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# SCTM-SSAS: Syntactic–Contextual Translation Framework This repository provides a research-oriented implementation of the two core components proposed in **“English translation semantic alignment mechanism combining syntactic mapping and context awareness for translation optimization”** by **Huiying Ye** and **Yanjie Ye**. :contentReference[oaicite:2]{index=2} The system integrates **syntactic mapping** and **contextual awareness** to build a translation process that is both grammatically coherent and semantically aligned. It consists of two main modules: - **SCTM — Syntactic-Contextual Translation Model** - **SSAS — Semantic-Syntactic Alignment Strategy** The goal is to bridge the gap between structural linguistic precision and dynamic contextual interpretation, enabling more fluent and semantically accurate translations. --- ## 1. Background Machine translation has long struggled with mismatches between: - syntactic structure - contextual nuances - semantic intentions Traditional rule-based systems preserved grammar but lacked adaptability, while statistical and deep learning models improved fluency at the cost of losing fine-grained structural control. This work proposes a unified mechanism that merges both syntactic dependency modeling and contextual embedding to enhance translation quality across diverse linguistic environments. --- ## 2. Model Overview ### **2.1 Syntactic Mapping Layer (from SCTM)** Based on the formulation in Section 3.3 (page 5–6) :contentReference[oaicite:3]{index=3}: - Dependency parsing constructs a syntactic graph - A syntactic relation matrix **S** encodes connection strengths - Structural similarity between words is computed using syntactic adjacency - Each word obtains a syntactic embedding aggregated from its neighbors This preserves grammatical relations and strengthens structural fidelity during translation. --- ### **2.2 Contextual Awareness Layer (SCTM)** As described in Section 3.3 (page 6–7) :contentReference[oaicite:4]{index=4}: - A bidirectional LSTM processes the full sentence - Global context vector **C** is computed - Hidden states are combined to form contextual embeddings - Word-level contextual similarity is calculated - Final contextual representation merges local and global signals This captures long-range dependencies and semantic nuances. --- ### **2.3 Attention Integration (SCTM)** Using Equation (15): - Syntactic embedding - Contextual embedding - Attention alignment are fused to generate target-side translation embeddings. --- ## 3. Semantic-Syntactic Alignment Strategy (SSAS) Section 3.4 (page 7–9) :contentReference[oaicite:5]{index=5} introduces SSAS as an optimization layer applied after SCTM output. ### SSAS consists of: 1. **Dynamic Syntactic Mapping** - Re-maps syntactic units between source and target - Computes syntactic distance & similarity - Improves structural alignment quality 2. **Contextual Adaptation** - BiLSTM-derived global context - Contextual similarity - Adjusted contextual embedding 3. **Adaptive Feedback Mechanism** - Alignment loss - Gradient-based updates - Feedback-corrected target embedding This iterative loop stabilizes translation outputs and increases consistency. --- ## 4. Key Features - Rich syntactic dependency modeling - Global-local contextual embedding fusion - Attention-based alignment for translation - Feedback-driven refinement with syntactic & semantic loss - Modular design suitable for multilingual or low-resource settings --- ## 5. Potential Applications - Machine translation systems - Syntactic parsing-based NLP pipelines - Context-aware language understanding - Cross-lingual semantic alignment research --- ## 6. Citation If you use this work, please cite: > Ye, H., & Ye, Y. *English translation semantic alignment mechanism combining syntactic mapping and context awareness for translation optimization.* :contentReference[oaicite:6]{index=6} --- ## 7. License You may choose any license suitable for academic or commercial use (MIT recommended).

