Deixy Ximena Ramos Rivadeneira, Javier Alejando Jiménez Toledo
In this article, we present a systematic review of the literature that explores the impact of Machine Learning as a teaching strategy in the educational field. Machine Learning, a branch of artificial intelligence, has gained relevance in teaching and learning due to its ability to personalize education and improve instructional effectiveness. The systematic review focuses on identifying studies investigating how Machine Learning has been used in educational settings. Through a thorough analysis, its impact on various areas related to teaching and learning, including student performance, knowledge retention, and curricular adaptability, is examined. The findings of this review indicate that Machine Learning has proven to be an effective strategy for tailoring instruction to individual student needs. As a result, engagement and academic performance are significantly improved. Furthermore, the review underscores the importance of future research. This future research will enable a deeper understanding of how Machine Learning can optimize education and address current challenges and emerging opportunities in this evolving field. This systematic review provides valuable information for educators, curriculum designers, and educational policymakers. It also emphasizes the continuing need to explore the potential of Machine Learning to enhance teaching and learning in the digital age of the 21st century.
{"title":"Machine learning as a teaching strategy education: A review","authors":"Deixy Ximena Ramos Rivadeneira, Javier Alejando Jiménez Toledo","doi":"10.4108/eetsis.5703","DOIUrl":"https://doi.org/10.4108/eetsis.5703","url":null,"abstract":"In this article, we present a systematic review of the literature that explores the impact of Machine Learning as a teaching strategy in the educational field. Machine Learning, a branch of artificial intelligence, has gained relevance in teaching and learning due to its ability to personalize education and improve instructional effectiveness. The systematic review focuses on identifying studies investigating how Machine Learning has been used in educational settings. Through a thorough analysis, its impact on various areas related to teaching and learning, including student performance, knowledge retention, and curricular adaptability, is examined. The findings of this review indicate that Machine Learning has proven to be an effective strategy for tailoring instruction to individual student needs. As a result, engagement and academic performance are significantly improved. Furthermore, the review underscores the importance of future research. This future research will enable a deeper understanding of how Machine Learning can optimize education and address current challenges and emerging opportunities in this evolving field. This systematic review provides valuable information for educators, curriculum designers, and educational policymakers. It also emphasizes the continuing need to explore the potential of Machine Learning to enhance teaching and learning in the digital age of the 21st century. ","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"104 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid evolution of network technologies, network attacks have become increasingly intricate and threatening. The escalating frequency of network intrusions has exerted a profound influence on both industrial settings and everyday activities. This underscores the urgent necessity for robust methods to detect malicious network traffic. While intrusion detection techniques employing Temporal Convolutional Networks (TCN) and Transformer architectures have exhibited commendable classification efficacy, most are confined to the temporal domain. These methods frequently fall short of encompassing the entirety of the frequency spectrum inherent in network data, thereby resulting in information loss. To mitigate this constraint, we present DTT, a novel dual-domain intrusion detection model that amalgamates TCN and Transformer architectures. DTT adeptly captures both high-frequency and low-frequency information, thereby facilitating the simultaneous extraction of local and global features. Specifically, we introduce a dual-domain feature extraction (DFE) block within the model. This block effectively extracts global frequency information and local temporal features through distinct branches, ensuring a comprehensive representation of the data. Moreover, we introduce an input encoding mechanism to transform the input into a format suitable for model training. Experiments conducted on two distinct datasets address concerns regarding data duplication and diverse attack types, respectively. Comparative experiments with recent intrusion detection models unequivocally demonstrate the superior performance of the proposed DTT model.
{"title":"DTT: A Dual-domain Transformer model for Network Intrusion Detection","authors":"Chenjian Xu, Weirui Sun, Mengxue Li","doi":"10.4108/eetsis.5445","DOIUrl":"https://doi.org/10.4108/eetsis.5445","url":null,"abstract":"With the rapid evolution of network technologies, network attacks have become increasingly intricate and threatening. The escalating frequency of network intrusions has exerted a profound influence on both industrial settings and everyday activities. This underscores the urgent necessity for robust methods to detect malicious network traffic. While intrusion detection techniques employing Temporal Convolutional Networks (TCN) and Transformer architectures have exhibited commendable classification efficacy, most are confined to the temporal domain. These methods frequently fall short of encompassing the entirety of the frequency spectrum inherent in network data, thereby resulting in information loss. To mitigate this constraint, we present DTT, a novel dual-domain intrusion detection model that amalgamates TCN and Transformer architectures. DTT adeptly captures both high-frequency and low-frequency information, thereby facilitating the simultaneous extraction of local and global features. Specifically, we introduce a dual-domain feature extraction (DFE) block within the model. This block effectively extracts global frequency information and local temporal features through distinct branches, ensuring a comprehensive representation of the data. Moreover, we introduce an input encoding mechanism to transform the input into a format suitable for model training. Experiments conducted on two distinct datasets address concerns regarding data duplication and diverse attack types, respectively. Comparative experiments with recent intrusion detection models unequivocally demonstrate the superior performance of the proposed DTT model.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141007803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.
