Journal of Web Engineering最新文献

{"title":"VisionGuard: Cost-Sensitive AI Attestation with Quorum-Verified Blockchain Enforcement","authors":"Sundara Srivathsan M.;Lighittha P. R.;Prithivraj S.;Suganya Ramamoorthy;Vijayan Sugumaran","doi":"10.13052/jwe1540-9589.2516","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2516","url":null,"abstract":"Web3 platforms face a critical challenge: once unsafe content is minted on-chain, it becomes immutable and irrevocable. Traditional NSFW classifiers operate off-chain without cryptographic guarantees, leaving blockchain ecosystems vulnerable to harmful content. We present VisionGuard, a unified moderation framework that integrates cost-sensitive AI decision-making with blockchain-based enforcement. Our system combines calibrated NSFW classification, abstention-based triage for uncertain cases, perceptual hashing for near-duplicate detection, and on-chain k-of-n quorum attestation using EIP-712 signatures. We establish formal guarantees for: (i) Bayes-optimal cost-sensitive thresholds minimizing asymmetric error costs, (ii) optimal abstention intervals for human review, (iii) monotone false-negative reduction under classifier-pHash fusion, (iv) quorum compromise bounds, and (v) end-to-end unsafe-mint probability. Empirical validation on a zero-shot NSFW task demonstrates 82% accuracy (AUC = 0.88), with the Bayes-optimal threshold <tex>$(tau^{*}=0.1)$</tex> reducing expected cost to 27,520 versus 54,942 at the F1-optimal threshold-a 50% improvement. Calibrated abstention further lowers harm (cost= 10,649.5), while a 3-of-5 quorum with oracle compromise <tex>$p=0.1$</tex> yields break probability <tex>$P_{text{break}} &lt; 1%$</tex>. Together, Vision Guard bridges decision theory, adversarial robustness, and cryptographic enforcement, providing the first provably safe AI moderation pathway for blockchain content.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 1","pages":"103-134"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370663","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098433","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":"Joint Models for Sentence Segmentation and Named Entity Recognition in Literary Sinitic Text","authors":"DongNyeong Heo;Yunhee Kang;Chul Heo;Heeyoul Choi;Kyounghun Jung","doi":"10.13052/jwe1540-9589.2512","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2512","url":null,"abstract":"It is challenging to understand Literary Sinitic text from the Joseon dynasty, since there is a lack of explicit word separators, which creates significant semantic ambiguity. To address this, both sentence segmentation and named entity recognition (NER) are essential. We propose a Transformer-based analyzer that performs these two tasks simultaneously. Trained on a labeled corpus from the Seungjeongwon Ilgi, our model effectively segments sentences and identifies named entities, thereby significantly improving the understanding of sentence structure and overall context.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 1","pages":"19-32"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098434","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 Novel Collaboration Representation Method of Combining PCANet with Occlusion Positioning for Non-cooperative Face Recognition","authors":"Zhi Zhang;Bingyu Sun","doi":"10.13052/jwe1540-9589.2513","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2513","url":null,"abstract":"At present, many researches on non-cooperative face recognition have achieved good results, but the representation ability of facial features still needs to be improved. Moreover, due to the existence of occlusion in test samples and the uncertainty of their locations, the task of face recognition is more challenging. To this end, (1) In this paper, multi-scale sample information is added to PCA Network (PCA Network) to obtain Multi-Scale PCANet(MSPCANet) to improve the expression ability of features, and further provide the criteria for selecting the optimal size of PCA filter. (2) The authors use the Markov random field to locate the occlusion position of the test sample, remove the feature information corresponding to the occlusion position in the original image from the feature map, and reduce the information interference caused by the occlusion by classifying the feature information excluding the occlusion. In order to verify the effectiveness of the method, uncoordinated face recognition experiments were carried out on AR and LFW data sets respectively. The results showed that the method with occlusion position information and multi-scale feature information fusion always achieved encouraging performance.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"25 1","pages":"33-50"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098436","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":"Real-Time Game—Broadcast Guidance Protocol for Spoiler Prevention in Live Game Streaming","authors":"YongJoon Joe;Sung-Il Jang;DongMyung Shin","doi":"10.13052/jwe1540-9589.2484","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2484","url":null,"abstract":"Spoiling story content during live streams of narrative games can undermine creator intent and expose creators to legal and policy risks. We present a real-time game—broadcast guidance protocol driven by a license-script interpreter. A game control module and a broadcasting SW plugin exchange authenticated states; the interpreter evaluates a DSL over synchronized game and broadcast schemas to issue actions, such as in-game choice limits and stream blackouts. Unlike DRM, this approach aims to prevent harm to both rights holders and streamers arising from accidental violations of license terms. This protocol does not burden streamers by operationalizing publisher guidelines that restrict spoilers and the disclosure of endings, and it supports wellintentioned creators in avoiding unintentional breaches caused by oversight or error. On a two-machine testbed at 1080p60, 1-hour streams achieved an average end-to-end block latency of about 65 ms, with minimal CPU overhead.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 8","pages":"1263-1282"},"PeriodicalIF":1.