Journal of Web Engineering最新文献

{"title":"Web Interactive Content Filtering Model Integrating Prompt Classifier and Intent Understanding","authors":"Yanghao Li;Shisong Wu;Pengfei Zhang;Shengyao Zhuang;Lulu Chen","doi":"10.13052/jwe1540-9589.2578","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2578","url":null,"abstract":"To accurately identify and intercept harmful content generated in user interactions on Web platforms such as social media and online forums, this study proposes a Web interactive content filtering model that integrates Prompt classifiers and intent understanding. The model adopts a hierarchical review architecture with dual-module collaboration. The front-end deploys a Prompt classifier based on Deep Contrastive Learning (DCL), and uses the attention mechanism and deep convolutional network to perform rapid semantic analysis and preliminary screening of real-time comments and conversations. The back-end introduces an intent understanding module based on search-enhanced generation and Large Language Models (LLMs) to conduct in-depth contextual analysis of preliminary screening warning content to distinguish malicious attacks from legitimate sensitive topic discussions. Experiments showed that the system performed well on a test set containing 52,397 Chinese Web interactive texts. The Prompt classifier module achieved a precision of 95.2% and a recall of 96.5%. After collaborative calibration with the intent understanding module, the false alarm rate of the system was significantly reduced to 4.4%, while the recall rate remained at 96.1%. From the results, this study provides a content security solution for Web interactive scenarios that combines high accuracy, strong semantic understanding capabilities, and real-time response performance. It can effectively purify the cyberspace environment while ensuring freedom of interaction, with important practical application value.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 7","pages":"1483-1508"},"PeriodicalIF":1.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11737706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Alignment-Driven Approach for Constructing SPARQL Federated Queries","authors":"Moussa Abdenour Beldi;Sihem Klai;Karima Mecheri;Seif Eddine Bouziane","doi":"10.13052/jwe1540-9589.2574","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2574","url":null,"abstract":"Integrating complementary information across distributed SPARQL end-points remains a significant challenge. While query federation enables access across multiple endpoints, constructing federated queries requires both SPARQL expertise and knowledge of the underlying ontological schemas. Current query rewriting techniques primarily focus on vocabulary translation to retrieve equivalent data, often overlooking the integration of complementary information spread across sources. To address these limitations, we propose an alignment-driven approach that assists users in constructing federated queries by leveraging ontology alignments. Our approach joins class paths – user-selected sequences of ontology classes and relationships – from source and target ontologies, guided by alignment-based filtering, into a unified path that serves as the foundation for generating federated SPARQL queries capturing complementary aspects of the user's intent. An experimental evaluation using OAEI Conference track datasets demonstrates that the generated queries consistently match reference queries in both structure and data retrieval across simple and complex mapping types, confirming the approach's effectiveness in integrating complementary data from heterogeneous sources.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 7","pages":"1349-1382"},"PeriodicalIF":1.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11737702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Evidence-Based License Compliance Verification for KOGL Open Government Works on the Web","authors":"Seyoung Jang;Byeongchan Park;Youngmo Kim;Sun-Jib Kim","doi":"10.13052/jwe1540-9589.2576","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2576","url":null,"abstract":"The reuse of open government works on the web has increased rapidly with the expansion of public data policies and artificial intelligence (AI)-assisted content utilization. In Korea, Korea Open Government Licenses (KOGLs) permit the reuse of public works under conditions such as attribution, non-commercial use, and no-derivatives. However, these conditions are frequently violated or misunderstood in web pages, advertisements, social media posts, and reused digital content. Since compliance-related evidence is distributed across textual descriptions, captions, license metadata, embedded image text, visual changes, and page-level context, manual inspection, and single-modal analysis are not sufficient for scalable web-based compliance review. This paper proposes a multimodal evidence-based method for KOGL license compliance risk prediction in web environments. The proposed method combines textual context, KOGL metadata, optical character recognition (OCR) extracted image text, and visual features to classify four risk types: attribution risk, non-commercial use risk, no-derivatives risk, and false KOGL claim risk. A lightweight sequential context model captures the contextual flow of compliance-related cues, while the decision module produces both risk probabilities and evidence-based explanations. Experiments on a KOGL-oriented dataset show that the proposed method achieves higher accuracy, macro-F1, and area under the curve (AUC) than text-only, image-only, and simple fusion baselines. The results demonstrate that multimodal web evidence improves compliance risk prediction and provides practical support for human review in public content management, open government data curation, and AI training data inspection.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 7","pages":"1419-1448"},"PeriodicalIF":1.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11737704","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitigating Volatility in P2E through Allowance Verification in Decentralized Platform","authors":"Md Shariful Islam;Hye-Young Kim","doi":"10.13052/jwe1540-9589.2575","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2575","url":null,"abstract":"Play-to-Earn (P2E) game economies integrated with Automated Market Maker (AMM) protocols face persistent sustainability challenges driven by whale-induced price volatility and the absence of effective token-burning mechanisms. These issues erode economic fairness and discourage long-term participation within web-based blockchain gaming platforms. This paper proposes EG-AMM (Epoch-Guarded Automated Market Maker), a software engineering framework that adds a governance-aware volatility protection layer to existing AMM-based P2E infrastructures without modifying the underlying pricing model. Inspired by circuit breaker mechanisms in traditional finance, EG-AMM enforces per-epoch dynamic swap allowances for individual players, computed from price impact tolerance and configurable through Decentralized Autonomous Organization (DAO) parameters. The framework employs Merkle-proof-based cryptographic verification and on-chain accounting to ensure allowance limits are transparent and tamper-resistant. Unlike existing pool-level approaches such as static transaction caps or dynamic fee schedules, EG-AMM operates at the individual player level, curbing whale dominance while preserving equitable market access for ordinary players and newcomers. Preliminary analysis and game-theoretic simulation confirm improvements in price stability, participation fairness, and economic resilience, offering a practical and deployable path toward sustainable token ecosystems in blockchain-integrated gaming applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 7","pages":"1383-1418"},"PeriodicalIF":1.