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HIPLE-CARSTA

# ๐Ÿงฉ HIPLE-CARSTA: A Multimodal Evaluation Framework for College English Teaching Effectiveness --- ## ๐ŸŒ Overview **HIPLE-CARSTA** introduces a comprehensive **deep learningโ€“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: - ๐Ÿง  **HIPLE** โ€” *Hierarchically Integrated Pedagogical Latent Estimator* A hierarchical neural architecture that models multimodal instructional interactions. - ๐ŸŽฏ **CARSTA** โ€” *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. --- ## ๐Ÿ”‘ Key Features - ๐ŸŽฅ **Multimodal Input Integration** Combines **audio**, **video**, **textual**, and **behavioral** signals from classroom interactions. - ๐Ÿงฉ **Hierarchical Pedagogical Encoding** Captures **micro-level** teaching cues and **macro-level** instructional structures. - ๐Ÿง  **Peer-Aware Relational Learning** Models **collaborative learning dynamics** among students using **Graph Neural Networks (GNNs)**. - ๐Ÿ” **Feedback-Driven Attention** Dynamically adjusts attention to critical teaching moments based on **student engagement feedback**. - ๐Ÿ“Š **Cognitive Alignment Indexing (CAI)** Quantifies how closely learning outcomes align with **Bloomโ€™s taxonomy** and cognitive development levels. - ๐Ÿš€ **Adaptive Feedback Optimization (AFO)** Uses **reinforcement learning** to continuously refine and optimize teaching strategies. - ๐Ÿ”ฎ **Predictive Instructional Simulation** Simulates **future teaching outcomes** based on current cognitive and behavioral trends. --- ## ๐Ÿ—๏ธ Model Architecture HIPLE-CARSTA is composed of two primary subsystems โ€” **HIPLE** and **CARSTA** โ€” that interact through multimodal and cognitive modeling. ### 1๏ธโƒฃ HIPLE โ€” *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*.) ### 2๏ธโƒฃ CARSTA โ€” *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 3โ€“4, pages 9โ€“10*.) > The integration of these modules leverages CNNs, attention mechanisms, and reinforcement-based reasoning for holistic multimodal assessment. --- ## ๐Ÿ“š 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 | --- ## ๐Ÿ“ˆ Experimental Highlights | Metric | Improvement | Baseline | |---------|--------------|-----------| | **Accuracy** | +2.54% | xDeepFM | | **F1 Score** | +3.66% | AutoInt | | **AUC** | +2.83% | Across all datasets | ### ๐Ÿงฉ Ablation Studies - Removing **HIPLE** or **CARSTA** modules leads to a **2โ€“3% reduction** in AUC, demonstrating both are essential for performance. - Visual comparisons (*Figures 5โ€“8, pages 14โ€“17*) highlight improved attention focus and multimodal interpretability. --- ## โš™๏ธ Implementation Details | Component | Configuration | |------------|---------------| | **Framework** | PyTorch | | **Optimizer** | AdamW (lr=1e-4, cosine decay) | | **Batch Size** | 32 | | **Modalities** | Audio โ†’ Wav2Vec2 / Video โ†’ TCN + Keypoint extractor / Text โ†’ Multilingual BERT | | **Training** | 100 epochs with early stopping (patience = 10) | | **Metrics** | Accuracy, Recall, F1, AUC, CCC, RMSE | --- ## ๐Ÿ”ฎ 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 --- ## ๐Ÿ“– 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.** --- ## ๐Ÿ“œ 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

# ๐Ÿšข ShipHealthNet: Deep Learning-Based Structural Health Monitoring for Ships --- ## ๐ŸŒ 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 โ€” including **strain**, **stress**, **vibration**, **acoustic**, and **temperature** signals โ€” to: - Detect anomalies - Estimate damage severity - Optimize maintenance scheduling --- ## ๐Ÿง  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*). --- ## ๐Ÿ—๏ธ 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. --- ## โš™๏ธ 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) | --- ## ๐Ÿ“ˆ Experimental Highlights - Achieved **>94% accuracy** and **>92% F1 scores** across all datasets. - Outperformed baselines (**ResNet50**, **ViT**, **Swin Transformer**, **CLIP**) by **4โ€“6%** across all metrics (*Tables 1โ€“2, pages 10โ€“11*). - **Ablation studies** (*Tables 3โ€“4, pages 11โ€“12*) show performance drops of up to **6% AUC** when removing any core module. - Demonstrated strong **robustness** under **noisy** and **nonstationary sea-state conditions**. --- ## ๐Ÿงฎ Implementation Details | Parameter | Configuration | |------------|---------------| | **Frameworks** | PyTorch 1.9 / TensorFlow 2.0 | | **Hardware** | NVIDIA RTX 3090 GPU | | **Optimizer** | Adam with learning rate decay ร—0.1 every 10 epochs | | **Batch Size** | 32 | | **Early Stopping** | Applied to prevent overfitting | | **Evaluation Metrics** | Precision, Recall, F1, AUC | --- ## โš“ Limitations and Future Work - Requires **large labeled datasets** for initial training โ€” 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. --- ## ๐Ÿ“– 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.** --- ## ๐Ÿ“œ 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

