Journal of Web Engineering最新文献_第9页

Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations 基于图注意力网络的可解释推荐实体和关系的联合表示

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-06-01 DOI: 10.13052/jwe1540-9589.2243

Rima Boughareb;Hassina Seridi-Bouchelaghem;Samia Beldjoudi

{"title":"Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations","authors":"Rima Boughareb;Hassina Seridi-Bouchelaghem;Samia Beldjoudi","doi":"10.13052/jwe1540-9589.2243","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2243","url":null,"abstract":"The latest advances in Graph Neural Networks (GNNs), have provided important new ideas for solving the Knowledge Graph (KG) representation problem for recommendation purposes. Although GNNs have an effective graph representation capability, the nonlinear transformations over the layers cause a loss of semantic information and make the generated embeddings hard to explain. In this paper, we investigate the potential of large KGs to perform interpretable recommendation using Graph Attention Networks (GATs). Our goal is to fully exploit the semantic information and preserve inherent knowledge ported in relations by jointly learning low-dimensional embeddings for nodes (i.e., entities) and edges (i.e., properties). Specifically, we feed the original data with additional knowledge from the Linked Open Data (LOD) cloud, and apply GATs to generate a vector representation for each node on the graph. Experiments conducted on three real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. In addition to improving predictive performance in terms of precision, recall, and diversity, our approach fully exploits the rich structured information provided by KGs to offer explanation for recommendations.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 4","pages":"615-638"},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903043","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}

引用次数: 0

The Case for Cross-Entity Delta Encoding in Web Compression Web压缩中跨实体增量编码的案例

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-04-20 DOI: 10.1007/978-3-031-09917-5_12

Benjamin Wollmer, Wolfram Wingerath, Sophie Ferrlein, Fabian Panse, Felix Gessert, N. Ritter

引用次数: 1

Validity Analysis of Network Big Data 网络大数据的有效性分析

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01 DOI: 10.13052/jwe1540-9589.2234

Peng Wang;Huaxia Lv;Xiaojing Zheng;Wenhui Ma;Weijin Wang

引用次数: 0

Pre-Trained Model-Based Software Defect Prediction for Edge-Cloud Systems 基于预训练模型的边缘云系统软件缺陷预测

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01 DOI: 10.13052/jwe1540-9589.2223

Sunjae Kwon;Sungu Lee;Duksan Ryu;Jongmoon Baik

{"title":"Pre-Trained Model-Based Software Defect Prediction for Edge-Cloud Systems","authors":"Sunjae Kwon;Sungu Lee;Duksan Ryu;Jongmoon Baik","doi":"10.13052/jwe1540-9589.2223","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2223","url":null,"abstract":"Edge-cloud computing is a distributed computing infrastructure that brings computation and data storage with low latency closer to clients. As interest in edge-cloud systems grows, research on testing the systems has also been actively studied. However, as with traditional systems, the amount of resources for testing is always limited. Thus, we suggest a function-level just-in-time (JIT) software defect prediction (SDP) model based on a pre-trained model to address the limitation by prioritizing the limited testing resources for the defect-prone functions. The pre-trained model is a transformer-based deep learning model trained on a large corpus of code snippets, and the fine-tuned pre-trained model can provide the defect proneness for the changed functions at a commit level. We evaluate the performance of the three popular pre-trained models (i.e., CodeBERT, GraphCodeBERT, UniXCoder) on edge-cloud systems in within-project and cross-project environments. To the best of our knowledge, it is the first attempt to analyse the performance of the three pre-trained model-based SDP models for edge-cloud systems. As a result, we can confirm that UniXCoder showed the best performance among the three in the WPDP environment. However, we also confirm that additional research is necessary to apply the SDP models to the CPDP environment.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 2","pages":"255-278"},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10243554/10243559/10247502.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50354809","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

Generating Automated Layout Design Using a Multi-Population Genetic Algorithm 用多群体遗传算法生成自动化布局设计

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01 DOI: 10.13052/jwe1540-9589.2227

