semvec/pypi-packages huggingface dataset:
brms 0.1.0.dev2
A bank risk management simulation framework.
Requires Python: >=3.11

# BRMS - Bank Risk Management Simulation

![LICENSE](https://img.shields.io/github/license/mgao6767/brms?color=blue)

BRMS is an educational tool designed to provide users with an in-depth understanding of bank risk management practices. It allows users to simulate and respond to various risk scenarios, and analyze the impact on a bank's financial health.

It is developed by Dr. [Mingze Gao](https://mingze-gao.com) from the Department of Applied Finance at [Macquarie Business School](https://www.mq.edu.au/macquarie-business-school).

> [!NOTE]  
> This application is under active development for use in my teaching AFIN8003 Banking and Financial Intermediation at the Macquarie University.

## How It Works

A simulation in BRMS begins with a predefined set of banking and trading books, covering instruments from standard mortgages to HTM, FVOCI, and FVTPL securities (e.g., Treasuries). Each simulation unfolds through daily scenarios, incorporating economic data such as yield curves. As users navigate these scenarios, the bank's performance is automatically computed and reported under IFRS accounting. Capital adequacy and liquidity ratios are assessed following the Basel III framework, with RWAs calculated based on the selected approach. Users can actively adjust exposures through various risk mitigation strategies, and their overall performance is analyzed at the simulation’s conclusion.  

## GUI Demo

Here is an overview of the user interface at this stage (2025-01-31).

![GUI Demo 1](docs/images/GUI-demo1-2025-01-30.png)

![GUI Demo 2](docs/images/GUI-demo2-2025-01-30.png)

![GUI Demo 3](docs/images/GUI-demo3-2025-01-31.png)
eye-tracking-collector 0.0.4
Tool for eye-tracking data collection
Requires Python: <4.0.0,>=3.12

# Eye Tracking Calibration Dataset

- [Eye Tracking Calibration Dataset](#eye-tracking-calibration-dataset)
  - [Overview](#overview)
  - [Installation](#installation)
  - [Usage](#usage)
  - [Dataset Contents](#dataset-contents)
  - [Methodology](#methodology)
  - [Potential Use Cases](#potential-use-cases)
  - [Acknowledgments](#acknowledgments)

## Overview

This dataset is a comprehensive collection of eye-tracking calibration data gathered from multiple participants. It is designed to support research and development in fields such as gaze estimation, eye-tracking systems, human-computer interaction, and computer vision. The dataset includes raw images, facial landmarks, and calibration metadata, making it a versatile resource for training machine learning models and conducting gaze analysis studies.

## Installation

`pip install eye-tracking-collector`

## Usage

`eye-tracking-collector collect --upload --api-key=<XXXXX>`


## Dataset Contents

1. Metadata (JSON):
   1. dataset.json:
        1. Landmark coordinates for each frame.
        2. Calibration dot positions and corresponding distances.
        3. Estimated distance of face from camera.
        4. Dot position ( place where eyes are looking at)
   1. screen_details.json:
        1. Screen and window size information to aid in experimental replication.

## Methodology

```math
R = 50, 70 cm \\
N = 10
```

1. Collection Process:
   1. Participants focused on points in a 5x5 grid displayed on a screen.
   2. Eye alignment, eyes position on screen and distance R were continuously monitored during data collection.
   3. N samples were recorded for each calibration point.
2. Participant Diversity:
   1. Data was collected from individuals of various age groups to ensure broad applicability.

## Potential Use Cases

1. Training gaze estimation models for real-time applications.
1. Developing assistive technologies for people with mobility impairments.
1. Conducting behavioral studies using gaze patterns.
1. Advancing research in cognitive science, computer vision, and HCI.

## Acknowledgments

This dataset was collected using a Python-based tool that leverages:

1. MediaPipe FaceMesh for landmark detection.
1. OpenCV for camera integration and visualization.

We thank all participants who contributed their data to make this dataset a valuable resource for the research community.