# ๐Ÿ›๏ธ EcomVolunteerNet: Behavioral Impact Modeling of Student Volunteering on E-commerce Consumer Dynamics --- ## ๐ŸŒ 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. --- ## ๐Ÿง  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**. | --- ## ๐Ÿ—๏ธ Model Architecture As illustrated in Figures 1โ€“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. --- ## ๐Ÿ“Š 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. | --- ## ๐Ÿ“ˆ 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% | ### ๐Ÿงฎ Performance Summary - High **interpretability** through hybrid symbolicโ€“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. --- ## ๐ŸŒŸ Highlights - ๐Ÿงฉ **Hybrid symbolicโ€“neural architecture** enhances interpretability. - โฑ **Temporal recurrence** models evolving consumer dynamics. - ๐ŸŒ **Ethical consumerism alignment** connects volunteering to sustainable purchasing. - โš™๏ธ **Scalable and domain-adaptable** for varied e-commerce ecosystems. - ๐Ÿ’ฌ **Probabilistic contextual weighting** improves both accuracy and transparency. --- ## ๐Ÿ”ฎ 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**. --- ## ๐Ÿ“– 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.** --- ## ๐Ÿ“œ 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

# โšก GridTimeNet: Deep Temporal Modeling for Fault Forecasting and Anomaly Detection in Power IoT Systems --- ## ๐ŸŒ 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**. --- ## ๐Ÿง  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. --- ## ๐Ÿ—๏ธ Architecture GridTimeNetโ€™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 1โ€“4 in the original paper illustrate the encoder design, attention propagation, and the TFMS control loop architecture. --- ## โš™๏ธ 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. --- ## ๐Ÿ“ˆ 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 โ€” the **Multimodal Encoder**, **Graphical Propagation Layer**, or **TFMS** โ€” leads to measurable drops in accuracy and recall, underscoring the necessity of each design element. --- ## ๐Ÿ”ฎ 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**. --- ## ๐Ÿ“– 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.** --- ## ๐Ÿ“œ 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

# ๐Ÿ’ผ DigitFusionNet: Structured and Unstructured Data Fusion for Enterprise Digital Transformation Path Identification --- ## ๐ŸŒ 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 โ€” such as **relational databases**, **textual reports**, **images**, and **sensor signals** โ€” DigitFusionNet produces actionable insights that align with **organizational goals** and **digital evolution strategies**. --- ## ๐Ÿง  Key Features - ๐Ÿ”„ **Multi-Modal Data Fusion** Integrates **structured (tabular)** and **unstructured (text, image)** data using deep neural encoders and transformer-based fusion layers. - ๐Ÿงญ **Cross-Modal Attention** Dynamically aligns and contextualizes **semantic relationships** across multiple data modalities. - ๐Ÿงฉ **Adaptive Fusion Strategy (AFS)** Adjusts the **weighting of data modalities** according to domain priorities, data quality, and relevance. - ๐Ÿ” **Interpretable Clustering Module** Organizes predictions into **semantically coherent clusters**, enhancing interpretability and strategic insight. - โš™๏ธ **Dynamic Feedback Loop** Continuously refines model performance through **adaptive parameter updates** and feedback-driven learning. - ๐Ÿ“ˆ **Scalable Enterprise Application** Designed for **large-scale**, **heterogeneous enterprise environments**, ensuring adaptability as data complexity grows. --- ## ๐Ÿ—๏ธ 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 1โ€“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. --- ## ๐Ÿงพ 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. | --- ## ๐Ÿ“ˆ Experimental Highlights - **Implementation:** PyTorch framework on **NVIDIA Tesla V100 GPUs** - **Baselines Compared:** ResNet, ViT, I3D, BLIP, DenseNet ### ๐Ÿงฎ Quantitative Results | Metric | Improvement Over SOTA | |---------|-----------------------| | **Recall** | +4.2% | | **F1-Score** | +3.9% | ### โœ… 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 --- ## ๐Ÿ”ฎ 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. --- ## ๐Ÿ“– 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.** --- ## ๐Ÿ“œ 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

# ๐Ÿƒโ€โ™‚๏ธ TradSportEvalNet: Intelligent Algorithm-Driven Assessment for Ethnic Sports Training --- ## ๐ŸŒ 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. --- ## ๐Ÿง  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**. --- ## ๐Ÿ—๏ธ 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 1โ€“4 in the paper provide a detailed schematic of the architecture. --- ## ๐Ÿ“Š 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 | ### โœ… Key Results - Consistent **performance gains (3.5โ€“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. --- ## ๐Ÿงฉ 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. | --- ## ๐Ÿ”ฎ 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. --- ## ๐Ÿ“– 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.** --- ## ๐Ÿ“œ 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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AgriSenseNet

