PNPM 



> The default pnpm store is usually at `~/.local/share/pnpm/store`. 
> You can change it with: `pnpm config set store-dir /path/to/pnpm/store`

```sh
# Install pnpm globally (one time only)
npm install -g pnpm

# This will create (if not present): '~/node_modules/', '~/package.json', '~/pnpm-lock.yaml', '~/.local/share/pnpm/store'
pnpm add -D typescript nodemon @biomejs/biome

# Expose user-level Node binaries in your shell
echo 'export PATH="$HOME/node_modules/.bin:$PATH"' >> ~/.bashrc
```

Interactive Scrolling HTML mail (Parallax effect like)

Complete HTML code with comments of an email made for BNNVARA with a parallax like effect. - The email consists of 3 layers of images: background, fixed image and a top layer image. The fixed image stays visible and fixed when scrolling through the mail, and is shown above the background and below the top image. This makes it look like it's "travelling" between these images. - In the comments (in the HTML) you'll find 🟢 emoticons everytime you'll have to make a change in the HTML. - The CSS part of the mail, has to be pasted in the CSS of the template in Marketing Cloud. Some usefull websites: - Basics of the used princaple: https://www.emailonacid.com/blog/article/email-development/how-we-created-out-interactive-scrolling-halloween-email/ - Check wether CSS styles work in different mail clients: https://www.caniemail.com/
<!DOCTYPE html>
<html>

<style>

    /*
    🟢 All CSS (everything between the style tags) has to be included in the template of the newsletter in Marketing Cloud.
    */

    /*
    Checks wether a device can handle @media CSS queries. If so, class .interactive will be activated 
    */
    @media screen {
        .interactive {
            display: block !important;
            max-height: none !important;
            overflow: visible !important;
        }

        .fallback {
            disp

keobongtrang.top 

<?php
add_action('wp_head',function(){
    ?>
    <style>
body.text-ui-light .content-area>.container{
	background:none!important;
}
body.text-ui-light .reading-content{
    font-size:16px;
        color: #fff;
}
.c-sidebar.c-top-second-sidebar{background:none!important;}
    </style>
    <?php
});

Fresh CC Cvv Non Vbv Fullz CashApp Bank Drop Wire Logs PayPal Zelle Venmo Apple Pay WU Transfer Bug 


______JEANSON ANCHETA______


Stop Paying for Fluff. Start Getting Results.


            U.S.A 🌍 


🛡️ Verified • HQ Access • Fast Delivery
💬 DM for escrow or direct 🔥
WESTERN UNION / MONEYGRAM / BANK LOGINS / BANK SWAPS / PAYPAL SWAPS GLOBAL / CASHAPP / ZELLE / APPLE PAY / SKRILL / VENMO SWAPS
©2025  Telegram: @JeansonCarder
https://t.me/+2__ynBAtFP00M2Fk
https://t.me/+CsF2t7HvV_ljMmU8


Hello fam, offering HQ services 💻💸 — got WU pluggz, bank logs w/ fullz, PayPal jobz, Skrill flips 🔥. HMU f

ChatGPT Search APU 

{
  "search_model_queries": {
    "type": "search_model_queries",
    "queries": [
      "how to find best electricity deal Fort Worth Texas",
      "electricity provider Fort Worth how to choose",
      "Texas electricity market Fort Worth compare plans"
    ]
  },
  "search_results": [
    {
      "domain": "comparepower.com",
      "url": "https://comparepower.com/electricity-rates/texas/fort-worth-electricity-rates-energy-plans/",
      "title": "Compare Fort Worth Electricity Rates & Plans

github

github
⁤

1526. Minimum Number of Increments on Subarrays to Form a Target Array

You are given an integer array target. You have an integer array initial of the same size as target with all elements initially zeros. In one operation you can choose any subarray from initial and increment each value by one. Return the minimum number of operations to form a target array from initial. The test cases are generated so that the answer fits in a 32-bit integer.
/**
 * @param {number[]} target
 * @return {number}
 */
var minNumberOperations = function(target) {
    // Initialize the total number of operations needed
    let operations = 0;

    // Loop through the target array
    for (let i = 0; i < target.length; i++) {
        if (i === 0) {
            // For the first element, we need to perform 'target[0]' operations
            operations += target[0];
        } else {
            // For subsequent elements, we only need to add the difference

AWS CloudWatch (Log Insights) Filters 

# Find all failed authentication attempts
fields @timestamp, userIdentity.principalId, errorCode, errorMessage
| filter errorCode like /Unauthorized|Denied|Failed/
| sort @timestamp desc

Legit CashApp Transfer Apple Pay Skrill PayPal Transfer WU Transfer Bug CC Fullz TopUp Bank Drop Wire Logs Ship Shop Administrative 


______JEANSON ANCHETA______


Stop Paying for Fluff. Start Getting Results.


