There are some problems in the existing English teaching network applications, such as low personalized adaptability, outdated teaching strategies and weak multimodal interaction, limited teaching paths, single feedback mechanism, weak adaptability of learners, low learning efficiency and low retention rate. In order to solve these problems, this paper proposes a Model-Driven Development (MDD) framework that integrates Web technology and artificial intelligence (Web-AI-I), and builds a lightweight, cross-device English intelligent teaching application through the Web native technology stack. Multi-modal data perception, AI adaptive decision-making and real-time feedback optimization are integrated to solve the coupling problem between teaching logic and Web application architecture through MDD mode. Through the experiment, the response time of learning path adaptation can be effectively shortened to 0.2 s, the English vocabulary mastery rate is 52% higher than that of traditional Web teaching applications, the grammar error correction accuracy rate is 91.8%, the average resource occupancy rate in cross-device scenarios is only 18.3%, and the MTA is 412, which is helpful to realize the large-scale landing of English intelligent teaching Web applications.
To address the problem of low service quality and efficiency caused by non-functional attributes in traditional Web service composition, this paper proposes a Web service composition model construction and optimization method that integrates trustworthiness calculation and QoS attributes. First, we conduct separate objective reputation assessments and subjective trust evaluations based on user needs. Next, we perform attribute normalization and calculate subjective and objective weights according to QoS attribute classifications to establish a comprehensive weighting method. Finally, we develop a multi-attribute QoS composite service model and optimize it using the IBBS algorithm to select the optimal Web service combination. Experimental results demonstrate that both the proposed reliability calculation method and QoS attribute service selection approach can effectively compute Web service reliability and optimize candidate service selections. Compared with advanced optimization algorithms, the IBBS algorithm exhibits significantly better time efficiency and optimization outcomes. When implemented in the constructed Web service combination system, this algorithm substantially enhances service quality, better meets user requirements, and demonstrates robust effectiveness and stability.
Efficient and explainable reasoning over dynamic, heterogeneous data remains a key challenge for intelligent diagnostic and monitoring systems. This paper presents a unified semantic-web-based framework that integrates ontology modeling, rule-driven inference, and interactive visualization into a scalable, service-oriented architecture. The proposed system couples Web Ontology Language (OWL)-based knowledge representation with dynamic Semantic Web Rule Language (SWRL) rule execution and control-theoretic feedback, forming a closed-loop semantic reasoning cycle that continuously refines ontology and rule parameters. To ensure real-time performance, the framework employs parallelized rule evaluation, adaptive caching, and incremental inference across distributed reasoning nodes. A modular semantic query interface bridges reasoning and visualization layers, enabling transparent inspection of causal relationships and human-in-the-loop knowledge refinement. Experimental results demonstrate that the proposed system achieves sub-linear latency growth with ontology size, reduces inference delay by up to 56% through indexing-caching synergy, and maintains detection accuracy above 95% under complex fault conditions. The end-to-end latency remains below 300 ms for medium-scale ontologies, validating its suitability for real-time diagnostic, telepresence, and edge-analytics applications. These findings establish a novel synthesis between symbolic reasoning and adaptive system control, offering both computational efficiency and semantic interpretability for circuit-oriented and cyber-physical diagnostic systems, while providing a foundation that may be extended to other semantic reasoning domains.
The widespread use of generative AI has intensified issues related to digital ownership, even as it enhances the efficiency of digital content creation. While watermarking is a primary method for protecting these rights, existing neural network-based approaches often prioritize robustness and imperceptibility, neglecting verifiable ownership. To address this limitation, this paper proposes ChainMark, a system that integrates an invertible neural network (INN) with blockchain technology. ChainMark employs an INN trained within the discrete wavelet transform domain to embed watermarks that are resilient to diverse signal processing attacks. Crucially, unlike traditional approaches that rely solely on watermark extraction, the proposed system secures the verification process through a blockchain smart contract. Experimental results validate the system's theoretical security and demonstrate that the joint LH-HL model configuration achieves an optimal trade-off between visual quality and extraction accuracy. Consequently, ChainMark effectively guarantees creator rights by ensuring both high-performance watermarking and trustworthy ownership verification.
