Journal of Computer Science and Frontier Technologies
Journal of Computer Science and Frontier Technologies ISSN Print: 3104-4204 ISSN Online: 3104-4212 CD ISSN: 3104-4220
Emotion-Aware Speech-Driven 3D Facial Animation with Diffusion Models
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.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
Research on Consistency-Driven Point Cloud Semantic Segmentation Method in Label Noise Scenarios
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.
Systematic Hyperparameter Optimisation of a U-Net Architecture for Robust Skin Lesion Segmentation
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.
A Method for the Identification and Intensity Prediction of DNA Enhancers Based on Feature Extraction Algorithms
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.
Member Reasoning Attack Detection and Privacy Disclosure Quantification in Federated Learning Gradient Swap Process
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.
A Verifiable Epidemic Monitoring and Early Warning Scheme for Privacy Protection under Malicious Cloud Server Threat Model
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.
Social Bot Detection via Spatio-Temporal Graph Self-Supervision
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.
HierFuse: Hierarchical Gated Fusion Network for Parkinson’s Disease Screening Based on Hand Tremor Characteristics
Parkinson's disease (PD) impairs motor control, making handwriting analysis a valuable non-invasive biomarker. This paper presents HierFuse, a hierarchical gated multimodal fusion network for PD screening on the NewHandPD dataset, which provides paired images and sensor signals from drawing tasks. HierFuse integrates temporal 1D and spatial 2D convolutional branches through cross-scale alignment, shared attentive compression, and progressive gated fusion with hidden propagation. Five-fold cross-validation shows state-of-the-art performance on Spiral (acc 0.912, F1 0.908) and Meander tasks (acc 0.929, F1 0.927), outperforming baselines. Ablations validate component contributions, with hierarchical fusion and hidden propagation most critical, while gating shows task-specific effects. Results demonstrate that hierarchical multimodal fusion effectively leverages handwriting dynamics for accurate PD detection.
Improved DeepLabv3+ for Semantic Segmentation via Multi-Scale Context Fusion and Feature Attention
Aiming at the problems of small object edge contour detail loss and insufficient feature representation capability in semantic segmentation tasks, the paper proposes an improved DeepLabv3+-based semantic segmentation algorithm, termed RDSC-DeepLabv3+, which integrates multi-scale contextual enhancement with feature attention optimization. Based on the original DeepLabv3+ framework, the model architecture is improved from multiple aspects, including backbone network selection, multi-scale contextual modeling, and feature attention enhancement: (1) ResNet-50 is adopted as the backbone network to reduce the number of model parameters and computational complexity while maintaining strong feature representation capability;(2) a multi-scale feature fusion module that combines DenseASPP with Strip Pooling (SP) is introduced to enhance the model’s ability to capture contextual information of objects at different scales;(3) the CBAM attention mechanism is incorporated into shallow features to adaptively enhance discriminative region features while suppressing redundant background information.Experimental results on the PASCAL VOC 2007 dataset demonstrate that, compared with the baseline model, the proposed method achieves improvements of 1.86%, 1.78%, and 1.49% in ACC, mPA, and mIoU, respectively. These results verify the comprehensive advantages of RDSC-DeepLabv3+ in terms of segmentation accuracy and computational efficiency, while also exhibiting stronger boundary delineation capability and robustness in complex scenes.
Forecasting Chaotic Multivariate Time Series Based on DB-ICDNet
Chaotic time series widely exist in complex scenarios such as nonlinear dynamic systems, financial markets, and meteorological observations. Its strong nonlinearity, sensitivity to initial values, and accumulation of long-term prediction errors make it possible to achieve stable, high-precision multi-step forecasting under limited observation conditions. Prediction is extremely challenging. Traditional linear models are difficult to approach nonlinear dynamics, while conventional deep learning models are prone to phase drift and error amplification during long-step extrapolation. Therefore, this study focuses on the problem of multivariate multi-step forecasting, and proposes a lightweight in-depth forecasting framework DB-ICDNet. In the representation learning stage, the framework extracts long-range dependencies and local perturbations through LSTM and causally inflated TCN, and realizes controlled fusion of information in the near-term window based on the residual gated fusion mechanism. In the prediction stage, it designs a step-size conditional dual-memory cooperative decoder, adaptively retrieves long-term and short-term contexts according to different prediction steps, and gradually generates future trajectories with end observations as anchors through incremental cumulative regression heads, supplemented by consistency constraints to suppress error diffusion. Experiments on Lorenz and Chen theoretical data sets and HS300 real data show that this method has higher accuracy and stability in multi-step prediction, especially in long-step extrapolation, it can effectively maintain the trajectory shape and alleviate error accumulation, which verifies its effectiveness in meeting the challenge of chaotic sequence prediction.
