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
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.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
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
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].