# Diffusion-Physics-FX: Dynamic Film Scene Generation with Physical-Guided Diffusion Diffusion-Physics-FX is an open-source implementation inspired by the paper *“A Film and Television Dynamic Scene Generation and Special Effects Synthesis System Integrating Diffusion Model and Physical Guidance Mechanism”* by Wenxiao Du and Xupeng Yao. This repository provides a research-oriented framework that blends diffusion-based generative modeling with physically guided constraints to produce dynamic, realistic, and coherent film scenes and special effects. The system introduces two main components: 1. **DDSSM — Diffusion-Driven Scene Synthesis Model** A multimodal diffusion architecture that iteratively refines latent representations of visual scenes using guidance from spatial–temporal features and transformer-enhanced multimodal encoders. 2. **DSSS — Dynamic Scene Synthesis Strategy** A physically grounded generation interface that enforces rules of gravity, momentum, collision, and structural constraints to ensure that the generated scenes are not just visually appealing but physically plausible. --- ## 1. Background Film and television productions increasingly require dynamic scenes that are: - High in visual fidelity - Spatially and temporally coherent - Physically realistic - Efficient to produce at scale Traditional systems rely on rule-based effects, handcrafted simulations, or pure generative models lacking physical consistency. Diffusion-Physics-FX addresses these gaps by integrating: - **Diffusion models** for high-quality iterative generation - **Graph-based physical constraints** rooted in real-world physics - **Multimodal encoders** capable of processing video, images, audio, text, and metadata - **Feedback refinement loops** that correct temporal drift, enforce scene continuity, and maintain narrative coherence --- ## 2. System Architecture Overview ### 2.1 Multimodal Encoder The multimodal encoder integrates signals from: - Visual frames - Audio cues - Textual scene descriptions - Motion or physical metadata As illustrated on page 5–6 of the paper :contentReference[oaicite:1]{index=1}, its transformer-based structure includes: - Cross-modal attention - Feature alignment modules - Joint latent representation - Policy & assessment submodules for guiding downstream diffusion This module transforms diverse signals into a unified latent space that conditions the diffusion generator. --- ## 3. Diffusion-Driven Scene Synthesis Model (DDSSM) DDSSM operates in the latent domain using a forward and reverse diffusion process: - **Forward process:** gradually injects Gaussian noise - **Reverse process:** denoises while injecting structural and physical constraints - **Graphical propagation layer:** enforces physical plausibility (gravity, collision, energy conservation) - **Contrastive refinement:** improves temporal alignment The architecture resembles Figure 1 in the paper (page 5) :contentReference[oaicite:2]{index=2}. The reverse denoising step incorporates physical guidance: - Corrects object positions - Ensures movements follow real-world laws - Stabilizes dynamic sequence transitions --- ## 4. Dynamic Scene Synthesis Strategy (DSSS) DSSS adds dynamic realism by embedding physics directly in the generation process. Its key components: - **Physics-guided constraints:** gravity, momentum, collision, rigid-body dynamics - **Feedback loop:** evaluates physical accuracy + visual coherence - **Iterative refinement:** corrects deviations frame-by-frame - **Scene-level optimization:** improves continuity between shots The feedback rule follows: Where C is coherence and P is physical accuracy. --- ## 5. Features - Multimodal transformer encoder - Diffusion-based temporal generation - Graphical propagation for physics constraints - Physically grounded motion and interaction - Feedback correction for coherence - Suitable for VFX, pre-visualization, animation, and creative pipelines --- ## 6. Applications - Film & TV visual effects - AI-assisted previsualization - Dynamic scene generation - Physics-aware animation - Special effects prototyping - Research in physics-guided generative modeling --- ## 7. Citation If you use this repository, cite the original paper: > Du, W., & Yao, X. *A film and television dynamic scene generation and special effects synthesis system integrating diffusion model and physical guidance mechanism.* --- ## 8. License You may apply MIT, Apache-2.0, or any license suited for your project’s intended use.

# AgriControlNet: Image Recognition-Driven Control Strategy for Agricultural Machinery AgriControlNet is a unified framework integrating advanced image recognition with adaptive control strategy optimization for agricultural machinery. It is derived from the paper *"AgriControlNet: Image Recognition-Driven Control Strategy Analysis for Agricultural Machinery"* by Runkun Guo and Liang Shang. The project provides a reference PyTorch implementation for the key components introduced in the paper, including its multimodal encoder, graphical propagation layer, adaptive region pooling module, and the Adaptive Control Strategy for Agricultural Machinery (ACSAM). --- ## 1. Overview Modern agriculture requires intelligent systems capable of adjusting to dynamic, uncertain, and heterogeneous farming environments. AgriControlNet addresses this need by combining a powerful visual recognition architecture with a domain-aware adaptive control strategy. Its goals include: - Robust feature extraction under challenging agricultural conditions - Real-time control signal generation from visual inputs - Adaptation to uncertainties such as weather, crop density, and terrain variability - Multi-objective optimization for sustainable farming operations The framework is built around three key modules: ### **1. AgriControl Recognition Network (ACRN)** A visual encoder combining: - Hierarchical Mixed Mamba blocks - Patch ghosting for multi-scale features - Spatial-temporal modeling - Graphical propagation for structured dependency learning - Adaptive Region Pooling for