Multivariate PV Power Forecasting via CPO-Optimized Deep Spatiotemporal Networks
Accurate photovoltaic (PV) power forecasting is critical for grid stability, yet it remains challenging due to the inherent volatility of meteorological conditions. Existing hybrid models often struggle with manual parameter sensitivity, inadequate local feature extraction, and computationally expensive cross-channel interactions. To address these gaps, this paper proposes a novel multivariate forecasting framework: CPO-VMD-TCN-SOFTS. The Crested Porcupine Optimizer (CPO) is employed to adaptively optimize Variational Mode Decomposition (VMD), effectively mitigating mode mixing. A Temporal Convolutional Network (TCN) extracts deep temporal dependencies, while the SOFTS architecture utilizes a centralized STAR mechanism to achieve cross-channel feature fusion with strict linear complexity. Comprehensive multi-resolution, all-season, and cross-site experiments demonstrate the model's superiority. The proposed architecture significantly enhances predictive accuracy and exhibits robust generalization, maintaining an average R2 above 0.9860 across diverse sites, thereby providing reliable support for smart grid dispatching.
GP-STCKS: A GeoPrefix-based Spatio-Temporal Controllable Keyword Search Scheme for Privacy-Preserving Electronic Health Systems
The proliferation of cloud-based e-health systems necessitates outsourcing sensitive Electronic Health Records (EHRs), raising critical privacy and access control concerns. While Attribute-Based Keyword Search (ABKS) enables secure encrypted retrieval, existing schemes inherently lack fine-grained spatio-temporal constraints. To address this, we propose GP-STCKS, a novel privacy-preserving spatio-temporal controllable keyword search scheme. By seamlessly integrating Geohash hierarchical indexing into the Ciphertext-Policy ABKS (CP-ABKS) framework, our scheme enforces strict dual access constraints over specific time intervals and spatial hierarchies. Rigorous formal analysis proves its resilience against chosen-keyword and multi-dimensional key collusion attacks while guaranteeing keyword confidentiality. Extensive experiments demonstrate that GP-STCKS maintains lightweight overhead and optimal search efficiency, perfectly balancing stringent privacy protection with real-world e-health deployability.
Research On Dual Evolution Pattern Mining Model For Time-Varying Community Detection In Dynamic Networks
Nowadays, more and more groups are studying community detection. Community detection also has many practical applications in real life, such as: modeling the brain in the field of neuroscience, effectively inferring relationships and preferences between users in social networks, etc. At present, most teams use time snapshots to simulate the dynamic community network, which ignores the small error caused by the change of time between each time snapshot. Therefore, in order to simulate the influence of time on this clique network, we add two time evolution matrices corresponding to both the sum matrix and the system matrix, and propose our new evolution model: double evolutionary pattern mining model for time-varying community detection in dynamic networks. At the same time we find that finally, the experimental results on the actual network show that the proposed algorithm has higher prediction accuracy in dynamic networks compared with other algorithms.
TECI-YOLO: An Efficient, Lightweight Model for Detecting Small Floating Objects on Water Surfaces
Timely detection of water-surface floating objects is critical for marine ecological protection, yet illumination variation, wave interference, and dense small-target distributions pose persistent challenges of low accuracy, high false-alarm rates, and excessive computational cost. This study proposes TECI-YOLO, a lightweight detection framework built upon YOLOv11s with four targeted improvements. The Tiny module adds a P2 layer while removing P5, preserving high-resolution spatial detail for small-target representation. The CEM–CFE module combines Channel-Enhanced MBConv and Channel-Fused Enhancer to strengthen feature discriminability and semantic robustness. The E_Head integrates coordinate attention, grouped convolution, and task-decoupled branches to reduce redundancy and improve localization. Inner-MPDIoU replaces standard MPDIoU with adaptive scaling and auxiliary bounding boxes for refined small-target geometric modeling. On FloW-IMG, TECI-YOLO improves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 2.4%, 3.8%, 3.3%, and 0.6% over YOLOv11s; on IWHR_AI_Label_Floater_V1, gains reach 1.3%, 1.4%, and 0.7%, respectively. Parameters are reduced by ~26% with 3.8% fewer FLOPs, demonstrating a compelling accuracy–efficiency tradeoff for real-time water-surface monitoring.