# AgriSenseNet: Spatio-Temporal Attention Fusion for Crop Condition Identification ## ๐ŸŒพ Overview **AgriSenseNet** is a deep learning framework designed for **crop condition identification** in smart agriculture. It integrates *spatial*, *temporal*, and *contextual* dependencies from multimodal data sources such as satellite imagery, sensor networks, and environmental signals. The architecture consists of two core components: - **AgriCureNet**: A hybrid convolutional-transformer model that performs spatio-temporal feature extraction and multimodal fusion. - **STGS (Spatio-Temporal Grounding Strategy)**: A post-inference calibration method that aligns predictions with real-world agricultural cycles, sensor reliability, and field topology. The system enables high-accuracy, interpretable, and scalable agricultural monitoring across heterogeneous farmlands. --- ## ๐Ÿšœ Key Contributions 1. **Multi-Scale Spatio-Temporal Attention** - Learns dynamic dependencies across both space and time using temporal memory and spatial attention blocks. 2. **Multimodal Fusion** - Integrates visual (RGB, NDVI), environmental (soil, humidity), and weather (temperature, rainfall) data. 3. **Graph-Based Reasoning** - Propagates structural relationships across neighboring parcels for spatial coherence. 4. **STGS Inference Calibration** - Aligns predictions with phenological phases, sensor fidelity, and topological field zones. 5. **Robustness and Interpretability** - Handles missing data and variable conditions while maintaining explainable feature attribution. --- ## ๐Ÿง  Architecture ### AgriCureNet - **Encoder:** Multi-scale CNN with dilated convolutions for spatial hierarchy. - **Temporal Module:** Transformer-like temporal attention mechanism for long-term dependencies. - **Graph Propagation:** Dynamic graph convolution to encode inter-parcel dependencies. - **Decoder:** MLP with confidence calibration head. ### STGS (Spatio-Temporal Grounding Strategy) - **Temporal Alignment:** Phase-aware reweighting using phenological phase priors. - **Spatial Reliability:** Sensor-based fidelity maps using NDVI entropy. - **Topological Adaptation:** Field zone priors for localized calibration. - **Self-Supervised Calibration:** Stability via perturbation consistency losses. For reference, see **Figure 1โ€“4** in the paper for architectural schematics&#8203;:contentReference[oaicite:1]{index=1}. --- ## ๐Ÿ“Š Experimental Results | Dataset | Accuracy | F1 Score | AUC | |----------|-----------|----------|------| | Crop Health Monitoring | 93.84% | 92.40% | 94.12% | | Smart Agriculture Sensor Data | 94.15% | 92.88% | 94.05% | | Spatio-Temporal Crop Analysis | 93.26% | 91.73% | 94.06% | | Agricultural Condition Imaging | 94.32% | 92.79% | 94.91% | These outperform Mask2Former, HRNet, and UNet++ by an average margin of **+3.5% F1** and **+3.8% AUC** across datasets&#8203;:contentReference[oaicite:2]{index=2}. --- ## โš™๏ธ Training Details - **Hardware:** NVIDIA RTX 3090 + Intel Xeon Gold 6226R - **Optimizer:** AdamW, lr = 1e-4 (cosine schedule) - **Losses:** Cross-entropy, confidence regularization, consistency penalty - **Augmentation:** Rotation, mirroring, NDVI perturbation, Gaussian noise - **Batch Size:** 16 - **Epochs:** 100 (early stop after 10 non-improving rounds) --- ## ๐Ÿงฉ License MIT License --- ## Acknowledgments This work was conducted at the School of Electronic Engineering and Computer Science, Queen Mary University of London, UK, and supported by the Smart Agriculture Innovation Program.
import torch
import torch.nn as nn
import torch.nn.functional as F


# ----------------------------------------------------
# 1. Multimodal Encoder: CNN + Temporal Attention
# ----------------------------------------------------
class MultiScaleEncoder(nn.Module):
    """
    Extracts spatial features using multi-scale dilated convolutions.
    """
    def __init__(self, in_channels=3, base_channels=64):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, base_ch

SNiFE-SCoRe

# SNiFE-SCoRe: Real-Time Mixed Reality Interior Design Feedback System with Edge Computing ## Overview This repository implements the architecture proposed in **โ€œReal-Time Immersive Interior Design Feedback with Mixed Reality and Edge Computingโ€** by *Tao Liu* and *Shan Lin* (Hubei University of Technology, China). The project introduces two tightly coupled modules: - **SNiFE (SceneGraph-Informed Neural Feedback Engine)** Generates real-time, interpretable feedback by analyzing scene graphs and inferred user intent. - **SCoRe (Spatial-Aware Constraint Reconciliation Engine)** Translates neural feedback into actionable, constraint-driven design guidance within mixed reality environments. Together, these components form a **closed-loop adaptive feedback system** for mixed reality interior design powered by **edge computing**. This design pipeline enhances responsiveness, interpretability, and creative efficiency, making it ideal for high-fidelity 3D design scenarios. --- ## Key Features - ๐Ÿงฉ **Scene Graph Embedding and Conditioning** Graph-based encoding of spatial relationships and object semantics (Figure 2 of the paper). - โš™๏ธ **Neural Feedback Engine (SNiFE)** Produces interpretable feedback vectors through latent user intent inference and differentiable constraint analysis. - ๐ŸŽฏ **Constraint Reconciliation Engine (SCoRe)** Dynamically projects corrective design actions based on spatial constraints, user behavior, and semantic consistency (see Figure 3). - ๐Ÿ•ถ๏ธ **Edge-Assisted Mixed Reality Loop** Enables sub-50ms feedback latency using local edge inference nodes, improving user immersion by 38% over cloud-only systems. - ๐Ÿง  **Context-Aware Learning** Incorporates gaze, gesture, and voice input from MR devices to predict user intent in real time. --- ## Architecture Summary 1. **SNiFE (SceneGraph-Informed Neural Feedback Engine)** - Embeds scene graphs as spatial-semantic vectors. - Infers user intent using GRU-based latent modeling. - Generates feedback vectors via MLP decoders aligned with constraint losses. - Enforces graph consistency during training. 2. **SCoRe (Spatial-Aware Constraint Reconciliation Engine)** - Prioritizes violated constraints. - Projects minimal-edit trajectories in design space. - Uses interaction attention masks to avoid redundant feedback. - Evolves over time to maintain stability and adaptivity. Refer to *Figure 1โ€“4* of the original paper for the workflow diagrams&#8203;:contentReference[oaicite:1]{index=1}. --- ## Experimental Highlights | Dataset | Accuracy | F1 Score | AUC | |----------|-----------|----------|------| | Interior Design Feedback | 89.37% | 87.12% | 91.43% | | Mixed Reality User Interaction | 88.74% | 86.40% | 90.52% | | Edge Optimization Dataset | 88.85% | 86.29% | 90.21% | | Immersive Spatial Experience | 89.72% | 87.11% | 91.08% | Average improvement over baselines: **+3.5%** on Accuracy and **+4.1%** on AUC. --- ## Technical Implementation **Frameworks:** PyTorch, Open3D, and PyTorch Geometric **Input:** 3D object layouts, semantic scene graphs, and interaction logs **Output:** Real-time feedback vectors and design suggestions **Compute Mode:** Hybrid โ€“ local edge inference + cloud synchronization --- ## License MIT License โ€“ free for academic and commercial use with attribution. --- ## Acknowledgments This implementation is inspired by the original paper and the detailed model schematics presented in Figures 1โ€“4, highlighting: Graph consistency objectives Constraint prioritization flow Edge-optimized perception loop 
import torch
import torch.nn as nn
import torch.nn.functional as F

# ------------------------------
# SceneGraph-Informed Neural Feedback Engine (SNiFE)
# ------------------------------

class SceneGraphEmbedding(nn.Module):
    """
    Scene graph embedding and conditioning module.
    Encodes geometric, semantic, and aesthetic object attributes.
    """
    def __init__(self, input_dim=128, hidden_dim=256):
        super().__init__()
        self.geo_encoder = nn.Linear(input_d

GEM-PNet

# GEM-PNet: Deep Learning-Based IoT Data Fusion Platform for Intelligent Decision-Making ## Overview This repository provides an implementation of **GEM-PNet** (Graph-Encoded Modular Perception Network) and **EARPO** (Energy-Aware Adaptive Routing with Predictive Offloading), proposed by **Rongfu Wang et al.** in *Construction of Internet of Things Data Fusion Platform Based on Deep Learning for Intelligent Decision-Making*. The framework aims to enhance **real-time decision-making** and **resource-efficient inference** across distributed Internet of Things (IoT) networks. GEM-PNet captures spatiotemporal dependencies through graph-based perception and attention-driven temporal modeling, while EARPO enables intelligent routing and adaptive offloading across heterogeneous edge environments. --- ## Key Features - **Dynamic Edge Encoding** Models communication reliability, bandwidth, and latency via learnable, time-sensitive adjacency matrices. - **Gated Temporal Inference** Learns short- and long-term dependencies with adaptive memory units similar to GRU architectures. - **Selective Communication Gating** Reduces redundant message exchange and optimizes bandwidth and energy consumption. - **EARPO (Energy-Aware Adaptive Routing with Predictive Offloading)** Balances computation between edge and cloud nodes, minimizing congestion and maximizing accuracy. - **Context-Aware Adaptation** Nodes learn to decide whether to process data locally, forward to peers, or offload to the cloud. --- ## System Architecture The complete architecture consists of: 1. **GEM-PNet Core** โ€“ Performs adaptive fusion of sensor data through attention-based dynamic graphs. 2. **EARPO Module** โ€“ Learns stochastic routing policies and congestion-aware offloading decisions. 3. **Energy Management Layer** โ€“ Tracks and adjusts node communication based on battery budgets and real-time load. Refer to Figures 1โ€“4 in the original paper for visual architecture breakdowns: - *Figure 1:* GEM-PNet pipeline with attention-based perception layers. - *Figure 2:* Selective Communication Gating module. - *Figure 3:* EARPO policy selection and routing diagram. - *Figure 4:* Congestion-Aware Offloading workflow&#8203;:contentReference[oaicite:1]{index=1}. --- ## Experimental Results | Dataset | Accuracy | F1 Score | Improvement vs. Baseline | |----------|-----------|----------|---------------------------| | SmartSantander | 91.67% | 90.02% | +2.56% | | OpenSense | 92.05% | 90.10% | +2.64% | | WISDM | 90.14% | 88.90% | +1.68% | | UNSW-NB15 | 91.02% | 89.75% | +3.14% | These results outperform state-of-the-art models including Transformers, GraphSAGE, ST-GCN, and CausalFormer across all benchmarks. --- ## Implementation Details - **Framework:** PyTorch - **Optimizer:** Adam (lr = 0.001) - **Batch Size:** 64 - **Dropout:** 0.3 - **Training Stop:** Early stopping after 20 stagnant epochs - **Loss Functions:** MSE (regression), BCE (classification) - **Metrics:** MAE, RMSE, Precision, Recall, F1-score --- ## Future Work - Improve adaptability to highly dynamic network topologies. - Integrate meta-learning for better cross-domain generalization. - Expand GEM-PNet for multi-agent reinforcement learning IoT systems. --- ## Citation If you use this repository, please cite: > Wang, R., Cai, J., Li, T., & Li, F. (2025). *Construction of Internet of Things Data Fusion Platform Based on Deep Learning for Intelligent Decision-Making.* > Guangdong University of Science and Technology, China. --- ## License Released under the MIT License. --- ## Acknowledgments This research was supported by the **Dongguan Science and Technology of Social Development Program (20221800905782)**. 
import torch
import torch.nn as nn
import torch.nn.functional as F