efficient feature aggregation The architecture is designed to handle agricultural image challenges such as: - Lighting variability - Dust and occlusion - Crop diversity - Machine motion noise ### **2. Adaptive Control Strategy for Agricultural Machinery (ACSAM)** ACSAM maps extracted visual features to actionable control signals. It includes: - Multimodal decision integration - Predictive modeling to anticipate future states - Real-time feedback-driven adaptation - Multi-objective optimization for efficiency and sustainability Control variables supported include: - Steering and direction - Machinery speed - Tool engagement and switching - Machine–crop interaction constraints --- ## 2. Architecture Summary ### **2.1 Multimodal Encoder (ACRN)** The encoder integrates: - Residual multi-scale CNN layers - Attention-based adjacency matrices - Graphical propagation (message passing across spatial regions) - Adaptive pooling based on semantic relevance It transforms raw images into a compact, robust feature representation suitable for control decision-making. ### **2.2 Graphical Propagation Layer** This layer models spatial dependencies using: - Learned adjacency matrices - Degree normalization - Context-aware feature propagation This is essential for recognizing machinery position, crop row alignment, obstacles, and field structure. ### **2.3 Adaptive Region Pooling (ARP)** ARP dynamically allocates pooling regions based on feature distributions. Unlike fixed pooling, ARP: - Preserves semantically relevant regions - Reduces computation - Increases robustness across environmental conditions ### **2.4 ACSAM Decision Module** ACSAM integrates: - Visual features - Operational metrics (soil resistance, crop density, weather data) - Predictive state modeling - Feedback loops for online improvement ACSAM can enforce domain constraints such as: - Maximum soil compaction - Minimum crop damage - Safety and equipment protection thresholds --- ## 3. Key Features - **High accuracy** in agricultural machinery recognition - **Robustness** to noise, occlusion, and environmental change - **Real-time capability** with lightweight architectural elements - **Multimodal integration** supporting visual and sensor fusion - **Adaptive optimization** aligning performance and sustainability - **Scalable and modular design** suitable for various agricultural tasks --- ## 4. Experimental Summary As reported in the paper, AgriControlNet outperforms prior baselines such as ResNet, ViT, I3D, DenseNet, MobileNet, and BLIP across multiple datasets: - Agricultural Machinery Image Dataset - Crop Field Machinery Control Dataset - Precision Farming Vision Dataset - Smart Agriculture Equipment Dataset Improvements include: - +3–4% accuracy - Higher robustness under noise and occlusion - Lower computational load compared to heavy backbones - Strong cross-dataset generalization --- --- ## 5. Usage ### **Training** Prepare datasets containing: - Field images - Machinery annotations - Control signals or labels - Environmental context metadata Define hyperparameters in the config file, then run the training script. ### **Inference** Call the model with: - ACRNEncoder(image) - ACSAM(decision_inputs) The output includes predicted control actions such as direction, velocity, tool adjustment, or operational state. --- ## 6. Citation If you use this repository, please cite the original paper: > Guo, R., & Shang, L. *AgriControlNet: Image Recognition-Driven Control Strategy Analysis for Agricultural Machinery.* --- ## 7. License You may assign any appropriate open-source license (MIT recommended). --- ## 8. Acknowledgment This implementation is inspired by the AgriControlNet framework introduced by **Runkun Guo** and **Liang Shang**.

# DissemiGraph-SEM-GUIDE: Semantic-Aware Content Dissemination in the Fourth Classroom This repository provides a reference implementation of **DissemiGraph** and **SEM-GUIDE**, a dual-module framework for modeling and optimizing content dissemination in *Fourth Classroom* environments. The approach is based on the paper: > "Recognition and Optimization of Fourth Classroom Content Dissemination Patterns Using Deep Learning Techniques" > by Gang Deng, Siyi Chen, and Yujie Huang. The goal of this project is to translate the core ideas of the paper into a practical, extensible codebase that can be used for research, prototyping, and educational experiments. --- ## 1. Background The **Fourth Classroom** refers to informal, flexible, and often decentralized learning spaces that arise on digital platforms and social media. Unlike traditional classrooms, these environments: - Are **peer-driven** and **interest-driven**. - Have highly **dynamic content** and user interactions. - Exhibit **complex dissemination patterns** that are hard to model with simple heuristics. Conventional dissemination models usually assume: - Static network structures. - Homogeneous users. - Limited or no semantic awareness of the content being propagated. As a result, they struggle to capture: - Semantically nuanced patterns of content flow. - Temporal dynamics and attention shifts. - The alignment between user interests and content topics. DissemiGraph and SEM-GUIDE are designed to address these issues in an integrated way. --- ## 2. Model Overview ### 2.1 DissemiGraph **DissemiGraph** is a graph-based deep learning model for predicting and analyzing how educational content spreads in a user network. It focuses on three aspects: 1. **Content-Aware Initialization (CAI)** - Builds initial node representations by combining: - User profile features (e.g., prior knowledge, preferences). - Content semantic embedding (e.g., from text, images, interactions). - Initial engagement or relevance scores. - Applies a learnable projection and an optional time-gating mechanism to inject temporal readiness (user activity patterns). 