Arun Kumar;Kamlesh Dutta;Abhishek Srivastava

{"title":"Generating Automated Layout Design Using a Multi-Population Genetic Algorithm","authors":"Arun Kumar;Kamlesh Dutta;Abhishek Srivastava","doi":"10.13052/jwe1540-9589.2227","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2227","url":null,"abstract":"The problem of space layout planning, constrained by a number of functional and non-functional requirements, not only challenges architects in coming up with a good solution, but is more difficult to give an alternative. Genetic algorithms (GAs) have been found suitable for solving the problem of providing alternative solutions. However, GAs have been found to be susceptible to the problem of local maxima and plateau conditions. To overcome these problems, the multi-population genetic algorithm (MPGA) improves the diversity of the population, thereby improving the quality of the solution. Algorithms are employed to automatically generate layout designs in best-connected ways, either rectangular or square. The area of the floor plans is optimized to minimize the extra area in the layout. The layouts are divided into four groups and these groups are related to each other based on highest proximity. Layout designs have been simulated using GA and MPGA algorithms and MPGA has shown significant improvement in computation time as well as quality over alternative solutions. In addition, the algorithm also provides the architect with the facility to interactively modify the dimensions and adjacent criteria during the design phase. The system works on clouds and shows the result for inputs passed by an architect.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 2","pages":"357-384"},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10243554/10243559/10247504.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50354916","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

Reliable and Scalable Big-Data Applications in Edge Cloud Environments 边缘云环境中可靠且可扩展的大数据应用

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01

In-Young Ko;Abhishek Srivastava;Michael Mrissa

引用次数: 0

Just-in-Time Defect Prediction for Self-Driving Software via a Deep Learning Model 基于深度学习模型的自动驾驶软件实时缺陷预测

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01 DOI: 10.13052/jwe1540-9589.2225

Jiwon Choi;Taeyoung Kim;Duksan Ryu;Jongmoon Baik;Suntae Kim

{"title":"Just-in-Time Defect Prediction for Self-Driving Software via a Deep Learning Model","authors":"Jiwon Choi;Taeyoung Kim;Duksan Ryu;Jongmoon Baik;Suntae Kim","doi":"10.13052/jwe1540-9589.2225","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2225","url":null,"abstract":"Edge computing is applied to various applications and is typically applied to autonomous driving software. As the self-driving system becomes complicated and the proportion of software increases, accidents caused by software defects increase. Just-in-time (JIT) defect prediction is a technique that identifies defects during the software development phase, which helps developers prioritize code inspection. Many researchers have proposed various JIT models, but it is difficult to find a case in which JIT defect prediction was performed on edge computing applications. In particular, due to the characteristic of self-driving software, which is frequently updated, there is a high risk of inducing defects into the update process. In this work, we propose a JIT defect prediction model via deep learning for edge computing applications called JIT4EA. Our research goal is to develop an effective model to predict defects in edge computing applications. To do this, we perform defect prediction on self-driving software, a representative edge computing application. We use pre-trained unified cross-modal pre-training for code representation (UniXCoder) to embed commit messages and code changes. We use bidirectional-LSTM(Bi-LSTM) for context and semantic learning. As a result of the experiment, it was confirmed that the proposed JIT4EA performed better than state-of-the-art methods and could reduce the code inspection effort.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 2","pages":"303-326"},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10243554/10243559/10243560.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50354808","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

Visual Quality Assessment of Point Clouds Compared to Natural Reference Images 点云与自然参考图像的视觉质量评估

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01 DOI: 10.13052/jwe1540-9589.2232

Aram Baek;Minseop Kim;Sohee Son;Sangwoo Ahn;Jeongil Seo;Hui Yong Kim;Haechul Choi

{"title":"Visual Quality Assessment of Point Clouds Compared to Natural Reference Images","authors":"Aram Baek;Minseop Kim;Sohee Son;Sangwoo Ahn;Jeongil Seo;Hui Yong Kim;Haechul Choi","doi":"10.13052/jwe1540-9589.2232","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2232","url":null,"abstract":"This paper proposes a point cloud (PC) visual quality assessment (VQA) framework that reflects the human visual system (HVS). The proposed framework compares natural images acquired using a digital camera and PC images generated via 2D projection in terms of appropriate objective quality evaluation metrics. Humans primarily consume natural images; thus, human knowledge is typically formed from natural images. Thus, natural images can be more reliable reference data than PC data. The proposed framework performs an image alignment process based on feature matching and image warping to use the natural images as a reference which enhances the similarities of the acquired natural and corresponding PC images. The framework facilitates identifying which objective VQA metrics can be used to reflect the HVS effectively. We constructed a database of natural images and three PC image qualities, and objective and subjective VQAs were conducted. The experimental result demonstrates that the acceptable consistency among different PC qualities appears in the metrics that compare the global structural similarity of images. We found that the SSIM, MAD, and GMSD achieved remarkable Spearman rank-order correlation coefficient scores of 0.882, 0.871, and 0.930, respectively. Thus, the proposed framework can reflect the HVS by comparing the global structural similarity between PC and natural reference images.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 3","pages":"405-432"},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10243554/10243555/10247500.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50346757","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