            U.S.A 🌍 


🛡️ Verified • HQ Access • Fast Delivery
💬 DM for escrow or direct 🔥
WESTERN UNION / MONEYGRAM / BANK LOGINS / BANK SWAPS / PAYPAL SWAPS GLOBAL / CASHAPP / ZELLE / APPLE PAY / SKRILL / VENMO SWAPS
©2025  Telegram: @JeansonCarder
https://t.me/+2__ynBAtFP00M2Fk
https://t.me/+CsF2t7HvV_ljMmU8


Hello fam, offering HQ services 💻💸 — got WU pluggz, bank logs w/ fullz, PayPal jobz, Skrill flips 🔥. HMU f

HIPLE-CARSTA

# 🧩 HIPLE-CARSTA: A Multimodal Evaluation Framework for College English Teaching Effectiveness --- ## 🌍 Overview **HIPLE-CARSTA** introduces a comprehensive **deep learning–based multimodal framework** for evaluating the **effectiveness of college English classroom teaching**. Unlike traditional systems that rely on static indicators (e.g., test scores or surveys), HIPLE-CARSTA captures the **dynamic, interactive, and multimodal nature** of language instruction. Proposed by **Yan Song (2025)**, the framework integrates two synergistic components: - 🧠 **HIPLE** — *Hierarchically Integrated Pedagogical Latent Estimator* A hierarchical neural architecture that models multimodal instructional interactions. - 🎯 **CARSTA** — *Cognitively Aligned Reflective Strategy for Teaching Assessment* A cognitive alignment and reinforcement-based strategy for interpreting and optimizing teaching effectiveness. Together, these systems provide a **data-driven**, **theory-grounded**, and **interpretable** approach to intelligent teaching assessment. --- ## 🔑 Key Features - 🎥 **Multimodal Input Integration** Combines **audio**, **video**, **textual**, and **behavioral** signals from classroom interactions. - 🧩 **Hierarchical Pedagogical Encoding** Captures **micro-level** teaching cues and **macro-level** instructional structures. - 🧠 **Peer-Aware Relational Learning** Models **collaborative learning dynamics** among students using **Graph Neural Networks (GNNs)**. - 🔁 **Feedback-Driven Attention** Dynamically adjusts attention to critical teaching moments based on **student engagement feedback**. - 📊 **Cognitive Alignment Indexing (CAI)** Quantifies how closely learning outcomes align with **Bloom’s taxonomy** and cognitive development levels. - 🚀 **Adaptive Feedback Optimization (AFO)** Uses **reinforcement learning** to continuously refine and optimize teaching strategies. - 🔮 **Predictive Instructional Simulation** Simulates **future teaching outcomes** based on current cognitive and behavioral trends. --- ## 🏗️ Model Architecture HIPLE-CARSTA is composed of two primary subsystems — **HIPLE** and **CARSTA** — that interact through multimodal and cognitive modeling. ### 1️⃣ HIPLE — *Hierarchically Integrated Pedagogical Latent Estimator* - Utilizes **multimodal transformers** to encode **instructional and learner responses**. - Incorporates **peer-aware relational modeling** via **GNNs** to represent classroom collaboration. - Employs a **feedback-driven attention refinement** module that aligns focus with effective teaching events. - (Illustrated in *Figure 2, page 7*.) ### 2️⃣ CARSTA — *Cognitively Aligned Reflective Strategy for