perf(docs): modify overclaim

docs/content/en/overview/system-overview.md

@@ -24,10 +24,10 @@ Machine learning has achieved great success in many fields but still faces vario
 Although many efforts focus on one of these issues separately, these efforts pay less attention to the fact that most issues are entangled in practice, and solving one may exacerbate others.
 
 The learnware dock system aims to address many of these challenges through a unified framework:
-- **Lack of Training Data/Skills**: Even for ordinary users with limited data and machine learning knowledge, they can obtain powerful machine learning models from the learnware dock system. Users can acquire high-performing learnwares and further customize or improve them without starting from scratch.
-- **Continuous Learning**: As high-performing learnwares are continually submitted for various tasks, the learnware doc system will continuously expand its knowledge base and improve its capabilities, naturally realizing lifelong learning.
-- **Catastrophic Forgetting**: Once a learnware passes the quality checks, it will always be stored in the learnware dock system. Therefore, old knowledge in the learnware dock system is always preserved and never forgotten.
-- **Data Privacy/Ownership**: Developers freely submit their high-performing models to the learnware doc system, without revealing their training data. The learnware paradigm enables developers to share their models that can be adequately identified and reused by future users without disclosing developer's original data.
+- **Lack of Training Data/Skills**: Even for ordinary users with limited data and machine learning knowledge, they can obtain powerful machine learning models from the learnware dock system. Users can acquire high-performance learnwares and further customize or improve them without starting from scratch.
+- **Continuous Learning**: As high-performance learnware is continually submitted for various tasks, the knowledge in the learnware dock system will naturally accumulate, achieving continuous and lifelong learning.
+- **Catastrophic Forgetting**: Once learnware is accepted, it will always be retained in the learnware dock system unless it can be replaced by other learnware in all aspects. Therefore, old knowledge in the learnware dock system is always preserved and never forgotten.
+- **Data Privacy/Ownership**: Developers only submit models without sharing private data, thus protecting data privacy and ownership. Although the possibility of reverse engineering the model cannot be completely eliminated, the risk of privacy leakage in the learnware dock system is less than many other privacy protection schemes.
 
 ## How Does the Learnware Dock System Work?
 

docs/content/en/user-guide/learnware-deploy.md

@@ -36,7 +36,7 @@ The `runnable_option` parameter includes three options (default is `None`), each
 - `"conda"`: Install a separate `conda` virtual environment for each learnware (automatically deleted after execution); run each learnware independently within its virtual environment.
 - `"docker"`: Install a `conda` virtual environment inside a Docker container (automatically destroyed after execution); run each learnware independently within the container (requires Docker privileges).
 
-It's important to note that while the system makes every effort to ensure the security of each learnware, the `None` and `"conda"` modes are **not secure** if there are any malicious learnwares. If the user cannot guarantee the security of the learnware they want to load, it's recommended to use the `"docker"` mode to load the learnware.
+It's important to note that while the system makes every effort to ensure the security of each learnware, the `None` and `"conda"` modes are **not secure** if there are any malicious learnwares. If the user cannot guarantee the security of the learnware they want to load, it's recommended to use the **relatively secure** `"docker"` mode to load the learnware.
 
 ### Loading Learnware from a ZIP File
 

docs/content/en/versions.md

@@ -10,7 +10,7 @@ The system is designed to help users efficiently solve machine learning tasks wi
 1. **Learnware Specification Generation**: The Beimingwu system provides a specification generation tool in the `learnware` Python package, supporting multiple data types (tables, images, and text), and can be efficiently generated locally.
 2. **Learnware Quality Detection**: The Beimingwu system has multiple detection mechanisms to ensure the quality of each learnware in the system.
 3. **Diverse Learnware Search**: The Beimingwu system supports both semantic specification and statistical specification search, covering data types including tables, images, and text. Additionally, for tabular tasks, the system preliminarily supports the search of heterogeneous table learnwares.
-4. **Local Learnware Deployment**: The Beimingwu system provides a unified user interface for learnware deployment and reuse in the `learnware` Python package, helping users to deploy and reuse arbitrary learnwares conveniently and securely.
+4. **Local Learnware Deployment**: The Beimingwu system provides a unified user interface for learnware deployment and reuse in the `learnware` Python package, helping users to deploy and reuse arbitrary learnwares conveniently.
 5. **Raw Data Protection**: The learnware submission, identification, and deployment of the Beimingwu system doesn't require users to upload raw data, and all involved statistical specifications are generated locally by users using open-source API.
 6. **Open Source System**: The source code of the Beimingwu system, including the `learnware` Python package and frontend and backend codes, are open source.
 