2. **Semantic-Guided Propagation (SGP)** - Performs message passing on the user graph. - Uses semantic-aware attention to decide how strongly each neighbor influences a target node. - Weights messages by: - The similarity between user profiles and the content embedding. - A learnable semantic projection space that adapts to the task. 3. **Temporal Dissemination Prediction (TDP)** - Uses recurrent units to evolve node states over time. - Incorporates: - Temporal decay to down-weight outdated interactions. - Bilinear compatibility between sender and receiver states. - Semantic similarity between users and content. - Produces a dissemination likelihood for each edge and time step, optionally smoothed over a temporal window. ### 2.2 SEM-GUIDE **SEM-GUIDE** (Strategic Enhancement Mechanism for Guided Dissemination) sits on top of DissemiGraph and focuses on **optimization** rather than pure prediction: 1. **Strategic Node Selection** - Computes an intervention score for each user based on: - Semantic alignment with content. - Temporal engagement volatility. - Attention availability (cognitive budget). - Network connectivity (how influential the user is). - Selects high-impact users as targets for intervention (e.g., targeted recommendations, reminders). 2. **Guided Message Recalibration** - Adjusts propagation probabilities for selected users. - Emphasizes neighbors whose profiles are compatible with the content. - Incorporates a time-dependent factor so that interventions are aligned with user activity rhythms. 3. **Adaptive Feedback Mechanism** - Uses a feedback loop (e.g., engagement rewards) to compare predicted vs. actual outcomes. - Updates intervention scores over time. - Reduces cumulative error and improves strategy quality as more interaction data is observed. --- ## 3. Repository Structure A suggested structure for this project is: - `model.py` Core implementation of DissemiGraph and SEM-GUIDE (PyTorch). - `datasets/` Utilities and placeholders for dataset loaders compatible with the paper’s setting. - `configs/` Configuration files (hyperparameters, architecture settings). - `scripts/` Example training, evaluation, and experimentation scripts. - `examples/` Small-scale synthetic or toy examples illustrating usage. - `README.md` You are here. --- ## 4. Data Representation The models expect data in a form that is consistent with the paper’s formalization: - **Content set** - A collection of content units, each with: - A semantic embedding vector (e.g., from a text encoder or multimodal encoder). - Optional time-dependent semantic drift representation. - **User set** - A collection of users or participants, each with: - A dynamic profile vector that may evolve over time. - Engagement features or history with specific content. - **Interaction graph** - A time-dependent graph where: - Nodes represent users. - Edges represent interactions or influence (e.g., shares, replies, mentions). - Edge weights store influence strengths, timestamps, or other interaction metadata. - **Dissemination tensor** - A tensor recording which content has reached which user at which time step, possibly with graded strengths. The provided `model.py` focuses on the modeling components; dataset handling and preprocessing can be customized to your concrete application. --- ## 5. Installation You can set up a Python environment along the following lines: 1. Create and activate a virtual environment. 2. Install dependencies (for example, a deep learning framework and common utilities). Typical dependencies include: - A deep learning library (such as PyTorch). - NumPy and SciPy. - Tools for handling graphs and mini-batch processing. - Logging and experiment tracking utilities. You can maintain a `requirements.txt` file in the repository and install everything with a single command. --- ## 6. Usage A typical workflow might look like this: 1. **Prepare data** - Build or load the interaction graph for your platform. - Compute or load semantic embeddings for content. - Extract user features and engagement histories. - Convert everything to tensors and adjacency structures. 2. **Configure the model** - Define: - Embedding dimensions. - Number of graph propagation layers. - Hidden dimensions for recurrent units. - Choice of activation functions, dropout, and other regularization. 3. **Train** - Instantiate `DissemiGraph` (and optionally `SEMGuide`). - Define a loss function based on: - Prediction tasks (e.g., next dissemination step, link prediction). - Optimization targets (e.g., gain, engagement metrics). - Iterate over time windows and mini-batches of user-content pairs. - Optionally log metrics like F1, AUC, and dissemination gain. 4. **Evaluate** - Measure: - Semantic coherence of predicted dissemination paths. - Efficiency (how quickly content reaches relevant users). - Controllability (how strongly interventions change the dissemination pattern). 5. **Deploy / Simulate** - Use the trained model to: - Predict how new content will spread. - Identify strategic users for content seeding. - Run simulations under different intervention policies. --- ## 7. Extensibility This repository is intended as a **research-friendly starting point**. You can extend it in multiple directions: - Plug in different text or multimodal encoders to generate content embeddings. - Swap GRU with other sequence models (e.g., LSTM or Transformer encoders). - Experiment with different attention mechanisms for message passing. - Integrate reinforcement learning agents to drive SEM-GUIDE’s intervention policy. - Add explanation modules or visualization tools for dissemination graphs. --- ## 8. Citation If you use this implementation or ideas derived from it in your research, please cite the original paper by Gang Deng, Siyi Chen, and Yujie Huang. --- ## 9. License You may choose an open-source license that fits your project goals (for example, MIT, Apache-2.0, or GPL). Add the corresponding `LICENSE` file at the root of the repository. --- ## 10. Acknowledgements This implementation is inspired by the formal framework, DissemiGraph model, and SEM-GUIDE optimization strategy described in the original paper on Fourth Classroom content dissemination. All credit for the conceptual design of the method goes to the authors.