We build and evaluate a concrete Zero-Knowledge Machine Learning (ZKML)-based pipeline for epidemic diagnosis and show that it can enforce computational integrity without exposing raw medical data in a Web3 setting. In response to security challenges posed by centralized data handling in medical AI applications, particularly during public health crises such as COVID-19, ZKML offers a privacy-preserving alternative by combining machine learning and Zero-Knowledge Proofs (ZKP). We experimentally applied ZKML to a CNN (Convolutional Neural Networks)-based COVID-19 diagnostic model, achieving 87% accuracy and 0.35 loss. All proof generation and verification processes were executed entirely off-chain, with the verified outputs represented as committed public_vals recorded on-chain via smart contracts. To ensure authenticity, the system enforces dual ECDSA signature verification from both the model provider and the data provider. This mechanism prevents unauthorized submissions and confirms the validity of the result before it is stored on-chain. The system was tested under both normal and adversarial conditions, demonstrating robust and reliable operation. By enabling decentralized trust and self-sovereign control over data, this architecture aligns well with Web3 principles. The results indicate that ZKML can support the development of privacy-preserving and verifiable AI systems.
With the continuous outsourcing of sensitive data to cloud platforms and third-party environments, how to achieve efficient and trustworthy data retrieval while preserving data confidentiality and query privacy has become a critical issue in outsourced data security management. Existing studies mainly focus on secure storage and privacy-preserving retrieval but suffer from limitations in the efficiency of multi-attribute conjunctive queries, the suppression of intermediate information leakage during auxiliary condition verification, and the trustworthy traceability of the retrieval process. To address these issues, this paper proposes a blockchain-assisted privacy-preserving retrieval framework for sensitive outsourced data. The framework adopts a collaborative paradigm of off-chain storage and retrieval together with on-chain commitment and auditing. By combining a frequency-aware primary search term selection strategy with a concealed auxiliary verification mechanism, it enables secure filtering of task-relevant data under multi-attribute conditions. Meanwhile, by recording index commitments, query digests, and result digests on the blockchain, the proposed framework enhances the verifiability and traceability of index states and access processes. Security analysis and experimental results demonstrate that the proposed scheme can achieve more efficient construction of auxiliary decision structures, more stable query performance, and better storage overhead while preserving data confidentiality and query privacy.
This paper focuses on the many problems that exist in the evaluation of AI to improve the usability of Web applications for multilingual learning platforms, including the poor alignment effect between learning efficiency and usability, the imbalance of cognitive load regulation, and the singularity of evaluation indicators. In order to solve these problems, this study innovatively constructs a multi-dimensional evaluation index system integrating language cognitive load and learning efficiency and designs a dynamic evaluation model AILA-WA driven by AI. This model can combine learning algorithms to interact with data from Web applications and can collect real-time data related to language learning behavior and cognitive state feedback data from Web applications. It enables the optimization direction of Web applications to be identified and accurately quantified. Subsequent experiments successfully prove that the index system and the evaluation model can effectively improve the comprehensiveness accuracy of the evaluation of Web application usability. For example, in the scenario of multilingual learning, the cognitive load fitting deviation rate of the Web application using the AILA-model is the best compared to the Web application using the comparative model. At the same time, learning efficiency and CSAT user satisfaction are also at the level; and the model is suitable for Web applications. System response delay on multiple terminals is reduced to 0.3 s. These breakthroughs provide strong support for design iteration and usability optimization of AI to improve multilingual learning Web applications.