class DynamicEdgeEncoder(nn.Module):
    """
    Dynamic Edge Encoding Module
    Learns time-varying adjacency matrices for IoT graphs
    using latency, reliability, and contextual metadata.
    """
    def __init__(self, hidden_dim, threshold=0.2):
        super().__init__()
        self.edge_fc = nn.Linear(hidden_dim, hidden_dim)
        self.att_proj = nn.Linear(hidden_dim, 1)
        self.threshold = thresh

NeuroSymbolic 3DGraph Modeling

# ๐Ÿงฉ NeuroSymbolic-3DGraph-Modeling ### A Neuro-Symbolic Reasoning Framework for Hierarchical Three-Dimensional Graph Modeling Based on Cognitive Structures --- ## ๐ŸŒ Overview **NeuroSymbolic-3DGraph-Modeling** implements two core components: - **Neuro-Symbolic Conceptual Transformer (NSCT)** - **Context-Aware Meta-Reasoning Scheme (CAMRS)** These were proposed in the paper *โ€œGraph Hierarchical Three-Dimensional Space Vector Modeling Method Based on Cognitive Modelโ€* by **Dan Chang (Changchun Sci-Tech University)**. The system bridges **symbolic reasoning** and **neural computation**, enabling **structured**, **interpretable**, and **adaptive reasoning** across complex domains. It models how humans perceive hierarchical relationships and perform **context-sensitive inference** through **neural-symbolic integration**. --- ## ๐Ÿง  Key Features ### ๐Ÿ”น Hierarchical Graph Embedding - Learns **spatially-aware 3D graph representations**. - Incorporates **cognitive priors** for semantic structure learning. ### ๐Ÿ”น Neuro-Symbolic Conceptual Transformer (NSCT) - Combines **symbolic rule propagation** with **neural attention**. - Enables **explainable reasoning** and concept-level interpretability. ### ๐Ÿ”น Symbol-Guided Attention - Integrates **logical priors** into attention heads. - Enforces **rule-compliant inference** during computation. ### ๐Ÿ”น Latent Concept Grounding - Maps **latent neural representations** to **interpretable symbolic concepts**. - Ensures **semantic transparency** in the reasoning process. ### ๐Ÿ”น Rule-Constrained State Evolution - Maintains **logical consistency** during inference. - Uses **rule-based neural updates** to constrain state transitions. ### ๐Ÿ”น Context-Aware Meta-Reasoning Scheme (CAMRS) - Dynamically adjusts **reasoning policies** based on contextual embeddings. - Supports **cross-task and cross-domain adaptation**. --- ## ๐Ÿงฉ Architecture Overview | Module | Description | |---------|--------------| | **Encoder** | Extracts multi-level graph features and spatial semantics. | | **Latent Concept Module** | Projects learned features into symbolic embeddings. | | **Reasoning Core (NSCT)** | Applies rule-based attention and memory updates. | | **Meta-Reasoning (CAMRS)** | Adapts reasoning parameters dynamically to context. | | **Decoder** | Generates interpretable, rule-consistent outputs. | > Figures 1โ€“3 in the paper illustrate the overall NSCT and CAMRS integration. --- ## ๐Ÿ“Š Experimental Datasets The framework was evaluated on the following datasets: | Dataset | Description | |----------|--------------| | **Human Connectome Project (HCP)** | Large-scale human brain connectivity data. | | **ATOM Dataset** | Abstract relational structures and symbolic reasoning data. | | **NeuroGraph Dataset** | Neurocognitive graph structures for 3D modeling. | | **Human Brainnetome Atlas** | High-resolution brain mapping dataset. | These datasets validate the frameworkโ€™s ability for **structured cognitive reasoning** and **symbolic alignment**. --- ## โš™๏ธ Requirements - **Python** โ‰ฅ 3.9 - **PyTorch** โ‰ฅ 1.13 - **Transformers** โ‰ฅ 4.0 - **NumPy**, **SciPy**, **tqdm**, **matplotlib** --- ## ๐Ÿ“ˆ Results Summary | Dataset | Accuracy | F1 Score | AUC | |----------|-----------|----------|-----| | **Human Connectome Project** | 88.94% | 86.89% | 90.31% | | **ATOM Dataset** | 89.77% | 87.42% | 91.25% | | **NeuroGraph** | 86.44% | 85.27% | 88.60% | | **Brainnetome Atlas** | 87.38% | 86.02% | 89.15% | **Key Achievements:** - Strong interpretability through neuro-symbolic fusion. - High generalization across cognitive and spatial domains. - Context-sensitive adaptability via meta-reasoning. --- ## ๐Ÿ”ฎ Future Work - Integrate **real-time adaptive feedback** for predictive reasoning. - Extend **CAMRS** for **multi-agent symbolic coordination**. - Apply the framework to **psychological state prediction** in ICU recovery monitoring. --- **NeuroSymbolic-3DGraph-Modeling** unites **neural intelligence** and **symbolic reasoning**โ€”advancing explainable AI through hierarchical 3D graph cognition. 
import torch
import torch.nn as nn
import torch.nn.functional as F

