With the rapid development and popularization of intelligent terminals, app software has also developed rapidly. The research and practical value of mining user experience (UX) of app software form interaction information are becoming increasingly prominent. The interactive information of app software is multisource homogeneous and heterogeneous. In order to obtain more accurate and more comprehensive app software UX results, the fused multisource information should be analyzed. In this paper, the app software UX analysis method based on multisource information fusion is proposed. First, feature engineering is carried out to extract the features. Then, the feature combination tree is constructed after feature correlation mining. Finally, the multisource app software interactive data are fused, and the result is further analyzed to obtain the information of app software UX. The experiments clearly show that the method can effectively fuse multisource app software interaction data and help to comprehensively mine the app software UX embodied in the data.
{"title":"Analysis Method of App Software User Experience Based on Multisource Information Fusion","authors":"Yongquan Chen, Ying Jiang, Haiyi Liu","doi":"10.4018/ijswis.325216","DOIUrl":"https://doi.org/10.4018/ijswis.325216","url":null,"abstract":"With the rapid development and popularization of intelligent terminals, app software has also developed rapidly. The research and practical value of mining user experience (UX) of app software form interaction information are becoming increasingly prominent. The interactive information of app software is multisource homogeneous and heterogeneous. In order to obtain more accurate and more comprehensive app software UX results, the fused multisource information should be analyzed. In this paper, the app software UX analysis method based on multisource information fusion is proposed. First, feature engineering is carried out to extract the features. Then, the feature combination tree is constructed after feature correlation mining. Finally, the multisource app software interactive data are fused, and the result is further analyzed to obtain the information of app software UX. The experiments clearly show that the method can effectively fuse multisource app software interaction data and help to comprehensively mine the app software UX embodied in the data.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"24 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80142359","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}
Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation across stages. In addition, previous studies did not correctly handle the situation where emotion clauses and cause clauses are the same clauses. To overcome these issues, the authors first use a multitask learning model that is based on graph from the perspective of sorting, which can simultaneously extract emotion clauses, cause clauses and emotion-cause pairs via an end-to-end strategy. Then the authors propose to convert text into graph structured data, and process this scenario through a unique graph convolutional neural network. Finally, the authors design a semantic decision mechanism to address the scenario in which there are multiple emotion-cause pairs in a text.
{"title":"Semantic Decision Internal-Attention Graph Convolutional Network for End-to-End Emotion-Cause Pair Extraction","authors":"Dianyuan Zhang, Zhenfang Zhu, Jiangtao Qi, Guangyuan Zhang, Linghui Zhong","doi":"10.4018/ijswis.325063","DOIUrl":"https://doi.org/10.4018/ijswis.325063","url":null,"abstract":"Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation across stages. In addition, previous studies did not correctly handle the situation where emotion clauses and cause clauses are the same clauses. To overcome these issues, the authors first use a multitask learning model that is based on graph from the perspective of sorting, which can simultaneously extract emotion clauses, cause clauses and emotion-cause pairs via an end-to-end strategy. Then the authors propose to convert text into graph structured data, and process this scenario through a unique graph convolutional neural network. Finally, the authors design a semantic decision mechanism to address the scenario in which there are multiple emotion-cause pairs in a text.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"7 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84335009","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}
Traditional landscape design methods rely entirely on the experience of designers and are difficult to adapt to the needs of modern society. This article proposes a landscape design method based on a distributed integrated model. Based on landscape design scheme data, the intelligent landscape design function is achieved by constructing a distributed geographic model, extracting features through data analysis and key point analysis, and using virtual environments in computer-aided design to display and restore the actual effects of landscape design. The results indicate that the landscape design method based on distributed integration mode is more in line with the needs of modern society and has significant advantages over traditional landscape design in terms of public interest and evaluation coefficient. The intelligent landscape design method based on distributed integrated models has important significance in modern urbanization construction, which can effectively improve the accuracy and speed of landscape design and create better living spaces for people.
{"title":"Research on Intelligent Landscape Design Based on Distributed Integrated Model","authors":"Xihui Tang","doi":"10.4018/ijswis.325002","DOIUrl":"https://doi.org/10.4018/ijswis.325002","url":null,"abstract":"Traditional landscape design methods rely entirely on the experience of designers and are difficult to adapt to the needs of modern society. This article proposes a landscape design method based on a distributed integrated model. Based on landscape design scheme data, the intelligent landscape design function is achieved by constructing a distributed geographic model, extracting features through data analysis and key point analysis, and using virtual environments in computer-aided design to display and restore the actual effects of landscape design. The results indicate that the landscape design method based on distributed integration mode is more in line with the needs of modern society and has significant advantages over traditional landscape design in terms of public interest and evaluation coefficient. The intelligent landscape design method based on distributed integrated models has important significance in modern urbanization construction, which can effectively improve the accuracy and speed of landscape design and create better living spaces for people.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"21 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77118519","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}
Meena Malik, C. Prabha, Punit Soni, Varsha Arya, Wadee Alhalabi, B. Gupta, A. Albeshri, Ammar Almomani
Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.