Teaching Assessment* - Defines a **Cognitive Alignment Index (CAI)** to measure progress consistency with **pedagogical theories**. - Applies **Adaptive Feedback Optimization (AFO)** using **cognitive reward signals** for reflective adaptation. - Includes **Predictive Instructional Simulation** to anticipate future learning trajectories and teaching outcomes. - (Visualized in *Figures 3–4, pages 9–10*.) > The integration of these modules leverages CNNs, attention mechanisms, and reinforcement-based reasoning for holistic multimodal assessment. --- ## 📚 Datasets HIPLE-CARSTA was trained and evaluated using **four public multimodal educational datasets**: | Dataset | Description | Key Use | |----------|--------------|----------| | **College English Teaching Effectiveness Dataset** | Annotated multimodal classroom videos | Evaluate teaching clarity and impact | | **Multimodal Classroom Interaction Dataset** | Behavioral and verbal exchanges between teachers and students | Model engagement and dialogue flow | | **Student Engagement in English Classes Dataset** | Eye gaze, posture, facial emotion tracking | Predict engagement and responsiveness | | **Teaching Method Evaluation Multimodal Dataset** | Various teaching styles and strategies | Compare pedagogical effectiveness | --- ## 📈 Experimental Highlights | Metric | Improvement | Baseline | |---------|--------------|-----------| | **Accuracy** | +2.54% | xDeepFM | | **F1 Score** | +3.66% | AutoInt | | **AUC** | +2.83% | Across all datasets | ### 🧩 Ablation Studies - Removing **HIPLE** or **CARSTA** modules leads to a **2–3% reduction** in AUC, demonstrating both are essential for performance. - Visual comparisons (*Figures 5–8, pages 14–17*) highlight improved attention focus and multimodal interpretability. --- ## ⚙️ Implementation Details | Component | Configuration | |------------|---------------| | **Framework** | PyTorch | | **Optimizer** | AdamW (lr=1e-4, cosine decay) | | **Batch Size** | 32 | | **Modalities** | Audio → Wav2Vec2 / Video → TCN + Keypoint extractor / Text → Multilingual BERT | | **Training** | 100 epochs with early stopping (patience = 10) | | **Metrics** | Accuracy, Recall, F1, AUC, CCC, RMSE | --- ## 🔮 Future Directions - **Semi-supervised learning** for low-label classroom environments - **Personalized learning analytics** for individual student trajectories - **Edge-AI deployment** for real-time classroom evaluation and feedback --- ## 📖 Citation If you use this work in your research, please cite: **Song, Yan. (2025).** *Exploration of the Evaluation Method of College English Classroom Teaching Effect Integrating Multimodal Data.* **Northwest Normal University.** --- ## 📜 License Released under the **MIT License**. Freely available for **research** and **educational use** with proper attribution. --- **HIPLE-CARSTA** bridges the gap between **cognitive pedagogy** and **deep learning**, offering a pioneering framework for **interpretable, multimodal, and adaptive evaluation** of college English teaching. 
import torch
import torch.nn as nn
import torch.nn.functional as F