{"title":"Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM","authors":"Yujie Guo","doi":"10.4108/eetsis.5771","DOIUrl":"https://doi.org/10.4108/eetsis.5771","url":null,"abstract":"Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"70 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: With the rapid development of the economy, more and more devices and sensors are connected to the Internet, and a large amount of data is transmitted in the network. However, this large-scale data transmission involves the problem of information security, especially in the transport layer. Therefore, there is an urgent need to study and design an information security data enhancement security strategy for the transport layer of ubiquitous networks (i.e., IoT). OBJECTIVES: This thesis aims to research and create a data enhancement security strategy for the transport layer of the Ubiquitous Web to ensure the confidentiality and integrity of data transmitted in the Ubiquitous Web. Specific objectives include evaluating the advantages and disadvantages of current ubiquitous network transport layer lifting security techniques, proposing a new lifting security strategy applicable to the transport layer of ubiquitous networks, and verifying the feasibility and security of the proposed standard.METHODS: First, a detailed study and evaluation of the current Ubiquitous Network Transport Layer Elevated Security Techniques is conducted, including analyzing and comparing the existing elevated security algorithms and protocols. Then, based on the obtained research results, a new lifting security strategy applicable to the transport layer of ubiquitous networks is proposed. The design process takes into account the characteristics and requirements of ubiquitous networks, such as resource constraints, dynamics of network topology, and cooperative communication of multiple devices. Subsequently, the feasibility and security of the proposed standard are verified through simulations and experiments. In the experiments, real ubiquitous network devices and network environments are used to evaluate the performance and attack resistance of the enhanced security algorithms.RESULTS: Through the research and analysis of ubiquitous network transport layer lifting security techniques, some limitations of the existing lifting security algorithms are identified, such as high resource consumption, insufficient security, and limited ability to adapt to the characteristics of ubiquitous networks. Therefore, this thesis proposes a new lifting security strategy applicable to the transport layer of ubiquitous networks. The experimental results show that the standard can guarantee data confidentiality and integrity while possessing high efficiency and attack resistance. In addition, the proposed standard meets the needs of resource-constrained devices in ubiquitous networks and can operate properly under multiple network topologies and cooperative device communications.CONCLUSION: This thesis proposes a new elevated security strategy applicable to ubiquitous networks through the study and design of transport layer elevated security techniques for ubiquitous networks. This standard can effectively protect the confidentiality and integrity of data transmitted in ubiquito
{"title":"Research and Design of Encryption Standards Based on IoT Network Layer Information Security of Data","authors":"Jia Wang","doi":"10.4108/eetsis.5826","DOIUrl":"https://doi.org/10.4108/eetsis.5826","url":null,"abstract":"INTRODUCTION: With the rapid development of the economy, more and more devices and sensors are connected to the Internet, and a large amount of data is transmitted in the network. However, this large-scale data transmission involves the problem of information security, especially in the transport layer. Therefore, there is an urgent need to study and design an information security data enhancement security strategy for the transport layer of ubiquitous networks (i.e., IoT). OBJECTIVES: This thesis aims to research and create a data enhancement security strategy for the transport layer of the Ubiquitous Web to ensure the confidentiality and integrity of data transmitted in the Ubiquitous Web. Specific objectives include evaluating the advantages and disadvantages of current ubiquitous network transport layer lifting security techniques, proposing a new lifting security strategy applicable to the transport layer of ubiquitous networks, and verifying the feasibility and security of the proposed standard.METHODS: First, a detailed study and evaluation of the current Ubiquitous Network Transport Layer Elevated Security Techniques is conducted, including analyzing and comparing the existing elevated security algorithms and protocols. Then, based on the obtained research results, a new lifting security strategy applicable to the transport layer of ubiquitous networks is proposed. The design process takes into account the characteristics and requirements of ubiquitous networks, such as resource constraints, dynamics of network topology, and cooperative communication of multiple devices. Subsequently, the feasibility and security of the proposed standard are verified through simulations and experiments. In the experiments, real ubiquitous network devices and network environments are used to evaluate the performance and attack resistance of the enhanced security algorithms.RESULTS: Through the research and analysis of ubiquitous network transport layer lifting security techniques, some limitations of the existing lifting security algorithms are identified, such as high resource consumption, insufficient security, and limited ability to adapt to the characteristics of ubiquitous networks. Therefore, this thesis proposes a new lifting security strategy applicable to the transport layer of ubiquitous networks. The experimental results show that the standard can guarantee data confidentiality and integrity while possessing high efficiency and attack resistance. In addition, the proposed standard meets the needs of resource-constrained devices in ubiquitous networks and can operate properly under multiple network topologies and cooperative device communications.CONCLUSION: This thesis proposes a new elevated security strategy applicable to ubiquitous networks through the study and design of transport layer elevated security techniques for ubiquitous networks. This standard can effectively protect the confidentiality and integrity of data transmitted in ubiquito","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"70 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability.