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766220","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 Containerized Microservices Architecture with Reinforcement Learning for Scalable, Adaptive Learning","authors":"Yanxia Gong;Kai Cai","doi":"10.13052/jwe1540-9589.2482","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2482","url":null,"abstract":"This paper presents the design and validation of a scalable, containerized web platform for real-time adaptive language instruction, integrating reinforcement learning into a modern microservices architecture. The system leverages Docker and Kubernetes for container orchestration, implements RESTful APIs and WebSocket communication for low-latency interaction, and utilizes Kafka for event-driven messaging between independently deployable services. Technical performance and operational health are monitored through a unified ELK stack dashboard, providing real-time insights for both developers and instructors. Empirical evaluation under authentic production conditions demonstrates the platform's ability to sustain high availability (99.95% uptime), rapid response times (frontend < 120 ms, backend ≈180 ms), and robust autoscaling for workloads exceeding 150 concurrent users. Rigorous stress testing confirms seamless failover and rapid recovery, while the analytics layer enables actionable monitoring of both infrastructure and learner engagement. A field study with 154 language learners further validates the platform's effectiveness, as reinforcement-learning-driven personalization resulted in a 24.7 percentage point gain in post-test performance and improved retention compared to rule-based controls. This work offers a transferable blueprint for web-native, AI-powered learning environments and demonstrates how service-oriented engineering can bridge the gap between pedagogical innovation and large-scale, real-world deployment.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 8","pages":"1203-1230"},"PeriodicalIF":1.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766198","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":"AI-Related Copyright Awareness Among Game Majors: A Cross-National Study in Vietnam and South Korea","authors":"Hye-Young Kim;GwanHyeon James Koh","doi":"10.13052/jwe1540-9589.2481","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2481","url":null,"abstract":"The rapid advancement of artificial intelligence (AI) has brought a paradigm shift to creative practices across various content industries, including the game industry. In particular, the application of AI technologies in areas such as character generation, storytelling, and level design has become increasingly prevalent in game development. As the use of AI-generated content expands, debates concerning its legal status and copyright protection have gained significant momentum. Understanding how future professionals perceive these issues is critical, especially in fields where AI-based creation is directly applied. In this study, we analyzed and compared the level of awareness regarding copyright technologies and related legal issues in AI-based game development among approximately 450 university students majoring in game-related disciplines in Vietnam and South Korea. By taking into account the cultural and institutional contexts of the two countries, we identified both commonalities and differences in students' perceptions and attitudes. For data analysis, a mathematical pattern analysis approach was applied to examine inter-item correlations and factor structures, with results visualized using Python in the Jupyter Notebook environment. Furthermore, Bartlett's test of sphericity was conducted to verify the statistical suitability of the dataset for factor analysis, confirming significant correlations among the survey items.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 8","pages":"1181-1202"},"PeriodicalIF":1.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766218","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-Based Framework for Unified Channel Access and Data Integrity in Smart Electricity Billing","authors":"Huawei Hong;Yimin Shen;Xiaorui Qian;Songyan Du;Xinling Zheng;Jing Ye;Xingye Lin","doi":"10.13052/jwe1540-9589.2486","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2486","url":null,"abstract":"Addressing heterogeneous data access and integrity assurance remains a critical challenge for modern smart electricity billing systems. Conventional solutions are often fragmented, with limited interoperability and inconsistent validation processes, which undermines scalability, data quality, and user trust. This paper proposes a Web engineering-driven framework that integrates service-oriented architecture, semantic modeling, and automated integrity verification into a unified, component-based Web application. Unlike prior energy-focused studies, the framework explicitly advances Web engineering principles by embedding metadata-driven routing, ontology-based interoperability, and real-time validation mechanisms into a distributed smart billing environment. The system architecture is designed for modular extensibility, with semantic reasoning enabling dynamic service orchestration and adaptive Web interfaces providing role-based usability across devices. Evaluation demonstrated low latency (120 ms), high availability (99.98%), and scalability to 5000 concurrent users, while the integrity module improved validation success rates from 96.2% to 98.9% over four weeks. Usability testing achieved a 97.5% task completion rate and a system usability score of 88.6, confirming accessibility and adaptability. By combining semantic-driven service orchestration, end-to-end integrity verification, and adaptive user interaction within a single Web framework, this work contributes a novel, holistic approach to Web engineering in critical infrastructure. Beyond smart billing, the proposed methods provide a transferable foundation for secure, interoperable, and user-centric Web applications in other distributed domains.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 8","pages":"1303-1330"},"PeriodicalIF":1.