In recent years, the advancement of quantum computing technology has posed potential security threats to RSA cryptography and elliptic curve cryptography. In response, the National Institute of Standards and Technology (NIST) published several Federal Information Processing Standards (FIPS) of post-quantum cryptography (PQC) in August 2024, including the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM), Module-Lattice-Based Digital Signature Algorithm (ML-DSA), and Stateless Hash-Based Digital Signature Algorithm (SLH-DSA). Although these PQC algorithms are designed to resist quantum computing attacks, they may not provide adequate security in certain specialized application scenarios. To address this issue, this study proposes quantum random number generator (QRNG)-based PQC algorithms. These algorithms leverage quantum computing to generate random numbers, which serve as the foundation for key pair generation, key encapsulation, and digital signature generation. A generalized architecture of QRNG is proposed, along with the design of six QRNGs. Each generator is evaluated according to the statistical validation procedures outlined in NIST SP 800-90B, including tests for verification of entropy sources and independent and identically distributed (IID) outputs. Experimental results assess the computation time of the six QRNGs, as well as the performance of QRNG-based ML-KEM, QRNG-based ML-DSA, and QRNG-based SLH-DSA. These findings provide valuable reference data for future deployment of PQC-based Transport Layer Security in web systems.
With the rapid development of the World Wide Web and the popularity of Internet applications, the generation and exchange of data have exploded. Large-scale data generation and transmission also bring severe security challenges. In response to the problems that existing anomaly detection methods are difficult to jointly model the semantic context and temporal dependencies in non-encrypted scenarios, and that single-modal feature information is insufficient in encrypted scenarios, resulting in limited detection accuracy, this study proposes two artificial intelligence anomaly detection methods that are adapted to different scenarios. For non-encrypted/low-encrypted scenarios, a BERT-LSTM-TextCNN parallel fusion architecture is proposed. This architecture extracts high-order semantic features, long-term dependency features, and multi-scale local features through parallel branches, and achieves complementary enhancement of multi-perspective information through feature concatenation, effectively solving the problem of difficult collaborative modeling of multiple types of features in non-encrypted scenarios. For multi-encrypted scenarios, a detection method based on improved ResNet and cross-modal feature fusion is proposed. Different from traditional methods that only rely on deep learning features, the study adaptively weights and fuses the deep semantic features extracted by ResNet with flow statistics features and temporal features and optimizes the fusion weights through a learnable random forest, breaking through the bottleneck of insufficient single-modal feature information in encrypted traffic. The precision reached 97.18%, the recall rate reached 95.26%, and the F1-score reached 96.21%. The AUC values were all greater than 0.97, the false positive rate was 8.12% lower than the traditional method, and the single-batch data detection time was only 37.25 s. In the multi-encryption scenario, the precision, recall rate and F1-score of the cross-modal feature fusion method were 98.48%, 87.30% and 92.57%, respectively. This effectively solves the detection limitations caused by feature ambiguity in encrypted environments. In summary, the artificial intelligence anomaly detection method effectively improves detection accuracy and efficiency and provides a feasible technical path for building a comprehensive World Wide Web data security monitoring system.
This study introduces task-injected layered hybrid retrieval-augmented generation (TILHR-RAG), a framework specifically designed for Persian to address the scarcity of native-language resources and the limitations of English-centric approaches. The architecture combines task-aware query augmentation, a layered retrieval strategy, and a hybrid semantic-lexical retriever, all supported by a multi-stage pipeline that includes preprocessing, document chunking, question generation, and embedding. A novel mechanism for injecting task-specific vectors directs retrieval toward domain intent while preserving comparability across documents. The layered design operates in three stages: per-task frequently asked questions (FAQ) retrieval, hybrid document search using FAISS semantic similarity combined with BM25 keyword matching, and a fallback response generated by a large language model (LLM). This structure ensures both precision and robustness. Comprehensive experiments across five progressively refined configurations demonstrate that TILHR-RAG achieves the best balance among accuracy, efficiency, and scalability, reaching 89.67% semantic accuracy with moderate latency and memory consumption on NVIDIA A100 hardware. Further evaluations on low-resource graphics processing units (GPUs) confirm that accuracy remains stable under hardware constraints, although latency increases significantly. Moreover, multilingual E5 embedding models substantially improve retrieval and generation quality for Persian – outperforming ParsBERT and Sentence-BERT (SBERT) – by mitigating challenges such as orthographic variation and complex compound word structures. Taken together, these findings establish task-injected layered hybrid retrieval-augmented generation as a practical, reproducible, and resource-efficient blueprint for Persian question answering, advancing retrieval-augmented generation for low-resource languages without requiring costly large language model fine-tuning, while also offering adaptable strategies for broader multilingual applications.