Client-Lightweight Soft Clustering Federated Learning
Clustered federated learning is a highly effective approach to addressing the non-independent and identically distributed (Non-IID) problem in conventional federated learning. Soft clustering federated learning allows clients to belong to multiple clusters, which not only captures the common characteristics of clients within the same cluster but also fully adapts to the unique properties of each client’s local data. This significantly improves model personalization and generalization ability, and greatly enhances model performance and training efficiency in Non-IID scenarios. Recent advanced soft clustering federated learning methods no longer rely on client model parameters for clustering. Instead, they generate asymmetric similarities via privacy-preserving set intersection operations on clients’ local datasets, which are then sent to the server for clustering. This avoids the privacy risks associated with uploading model parameters from clients. In this work, we optimize this clustering paradigm by adopting locality-sensitive hashing and an improved privacy-preserving set intersection scheme. Our method substantially reduces the communication and computational overhead among clients while maintaining competitive model performance.
FreeMatch-PG: A Lightweight Semi-supervised Learning Framework for IoT Device Identification
IoT device identification is a critical component of cybersecurity, yet its practical deployment faces the dual challenges of scarce labeled data and imbalanced device class distribution. To address these issues, this paper proposes a lightweight identification framework based on semi-supervised learning. The core innovations of the framework include: 1) A prior-guided adaptive threshold mechanism (FreeMatch-PG), which sets differentiated learning thresholds for various classes by simulating initial cognitive states, alleviating class imbalance from the source and significantly improving pseudo-label quality; 2) Domain-customized data augmentation and a weighted focal loss function, which jointly enhance model robustness in noisy environments; 3) A lightweight architecture based on an improved ResNet-18, substantially reducing model complexity. Experiments on two public datasets demonstrate that using only 8% of labeled data, the proposed method achieves recognition accuracies of 98.25% and 97.72% on the UNSW and CICIOT datasets, respectively. It significantly outperforms multiple baseline models and achieves an excellent balance between accuracy, efficiency, and deployment feasibility, offering a practical solution for resource-constrained real-world IoT environments.
Dynamic Modeling of Agroecosystems and Natural Restoration Mechanisms: Seasonal Evolution Analysis Based on Four-Dimensional Food Chain
This study presents a computational framework for modeling the dynamics of agricultural ecosystems transformed from forest areas, with a focus on quantifying natural recovery processes under the influence of invasive species and seasonal fluctuations. Addressing the limitations of existing models that overlook the synergistic effects of invasive competition and agricultural cycles, we introduce a four-dimensional system of ordinary differential equations (ODEs) that integrates crop biomass, primary consumers (native pests), secondary consumers (natural predators), and pesticide concentrations. The model employs logistic growth functions and Monod kinetics to capture ecological interactions, while incorporating seasonal parameters and invasive competitors represented by forestry pests. Through extensive numerical simulations spanning 20 years, utilizing the odeint solver, and validated via R² goodness-of-fit (0.87-0.93) and polar coordinate visualization, our results indicate that invasive forestry pests diminish crop yields by approximately 18.7±2.3% due to resource competition. Furthermore, we establish a strong correlation between species return rates and ecosystem recovery efficiency (r=0.82, p<0.01), and identify three distinct stability regimes through phase space analysis. These findings underscore the pivotal roles of seasonal variability and invasive species dynamics in ecosystem resilience, offering actionable insights for restoration management. The proposed model serves as a versatile tool for assessing ecological stability in anthropogenically altered landscapes, facilitating predictive analytics for sustainable land use practices.
Research on Olympic Medals Prediction by SVM and Random Forest
The goal of this article is to predict the likelihood of countries without Olympic gold medals winning their first in 2028. Our study create binary classification labels and countries that have never won gold are identified and relevant features are extracted. SVM is employed for classification, with an AUC score close to 1, indicating high accuracy. Eventually, the relationship between event selection and medal counts is analyzed. The data is preprocessed and event types are converted into categorical variables. Random Forest regression is used, revealing that host country event selection affects medal performance but has minimal impact on overall rankings. The model’s performance is validated using MAE, MSE, and other metrics.
