[DOC] update system features doc

docs/references/beimingwu.rst

@@ -22,7 +22,7 @@ In addition, the Beimingwu system also has the following features:
 - ``Learnware Specification Generation``: The Beimingwu system provides specification generation interfaces in the ``learnware`` 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 preliminarily supports the search for heterogeneous table learnwares.
-- ``Local Learnware Deployment``: The Beimingwu system provides a unified interface for learnware deployment and learnware reuse in the ``learnware`` package, facilitating users' convenient and secure deployment and reuse of arbitrary learnwares.
+- ``Local Learnware Deployment``: The Beimingwu system provides a unified interface for learnware deployment and learnware reuse in the ``learnware`` package, facilitating users' convenient deployment and reuse of arbitrary learnwares.
 - ``Raw Data Protection``: The Beimingwu system operations, including learnware upload, search, 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 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.
 

docs/start/intro.rst

@@ -30,7 +30,7 @@ Why do we need Learnware?
 The Benefits of Learnware Paradigm
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-Machine learning has achieved great success in many fields but still faces various challenges, such as the need for extensive training data and advanced training techniques, the difficulty of continuous learning, the risk of catastrophic forgetting, and the leakage of data privacy.
+Machine learning has achieved great success in many fields but still faces various challenges, such as the need for extensive training data and advanced training techniques, the difficulty of continuous learning, the risk of catastrophic forgetting, and the risk of data privacy breach.
 
 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. The learnware paradigm aims to tackle many of these challenges through a unified framework: