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
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
Unlearning in Tabular-to-Hypergraph Learning via Selective Distillation
Machine unlearning requires efficiently removing the influence of specified information (rows/columns/values) from a deployed model to satisfy privacy and regulatory constraints. Converting tables into hypergraphs can capture high-order relations such as “same column”, “same value”, and multi-column interactions, but unlearning for hypergraph-based models is often implemented via costly retraining or gradient/Hessian-based approximations, and becomes especially challenging for column- and value-level deletion due to broad structural dependencies and limited verifiability. We propose HERMES, a fundamentally different unlearning paradigm for tabular-to-hypergraph modeling based on selective knowledge transfer rather than parameter rollback or second-order correction. HERMES freezes the original trained model as a teacher and trains a student as the released unlearned model: on the retained set, the student learns from the teacher through structure-augmented distillation and consistency regularization to preserve utility; on the forget set, the student is driven toward maximum-entropy predictions and explicitly pushed away from the teacher via anti-distillation divergence, actively erasing memorized behaviors that can be exploited by membership inference. For column/value deletion, HERMES introduces counterfactual invariance by replacing deleted attributes/values and enforcing prediction consistency, preventing the student from relying on prohibited information even through hypergraph message-passing “detours”. The framework requires neither graph partitioning nor second-order information, and can be implemented as a lightweight post-processing stage that completes in a few epochs while offering practical verification signals based on forget-set entropy, teacher–student disagreement, and membership inference risk.
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.
FCSC: Weakly-Supervised Temporal Action Localization via Feature Calibration-assisted Sequence Comparison
Weakly-supervised temporal action localization task is to identify action categories and start and end times in unedited videos. How to achieve feature calibration between different modalities in this task, and how to further optimize action boundaries based on the similarity of action common sequences remains an urgent problem to be solved. Based on the above issues, we propose a novel network framework, weakly supervised temporal action localization via feature calibration-assisted sequence comparison (FCSC). The core of the FCSC framework lies in the Multi-Modal Feature Calibration Module (MFCM), which utilizes global and local contextual information from the primary and auxiliary modalities to enhance RGB and FLOW features, respectively, achieving deep feature calibration. In addition, the framework introduces an improved distinguishable edit distance metric to sequence similarity optimize (SSO) and maximum consistent subsequence (MCS) to narrow the gap between classification and localization tasks. After multiple experiments, it has been proven that FCSC achieved maps of 47.7% and 27.9%, respectively on the THUMOS14 and ActiveNet1.2 temporal action recognition benchmark test sets, fully verifying the effectiveness of the model.
Copyright (c) 2025 Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
Blockchain-Enhanced Differential Privacy Federated Learning Framework (B-DPFL) for Healthcare IoT Applications
The proliferation of Internet of Medical Things (IoMT) devices has generated massive amounts of sensitive healthcare data, presenting unprecedented opportunities for machine learning applications while raising critical privacy concerns. This paper proposes a novel Blockchain-Enhanced Differential Privacy Federated Learning Framework (B-DPFL) that integrates federated learning, differential privacy mechanisms, and blockchain technology to enable secure, privacy-preserving collaborative learning in healthcare IoT environments. Our framework addresses three fundamental challenges: (1) preserving patient privacy during model training, (2) ensuring data integrity and model transparency, and (3) preventing malicious attacks in decentralized learning systems. Extensive experiments on real-world medical datasets demonstrate that B-DPFL achieves superior performance with 94.3% accuracy while providing strong privacy guarantees (ε = 1.0) and maintaining Byzantine fault tolerance. Compared to conventional federated learning approaches, our framework reduces privacy leakage by 78.5% and improves model convergence speed by 34.2%.
Copyright (c) 2025 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
DBRS-Net: A Hybrid Framework for Food Image Classification
Food image classification holds significant importance in applications such as food retrieval, nutritional assessment, and dietary management. However, food images typically exhibit pronounced intra-class variations, high inter-class similarity, and complex shooting environments, limiting the classification performance of traditional deep learning approaches. To address this, this paper proposes a novel hybrid network architecture, the Dual-Branch ResNet-Swin Network (DBRS-Net). By integrating ResNet with the window-based attention mechanism of Swin Transformer, it leverages the complementary strengths of both architectures in local fine-grained feature extraction and global context modelling. Specifically, the ResNet branch captures local features such as texture and shape, while the Swin Transformer branch learns overall structure and long-range dependencies. A simple yet effective feature fusion strategy then synthesises comprehensive image representations. Experimental results demonstrate that even without introducing additional complex modules, DBRS-Net achieves stable classification performance on the Food11 dataset, attaining a top-1 accuracy of 94.83%. This provides a reliable foundation for research into food image recognition on larger-scale or more diverse datasets.
