Search Results for "source code plagiarism detection"

With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that...

Microsoft Application Inspector is a software source code characterization tool that helps identify coding features of first or third party software components based on well-known library/API calls and is helpful in security and non-security use cases. It uses hundreds of rules and regex patterns to surface interesting characteristics of source code to aid in determining what the software is or what it does from what file operations it uses, encryption, shell operations, cloud API's, frameworks and more and has received industry attention as a new and valuable contribution to OSS on ZDNet, SecurityWeek, CSOOnline, Linux.com/news, HelpNetSecurity, Twitter and more and was first featured on Microsoft.com. ...

CameraVoyeur is a small Windows-oriented utility that demonstrates how to capture, preview, and possibly log frames from a connected camera device in a lightweight way. It’s set up more like an experiment or proof-of-concept than a polished end-user app, focusing on the plumbing for grabbing images from the webcam. The project shows how to access the camera stream, keep it running, and expose the captured output for later use or display. Because it’s in a regular GitHub repo and not a closed...

CodeQL is a semantic code analysis engine that treats programs as queryable databases, enabling users to write expressive queries that identify security vulnerabilities, logic bugs, and code quality issues across large codebases. Instead of just pattern matching text, CodeQL ingests source code, builds rich representations of structure and data flow, and allows queries that reason about control flow, type systems, and interprocedural relationships.

JPPF makes it easy to parallelize computationally intensive tasks and execute them on a Grid.

Medical Datasets (In a text file, with space separated values) can be loaded to the system. By choosing either one of the two classifiers, Neural network or Decision Tree, the system can be trained and evaluated.

The plugin eases the generation of compilers with Coco/R by: syntax highlighting, automatic compilation, error detection, and much more.