# === 1. Multimodal Pedagogical Encoder (HIPLE Core) ===
class MultimodalPedagogicalEncoder(nn.Module):
    """Processes multimodal teaching signals and student responses."""
    def __init__(self, input_dim=256, hidden_dim=512, num_layers=2):
        super().__init__()
        self.attn = nn.MultiheadAttention(hidden_dim, num_heads=4, batch_first=True)
        self.rnn = nn.GRU(hidden_dim, hidden_dim, num_layers=num

ShipHealthNet

# 🚢 ShipHealthNet: Deep Learning-Based Structural Health Monitoring for Ships --- ## 🌍 Overview **ShipHealthNet** is an advanced **deep learning framework** designed for **real-time Structural Health Monitoring (SHM)** of maritime vessels. Traditional SHM systems based on fixed thresholds or heuristic rules often fail to detect **early-stage structural damage** or evolving anomalies under **dynamic sea conditions**. ShipHealthNet overcomes these limitations through **dynamic state-space modeling**, **multimodal sensor fusion**, and **adaptive learning algorithms**, enabling **intelligent, data-driven insights** into ship integrity and predictive maintenance. The framework processes large-scale multimodal sensor data — including **strain**, **stress**, **vibration**, **acoustic**, and **temperature** signals — to: - Detect anomalies - Estimate damage severity - Optimize maintenance scheduling --- ## 🧠 Key Contributions ### 1. Dynamic Structural Modeling (DSM) - Uses **time-varying state-space equations** combined with **RNN/LSTM networks**. - Captures **nonlinear** and **time-dependent** ship structural behavior under variable sea conditions. ### 2. Multimodal Encoder Architecture - Encodes **heterogeneous sensor inputs** via **dynamic spatial attention**. - Learns **local and global dependencies** between sensor features (see *Figure 2, page 6*). ### 3. Graphical Propagation Layer - Models **inter-sensor spatial relationships** across ship components. - Learns **nonlinear correlations** adapting to **environmental and operational variations**. ### 4. Damage Detection Module - Implements **adaptive anomaly detection** comparing **predicted vs. observed** structural states. - Flags **early-stage damage** for proactive maintenance. ### 5. Adaptive Monitoring Strategy - Integrates **real-time sensor fusion**, **LSTM-based adaptation**, and **hierarchical decision-making**. - Enables **proactive scheduling** balancing operational risk and maintenance cost (*Figure 3, page 8*). --- ## 🏗️ Architecture As depicted in *Figure 1 (page 5)*, ShipHealthNet includes four major modules: | Module | Function | |---------|-----------| | **Multimodal Encoder Architecture** | Extracts fused latent features from multimodal sensors (strain, vibration, acoustic, temperature). | | **Graphical Propagation Layer** | Propagates contextual and temporal dependencies between sensor nodes. | | **Damage Detection Module** | Generates adaptive indicators using learned embeddings for anomaly localization. | | **Adaptive Monitoring Strategy** | Integrates outputs into a decision engine optimizing maintenance and operational safety. | > The **Multimodal Sensor Fusion Framework** (*Figure 4, page 9*) employs **recursive Bayesian filtering** and **temporal attention** to merge heterogeneous data streams in real time. --- ## ⚙️ Datasets ShipHealthNet was trained and validated using **four key maritime datasets**: | Dataset | Focus | Data Type | Purpose | |----------|--------|------------|----------| | **Ship Structural Integrity Dataset** | Stress & strain analysis | Sensor time series | Evaluate hull and panel fatigue | | **Maritime Vessel Stress Analysis Dataset** | Stress distribution | Real-world vessel data | Predict mechanical stress | | **Deep Learning Ship Damage Detection Dataset** | Damage classification | Image + sensor data | Train deep models for damage localization | | **Oceanic Ship Fatigue Monitoring Dataset** | Long-term fatigue | Wave, motion, stress data | Track degradation and remaining useful life (RUL) | --- ## 📈 Experimental Highlights - Achieved **>94% accuracy** and **>92% F1 scores** across all datasets. - Outperformed baselines (**ResNet50**, **ViT**, **Swin Transformer**, **CLIP**) by **4–6%** across all metrics (*Tables 1–2, pages 10–11*). - **Ablation studies** (*Tables 3–4, pages 11–12*) show performance drops of up to **6% AUC** when removing any core module. - Demonstrated strong **robustness** under **noisy** and **nonstationary sea-state conditions**. --- ## 🧮 Implementation Details | Parameter | Configuration | |------------|---------------| | **Frameworks** | PyTorch 1.9 / TensorFlow 2.0 | | **Hardware** | NVIDIA RTX 3090 GPU | | **Optimizer** | Adam with learning rate decay ×0.1 every 10 epochs | | **Batch Size** | 32 | | **Early Stopping** | Applied to prevent overfitting | | **Evaluation Metrics** | Precision, Recall, F1, AUC | --- ## ⚓ Limitations and Future Work - Requires **large labeled datasets** for initial training — ongoing work focuses on **low-data domain adaptation**. - Deployment on **low-power onboard systems** needs **model compression** and **quantization**. - Future research will integrate **edge-AI deployment** and **hybrid reinforcement-learning control** for **autonomous maintenance** in real maritime environments. --- ## 📖 Citation If you use this work in your research, please cite: **Chen, Zixuan & Li, Qiaozhong. (2025).** *Deep Learning-Based Structural Health Monitoring for Ships.* **School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology.** --- ## 📜 License Released under the **MIT License**. Free for **research** and **educational use** with proper attribution. --- **ShipHealthNet** establishes a new standard for **intelligent maritime structural monitoring**, combining **deep temporal learning**, **graph-based reasoning**, and **adaptive sensor fusion** to safeguard the future of **smart and resilient ocean engineering**. 
import torch
import torch.nn as nn
import torch.nn.functional as F


class MultimodalEncoder(nn.Module):
    """Encodes strain, vibration, and temperature sensor data using dynamic spatial attention."""
    def __init__(self, input_dim=128, hidden_dim=256):
        super().__init__()
        self.ln = nn.LayerNorm(input_dim)
        self.proj_q = nn.Linear(input_dim, hidden_dim)
        self.proj_k = nn.Linear(input_dim, hidden_dim)
        self.proj_v = nn.Linear(input_dim, hidden_d

