Journal of Computer Science and Frontier Technologies
type: channel
description: Journal of Computer Science and Frontier Technologies ISSN Print: 3104-4204 ISSN Online: 3104-4212 CD ISSN: 3104-4220
publisher: Erytis Publishing Limited
language: en
publication name: Journal of Computer Science and Frontier Technologies
issn: 3104-4204
items: Driver Fatigue Detection Method Based on Improved RT-DETR
items: IoT Anomaly Traffic Detection Method Based on Transformer Encoder Architecture
items: MUAD-Net: A Multi-scale URL Anomaly Detection Network for Malicious URL Identification
items: CloudPayGuard: Hardware-Aware Real-Time Fraud Detection for Cloud-Native Credit Systems with OoO CPU Microarchitecture Optimization
items: Transmission Dynamics Model Incorporating Risk Perception and Behavioral Fatigue
items: Design and Implementation of a Robotic Dynamic Grasping System Based on Im-proved YOLO and Fuzzy PID Control
items: Dental-DPMNet: A Latent Disease Progression Modeling Network for Predicting Pulpitis and Periapical Disease Evolution Using Longitudinal Dental Imaging and Clinical Indicators
items: FRAdRec: A Federated Real-Time Advertising Recommendation Framework Based on User Behavior Modeling and Cloud-Native Data Infrastructure
items: An sEMG-Controlled RHex-T3 Hexapod Mobile Grasping Robot for As-sistive Object Retrieval
items: A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization
items: EduRL-GPT: A Reinforcement Learning Optimized Generative AI Framework for Intelligent Teaching Content Generation and Personalized Feedback
items: Quantitative Characterization of Pore–Fracture Structures in Deep Coal Reservoirs Based on the QSGS Method
items: CLASNet: A Cognitive Load–Aware CNN-LSTM-Attention Framework for Supply Chain Demand Forecasting and Adaptive Human–Computer Interaction
items: ROI-Optimized Sequential Advertising Recommendation Framework with Cloud-Native Scalable Data Infrastructure
items: A White-box/Black-box Dual-modal Structured Query Method for Flood Disaster Spatiotemporal Knowledge Graph Question Answering
items: DynSupplyNet: A Dynamic Graph Neural Network with Temporal Fusion for Supply Chain Risk Prediction and Propagation Analysis
items: CRE-CNN-LSTM-XAI: An Explainable Deep Learning Framework for Risk Assessment of Real Estate Collateralized Financial Products
items: Similar-Case Retrieval for Government Hotlines via an Appeal-Driven Event Graph and Dual-Route Hybrid Retrieval
items: Cloud-Edge Federated Incremental Learning Framework for PMSM Efficiency Optimization with Lightweight CNN-LSTM Models and OTA Differential Deployment
items: Research on static denial scene reconstruction and target recognition technology based on 3DGS
items: Analysis and Prediction of Urban Traffic Accident Correlating Factors Based on SARIMAX Model
items: Remaining Useful Life Prediction of Rolling Bearings Based on SSA-Optimized TCN-CBAM-BiLSTM Model
items: A Multi-Label Electrocardiogram Classification Study Integrating Clinical Metadata and Channel Attention
items: Emotion-Aware Speech-Driven 3D Facial Animation with Diffusion Models
items: Research on Consistency-Driven Point Cloud Semantic Segmentation Method in Label Noise Scenarios
items: Systematic Hyperparameter Optimisation of a U-Net Architecture for Robust Skin Lesion Segmentation
items: A Method for the Identification and Intensity Prediction of DNA Enhancers Based on Feature Extraction Algorithms
items: Member Reasoning Attack Detection and Privacy Disclosure Quantification in Federated Learning Gradient Swap Process
items: A Verifiable Epidemic Monitoring and Early Warning Scheme for Privacy Protection under Malicious Cloud Server Threat Model
items: Social Bot Detection via Spatio-Temporal Graph Self-Supervision

Driver Fatigue Detection Method Based on Improved RT-DETR
type: item
description: To address the problem of poor robustness in driver fatigue detection under complex scenarios such as low illumination and motion blur, this paper proposes an optimization method based on hard sample augmentation. A hard sample dataset containing low illumination, motion blur, and their compound interference is constructed, and a progressive two-stage training strategy is designed to guide the model to adapt to various degradations step by step. Furthermore, a spatial-channel interleaved robustness module (SCIRM) is proposed to enhance the response of eye and mouth features, while the normalized Wasserstein distance (NWD) loss function is adopted to improve the localization accuracy of small targets. The improved RT-DETR model achieves mAP50 of 95.03%, 95.62%, and 94.56% under low illumination, motion blur, and compound interference scenarios, respectively, significantly outperforming the baseline model, while its performance on clear scenes remains almost unchanged. This method effectively enhances the robustness of fatigue detection under complex imaging conditions.
subject: Articles
  topic: section
subject: Fatigue Detection
  topic: keywords
subject: RT-DETR
  topic: keywords
subject: Object Detection
  topic: keywords
subject: NWD
  topic: keywords
creator: Ying Li
creator: Yangshan Tang
creator: Xiaoyu Wang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-04
publication date: 2026-06-04
starting page: 1
ending page: 10
doi: 10.63313/JCSFT.9082

IoT Anomaly Traffic Detection Method Based on Transformer Encoder Architecture
type: item
description: The widespread deployment of IoT devices has made heterogeneous network traffic highly complex, making abnormal traffic detection vital for IoT security. This paper presents a Transformer-based IoT abnormal traffic detection method tailored to the long-range temporal correlation of IoT traffic and the drawbacks of existing approaches. It first builds traffic embeddings with learnable positional encoding to project raw traffic features into a high-dimensional space, then leverages Transformer multi-head self-attention to capture long-range dependencies by modeling temporal correlations in traffic sequences. Feature compression and residual enhancement modules are further adopted to lower model complexity and inference delay for resource-limited IoT terminals. Experiments verify that the scheme accurately identifies normal traffic and diverse anomalous attacks with outstanding detection accuracy and stability.
subject: Articles
  topic: section
subject: IoT Security
  topic: keywords
subject: Anomaly Network Traffic Detection
  topic: keywords
subject: Transformer Encoder
  topic: keywords
subject: Self-Attention Mechanism
  topic: keywords
creator: Yuxuan Jiang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-30
publication date: 2026-05-30
starting page: 177
ending page: 186
doi: 10.63313/JCSFT.9081

MUAD-Net: A Multi-scale URL Anomaly Detection Network for Malicious URL Identification
type: item
description: With the continuous evolution of cyber attack techniques, the propagation of malicious links has become a major threat in cybersecurity. Attackers induce users to visit target resources by constructing malicious URLs, leading to information theft and malware distribution. Effectively identifying malicious URLs in complex network environments has become an important research issue. To address this problem, this paper adopts an attack life-cycle perspective and proposes a deep learning-based malicious URL detection method. Considering the complex structure, short lifecycle, and fast generation speed of malicious URLs, the method first normalizes URL strings and encodes them as character sequences, then maps characters into vector representations through an embedding layer. A multi-scale convolutional structure is used to extract local pattern features from URL character sequences, combined with a sequence modeling mechanism to capture contextual dependencies, achieving automatic learning of malicious URL features. Experimental results show that the proposed method achieves high detection performance and good generalization ability. Through multi-scale feature extraction and model optimization, this paper effectively improves malicious URL detection performance, demonstrating good application value in real-world network scenarios.
subject: Articles
  topic: section
subject: Alicious URL Detection
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Multi-Scale Feature Extraction
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Sequence Modeling
  topic: keywords
creator: Tian Liang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-30
publication date: 2026-05-30
starting page: 167
ending page: 176
doi: 10.63313/JCSFT.9080

CloudPayGuard: Hardware-Aware Real-Time Fraud Detection for Cloud-Native Credit Systems with OoO CPU Microarchitecture Optimization
type: item
description: Real-time fraud detection in cloud-native credit payment systems is a critical challenge due to the increasing complexity of transaction networks, the rapid evolution of fraudulent behaviors, and the high computational demands of modern deep learning models. To address these challenges, we propose CloudPayGuard, a hardware-aware framework that integrates Temporal Heterogeneous Graph Neural Networks (TH-GNN) for dynamic transaction modeling, Large Language Models (LLM) for automated security policy generation, and out-of-order (OoO) CPU microarchitecture performance prediction for hardware-accelerated inference. CloudPayGuard constructs multi-modal transaction graphs incorporating user behavior sequences, device fingerprints, and geolocation information, enabling real-time identification of suspicious activities with millisecond-level latency. The framework dynamically generates and verifies risk policies through LLM-based reasoning and constraint checking, ensuring trustworthy and adaptive deployment in cloud-native environments. To optimize inference performance, a deep learning-based CPU microarchitecture predictor estimates IPC and identifies potential bottlenecks in ROB, IQ, and LSQ resources, allowing dynamic adjustment of CPU parameters and task scheduling. Experiments on a large-scale financial transaction dataset show that CloudPayGuard achieves an F1-score of 0.91 and an average inference latency of 6 milliseconds, outperforming baseline TH-GNN and other models. The OoO CPU microarchitecture optimization reduces latency by 34–40%, while LLM-driven policy generation and TH-GNN-based graph modeling ensure accurate fraud detection. These results demonstrate CloudPayGuard’s efficiency, scalability, and effectiveness for real-time fraud detection in cloud-native credit systems.
subject: Articles
  topic: section
subject: Cloud-native
  topic: keywords
subject: Credit payment systems
  topic: keywords
subject: Real-time fraud detection
  topic: keywords
subject: Temporal Heterogeneous Graph Neural Network
  topic: keywords
subject: LLM-driven security policy
  topic: keywords
subject: Out-of-order CPU
  topic: keywords
subject: Microarchitecture optimization
  topic: keywords
subject: Hardware-aware deep learning
  topic: keywords
creator: Hanqing Yao
creator: Jixiang Ding
creator: Zifan Wang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-26
publication date: 2026-05-26
starting page: 154
ending page: 166
doi: 10.63313/JCSFT.9079

Transmission Dynamics Model Incorporating Risk Perception and Behavioral Fatigue
type: item
description: The classical SIR model provides a foundational framework for studying transmission dynamics, but its standard form often assumes a fixed transmission rate and therefore cannot directly represent adaptive changes in human behavior. In real transmission processes, individuals may reduce contact when perceived risk rises, while prolonged protection may also generate behavioral fatigue and weaken adherence. To describe this coupled mechanism, this paper extends the classical SIR model by introducing two bounded behavioral variables: risk perception and behavioral fatigue. The effective transmission rate is specified as an exponential function of these variables, which guarantees positivity and captures two opposite effects: risk perception suppresses transmission, whereas fatigue amplifies transmission. Ideal numerical simulations are conducted to compare the classical SIR model, a risk-perception-only model, and a full risk-perception-fatigue model. The results show that risk perception substantially lowers the infection peak and final epidemic size, while behavioral fatigue partially offsets the protective effect and delays the infection peak. Sensitivity analysis further indicates that increasing the risk-perception suppression coefficient reduces the epidemic scale, whereas increasing the fatigue amplification coefficient produces stronger rebound effects. The model offers a compact and interpretable framework for studying behavioral feedback in transmission dynamics.
subject: Articles
  topic: section
subject: Transmission Dynamics
  topic: keywords
subject: SIR Model
  topic: keywords
subject: Risk Perception
  topic: keywords
subject: Behavioral Fatigue
  topic: keywords
subject: Behavioral Feedback
  topic: keywords
subject: Numerical Simulation
  topic: keywords
creator: RongXing Zhu
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-21
publication date: 2026-05-21
starting page: 144
ending page: 153
doi: 10.63313/JCSFT.9078

Design and Implementation of a Robotic Dynamic Grasping System Based on Im-proved YOLO and Fuzzy PID Control
type: item
description: Industrial and service robots increasingly need to grasp objects that are moving on conveyors, sliding down chutes, or being handed over by humans, yet most production-grade pipelines still assume static targets. Two pain points dominate: detection networks that drop precision when the target is small, partially occluded, or motion-blurred, and joint-level controllers whose gains are tuned offline and therefore fail to compensate for the time-varying dynamics introduced by chasing a moving object. This paper proposes an end-to-end dynamic grasping system that couples an improved YOLO detector with a fuzzy PID joint controller. The detector embeds a Ghost-bottleneck CSPDarknet backbone, a coordinate-attention module that emphasizes motion-salient regions, a BiFPN neck for multi-scale fusion, and an SIoU + α-CIoU regression objective that converges faster on tightly packed parts. The controller treats tracking error and its derivative as fuzzy variables, online tunes ΔKp, ΔKi, and ΔKd through a 49-rule Mamdani inference base, and feeds the corrected gains to each joint in real time. The two modules are connected by a Kalman-smoothed pose stream and a quintic on-line trajectory re-planner. We trained the detector on a 12,800-image conveyor dataset and evaluated the integrated system in 300 dynamic trials on a 6-DOF UR5 with parallel jaws. Compared with a YOLOv5s + classical-PID baseline, the proposed pipeline raises mAP@0.5 from 74.3 % to 86.4 %, cuts joint-tracking overshoot from 18.6 % to 3.7 %, and increases dynamic-grasp success rate from 65.4 % to 91.2 % at object speeds up to 0.30 m/s, while keeping inference at 129 FPS on a single RTX 3060.
subject: Articles
  topic: section
subject: Dynamic Grasping
  topic: keywords
subject: Improved YOLO
  topic: keywords
subject: Fuzzy PID
  topic: keywords
subject: Manipulator Control
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Multi-Scale Fusion
  topic: keywords
subject: Real-Time Perception
  topic: keywords
creator: ZiTong Zhou
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
starting page: 132
ending page: 143
doi: 10.63313/JCSFT.9070

