如下图所示，北冥坞首次系统性地实现了学件范式中的核心流程：
+如下图所示，北冥坞学件基座系统作为学件范式的科研平台，首次系统性地实现了学件范式中的核心流程：
 
 - **提交阶段**：系统内置了多重检测机制，以确保上传学件的质量。另外，系统会根据已有的学件规约，训练一个异构引擎，用于合并不同的规约岛屿，以及为学件赋予新规约。随着更多学件的上传，异构引擎将持续更新，实现学件规约的持续迭代，构建更精准的规约世界。
 - **部署阶段**：用户上传任务需求后，系统会自动选择是推荐单学件还是多学件组合，并提供高效的部署方式。无论是单个学件还是多学件组合，系统均提供了便捷的学件复用接口。
@@ -210,8 +212,7 @@ print(f"Classification accuracy: {accuracy_score(target, y_pred)}")
 
 ## 如何贡献
 
-北冥坞系统还很年轻，可能存在错误和问题。我们非常欢迎大家为北冥坞系统做出贡献。
-我们为所有的开发者提供了详细的[系统开发指南](https://docs.bmwu.cloud/zh-CN/developer-guide/structure-and-guidelines.html)，并制定了相应的[系统开发规范](https://docs.bmwu.cloud/zh-CN/developer-guide/structure-and-guidelines.html#development-standards)，请大家遵守。
+学件范式的构建需要社区的共同努力。作为首个学件基座系统，北冥坞还很年轻，可能存在错误和问题。我们诚邀大家基于北冥坞系统上传模型、共同开发完善系统，或进行学件相关算法的研究与改进。我们为所有的开发者提供了详细的[系统开发指南](https://docs.bmwu.cloud/zh-CN/developer-guide/structure-and-guidelines.html)，并制定了相应的[系统开发规范](https://docs.bmwu.cloud/zh-CN/developer-guide/structure-and-guidelines.html#development-standards)，请大家遵守。
 非常感谢大家的贡献！
 
 ## 关于我们