EcomVolunteerNet

# 🛍️ EcomVolunteerNet: Behavioral Impact Modeling of Student Volunteering on E-commerce Consumer Dynamics --- ## 🌍 Overview **EcomVolunteerNet** is an innovative **deep learning framework** designed to model the **behavioral impact of student volunteering** on **e-commerce consumer dynamics**. By combining **symbolic reasoning**, **probabilistic modeling**, and **neural architectures**, it captures how **volunteering activities** influence **purchasing behavior**, **brand loyalty**, and **ethical consumerism**. Through the unification of **contextual volunteering representations** and **dynamic temporal modeling**, EcomVolunteerNet bridges the gap between **social responsibility** and **consumer decision-making**, offering **data-driven insights** for sustainable and ethical commerce. --- ## 🧠 Core Components EcomVolunteerNet consists of **three main components**: | Component | Description | |------------|--------------| | **Preliminaries** | Defines the foundational constructs, relationships, and influence variables linking volunteering activities with consumer behavior. | | **EcomBehaviorNet** | A neural architecture modeling **direct and indirect effects** of volunteering on purchasing patterns through **contextual representations** and **temporal sequence modeling**. | | **Behavioral Dynamics Integration Strategy (BDIS)** | An **adaptive integration mechanism** capturing **temporal-contextual dependencies** using **probabilistic weighting** and **dynamic sequence modeling**. | --- ## 🏗️ Model Architecture As illustrated in Figures 1–4 of the paper, the model architecture includes four interconnected layers: 1. **Contextual Volunteering Representation Module** - Learns **latent vectors** for volunteering activities and consumer tendencies. - Uses a **bilinear interaction mechanism** to combine behavioral and contextual features. 2. **Dynamic Consumer Modeling Layer** - Employs **GRU/LSTM recurrent structures** to model **evolving purchasing tendencies** influenced by volunteering. - Captures both **short-term and long-term temporal dependencies**. 3. **Behavioral Dynamics Integration Strategy (BDIS)** - Implements **temporal-contextual dependency modeling** and **dynamic weighting**. - Simulates **adaptive, long-term consumer behavior** in response to social engagement. 4. **Temporal-Contextual Fusion Mechanism** - Leverages **frozen vision and language transformers** for **multimodal fusion**. - Ensures **semantic alignment** between volunteering events and e-commerce interactions. --- ## 📊 Datasets EcomVolunteerNet was trained and evaluated on **four key datasets** that integrate social, behavioral, and commercial dimensions: | Dataset | Description | |----------|--------------| | **Student Volunteering Behavior Dataset** | Records volunteering frequency, type, and motivation of students. | | **E-commerce Consumer Interaction Dataset** | Contains browsing, purchasing, and interaction histories. | | **Volunteer Impact on Consumer Preferences Dataset** | Links volunteering participation with changes in product category interests. | | **Online Shopping & Volunteering Dataset** | Tracks how volunteering correlates with sustainable and ethical purchasing patterns. | --- ## 📈 Key Results EcomVolunteerNet outperforms leading baseline models (**ResNet**, **ViT**, **I3D**, **BLIP**, **DenseNet**, **MobileNet**) across all datasets: | Dataset | Accuracy Gain | F1 Score Gain | |----------|----------------|----------------| | **Student Volunteering Behavior** | +3.8% | +2.7% | | **E-commerce Consumer Interaction** | +4.2% | +3.1% | | **Volunteer Impact Dataset** | +3.5% | +3.0% | | **Online Shopping Dataset** | +3.8% | +2.9% | ### 🧮 Performance Summary - High **interpretability** through hybrid symbolic–neural modeling. - Strong **generalization** across multiple e-commerce domains. - Excellent **temporal adaptability**, modeling dynamic consumer evolution. - Suitable for **real-world deployment** in recommendation systems and behavior analytics. --- ## 🌟 Highlights - 🧩 **Hybrid symbolic–neural architecture** enhances interpretability. - ⏱ **Temporal recurrence** models evolving consumer dynamics. - 🌍 **Ethical consumerism alignment** connects volunteering to sustainable purchasing. - ⚙️ **Scalable and domain-adaptable** for varied e-commerce ecosystems. - 💬 **Probabilistic contextual weighting** improves both accuracy and transparency. --- ## 🔮 Future Work - Integrate **real-time consumer feedback** for adaptive decision-making. - Expand **multimodal reasoning layers** to include visual and linguistic sentiment analysis. - Explore **transfer learning** for **limited-data social impact modeling**. --- ## 📖 Citation If you use this framework in your research, please cite: **Bu, Zhiqiong. (2025).** *EcomVolunteerNet: Behavioral Impact Modeling of Student Volunteering on E-commerce Consumer Dynamics.* **Guangdong Polytechnic Normal University.** --- ## 📜 License Released under the **MIT License**. You may freely **use**, **modify**, and **redistribute** this project with appropriate credit. --- **EcomVolunteerNet** represents a pioneering step toward uniting **social engagement** and **consumer analytics**, empowering e-commerce systems to align **business intelligence** with **ethical and community-driven values**. 
import torch
import torch.nn as nn
import torch.nn.functional as F


class VolunteeringEncoder(nn.Module):
    """Encodes volunteering activities into contextual latent representations."""
    def __init__(self, input_dim, hidden_dim=256):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