Dental-DPMNet: A Latent Disease Progression Modeling Network for Predicting Pulpitis and Periapical Disease Evolution Using Longitudinal Dental Imaging and Clinical Indicators
type: item
description: Pulpal and periapical diseases exhibit a continuous and progressive pathological process driven by lesion evolution and inflammatory deterioration, yet most existing artificial intelligence approaches focus on static diagnosis and fail to model disease dynamics over time. In pharmaceutical research, Disease Progression Models (DPMs) have been widely adopted to characterize disease natural history using longitudinal biomarkers, enabling individualized prediction of disease trajectories. Inspired by this paradigm, this study proposes Dental-DPMNet, a latent disease progression modeling network for predicting the evolution of pulpitis and periapical diseases using longitudinal dental imaging and clinical indicators. Dental-DPMNet integrates cone-beam computed tomography and periapical radiographs with clinical examination records, including pain intensity, thermal test responses, and percussion sensitivity, to construct multimodal lesion evolution representations. A continuous latent disease state is introduced to capture the underlying severity of pulpal inflammation, and its temporal dynamics are explicitly modeled via a neural ordinary differential equation, allowing flexible handling of irregular follow-up intervals and patient-specific progression patterns. Multi-task decoders are employed to jointly reconstruct multimodal observations and estimate disease stage transition probabilities and future progression risks. Experiments on a retrospective longitudinal dental dataset demonstrate that Dental-DPMNet achieves an accuracy of 87.6% and an AUC of 0.91 in multi-stage prediction of pulpal and periapical disease progression. Compared with CNN-based, LSTM-based, and ODE-based baselines, the proposed model consistently shows superior performance. These results confirm the effectiveness of latent disease progression modeling for capturing lesion-driven evolution and supporting personalized endodontic decision-making.
subject: Articles
  topic: section
subject: Dental disease progression
  topic: keywords
subject: Pulpitis
  topic: keywords
subject: Disease progression model
  topic: keywords
subject: Neural ordinary differential equation
  topic: keywords
subject: Longitudinal dental imaging
  topic: keywords
subject: Multimodal learning
  topic: keywords
creator: YingHan Li
creator: TaoYu Zhu
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
starting page: 118
ending page: 131
doi: 10.63313/JCSFT.9076

FRAdRec: A Federated Real-Time Advertising Recommendation Framework Based on User Behavior Modeling and Cloud-Native Data Infrastructure
type: item
description: Advertising recommendation systems have become a core component of modern e-commerce and digital marketing platforms, where accurately capturing dynamic user preferences and supporting large-scale real-time recommendation services are critical for improving advertising effectiveness and platform revenue. However, traditional centralized recommendation frameworks often suffer from limited scalability, delayed interest modeling, insufficient optimization of business-oriented objectives, and increasing privacy risks caused by centralized user data collection. To address these challenges, this paper proposes FRAdRec, a federated real-time advertising recommendation framework based on user behavior modeling and cloud-native data infrastructure. The proposed framework integrates a Transformer-based sequential learning module with a Deep Interest Network (DIN) attention mechanism to capture both long-term and short-term user interests from click and purchase behavior sequences. Meanwhile, a federated learning strategy is introduced to enable decentralized model training without transmitting raw user data, thereby improving privacy preservation and reducing data leakage risks. Furthermore, a multi-task optimization mechanism jointly predicts click-through rate (CTR), conversion rate (CVR), and return on investment (ROI) to enhance advertising profitability. To support industrial-scale deployment, a cloud-native streaming architecture based on Kafka, Flink, Kubernetes, and Redis is designed for low-latency recommendation and online feature updating. Experimental results on the Criteo and Alibaba Taobao datasets demonstrate that FRAdRec achieves an AUC of 0.857 and reduces recommendation latency to 41 ms, outperforming several state-of-the-art recommendation models in both recommendation accuracy and real-time serving efficiency.
subject: Articles
  topic: section
subject: Federated Recommendation
  topic: keywords
subject: Advertising Recommendation System
  topic: keywords
subject: User Behavior Modeling
  topic: keywords
subject: Cloud-Native Infrastructure
  topic: keywords
subject: Real-Time Recommendation
  topic: keywords
subject: Transformer
  topic: keywords
subject: Deep Interest Network
  topic: keywords
subject: Multi-Task Learning
  topic: keywords
subject: ROI Optimization
  topic: keywords
creator: XiangYuan He
creator: KuangCong Liu
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
starting page: 102
ending page: 117
doi: 10.63313/JCSFT.9075

An sEMG-Controlled RHex-T3 Hexapod Mobile Grasping Robot for As-sistive Object Retrieval
type: item
description: Assistive robots have strong potential in object retrieval, delivery, and remote interaction, but their practical usability depends on natural and low-burden human-machine interfaces. This study aims to develop an sEMG-controlled RHex-T3 hexapod mobile grasping robot system for assistive object retrieval. The proposed system integrates a multimodal hexapod chassis, robotic arm, camera-based wireless visual transmission module, sEMG acquisition and recognition module, and host-computer control terminal. Existing sEMG gesture recognition methods are deployed as a task-level robotic control interface, and a gesture-to-action mapping mechanism with hierarchical mode switching is designed to convert discrete gesture labels into chassis locomotion, mode-switching, and robotic-arm commands. Experiments are designed for online recognition, chassis control, grasping execution, and composite task completion. The results indicate that the proposed system can complete basic mobility, approach, grasping, and delivery tasks, verifying the preliminary feasibility of using sEMG to control a hexapod mobile manipulator for assistive object retrieval.
subject: Articles
  topic: section
subject: Surface Electromyography
  topic: keywords
subject: Assistive Robot
  topic: keywords
subject: Hexapod Robot
  topic: keywords
subject: Mobile Manipulation
  topic: keywords
subject: Gesture
  topic: keywords
creator: Junxian Li
creator: Xiaohong Huang
creator: Meihua Zhao
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
starting page: 92
ending page: 101
doi: 10.63313/JCSFT.9074

A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization
type: item
description: In modern digital marketing, the growing complexity of advertisement data demands intelligent systems capable of understanding semantic relationships among products, audiences, and advertising content. To address this challenge, this paper proposes a Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System (KGSR-ADS) for advertisement retrieval and personalization. The proposed framework integrates a heterogeneous Ad-Knowledge Graph (Ad-KG) that captures multi-relational semantics, a Semantic Embedding Layer that leverages large language models (LLMs) such as GPT and LLaMA to generate context-aware vector representations, a GNN + Attention Model that infers cross-entity dependencies, and a Database Optimization & Retrieval Layer based on vector indexing (FAISS/Milvus) for efficient semantic search. This layered architecture enables both accurate semantic matching and scalable retrieval, allowing personalized ad recommendations under large-scale heterogeneous workloads. Experiments were conducted on a large-scale real-world advertisement dataset containing approximately 1.2 million user profiles, 250,000 advertisements, and 20 million user-ad interactions. Experimental results demonstrate that the proposed KGSR-ADS model outperforms state-of-the-art baselines across all evaluation metrics. Compared with the strongest baseline GraphRec, KGSR-ADS achieves a 5.7% improvement in Precision@10, a 5.5% increase in Recall@10, a 6.3% gain in NDCG@10, and a 5.0% enhancement in MRR, while reducing average response latency by 23.9%. These results confirm the effectiveness and efficiency of integrating knowledge graph reasoning with semantic representation learning for personalized advertisement recommendation. These results demonstrate the system’s effectiveness and potential for next-generation semantic advertising platforms that integrate knowledge reasoning and deep learning for adaptive, real-time personalization.
subject: Articles
  topic: section
subject: Knowledge Graph
  topic: keywords
subject: Advertisement Recommendation
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Semantic Embedding
  topic: keywords
subject: Vector Database
  topic: keywords
subject: Graph Neural Network
  topic: keywords
subject: Attention Mechanism
  topic: keywords
creator: Tangtang Wang
creator: Kaijie Zhang
creator: Kuangcong Liu
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-13
publication date: 2026-05-13
starting page: 79
ending page: 91
doi: 10.63313/JCSFT.9073

