AI Analysis
Final verdict: SAFE
The package appears safe with low risks across all categories except for a moderate obfuscation risk. There's no evidence of malicious activity, and the obfuscation might serve legitimate purposes within the framework.
- moderate obfuscation risk
- low risks in other categories
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
- Obfuscation: The code shows signs of obfuscation which may indicate an attempt to hide functionality, but it could also be part of a complex internal logic.
- Credentials: No clear patterns of credential harvesting are present.
- Metadata: The maintainer has only one package, which could indicate a new or less active account.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 4.0
Found 2 obfuscation pattern(s)
return span def eval(self, x: float) -> float: # Evaluate the piecewise llensSetting = self.cfg.map.eval(self.fsmooth) self.afstatus.lensSetting = int(lensSe
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Idein/actfw-core appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Idein Inc." appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with actfw-core
Create a real-time activity recognition system using the Python package 'actfw-core'. This system will be designed to analyze sensor data from wearable devices to recognize various human activities such as walking, running, sitting, and standing. The goal is to develop a fully functional mini-application that can process live data streams and output recognized activities in real-time. Step 1: Set up your development environment by installing Python and the 'actfw-core' package. Ensure you have the necessary dependencies installed to support real-time data processing. Step 2: Design the architecture of your application. It should include modules for data acquisition, preprocessing, feature extraction, activity recognition, and result visualization. Step 3: Implement the data acquisition module which interfaces with simulated or actual wearable devices to collect sensor data. Use 'actfw-core' to handle generic data processing tasks that are device-independent. Step 4: Develop the preprocessing module to clean and normalize the raw sensor data. This module should leverage 'actfw-core' functionalities for efficient data manipulation. Step 5: Create the feature extraction module to derive meaningful features from the preprocessed data. Utilize 'actfw-core' for any required mathematical operations or transformations. Step 6: Build the activity recognition module using machine learning or deep learning models trained on labeled activity datasets. Integrate 'actfw-core' for model deployment and inference processes. Step 7: Implement the result visualization module to display recognized activities in real-time. This could include graphical representations or simple text outputs. Suggested Features: - Real-time data streaming support - User-friendly interface for monitoring activities - Adjustable sensitivity levels for differentiating between similar activities - Logging capabilities for storing historical activity data - Compatibility with multiple types of wearable sensors Utilization of 'actfw-core': - For handling generic data processing tasks across different devices - To provide robust and efficient computational methods for feature extraction - For deploying and running machine learning models in real-time scenarios - As a foundation for building scalable and maintainable applications
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