    def forward(self

GridTimeNet

# ⚡ GridTimeNet: Deep Temporal Modeling for Fault Forecasting and Anomaly Detection in Power IoT Systems --- ## 🌍 Overview **GridTimeNet** is an advanced **deep temporal modeling framework** designed to enhance **fault forecasting** and **anomaly detection** in modern **Power IoT systems**. It addresses the limitations of traditional rule-based and machine learning approaches by introducing a **hybrid deep neural architecture** capable of modeling both **short-term** and **long-term dependencies** in temporal grid data. By combining **hierarchical temporal modeling**, **attention-based interpretation**, and **domain-specific regularization**, GridTimeNet delivers **interpretable**, **robust**, and **real-time** insights for large-scale power networks. A complementary module, the **Temporal Fault Mitigation Strategy (TFMS)**, further strengthens the system by enabling **adaptive fault management** through **probabilistic inference** and **reinforcement learning**. --- ## 🧠 Key Features - **Hierarchical Temporal Modeling** Captures **short- and long-term temporal dependencies** using **LSTMs**, **CNNs**, and **cross-attention mechanisms**. - **Graphical Propagation Layer** Enhances interpretability by emphasizing **critical temporal dependencies** within grid data sequences. - **Domain-Specific Regularization** Embeds **physical constraints** from power systems to ensure **plausible and consistent predictions**. - **Probabilistic Anomaly Detection** Learns latent operational states using a **variational inference framework** for robust fault detection. - **Adaptive Fault Mitigation (TFMS)** Integrates **reinforcement learning** to support **real-time fault control** and **preventive maintenance**. - **Multimodal Encoder** Fuses **spatial, temporal, and sensor-level data** for comprehensive and noise-tolerant feature representation. --- ## 🏗️ Architecture GridTimeNet’s architecture is composed of multiple interconnected modules: | Module | Description | |---------|--------------| | **Multimodal Encoder** | Combines **LSTM** layers for sequential modeling with **multi-scale CNNs** for local temporal pattern extraction. Captures spatial-temporal correlations across diverse IoT signals. | | **Graphical Propagation Layer** | Uses **attention-based message passing** to highlight crucial time steps and inter-node dependencies across the grid. | | **Domain-Specific Regularization** | Adds **physics-informed constraints**, ensuring predictions conform to operational grid dynamics. | | **Temporal Fault Mitigation Strategy (TFMS)** | Integrates **hierarchical attention**, **variational modeling**, and **reinforcement learning** for fault anticipation and adaptive decision-making. | > Figures 1–4 in the original paper illustrate the encoder design, attention propagation, and the TFMS control loop architecture. --- ## ⚙️ Datasets GridTimeNet was evaluated using **four Power IoT benchmark datasets**, representing a broad range of operational conditions: | Dataset | Focus | Key Application | |----------|--------|-----------------| | **Power IoT Fault Events Dataset** | Fault forecasting | Grid fault diagnostics | | **Smart Grid Anomaly Signals Dataset** | Anomaly detection | Cyber-attack and noise pattern identification | | **Temporal Power System Metrics Dataset** | Temporal prediction | Load forecasting and stability analysis | | **IoT Energy Network Fault Logs Dataset** | Fault propagation | Predictive maintenance and risk mitigation | These datasets ensure a comprehensive evaluation across **fault diagnosis**, **temporal reasoning**, and **anomaly management** tasks. --- ## 📈 Experimental Results GridTimeNet demonstrates **consistent superiority** over strong deep learning baselines (**ResNet**, **ViT**, **I3D**, **BLIP**, **DenseNet**): | Metric | Improvement | |---------|-------------| | **Accuracy (Power IoT Fault Events)** | +3.5% | | **Robustness under noise/occlusion** | +4.2% | | **F1 / AUC / Recall** | Significantly higher across all datasets | **Ablation studies** confirm that removing any of the three core components — the **Multimodal Encoder**, **Graphical Propagation Layer**, or **TFMS** — leads to measurable drops in accuracy and recall, underscoring the necessity of each design element. --- ## 🔮 Future Work - Extend GridTimeNet for **real-time grid management** on **edge and embedded systems**. - Develop **lightweight model variants** for **resource-limited IoT deployments**. - Integrate **Explainable AI (XAI)** modules for **operator-facing interpretability dashboards**. --- ## 📖 Citation If you use this work in your research, please cite: **Yang, Chun, & Ma, Yining. (2025).** *GridTimeNet: Deep Temporal Modeling for Fault Forecasting and Anomaly Detection in Power IoT Systems.* **Energy Development Research Institute, China Southern Power Grid.** --- ## 📜 License This project is released under the **MIT License**. You are free to **use**, **modify**, and **distribute** the framework with proper attribution. --- **GridTimeNet** redefines **temporal intelligence** for the **Power IoT era**, combining deep neural reasoning, physical consistency, and adaptive control to deliver a **resilient and interpretable digital power grid solution**. 
import torch
import torch.nn as nn
import torch.nn.functional as F


class LSTMEncoder(nn.Module):
    """Encodes sequential input using stacked LSTM layers."""
    def __init__(self, input_dim, hidden_dim, num_layers=2):
        super().__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True, dropout=0.2)
    
