Journal of Web Engineering最新文献_第2页

A Memory Driven Self-learning Combat Agent Architecture in a 3D Virtual Environment 三维虚拟环境中记忆驱动的自学习战斗代理体系结构

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-07-01 DOI: 10.13052/jwe1540-9589.2451

Tianci Zhang;Yongyong Wei;Hao Fang

{"title":"A Memory Driven Self-learning Combat Agent Architecture in a 3D Virtual Environment","authors":"Tianci Zhang;Yongyong Wei;Hao Fang","doi":"10.13052/jwe1540-9589.2451","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2451","url":null,"abstract":"Agent behavior modeling in 3D virtual environments is a critical challenge in artificial intelligence and military simulation. While rule-based methods (e.g., finite state machines) are widely used, their limitations in adaptability and development efficiency hinder their application in dynamic combat scenarios. To address this, a memory-driven self-learning agent (MDSLA) architecture is proposed, integrating visual, auditory, and game features to simulate human-like battlefield decision-making. The architecture employs an asynchronous advantage actor-critic (A3C) framework to enhance training efficiency and incorporates a memory module for processing historical perception data. Experimental validation in the Vizdoom environment demonstrates that MDSLA outperforms traditional rule-based methods and mainstream reinforcement learning algorithms in convergence speed and combat effectiveness. Furthermore, a parallel simulation mechanism is implemented via high-speed middleware, enabling seamless deployment of the model on both Vizdoom and a high-precision simulation platform (HPSP). Results from HPSP experiments show a 33% reduction in task execution time and a 24.1% improvement in lethality compared to finite state machine-driven agents. This work provides a scalable framework for developing intelligent combat agents with enhanced adaptability and realism in 3D virtual environments.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 5","pages":"687-712"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11135462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896762","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

Integration and Application of an Intelligent Content Classification Model Based on Artificial Intelligence Technology and Metadata in Web Applications 基于人工智能技术和元数据的智能内容分类模型在Web应用中的集成与应用

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-07-01 DOI: 10.13052/jwe1540-9589.2455

Guoxin Han;Hai Lin;Genchao Yan;Kaiye Dai

{"title":"Integration and Application of an Intelligent Content Classification Model Based on Artificial Intelligence Technology and Metadata in Web Applications","authors":"Guoxin Han;Hai Lin;Genchao Yan;Kaiye Dai","doi":"10.13052/jwe1540-9589.2455","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2455","url":null,"abstract":"With the explosive growth of Internet information, Web applications are facing the challenge of efficient classification and management of a massive amount of content. Traditional classification methods rely on manual rules, which are inefficient and difficult to adapt to dynamically changing content. This study proposes an intelligent content classification model based on artificial intelligence technology and metadata, and integrates it into web applications to achieve automated and precise content classification and management. Preprocessing operations such as cleaning, deduplication, and word segmentation on multimodal data such as text, images, and videos in web applications, and extract key metadata information such as title, author, publication time, tags, etc., are performed. Pre-trained language models and image feature extraction models are used to extract high-dimensional feature representations of text and images, respectively, and metadata information are combined to construct a comprehensive feature vector. Deep neural networks are used to learn from annotated training data and construct a classification model. The experimental results illustrate that compared with traditional methods, the proposed model has significantly improved in accuracy, recall, and F1 score, reaching 95.2%, 94.8%, and 95.0%, respectively. The proposed intelligent content classification model based on artificial intelligence technology and metadata can effectively solve the problem of content classification in web applications, and improve content management efficiency and user experience.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 5","pages":"805-826"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11135460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896810","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

TrADS: A Trust-Aware Decentralized Social Network TrADS：一个具有信任意识的去中心化社会网络

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-06-01 DOI: 10.13052/jwe1540-9589.2443

Valentin Siegert;Dirk Leichsenring;Alejandro Wurts Santos;Martin Gaedke

{"title":"TrADS: A Trust-Aware Decentralized Social Network","authors":"Valentin Siegert;Dirk Leichsenring;Alejandro Wurts Santos;Martin Gaedke","doi":"10.13052/jwe1540-9589.2443","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2443","url":null,"abstract":"In today's data-driven world, people are increasingly prioritising data privacy and control. This awareness has sparked an initiative for a decentralized web, where web applications no longer rely on centralized data storage. Solid, a prominent approach for a decentralized web, allows users to store their data in decentralized pods of their control. However, the integration of data from various and potentially untrusted sources can lead to malicious or harmful results and impair user experience. To solve this problem for social networks, we propose a trustaware and decentralized web application called TrADS. It utilises the MVC pattern to integrate external data from Solid pods based on trust evaluations. In this article, we extend our first paper on TrADS with further details on related work assessment, concept and implementation. We are also extending our evaluation to a second international user study with another 64 participants. We measure the user experience instead of pure usability and form two separate groups of participants, one of which experiences the trust awareness of TrADS and one of which does not. The results do not yet show significant improvement in the user experience but show that after using trust awareness in a social network, users favour a network with such features.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"529-562"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112760","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773296","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