# BACE-CICI: Multimodal Learning Behavior Analysis and Competence-Aware Curriculum Modeling This repository provides a research-oriented implementation inspired by the paper *“Multimodal Fusion for Analyzing English Learning Behaviors and Competence Assessment”* by **Long Shi**. :contentReference[oaicite:2]{index=2} It contains two core components: - **BACE — Behaviorally Augmented Competence Embedding** - **CICI — Competence-Informed Curriculum Inference** The project aims to support computational modeling of English learning behaviors using multimodal signals such as linguistic content, behavioral logs, temporal indicators, and task complexity descriptors. --- ## 1. Background Modern digital learning environments generate abundant multimodal interaction data, including: - correctness logs - response times - revision behaviors - eye-gaze, speech, gesture, or platform interactions - task difficulty descriptors Traditional learning analytics approaches rely on static test scores or manually curated features, which overlook rich behavioral dynamics and temporal competence evolution. As discussed in Section 1 of the paper, these limitations hinder personalized feedback, adaptive sequencing, and cognitive-aware modeling. :contentReference[oaicite:3]{index=3} The BACE–CICI framework integrates deep multimodal representation learning with curriculum optimization to address these gaps. --- ## 2. Model Overview ### 2.1 Multimodal Encoder Architecture (BACE) Figure 1 and Figure 2 in the paper describe an encoder architecture combining: - pretrained text encoder for linguistic complexity - nonlinear behavior encoder for correctness, latency, and behavioral vectors - task complexity embedding - wavelet-enhanced blocks, SEB blocks, and channel-attention mechanisms - graphical propagation to refine fused representations - gated residual fusion combining behavioral and linguistic inputs Together these produce unified representations of learner–task interactions. :contentReference[oaicite:4]{index=4} --- ### 2.2 Behaviorally Augmented Competence Embedding (BACE) BACE unifies three multimodal streams: - **lt:** textual embedding of task-level linguistic input (Eq. 10) - **bu,t:** behavioral embedding (Eq. 11) - **ct:** task difficulty & structural features (Eq. 12) The three components are joined with a learned gated residual mechanism (Eq. 13–15), forming: A self-attention pooling layer aggregates learner histories into a competence embedding hu (Eq. 16), which then passes through a projection network to produce the final competence estimate cu (Eq. 17). Task-level correctness prediction is performed via Eq. (18). Competence smoothness regularization (Eq. 19) stabilizes trajectories across sessions. :contentReference[oaicite:5]{index=5} --- ## 3. Competence-Informed Curriculum Inference (CICI) CICI formulates curriculum sequencing as a constrained optimization problem over competence trajectories. ### 3.1 Competence Gap Projection & Feasibility Masking For each candidate task tj: - compute competence gap Δ (Eq. 21) - filter tasks exceeding cognitive stretch δmax (Eq. 22) Figure 4 illustrates how diverse task representations are processed via dilated depthwise convolutions and feasibility constraints. :contentReference[oaicite:6]{index=6} --- ### 3.2 Learning Gain Estimation Expected learning gain from a task is modeled by: (Equation 23). A curriculum smoothness constraint ensures topical continuity using cosine similarity (Eq. 24). --- ### 3.3 Curriculum Optimization & Diversity Regularization Candidate tasks are sampled via a softmax strategy (Eq. 25) with exploration controlled by τ. Competence evolves through expected increments (Eq. 26). Curriculum utility includes penalties for incoherent topic transitions (Eq. 27) and diversity regularization across selected tasks (Eq. 29). The optimal curriculum segment S* maximizes expected reward (Eq. 28). :contentReference[oaicite:7]{index=7} --- ## 4. Features - Multimodal linguistic + behavioral fusion - Wavelet / SEB / CA-based feature hierarchy - Graphical propagation for temporal and semantic task structure - Competence trajectory modeling with smoothness regularization - CICI curriculum inference integrating feasibility, gain, and diversity - Personalized task sequencing for real-time adaptation --- ## 5. Potential Applications - English learning analytics - Personalized task recommendation - Learner modeling and competence prediction - Adaptive curriculum design - Educational data mining - Multimodal behavior analysis --- ## 6. Citation If you use this repository: > Shi, L. *Multimodal Fusion for Analyzing English Learning Behaviors and Competence Assessment.* :contentReference[oaicite:8]{index=8} --- ## 7. License You may release the project under MIT, Apache-2.0, or another open-source license depending on your intended distribution model.