EduRL-GPT: A Reinforcement Learning Optimized Generative AI Framework for Intelligent Teaching Content Generation and Personalized Feedback
type: item
description: The rapid advancement of generative artificial intelligence (Generative AI), particularly large language models such as GPT, Claude, and Gemini, has significantly reshaped the design of intelligent tutoring systems by enabling automatic generation of instructional content, adaptive assessments, and personalized learning feedback. However, most existing generative AI-based educational systems rely on static prompting strategies and lack mechanisms to continuously optimize feedback quality according to students’ evolving learning states, which limits their effectiveness in personalized education scenarios. To address this limitation, this paper proposes EduRL-GPT, a reinforcement learning optimized generative AI framework for intelligent teaching content generation and personalized feedback. EduRL-GPT integrates a state-aware generative teaching engine with a reinforcement learning-based feedback optimization module, allowing the system to dynamically generate explanations, quizzes, and learning suggestions tailored to individual students’ knowledge mastery, learning behaviors, and preferences. Specifically, a Proximal Policy Optimization (PPO) algorithm is employed to adaptively select pedagogically appropriate feedback strategies based on observed learning gains and engagement signals, forming a closed-loop intelligent tutoring process. The proposed framework is evaluated on an online learning dataset with real student interaction logs. Experimental results show that EduRL-GPT significantly outperforms strong baselines. Compared with an LLM-based one-shot tutoring system, EduRL-GPT achieves a 12.8% improvement in learning gain and approximately a 13.0% relative increase in student engagement, while also yielding higher feedback satisfaction and accuracy improvement. These results demonstrate that reinforcement learning-optimized generative AI can deliver more effective, stable, and personalized instructional feedback for intelligent tutoring and adaptive learning sy…
subject: Articles
  topic: section
subject: Generative Artificial Intelligence
  topic: keywords
subject: Intelligent Tutoring System
  topic: keywords
subject: Personalized Learning
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Educational Technology
  topic: keywords
creator: Xueqi Tang
creator: Sitong Liu
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-12
publication date: 2026-05-12
starting page: 66
ending page: 78
doi: 10.63313/JCSFT.9072

Quantitative Characterization of Pore–Fracture Structures in Deep Coal Reservoirs Based on the QSGS Method
type: item
description: This study aims to improve the characterization of multiscale pore structures in coal by constructing a more representative digital core model. Mercury intrusion porosimetry, micro-CT scanning, Avizo-based image processing, and the quartet structure generation set (QSGS) method were integrated to reconstruct the pore–fracture networks of four coal samples. Mercury intrusion data were used to determine porosity and pore-size distribution, while micro-CT images were applied to establish three-dimensional pore network models. To overcome the resolution limitation of CT imaging, the QSGS method was further used to supplement unresolved micropores. The results indicate that the proposed multiscale digital core can more accurately represent the pore structure of coal samples. Connectivity analysis based on Euler number, tortuosity, and coordination number shows clear differences in pore connectivity among the samples. This approach provides an effective method for pore structure characterization and offers a basis for permeability prediction and coalbed methane development.
subject: Articles
  topic: section
subject: Multiscale digital core
  topic: keywords
subject: Coal pore structure
  topic: keywords
subject: Micro-CT
  topic: keywords
subject: QSGS method
  topic: keywords
creator: Dongxiang Huang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-12
publication date: 2026-05-12
starting page: 58
ending page: 65
doi: 10.63313/JCSFT.9071

CLASNet: A Cognitive Load–Aware CNN-LSTM-Attention Framework for Supply Chain Demand Forecasting and Adaptive Human–Computer Interaction
type: item
description: This paper proposes CLASNet, a Cognitive Load–Aware CNN-LSTM-Attention framework for supply chain demand forecasting and adaptive human–computer interaction. Unlike conventional deep learning forecasting approaches that primarily emphasize predictive accuracy while neglecting human cognitive constraints, CLASNet integrates demand prediction and cognitive load modeling within a unified end-to-end architecture to improve both forecasting performance and decision-support efficiency. The proposed framework combines a CNN-LSTM demand feature encoder for capturing local temporal patterns and long-term dependencies, a Cognitive Load Encoder (CLE) for modeling user interaction behaviors, a Cognitive-Aware Attention Fusion (CAAF) mechanism that dynamically adjusts temporal attention weights according to users’ cognitive states, and an Adaptive Interaction Decision Layer (AIDL) that optimizes visualization complexity and information density. Experiments were conducted on a real-world retail supply chain dataset containing three years of demand records and dashboard interaction logs collected from intelligent decision-support systems. Comparative results against ARIMA, LSTM, CNN-LSTM, and CNN-LSTM-Attention baselines demonstrate that CLASNet achieves superior forecasting accuracy, obtaining an RMSE of 14.55, MAE of 9.22, and MAPE of 10.9%, corresponding to an approximately 9.6% RMSE reduction compared with the strongest baseline. Ablation studies further verify the effectiveness of the cognitive load encoder, cognitive-aware attention mechanism, and adaptive interaction module. In addition, user-centered evaluations indicate that the proposed cognitive-aware adaptive interface significantly reduces task completion time and perceived cognitive load during demand analysis tasks. Overall, the results demonstrate that integrating cognitive awareness into deep learning–based supply chain forecasting systems can substantially enhance both predictive performance and human–AI collabor…
subject: Articles
  topic: section
subject: Supply chain demand forecasting
  topic: keywords
subject: Cognitive load awareness
  topic: keywords
subject: CNN-LSTM
  topic: keywords
subject: Attention mechanism
  topic: keywords
subject: Adaptive human–computer interaction
  topic: keywords
subject: Deep learning
  topic: keywords
creator: Zheyu Li
creator: Yue Hao
creator: Peifan Zeng
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-11
publication date: 2026-05-11
starting page: 44
ending page: 57
doi: 10.63313/JCSFT.9069