A Novel Approach to Integration of Manual Changes in Generated Code: SeamlessMDD 在生成的代码中集成手工更改的新方法：无缝mdd

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-06-01 DOI: 10.13052/jwe1540-9589.2442

Bojana Dragaš;Nenad Todorović;Tijana Rajačić;Gordana Milosavljević;Željko Vuković

{"title":"A Novel Approach to Integration of Manual Changes in Generated Code: SeamlessMDD","authors":"Bojana Dragaš;Nenad Todorović;Tijana Rajačić;Gordana Milosavljević;Željko Vuković","doi":"10.13052/jwe1540-9589.2442","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2442","url":null,"abstract":"Model-driven development (MDD) significantly improves web development by generating source code from models at higher abstraction levels, which enhances productivity and enables shorter, less expensive development cycles. However, not all aspects of a web application can be captured in a model, necessitating manual changes to the generated code. To prevent the loss of manual changes due to subsequent code generation, various strategies are recommended in the literature: keeping handwritten and generated code in separate files, utilizing protected regions, or employing a version control system (VCS) to merge handwritten and generated code. Unfortunately, these approaches can introduce complexity into web application architecture and impose an additional burden on developers. This paper presents SeamlessMDD, our novel open-source framework for seamless integration that allows us to maintain handwritten and generated code intertwined without the need to adjust the web application architecture or established workflows. The framework provides the following features: (1) incremental and iterative transformations derived from model versions comparison ensuring that only code for affected model elements is generated or modified; (2) integration of generated and handwritten code using API-based code generators (ABG) that operate on the syntax trees of target programming languages; (3) case-specific support for change propagation and conflict resolution, as opposed to VCS-based systems that operate on a single line. The proposed features were tested in practice within a complex industrial MDD tool. This paper is an extended version of a paper presented at the 24th International Conference on Web Engineering, held in Tampere, Finland.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"499-528"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773293","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

SPARQL Query Candidate Filtering for Improving the Quality of Multilingual Question Answering Over Knowledge Graphs Using Language Models 使用语言模型提高知识图多语种问答质量的SPARQL查询候选过滤

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-06-01 DOI: 10.13052/jwe1540-9589.2444

Aleksandr Perevalov;Aleksandr Gashkov;Maria Eltsova;Andreas Both

{"title":"SPARQL Query Candidate Filtering for Improving the Quality of Multilingual Question Answering Over Knowledge Graphs Using Language Models","authors":"Aleksandr Perevalov;Aleksandr Gashkov;Maria Eltsova;Andreas Both","doi":"10.13052/jwe1540-9589.2444","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2444","url":null,"abstract":"Question answering is an approach to retrieving information from a knowledge base using natural language. Within question answering systems that work over knowledge graphs (KGQA), a ranked list of SPARQL query candidates is typically computed for the given natural-language input, where the top-ranked query should reflect the intention and semantics of the given user's question. This article follows our long-term research agenda of providing trustworthy KGQA systems by presenting an approach for filtering incorrect queries. Here, we employ (large) language models (LMs/LLMs) to distinguish between correct and incorrect queries. The main difference to the previous work is that we address here multilingual questions represented in major languages (English, German, French, Spanish, and Russian), and confirm the generalizability of the approach by also evaluating it on some low-resource languages (Ukrainian, Armenian, Lithuanian, Belarusian, and Bashkir). The considered LMs (BERT, DistilBERT, Mistral, Zephyr, GPT-3.5, and GPT-4) were applied to the KGQA systems - QAnswer (real-world system) and MemQA (idealized system) – as SPARQL query filters. The approach was evaluated on the multilingual dataset QALD-9-plus, which is based on the Wikidata knowledge graph. The experimental results imply that the considered KGQA systems achieve quality improvements for all languages when using our query-filtering approach.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"563-592"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112782","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773294","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

Application and Optimization of Semantic-Enriched Keyword Prefetching Driven by Intelligent Technology in Language Education Network Platforms 智能技术驱动的富语义关键词预取在语言教育网络平台中的应用与优化

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-06-01 DOI: 10.13052/jwe1540-9589.2446

Zhubin Luo;Feifei Guo

{"title":"Application and Optimization of Semantic-Enriched Keyword Prefetching Driven by Intelligent Technology in Language Education Network Platforms","authors":"Zhubin Luo;Feifei Guo","doi":"10.13052/jwe1540-9589.2446","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2446","url":null,"abstract":"With the rapid development of intelligent technology, the application of semantically rich keyword prefetching in language education network platforms has gradually become a key technology to improve learning efficiency and user experience. This article proposes a semantic rich keyword prefetching model driven by intelligent technology, aiming to achieve accurate keyword prefetching and recommendation in language education platforms through modeling and optimization. Firstly, based on user behavior data and semantic analysis techniques, a user interest model and a semantic association model were constructed to capture the semantic relationship between users' learning intentions and keywords. Secondly, by introducing a time decay factor and context aware mechanism, the real-time and accuracy of keyword prefetching have been optimized. Experimental data shows that the MAF1 value, keyword prefetching accuracy and user satisfaction of the model are 0.897, 89.8% and 96%, which are higher than those of compared models, while increasing user satisfaction by 20%. In addition, this article proposes an optimization framework based on A/B testing, which further verifies the robustness and scalability of the model by comparing the effects of different prefetching strategies. The research results indicate that intelligent technology driven semantic rich keyword prefetching can significantly improve the personalized recommendation ability and learning efficiency of language education platforms, providing new ideas for the future development of educational technology.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"635-654"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112808","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773295","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

