M3DecideNet: Multi-Modal Attention-Driven Fusion for Enterprise Management Decision Support

Overview M3DecideNet is a cutting-edge multi-modal attention-driven fusion framework designed to enhance enterprise management decision-making by integrating diverse data sources, such as financial metrics, operational statistics, and external market indicators. This system utilizes a dynamic attention mechanism to optimize predictive accuracy and interpretability in real-time decision-making. Key Features Multi-Modal Fusion: Integrates various data types (text, numerical, visual) using attention mechanisms. Adaptive Decision Strategy: Context-aware fusion strategy to adapt to different enterprise environments. State-of-the-art Performance: Empirical results show superior predictive performance across enterprise management scenarios. Scalability & Flexibility: Suitable for real-time business applications and adaptable to different enterprise contexts. Installation bash # Clone the repository git clone https://github.com/yourusername/M3DecideNet.git cd M3DecideNet # Install dependencies pip install -r requirements.txt Usage python from m3decidenet import M3DecideNet # Initialize the model model = M3DecideNet() # Train the model with your enterprise data model.train(training_data) # Make predictions predictions = model.predict(new_data) Documentation Components Preliminaries: Defines the mathematical foundation for enterprise decision-making, addressing multi-modal data fusion challenges. M3FusionNet: The core model that uses attention mechanisms for dynamic data integration. Adaptive Decision Fusion Strategy (ADFS): A robust strategy to optimize decision-making through attention modulation and regularization. For a deeper dive into the methodology, refer to the research paper linked in the References section. Experiments The framework has been evaluated using multiple datasets, including Enterprise Decision Support Data, Multi-Modal Management Insights, and others. Performance metrics such as Accuracy, Precision, Recall, and AUC show that M3DecideNet outperforms leading models. Evaluation Results Accuracy: 90.45% (on Multi-Modal Management Insights Dataset) Precision: 89.78% Recall: 89.23% AUC: 90.12% Contributing We welcome contributions to improve and expand M3DecideNet. Please follow these steps to contribute: Fork the repository. Create a new branch for your feature or fix. Submit a pull request with a clear description of your changes. License This project is licensed under the MIT License - see the LICENSE file for details. References [Bi et al., 2022] Enterprise strategic management using multi-modal emotion recognition. Frontiers in Psychology. [Ren et al., 2024] Multi-modal fusion for review helpfulness prediction. Information Processing & Management. [Wang et al., 2023] Attentive statement fraud detection with multi-modal financial data. Decision Support Systems.
import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiModalAttention(nn.Module):
    def __init__(self, input_dims, attention_dims):
        super(MultiModalAttention, self).__init__()
        
        self.attention_layers = nn.ModuleList([
            nn.Linear(input_dim, attention_dims) for input_dim in input_dims
        ])
        self.attention_weights = nn.Parameter(torch.ones(len(input_dims)))

    def forward(self, inputs):
        attention_scores

Multimodal Data Fusion for Evaluating the Effectiveness of Cadre Training

Overview This repository implements the research presented in “Multimodal Data Fusion for Evaluating the Effectiveness of Cadre Training.” The project proposes a unified framework that integrates multimodal data—text, audio, video, and physiological signals—to assess the effectiveness of cadre (leadership) training. It emphasizes interpretability, adaptability, and policy alignment in performance evaluation. 🧠 Key Components Hierarchically Attentive Progression Encoder (HAPE): A temporal encoder that captures cross-time and cross-unit dependencies in performance data using hierarchical attention and GRU-based modeling. (See architecture diagram in Figure 1, page 8 of the paper.) Policy-Aligned Knowledge-Guided Adaptation (PAKGA): A reinforcement learning strategy that integrates institutional policies and domain knowledge into adaptive decision-making. (See schematic on page 11 for workflow illustration.) Multimodal Fusion Framework: Combines heterogeneous data modalities (video, text, audio, sensor data) into interpretable embeddings, improving robustness and accuracy. 📊 Experimental Highlights Achieved ~91% accuracy and ~92% AUC on benchmark datasets: Cadre Training Performance Dataset Multimodal Leadership Assessment Dataset Training Effectiveness Metrics Dataset Behavioral Insights Fusion Dataset Outperforms baseline models like OC-SVM, TranAD, and MSCRED by 4–6%. ⚙️ Implementation Details Frameworks: PyTorch, Hugging Face Transformers Optimizers: AdamW with cosine annealing Learning Rate: 3e-4 (with warmup and decay) Batch Size: 64 per GPU Evaluation Metrics: Accuracy, F1-Score, AUC, MSE, Pearson Correlation 🧩 Folder Structure graphql ├── data/ # Sample datasets or data loading scripts ├── models/ # HAPE and PAKGA model definitions ├── utils/ # Helper functions (training, evaluation, visualization) ├── experiments/ # Configuration files and logs ├── figures/ # Architecture diagrams and result visualizations └── README.md # Project documentation 🚀 Usage Install Dependencies bash pip install -r requirements.txt Train Model bash python train.py --config configs/hape_pakga.yaml Evaluate bash python evaluate.py --checkpoint checkpoints/best_model.pth 🧩 Citation If you use this work, please cite: scss @article{wang2025multimodal, title={Multimodal Data Fusion for Evaluating the Effectiveness of Cadre Training}, author={Wang, Ke}, journal={Shengli Oilfield Party School (Training Center)}, year={2025} } 📜 License This project is released under the MIT License. See LICENSE for details. 
# models/hape.py
from __future__ import annotations