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766228","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":"Decentralized Federated Learning on a High-Performance Computing Platform: Blockchain-Based Security and Optimization","authors":"Yejin Kwon;Jeongcheol Lee;Yongbom Park","doi":"10.13052/jwe1540-9589.2483","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2483","url":null,"abstract":"As various artificial intelligence (AI)-related services emerge, attempts to apply AI models to industrial and service fields are ongoing. In preparation for the increase in demand for such AI services, the importance of data required to create AI models is increasing. As data for applying services is a key element in training AI models and improving performance, the privacy and security of data have also recently emerged as significant issues. Although various AI learning platforms exist, users must share data that they have personally created and refined. Consequently, users who do not want to share personal data tend to be reluctant to access and use these platforms. Therefore, in this paper, we built a platform that can create and share specific AI models without sharing data. We designed and built a platform that can perform blockchain-based federated learning to ensure the authenticity and privacy of the user's learning model and global model, allowing the learning of the final result model according to the sharing model of each dataset during each learning session. Each user learns based on their personal dataset, and the weights are integrated based on the learned models to create a single global model. The contribution of each user is measured based on the blockchain-based model creation, leading to the development of a high-performance AI model. We conducted experiments using the MNIST and COVID-19 datasets, focusing on data independence. The results showed that significant results could be achieved with just 10-20% data sharing. These experimental results confirmed that federated learning is possible in an environment where data independence is maintained and individual users do not share data. By applying this learning method to the platform, it is expected that the collaboration barriers between researchers in computational science and engineering will be lowered and there will be an increase in the usability of integrated research.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 8","pages":"1231-1262"},"PeriodicalIF":1.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765640","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 Hyper-Personalized, Context-Aware Café Recommendation Mobile Application Integrating Real-Time Environmental Sensing and Augmented Reality","authors":"Haekyung Chung;Janghyok Ko","doi":"10.13052/jwe1540-9589.2485","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2485","url":null,"abstract":"This study aims to develop an innovative recommendation system that provides a tailored café experience by integrating users' nuanced preferences with a real-time environmental context. With the recent surge in domestic coffee consumption, the café market has reached a saturation point, leading consumers to seek spaces that match a specific atmosphere or purpose beyond merely consuming beverages. Existing recommendation systems, which primarily rely on past ratings or static information, have shown limitations in meeting these dynamic and multidimensional demands. To overcome these limitations, this paper proposes a novel framework that fuses hyper-personalization, context-aware recommendation, acoustic scene classification (ASC), passive crowd density estimation, and location-based augmented reality (AR). The proposed system utilizes a large language model (LLM) to extract abstract atmospheric characteristics such as “cozy,” “vibrant,” or “suitable for work” from unstructured text data collected from social media and review platforms. Simultaneously, it quantifies the actual environment of a space by analyzing real-time data collected through in-store sensors (or user devices). Specifically, ASC technology identifies the qualitative characteristics of sound - such as conversations, background music, and machine noise -going beyond simple noise levels. Passive detection of smartphone Wi-Fi probe signals accurately estimates indoor crowd density without infringing on personal privacy. This multimodal data is combined with user profiles to generate a list of recommendations optimized for each individual. Finally, users can have an immersive exploration experience through a location-based AR interface, visually confirming recommended cafés, friends' reviews, and personalized notes overlaid on their real-world surroundings. Through the synergistic combination of these advanced technologies, this research presents the design and implementation potential of a system that shifts the paradigm of physical space recommendation and provides a truly experience-centric service.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 8","pages":"1283-1302"},"PeriodicalIF":1.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766211","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":"Web-Engineered ECC-Based Group Key Protocol for Secure and Scalable Metering Communication","authors":"Hao Yang;Yiming Zhang","doi":"10.13052/jwe1540-9589.2473","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2473","url":null,"abstract":"This paper presents a Web-native group key negotiation framework for secure and scalable communication in smart metering networks. Leveraging lightweight elliptic curve cryptography (ECC), the proposed protocol supports dynamic group membership, forward and backward secrecy, and resistance to impersonation and replay attacks – without relying on persistent sessions or centralized trust brokers. Unlike traditional TLS- or MQTT-based approaches, our design adopts stateless REST and CoAP messaging, enabling seamless integration with constrained IoT devices and cloud-native microservice platforms. We architect a modular system comprising smart meters, a secure gateway, and a REST-compliant backend, each aligned with Web of Things (WoT) standards. Group keys are established through a contributory ECC-based exchange that ensures decentralized key computation and rekeying across heterogeneous nodes. The gateway acts as a protocol adapter, translating CoAP messages into REST APIs while enforcing cryptographic policy and interoperability with tools such as Node-RED, Eclipse Leshan, and AWS IoT Core. Performance analysis shows that our protocol achieves group key negotiation in under 450 ms for 25 nodes with message sizes below 220 bytes, outperforming traditional LKH and centralized DH schemes by 30–40% in latency and bandwidth usage. Real-world case studies demonstrate successful deployment in rural microgrids and urban energy-sharing networks. By aligning cryptographic rigor with Web engineering principles, this work offers a practical and extensible solution for secure group communication in emerging energy and IoT infrastructures.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 7","pages":"1073-1102"},"PeriodicalIF":1.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11251116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533028","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}