{"title":"Analysis of Employment Competitiveness of College Students Based on Binary Association Rule Extraction Algorithm","authors":"Lixia Guo","doi":"10.4108/eetsis.5765","DOIUrl":"https://doi.org/10.4108/eetsis.5765","url":null,"abstract":" Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141018653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of political ideology teaching evaluation. Aiming at the problem that the current political ideology examination paper-grouping strategy method easily falls into the local optimum, a kind of intelligent paper-grouping method for political ideology examination based on the improved stock market trading optimisation algorithm is proposed. Firstly, by analyzing the traditional steps of political thought grouping, according to the index genus of the grouping problem and the condition constraints, we construct the grouping model of political thought test questions; then, combining the segmented real number coding method and the fitness function, we use the securities market trading optimization algorithm based on the Circle chaotic mapping initialization strategy and adaptive t-distribution variability strategy to solve the grouping problem of the political thought test. The experimental results show that the method can effectively find the optimal strategy of political thought exam grouping, and the test questions have higher knowledge point coverage, moderate difficulty, and more stable performance.
{"title":"Design of Intelligent Political Test Paper Generation Method Based on Improved Intelligent Optimization Algorithm","authors":"Qing Wan","doi":"10.4108/eetsis.5862","DOIUrl":"https://doi.org/10.4108/eetsis.5862","url":null,"abstract":"With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of political ideology teaching evaluation. Aiming at the problem that the current political ideology examination paper-grouping strategy method easily falls into the local optimum, a kind of intelligent paper-grouping method for political ideology examination based on the improved stock market trading optimisation algorithm is proposed. Firstly, by analyzing the traditional steps of political thought grouping, according to the index genus of the grouping problem and the condition constraints, we construct the grouping model of political thought test questions; then, combining the segmented real number coding method and the fitness function, we use the securities market trading optimization algorithm based on the Circle chaotic mapping initialization strategy and adaptive t-distribution variability strategy to solve the grouping problem of the political thought test. The experimental results show that the method can effectively find the optimal strategy of political thought exam grouping, and the test questions have higher knowledge point coverage, moderate difficulty, and more stable performance.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"78 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141021359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.
{"title":"Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests","authors":"Xiaoyan Wang","doi":"10.4108/eetsis.5785","DOIUrl":"https://doi.org/10.4108/eetsis.5785","url":null,"abstract":"INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system. ","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"21 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141022712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: With the continuous progress of urbanization, people's perceptions and experiences of the urban environment are increasingly concerned. Traditional forms of artistic expression can no longer fully meet people's needs for urban perception. Therefore, it is especially important to explore new possibilities of urban perception art with the help of modern technology, especially intelligent technology.OBJECTIVES: The main purpose of this study is to explore the feasibility and effectiveness of utilizing advanced technology for urban perception art expression. Through an in-depth understanding of the urban environment and the perceptual needs of urban residents, as well as existing technological means, artistic expressions that can present urban perceptions more intuitively and vividly are developed.METHODS: This study adopts a combination of field research and art practice. Through urban observation and questionnaire surveys, the subjective experience and needs of urban residents for urban perception were collected. Then, using digital painting and video technology, combined with the principles of perception psychology, urban perception works with artistic and technological senses were designed.RESULTS: A series of urban perception artworks were designed in this study, covering all aspects of urban life, including architectural landscapes, transportation scenes, and humanistic customs. These works enable viewers to perceive the urban environment in a more intuitive and immersive way through digital painting and video technology, as well as real-time data and perceptual feedback.CONCLUSION: By exploring new ways of artistic expression of urban perception, this study provides urban residents with a richer and deeper experience of urban perception. The application of digital painting and video technology, as well as the interaction and feedback with urban residents, opens up new possibilities for the development of urban perceptual art.