import math
from typing import Optional, Tuple, Dict

import torch
from torch import nn, Tensor
import torch.nn.functional as F


class SinusoidalPositionalEncoding(nn.Module):
    """
    Classic transformer-style fixed positional encoding.

    Args:
        dim: feature dimension
        max_len: maximum sequence length supported
    """
    def __init__(self, dim: int, max_len: int = 10_000):
        super().__init__()
        pe = torch.z

Verifica Objetos inválidos 

DECLARE
    v_total_pkg     NUMBER := 0;
    v_success_pkg   NUMBER := 0;
    v_error_pkg     NUMBER := 0;
    v_total_prc     NUMBER := 0;
    v_success_prc   NUMBER := 0;
    v_error_prc     NUMBER := 0;
    v_total_fnc     NUMBER := 0;
    v_success_fnc   NUMBER := 0;
    v_error_fnc     NUMBER := 0;
    v_total_trg     NUMBER := 0;
    v_success_trg   NUMBER := 0;
    v_error_trg     NUMBER := 0;
BEGIN
    -- PACKAGES
    FOR pkg IN (SELECT DISTINCT object_name FROM user_objects WHERE object

shareX audio recording parameters 

# shareX audio recording parameters

![](https://cdn.cacher.io/attachments/u/3fx93fy4dqwj6/wKxhyPsYdB5zhLVZ92KkCxnxrgaaM702/b2jobpocb.png)

フロントエンドだけでバリデーションしても意味がないサンプル 

<form id="purchase-form">
  <label>
    個数（最大2個まで）:
    <input type="number" id="quantity" name="quantity" min="1" max="2" required />
  </label>
  <button type="submit">購入</button>
</form>

<script>
  document.getElementById("purchase-form").addEventListener("submit", async (e) => {
    e.preventDefault();

    const quantity = parseInt(document.getElementById("quantity").value, 10);

    // フロント側バリデーション
    if (quantity > 2) {
      alert("2個までしか購入できません");
      return;
    }

    // APIに送信

php artisan tinker - add user 

Abrir PHP Artisan Tinker
```php
php artisan tinker
```

```
use App\Models\User;
User::create([
    'name' => 'test',
    'email' => 'test@testuser.com',
    'password' => bcrypt('0QcrNCKSQ7uuay2@'),
]);

```

🚧 Ingester 

## Exemple d'URL à requêter
https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2025-01.parquet

## Paramètres de `__init__`
BASE_URL = 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata'
YEAR = '2025'
DATA_DIR = 🚧 Définir avec path relatif par rapport à la racine du projet

🚧 Il faut éventuellement générer le folder data (dans le `__init__` ou dans un 
autre script?)

3350. Adjacent Increasing Subarrays Detection II

Given an array nums of n integers, your task is to find the maximum value of k for which there exist two adjacent subarrays of length k each, such that both subarrays are strictly increasing. Specifically, check if there are two subarrays of length k starting at indices a and b (a < b), where: Both subarrays nums[a..a + k - 1] and nums[b..b + k - 1] are strictly increasing. The subarrays must be adjacent, meaning b = a + k. Return the maximum possible value of k. A subarray is a contiguous non-empty sequence of elements within an array.
/**
 * @param {number[]} nums
 * @return {number}
 */
var maxIncreasingSubarrays = function (nums) {
    let maxK = 0;       // Stores the maximum valid k found so far
    let curLen = 1;     // Length of the current strictly increasing run
    let prevLen = 0;    // Length of the previous strictly increasing run

    for (let i = 1; i < nums.length; i++) {
        if (nums[i] > nums[i - 1]) {
            // Continue the current increasing run
            curLen++;
        } else {
            /

