Journal of Web Engineering最新文献

{"title":"The Potential of Serverless Edge-Powered Islands for Web Development","authors":"Juho Vepsäläinen;Petri Vuorimaa;Arto Hellas","doi":"10.13052/jwe1540-9589.2411","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2411","url":null,"abstract":"Web developers face two significant challenges when developing their applications and websites: latency and payload size. Given that web services rely on servers, the related communication incurs a cost in terms of latency. In contrast, the payload passed to the client incurs a communication cost, not to mention the computational cost to the client. The concept of serverless edge computing, built on top of content delivery networks (CDNs), is an approach that has begun to gain the attention of web developers for its promise of lower latencies due to its efficiencies in communication thanks to globally distributed networks and replication. Islands architecture is a technical approach that addresses payload size by giving developers easy ways to defer and potentially even avoid the cost of loading content. Combined, these two approaches form edge-powered islands and, in this article, we examine how the combination can help to address these two notable costs web developers have to consider in their daily work. Our findings indicate that edge-powered islands can provide a way to introduce interactivity to otherwise static websites while wrapping dynamic portions of a page within islands to gain the benefits of static approaches in more dynamic contexts, such as storefronts. In addition, islands can provide loading benefits even for more application-like websites, such as social networks, and give web developers an additional control layer in their development work.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"1-38"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602042","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 User Behavior Prediction Method for Web Applications Based on Deep Forest","authors":"Chang-Sheng Ma;Xiang-Ran Du;Jing Lou;Ming-Qian Wang","doi":"10.13052/jwe1540-9589.2412","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2412","url":null,"abstract":"To increase the sales of agricultural products in e-commerce, understanding customer preferences is essential. In agricultural web applications, data mining techniques can help farmers analyze customer behavior patterns and identify preferences, thus optimizing product design or offering more precise personalized services, which, in turn, can enhance farmers' decision-making in agricultural production. This study proposes a web application user behavior prediction method based on deep forest, which addresses the issue of traditional learning methods requiring a large number of hyperparameter settings. Analysis results show that the Mondrian deep forest model has an accuracy of 95.42% and a running time of 55 s. The accuracy and efficiency of the Mondrian deep forest model are higher than for other models, and the proposed model can improve the accuracy of predicting user behavior in web applications. The effectiveness of the algorithm has been validated through practical testing.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"39-56"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602017","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 Top Features Extraction Model for Detecting X Rumor Events Using an Ensemble Method","authors":"Taukir Alam;Wei Chung Shia;Fang Rong Hsu;Taimoor Hassan;Pei-Chun Lin;Eric Odle;Junzo Watada","doi":"10.13052/jwe1540-9589.2414","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2414","url":null,"abstract":"The paper describes a novel a hybrid ensemble algorithm (HEA) that combines ensemble learning, class imbalance handling, and feature extraction. To address class imbalance in the dataset, the suggested approach integrates SMOTE oversampling and random under sampling (RU) feature extraction. To begin, Pearson correlation analysis is used to detect highly associated features in a dataset. This analysis aids in the selection of the most relevant features, which are either substantially related to the target variable or have a strong association with other features. The method seeks to improve classification performance by focusing on these correlated features. Following that, the SMOTE oversampling and RU algorithms are used to balance the majority and minority categorization characteristics. The SMOTE (synthetic minority oversampling technique) develops synthetic cases for the minority class by interpolating between existing instances, enhancing minority class representation. RU, on the other hand, removes instances from the majority class at random to obtain a balanced distribution. Furthermore, the random forest classifier (RFC) model's key features are input into an ensemble of decision tree (DT), k-nearest neighbor (KNN), adaptive boosting (AdaBoost), and convolutional neural network (CNN) approaches. This ensemble approach combines multiple models' predictions, exploiting their particular strengths and catching varied patterns in the data. Popular machine learning algorithms include DT, KNN, AdaBoost, and CNN, which are notable for their capacity to handle many types of data and capture complicated relationships. The evaluation findings show that the suggested HEA approach is effective, with a maximum precision, recall, F-score, and accuracy of 90%. The proposed methodology produces encouraging results, proving its applicability to a variety of categorization problems.