Regulatory Effects of Artificial Intervention on Agricultural Ecosystems: A Comparative Simulation Study of Pesticide Removal and Biological Control
Agricultural ecosystems face mounting challenges from pesticide overuse and biodiversity loss, necessitating innovative management strategies. This study introduces a computational framework to evaluate the impacts of pesticide control versus bat-mediated biological control on ecosystem stability. We developed a six-dimensional intervention matrix that integrates time-dependent pesticide decay dynamics and the dual roles of bats as predators and pollinators. Employing advanced simulation techniques and Lyapunov stability criteria, we analyzed 12 intervention scenarios, incorporating parameter sensitivity analysis and residual distribution modeling. Simulations reveal that bat introduction enhances ecosystem resilience by 28.7% compared to conventional pesticide applications, with a 15.3% reduction in predator-prey variance. Optimal intervention parameters were identified at 0.45–0.68 pesticide reduction coefficient and 0.32–0.41 bat population ratio. These findings underscore the potential of computational modeling to inform sustainable agricultural practices, advocating for biological control in ecosystem management policies.
Optimizing Multidimensional Decision-Making for Sustainable Agricultural Models: Establishing an Evaluation System Based on Dynamic Entropy Weight Method and Monte Carlo Validation
This study introduces a novel computational framework for evaluating sustainable agricultural practices, leveraging advanced decision-making techniques from computer science. Addressing the limitations of existing assessment methods, which often fail to account for dynamic weight allocation and reliability validation, we propose an integrated evaluation system. This system incorporates 17 indicators across six dimensions (pest control, crop health, biodiversity, etc.), utilizing the dynamic entropy weight method (DEWM) for adaptive index weighting and Monte Carlo simulation for robust uncertainty analysis. Our approach features a three-stage computational workflow: (1) generation of a 3D radar evaluation map to visually compare organic, conventional, and integrated farming practices; (2) application of TOPSIS-based decision optimization enhanced with Pareto frontier analysis to identify optimal agricultural modes; and (3) rigorous cross-validation via 10,000-iteration Monte Carlo sampling to ensure model reliability. Empirical results indicate that integrated farming achieves the highest composite scores (0.82±0.03), significantly outperforming conventional methods in biodiversity enhancement (+38.7%) and long-term sustainability (+25.4%). The model's exceptional reliability is confirmed by a cross-validation mean squared error (MSE) less than 1e-24. Moreover, the proposed framework demonstrates superior performance compared to traditional methods, achieving a 24% higher consistency ratio and 37% reduced computational time. This research marks the first implementation of a cross-validated decision optimization framework that synergistically combines TOPSIS, Pareto frontier analysis, and Monte Carlo validation, offering a scientifically rigorous decision support system for sustainable agriculture. Furthermore, the framework's scalability and adaptability pave the way for integration with emerging technologies such as IoT and machine learning, enabling real-time calibration and enhanced decision-making. This work not only advances computational sustainability and agricultural informatics but also provides policymakers with actionable insights for transitioning to more sustainable agricultural practices.
Universal Village Oriented Solution for Elderly Emotional Support
As the global population continues to age, the number of the elderly significantly grows. The aging of the body, combined with the awareness of physical decline, can negatively affect the elderly's mental health [4]. The negative impacts on physical and mental health caused by loneliness and social isolation could increase the use of healthcare services, leading to burdens on the healthcare system [2]. The gradually decreasing potential support ratio also increases the pressure on society to provide care for the elderly [1]. This means that the problems of elderly care are becoming a growing challenge for the working-age population. Loneliness could be improved by interactions [2], and the emergence of large language models (LLMs) allows smart technologies to manage interactions. In the past, chatbots based on smart technology had limited levels of personalization and emotional interaction. However, achieving greater personalization requires collecting more user information, which can lead to privacy concerns [3]. This study uses a literature review approach to examine existing research in smart technologies from Universal Village's perspectives. We propose an elderly emotional support chatbot with emotional understanding and personalized memory management that can effectively mitigate LLM hallucination, enhance information security, and increase privacy protection. This could enhance elderly's mental health and improve the quality of life, reducing the burden on family caregivers and the healthcare system.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
Incomplete Multi-View Clustering based on Semi-Definite Constraint and Tensor Low-Rank Learning
Incomplete Multi-View Clustering (IMVC) faces significant challenges in preserving high-order correlations and the Positive Semi-Definite (PSD) property of missing kernels. This paper proposes a novel approach, a unified framework that simultaneously performs kernel completion and consensus graph learning. The method leverages tensor low-rank constraints to capture global consistency across views and explicitly enforces a strict PSD constraint to ensure kernel validity. An efficient Augmented Lagrangian Multiplier (ALM)-based algorithm is developed to solve the optimization problem. Experimental results demonstrate the superiority of the method over state-of-the-art methods.