START_END 

import pandas as pd
from datetime import datetime, timedelta
import yfinance as yf

# Define the end date
end = str(pd.Timestamp.today().strftime('%Y-%m-%d'))

# Calculate the start date (20 years before the end date)
no_years = 20
start = (datetime.strptime(end, '%Y-%m-%d') - timedelta(days=no_years*365)).strftime('%Y-%m-%d')

# Generate the date range
date_range = pd.date_range(start, end, freq='D')

print(date_range, '\n\n')

tickers = ['SPY', 'MDY']
data = yf.download(ticker

Decompose Flow 

Если задача с типом **decomp**

![](https://cdn.cacher.io/attachments/u/3kcbpjvt3jkry/PNj6DgUJ0EqxM_I_aaMwYKXgEuvfRa3G/wq1oqnqid.png)

- нужно создать новую задачу на разработку, где производится описание задачи.
- задачу нужно оценить самому и отдать на оценку QA переведя в статус Requirements Review  (RR).
QA уже дальше переведет в ready to develop

### Линковки
- Задачу по декомпозиции линкуем children задача для разработки
- Задачу по разработке линкуем с задачей по декомпозу parent
- Задачу

🛠️ Setting Up Pre-commit Hooks with UV 

# Setting Up Pre-commit Hooks with UV

Pre-commit hooks automatically check your code before each commit, catching issues early and enforcing consistent code quality.

## Installation

Add pre-commit as a development dependency:

```bash
uv add --dev pre-commit
```

## Configuration

Create `.pre-commit-config.yaml` in your project root:

```yaml
repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.5.0
    hooks:
      - id: trailing-whitespace

git 

# 查看远程仓库
git remote -v

# 断联
git remote remove origin

cartopy 

import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.io.shapereader import Reader

sys.path.insert(0, "/data8/xuyf/Project/shouxian")
from configs import MAP_DIR
sheng = Reader(os.path.join(MAP_DIR, 'sheng.shp'))

BDY_DIR = "/data8/xuyf/Data/Static/boundary/GS(2024)0650-SHP"
sheng = Reader(os.path.join(BDY_DIR, 'sheng.shp'))

fig = plt.figure(figsize=(12, 8), dpi=300)
ax = fig.subplots(1, 1, subplot_kw={'projection': ccrs.PlateCarree()})

ax.add_feature(cfeatu

Mount/Dismount Registry Hive File 

function Mount-RegistryHive {
    [CmdletBinding()]
    param(
        [Parameter(Mandatory = $true, Position = 0, ValueFromPipeline = $true, ValueFromPipelineByPropertyName = $true)]
        $KeyName
        ,
        [Parameter(Mandatory = $true, Position = 1, ValueFromPipeline = $true, ValueFromPipelineByPropertyName = $true)]
        $FileName
    )

    begin {
            Add-Type -Name LoadHive -NameSpace RegistryHelper -MemberDefinition @"

[DllImport("advapi32.dll", SetLast

3349. Adjacent Increasing Subarrays Detection I

Given an array nums of n integers and an integer k, determine whether there exist two adjacent subarrays of length k such that both subarrays are strictly increasing. Specifically, check if there are two subarrays starting at indices a and b (a < b), where: Both subarrays nums[a..a + k - 1] and nums[b..b + k - 1] are strictly increasing. The subarrays must be adjacent, meaning b = a + k. Return true if it is possible to find two such subarrays, and false otherwise.
/**
 * @param {number[]} nums
 * @param {number} k
 * @return {boolean}
 */
var hasIncreasingSubarrays = function(nums, k) {
    // Helper function to check if a subarray is strictly increasing
    function isStrictlyIncreasing(start, end) {
        for (let i = start; i < end; i++) {
            if (nums[i] >= nums[i + 1]) {
                return false; // Not strictly increasing
            }
        }
        return true;
    }

    // Total length needed for two adjacent subarrays of length

Vanilla Stats CountUp

using matrix and some js, count up/down to select values. Respects decimals. # 
<div class="pp-stats-countup" data-id="pp-{{Matrix.MatrixId}}">
   <div class="section-copy">{{Module.FieldValues.SectionCopy}}</div>
   <ul class="stats-countup-list" data-layout="{{Module.FieldValues.Layout | default: 'quarter'}}">
      {% for item in List.Items %}
         {% assign el = item.FieldValues %}
         <li
            class="single-stat"
            data-start-value="{{el.StartingValue}}"
            data-value="{{el.Value}}"
         >
            <h3>