Large-scale interactive online education platforms present significant challenges to traditional elastic scaling strategies based on static thresholds due to their dynamic and unpredictable load characteristics. This article designs and implements a cloud native high-availability network infrastructure centered around an intelligent elastic scaling model that integrates time series prediction and reinforcement learning. This architecture deeply integrates microservices and service mesh technology, predicting short-term resource requirements through historical load and contextual information (such as course schedules), and driving Kubernetes clusters to perform pre-scaling. The research is validated through simulation analysis and real prototype system experiments. The results show that in the simulation environment, the model improves resource prediction accuracy by 25% compared to traditional Horizontal Pod Autoscaler (HPA) strategies, and reduces service level agreement (SLA) violation rates by more than 60% during sudden traffic. In practical systems, the average response delay during peak periods is reduced by 40%, resource utilization increases by 35%, and system availability reaches 99.99%, significantly improving service quality and resource utilization efficiency.
The Semantic Web aims to make information intelligible for computers. In the Semantic Web, unstructured information from text is represented using ontologies, such that computers can understand text better. However, adding text information to existing ontologies by hand is time-consuming. Information extraction rules can help to automate this process. In the process of learning information extraction rules, patterns are constructed that consist of lexico-syntactic and lexico-semantic features from text, which aim to extract Resource Description Framework subject-predicate-object expressions. In this paper, we investigate the following four metaheuristics for learning ontology-based information extraction rules: Particle Swarm Optimization, 2-Phase Optimization, Ant Colony Optimization, and Genetic Algorithm (GA). We evaluate all methods using financial news data. GA gives the best $F_{1}$-measure results, but the other metaheuristics are faster.
To address the limitations of existing knowledge graph-enhanced recommendation systems - particularly their reliance on static fusion mechanisms that fail to capture the dynamic evolution of user interests and their inadequate modeling of heterogeneous information interactions - this paper proposes AdaTKGR, an adaptive time-decay weighted framework for knowledge graph-enhanced recommendations. First, a time-aware self-attention mechanism is introduced to effectively model temporal dependencies in user behavior sequences, thereby capturing fine-grained patterns of interest shift over time. Second, we integrate the RippleNet-style knowledge propagation strategy with a learnable temporal decay kernel, enabling dual-weighted representation learning based on both relational distance within the knowledge graph and temporal recency. Third, a cross-compression unit leveraging low-rank bilinear transformations is designed to facilitate deep semantic interaction between user-item interaction embeddings and knowledge graph entity representations. Finally, a time-gated multitask learning objective is formulated to dynamically balance the primary recommendation task with auxiliary knowledge graph link prediction, enhancing joint optimization. Extensive experiments are conducted on three benchmark datasets - Book-Crossing, Last-FM, and MovieLens-1M- where AdaTKGR achieves average improvements of 6.1% and 8.4% in HR@10 and NDCG@10, respectively, over the strongest baseline methods. Notably, the proposed framework exhibits enhanced generalization performance and interpretability, particularly under data-sparse conditions. This work presents a principled approach to jointly optimizing temporal dynamics modeling and semantic knowledge integration in recommender systems.
