Journal of Web Engineering最新文献_第8页

Fine-Grained Sentiment-Enhanced Collaborative Filtering-Based Hybrid Recommender System 基于细粒度句子增强协同过滤的混合推荐系统

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-10-01 DOI: 10.13052/jwe1540-9589.2273

Rawaa Alatrash;Rojalina Priyadarshini

{"title":"Fine-Grained Sentiment-Enhanced Collaborative Filtering-Based Hybrid Recommender System","authors":"Rawaa Alatrash;Rojalina Priyadarshini","doi":"10.13052/jwe1540-9589.2273","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2273","url":null,"abstract":"Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner's latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 7","pages":"983-1035"},"PeriodicalIF":0.8,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10431746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715188","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}

引用次数: 0

Integrating Citizens' Avatars in Urban Digital Twins 在城市数字孪生中整合市民头像

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-09-01 DOI: 10.13052/jwe1540-9589.2264

Rafael García-Luque;Lorenzo Toro-Gálvez;Nathalie Moreno;Javier Troya;Carlos Canal;Ernesto Pimentel

引用次数: 0

Fully Decentralized Horizontal Autoscaling for Burst of Load in Fog Computing 完全分散的水平自动伸缩技术，用于雾计算中的突发负载

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-09-01 DOI: 10.13052/jwe1540-9589.2261

Eunchan Park;Kyeongdeok Baek;Eunho Cho;In-Young Ko

{"title":"Fully Decentralized Horizontal Autoscaling for Burst of Load in Fog Computing","authors":"Eunchan Park;Kyeongdeok Baek;Eunho Cho;In-Young Ko","doi":"10.13052/jwe1540-9589.2261","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2261","url":null,"abstract":"With the increasing number of Web of Things devices, the network and processing delays in the cloud have also increased. As a solution, fog computing has emerged, placing computational resources closer to the user to lower the communication overhead and congestion in the cloud. In fog computing systems, microservices are deployed as containers, which require an orchestration tool like Kubernetes to support service discovery, placement, and recovery. A key challenge in the orchestration of microservices is automatically scaling the microservices in case of an unpredictable burst of load. In cloud computing, a centralized autoscaler can monitor the deployed microservice instances and make scaling actions based on the monitored metric values. However, monitoring an increasing number of microservices in fog computing can cause excessive network overhead and thereby delay the time to scaling action. We propose DESA, a fully DEcentralized Self-adaptive Autoscaler through which microservice instances make their own scaling decisions, cloning or terminating themselves through self-monitoring. We evaluate DESA in a simulated fog computing environment with different numbers of fog nodes. Furthermore, we conduct a case study with the 1998 World Cup website access log, examining DESA's performance in a realistic scenario. The results show that DESA successfully reduces the scaling reaction time in large-scale fog computing systems compared to the centralized approach. Moreover, DESA resulted in a similar maximum number of instances and lower average CPU utilization during bursts of load.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 6","pages":"849-870"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060246","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}

引用次数: 0

Efficient AI Applications in Edge-Cloud Environments 边缘云环境中的高效人工智能应用

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-09-01

In-Young Ko;Michael Mrissa;Juan Manuel Murillo;Abhishek Srivastava

引用次数: 0

Priority Based Scheduler for Asymmetric Multi-Core Edge Computing 基于优先级的非对称多核边缘计算调度器

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-09-01 DOI: 10.13052/jwe1540-9589.2262

Rupendra Pratap Singh Hada;Abhishek Srivastava

{"title":"Priority Based Scheduler for Asymmetric Multi-Core Edge Computing","authors":"Rupendra Pratap Singh Hada;Abhishek Srivastava","doi":"10.13052/jwe1540-9589.2262","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2262","url":null,"abstract":"Edge computing technology has gained popularity due to its ability to process data near the source or collection device, benefiting from low bandwidth utilization and enhanced security. Edge devices are typically equipped with multiple devices that employ asymmetric multi-cores for efficient data processing. To ensure optimal performance, it is crucial to carefully assign tasks to the appropriate cores in asymmetric multi-core processors. However, the current Linux scheduler needs to consider the capabilities of individual cores when assigning tasks. Consequently, high-priority tasks may be assigned to energy-efficient cores, while low-priority tasks end up on high-performance cores. This sub-optimal task assignment negatively impacts the overall system performance. To address this issue, a new algorithm has been proposed. This algorithm considers both the core's capabilities and the task's priority. However, due to the asymmetric nature of the cores, prior knowledge of each core's speed is necessary. The algorithm fetches the priorities of the tasks and classifies them into high, medium, and low-priority categories. Highpriority tasks are scheduled on high-performance cores, while medium and low-priority tasks are allocated to energy-efficient cores. The proposed algorithm demonstrates superior performance for high-priority tasks compared to the existing Linux task scheduling algorithm. It significantly improves task scheduling time by up to 16%, thereby enhancing the system's overall efficiency.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 6","pages":"871-888"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060172","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}