Features for a Style for Push-Communication Integrated Rich Web-Based Applications 一种推送通信风格的特征集成了基于富Web的应用程序

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01 DOI: 10.13052/jwe1540-9589.2236

Nalaka R. Dissanayake;Dharshana Kasthurirathna;Shantha Jayalal

{"title":"Features for a Style for Push-Communication Integrated Rich Web-Based Applications","authors":"Nalaka R. Dissanayake;Dharshana Kasthurirathna;Shantha Jayalal","doi":"10.13052/jwe1540-9589.2236","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2236","url":null,"abstract":"The development aspects of rich web-based applications have evolved; however, abstract concepts, like styles and patterns, are still lacking. If an abstract style for rich web-based applications is available, it can support the whole engineering process in many ways, like assisting in designing aspects and the system's evolution. We have produced an abstract architectural style named RiWAArch style for standard rich web-based applications, and we are working on extending the same to realize integrating push-communication. Push-communication has become a contemporary requirement in developing features like real-time notifications in rich web-based applications. However, the features to be expected from a style to realize the integration of the push-communication are not yet recognized. This concept paper proposes a set of features to be expected from a style for push-communication-integrated rich web-based applications. Our ongoing research will later utilize these features to form requirements and design a comprehensive style by extending the RiWAArch style to realize the abstract features of integrating true push-communication into rich web-based applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 3","pages":"515-542"},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10243554/10243555/10243556.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50346759","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 Phishing Website Detection using a Hybrid Two-level Framework for Feature Selection and XGBoost Tuning 使用用于特征选择和XGBoost调整的混合两级框架改进钓鱼网站检测

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-03-01 DOI: 10.13052/jwe1540-9589.2237

Luka Jovanovic;Dijana Jovanovic;Milos Antonijevic;Bosko Nikolic;Nebojsa Bacanin;Miodrag Zivkovic;Ivana Strumberger

{"title":"Improving Phishing Website Detection using a Hybrid Two-level Framework for Feature Selection and XGBoost Tuning","authors":"Luka Jovanovic;Dijana Jovanovic;Milos Antonijevic;Bosko Nikolic;Nebojsa Bacanin;Miodrag Zivkovic;Ivana Strumberger","doi":"10.13052/jwe1540-9589.2237","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2237","url":null,"abstract":"In the last few decades, the World Wide Web has become a necessity that offers numerous services to end users. The number of online transactions increases daily, as well as that of malicious actors. Machine learning plays a vital role in the majority of modern solutions. To further improve Web security, this paper proposes a hybrid approach based on the eXtreme Gradient Boosting (XGBoost) machine learning model optimized by an improved version of the well-known metaheuristics algorithm. In this research, the improved firefly algorithm is employed in the two-tier framework, which was also developed as part of the research, to perform both the feature selection and adjustment of the XGBoost hyper-parameters. The performance of the introduced hybrid model is evaluated against three instances of well-known publicly available phishing website datasets. The performance of novel introduced algorithms is additionally compared against cutting-edge metaheuristics that are utilized in the same framework. The first two datasets were provided by Mendeley Data, while the third was acquired from the University of California, Irvine machine learning repository. Additionally, the best performing models have been subjected to SHapley Additive exPlanations (SHAP) analysis to determine the impact of each feature on model decisions. The obtained results suggest that the proposed hybrid solution achieves a superior performance level in comparison to other approaches, and that it represents a perspective solution in the domain of web security.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 3","pages":"543-574"},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10243554/10243555/10247501.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50424078","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: 1