Journal of Web Engineering最新文献

{"title":"Proposed Secure Hypertext Model in Web Engineering","authors":"Madhuri N. Gedam;Bandu B. Meshram","doi":"10.13052/jwe1540-9589.2241","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2241","url":null,"abstract":"Secure web application development is one of the prime challenges for the software industry. In the last decade, web applications have rapidly developed but web engineering methods have some limitations while designing web applications. The extensive literature survey explores various concepts like web engineering, hypertext modelling, web applications hypertext modelling methods, attacks on web applications, same origin policy (SOP) and cross origin resource sharing (CORS). The complexity of web pages is a major concern for security. The proposed secure hypertext model (SHM) provides hypertext modelling of web applications and helps in the identification of attacks on hypertext links. It provides security stereotypes and precisely specifies vulnerability defences in web application design. This standardized attack vector and defence mechanism will help developers to build more secure applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 4","pages":"575-596"},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903044","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":"Metaheuristic Aided Improved LSTM for Multi-document Summarization: A Hybrid Optimization Model","authors":"Sunilkumar Ketineni;Sheela J","doi":"10.13052/jwe1540-9589.2246","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2246","url":null,"abstract":"Multi-document summarization (MDS) is an automated process designed to extract information from various texts that have been written regarding the same subject. Here, we present a generic, extractive, MDS approach that employs steps like preprocessing, feature extraction, score generation, and summarization. The input text goes preprocessing steps such as lemmatization, stemming, and tokenization in the first stage. After preprocessing, features are extracted, including improved semantic similarity-based features, term frequency-inverse document frequency (TF-IDF-based features), and thematic-based features. Finally, an improved LSTM model will be proposed to summarize the document based on the scores considered under the objectives such as content coverage and redundancy reduction. The Blue Monkey Integrated Coot Optimization (BMICO) algorithm is proposed in this paper for fine-tuning the optimal weight of the LSTM model that ensures precise summarization. Finally, the suggested BMICO's effectiveness is evaluated, and the outcome is successfully verified.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 4","pages":"701-730"},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903507","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":"Web-based Non-contact Edge Computing Solution for Suspected COVID-19 Infection Classification Model","authors":"Tae-Ho Hwang;KangYoon Lee","doi":"10.13052/jwe1540-9589.2242","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2242","url":null,"abstract":"The recent outbreak of the COVID-19 coronavirus pandemic has necessitated the development of web-based, non-contact edge analytics solutions. Non-contact sensors serve as the interface between web servers and edge analytics through web engineering technology. The need for an edge device classification model that can identify COVID-19 patients based on early symptoms has become evident. In particular a non-contact implementation of such a classification model is required to efficiently prevent viral infection and minimize cross-infection. In this work, we investigate the use of diverse non-contact biosensors (e.g., remote photoplethysmography, radar, and infrared sensors) for reducing effective physical contact with patients and for measuring their biometric data and vital signs. We further explain a classification method for suspected COVID-19 infection based on the measured vital signs and symptoms. The results of this study can be applied in patient classification by mobile-based edge computing applications. The correlation between symptoms comprising cough, sore throat, fever, headache, myalgia, and arthralgia are analyzed in the model. We implement a machine learning classification model using vital signs for performance evaluation, and propose an ensemble model realized by fine-tuning the high-performing classification models. The proposed ensemble model successfully distinguishes suspected patients with an accuracy, area under curve, and F1 scores of 94.4%, 98.4%, and 94.4%, respectively.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 4","pages":"597-613"},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903045","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":"Proposition of Rubustness Indicators for Immersive Content Filtering","authors":"Youngmo Kim;Seok-Yoon Kim;Chyapol Kamyod;Byeongchan Park","doi":"10.13052/jwe1540-9589.2247","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2247","url":null,"abstract":"With the full-fledged service of mobile carrier 5G networks, it is possible to use large-capacity, immersive content at high speed anytime, anywhere. It can be illegally distributed in web-hard and torrents through DRM dismantling and various transformation attacks; however, evaluation indicators that can objectively evaluate the filtering performance for copyright protection are required. Since applying existing 2D filtering techniques to immersive content directly is not possible, in this paper we propose a set of robustness indicators for immersive content. The proposed indicators modify and enlarge the existing 2D video robustness indicators to consider the projection and reproduction method, which are the characteristics of immersive content. A performance evaluation experiment has been carried out for a sample filtering system and it is verified that an excellent recognition rate of 95% or more is achieved in about 3 s of execution time.