Aiming at the challenges of anomaly detection of virtual machine memory, network, CPU and hard disk in the IaaS cloud environment, this study proposes an adaptive anomaly detection system based on a deep Q-network. The system constructs a hierarchical detection framework: a spatiotemporal feature extraction module via fused temporal convolutional networks (TCN) for sequential pattern mining and convolutional neural networks (CNN) for cross-metric correlation learning; a transfer learning module to enhance generalization; and a deep Q-network (DQN) based central controller that dynamically adjusts detection parameters through reinforcement learning. This architecture integrates with cloud workload schedulers by operating at the VM-level (anomaly detection) and edge-server level (DQN control), minimizing core network overhead. Experiments show that the research method achieves a detection accuracy rate of 99.8% in the benchmark test, with an F1 score of 98.7%, which is significantly superior to the accuracy rate of 96.5% of the single convolutional neural network, 92.3% of the multilayer perceptron, and 97.8% of Google Net. The transfer training experiments show that the accuracy rate of the untuned model on the new dataset is only 70% to 80%, while the detection accuracy can be stably improved to 98% through the adaptive system driven by the DQN. The system shows low volatility during the dynamic adjustment process. The number of training iterations is reduced by 32.3% to 69.8% compared with the traditional static model, indicating that the research method does not affect the time complexity. Research shows that this framework effectively solves the problem of insufficient adaptability of static models to unknown data in the cloud environment through the collaborative mechanism of spatiotemporal feature extraction and reinforcement learning decision-making, providing intelligent operation and maintenance solutions for fields with high reliability requirements such as finance and healthcare.
Over-the-top (OTT) platforms must expose metadata and digital rights from numerous content providers (CPs) through the web while maintaining low latency and verifiable integrity. This paper presents HBCMS, a hierarchical blockchain-based content management system that separates contract-aware governance in the upper layer from high-rate metadata management in CP-owned lower chains. The web layer is realized through a contract-aware API, a consistency model aligned with edge and browser caching, and HTTP-level service-level objectives (SLOs) linking blockchain verification to observable web behavior. The upper and lower chains are connected via Merkle-root anchoring, and verification proceeds through contract validation, anchor matching, and Merkle proof verification exposed as RESTful endpoints. Anchoring is modeled as a Poisson process that determines the rate required to satisfy verification windows and guides content delivery network (CDN) cache-control policies. In large-scale experiments with up to 1000 CPs, HBCMS achieved about 2.6 k transactions per second (TPS), 0.185 s end-to-end latency, and 99.4% verification success, with lower-chain queries dominating delay. These results provide reproducible guidance for API versioning, cache invalidation, and observability in scalable OTT web architectures.
Latent diffusion models (LDMs) have rapidly become the de facto backbone of web-scale generative systems, powering text-to-image platforms such as Stable Diffusion and their video, 3D, and domain-specific extensions. By performing the diffusion process in a compressed latent space rather than directly in pixel space, LDMs achieve a favorable tradeoff between computational efficiency and generative fidelity, enabling deployment in interactive web applications and large-scale content pipelines. This paper presents a comprehensive survey of LDMs from the perspective of both foundational modeling and web engineering. We first review the background of diffusion models and latent representations, contrasting LDMs with classical VAEs, GANs, and pixel-space diffusion models. We then dissect the architectural design of LDMs, including autoencoder backbones, latent-space U-Nets and diffusion transformers, conditioning mechanisms, training objectives, and sampling accelerations. Building on recent general surveys of diffusion models in vision, temporal data, and inverse problems, we propose a taxonomy of LDM variants, covering 2D image models, video and 4D models, and domain-specific LDMs in medical imaging, watermarking, time series, and text. From a web engineering viewpoint, we analyze LDM-based services exposed via web APIs, hosted user interfaces, and developer platforms, and discuss system-level concerns such as scalability, latency, cost, safety, and governance. We review current evaluation methodologies (quality, diversity, downstream task performance, robustness, watermarking) and highlight open challenges in controllability, interpretability, resource efficiency, and regulatory compliance, especially in light of recent legal and societal developments around generative deepfakes and copyright. This survey aims to provide both a conceptual map of LDM research and practical guidance for designing, deploying, and governing LDM-driven web systems.