Vulnerability Detection of Blockchain Smart Contracts Based on GNN with Multi-Head Attention Mechanism
Smart contracts have been widely applied in various fields. Due to the immutability of data on the blockchain, it is of great significance to conduct smart contract vulnerability detection before data is uploaded to the chain. To address the problems of low accuracy and single vulnerability type in traditional detection methods, a blockchain smart contract vulnerability detection method based on Graph Neural Network (GNN) is proposed. This method abstracts the functions and key code segments in smart contracts into nodes in a graph, and constructs edges by leveraging data and control dependencies during code execution, thereby accurately depicting the specific graph structures of reentrancy attacks and timestamp-dependent vulnerabilities. To further enhance the model’s sensitivity to key vulnerability patterns, the multi-head attention mechanism is innovatively introduced, which can effectively screen out the nodes and edges that contribute the most to vulnerability detection, suppress irrelevant or noisy information, and significantly improve the accuracy and robustness of vulnerability detection. Experimental results show that the proposed method achieves an accuracy of 85.19% in reentrancy vulnerability detection and 82.37% in timestamp-dependent vulnerability detection, demonstrating excellent vulnerability identification capability.
Automated Deployment and Application of Intelligent Medical Assistant Private Cloud Platform Based on Ansible
With the rapid development of cloud computing and artificial intelligence technologies, intelligent health consultation services have become an effective means to make up for the uneven distribution of medical resources. This paper builds a private cloud platform based on OpenStack, realizes automated deployment using Ansible, integrates DeepSeek intel-ligent medical consultation services, and achieves realtime monitoring and visualization of system resources through Prometheus and Grafana. Experimental results show that the platform has high availability, scalability and maintainability, and can provide users with stable and efficient remote health consultation services.
Research on an Intelligent Prospecting Workflow for Bauxite Deposits: Integrating AI Prediction with Surpac 3D Visualization
The exploration for concealed bauxite deposits presents significant challenges for traditional methods. This paper proposes a novel intelligent prospecting workflow that seamlessly integrates artificial intelligence (AI) prediction with professional three-dimensional (3D) visualization and analysis in Surpac. The workflow begins with the systematic processing and standardization of multi-source geospatial data, including geological maps, geophysical data, and remote sensing imagery, into a unified gridded format. This data serves as input for machine learning models, such as Random Forest, which are trained on known mineral occurrences to generate a predictive mineral prospectivity map. The core innovation lies in the closed-loop workflow where the AI-generated prospectivity model, exported as an ASCII grid file, is directly imported into Surpac. Within Surpac's powerful 3D environment, geologists can dynamically visualize the probability model fused with other geological data, perform interactive 3D interpretation, and delineate exploration targets. This integration creates a synergistic cycle where quantitative AI-driven insights directly inform qualitative spatial reasoning and decision-making within a robust 3D platform, significantly enhancing the efficiency and effectiveness of bauxite exploration.
Multi-Level Safety Filtering Method for LLM Generation Security
Large Language Models (LLMs) exhibit limitations due to their static para-metric knowledge, leading to potential inaccuracies and an inability to incor-porate updated information. Retrieval-Augmented Generation (RAG) mitigates this by integrating external knowledge bases. However, existing RAG paradigms face challenges such as information forgetting in long conversational contexts and vulnerability to knowledge poisoning attacks, which compromise the accu-racy and security of generated content. This research aims to enhance the capa-bilities of LLMs augmented by external knowledge by addressing these two crit-ical issues. To tackle information forgetting during conversational query rewriting, we propose KwRewriter, a keyword-integrated query rewriting framework. It em-ploys a document keyword extractor to enrich document representations and a query rewriter that fuses generated keywords with the rewritten query, empha-sizing core user intent. For security concerns, we introduce MLSF, a multi-level safety filtering method. MLSF implements a three-stage defense: vector-level coarse screening via K-means clustering, fine screening combining keyword consensus and BERTScore semantic matching, and final fact verification using the LLM's internal knowledge. Experimental results on open-domain QA datasets demonstrate KwRewrit-er's effectiveness, significantly improving retrieval metrics like MRR and Re-call@k over strong baselines, proving its ability to alleviate information loss. MLSF was validated on datasets subjected to knowledge poisoning attacks, where it substantially increased answer accuracy and reduced the attack success rate, especially under high poisoning ratios, confirming its robust defensive capability. In conclusion, this work successfully enhances RAG systems by improving both the accuracy of retrieval through context-aware query rewriting and the security of generation via hierarchical document filtering. The proposed meth-ods were integrated into a practical medical retrieval QA system, underscoring their applicability and value in providing reliable, knowledge-grounded re-sponses. Future work will focus on optimizing keyword integration, improving method generalizability, and exploring defenses against a broader spectrum of RAG security threats.
