Journal of Web Engineering最新文献

{"title":"Evaluation of the Usability of a Web Application for AI-Enhanced Multilingual Learning Platform: Based on the Indicator System of Language Cognitive Load Learning Efficiency","authors":"Rui Zhang;Zinan Wang;Jing He;Bing Li","doi":"10.13052/jwe1540-9589.2559","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2559","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"977-1014"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442452","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":"ChainMark: Integrating an Invertible Neural Network and Blockchain for Ensuring Ownership Rights in Image Watermarking","authors":"Haeun Jo;Jungwon Seo","doi":"10.13052/jwe1540-9589.2558","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2558","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"945-976"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442906","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":"Blockchain-Assisted Privacy-Preserving Retrieval for Sensitive Outsourced Data","authors":"Bian Zhu;Ling Niu","doi":"10.13052/jwe1540-9589.25510","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.25510","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"1015-1044"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442865","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":"Application of ZKML for Unpredictive Epidemic Response","authors":"Jin Ah Seo;Kun Hwa Lee;Vijayan Sugumaran;Jo Yeon Park;Soo Yong Park","doi":"10.13052/jwe1540-9589.2556","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2556","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"889-914"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442905","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":"The Case for HTML First Web Development","authors":"Juho Vepsäläinen","doi":"10.13052/jwe1540-9589.2553","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2553","url":null,"abstract":"Since its introduction, the web has become the largest application platform available globally. HyperText Markup Language (HTML) has been an essential part of the web since the beginning, as it allows defining webpages in a tree-like manner, including semantics and content. Although the web was never meant to be an application platform, it evolved as such, especially since the early 2000s, as web application frameworks became available. While the emergence of frameworks made it easier than ever to develop complex applications, it also put HTML on the back burner. As web standards caught up, especially with milestones such as HTML5, the gap between the web platform and frameworks was reduced. HTML First development emphasizes this shift and puts focus on literally using HTML first when possible, while encouraging minimalism familiar from the early days of the web. It seems HTML-oriented web development can provide clear benefits to developers, especially when it is combined with complementary approaches, such as embracing hypermedia and moving a large part of application logic to the server side. In the context of the htmx project, it was observed that moving towards HTML can reduce the size of a codebase greatly while leading to maintenance and development benefits due to the increased conceptual simplicity. Holotype-based comparisons for content-oriented websites show performance benefits, and the same observation was confirmed by a small case study where the Yle website was converted to follow HTML First principles. In short, the HTML First approach seems to have clear advantages for web developers, while there are open questions related to the magnitude of the benefits and the alignment with the recent trend of AI-driven web development.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"797-822"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442238","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":"Artificial Intelligence-Based Anomaly Detection for Large-Scale Web Data Security Monitoring","authors":"Siyao Xu;Yan Li;Kai Zhang;Weiming Li;Jieshao Lai","doi":"10.13052/jwe1540-9589.2557","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2557","url":null,"abstract":"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.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 5","pages":"915-944"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442643","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 Metadata-Driven Architecture for Federated Data Asset Management and Visualization in Energy Monitoring Networks","authors":"Qing Rao;Jianxia Wu;Shihong Chen;Zhongkai Pan;Qing Lei;Yinfeng Liu;Yangjinglan Feng;Xianping Jia","doi":"10.13052/jwe1540-9589.2522","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2522","url":null,"abstract":"Distributed energy systems increasingly consist of heterogeneous assets and organizations that must exchange operational data while preserving inter-operability, security, and regulatory compliance. Existing integration solutions often rely on syntactic adapters or centralized data hubs, which scale poorly and offer limited transparency or governance. This paper presents a metadata-driven federated monitoring architecture that integrates ontology-based metadata federation, event-driven microservices, and governance-aware provenance tracking to enable secure, scalable, and auditable data sharing across distributed energy infrastructures. The proposed system models all assets and data streams through a unified semantic graph, aligning heterogeneous schemas via automated ontology matching and combined lexical-structural similarity scoring. A microservices pipeline ingests multi-protocol data (OPC-UA, MQTT, REST), applies stream analytics for anomaly detection, and enforces access and compliance policies at the metadata layer. A Web-based interface allows operators to issue GraphQL queries, visualize distributed assets, and monitor real-time alerts linked to provenance records. A prototype implementation demonstrates operational-scale efficiency, achieving low-latency response (<tex>$leq 540$</tex> ms for hybrid metadata-telemetry queries over 10,000 assets), near-linear scalability (~4.5% CPU growth per added node), and high governance accuracy (precision 0.90, recall 0.95, median detection 1.6 s) while maintaining minimal overhead (<8% added latency). These results highlight that the proposed metadata-driven federation delivers both technical performance and governance reliability unmatched by existing Web-based integration frameworks. These results show that metadata federation can be deployed at operational scale while providing explainable compliance and trustworthy data sharing across organizational boundaries. This research advances the state of the art in Web-based system engineering by combining semantic modeling, distributed processing, and security governance into a single deployable framework. Beyond energy systems, the approach offers a foundation for interoperable and auditable monitoring in other critical cyber-physical domains such as industrial IoT, urban infrastructure, and healthcare telemetry.