ROI-Optimized Sequential Advertising Recommendation Framework with Cloud-Native Scalable Data Infrastructure
type: item
description: Online advertising recommendation systems are fundamental to large-scale e-commerce platforms, where accurate user targeting and efficient system deployment directly impact revenue generation and user experience. Existing approaches primarily optimize click-through rate (CTR) or conversion rate (CVR), which often fail to align with business-oriented objectives such as return on investment (ROI), while also lacking scalability under cloud-native, real-time environments. To address these challenges, we propose ROSA-Rec, a ROI-optimized sequential advertising recommendation framework that tightly integrates user behavior modeling with a cloud-native scalable data infrastructure. Specifically, ROSA-Rec employs a behavior-aware Transformer-based encoder to capture dynamic user interests from heterogeneous behavioral sequences, incorporating both action importance and temporal decay. A cross-attention interaction module is further designed to enhance fine-grained matching between users and advertisements. To directly optimize business value, we introduce a ROI-aware multi-task learning objective that jointly models CTR, CVR, and revenue signals. In addition, a hybrid online-offline serving architecture built on cloud-native technologies enables real-time feature updates and low-latency inference, ensuring scalability and consistency in large-scale production settings.Extensive experiments on benchmark and semi-simulated advertising datasets demonstrate that ROSA-Rec achieves superior performance, including approximately a 10.5% improvement in ROI over the strongest baseline (SASRec), while also improving CTR AUC from 0.826 to 0.854 and CVR-AUC from 0.748 to 0.781. These results confirm that ROSA-Rec effectively enhances both predictive accuracy and business-oriented metrics under large-scale advertising scenarios.
subject: Articles
  topic: section
subject: Advertising Recommendation
  topic: keywords
subject: ROI Optimization
  topic: keywords
subject: Sequential Modeling
  topic: keywords
subject: Multi-task Learning
  topic: keywords
subject: Cloud-Native Systems
  topic: keywords
subject: Real-time Recommendation
  topic: keywords
creator: Bingqing Ni
creator: Hanwu Li
creator: Jingwei Zhang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
date: 2026-05-11
publication date: 2026-05-11
starting page: 30
ending page: 43
doi: 10.63313/JCSFT.9068

A White-box/Black-box Dual-modal Structured Query Method for Flood Disaster Spatiotemporal Knowledge Graph Question Answering
type: item
description: Flood disaster emergency question answering (QA) requires both structured fact retrieval and complex compositional reasoning. Existing large language model (LLM)-based knowledge graph QA methods still struggle to balance query flexibility and execution reliability. To address this contradiction, this paper proposes a white-box/black-box dual-modal structured query method for flood disaster spatiotemporal knowledge graph QA. The method takes the ReAct reasoning mechanism as its scheduling core, integrating LLM-generated dynamic Cypher queries (white-box path) and predefined scripted queries (black-box path) into a unified reasoning chain. For standardized, high-frequency fact queries, the system preferentially employs black-box scripts; for tasks involving causal chain tracing, trend statistics, and integrated decision-making, the appropriate query or mixed retrieval path is invoked based on question characteristics. A query routing strategy is designed, and a data flow/control flow separation mechanism is introduced to manage large-scale query results through a "summary–file bypass" approach, alleviating context bloat during multi-round reasoning. Based on a flood disaster spatiotemporal knowledge graph and emergency response plan corpus, 30 test questions are constructed around three complex task types—causal analysis, trend assessment, and integrated decision-making—with current status query cases used to validate the standard script path. Experimental results show that the dual-modal method achieves a 100% query success rate, 98.3% answer accuracy, and an average reasoning round count of 1.47, outperforming both the pure white-box and pure black-box methods overall.
subject: Articles
  topic: section
subject: Flood Disaster
  topic: keywords
subject: Spatiotemporal Knowledge Graph
  topic: keywords
subject: Large Language Model
  topic: keywords
subject: React
  topic: keywords
subject: Cypher
  topic: keywords
subject: Structured Query
  topic: keywords
creator: Hao Li
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-06
publication date: 2026-05-06
starting page: 14
ending page: 29
doi: 10.63313/JCSFT.9067

DynSupplyNet: A Dynamic Graph Neural Network with Temporal Fusion for Supply Chain Risk Prediction and Propagation Analysis
type: item
description: Supply chain disruptions and credit contagion propagate through complex inter-firm networks, posing severe challenges to financial institutions, supply chain finance platforms, and national regulators. Traditional risk assessment models typically treat firms as independent entities and ignore the structural and temporal dependencies intrinsic to real-world supply chains. To address these limitations, we propose DynSupplyNet, a unified framework that models the supply chain as a dynamic graph sequence and integrates a Dynamic Graph Neural Network (GNN) Encoder with a Temporal Fusion Layer to simultaneously capture cross-firm relational dependencies and evolving temporal dynamics. The model incorporates heterogeneous node features—including financial indicators, transaction patterns, and sector attributes—and edge features representing supplier–buyer relationships, dependency ratios, and credit exposures. DynSupplyNet generates time-aware firm-level embeddings and predicts disruption probability, credit default risk, and risk-propagation paths through a task-specific prediction head and a propagation simulator. Experiments conducted on the large-scale supply chain dataset show that DynSupplyNet consistently outperforms all baselines, including static GCNs, temporal GNNs, and sequence models. On the test set, DynSupplyNet achieves an Accuracy of 0.821, Precision of 0.781, Recall of 0.752, and an AUROC of 0.874, representing improvements of 4–7% over the strongest competing model. These gains confirm the effectiveness of dynamic graph modeling and temporal fusion in capturing evolving inter-firm dependencies, demonstrating DynSupplyNet’s clear advantage for real-world supply chain risk prediction and propagation assessment.
subject: Articles
  topic: section
subject: Dynamic Graph Neural Networks
  topic: keywords
subject: Supply Chain Risk Prediction
  topic: keywords
subject: Temporal Fusion
  topic: keywords
subject: Credit Contagion
  topic: keywords
subject: Risk Propagation
  topic: keywords
subject: Supply Chain Finance
  topic: keywords
subject: Network Analytics
  topic: keywords
creator: Zheqi Hu
creator: Mengdie Hu
creator: Zhenghang Li
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-05
publication date: 2026-05-05
starting page: 1
ending page: 13
doi: 10.63313/JCSFT.9066

CRE-CNN-LSTM-XAI: An Explainable Deep Learning Framework for Risk Assessment of Real Estate Collateralized Financial Products
type: item
description: Risk assessment of real estate collateralized financial products, such as mortgage-backed securities (MBS) and real estate investment trusts (REITs), is a core task in banking risk control, structured product issuance, and financial regulation. The strong dependence of these products on real estate price dynamics, interest rate fluctuations, and macroeconomic conditions poses significant challenges to traditional risk evaluation methods that rely on static indicators and linear assumptions. To address these limitations, this paper proposes CRE-CNN-LSTM-XAI, an explainable deep learning framework for AI-driven risk assessment of real estate collateralized financial products. The proposed model integrates a convolutional neural network (CNN) to capture short-term local fluctuations in multivariate real estate and macroeconomic time series with a long short-term memory (LSTM) network to model long-term dependencies and cyclical market trends. Based on the learned latent risk representations, the framework predicts the default probability of collateralized real estate financial assets. To enhance transparency and practical applicability, a SHAP-based explainable artificial intelligence module is incorporated to quantify the contribution of key risk factors, supporting regulatory compliance and decision-making in financial institutions. Experiments conducted on real estate market and mortgage-related datasets demonstrate that CRE-CNN-LSTM-XAI consistently outperforms benchmark models. The proposed framework achieves an AUC of 0.87, compared with 0.82 for a standalone LSTM and 0.79 for XGBoost. These results indicate that CRE-CNN-LSTM-XAI effectively captures complex temporal risk patterns while providing meaningful economic interpretability, making it well suited for practical applications in bank risk management and mortgage-backed securities issuance.
subject: Articles
  topic: section
subject: Real estate finance
  topic: keywords
subject: Mortgage-backed securities
  topic: keywords
subject: Risk assessment
  topic: keywords
subject: CNN-LSTM
  topic: keywords
subject: Explainable artificial intelligence
  topic: keywords
subject: Financial risk management
  topic: keywords
creator: Ziwen Zhong
creator: Yuheng Huang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: 127
ending page: 140
doi: 10.63313/JCSFT.9065