class SymbolGuidedAttention(nn.Module):
    """
    Symbol-Guided Multi-Head Attention with symbolic bias vectors.
    Implements the symbolic-aware attention mechanism from NSCT.
    """
    def __init__(self, d_model, n_heads):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.scale = (d_model // n_heads) ** 0.5
        self.WQ = nn.Linear(d_model, d_model)

Enterprise Financial Risk Early Warning Model

# Enterprise Financial Risk Early Warning Model ## Overview This repository presents an implementation inspired by the research paper *"Research on Enterprise Financial Risk Early Warning Model Based on Deep Learning"* authored by **Yao Kang**. The project introduces a **Hierarchical Strategic Synthesis Model (HSSM)** integrated with **Coordinated Adaptive Policy Refinement (CAPR)** โ€” two synergistic frameworks that together enable dynamic, interpretable, and multi-level financial risk forecasting. In the face of increasing market volatility, enterprises need intelligent systems that can capture the interdependencies among strategic, tactical, and operational factors. The HSSM model addresses this need by combining deep neural networks, hierarchical information flows, and feedback-driven adaptive policies. --- ## Key Features - **Hierarchical Strategic Synthesis Model (HSSM)** Captures the multi-level organizational decision flow between strategic, tactical, and operational layers. Integrates cross-level dynamics and structured coherence regularization to ensure consistent decision propagation. - **Coordinated Adaptive Policy Refinement (CAPR)** Dynamically refines decision pathways based on contextual feedback, enabling adaptation to changing financial and market environments. - **Interpretability & Transparency** Includes visualization and feature attribution mechanisms (e.g., SHAP, attention weights) to make deep learning outcomes explainable for decision-makers. - **Dataset Flexibility** Designed for diverse datasets such as: - SMEs Dataset (Small and Medium Enterprises) - ORBIS Global Corporate Database - SECโ€™s EDGAR Financial Filings - PaySim Financial Transaction Simulation Data --- ## Architecture The system is composed of three key modules: 1. **Cross-Level Dynamics** Models bidirectional dependencies between organizational layers using attention-based neural encoders. Ensures top-down directives and bottom-up feedback are harmonized. 2. **Hierarchical Policy Aggregation** Aggregates multi-agent policies into higher-level strategies using attention-weighted fusion and temporal smoothness constraints. Reflects real-world decision coordination between departments. 3. **Structured Coherence Regularization** Enforces alignment between policy abstraction levels, improving both consistency and interpretability. *(See Figure 1 and Figure 2 in the original paper for schematic diagrams.)* --- ## Experimental Highlights - **Accuracy:** 91.34% on SMEs dataset; 90.76% on ORBIS; 89.27% on SECโ€™s EDGAR; 90.02% on PaySim. - **Robustness:** Consistent performance across multiple random seeds and datasets. - **Efficiency:** Converges faster than transformer-based baselines such as BERT, RoBERTa, and ELECTRA. --- ## Methodology The model employs a multi-stage deep learning framework combining: - Temporal Convolutional Networks (TCN) - Transformers for sequential modeling - LSTM units for long-term dependencies - Gradient boosting (XGBoost) for comparative baselines - SHAP for model interpretability Each module contributes to enhanced predictive stability, adaptive learning, and explainability โ€” addressing both financial accuracy and managerial usability. --- ## Future Work - Simplify deployment via model compression and knowledge distillation. - Extend CAPR for real-time risk monitoring systems. - Integrate live financial streaming data for automated forecasting dashboards. --- ## Citation If you use this repository or the derived methods, please cite: > Yao Kang. *Research on Enterprise Financial Risk Early Warning Model Based on Deep Learning*. > Nanjing Pukou District Traditional Chinese Medicine Hospital, Nanjing, China, 2024. --- ## License MIT License โ€“ free for academic and commercial use with attribution. --- ## Acknowledgments This work is inspired by the theoretical framework proposed by **Dr. Yao Kang**, whose interdisciplinary approach bridges computational intelligence and enterprise governance. 
import torch
import torch.nn as nn
import torch.nn.functional as F


class CrossLevelDynamics(nn.Module):
    """
    Cross-Level Dynamics (CLD)
    Models top-down and bottom-up interactions between decision layers
    using attention and temporal CNNs.
    """
    def __init__(self, input_dim, hidden_dim, num_heads=4):
        super(CrossLevelDynamics, self).__init__()
        self.attention = nn.MultiheadAttention(hidden_dim, num_heads)
        self.temporal_encoder = nn.Conv1d(