Adaptive Multi-Scale Deep Learning Framework for Intelligent Environmental Anomaly Detection and Predictive Analytics in Smart Cities
Smart cities generate massive volumes of heterogeneous environmental data through distributed sensor networks, satellite imagery, and IoT devices. This paper introduces an innovative Adaptive Multi-Scale Deep Learning Framework (AMSDLF) for intelligent environmental anomaly detection and predictive analytics in urban environments. Our framework employs a hierarchical architecture combining Multi-Resolution Convolutional Neural Networks (MR-CNN), Long Short-Term Memory networks with attention mechanisms (Att-LSTM), and Dynamic Graph Neural Networks (DGNN) to process multi-temporal and multi-spatial environmental data streams. The proposed system achieves real-time anomaly detection with 98.4% accuracy, 96.7% precision, and 97.8% recall across diverse environmental parameters including air quality, noise pollution, temperature variations, and urban heat islands. Experimental validation on three metropolitan datasets demonstrates significant improvements over existing methods, with 23% reduction in false positive rates and 31% improvement in early warning capabilities. The framework's adaptive learning mechanism enables continuous model refinement and handles concept drift in evolving urban environments.
Design and research of agricultural underwater robots based on artificial intelligence algorithms
With the rapid development of artificial intelligence technology, its application in the agricultural field has gradually become a research hotspot. This paper focuses on the design and research of agricultural underwater robots based on artificial intelligence algorithms, aiming to explore an intelligent system that can efficiently and accurately complete underwater agricultural tasks. The introduction of artificial intelligence algorithms can significantly improve the autonomous decision-making ability and environmental perception level of underwater robots. This paper first analyzes the characteristics of the agricultural underwater environment and the shortcomings of existing underwater robot technology, and clarifies the background and significance of this study. Subsequently, the basic principles of artificial intelligence algorithms and their application potential in underwater robots are introduced, and an algorithm model suitable for underwater operations is constructed based on the needs of agricultural scenarios. In terms of algorithm design, this paper adopts the method of combining deep learning and reinforcement learning to optimize the performance of the algorithm through data collection and processing, and improve the recognition and decision-making ability of underwater robots in complex environments. At the same time, in view of the problem of limited underwater communication, an efficient communication and control mechanism is designed to ensure the stable operation of the system. Finally, the integration and testing of the underwater robot system is completed, including hardware platform construction, software system development and environmental simulation testing, which verifies the feasibility and effectiveness of the system in practical application scenarios. Experimental results show that agricultural underwater robots based on artificial intelligence algorithms can achieve precise operations in different underwater environments, with good adaptability and stability, providing new technical support for the development of smart agriculture in the future.
Intelligent Matching and Optimization of Ideological and Political Elements in Computer Science Courses Based on AI Agent
This study presents an AI Agent framework for intelligently matching and optimizing ideological and political elements in computer science (CS) curricula based on Outcome-Based Education (OBE) principles. The proposed system employs a hybrid recommendation approach, combining knowledge graph-based semantic reasoning (with 86+ validated ideological elements) and collaborative filtering (leveraging 320+ instructor profiles), achieving 40% higher matching accuracy (F1-score=0.79) than conventional methods. Key innovations include: (1) a three-dimensional taxonomy of CS ideological elements (ethical/legal, technological narratives, professional competencies), (2) an explainable recommendation engine providing justification paths (e.g., linking "sorting algorithms" to "engineering ethics"), and (3) continuous improvement mechanisms via real-time regulatory updates and longitudinal graduate tracking. Experimental results across five core CS courses demonstrate significant improvements in instructor satisfaction (4.1/5 vs 3.2/5 baseline) and student ideological awareness (+42% post-test scores). The framework addresses critical gaps in curriculum design by balancing technical rigor with value cultivation, offering a replicable model for STEM education.