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"79-106"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602018","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":"Enhancing Collaborative Filtering with Game Theory for Educational Recommendations: The Edu-CF-GT Approach","authors":"Rezoug Nachida;Selma Benkessirat;Fatima Boumahdi","doi":"10.13052/jwe1540-9589.2413","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2413","url":null,"abstract":"In the field of education, the proliferation of e-learning platforms has considerably increased access to teaching material. However, this abundance of resources poses a serious challenge to learners in the form of information overload that hinders the learning process. To meet this challenge, effective mechanisms need to be put in place to guide learners towards resources that are tailored to their individual needs and preferences. Recommendation systems appear to be essential tools in this context, aiming to personalise the learning experience by offering targeted suggestions based on the user's preferences. This article presents EDU-CF-GT, a new educational recommendation model, as a solution to this challenge. Based on our generic CF-GT model, EDU-CF-GT is adapted to the complexities of the educational domain, improving learning efficiency by simplifying access to resources. Through evaluation on an educational dataset, EDU-CF-GT demonstrates significant improvements in recommendation relevance and learner satisfaction compared to existing models.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"57-78"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601929","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":"Generative AI-Driven Graphic Pipeline for Web-Based Editing of 4D Volumetric Data","authors":"Ye-Won Jang;Jung-Woo Kim;Hak-Bum Lee;Young-Ho Seo","doi":"10.13052/jwe1540-9589.2416","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2416","url":null,"abstract":"This paper proposes a novel approach to adding and editing clothing and movement of 4D volumetric video data in a web-based environment. While significant advancements have been made in 3D modeling and animation, efficiently editing 3D mesh data produced in sequence remains a challenging problem. Since 3D mesh data synthesized from multiple cameras exists continuously over time, modifying a single 3D mesh model requires consistent editing across multiple frames. Most existing methods focus on single meshes or static 3D models, limiting their ability to handle the complexity of timevarying 3D mesh sequences. The method proposed in this paper targets 3D volumetric sequences synthesized from multiple cameras. It utilizes deep learning networks to estimate body poses, facial features, and hand shapes from RGB images, generating 3D models using the SMPL-X method. Subsequently, an algorithm is applied to segment the 3D mesh, separating and combining the head and torso of the model to create a new 3D model. In the web-based environment, this process makes the data editable, allowing for adding new motions or replacing clothing, which can be seamlessly composited into the existing sequence video. The proposed method enables editing and modification of various types of 3D mesh sequences, facilitating enhancements to existing sequences, such as changing the motion of characters or replacing their clothing, thereby improving the overall quality of 3D content creation in online applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"135-162"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601939","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":"Mesh Enhancement of a 3D Volumetric Model Using Generative AI for a Web 3.0-Based Graphic Service","authors":"Byung-Seo Park;Ye-Won Jang;Hak-Bum Lee;Young-Ho Seo","doi":"10.13052/jwe1540-9589.2415","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2415","url":null,"abstract":"Using depth images from RGB-D cameras simplifies reconstructing 3D information for adaptive online transmission. However, depth sensors often produce distance-related distortions, leading to 3D distortions in reconstructed point clouds or meshes. This paper addresses these issues by proposing a method to enhance volumetric 3D data quality using synthesized point clouds and generating meshes with low-cost RGB-D cameras for Web 3.0 graphic services. We utilize calibration and reconstruction techniques from previous studies to create point clouds, refine them, and convert them into meshes. Finally, we improve the mesh surface using a latent diffusion model (LDM). The proposed calibration method reduced errors to 0.00926 mm in the 3D Charuco board experiment. For the Moai statue, the alignment accuracy achieved an average error of 8 mm and a standard deviation of 3.9 mm. Using LDM, the mesh surface improvement reduced the average error by 54.8% and the standard deviation by 65.9%.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 1","pages":"107-133"},"PeriodicalIF":0.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924704","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601997","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":"Code Smell-Guided Prompting for LLM-Based Defect Prediction in Ansible Scripts","authors":"Hyunsun Hong;Sungu Lee;Duksan Ryu;Jongmoon Baik","doi":"10.13052/jwe1540-9589.2383","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2383","url":null,"abstract":"Ensuring the reliability of infrastructure as code (IaC) scripts, like those written in Ansible, is vital for maintaining the performance and security of edge-cloud systems. However, the scale and complexity of these scripts make exhaustive testing impractical. To address this, we propose a large language model (LLM)-based software defect prediction (SDP) approach that uses code-smell-guided prompting (CSP). In some cases, CSP enhances LLM performance in defect prediction by embedding specific code smell indicators directly into the prompts. We explore various prompting strategies, including zero-shot, one-shot, and chain of thought CSP (CoT-CSP), to evaluate how code smell information can improve defect detection. Unlike traditional prompting, CSP uniquely leverages code context to guide LLMs in identifying defect-prone code segments. Experimental results reveal that while zero-shot prompting achieves high baseline performance, CSP variants provide nuanced insights into the role of code smells in improving SDP. This study represents exploration of LLMs for defect prediction in Ansible scripts, offering a new perspective on enhancing software quality in edge-cloud deployments.