The Construction and Practice of the QHSE Information Management Platform for Oil Fields
In order to improve the informatization and standardization level of oilfield enterprise safety management, this paper designs and implements a QHSE management system based on the open-source framework RuoYi. The system uses Spring Boot + Thymeleaf + MySQL technology stack, integrates Apache Shiro permission control module, and adds new core functions such as check entry, experience sharing, and "checking-rectification-verification" closed-loop management. It focuses on solving the inherent problems of data acquisition and synchronization in oil field under complex geographical environment. The security and rationality of the access rights of users with different roles are ensured through the permission hierarchical control and data-level isolation mechanism. The supporting mobile App has been put into use, which improves the convenience and real-time performance of on-site operation. The system is deployed in Windows environment, and the page response time is short and the operation is stable. The practical application shows that the system effectively standardizes the safety management process and improves the efficiency of hidden dangers rectification.
The inspection, rectification and verification mechanism
MATE-YOLO: Multi-scale Attention Task-aligned Enhanced Detection Network for Apple Fruit in Complex Orchard Environments
Accurate apple detection in complex orchards remains challenging due to foliage occlusion, illumination variations, and cluttered backgrounds. This study proposes an enhanced YOLOv11n framework integrating three architectural innovations. First, the EMCSP (EMA-enhanced Cross-Stage Partial) module is introduced into the backbone, synergistically incorporating multi-scale attention within cross-stage partial topology to strengthen discriminative feature extraction. Second, the ELA-HSFPN (Efficient Local Attention enhanced Hierarchical Scale Feature Pyramid Network) is devised for the neck, leveraging decoupled spatial attention and bidirectional hierarchical fusion to enhance multi-scale representation. Third, the TADDH (Task-Aligned Dynamic Detection Head) supersedes the conventional head, employing task decomposition, dynamic deformable convolution, and probabilistic feature modulation to achieve optimal classification-localization alignment. Extensive experiments demonstrate substantial improvements over baseline YOLOv11n: Precision+1.4%, Recall+2.3%, mAP@0.5+3.0%, and mAP@0.5:0.95 +1.7%. These results validate the efficacy of our methodology for intelligent fruit harvesting applications.
An Improved YOLOv10-Based Real-Time Detection Method for Apple Leaf Diseases in Complex Environments
Efficient and accurate detection of apple leaf diseases is important for timely field management and yield protection. However, real orchard images often contain complex backgrounds, uneven illumination, and small, scattered lesions, which still challenge standard one-stage detectors. In this study, we propose an enhanced real-time detector built on YOLOv10 to improve robustness and small-lesion sensitivity. First, Coordinate Attention is inserted into the backbone to encode long-range dependency while preserving precise positional cues, thereby suppressing background interference. Second, a Bidirectional Feature Pyramid Network is adopted to strengthen multi-scale feature fusion and to better retain shallow details for small targets. Third, the SIoU regression loss is introduced to accelerate convergence and improve bounding-box localization by considering distance, angle, and shape consistency. Experiments on an apple leaf disease dataset with complex field scenes show that the proposed method achieves 84.2% mAP@0.5, improving the baseline YOLOv10 by 3.5 percentage points. The detector runs at 85 FPS on a single GPU and remains suitable for real-time monitoring on edge or mobile devices. Overall, the proposed approach provides a practical and accurate solution for apple leaf disease detection in complex environments.
Copyright (c) 2025 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
Retraction: Unlearning in Tabular-to-Hypergraph Learning via Selective Distillation
At request of the authors, this article has been retracted by Erytis Publishing Limited in light of clear evidence that the results and conclusions are no longer valid. We thank the authors for notifying us so that the publication record can be amended accordingly. Retraction published: March 10, 2026.