GridaNet-IntelliFlow

# โšก GridaNet-IntelliFlow: Intelligent Big Data Framework for Power Grid Digitalization --- ## ๐ŸŒ Overview **GridaNet-IntelliFlow** is a unified architecture for **power grid digitalization**, integrating **graph-based digital twin modeling** and **risk-aware intelligent control**. The framework enhances **situational awareness**, **real-time optimization**, and **information flow efficiency** across **large-scale cyber-physical energy systems**. It consists of two synergistic components: - **GridaNet** โ€” a physics-informed digital twin for grid modeling. - **IntelliFlow** โ€” a decentralized, risk-aware control system for intelligent decision-making. --- ## ๐Ÿง  Key Components ### โš™๏ธ GridaNet โ€“ Physics-Informed Digital Twin Model - Encodes **spatio-temporal dependencies** in power grids using **Graph Neural Networks (GNNs)**. - Integrates **physics-consistent regularization** derived from **AC power flow laws**. - Employs a **Swin-Transformer backbone** and **uncertainty-aware decoding** for robust performance. ### ๐Ÿ” IntelliFlow โ€“ Decentralized Intelligent Control - Operates as a **belief-state-driven policy optimizer** under **partial observability**. - Incorporates **risk-aware control (CVaR)** and **safety-constrained optimization**. - Uses a **hierarchical control structure** for coordinated global and local decision-making. --- ## ๐Ÿ—๏ธ Architecture Highlights According to Figures 1โ€“4 in the paper: - **Figure 1 (p.5):** Hierarchical GridaNet pipeline showing graphical encoding, physical regularization, and transformer-based region extraction. - **Figure 3 (p.8):** IntelliFlowโ€™s multi-stage control combining **Mamba Vision Mixers**, **self-attention**, and **safety-constrained controllers**. - **Figure 4 (p.9):** Belief-Driven Policy Optimization mechanism with convolutional and transformer layers for adaptive learning. --- ## ๐Ÿ“Š Experimental Datasets GridaNet-IntelliFlow was evaluated using four major datasets: | Dataset | Description | |----------|--------------| | **Power Grid Information Flow Dataset** | Captures control center and IoT-level communications. | | **Digitalized Energy Network Dataset** | Contains multi-modal energy and sensor logs. | | **Big Data Power Grid Architecture Dataset** | Provides structural metadata for throughput and latency. | | **Smart Grid Data Flow Dataset** | Represents synchronization between cyber and physical layers. | > Refer to **Tables 1โ€“4** in the paper for detailed comparison and ablation study results. --- ## ๐Ÿ“ˆ Performance Summary GridaNet-IntelliFlow demonstrates **up to +5.2% F1-score improvement** compared to **ConvNeXt**, **ViT**, and **DenseNet** baselines, achieving **94%+ accuracy** across all benchmarks. ### โœ… Key Improvements - **โ†‘ Robustness:** Handles noise and partial observability effectively. - **โ†‘ Efficiency:** Reduces latency via **edge-cloud fusion** and parallel control mechanisms. - **โ†‘ Interpretability:** Physics-informed objectives ensure alignment with real-world grid constraints. --- ## โš™๏ธ Implementation Details | Parameter | Setting | |------------|----------| | **Framework** | PyTorch 2.1 | | **Optimizer** | AdamW (lr = 1e-4, cosine annealing, weight_decay = 0.01) | | **Batch Size** | 64 | | **Sequence Length** | 256 | | **Training** | 5-fold Cross Validation, Mixed Precision (FP16) | | **Metrics** | F1, AUC, Recall, RMSE, MAE | --- ## ๐Ÿงฉ Model Characteristics | Module | Function | |---------|-----------| | **Graph Encoder** | Learns topological dependencies in the power grid | | **Physics Regularizer** | Ensures consistency with AC power flow equations | | **Transformer Region Extractor** | Captures spatial-temporal energy distribution | | **Belief-State Controller** | Adjusts decisions dynamically based on uncertainty | | **Safety Layer** | Maintains operational security under risk constraints | --- ## ๐Ÿ’ก Summary **GridaNet-IntelliFlow** represents a powerful step toward **digitalized, intelligent, and resilient power grids**. By combining **GNN-based digital twins** and **risk-aware adaptive control**, it enables: - Real-time situational awareness - Efficient information flow - Safe and explainable energy management --- **Result:** A next-generation, **interpretable**, **scalable**, and **energy-efficient** framework for smart grid digital transformation. 
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

# ======================================================
# 1. Graphical Neural Encoding (from GridaNet)
# ======================================================
class GraphicalEncoding(nn.Module):
    def __init__(self, d_model=256):
        super().__init__()
        self.linear_q = nn.Linear(d_model, d_model)
        self.linear_k = nn.Linear(d_model, d_model)
        self.linear_v = nn.Linear(d_model,

