perf(docs): polish docs

README.md

@@ -4,7 +4,7 @@
  <br/>
 </div>
 
-<p align="center">
+<div align="center">
  <a href="LICENSE">
   <img alt="LICENSE" src="https://img.shields.io/pypi/l/learnware?color=blue">
  </a>
@@ -23,32 +23,34 @@
  <a href="https://img.shields.io/pypi/dm/example-package">
   <img alt="PyPI Downloads" src="https://img.shields.io/pypi/dm/example-package">
  </a>
-</p>
+</div>
 
-<p>
+<div>
  <h3 align="center">Beimingwu: The First Learnware Dock System</h3>
- <h3 align="center">A Systematic Implementation of Learnware Paradigm</h3>
+ <h3 align="center">A Research Platform for Learnware</h3>
  <h3 align="center">
   <a href="README_zh.md">中文</a> |
   <b>English</b>
  </h3>
-</p>
+</div>
 
 # Introduction
 
-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.
+_Learnware_ was proposed by Professor Zhi-Hua Zhou in 2016 [1, 2]. In the _learnware paradigm_, developers worldwide can share models with the _learnware dock system_, which effectively searches for and reuse learnware(s) to help users solve machine learning tasks efficiently without starting from scratch.
+
+Beimingwu is the first systematic open-source implementation of learnware dock system, providing a preliminary research platform for learnware studies. Developers worldwide can submit their models freely to the learnware dock. They can generate specifications for the model with the help of Beimingwu without disclosing their raw data, and then the model and specification can be assembled into a learnware, which will be accommodated in the learnware dock. Future users can solve their tasks by submitting their requirements and reusing helpful learnwares returned by Beimingwu, while also not disclosing their own data. It is anticipated that after Beimingwu accumulates millions of learnwares, an "emergent" behavior may occur: machine learning tasks that have never been specifically tackled may be solved by assembling and reusing some existing learnwares.
 
-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.
+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.
 
 [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 preliminary research platform for learnware, systematically implements the core processes of the learnware 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.
+- **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 the submission of more learnwares, the heterogeneous engine will continually update, aiming to construct a more precise specification world through the constant iteration of learnware specifications.
+- **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 baseline learnware reuse methods in a uniform format for convenient usage.
 
 <div align=center>
  <img src="docs/content/public/overview/learnware-workflow-en.svg" width="700" height="auto" style="max-width: 100%;"/>
@@ -58,9 +60,9 @@ In addition, the Beimingwu system also has the following features:
 
 - **Learnware Specification Generation**: The Beimingwu system provides specification generation interfaces in the `learnware` Python package, supporting various data types (tables, images, and text) for efficient local generation.
 - **Learnware Quality Inspection**: The Beimingwu system includes multiple detection mechanisms to ensure the quality of each learnware in the system.
-- **Diverse Learnware Search**: The Beimingwu system supports both semantic specifications and statistical specifications searches, covering data types such as tables, images, and text. In addition, for table-based tasks, the system also supports the search for heterogeneous table learnwares.
-- **Local Learnware Deployment**: The Beimingwu system provides interfaces for learnware deployment and learnware reuse in the `learnware` Python package, facilitating users' convenient and secure learnware deployment.
-- **Data Privacy Protection**: The Beimingwu system operations, including learnware upload, search, and deployment, do not require users to upload local data. All relevant statistical specifications are generated locally by users, ensuring data privacy.
+- **Diverse Learnware Search**: The Beimingwu system supports both semantic specifications and statistical specifications searches, covering data types such as tables, images, and text. In addition, for table-based tasks, the system preliminarily supports the search for heterogeneous table learnwares.
+- **Local Learnware Deployment**: The Beimingwu system provides a unified user interface for learnware deployment and reuse in the `learnware` Python package, facilitating users' convenient deployment and reuse of arbitrary learnwares.
+- **Raw Data Protection**: The Beimingwu system operations, including learnware submission, identification, and deployment, do not require users to upload raw data. All relevant statistical specifications are generated locally by users using open-source API.
 - **Open Source System**: The Beimingwu system's source code is open-source, including the `learnware` Python package and frontend/backend code. The `learnware` package is highly extensible, making it easy to integrate new specification designs, learnware system designs, and learnware reuse methods in the future.
 
 ## How is Beimingwu organized?
@@ -82,15 +84,15 @@ The functionalities of the four layers are described as follows:
 ### Project Structure
 