# LSAN-CGAT: Deep Learning–Based Adaptive Tutoring for English Language Acquisition **LSAN-CGAT** is a research-oriented implementation of a **deep learning–based adaptive tutoring framework** for English language acquisition. It is inspired by the paper: *“Enhancing English Language Acquisition through Deep Learning-Based Adaptive Tutoring Systems”* by Xi Yang and Ling Li. The framework integrates: - **Lexical-Semantic Alignment Network (LSAN)** for multimodal learner modeling - **Curriculum-Guided Adaptive Transfer (CGAT)** for adaptive training and deployment to provide **personalized, multimodal, and curriculum-aware English tutoring**. --- ## 1. Motivation Traditional digital language learning systems suffer from: - static content and fixed exercise sequences - limited personalization beyond basic difficulty levels - rule-based feedback that fails to capture rich learner errors - weak integration of speech, text, and semantic information These issues reduce engagement and limit support for diverse learners. Recent progress in deep learning and large-scale language models enables: - joint lexical, semantic, and phonological learner modeling - modeling of complex interaction histories - dynamic content and difficulty adaptation based on real-time performance - curriculum design aligned with cognitive principles of second language acquisition **LSAN-CGAT provides a computational framework that incorporates these capabilities.** --- ## 2. System Overview LSAN-CGAT consists of two tightly coupled modules: ### **1. Lexical-Semantic Alignment Network (LSAN)** A multimodal language model that jointly learns lexical, semantic, and phonological representations. ### **2. Curriculum-Guided Adaptive Transfer (CGAT)** A curriculum-aware training and scheduling strategy that adapts to learner profiles and supports cross-task transfer. Together, they enable: - fine-grained learner modeling - task-specific supervision - curriculum-aware learning trajectories - cross-domain robustness for different learning settings --- ## 3. Lexical-Semantic Alignment Network (LSAN) LSAN is a multimodal language model tailored for English education tasks. ### 3.1 Multimodal Lexical Representation For each token, LSAN builds **three parallel embeddings**: - **Lexical embedding** (subword + token identity) - **Semantic embedding** (context windows + gloss meanings) - **Phonological embedding** (IPA / phoneme sequences) These are concatenated into a **multimodal lexical vector**, representing: - orthographic form - contextual semantics - pronunciation features Figure 1 (page 9) illustrates the patch-like embeddings and multimodal pathway. --- ### 3.2 Dual-Path Encoder LSAN uses two complementary encoding paths: 1. **Structural transformer** — models syntax and sequence patterns 2. **Semantic graph encoder** — operates on dependency and sense graphs A fusion module aligns outputs from both paths, producing rich token-level representations that encode: - grammatical relations (e.g., tense, agreement) - semantic disambiguation - graph-based syntactic and semantic propagation --- ### 3.3 Educational Objectives and Losses LSAN is trained using multiple pedagogically motivated objectives: - **Form-oriented loss** (grammar correctness, POS, error type prediction) - **Semantic alignment loss** (embedding ↔ gloss consistency) - **Phonological contrastive loss** (distinguish sound-alike words) - **Task-specific loss** (classification, tagging, generation) Additional self-supervised objectives include: - masked token modeling - multimodal embedding reconstruction These correspond to Section 3.3 and Figures on pages 9–10 of the paper. --- ## 4. Curriculum-Guided Adaptive Transfer (CGAT) CGAT governs training, task ordering, adaptation, and deployment. ### 4.1 Curriculum Scheduling CGAT organizes tasks such as: - grammar recognition - vocabulary expansion - sentence ordering - reading comprehension - listening discrimination Each task has: - difficulty level - performance score - curriculum phase role A **soft curriculum scheduler** balances: - intrinsic difficulty - performance deficits This enables: - prioritizing tasks where the learner struggles - maintaining exposure to mastered skills - smooth progression from simple → complex abilities --- ### 4.2 Transfer-Aware Training CGAT maintains a **task transfer matrix** that models: - positive transfer - interference - cross-task relationships Gradients are reweighted during optimization to: - enhance beneficial transfer - suppress harmful interference Especially helpful when dealing with: - shared linguistic structures - domain differences - mixed educational corpora --- ### 4.3 Learner-Specific Adaptation Each learner is represented by a **latent learner vector** encoding: - proficiency level - native language influence - cognitive style - interaction history This vector modulates: - task sampling probabilities - curriculum progression - per-sample loss weighting A **feedback gating mechanism** uses correctness and confidence to adjust learning contribution—enabling fine-grained personalization. --- ### 4.4 Domain Robustness CGAT uses **domain-adversarial training** to build domain-invariant representations while preserving useful domain distinctions. Figure 3 shows the architecture: LSAN + scheduler + transfer module + learner adaptation. --- ## 5. Datasets and Evaluation The LSAN-CGAT framework is evaluated on multiple English language learning datasets: - **Cambridge English Profile (CEP)** - **EFCAMDAT** - **Duolingo SLAM** - **CALLHOME / CALLFRIEND English** **Experimental setup:** - Transformer-based backbone - Adam optimizer + mixed precision - Cross-entropy / MSE losses - Metrics: accuracy, F1, precision, recall, AUC, error-based metrics **LSAN-CGAT consistently outperforms** strong baselines (3D CNNs, transformer trackers, multimodal models), showing robust and adaptive learning behavior. --- ## 6. Integration in an Adaptive Tutoring System A practical LSAN-CGAT pipeline includes: ### **1. Interaction Collection** - Collect learner responses - Log timings, errors, metadata ### **2. LSAN-Based Analysis** - Convert input into multimodal embeddings - Use dual-path encoders - Predict grammatical labels, semantic matches, or rewritten text ### **3. Feedback & Recommendation** - Provide natural-language feedback - Highlight semantic issues & grammar errors - Tailor suggestions to learner level ### **4. CGAT Curriculum Adaptation** - Update difficulty & performance estimates - Schedule next tasks - Update learner profile vectors ### **5. Continuous Improvement** - Ongoing model updates - Support for new domains or corpora --- ## 7. Extensibility The LSAN-CGAT framework is modular and can be extended by: - incorporating pretrained LMs or speech encoders - adding image/text multimodal components (as hinted in Figure 2) - integrating explainable AI for instructor dashboards - linking with institutional learning analytics - combining with conversational agents for dialogue practice --- ## 8. Citation If you use or adapt this project, please cite: > Yang, X., & Li, L. > *Enhancing English Language Acquisition through Deep Learning-Based Adaptive Tutoring Systems.* --- ## 9. Disclaimer This repository is a **research prototype**, not a production-ready tutoring platform. Any real-world deployment must ensure: - robust privacy and data protection - fairness and bias mitigation - accessibility and inclusivity - evaluation with target populations Use the framework responsibly within legal and institutional constraints.

# CreditGraphFormer: Privacy-Aware Federated Enterprise Credit Rating **CreditGraphFormer** is a research-oriented implementation of a **privacy-aware, federated learning framework** for enterprise credit rating with fiscal big data. It is inspired by the paper: *“Privacy-Aware Federated Learning for Enterprise Credit Rating with Fiscal Big Data”* by Xuan Huang. The framework integrates: - **CreditGraphFormer** — a graph-based transformer encoder, - **RegulaLogic** — a compliance-driven reasoning layer, - **A privacy-aware federated training pipeline**, to support trustworthy enterprise credit assessment under regulatory and data-protection constraints. --- ## 1. Background and Motivation Enterprise credit rating is essential for: - financial risk management, - lending and loan approval, - regulatory supervision. ### Modern fiscal environments feature: - Massive heterogeneous data: tax records, invoices, regulatory filings, transaction logs. - Distributed data silos across tax bureaus, banks, payment platforms, regulators. - Data protection laws (e.g., GDPR and local regulations) that restrict centralized data pooling. ### Challenges in traditional methods: - Dependence on static statements and handcrafted indicators. - Insufficient capacity for large-scale fiscal big data. - Centralized training pipelines that raise privacy and compliance risks. - Lack of alignment with regulatory logic and audit semantics. Federated learning provides a collaborative training structure without data sharing—but requires: - fiscal-domain architectures, - robust privacy mechanisms, - regulatory-aware reasoning. **CreditGraphFormer** addresses these needs. --- ## 2. Problem Setting Enterprise credit rating is formulated as **representation learning on a fiscal knowledge graph**. ### Graph structure: - **Nodes**: enterprises with high-dimensional fiscal attributes. - **Edges**: invoice flows, supply-chain links, equity holdings, legal ties. - **Temporal attributes**: tax indicators, revenue sequences, audits over time. ### Formalization: - Enterprises form the set of rating targets. - Ratings use **ordinal labels** (e.g., AAA → C). - A **fiscal knowledge graph** contains: - nodes for enterprises and regulators, - heterogeneous relations, - time-varying enterprise feature vectors. ### Goals: - Predict enterprise ratings, - Integrate regulatory compliance, - Maintain cross-institution privacy, - Achieve robustness under distribution shifts. --- ## 3. Framework Overview The framework consists of three coordinated components: ### **1. CreditGraphFormer** A transformer-based graph encoder for fiscal networks that are: - **asymmetric**, - **sparse**, - **temporal**. It integrates: - multimodal feature projection, - relation-specific message passing, - temporal stability embeddings, - a regulatory-aware transformer block. (Architecture shown on page 5 of the paper.) --- ### **2. RegulaLogic** A compliance-oriented reasoning layer incorporating: - compliance constraints, - audit overrides, - industry-specific rating floors, - temporal smoothing, - policy-rule penalties, - confidence calibration. (Shown in diagrams on pages 8 and 14.) --- ### **3. Privacy-Aware Federated Pipeline** A federated setup where: - Clients keep data locally, - Knowledge is shared via secure aggregation and differential privacy, - Heterogeneous data distributions are handled via domain-specific modeling and local personalization. --- ## 4. CreditGraphFormer: Graph-Based Transformer for Fiscal Big Data ### 4.1 Multimodal Feature Projection Attributes are projected into multiple **fiscal views**, such as: - tax declarations & VAT indicators, - balance-sheet metrics, - audit statistics, - invoice and transaction patterns. Each view uses its own projection matrix, producing a multimodal embedding. (Shown on page 6 of the paper.) --- ### 4.2 Heterogeneous Relational Attention For each relation type, the model applies: - relation-specific transformations, - neighbor attention mechanisms, - bilinear compatibility scoring. This supports: - risk propagation across invoice networks, - supply-chain dependency modeling, - reputation propagation through ownership/legal links. --- ### 4.3 Regulatory-Aware Transformer Encoder A regulatory mask influences transformer attention: - Audited/flagged nodes can be masked or down-weighted. - Attention computations incorporate compliance constraints. - Residual and feed-forward blocks follow transformer standards. This ensures regulatory knowledge is encoded structurally. --- ### 4.4 Temporal Stability Embedding Temporal differences between consecutive periods produce a **stability descriptor**: - Small changes → stable enterprises, - Large changes → high volatility. Combined with graph embeddings, this forms the final enterprise representation. --- ## 5. RegulaLogic: Compliance-Driven Reasoning Layer RegulaLogic ensures predictions are **policy-consistent and interpretable**. ### Mechanisms include: - **Compliance masks** (filing timeliness, penalties, transparency). - **Rule-based penalties** for: - high audit risk, - recurring compliance violations, - tax anomalies or invoice irregularities. - **Sector-based rating floors** ensuring industry-specific minimum caps. - **Temporal smoothing** to avoid unstable predictions. - **Confidence calibration**: - High uncertainty → fallback to sector median. RegulaLogic bridges machine learning and regulatory logic. --- ## 6. Privacy-Aware Federated Learning Designed for multi-institution collaboration: - Local data never leaves the client. - Secure aggregation protects model updates. - Differential privacy controls information leakage. ### Typical FL parameters: - Local epochs per communication round, - Global aggregation rounds, - Client sampling strategies, - DP noise multipliers and clipping norms. Experiments in the paper show improvements in: - accuracy and F1, - robustness under label noise and distribution shifts, - regulatory adherence versus baselines (GraphSAGE, CNN-LSTM, Transformers, TabNet). --- ## 7. Potential Usage Scenarios CreditGraphFormer + RegulaLogic can support: - enterprise credit rating and risk tiering, - regulatory early-warning systems, - bank loan approval and credit limit setting, - supply-chain risk propagation, - scenario simulations under regulatory policy changes. The architecture can generalize to other privacy-sensitive financial graph tasks. --- ## 8. Extensibility Possible extensions include: - Integrating legal documents or scanned contracts, - Exploring personalized FL or advanced federated optimizers, - Adding adversarial defenses for malicious clients, - Hybridizing RegulaLogic with neural-symbolic rule learning, - Using advanced temporal models or change-point detection. --- ## 9. Citation If using this framework, please cite: > Huang, X. > *Privacy-Aware Federated Learning for Enterprise Credit Rating with Fiscal Big Data.* --- ## 10. Disclaimer This repository provides a **conceptual, research-style implementation**. Not suitable for production deployment without: - rigorous testing, - domain expert evaluation, - legal and compliance review, - strict adherence to relevant data protection laws. Use responsibly and within applicable regulatory frameworks.