Embodied Intelligence: A Literature Review
This paper systematically reviews the theoretical foundations, technological evolution, and current research landscape of embodied intelligence (Embodied AI), with particular attention to its interdisciplinary value and applications in the field of design. It first revisits key theories such as embodied cognition, in-telligence without representation, and morphological computation, illustrating how embodied intelligence promotes a paradigm shift in design logic through the coupling of “body–environment” interactions. It then outlines the de-velopment trajectory of technologies ranging from robotic hardware and mul-timodal fusion to large multimodal and world models, revealing their applica-tions across product, architectural, fashion, and service design. Furthermore, the study analyzes theoretical extensions and technical applications of embodied intelligence in user experience, interaction design, and sustainable design — including motion capture, multimodal interaction, and digital twins. Finally, it summarizes the technical, ethical, and interdisciplinary challenges currently faced and proposes future research directions: focusing on affective and light-weight intelligent agents, promoting paradigm innovation in “intelligent co-design,” and constructing a responsible interdisciplinary ecosystem.
The improved model of YOLOV11 integrating Starnet
Due to the small size, widespread distribution, and complex background of road cracks, traditional detection methods are prone to misdetection and missed detection. To improve the model's detection performance, we propose an im-proved YOLOv11 model for road crack detection. The model uses Starnet as the backbone network and dynamically adjusts its large spatial receptive field to more accurately capture the details of road cracks. Validation on the RDD2022 dataset shows that the improved model achieves an average precision (mAP) of 0.586, which is a 4.8 percentage point improvement over the traditional model. These improvements significantly enhance detection accuracy and efficiency, providing a more reliable and efficient solution for road crack detection.
Architecting Trustworthy LLMs: A Unified TRUST Framework for Mitigating AI Hallucination
The use of large language models (LLMs) in high-stakes domains like engineer-ing and public policy is hindered by their tendency to hallucinate—generating factually incorrect or unverifiable information. Although technical mitigations such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning from Human Feedback (RLHF) exist, they are typically applied as isolated point-solutions, lacking a unifying framework. This paper analyses the archi-tectural roots of AI hallucination, classifying them into cognitive gaps and sta-tistical biases, and then shows the shortcomings in current mitigation strategies. To address this, we introduce the TRUST model, which is a systematic structure built on five interconnected pillars: Transparency, Reliability, Uncertainty, Su-pervision, and Traceability. The TRUST framework serves as the architectural basis to orchestrate existing technologies and establish accountability in the lifecycle of AI and to build robust and accountable AIs for vital societal applications [2].
Chord Ring Distributed Storage Method for Ethereum Data
To address the limitations of blockchain data storage capacity and uneven dis-tribution, this paper proposes a Chord dual-ring distributed storage method based on virtual nodes. Building upon the original Chord protocol, this approach introduces virtual rings to construct a “storage ring-virtual ring” dual-ring structure. Target virtual nodes are located through routing table lookups, and data is distributed across the storage ring via a name mapping mechanism. Sim-ulation experiments validate the proposed scheme's effectiveness by evaluating load balancing and query success rate across varying sharding granularities. Results demonstrate that this approach not only efficiently achieves sharded storage for blockchain data but also ensures balanced distribution of block data.
AI driven intelligent health management systems in telemedicine: An applied research study
The current imbalance in healthcare resource distribution urgently requires innovative solutions through telemedicine technology. This study designed a telemedicine health management system integrating AI technology, innovatively constructing three core functional modules: remote diagnosis and treatment, AI-assisted decision-making, and cross-regional collaboration. This enables multi-scenario applications including remote consultations, primary healthcare, and home monitoring. Employing a distributed microservices architecture alongside deep learning algorithms, the system delivers intelligent healthcare services. It has significantly enhanced medical accessibility and diagnostic quality, offering novel insights for advancing telemedicine development.