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 2","pages":"153-186"},"PeriodicalIF":1.0,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11424362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362412","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":"Service-Oriented Web Framework for Real-time Data Flow Tracing and Threat Propagation Analysis in Distributed Energy Systems","authors":"Qing Rao;Yunhao Yu;Yizhou Fu;Boda Zhang;Shihong Chen;Jianxia Wu;Zhongkai Pan;Qing Lei","doi":"10.13052/jwe1540-9589.2525","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2525","url":null,"abstract":"Ensuring data-flow integrity and rapid threat containment in renewable-integrated, distributed energy systems requires monitoring solutions that are technically rigorous yet lightweight in operation. This paper presents a service-oriented web framework for real-time data-flow tracing and threat propagation analysis in heterogeneous industrial control and energy networks. The framework integrates lightweight provenance tokens embedded in event streams, an incrementally maintained lineage graph with probability-weighted edges, and propagation-aware risk indicators that drive adaptive response orchestration through open web APIs. A progressive web dashboard provides sub-second visualization of dynamic topologies, risk heat maps, and operator controls. Implemented on a Kafka/Flink streaming backbone with a graph database and deployed in an eight-node Kubernetes testbed emulating substations, gateways, and adversarial nodes using OPC UA, MQTT, and REST, the system achieved tracing coverage of <tex>$0.96pm 0.02$</tex> and fidelity of <tex>$0.92 pm 0.03$</tex>, with forward propagation prediction reaching precision 0.91 and recall 0.88, outperforming static-topology baselines. Adaptive containment reduced the flow reproduction factor from 1.42 to 0.64, achieved a median containment efficacy of 0.71, and stabilized risk trajectories within two minutes, while operational cost remained low with payload expansion under 12%, CPU overhead below 4%, and service availability above 0.99 for critical assets. User studies showed 38% faster incident response and higher comprehension and confidence compared with static log viewers. These results demonstrate that modern web-engineering practices such as microservices, event-driven streaming, and progressive web interfaces can enable practical, real-time cyber defense for distributed energy infrastructures by bridging static security guidelines with deployable, adaptive situational awareness and containment.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 2","pages":"249-282"},"PeriodicalIF":1.0,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11424361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362472","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 Software Engineering: Something Old, Something New; Something Borrowed, Something Blue","authors":"Jose Garcia-Alonso;Majid Haghparast;Tommi Mikkonen;Juan Manuel Murillo Rodríguez;Vlad Stirbu","doi":"10.13052/jwe1540-9589.2526","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2526","url":null,"abstract":"Quantum software engineering has gained a lot of attention recently. Multiple traditional software engineering events have introduced a quantum software track, or a co-located quantum related workshop or other side event, indicating that quantum software is becoming a popular research topic, with more and more software engineering researchers contributing to its evolution. In this paper, we address software engineering research that aims at solving problems that emerge when quantum programs are used on industry domains. The paper is based on the keynote at the IEEE Symposium on Quantum Software: Quantum Software Engineering 2025, which took place in Helsinki, Finland, Summer of 2025. In particular, we address the state of research in quantum software engineering, its novel aspects as well as its connections to other branches of software engineering. Furthermore, in the light of this research, we also assess the maturity of quantum software engineering in the light of industry expectations.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 2","pages":"283-298"},"PeriodicalIF":1.0,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11424365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362398","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":"Causal Cross-Embedded Spatio-Temporal LSTM for Web Traffic Prediction","authors":"Zhao Na;Mao Yanying","doi":"10.13052/jwe1540-9589.2524","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2524","url":null,"abstract":"Web service traffic forecasting is vital for dynamic resource scaling, load balancing, and anomaly detection, but remains challenging due to frequent large-scale fluctuations caused by heterogeneous user behaviors. Traditional time-series models and recent deep neural networks have made progress by capturing temporal patterns, yet they largely overlook latent causal relationships between services that can significantly influence traffic dynamics. In this paper, we propose a novel causal cross-embedded spatio-temporal LSTM (CEST-LSTM) architecture that integrates spatio-temporal modelling with a causal inference mechanism to improve web traffic prediction. The model consists of a spatio-temporal LSTM branch for capturing temporal dependencies across services and a causal branch that leverages convergent cross mapping-based cross-embedding to uncover and incorporate latent inter-service causal influences. A cross-embedding fusion mechanism seamlessly combines these causal features with spatio-temporal representations. On real-world datasets (e.g., Microsoft Azure and Alibaba Cloud), CEST-LSTM achieves a variance-explained prediction accuracy of approximately 93%, surpassing state-of-the-art baselines such as temporal graph convolutional networks (T-GCN) and spatio-temporal attention GCNs (STA-GCN). Comparative experiments and ablation studies confirm that the causal branch consistently improves forecasting accuracy - for example, removing the causal module reduces accuracy by several percentage points. These results demonstrate that integrating latent causal relationship modelling into spatio-temporal neural networks yields substantial improvements in web traffic prediction, offering a promising direction for robust and interpretable forecasting in complex web systems.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 2","pages":"215-248"},"PeriodicalIF":1.0,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11424363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362574","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}