docs/content/zh-CN/overview/system-overview.md

@@ -27,7 +27,7 @@
 - **缺乏训练数据/技能**：即使对于缺乏训练技能或数据量较小的普通用户，也可以获得强大的机器学习模型，因为用户可以从学件基座系统中获取性能优良的学件，并进一步调整或改进，而不是自己从头开始构建模型。
 - **持续学习**：随着在各种不同任务上训练得到的、性能优良的学件被不断提交，学件基座系统中的知识将不断丰富，进而自然地实现持续和终身学习。
 - **灾难性遗忘**：一旦学件被接收，它将永远被容纳在学件基座系统中，除非其各方面功能都可以被其他学件所替代。因此，学件基座系统中的旧知识总是会被保留，而不会被遗忘。
-- **数据隐私/所有权**：开发者只提交模型而不共享私有数据，因此数据隐私/所有权可以得到很好的保护。尽管无法完全排除对模型进行逆向工程的可能性，但与许多其它隐私保护方案相比，学件基座系统泄露隐私的风险非常小。
+- **数据隐私/所有权**：开发者通过仅提交模型而不共享私有数据的方式，有助于更好地保护数据隐私和所有权。尽管无法完全排除对模型进行逆向工程的可能性，但与许多其它隐私保护方案相比，学件基座系统泄露隐私的风险更低。
 
 
 ## 学件基座系统是如何工作的？

docs/content/zh-CN/user-guide/learnware-deploy.md

@@ -36,7 +36,7 @@ learnware_list = client.load_learnware(
 - `"conda"`：为每个学件安装独立的 `conda` 虚拟环境（运行结束后自动删除），在虚拟环境内独立运行每个学件；
 - `"docker"`：在 `docker` 容器内安装 `conda` 虚拟环境（运行结束后自动销毁），在容器内独立运行每个学件（用户需要有 `docker` 权限）。
 
-需要注意的是，尽管系统已尽最大努力确保每个学件的安全，但如果仍有包含恶意代码的漏网之鱼，则 `None` 和 `"conda"` 两种模式是**不安全**的。如果用户不能确保需要加载的学件的安全性，请使用 `"docker"` 模式载入学件。
+需要注意的是，尽管系统已尽最大努力确保每个学件的安全，但如果仍有包含恶意代码的漏网之鱼，则 `None` 和 `"conda"` 两种模式是**不安全**的。如果用户不能确保需要加载的学件的安全性，请使用**较为安全** `"docker"` 模式载入学件。
 
 
 ### 根据学件 `zip` 文件载入学件

docs/content/zh-CN/versions.md

@@ -2,14 +2,14 @@
 
 ## 北冥坞 v1.0 版本 (2024.01)
 
-北冥坞学件基座系统 v1.0 版本是学件的第一个系统性开源实现，为学件相关研究提供了一个初步科研平台。该系统旨在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。
+北冥坞学件基座系统 v1.0 版本是学件的第一个系统性开源实现，为学件相关研究提供了一个初步科研平台。该系统旨在通过查搜和复用学件，帮助用户便捷解决机器学习任务，而无需从零开始构建机器学习模型。
 
 ### v1.0 系统特点
 
 1. **学件规约生成**：北冥坞系统在 `learnware` Python 包中提供规约生成接口，支持多种数据类型（表格、图像和文本），可以在本地高效生成。
 2. **学件质量检测**：北冥坞系统内置了多重检测机制，以确保系统中每个学件的质量。
-3. **学件多样查搜**：北冥坞系统同时支持语义规约和统计规约的查搜，覆盖的数据类型包括表格、图像、文本。另外，对于表格型任务，系统初步支持异构表格学件的查搜。
-4. **学件本地部署**：北冥坞系统在 `learnware` Python 包中同时提供学件部署与学件复用的接口，帮助用户以统一的方式便携地部署与复用学件。
+3. **学件多样查搜**：北冥坞系统同时支持语义规约和统计规约的查搜，覆盖的数据类型包括表格、图像、文本。另外，对于表格型任务，系统额外支持初步的异构表格学件查搜。
+4. **学件部署复用**：北冥坞系统在 `learnware` Python 包中提供统一的学件部署与复用的接口，帮助用户便捷的使用学件。
 5. **保护原始数据**：北冥坞系统所涉及的学件上传、查搜、部署均无需用户上传本地数据，生成统计规约的过程在用户本地进行且代码公开。
 6. **面向社区开源**：北冥坞系统面向社区开源，包括 `learnware` Python 包与前后端代码。