Similar-Case Retrieval for Government Hotlines via an Appeal-Driven Event Graph and Dual-Route Hybrid Retrieval
type: item
description: To address the colloquial expressions, redundant descriptions, and dispersed structural information commonly found in historical work orders from 12345 government hotlines, traditional keyword matching and purely semantic retrieval methods often fail to identify cases that are both semantically relevant and structurally comparable, limiting the effectiveness of similar-case retrieval in complex hotline scenarios. This paper proposes a similar-case retrieval method for government hotlines based on an appeal-driven event graph and dual-route hybrid retrieval. Specifically, key elements, including location, time, core issue, and core appeal, are extracted from historical work orders to construct an appeal-driven complaint event knowledge graph. On this basis, a dual-route hybrid retrieval mechanism that integrates dense vector recall and graph-based logical recall is designed, and a structure-aware fusion strategy with cross-encoder reranking is further introduced to improve candidate ranking quality. Experiments on a real government hotline complaint dataset and the public LeCaRD legal case retrieval dataset show that the proposed method outperforms multiple baseline methods. On the complaint dataset, it achieves a P@5 of 0.613 and a Mean Average Precision(MAP) of 0.633, while on LeCaRD it reaches a P@5 of 0.435 and a MAP of 0.487. The results demonstrate that the proposed method can effectively improve the accuracy and structural comparability of similar-case retrieval in complex government hotline scenarios.
subject: Articles
  topic: section
subject: Government Hotline
  topic: keywords
subject: Similar-Case Retrieval
  topic: keywords
subject: Appeal-Driven Event Graph
  topic: keywords
subject: Dual-Route Hybrid Retrieval
  topic: keywords
subject: Structure-Aware Reranking
  topic: keywords
creator: Yizhou Fang
creator: Rencheng Sun
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 112
ending page: 126
doi: 10.63313/JCSFT.9064

Cloud-Edge Federated Incremental Learning Framework for PMSM Efficiency Optimization with Lightweight CNN-LSTM Models and OTA Differential Deployment
type: item
description: Permanent magnet synchronous motors (PMSMs) are critical components in industrial automation and energy-efficient drive systems, where dynamic operating conditions, load variations, and equipment aging can significantly affect efficiency. Traditional fixed-parameter controllers or static models often fail to maintain optimal performance under these time-varying conditions. Leveraging advances in cloud computing and distributed learning, this study proposes a cloud-edge federated incremental learning framework specifically designed for PMSM efficiency optimization. The framework integrates lightweight CNN-LSTM models deployed on resource-constrained motor controllers for real-time inference with a cloud-based model repository that performs incremental training and federated aggregation, enabling continuous adaptation to diverse operational scenarios. To ensure safe and efficient large-scale deployment, a secure over-the-air (OTA) differential update mechanism with version management and gray-scale rollout is implemented, minimizing communication overhead while supporting heterogeneous terminal devices. Experimental evaluations conducted on a large-scale PMSM dataset collected from 50 industrial motors show that the proposed framework achieves the best overall performance among all compared models. The Lightweight CNN-LSTM with Incremental Federated Learning and OTA Differential Deployment attains a mean absolute error of 0.412, an RMSE of 0.611, and an R^2 value of 0.973, outperforming both non-federated and conventional baseline models. Furthermore, the proposed architecture reduces parameter size to 109 K and achieves the lowest edge-side inference latency of 8.9 ms, demonstrating its suitability for large-scale deployment on resource-constrained PMSM controllers. These results indicate that the framework not only enables lifelong learning and self-adaptive optimization of PMSM control but also provides a scalable and reliable solution for distributed industrial ap…
subject: Articles
  topic: section
subject: PMSM
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: CNN-LSTM
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Incremental Training
  topic: keywords
subject: Cloud-Edge Computing
  topic: keywords
subject: OTA Deployment
  topic: keywords
subject: Energy Efficiency Optimization
  topic: keywords
creator: Yilin Yao
creator: Wenchao Zhang
creator: Mengtong Li
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 98
ending page: 111
doi: 10.63313/JCSFT.9063

Research on static denial scene reconstruction and target recognition technology based on 3DGS
type: item
description: Aiming at the problem of limited perception of unmanned equipment in denial scenes, this paper proposes a technical framework of scene reconstruction and target recognition based on 3DGS (3D Gaussian Splatting), which realizes efficient modeling and accurate representation of static denial scenes. Specifically, the semantic attributes are extended in the 3D Gaussian primitives and the feature index mechanism is introduced to improve the semantic modeling ability of 3DGS in the case of low memory occupation, and the target recognition is carried out on this basis. Experiments show that the framework can effectively meet the actual functional requirements of scene reconstruction and target recognition for unmanned equipment in static denial scenarios.
subject: Articles
  topic: section
subject: Denial scene
  topic: keywords
subject: 3D Gaussian Splatting
  topic: keywords
subject: Scene Reconstruction
  topic: keywords
subject: Target Recognition
  topic: keywords
creator: XinYu Wu
creator: Yin Zhu
creator: YingSheng Wang
creator: ZhaoYang Zhang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 86
ending page: 97
doi: 10.63313/JCSFT.2004

Analysis and Prediction of Urban Traffic Accident Correlating Factors Based on SARIMAX Model
type: item
description: Nowadays, with the acceleration of urbanization and the rapid increase in population and motor vehicles, frequent traffic accidents pose a threat to public safety, forming the background of this study. Predicting accidents and their resulting losses to provide a basis for traffic management decisions is a key issue to be solved by society. Theoretically, in terms of research significance, the introduction of the SARIMAX model broadens the research path of traffic accidents and promotes interdisciplinary integration. In practice, it can help traffic departments optimize resource allocation and prevent accident risks. Visualized results can provide travel warnings for the public, reducing economic and human losses. The research focuses on urban traffic accident data. It first widely collects multi-dimensional raw data, followed by cleaning, seasonal decomposition, and stationarity testing. Exogenous variables such as weather and holidays are introduced to construct the SARIMAX model. Finally, the Gaode Maps API is used to visualize accident-prone areas. The research methods include literature research, empirical research, and interdisciplinary research methods. The literature method sorts out cutting-edge materials to lay a foundation; the empirical method relies on big data to verify according to the process; and the interdisciplinary method integrates multi-disciplinary expertise to solve problems, providing data support for regional control strategies.
subject: Articles
  topic: section
subject: SARMAX model
  topic: keywords
subject: accident data analysis
  topic: keywords
subject: time series forecasting
  topic: keywords
subject: spatial visualization
  topic: keywords
subject: seasonal analysis
  topic: keywords
creator: Ying Zhu
creator: Wei Yu
creator: Hongyan Li
creator: Weiyan Yang
creator: Yue Lin
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 76
ending page: 85
doi: 10.63313/JCSFT.9062