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 4","pages":"731-755"},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903508","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":"Embedding a Microblog Context in Ephemeral Queries for Document Retrieval","authors":"Shilpa Sethi","doi":"10.13052/jwe1540-9589.2245","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2245","url":null,"abstract":"With the proliferation of information globally, the search engine had become an indispensable tool that helps the user to search for information in a simple, easy and quick way. These search engines employ sophisticated document ranking algorithms based on query context, link structure and user behavior characterization. However, all these features keep changing in the real scenario. Ideally, ranking algorithms must be robust enough to time-sensitive queries. Microblog content is typically short-lived as it is often intended to provide quick updates or share brief information in a concise manner. The technique first determines if a query is currently in high demand, then it automatically appends a time-sensitive context to the query by mining those microblogs whose torrent matches with query-in-demand. The extracted contextual terms are further used in re-ranking the search results. The experimental results reveal the existence of a strong correlation between ephemeral search queries and microblog volumes. These volumes are analyzed to identify the temporal proximity of their torrents. It is observed that approximately 70% of search torrents occurred one day before or after blog torrents for lower threshold values. When the threshold is increased, the match ratio of torrent is raised to ~90%. In addition, the performance of the proposed model is analyzed for different combining principles namely, aggregate relevance (AR) and disjunctive relevance (DR). It is found that the DR variant of the proposed model outperforms the AR variant of the proposed model in terms of relevance and interest scores. Further, the proposed model's performance is compared with three categories of retrieval models: log-logistic model, sequential dependence model (SDM) and embedding based query expansion model (EQE1). The experimental results reveal the effectiveness of the proposed technique in terms of result relevancy and user satisfaction. There is a significant improvement of ~25% in the result relevance score and ~35% in the user satisfaction score compared to underlying retrieval models. The work can be expanded in many directions in the future as various researchers can combine these strategies to build a recommendation system, auto query reformulation system, Chatbot, and NLP professional toolkit.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 4","pages":"679-700"},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903509","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":"Federated Latent Dirichlet Allocation for User Preference Mining","authors":"Xing Wu;Yushun Fan;Jia Zhang;Zhenfeng Gao","doi":"10.13052/jwe1540-9589.2244","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2244","url":null,"abstract":"In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people's awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 4","pages":"639-677"},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903510","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":"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}

{"title":"The Case for Cross-Entity Delta Encoding in Web Compression","authors":"Benjamin Wollmer, Wolfram Wingerath, Sophie Ferrlein, Fabian Panse, Felix Gessert, N. Ritter","doi":"10.1007/978-3-031-09917-5_12","DOIUrl":"https://doi.org/10.1007/978-3-031-09917-5_12","url":null,"abstract":"","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"16 1","pages":"131-146"},"PeriodicalIF":0.8,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74991110","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":"Validity Analysis of Network Big Data","authors":"Peng Wang;Huaxia Lv;Xiaojing Zheng;Wenhui Ma;Weijin Wang","doi":"10.13052/jwe1540-9589.2234","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2234","url":null,"abstract":"False data in network big data has led to considerable ineffectiveness in perceiving the property of fact. Correct conclusions can be drawn only by accurately identifying and eliminating these false data. In other words, analysis is the premise to reaching a correct conclusion. This paper develops a big data network dissemination model based on the properties of the network. We also analyze the attributes of the big data random complex network based on the revised F-J model. Then, based on the scale-free nature of network big data, the evolution law of connected giant components and Bayesian inference, we propose an identification method of effective data in networks. Finally, after obtaining the real data, we analyze the dissemination and evolution characteristics of the network big data. The results show that if some online users intentionally spread false data on a large-scale website, the entire network data becomes false, despite a minimal probability of choosing these dissemination sources.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 3","pages":"465-496"},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10243554/10243555/10247498.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50424077","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":"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}