    def forward(self, x):
        out, _ = self.lstm(x)
        return out


class MultiScaleConv(nn.Module):
    """Extracts temporal

DigitFusionNet

# 💼 DigitFusionNet: Structured and Unstructured Data Fusion for Enterprise Digital Transformation Path Identification --- ## 🌐 Overview **DigitFusionNet** is a hybrid **deep learning framework** designed to integrate **structured** and **unstructured enterprise data** for identifying optimal **digital transformation paths**. It provides a **scalable**, **interpretable**, and **data-driven** solution for enterprise decision-making by leveraging **multi-modal representation learning**, **cross-modal attention fusion**, and **adaptive fusion strategies**. By unifying diverse enterprise data sources — such as **relational databases**, **textual reports**, **images**, and **sensor signals** — DigitFusionNet produces actionable insights that align with **organizational goals** and **digital evolution strategies**. --- ## 🧠 Key Features - 🔄 **Multi-Modal Data Fusion** Integrates **structured (tabular)** and **unstructured (text, image)** data using deep neural encoders and transformer-based fusion layers. - 🧭 **Cross-Modal Attention** Dynamically aligns and contextualizes **semantic relationships** across multiple data modalities. - 🧩 **Adaptive Fusion Strategy (AFS)** Adjusts the **weighting of data modalities** according to domain priorities, data quality, and relevance. - 🔍 **Interpretable Clustering Module** Organizes predictions into **semantically coherent clusters**, enhancing interpretability and strategic insight. - ⚙️ **Dynamic Feedback Loop** Continuously refines model performance through **adaptive parameter updates** and feedback-driven learning. - 📈 **Scalable Enterprise Application** Designed for **large-scale**, **heterogeneous enterprise environments**, ensuring adaptability as data complexity grows. --- ## 🏗️ Architecture The **DigitFusionNet** architecture is composed of five core modules: | Module | Description | |---------|--------------| | **Structured Encoder (Fs)** | Encodes relational or tabular data into dense embeddings using feed-forward networks. | | **Unstructured Encoder (Fu)** | Processes unstructured data (text, image, etc.) via CNNs and transformer-based encoders. | | **Cross-Modal Attention Fusion (A)** | Aligns latent representations from structured and unstructured data into a unified embedding space. | | **Hierarchical Clustering Layer (H)** | Groups fused embeddings into interpretable clusters balancing semantic structure and accuracy. | | **Adaptive Fusion Strategy (AFS)** | Optimizes fusion weights through a combination of rule-based and learning-based feedback mechanisms. | > Figures 1–4 in the paper illustrate the **multi-modal alignment process**, **attention-based fusion layers**, and **dynamic multi-level architecture** used to achieve high adaptability across enterprise data modalities. --- ## 🧾 Datasets DigitFusionNet was evaluated on **four enterprise datasets**, capturing a variety of organizational and transformation scenarios: | Dataset | Description | |----------|--------------| | **Enterprise Digital Transformation Path Dataset** | Contains organizational timelines and transformation milestones. | | **Structured and Unstructured Data Fusion Records** | Integrates databases, textual documentation, and sensor data for multimodal learning. | | **Organizational Data Integration Benchmark** | Standardized dataset for evaluating cross-domain data fusion systems. | | **Business Process Evolution Tracking Dataset** | Monitors the longitudinal progression of enterprise performance metrics and workflows. | --- ## 📈 Experimental Highlights - **Implementation:** PyTorch framework on **NVIDIA Tesla V100 GPUs** - **Baselines Compared:** ResNet, ViT, I3D, BLIP, DenseNet ### 🧮 Quantitative Results | Metric | Improvement Over SOTA | |---------|-----------------------| | **Recall** | +4.2% | | **F1-Score** | +3.9% | ### ✅ Performance Summary - **Superior generalization** across diverse data distributions - **High interpretability** via hierarchical clustering and cross-modal explainability - **Stable performance** across varying enterprise datasets and industries --- ## 🔮 Future Work - Develop **lightweight DigitFusionNet variants** for **resource-constrained enterprise systems**. - Enhance the **adaptive fusion mechanism** to better handle dynamic business contexts. - Integrate **Explainable AI (XAI) modules** for greater transparency in model-driven enterprise decision-making. --- ## 📖 Citation If you use this framework in your research, please cite: **Li, Qinyou. (2025).** *DigitFusionNet: Structured and Unstructured Data Fusion for Enterprise Digital Transformation Path Identification.* **Zhangzhou College of Science and Technology.** --- ## 📜 License Released under the **MIT License**. You are free to **use**, **modify**, and **distribute** this project with appropriate attribution. --- **DigitFusionNet** bridges **data heterogeneity and enterprise intelligence**, enabling organizations to **discover actionable digital transformation pathways** through **interpretable, adaptive, and scalable multimodal learning**. 
import torch
import torch.nn as nn
import torch.nn.functional as F


class StructuredEncoder(nn.Module):
    """
    Feedforward encoder for structured data (tabular, KPIs, etc.)
    """
    def __init__(self, input_dim, hidden_dim=256):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.BatchNorm1d(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU()