{"title":"Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities","authors":"Meena Malik, C. Prabha, Punit Soni, Varsha Arya, Wadee Alhalabi, B. Gupta, A. Albeshri, Ammar Almomani","doi":"10.4018/ijswis.324105","DOIUrl":"https://doi.org/10.4018/ijswis.324105","url":null,"abstract":"Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"2 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74948483","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}
Pu Li, Guohao Zhou, Zhilei Yin, Rui Chen, Suzhi Zhang
Discover the deep semantics from the massively structured data in knowledge graph and provide reasonable explanations are a series of important foundational research issues of artificial intelligence. However, the deep semantics hidden between entities in knowledge graph cannot be well expressed. Moreover, considering many predicates express fuzzy relationships, the existing reasoning methods cannot effectively deal with these fuzzy semantics and interpret the corresponding reasoning process. To counter the above problems, in this article, a new interpretable reasoning schema is proposed by introducing fuzzy theory. The presented method focuses on analyzing the fuzzy semantic between related entities in a knowledge graph. By annotating the fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantic extension over knowledge graph. The evaluation, based on both visualization and query experiments, shows that this proposal has advantages over the initial knowledge graph and can discover more valid semantic information.
{"title":"A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning","authors":"Pu Li, Guohao Zhou, Zhilei Yin, Rui Chen, Suzhi Zhang","doi":"10.4018/ijswis.323921","DOIUrl":"https://doi.org/10.4018/ijswis.323921","url":null,"abstract":"Discover the deep semantics from the massively structured data in knowledge graph and provide reasonable explanations are a series of important foundational research issues of artificial intelligence. However, the deep semantics hidden between entities in knowledge graph cannot be well expressed. Moreover, considering many predicates express fuzzy relationships, the existing reasoning methods cannot effectively deal with these fuzzy semantics and interpret the corresponding reasoning process. To counter the above problems, in this article, a new interpretable reasoning schema is proposed by introducing fuzzy theory. The presented method focuses on analyzing the fuzzy semantic between related entities in a knowledge graph. By annotating the fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantic extension over knowledge graph. The evaluation, based on both visualization and query experiments, shows that this proposal has advantages over the initial knowledge graph and can discover more valid semantic information.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"4 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88940184","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}
A citation is a reference to the source of information used in an article. Citations are very useful for students and researchers to locate relevant information on a topic. Proper citation is also important in the academic ethics of article writing. Due to the rapid growth of scientific works published each year, how to automatically recommend citations to students and researchers has become an interesting but challenging research problem. In particular, a citation recommendation system can assist students to identify relevant papers and literature for academic writing. Citation recommendation can be classified into local and global citation recommendation depending on whether a specific local citation context is given; e.g., the text surrounding a citation placeholder. This article provides a systematic review on global citation recommendation models and compares the reviewed methods from the traditional topic- based models to the recent models embedded with deep neural networks, aiming to summarize this field to facilitate researchers working on citation recommendation.
{"title":"A Systematic Review of Citation Recommendation Over the Past Two Decades","authors":"Yicong Liang, Lap-Kei Lee","doi":"10.4018/ijswis.324071","DOIUrl":"https://doi.org/10.4018/ijswis.324071","url":null,"abstract":"A citation is a reference to the source of information used in an article. Citations are very useful for students and researchers to locate relevant information on a topic. Proper citation is also important in the academic ethics of article writing. Due to the rapid growth of scientific works published each year, how to automatically recommend citations to students and researchers has become an interesting but challenging research problem. In particular, a citation recommendation system can assist students to identify relevant papers and literature for academic writing. Citation recommendation can be classified into local and global citation recommendation depending on whether a specific local citation context is given; e.g., the text surrounding a citation placeholder. This article provides a systematic review on global citation recommendation models and compares the reviewed methods from the traditional topic- based models to the recent models embedded with deep neural networks, aiming to summarize this field to facilitate researchers working on citation recommendation.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"50 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75433975","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}
Xueqiang Lv, Zhaonan Liu, Ying Zhao, Ge Xu, Xindong You
With the emergence of a large-scale pre-training model based on the transformer model, the effect of all-natural language processing tasks has been pushed to a new level. However, due to the high complexity of the transformer's self-attention mechanism, these models have poor processing ability for long text. Aiming at solving this problem, a long text processing method named HBert based on Bert and hierarchical attention neural network is proposed. Firstly, the long text is divided into multiple sentences whose vectors are obtained through the word encoder composed of Bert and the word attention layer. And the article vector is obtained through the sentence encoder that is composed of transformer and sentence attention. Then the article vector is used to complete the subsequent tasks. The experimental results show that the proposed HBert method achieves good results in text classification and QA tasks. The F1 value is 95.7% in longer text classification tasks and 75.2% in QA tasks, which are better than the state-of-the-art model longformer.