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1107-1126"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy and Performance in Virtual Reality: The Advantages of Federated Learning in Collaborative Environments","authors":"Daniel Flores-Martin;Francisco Díaz-Barrancas;Pedro J. Pardo;Javier Berrocal;Juan M. Murillo","doi":"10.13052/jwe1540-9589.2382","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2382","url":null,"abstract":"Federated Learning has emerged as a promising approach for maintaining data privacy across distributed environments, enabling training on a diverse range of devices from high-performance servers to low-power gadgets. Despite its potential, managing numerous data sources can strain these devices, particularly those with limited capabilities, leading to increased latency. This is especially critical in virtual reality, where real-time responsiveness is crucial due to the need for constant data connectivity. Historically, virtual reality systems have relied on tethered computer setups, restricting their flexibility and the benefits of wireless technology. However, recent advancements have enhanced the computational power of VR devices, allowing them to perform certain tasks independently. This work explores the feasibility of training a neural network on VR devices, using a federated learning approach, to develop a collaborative model aggregated and stored in the cloud. The goal is to assess the computational demands and explore the potential and constraints of leveraging VR devices for artificial intelligence applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1085-1106"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized User Models in a Real-World Edge Computing Environment: A Peer-to-Peer Federated Learning Framework","authors":"Xiangchi Song;Zhaoyan Wang;KyeongDeok Baek;In-Young Ko","doi":"10.13052/jwe1540-9589.2381","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2381","url":null,"abstract":"As the number of IoT devices and the volume of data increase, distributed computing systems have become the primary deployment solution for large-scale Internet of Things (IoT) environments. Federated learning (FL) is a collaborative machine learning framework that allows for model training using data from all participants while protecting their privacy. However, traditional FL suffers from low computational and communication efficiency in large-scale hierarchical cloud-edge collaborative IoT systems. Additionally, due to heterogeneity issues, not all IoT devices necessarily benefit from the global model of traditional FL, but instead require the maintenance of personalized levels in the global training process. Therefore we extend FL into a horizontal peer-to-peer (P2P) structure and introduce our P2PFL framework: efficient peer-to-peer federated learning for users (EPFLU). EPFLU transitions the paradigms from vertical FL to a horizontal P2P structure from the user perspective and incorporates personalized enhancement techniques using private information. Through horizontal consensus information aggregation and private information supplementation, EPFLU solves the weakness of traditional FL that dilutes the characteristics of individual client data and leads to model deviation. This structural transformation also significantly alleviates the original communication issues. Additionally, EPFLU has a customized simulation evaluation framework, and uses the EUA dataset containing real-world edge server distribution, making it more suitable for real-world large-scale IoT. Within this framework, we design two extreme data distribution scenarios and conduct detailed experiments of EPFLU and selected baselines on the MNIST and CIFAR-10 datasets. The results demonstrate that the robust and adaptive EPFLU framework can consistently converge to optimal performance even under challenging data distribution scenarios. Compared with the traditional FL and selected P2PFL methods, EPFLU achieves communication time improvements of 39% and 16% respectively.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1057-1083"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379599","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":"Efficient Machine Learning Systems in Edge Cloud Environments","authors":"In-Young Ko;Michael Mrissa;Juan Manuel Murillo;Abhishek Srivastava","doi":"","DOIUrl":"https://doi.org/","url":null,"abstract":"The international workshop on Big Data-Driven Edge Cloud Services (BECS) aims to provide a platform for scholars and practitioners to share their experiences and present ongoing work in developing data-driven AI applications and services within distributed computing environments, commonly referred to as the edge cloud.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"v-vii"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379539","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}