IDEIM: Integrated Driving-Environment Interaction Model

# ๐Ÿš— IDEIM: Integrated Driving-Environment Interaction Model ### Design and Marketing Optimization of Vehicle Insurance UBI Products Based on Driving Behavior and Environment --- ## ๐ŸŒ Overview **IDEIM** is a novel framework designed to optimize **Usage-Based Insurance (UBI)** products by integrating **driving behavior** and **environmental factors**. Unlike traditional insurance pricing models that rely on static demographics, IDEIM leverages **multi-modal driving data**, **sensor fusion**, and **deep learning** to enable **adaptive**, **data-driven**, and **fair risk assessments**. The framework introduces a **multi-stage intelligent modeling architecture** that unifies: - Driver behavior modeling - Vehicle motion dynamics - Environmental factor integration - Real-time adaptive feedback loops By fusing these components, IDEIM enables **real-time pricing and decision optimization** for insurersโ€”resulting in safer, more efficient, and more equitable insurance systems. --- ## ๐Ÿงฉ Model Components ### 1๏ธโƒฃ Driver-Vehicle-Environment Coupling - Captures interactions among **driver cognition**, **vehicle dynamics**, and **external environment**. - Utilizes **Temporal Multi-Head Self-Attention (MSA)** to model driver reactions under variable road conditions. ### 2๏ธโƒฃ Vehicle Motion Modeling - Applies **Newtonian mechanics** to predict motion trajectories under drag, slope, and throttle effects. - Uses **discretized numerical integration** for stable real-time simulation. ### 3๏ธโƒฃ Environmental Factor Interaction - Introduces an **Environment-Aware Attention Module (EAAM)** (see *Figure 2, page 7*), integrating: - Pointwise & dilated convolutions - Bottleneck Attention Module (BAM) - Queryโ€“Keyโ€“Value attention for spatial dependencies ### 4๏ธโƒฃ Adaptive Behavior Mechanism - Learns to **dynamically adjust decision strategies** based on feedback and driver state. - Uses reinforcement learningโ€“style **reward functions** and **temporal regularization** for stability. ### 5๏ธโƒฃ Real-Time Feedback Loop - Continuously updates decisions according to **sensor uncertainty** and **context changes**. - Implements **adaptive gain control** through a context-sensitive modulation function ฯ‰(t). --- ## ๐Ÿ›ฐ๏ธ Datasets IDEIM was evaluated using four benchmark datasets: | Dataset | Description | |----------|--------------| | **EarthNet2021** | Remote sensing environmental imagery | | **UTDrive** | Urban GPS trajectory dataset | | **T-Drive** | Large-scale taxi GPS dataset from Beijing | | **PEMS-BAY** | Traffic sensor data for real-time vehicle flow | Each dataset captures a distinct facet of **environmental or behavioral influence**, demonstrating IDEIMโ€™s generalization capability. --- ## ๐Ÿง  Methodology Summary The IDEIM framework integrates: - Dynamic equations of vehicle motion - Environmental potential field modeling - Multi-task learning across **trajectory**, **decision**, and **environmental prediction** tasks This multi-objective optimization ensures a balance between **trajectory accuracy**, **decision interpretability**, and **environmental adaptability**. --- ## โš™๏ธ Training Configuration | Parameter | Setting | |------------|----------| | **Framework** | PyTorch 2.0 | | **GPU** | NVIDIA RTX 3090 | | **Optimizer** | Adam (lr=1e-3 โ†’ decay 0.1/10 epochs) | | **Batch Size** | 32 | | **Epochs** | 50 | | **Loss Function** | MSE + CrossEntropy (multi-task) | | **Metrics** | Accuracy, Recall, F1, AUC | --- ## ๐Ÿ“ˆ Experimental Results IDEIM achieved top-tier results across multiple datasets: | Dataset | Accuracy | Recall | F1 | AUC | |----------|-----------|--------|----|-----| | **EarthNet2021** | 95.63 | 93.47 | 94.29 | 96.42 | | **UTDrive** | 96.88 | 95.14 | 94.91 | 97.03 | | **T-Drive** | 95.87 | 93.82 | 94.23 | 96.27 | | **PEMS-BAY** | 95.94 | 94.15 | 94.56 | 96.41 | IDEIM consistently outperformed advanced models like **CLIP**, **ViT**, **BLIP**, and **Wav2Vec 2.0**. --- ## ๐Ÿ’ก Key Advantages - โš™๏ธ **Adaptive real-time learning** via sensor fusion - ๐Ÿงญ **Interpretable multi-modal attention** for behavioral understanding - ๐ŸŒ† **Scalable deployment** across cities, fleets, and insurance systems - โš–๏ธ **Fair and transparent** risk-based pricing - ๐Ÿšฆ **Data-driven adaptation** that encourages safer driving behavior --- ## ๐Ÿ”’ Future Work - Explore **privacy-preserving telematics** through **federated learning** - Extend framework for **EV-specific risk modeling** (battery, terrain, and charging data) - Integrate **marketing analytics** for personalized and dynamic insurance product design --- **IDEIM** represents a significant advancement in smart insurance modelingโ€”merging deep learning, physics-based simulation, and environmental intelligence to drive next-generation, fair, and adaptive UBI systems. 
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

# ============================================================
# Environment-Aware Attention Module (EAAM)
# ============================================================
class EAAM(nn.Module):
    def __init__(self, in_channels, reduction=4):
        super().__init__()
        self.query = nn.Conv2d(in_channels, in_channels // reduction, 1)
        self.key = nn.Conv2d(in_channels, in_channels // reductio