{"title":"HBert","authors":"Xueqiang Lv, Zhaonan Liu, Ying Zhao, Ge Xu, Xindong You","doi":"10.4018/ijswis.322769","DOIUrl":"https://doi.org/10.4018/ijswis.322769","url":null,"abstract":"With the emergence of a large-scale pre-training model based on the transformer model, the effect of all-natural language processing tasks has been pushed to a new level. However, due to the high complexity of the transformer's self-attention mechanism, these models have poor processing ability for long text. Aiming at solving this problem, a long text processing method named HBert based on Bert and hierarchical attention neural network is proposed. Firstly, the long text is divided into multiple sentences whose vectors are obtained through the word encoder composed of Bert and the word attention layer. And the article vector is obtained through the sentence encoder that is composed of transformer and sentence attention. Then the article vector is used to complete the subsequent tasks. The experimental results show that the proposed HBert method achieves good results in text classification and QA tasks. The F1 value is 95.7% in longer text classification tasks and 75.2% in QA tasks, which are better than the state-of-the-art model longformer.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"31 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76777743","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}
Many existing works compute the binary vulnerability similarity based on binary procedure, which has coarse detection granularity and cannot locate the vulnerability trigger position accurately, and have a higher false positive rate, so a new binary vulnerability similarity detection method based on function parameter dependency in hazard API is proposed. First, convert the instructions of different architectures into an intermediate language, and use the compiler with a back-end optimizer to optimize and normalize the binary procedure. Then, locate the hazard API that appears in the binary procedure, and perform the function parameters dependency analysis to generate a set of parameter slices on the hazard API. Experiments show that the method has a higher recall rate (up to 14.3% better than the baseline model) in real-world scenarios, and not only locates the triggering position of the vulnerability but also identifies the fixed vulnerability.
{"title":"Binary Vulnerability Similarity Detection Based on Function Parameter Dependency","authors":"Bing Xia, Wenbo Liu, Qudong He, Fudong Liu, Jianmin Pang, Ruinan Yang, Jiabin Yin, Yunxiang Ge","doi":"10.4018/ijswis.322392","DOIUrl":"https://doi.org/10.4018/ijswis.322392","url":null,"abstract":"Many existing works compute the binary vulnerability similarity based on binary procedure, which has coarse detection granularity and cannot locate the vulnerability trigger position accurately, and have a higher false positive rate, so a new binary vulnerability similarity detection method based on function parameter dependency in hazard API is proposed. First, convert the instructions of different architectures into an intermediate language, and use the compiler with a back-end optimizer to optimize and normalize the binary procedure. Then, locate the hazard API that appears in the binary procedure, and perform the function parameters dependency analysis to generate a set of parameter slices on the hazard API. Experiments show that the method has a higher recall rate (up to 14.3% better than the baseline model) in real-world scenarios, and not only locates the triggering position of the vulnerability but also identifies the fixed vulnerability.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"4 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91388039","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}
As one of the most widely used federated chains, hyperledger fabric uses many cryptographic algorithms to ensure the security of information on the chain, but the ECDSA cryptographic algorithm used in the fabric system has backdoor security risks. In this paper, the authors adopt SM2 algorithm to replace the corresponding ECDSA algorithm for blockchain design based on fabric platform. Firstly, they optimize the part of SM2 signature algorithm process with inverse operation and effectively reduce the time complexity by reducing the inverse operation in the whole process, and the experimental results show that the improved SM2 algorithm improves the signature and verification efficiency by about 5.7%. Secondly, by adding SM2 algorithm template and interface to the BCCSP module of fabric platform to realize the shift value of SM2 algorithm and compare the performance with the native fabric system, the network startup time is reduced by about 29%. The experimental results show the effectiveness of the improved SM2 algorithm, and also the performance of the optimized fabric system is improved.
{"title":"Fabric Blockchain Design Based on Improved SM2 Algorithm","authors":"Jinhua Fu, Wenhui Zhou, Suzhi Zhang","doi":"10.4018/ijswis.322403","DOIUrl":"https://doi.org/10.4018/ijswis.322403","url":null,"abstract":"As one of the most widely used federated chains, hyperledger fabric uses many cryptographic algorithms to ensure the security of information on the chain, but the ECDSA cryptographic algorithm used in the fabric system has backdoor security risks. In this paper, the authors adopt SM2 algorithm to replace the corresponding ECDSA algorithm for blockchain design based on fabric platform. Firstly, they optimize the part of SM2 signature algorithm process with inverse operation and effectively reduce the time complexity by reducing the inverse operation in the whole process, and the experimental results show that the improved SM2 algorithm improves the signature and verification efficiency by about 5.7%. Secondly, by adding SM2 algorithm template and interface to the BCCSP module of fabric platform to realize the shift value of SM2 algorithm and compare the performance with the native fabric system, the network startup time is reduced by about 29%. The experimental results show the effectiveness of the improved SM2 algorithm, and also the performance of the optimized fabric system is improved.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"190 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73738861","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}
Xinkun Tang, Ying Xu, Ouyang Feng, Ligu Zhu, Bo Peng
Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.
{"title":"A Cloud-Edge Collaborative Gaming Framework Using AI-Powered Foveated Rendering and Super Resolution","authors":"Xinkun Tang, Ying Xu, Ouyang Feng, Ligu Zhu, Bo Peng","doi":"10.4018/ijswis.321751","DOIUrl":"https://doi.org/10.4018/ijswis.321751","url":null,"abstract":"Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"10 4","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72593865","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}
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