 Based on the system architecture, Beimingwu is developed with five sub-projects:
-- [`Engine`](https://gitee.com/beimingwu/learnware): Encompassing core components and algorithms within the learnware paradigm, and providing a command-line client for user interaction, it has been releasead as the [learnware](https://pypi.org/project/learnware/) package.
-- [`Frontend`](https://gitee.com/beimingwu/beimingwu/tree/main/frontend): Provide the interface and functionality for user interaction with the learnware dock system, including the main system and administrator system.
-- [`Backend`](https://gitee.com/beimingwu/beimingwu/tree/main/backend): Responsible for handling the dock system's operation logic and data operations, it ensures system stability and high performance.
-- [`Docs`](https://gitee.com/beimingwu/beimingwu/tree/main/docs): Maintain system documentation, including user guides, development guides, etc., ensuring system usability.
-- [`Deploy`](https://gitee.com/beimingwu/beimingwu/tree/main/deploy): Manage the system deployment configuration, including frontend and backend deployment files.
+- [`Engine`](https://www.gitlink.org.cn/beimingwu/learnware): Encompassing core components and algorithms within the learnware paradigm, and providing a command-line client for user interaction, it has been releasead as the [learnware](https://pypi.org/project/learnware/) package.
+- [`Frontend`](https://www.gitlink.org.cn/beimingwu/beimingwu/tree/main/frontend): Provide the interface and functionality for user interaction with the learnware dock system, including the main system and administrator system.
+- [`Backend`](https://www.gitlink.org.cn/beimingwu/beimingwu/tree/main/backend): Responsible for handling the dock system's operation logic and data operations, it ensures system stability and high performance.
+- [`Docs`](https://www.gitlink.org.cn/beimingwu/beimingwu/tree/main/docs): Maintain system documentation, including user guides, development guides, etc., ensuring system usability.
+- [`Deploy`](https://www.gitlink.org.cn/beimingwu/beimingwu/tree/main/deploy): Manage the system deployment configuration, including frontend and backend deployment files.
 
 # Quick Start
 
-Welcome to experience [Beimingwu](https://bmwu.cloud/). The following instructions will assist you in quickly exploring the search functionality on the system website and provide two demo cases from learnware search to learnware deployment using the [learnware](https://gitee.com/beimingwu/learnware) package.
+Welcome to experience [Beimingwu](https://bmwu.cloud/). The following instructions will assist you in quickly exploring the search functionality on the system website and provide two demo cases from learnware search to learnware deployment using the [learnware](https://www.gitlink.org.cn/beimingwu/learnware) package.
 
 The installation instructions for the learnware package can be found here: [Installation Guide](docs/content/en/overview/installation.md).
 
@@ -102,7 +104,7 @@ When searching with semantic information, you can fill in the information about
 
 ![image](docs/content/public/quick-start/semantic-search-en.jpg)
 
-When searching with statistical information, you need to submit the statistical information of the task. The tools we provide will generate approximate statistical information for your task locally while protecting your data privacy. With the following code, you can easily generate approximate statistical information for your task.
+When searching with statistical information, you need to generate and submit a statistical specification, which captures the data distribution while not disclosing your original data. Using the API we provided, you can easily generate this statistical specification locally.
 
 ```python
 from learnware.specification import generate_stat_spec
@@ -116,7 +118,7 @@ By uploading the JSON file containing statistical information, the system will m
 
 ![image](docs/content/public/quick-start/stat-search-single-en.jpg)
 
-In some cases, combining multiple learnwares with similar statistical information may be more suitable for your task. The system will recommend these learnwares as a package to you. You can download all of them with the "Download All" button in the upper right corner.
+In some cases, assembling multiple helpful learnwares may be more beneficial for your task. The system will accordingly recommend a combination of these learnwares as a package. You can download the package using the "Download All" button in the upper right corner.
 
 ![image](docs/content/public/quick-start/stat-search-multiple-en.jpg)
 
@@ -194,26 +196,30 @@ print(f"Classification accuracy: {accuracy_score(target, y_pred)}")
 If you use our project in your research or work, we kindly request that you cite the following papers:
 
 ```bibtex
-@article{zhou2022learnware,
+@article{zhou2024learnware,
+ title = {Learnware: Small models do big},
  author = {Zhou, Zhi-Hua and Tan, Zhi-Hao},
- title = {Learnware: Small Models Do Big},
- journal = {SCIENCE CHINA Information Sciences},
- year = {2024},
+ journal = {Science China Information Sciences},
  volume = {67},
  number = {1},
- pages = {1--12},
+ pages = {112102},
+ year = {2024}
 }
-```
 
-Please acknowledge the use of our project by citing these papers in your work. Thank you for your support!
+@article{tan2024beimingwu,
+ title = {Beimingwu: A learnware dock system}, 
+ author = {Tan, Zhi-Hao and Liu, Jian-Dong and Bi, Xiao-Dong and Tan, Peng and Zheng, Qin-Cheng and Liu, Hai-Tian and Xie, Yi and Zou, Xiao-Chuan and Yu, Yang and Zhou, Zhi-Hua},
+ journal = {arXiv preprint arXiv:2401.14427},
+ year = {2024}
+}
+```
 
 # About
 
 ## 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
 
-The Beimingwu repository is developed and maintained by the LAMDA Beimingwu R&D Team.
-To learn more about our team, please visit the [Team Overview](https://docs.bmwu.cloud/en/about-us.html).
+The Beimingwu repository is developed and maintained by the LAMDA Beimingwu R&D (Research and Development) Team. To learn more about our team, please visit the [Team Overview](https://docs.bmwu.cloud/en/about-us.html).