As modern smart-factory environments increasingly require real-time remote operation and lightweight cloud-based control, routing intelligence for OHT systems must be fully web-app compatible, supporting scalable deployment without reliance on high-end local infrastructure. To address these demands and the limitations of static algorithms in large-scale OHT systems, this study proposes a multi-agent reinforcement learning model based on proximal policy optimization, incorporating a state space that accounts for chain blockage probability. The key metric, “movement success probability,” integrates preceding agent states to predictively assess chain-reaction congestion, enabling agents to proactively select stable detours. To enhance scalability in high-density environments, the model stabilizes learning through a lightweight policy initialization approach rather than requiring large-scale training from scratch. Moreover, the proposed decentralized structure minimizes central computational overhead, aligning naturally with web-app deployment and enabling real-time monitoring across distributed environments. In a simulation with 1333 nodes and 100 OHTs, the proposed model achieved an average task completion distance of 166,809 mm, improving efficiency by 4.1% over the rule-based Floyd-Warshall method (173,940 mm). Notably, in worst-case scenarios where the rule-based method surged to 321,753 mm due to congestion, the AI model maintained 176,268 mm, achieving a 45.2% reduction and demonstrating superior operational stability.
Online public opinion has become a critical component of web-based social systems, where large-scale user interactions generate complex propagation behaviors and evolving topic structures. With the rapid growth of social networking platforms, public opinion exhibits network-driven diffusion, temporal volatility, and fragmented topic evolution, posing challenges for web platform monitoring and governance. Existing studies typically rely on either epidemic propagation models or standalone topic modeling methods, limiting their ability to jointly capture diffusion mechanisms and content evolution. To address this issue, this study proposes an integrated web analytics framework that combines epidemic-based propagation modeling with topic mining. Using real data from the Weibo platform, an improved epidemic dynamics model is developed to simulate opinion diffusion over complex networks, with parameters calibrated from observed user interactions. In parallel, latent Dirichlet allocation (LDA) is applied to large-scale textual data to extract latent topics and analyze their temporal evolution. The results show that the network positions of initial propagators and key topological characteristics significantly influence propagation dynamics. Topic mining further reveals six stable thematic clusters with distinct evolutionary patterns across time windows. The proposed framework provides an interpretable system-level approach for analyzing online public opinion, offering practical support for real-time monitoring, moderation workflows, and decision-support systems in web governance.
Smart distribution networks (SDNs) now integrate more distributed energy resources, IoT devices, and multi-stakeholder systems, raising service collaboration complexity. This creates key challenges for real-time fault localization, cross-organizational service compatibility, and performance oversight. This paper presents a novel service-integrated web framework designed to address the challenges of reliability tracing, service integration, and performance monitoring SDNs. The framework leverages a modular architecture that integrates advanced methodologies, including a service-oriented architecture (SOA) for seamless cross-organizational collaboration, metadata management using semantic web technologies for enhanced interoperability, and real-time performance monitoring with anomaly detection. An end-to-end reliability tracing mechanism that combines event logging with causal relationship analysis is implemented to localize faults with high accuracy. The development process adopts a model-driven approach, utilizing UML and SysML for architectural modeling, and employs containerized deployment via Kubernetes for scalability. Unlike existing web-based reliability management systems that operate as isolated analytics or visualization layers, the proposed framework integrates service orchestration, semantic meta-data reasoning, and fault-tracing analytics into a unified architecture. This service-integrated design enables end-to-end information flow - from data acquisition to reliability inference - under a common web infrastructure, representing a substantive advancement in the web engineering of power system reliability applications. Experimental validation in a simulated SDN environment demonstrates that the framework achieves a reliability tracing accuracy of 97.2%, a detection-to-reporting time of 1.8 s, and resource utilization increases of less than 5% per node. These metrics - tracing accuracy, latency, and resource efficiency - are directly aligned with the reliability evaluation indices defined in IEEE 762 and IEC 62559 standards for smart distribution networks, ensuring comparability with established system reliability benchmarks. These results highlight the framework's ability to meet the demands of dynamic distributed systems while providing a foundation for future advancements.
In the era of artificial intelligence (AI), smart devices such as autonomous vehicles, drones, and service robots are increasingly collaborating to perform complex tasks for humans. However, the centralized control and optimization of these widely distributed devices suffer from significant scalability limitations. At the same time, traditional cloud infrastructures are struggling to meet the demands of collecting and processing massive volumes of data from countless devices, often leading to increased latency and reduced service responsiveness.