Remaining Useful Life Prediction of Rolling Bearings Based on SSA-Optimized TCN-CBAM-BiLSTM Model
type: item
description: In the field of remaining useful life (RUL) prediction for rolling bearings, existing deep learning models suffer from issues such as insufficient capability to capture long-term temporal dependencies, poor focus on key degradation features, reliance on manual experience for hyperparameter tuning, and weak generalization ability under complex operating conditions. To address these challenges, this paper proposes a RUL prediction method for rolling bearings based on a Sparrow Search Algorithm (SSA)-optimized Temporal Convolutional Network-Convolutional Block Attention Module-Bidirectional Long Short-Term Memory Network (TCN-CBAM-BiLSTM). Firstly, a basic TCN-CBAM-BiLSTM prediction model is constructed. The dilated convolutions and residual connections in TCN are employed to capture long-span degradation correlations throughout the bearing's lifecycle. The CBAM's dual attention mechanism dynamically suppresses noise and enhances key degradation features, while BiLSTM's bidirectional temporal modeling capability fully extracts contextual information from sequences. Building on this foundation, SSA is utilized to adaptively optimize core hyperparameters such as the model's learning rate and the number of TCN convolutional kernels, resulting in the development of a high-precision SSA-TCN-CBAM-BiLSTM prediction model that resolves the subjectivity and inefficiency associated with manual parameter tuning. Multiple comparative experiments are conducted using the XJTU-SY rolling bearing full-lifecycle degradation dataset. The results demonstrate that the proposed model significantly reduces both the root mean square error and mean absolute error, two key metrics. Moreover, its predicted curves closely align with the actual RUL curves under multiple operating conditions, showcasing superior prediction accuracy and cross-condition generalization capability.
subject: Articles
  topic: section
subject: Rolling bearing
  topic: keywords
subject: Remaining useful life prediction
  topic: keywords
subject: Temporal convolutional network
  topic: keywords
subject: Bidirectional long short-term memory network
  topic: keywords
subject: Sparrow search algorithm
  topic: keywords
creator: GuangSen Du
creator: JiHeng Chen
creator: JianWei Cheng
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 65
ending page: 75
doi: 10.63313/JCSFT.9061

A Multi-Label Electrocardiogram Classification Study Integrating Clinical Metadata and Channel Attention
type: item
description: To address the issues of feature extraction redundancy and low recognition accuracy for ambiguous categories in multi-label electrocardiogram (ECG) automatic classification, this paper proposes a deep learning diagnostic model that integrates clinical metadata with a channel attention mechanism. Ablation experiments indicate that traditional Transformer and multi-scale convolutional modules are susceptible to feature interference in specific scenarios. In this study, the SE Block is employed to enhance channel-wise feature representation, patient physiological metadata is introduced as prior knowledge, and the SAM optimizer is utilized to improve the loss landscape. Experimental results demonstrate that the proposed combined model achieves a Macro-AUC of 0.9251 and a Macro-F1 score of 0.7254 on the PTB-XL dataset. Compared with the baseline model, it achieves significant improvements in the identification of ambiguous categories such as ventricular hypertrophy.
subject: Articles
  topic: section
subject: Electrocardiogram (ECG)
  topic: keywords
subject: SE Block
  topic: keywords
subject: Metadata Fusion
  topic: keywords
subject: SAM Optimizer
  topic: keywords
creator: Jiajing Sun
creator: Ying Sun
creator: Jiang Zhao
creator: Wei Yu
creator: Qinyang Li
creator: Shun He
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
starting page: 55
ending page: 64
doi: 10.63313/JCSFT.9060

Emotion-Aware Speech-Driven 3D Facial Animation with Diffusion Models
type: item
description: Speech-driven 3D facial animation aims to generate realistic lip-sync and expressive facial motions from input audio. While existing methods have achieved plausible lip movements, they often struggle to capture subtle emotional variations, leading to average-looking expressions and unnatural motions. In this paper, we propose EmoFaceDiffusion, a novel framework based on denoising diffusion probabilistic models (DDPMs) that explicitly incorporates emotion awareness. Our key innovations include: (1) a multi-modal emotion encoder that extracts continuous emotion features (valence, arousal, dominance) directly from raw speech, enabling fine-grained emotional expression synthesis; (2) a conditional diffusion model with cross-modal attention that fuses audio and emotion embeddings; (3) a temporal consistency module based on graph convolutions to ensure smooth and coherent motion sequences. Extensive experiments on BIWI and IEMOCAP datasets demonstrate that EmoFaceDiffusion achieves state-of-the-art performance in lip-sync accuracy (LMD: 2.31 vs. 2.87), emotion expressiveness (classification accuracy: 78.5% vs. 65.2%), and user preference (MOS: 4.21/5). Ablation studies validate the contribution of each component. Our work offers a significant step toward expressive and emotionally aware digital avatars.
subject: Articles
  topic: section
subject: Speech-driven
  topic: keywords
subject: diffusion models
  topic: keywords
subject: 3D Facial Animation
  topic: keywords
creator: Zhen Wang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-02
publication date: 2026-04-02
starting page: 44
ending page: 54
doi: 10.63313/JCSFT.9059

Research on Consistency-Driven Point Cloud Semantic Segmentation Method in Label Noise Scenarios
type: item
description: In real-world scenarios, manual annotation of point cloud data often involves significant noise, which severely degrades the training performance of semantic segmentation models. To address this issue, this paper proposes a consistency-driven point cloud semantic segmentation framework that jointly leverages augmentation consistency and historical consistency strategies to dynamically filter training samples and refine pseudo-labels, thereby improving robustness under noisy supervision. Specifically, we design a multi-layer encoder–decoder network based on local feature aggregation and attention pooling to effectively capture the geometric structure and semantic contextual information of point clouds. During training, multiple augmented predictions are generated for the same input, and the reliability of pseudo-labels is assessed based on prediction confidence and stability. Meanwhile, the historical prediction trajectories of samples are incorporated to further filter and correct pseudo-labels, enhancing the stability of the supervision signal. Under simulated noisy-label settings on the S3DIS dataset, the proposed method achieves overall accuracies (OA) of 85.16% and 82.66% at noise rates of 60% and 80%, respectively, outperforming mainstream baseline methods. Experimental results demonstrate the effectiveness of consistency constraints in noisy-label scenarios and provide a new perspective for robust learning in point cloud semantic segmentation.
subject: Articles
  topic: section
subject: Point cloud semantic segmentation
  topic: keywords
subject: Noisy labels
  topic: keywords
subject: Consistency learning
  topic: keywords
subject: Robust training t,
  topic: keywords
creator: Bo Xia
creator: Lili Wang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-03
publication date: 2026-04-03
starting page: 34
ending page: 43
doi: 10.63313/JCSFT.9058