Construction and Optimization of Faculty Teams in Vocational Colleges under the Paradigm of Industry-Education Integration
Amid accelerating industrial transformation driven by artificial intelligence and digital manufacturing, vocational education faces an acute shortage of dual-qualified teachers who can bridge academic instruction and enterprise practice. This study investigates the construction and optimisation of faculty teams in Shandong’s vocational colleges within the paradigm of industry–education integration. Drawing upon Human Capital Theory, the TVET-oriented TPACK framework, and the AI-enabled professional learning model, a mixed-methods design combined questionnaire data (132 valid responses) with semi-structured interviews and a case study at Shandong X College. Findings reveal structural and competence imbalances: only 49.4 per cent of teachers hold dual qualifications, fewer than 30 per cent engage in enterprise practice exceeding three months annually, and merely 22.7 per cent employ AI-assisted or digital-twin pedagogy. Three interlocking barriers were identified—rigid institutional mechanisms, resource scarcity, and cognitive constraints regarding digital and industrial teaching. To address these challenges, the study proposes a Triple-Entity Collaboration and Dual-Drive framework linking full-time teachers, enterprise engineers, and part-time practitioners, supported by policy incentives and AI-based capability development. Implementation at Shandong X College increased dual-qualified faculty to 58.2 per cent, raised digital-teaching coverage to 65.3 per cent, and improved enterprise participation by 40 per cent. Theoretically, this research extends Human Capital Theory by integrating AI-mediated capacity growth and enriches the TVET-TPACK model through quantitative validation. Practically, it offers a replicable approach for aligning vocational faculty development with industrial upgrading in China’s digital economy.
MENN: A Hybrid Model for User Preference Mining
User preference mining uses rating data, item content or comments to learn additional knowledge to support the prediction task. For the use of rating data, the usual approach is to take rating matrix as data source, and collaborative filtering as the algorithm to predict user preferences. Item content and comments are usually used in sentiment analysis or as auxiliary information for other algorithms. However, factors such as data sparsity, category diversity, and numerical processing requirements for aspect sentiment analysis affect model performance. This paper proposes a hybrid method, which uses the deep neural network as the basic structure, considers the complementarity of text and numeric data, and integrates the numeric and text embedding into the model. In the construction of text-based embedding, extracts the text summary of each text-based review, and uses the Doc2vec to convert the text summary into multi-dimensional vector. Experiments on two Amazon product datasets show that the proposed model consistently outperforms other baseline models, achieving an average reduction of 15.72% in RMSE, 24.13% in MAE, and 28.91% in MSE. These results confirm the effectiveness of our proposed method for learning user preferences.
A High-Precision Object Detection Algorithm for UAV Aerial Images: WI-YOLO
To tackle the challenges of low small-target detection accuracy, intense background interference, and high model computational complexity in unmanned aerial vehicle (UAV) aerial images, this study presents an enhanced lightweight object detection algorithm, namely WI-YOLO. Built on the YOLOv11n architecture, the algorithm integrates two key improvements: first, an improved efficient multi-scale attention mechanism (iEMA) is embedded into the backbone network, which strengthens the discriminability of low-contrast targets by fusing standard deviation and local contrast features; second, a wavelet transform convolution module (WTConv) is designed to expand the receptive field and preserve multi-scale structural information via a multi-level frequency-domain decomposition and reconstruction mechanism. Experimental evaluations on the DOTA dataset show that WI-YOLO achieves 61.5% in mAP@50 and 35.1% in mAP@50:95—marking significant gains over the baseline model. Concurrently, the model’s parameter count is reduced to 5.9 MB, and its inference speed is increased to 30.5 FPS. These results demonstrate the coordinated optimization of detection accuracy and computational efficiency, making WI-YOLO well-suited for real-time detection tasks on UAV embedded platforms.
An Initial Exploration of Core Network Security Defense Technologies
With the rapid development of Internet technology, network security issues such as hacker attacks, ransomware, and DDoS attacks have gradually come into public view. This paper sorts out two core defense technologies—firewall technology and Intrusion Prevention System (IPS)—from the perspectives of technical principles, development trends, and application scenarios. Meanwhile, combined with current network security hotspots such as zero-day vulnerabilities and AI-driven attacks, it analyzes the challenges and development trends faced by the aforementioned defense technologies.
How Do Social Bots Become the "Other" ? —Identity Camouflage of Generative AI in Social Platforms
Under the research paradigm of "data-driven, behavior modeling, mechanism explanation," the deep identity camouflage achieved by social bots through generative AI technology has become a core risk intervening in the information ecology of social media. This paper takes 206 labeled social bot accounts on the Weibo platform as the research object, integrates 4823 valid post data points and multimodal visualization results, and constructs a quantitative analysis framework from four dimensions: behavioral consistency, content homogeneity, metadata contradiction, and camouflage depth. The study finds that Weibo social bots achieve identity camouflage through four major strategies: "dynamic temporal mimicry," "entertainment content cloning," "cluster interaction fabrication," and "superficial persona construction." Furthermore, generative AI technology drives their camouflage from "mechanical replication" to "semantically coherent, lifelike narration." The research not only provides a three-dimensional quantitative indicator of "behavior-content-metadata" for social bot detection on Chinese platforms but also supplements the theoretical gap of "machine communicator identity construction" in computational communication studies, offering empirical support for platform governance and AI content regulation.