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11737707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security Architecture of WEB-Based Education Management Platform Based on Zero-Trust Model: Protecting Sensitive Student Data and Teaching Resources","authors":"Baowang Yang","doi":"10.13052/jwe1540-9589.2573","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2573","url":null,"abstract":"In response to security risks such as data leakage and unauthorized access in educational Web platforms during digital transformation, this paper proposes a five-layer security architecture based on the zero-trust model. Following the principle of “never trust, always verify,” the architecture integrates identity management and access proxy technologies to build a collaborative protection system. It employs a multi-dimensional permission model for fine-grained access control, combines signature and behavior detection to strengthen Web attack protection, and leverages dynamic authorization evaluation, three-factor authentication, AES256 encryption, and distributed storage to ensure authentication and data security. Comparative experiments show that the architecture effectively mitigates unauthorized access, session hijacking, and abnormal authentication attacks, achieving a 97.0% defense success rate. Under 50 concurrent users, authentication delay remains within 4.2 ms, and the average response time in high-frequency access scenarios is 68 ms. Throughput meets the peak demands of education systems, with performance improved by 57.5% compared to traditional Web education platform architectures (TWEPA) and by 33.3% compared to general zero-trust architectures (ZTA). This solution provides a feasible approach for security enhancement and data compliance management in educational Web platforms.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 7","pages":"1319-1348"},"PeriodicalIF":1.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11737705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Web Engineering Approach to AI-Enabled Japanese Tutoring via Instructional Design Pattern Mining","authors":"Xiaomei Gai;Yanjia Tian","doi":"10.13052/jwe1540-9589.2577","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2577","url":null,"abstract":"Despite the proliferation of artificial intelligence (AI)-enabled web applications for Japanese language learning, existing platforms suffer from three critical challenges: insufficient integration of AI capabilities with pedagogical design, limited scenario adaptability across proficiency levels, and suboptimal lightweight deployment on the browser side. To address these gaps and explore instructional design patterns tailored to diverse Japanese learning needs, this study focuses on web application technology implementation and interaction design as the core analytical dimensions. Three typical AI-enabled Japanese tutoring web applications were selected as research objects, and 192 Japanese learners participated in the empirical research. Quantitative data were obtained through technical disassembly, questionnaire surveys, and web-based behavior tracking; learning effects were verified by comparative experiments, and the weight of instructional design elements was determined via the analytic hierarchy process, finally extracting core instructional design modes suitable for the mainstream Japanese learning stages from beginner to upper-intermediate (Japanese-Language Proficiency Test [JLPT] N5–N2), with preliminary applicability discussion for the advanced stage (N1 and above). The results show that web-based lightweight AI technology deployment and real-time AI interactive feedback design are the core elements, with a combined weight of over 40%. There is a significant positive correlation between web-based AI technology implementation and user satisfaction, with the highest implementation effect and user acceptance in listening, speaking, and grammar modules. Compared with traditional Japanese web learning platforms, AI-enabled instructional design can improve learners' listening and speaking abilities by <tex>$58.0 %(M = 58.0, text{SD} = 4.2,95 % text{CI} [56.4, 59.6]$</tex>, Cohen's <tex>$d=1.82$</tex>, large effect), grammar analysis abilities by 46.5% (<tex>$M = 46.5, text{SD} = 5.1, 95% text{CI} [44.8, 48.2]$</tex>, Cohen's <tex>$d=1.45$</tex>, large effect), and enhance learning sustainability by 85.7% <tex>$(M=85.7, text{SD} = 6.3, 95% text{CI} [83.5, 87.9]$</tex>, Cohen's <tex>$d=2.01$</tex>, large effect), based on N5–N2 learner data with robust statistical power. Three core instructional design modes were identified, adapted to zero-based-N4 fragmented learning, N3–N2 systematic learning, and N1 + immersive learning, respectively. This study clarifies the core elements and optimization direction of such applications, providing standardized and practical theoretical references and paths for their design, development, and teaching practice.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 7","pages":"1449-1482"},"PeriodicalIF":1.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11737703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149175967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Layered Retrieval and Task-Aware Embeddings for Efficient Persian RAG Systems","authors":"Toktam Zoughi;Ehsan Arianyan;Maryam Mahmoudi;Mahtab Aghdamifard;Mohadese Nikoogoftar","doi":"10.13052/jwe1540-9589.2552","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2552","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"765-796"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum Random Number Generators for NIST Post-Quantum Cryptography Standard Algorithms","authors":"Abel C. H. Chen","doi":"10.13052/jwe1540-9589.2555","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2555","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"861-888"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination Optimization of Web Services with Credibility and QoS Attributes","authors":"Xiang Sun","doi":"10.13052/jwe1540-9589.2551","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2551","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"737-764"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Semantic-Web-Driven Visualization and Fault-Reasoning Framework for Circuit-Oriented Systems","authors":"Liu Yin;Zhang Nanjing;Zheng Qiang;Tang Yadong;Song Fuping;Li Senwei","doi":"10.13052/jwe1540-9589.2554","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2554","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"823-860"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}