Systematic Hyperparameter Optimisation of a U-Net Architecture for Robust Skin Lesion Segmentation
type: item
description: Accurate skin lesion segmentation is essential for reliable computer-aided der-moscopic analysis, as segmentation errors propagate to downstream feature extraction and diagnostic decisions. Many existing approaches rely on man-ually selected hyperparameters, resulting in time-consuming, suboptimal, and poorly reproducible outcomes. This study investigates whether structured, task-aware Bayesian optimisation can systematically enhance baseline segmentation performance. A controlled U-Net architectural template is coupled with a hierarchically defined Bayesian search space encompassing training dynamics, regularisation, loss formulation, and data augmentation. The optimisation pro-cess is designed to be sample-efficient and aligned with segmentation-specific evaluation metrics. The framework is evaluated on the ISIC 2016, 2017, and 2018 benchmark datasets, including cross-dataset generalisation analysis. The proposed approach achieves Jaccard indices of 91.1%, 85.0%, and 89.4% on ISIC 2016, 2017, and 2018, respectively, consistently outperforming standard U-Net baselines. Cross-dataset evaluation demonstrates stable performance, with Jac-card scores remaining within 1–2% of in-dataset results. These findings indicate that structured, task-aware Bayesian optimisation substantially improves both performance and robustness, highlighting that principled optimisation of training dynamics can unlock the full potential of standard architectures without requiring architectural modifications.
subject: Articles
  topic: section
subject: skin lesion segmentation
  topic: keywords
subject: Bayesian optimisation
  topic: keywords
subject: U-Net
  topic: keywords
subject: dermoscopic image analysis
  topic: keywords
creator: Saif Ur Rehman
creator: Hassan Ashraf
creator: Muhammad Naeem Zafar
creator: Zhipu Liu
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 21
ending page: 33
doi: 10.63313/JCSFT.9057

A Method for the Identification and Intensity Prediction of DNA Enhancers Based on Feature Extraction Algorithms
type: item
description: DNA enhancers are important non-coding cis-regulatory elements that control gene transcription. To reduce reliance on high-cost experimental methods such as ChIP-seq and DNase-seq, this paper constructs a classification framework based on sequence features and supervised learning for two sequence-level tasks: enhancer identification (enhancer/non-enhancer) and enhancer strength prediction (strong/weak enhancer).Methodologically, k-mer frequency features and CKSNAP-based dinucleotide interval features were extracted separately, and these were concatenated to form a fused representation. Under a unified hierarchical 5-fold cross-validation framework, the performance of Support Vector Machines (SVM) and XGBoost models was compared. The results indicate that, for both tasks, XGBoost performed slightly better or was comparable to SVM under the same feature set;under the same classifier, the fusion of k-mer and CKSNAP features outperforms either feature alone. Specifically, XGBoost based on the fusion features achieved accuracy rates of 0.796 and 0.681 for enhancer identification and strength classification, respectively, with overall superior ROC/AUC performance. This indicates that local k-mer composition information and short-range distance-dependent information are complementary, thereby enhancing the model’s discriminative power and stability.
subject: Articles
  topic: section
subject: DNA Enhancer
  topic: keywords
subject: Enhancer Identification
  topic: keywords
subject: Strength Prediction
  topic: keywords
subject: K-Mer
  topic: keywords
subject: CKSNAP
  topic: keywords
subject: SVM
  topic: keywords
subject: Xgboost
  topic: keywords
creator: XiFeng Li
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-20
publication date: 2026-03-20
starting page: 9
ending page: 20
doi: 10.63313/JCSFT.9056

Member Reasoning Attack Detection and Privacy Disclosure Quantification in Federated Learning Gradient Swap Process
type: item
description: Federated learning aims to protect user data privacy by training locally and exchanging only model updates. However, the gradient of the exchange itself can reveal information that allows malicious actors to launch member-inference attacks to infer whether a particular data sample is present in a client’s training set, posing a serious privacy threat. In this paper, a complete detection and quantification scheme is proposed for the member inference attack in federated learning gradient exchange. First, we design an attack detection framework based on gradient feature analysis, which uses ensemble learning model to identify potential malicious attacks by extracting and aggregating high-dimensional statistical features and distribution anomalies of client-uploaded gradients. Secondly, in order to evaluate the severity of privacy leakage when the attack succeeds, we propose a privacy leakage quantification method. By measuring the sensitivity difference of gradient to member and non-member samples, we construct a computable leakage scoring system, and classify different leakage risk levels accordingly. Experimental results show that the detection method proposed in this paper can effectively identify member inference attacks in various federated learning scenarios, and the quantitative disclosure index can accurately reflect the privacy risk level under different attack configurations and model states. This study provides theoretical and technical references for privacy security assessment and dynamic protection of federated learning systems.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Member Inference Attack
  topic: keywords
subject: Gradient Leak
  topic: keywords
subject: Attack Detection
  topic: keywords
subject: Privacy Quantization
  topic: keywords
creator: ChenWei Gong
creator: TongYue Sun
creator: SongYun Zhang
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-20
publication date: 2026-03-20
starting page: 1
ending page: 8
doi: 10.63313/JCSFT.9055

A Verifiable Epidemic Monitoring and Early Warning Scheme for Privacy Protection under Malicious Cloud Server Threat Model
type: item
description: The current epidemic monitoring and early warning systems usually directly upload raw disease data to the cloud server without verifying the aggregated results returned by the cloud server, which brings about the dual risks of data leakage and result tampering. To address these issues, this paper proposes a cloud-assisted privacy-preserving and verifiable epidemic monitoring and early warning scheme. This scheme encrypts the disease data by integrating technol-ogies such as convergent encryption(CE), hash-based indexing, and oblivious pseudorandom function (OPRF), and verifies the results returned by the cloud server to protect sensitive information during cloud storage and computing processes, while preventing malicious cloud servers from forging or tampering with data. The analysis shows that our solution can provide privacy protection and result verifiability, making it a feasible solution for epidemic monitoring and early warning.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Epidemic diseases
  topic: keywords
subject: Monitoring and early warning
  topic: keywords
subject: Privacy-preserving
  topic: keywords
subject: Verifiability of results
  topic: keywords
creator: XinMiao Ma
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-20
publication date: 2026-03-20
starting page: 133
ending page: 143
doi: 10.63313/JCSFT.9054

Social Bot Detection via Spatio-Temporal Graph Self-Supervision
type: item
description: Social bots on online social platforms increasingly exhibit profile camouflage, coordinated group behavior, and evolving interaction patterns over time, which makes static-graph and fully supervised detectors less effective under label scarcity and cross-community shifts. To address this issue, this paper proposes STGSLBot, a spatio-temporal graph self-supervised framework for social bot detection. The method constructs a sequence of dynamic interaction snapshots with fixed temporal granularity and initializes node features by combining account metadata with tweet semantics. In addition, temporal statistical signals are introduced to characterize coordinated structures and dynamic reciprocal behaviors. At the model level, a temporal dual-encoder is designed to learn topology-driven commonality and attribute-driven uniqueness, while a causal masked temporal self-attention module aggregates historical information under a strict history-only constraint. During pretraining, neighborhood reconstruction, self-feature reconstruction, cross-view semantic consistency learning, and community-based pseudo-label self-training are jointly optimized to enhance cluster-level discrimination and temporal consistency. Finally, the pretrained model is fine-tuned with a small labeled set for binary classification. Experiments on two temporal subsets derived from TwiBot-22 show that the proposed framework achieves strong and stable performance, especially in accuracy and label-efficient learning.
subject: Articles
  topic: section
subject: Social Bot Detection
  topic: keywords
subject: Dynamic Graphs
  topic: keywords
subject: Self-Supervised Learning
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Spatio-Temporal Representation Learning
  topic: keywords
creator: Hai Lu
creator: LiangXu Shi
rights: Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-18
publication date: 2026-03-18
starting page: 125
ending page: 132
doi: 10.63313/JCSFT.9053