引用次数: 0

Exploring LLM-Based Automated Repairing of Ansible Script in Edge-Cloud Infrastructures 探索边缘云基础设施中基于 LLM 的 Ansible 脚本自动修复功能

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-09-01 DOI: 10.13052/jwe1540-9589.2263

Sunjae Kwon;Sungu Lee;Taehyoun Kim;Duksan Ryu;Jongmoon Baik

{"title":"Exploring LLM-Based Automated Repairing of Ansible Script in Edge-Cloud Infrastructures","authors":"Sunjae Kwon;Sungu Lee;Taehyoun Kim;Duksan Ryu;Jongmoon Baik","doi":"10.13052/jwe1540-9589.2263","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2263","url":null,"abstract":"Edge-Cloud system requires massive infrastructures located in closer to the user to minimize latencies in handling Big data. Ansible is one of the most popular Infrastructure as Code (IaC) tools crucial for deploying these infrastructures of the Edge-cloud system. However, Ansible also consists of code, and its code quality is critical in ensuring the delivery of high-quality services within the Edge-Cloud system. On the other hand, the Large Langue Model (LLM) has performed remarkably on various Software Engineering (SE) tasks in recent years. One such task is Automated Program Repairing (APR), where LLMs assist developers in proposing code fixes for identified bugs. Nevertheless, prior studies in LLM-based APR have predominantly concentrated on widely used programming languages (PL), such as Java and C, and there has yet to be an attempt to apply it to Ansible. Hence, we explore the applicability of LLM-based APR on Ansible. We assess LLMs' performance (ChatGPT and Bard) on 58 Ansible script revision cases from Open Source Software (OSS). Our findings reveal promising prospects, with LLMs generating helpful responses in 70% of the sampled cases. Nonetheless, further research is necessary to harness this approach's potential fully.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 6","pages":"889-912"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060308","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}

引用次数: 0

Improving Ranking Using Hybrid Custom Embedding Models on Persian Web 使用混合自定义嵌入模型提高波斯语网络排名

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-07-01 DOI: 10.13052/jwe1540-9589.2253

Shekoofe Bostan;Ali Mohammad Zareh Bidoki;Mohammad-Reza Pajoohan

引用次数: 0

Development of Web Content for Music Education Using AR Human Facial Recognition Technology 利用 AR 人脸识别技术开发音乐教育网络内容

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-07-01 DOI: 10.13052/jwe1540-9589.2252

Eunee Park

引用次数: 0

Machine Learning Approaches for Fake Reviews Detection: A Systematic Literature Review 虚假评论检测的机器学习方法：系统性文献综述

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-07-01 DOI: 10.13052/jwe1540-9589.2254

Mohammed Ennaouri;Ahmed Zellou

{"title":"Machine Learning Approaches for Fake Reviews Detection: A Systematic Literature Review","authors":"Mohammed Ennaouri;Ahmed Zellou","doi":"10.13052/jwe1540-9589.2254","DOIUrl":"10.13052/jwe1540-9589.2254","url":null,"abstract":"These days, most people refer to user reviews to purchase an online product. Unfortunately, spammers exploit this situation by posting deceptive reviews and misleading consumers either to promote a product with poor quality or to demote a brand and damage its reputation. Among the solutions to this problem is human verification. Unfortunately, the real-time nature of fake reviews makes the task more difficult, especially on e-commerce platforms. The purpose of this study is to conduct a systematic literature review to analyze solutions put out by researchers who have worked on setting up an automatic and efficient framework to identify fake reviews, unsolved problems in the domain, and the future research direction. Our findings emphasize the importance of the use of certain features and provide researchers and practitioners with insights on proposed solutions and their limitations. Thus, the findings of the study reveals that most approaches focus on sentiment analysis, opinion mining and, in particular, machine learning (ML), which contributes to the development of more powerful models that can significantly solve the problem and thus enhance further the accuracy and efficiency of detecting fake reviews.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 5","pages":"821-848"},"PeriodicalIF":0.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10374425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953298","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}

引用次数: 0

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction HCNN-LSTM：集成了长短期记忆的混合卷积神经网络，用于合法网络预测

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-07-01 DOI: 10.13052/jwe1540-9589.2251

Candra Zonyfar;Jung-Been Lee;Jeong-Dong Kim

{"title":"HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction","authors":"Candra Zonyfar;Jung-Been Lee;Jeong-Dong Kim","doi":"10.13052/jwe1540-9589.2251","DOIUrl":"10.13052/jwe1540-9589.2251","url":null,"abstract":"Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 5","pages":"757-782"},"PeriodicalIF":0.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10374423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950485","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}

引用次数: 0