TradSportEvalNet

# 🏃‍♂️ TradSportEvalNet: Intelligent Algorithm-Driven Assessment for Ethnic Sports Training --- ## 🌍 Overview **TradSportEvalNet** is an intelligent, **algorithm-driven framework** for **performance evaluation in ethnic sports training**. Traditional assessment systems often depend on **subjective judgments** and **static evaluation criteria**, limiting **scalability**, **objectivity**, and **cultural adaptability**. TradSportEvalNet introduces a **hybrid deep learning approach** that combines **multimodal data encoding**, **graphical propagation**, and **adaptive knowledge integration** to achieve **accurate, interpretable**, and **culturally sensitive** athletic assessments. By embedding **cultural context** into computational modeling, the framework ensures that performance evaluations remain **contextually relevant** across diverse ethnic sports. --- ## 🧠 Key Features - **Multimodal Encoder** Processes **visual**, **audio**, and **textual sensor data** using deep hierarchical architectures. - **Hierarchical Feature Extraction** Captures both **coarse-grained** and **fine-grained** aspects of athlete performance. - **Attention-Based Propagation** Utilizes **attention mechanisms** to emphasize critical training signals and patterns. - **Adaptive Knowledge Integration Mechanism (AKIM)** Dynamically embeds **domain-specific** and **cultural knowledge** into model learning for adaptive inference. - **Rule-Based Explainability** Generates **human-readable assessment reports**, enhancing interpretability for coaches and researchers. - **Scalable Architecture** Easily adaptable for various ethnic sports and new datasets through **modular design**. --- ## 🏗️ Architecture TradSportEvalNet is organized into three core modules: | Module | Function | |---------|-----------| | **Multimodal Encoder** | Converts multimodal inputs (visual, sensor, text) into high-level embeddings via convolutional and transformer blocks. | | **Graphical Propagation Layer** | Models dependencies between performance indicators using graph structures and attention propagation. | | **Adaptive Knowledge Integration Mechanism (AKIM)** | Incorporates cultural and domain knowledge to refine decision boundaries dynamically. | > Figures 1–4 in the paper provide a detailed schematic of the architecture. --- ## 📊 Experimental Highlights TradSportEvalNet achieved **superior performance** compared to state-of-the-art baselines across multiple datasets. | Baseline Models | Improvement | |------------------|--------------| | **ResNet** | +3.5% Accuracy | | **ViT** | +3.8% Accuracy | | **I3D** | +4.0% Accuracy | | **BLIP** | +4.2% Accuracy | ### ✅ Key Results - Consistent **performance gains (3.5–4.2%)** over leading deep learning baselines. - Strong **interpretability** through rule-aware and knowledge-integrated attention maps. - Demonstrated **efficiency** in processing multimodal inputs across different cultural sports contexts. --- ## 🧩 Datasets TradSportEvalNet was validated using **four primary datasets**, representing a variety of ethnic and cultural sports contexts: | Dataset | Description | |----------|--------------| | **Ethnic Sports Performance Dataset** | Includes biomechanical and cultural parameters from ethnic sports activities. | | **Intelligent Sports Training Dataset** | Combines video, motion sensors, and AI-assisted performance data. | | **Cultural Sports Assessment Dataset** | Captures cultural influences on training and evaluation outcomes. | | **Algorithmic Sports Evaluation Dataset** | Provides benchmark data for algorithmic assessment and performance prediction. | --- ## 🔮 Future Directions - Integrate **real-time feedback loops** for continuous and adaptive cultural learning. - Extend the framework to **mainstream and Olympic sports analytics**. - Develop a **standardized repository of cultural ontologies** for AI-driven sports research. --- ## 📖 Citation If you use this framework in your research, please cite: **Xu, Yibing. (2025).** *TradSportEvalNet: Intelligent Algorithm-Driven Assessment Modeling for Ethnic Sports Training.* **Beijing Sport University.** --- ## 📜 License This project is released under the **MIT License**. You are free to **use**, **modify**, and **distribute** this work with proper attribution. --- **TradSportEvalNet** pioneers the fusion of **cultural intelligence** and **deep learning**, advancing **AI-based assessment systems** that honor the diversity and richness of **ethnic sports traditions**. 
import torch
import torch.nn as nn
import torch.nn.functional as F

class HierarchicalBlock(nn.Module):
    """
    Hierarchical block that refines feature representations
    through multiple nonlinear transformations.
    """
    def __init__(self, in_dim, hidden_dim, num_layers=3):
        super().__init__()
        self.layers = nn.ModuleList()
        for i in range(num_layers):
            self.layers.append(nn.Sequential(
                nn.Linear(in_dim if i == 0 else hidde

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