ISSF: An Intelligent Security Service Framework for Cloud-Native Operations 面向云原生操作的智能安全服务框架

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-06-01 DOI: 10.13052/jwe1540-9589.2447

Yikuan Yan;Keman Huang;Michael Siegel

{"title":"ISSF: An Intelligent Security Service Framework for Cloud-Native Operations","authors":"Yikuan Yan;Keman Huang;Michael Siegel","doi":"10.13052/jwe1540-9589.2447","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2447","url":null,"abstract":"The growing system complexity of microservice architectures and the bilateral enhancement of artificial intelligence (AI) for both attackers and defenders present increasing security challenges for cloud-native operations. In particular, cloud-native operators require a holistic view of the dynamic security posture for the microservice-based cloud-native environment from a defense aspect. Additionally, both attackers and defenders can adopt advanced AI technologies. This makes the dynamic interaction and benchmark among different intelligent offense and defense strategies more crucial. Hence, following the multi-agent deep reinforcement learning (RL) paradigm, this research develops an agent-based intelligent security service framework (ISSF) for cloud-native operations. It includes a dynamic attack graph model to represent the cloud-native environment and an action model to represent offense and defense actions. Then we develop an approach to enable the training, publishing, and evaluating of intelligent security services using diverse deep RL algorithms and training strategies, facilitating their systematic development and benchmarking. The experiments demonstrate that our framework can sufficiently model the security posture of a cloudnative system for defenders, effectively develop and quantitatively benchmark different intelligent security services for both attackers and defenders, and guide further optimization.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"655-686"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773282","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

A Web Engineering-Based Robust Watermark Restoration and Recognition Method for Protecting Online Video Content 基于Web工程的在线视频内容鲁棒水印恢复与识别方法

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-06-01 DOI: 10.13052/jwe1540-9589.2441

Jieun Lee;Byeongchan Park;Uijin Jang;Yongtae Shin

{"title":"A Web Engineering-Based Robust Watermark Restoration and Recognition Method for Protecting Online Video Content","authors":"Jieun Lee;Byeongchan Park;Uijin Jang;Yongtae Shin","doi":"10.13052/jwe1540-9589.2441","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2441","url":null,"abstract":"With the rapid expansion of over-the-top (OTT) services and web-based video streaming platforms, copyright protection has become a critical concern. Unauthorized redistribution and modification of digital content via composite transformations and distortions threaten content security. While watermarking and digital rights management (DRM) offer protection, existing methods often fail under real-world web-based attack scenarios. In this paper, we present a web engineering-based robust watermark restoration and recognition method to enhance the security of online video content. Our approach employs AKAZE feature detection to extract robust feature points, while a discrete wavelet transform (DWT) is used for subband decomposition, embedding the watermark in the lowest-energy subband near the detected feature points. To ensure resilience against distortions common in web environments, we evaluate our method under four types of noise (Gaussian, salt-and-pepper, uniform, and Poisson) and four rotation angles (0°, 90°, 180°, and 270°). AKAZE-based feature matching compensates for rotation distortions, while noise removal is handled using Gaussian, Median, or BM3D filtering. Performance evaluation using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation (NC), and bit error rate (BER) confirms the effectiveness of our method. Results show that BM3D filtering achieves the highest average NC (0.8996) and the lowest BER (0.1137), demonstrating strong robustness against composite transformation attacks. This study contributes to web-based video security by integrating feature-based watermarking techniques with web engineering principles, ensuring effective protection for modern web applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 4","pages":"473-498"},"PeriodicalIF":1.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11112761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773280","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

Foundational Components for B2B Data Sharing Using the Solid Protocol 使用Solid协议的B2B数据共享的基本组件

IF 1 4区计算机科学

Journal of Web Engineering Pub Date : 2025-06-01 DOI: 10.13052/jwe1540-9589.2445

Andreas Both;Thorsten Kastner;Dustin Yeboah;Christoph Braun;Daniel Schraudner;Sebastian Schmid;Tobias Käfer;Andreas Harth

引用次数: 0

A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book 一种基于BERT的图书分类推荐方法，以加强图书的Web服务

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2025-03-01 DOI: 10.13052/jwe1540-9589.2422

Youngmo Kim;Seok-Yoon Kim;Byeongchan Park

{"title":"A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book","authors":"Youngmo Kim;Seok-Yoon Kim;Byeongchan Park","doi":"10.13052/jwe1540-9589.2422","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2422","url":null,"abstract":"In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user's recent interest without considering the changes in preference over time, and the user's satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users' past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users' recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"24 2","pages":"199-216"},"PeriodicalIF":0.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888413","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