Design of Deep Service Awareness and Analysis System for International Education Park Network
Given the characteristics of the International Education Park network, such as a large user scale, complex services, and high quality requirements, this project aims to achieve a "visible, manageable, controllable, and trustworthy" network. It plans to build a centralized-distributed data storage and sharing system, collaborate with CDN/Cache to optimize resource scheduling, and construct a full-process security monitoring and big data performance management platform to meet eight core functional requirements, including multi-dimensional visualization, resource analysis, and quality evaluation. By comparing IP Account, Flow, and DPI/DFI technologies, DPI/DFI is selected as the core technology. A four-layer architecture (Application Layer, Sharing Layer, Decoding Layer, and Collection Layer) is designed, and its technical maturity, industrial chain completeness, and deployment feasibility are analyzed. The equipment deployment and interface design schemes are clarified to provide support for the digital transformation and efficient operation of the Education Park network.
Research on Optimization of Markov Blanket Algorithm Based on Bayesian Network
With the advent of the big data era, the scale and complexity of data are con-stantly increasing, and feature selection has become one of the key issues in improving the performance and computational efficiency of machine learning models.15 The Markov carpet algorithm is widely used in feature selection and data dimensionality reduction tasks because it can effectively describe the con-ditional dependencies among variables in Bayesian networks. This paper pro-poses an optimization method for the computational efficiency and accuracy problems of the existing IPC-MB and IAMB algorithms in high-dimensional data processing. The optimization methods include the optimization of sorting strat-egies, the improvement of filtering strategies, and the application of symmetry principles. Through theoretical analysis and experimental verification, this pa-per proves the performance improvement of the optimized algorithm on multi-ple datasets. The optimized IPC-MB and IAMB algorithms are significantly supe-rior to the original algorithms in terms of computing time and accuracy. The experimental results show that the optimized algorithm can efficiently process high-dimensional data and has strong stability and application prospects.
Research Progress on Electric-Driven Precision Seed-Metering Technology
In recent years, with the continuous development of agricultural intelligence and precision farming, electrically driven seeding technology has gradually be-come an important method for modern crop planting. This paper, based on an extensive review of relevant literature, provides a detailed introduction to the basic principles and core components of electric-driven seeding, with a focus on analyzing the working mechanisms and technical characteristics of such sys-tems. Meanwhile, the study offers an in-depth review of the research status of electric-driven seeding both domestically and internationally, summarizing the progress and achievements of various countries in this field, particularly re-garding seeding accuracy, adaptability, and intelligent control innovations. Fi-nally, in view of the current technological development, the paper identifies the challenges faced by electric-driven seeding technology and proposes future de-velopment recommendations, envisioning its broad application prospects in future agriculture, especially in improving seeding efficiency, operational preci-sion, and intelligent management.
Research on Core Algorithms of Desktop Scanning Platform
With the in-depth advancement of informatization and digitalization, the demand for rapid and precise scanning of paper documents in various industries is increasing day by day. Traditional scanning devices have become difficult to meet modern demands in terms of efficiency, accuracy and ease of use. This paper introduces the design and implementation of an artificial intelligence-based desktop scanning platform, which utilizes the powerful machine vision system OpenCV and combines advanced image recognition, enhancement and correction algorithms. Using a personal computer as the hardware platform, the images of paper documents are collected through an external camera. The OpenCV image processing technology is utilized for image segmentation, color correction and shape correction, and ultimately high-quality JPG and PDF format documents are generated. The system also supports contactless scanning and OCR preprocessing, making it suitable for long-distance text recognition and the digitization of precious documents. The research adopts the joint programming of C and OpenCV, designs a user-friendly interactive interface, and realizes automatic correction through edge detection and 3D reconstruction technology. The test results show that the system performs outstandingly in terms of scanning accuracy, efficiency and stability. It is suitable for personal and professional use, especially for the needs of enterprises, educational institutions and government departments.