Research on Optimization of Large Model Code Generation Based on Improved RAG in Embedded Environments
In embedded environments, large model code generation faces a series of issues such as high response latency, memory overflow, and poor adaptability of generated code due to limited computing power and tight memory resources. To address these problems, this paper proposes an improved RAG architecture (EmbedCode-RAG) based on fine-grained compression and dual-dimensional retrieval. Firstly, the architecture utilizes Abstract Syntax Tree (AST)-guided code snippet segmentation and lightweight block embedding compression technology to reduce the volume of reference code by 16-32 times. Subsequently, a "task-situation" dual-dimensional retrieval mechanism is designed to integrate code function similarity and embedded hardware constraint features. Finally, a dynamic knowledge base update module is incorporated to achieve progressive accumulation of generation experience. We conducted experiments on two hardware platforms: ARM Cortex-M7 and NVIDIA Jetson Nano. The dataset used is a self-constructed embedded code corpus, including MCU drivers, IoT protocols, and other related tasks. Experimental results show that compared with the traditional RAG method, EmbedCode-RAG achieves an 8.3% improvement in the CodeBLEU score of generated code [20], reduces the Time to First Token (TTFT) by 82.6%, and decreases memory usage by 79.2%. Furthermore, the proposed method operates without crashes on low-power hardware. This research provides a new solution for realizing efficient and reliable code generation in embedded scenarios.
WorkLife +: A Conceptual Design of an Al-Driven Adaptive Office Environment Regulation System
Focusing on the high energy consumption and discomfort issues of office environments, this research conducted a preliminary survey of 115 office workers to identify major pain points of traditional office environment. Taking IoT and AI as the core, the research integrates multi-modal data and proposes a conceptual design of an intelligent environmental perception system. It aims to establish an adaptive environmental regulation system through standardized sensors and intelligent control models, balancing energy conservation and health. The research is expected to form reusable solutions and prototype systems.
Application of Confidence-Based Semi-Supervised Learning Algorithms in Viral Subtype Prediction
Rapid and accurate identification of viral variants is essential for disease surveillance and public health decision-making. However, large amounts of viral genomic data often lack clear variant labels, and the high cost of expert annotation limits the effectiveness of supervised learning models. To address this, we propose a confidence-based semi-supervised learning framework for predicting SARS-CoV-2 variants, specifically Alpha, Beta, and Omicron. The approach begins with a small labeled dataset (48 Alpha and 50 Beta samples) for initial training. To address class imbalance, we apply the SMOTE technique, and use Lasso for feature selection to enhance model efficiency. The key innovation is an ensemble model combining Random Forest, Gradient Boosting Trees, and Logistic Regression, with a consistency regularization mechanism. This mechanism iteratively assigns pseudo-labels to a large pool of unlabeled data, including only samples with high prediction confidence (above 0.7) into the training set. This approach refines the model’s decision boundaries without additional manual annotation. Experiments show that the proposed method effectively uses unlabeled data to improve model performance and provides reliable predictions for Alpha, Beta, and Omicron variants. This confidence-based semi-supervised learning framework offers a practical solution for accurate pathogen subtyping when labeled data is scarce.
Research on the Evolution Mechanism of Weibo Online Public Opinion Emotions — A Computational Communication Analysis Based on the 2024 Juvenile Crime Incident in Handan, Hebei Province
This study takes the extreme juvenile crime incident where three junior high school students killed their classmate in Handan, Hebei Province in 2024 as the research object. Employing computational communication research methods, it conducts sentiment analysis, topic mining, and social network analysis on 447,918 relevant original Weibo data. The research aims to reveal the evolutionary path of Weibo online public opinion emotions related to cross-cutting issues of technology and morality in the context of sudden social events. The results indicate that the public opinion emotions triggered by the incident exhibit a three-stage evolutionary characteristic of "shock and anger — in-depth reflection — rational attention". The technical issue of AI campus monitoring was reframed by opinion leaders during the incident investigation stage and further deeply bound to public emotions during the trial stage. Official media and opinion leaders play key roles as "emotional brakes" and "issue reframes" respectively in the public opinion field, jointly influencing the emotional evolution process. This study not only provides new empirical evidence for understanding the media generation mechanism of technological stigmatization but also offers practically significant strategic suggestions for the precise governance and guidance of public opinion related to juvenile crimes, as well as the improvement of the juvenile crime prevention system.