{"title":"Realization of Urban Perception Art: Painting Expressions of Internet of Things Technologies in Urban Environments","authors":"Hong Zhu, Lu Yao","doi":"10.4108/eetsis.5713","DOIUrl":"https://doi.org/10.4108/eetsis.5713","url":null,"abstract":"INTRODUCTION: With the continuous progress of urbanization, people's perceptions and experiences of the urban environment are increasingly concerned. Traditional forms of artistic expression can no longer fully meet people's needs for urban perception. Therefore, it is especially important to explore new possibilities of urban perception art with the help of modern technology, especially intelligent technology.OBJECTIVES: The main purpose of this study is to explore the feasibility and effectiveness of utilizing advanced technology for urban perception art expression. Through an in-depth understanding of the urban environment and the perceptual needs of urban residents, as well as existing technological means, artistic expressions that can present urban perceptions more intuitively and vividly are developed.METHODS: This study adopts a combination of field research and art practice. Through urban observation and questionnaire surveys, the subjective experience and needs of urban residents for urban perception were collected. Then, using digital painting and video technology, combined with the principles of perception psychology, urban perception works with artistic and technological senses were designed.RESULTS: A series of urban perception artworks were designed in this study, covering all aspects of urban life, including architectural landscapes, transportation scenes, and humanistic customs. These works enable viewers to perceive the urban environment in a more intuitive and immersive way through digital painting and video technology, as well as real-time data and perceptual feedback.CONCLUSION: By exploring new ways of artistic expression of urban perception, this study provides urban residents with a richer and deeper experience of urban perception. The application of digital painting and video technology, as well as the interaction and feedback with urban residents, opens up new possibilities for the development of urban perceptual art. ","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141021736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article aims to explore the learning characteristics of online learners within the smart education framework, with a specific emphasis on how they might use Internet of Things (IoT) technologies to improve their educational experience. The term "online learning" refers to the process of acquiring knowledge via electronic means, most often the global web. Online education, e-learning, web-based learning, and computer-assisted learning all share this term. The challenging characteristics of such online learners for students are technical issues, lack of motivation, and slow loading times in online courses. Hence, in this research, the Internet of Things-empowered Smart Education (IoT-SE) Framework has been improved for online learners for students by leveraging IoT tech that tracks how learners interact with learning resources and their environment. This paper aims to revolutionize web-based education through tailored instructions targeting individuals' unique needs and fads as availed by the IoT-SE system. This paper offers evaluation parameters such as level of engagement among learners, retention rates on knowledge acquired while studying e-courses, and satisfaction from an online program. Besides overcoming limitations associated with conventional e-learning approaches, such systems like IoT-SE technology promise more effective pedagogy and student satisfaction for online learners.
{"title":"Analysis of Learning Characteristics of Online Learners in the Context of Smart Education","authors":"Weihua Weihua","doi":"10.4108/eetsis.5764","DOIUrl":"https://doi.org/10.4108/eetsis.5764","url":null,"abstract":" This article aims to explore the learning characteristics of online learners within the smart education framework, with a specific emphasis on how they might use Internet of Things (IoT) technologies to improve their educational experience. The term \"online learning\" refers to the process of acquiring knowledge via electronic means, most often the global web. Online education, e-learning, web-based learning, and computer-assisted learning all share this term. The challenging characteristics of such online learners for students are technical issues, lack of motivation, and slow loading times in online courses. Hence, in this research, the Internet of Things-empowered Smart Education (IoT-SE) Framework has been improved for online learners for students by leveraging IoT tech that tracks how learners interact with learning resources and their environment. This paper aims to revolutionize web-based education through tailored instructions targeting individuals' unique needs and fads as availed by the IoT-SE system. This paper offers evaluation parameters such as level of engagement among learners, retention rates on knowledge acquired while studying e-courses, and satisfaction from an online program. Besides overcoming limitations associated with conventional e-learning approaches, such systems like IoT-SE technology promise more effective pedagogy and student satisfaction for online learners.","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"26 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141023137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: The rapid development of virtual reality technology and the Internet of Things (IoT) has provided new possibilities for user experience, and a variety of new products have emerged, especially in the field of painting, where the combination of these two provides a new platform for innovative artistic expression.OBJECTIVES: This study takes IoT products in the art field as an example to analyze the user experience in virtual reality environments and the impact of technological innovations on IoT products, as well as to explore the potentials and limitations of this emerging form of products and forms of painting.METHODS: In this study, the author constructed a virtual reality painting environment, utilized IoT technology to collect data from the user's painting process, and combined quantitative and qualitative analysis methods to assess user experience and technological innovation comprehensively.RESULTS: In the virtual reality environment, the user experience was significantly improved, and the users were more immersed in the painting process and felt more robust creativity and expression. Meanwhile, the application of Internet of Things (IoT) technology also provides more possibilities for drawing; for example, using smartpens makes the drawing process more smooth and natural.CONCLUSION: IoT painting with user experience and technological innovation in a virtual reality environment can provide a new creative platform for artists and bring a richer artistic experience to the audience, showing the feasibility and broad prospect of IoT products based on a virtual reality environment.
{"title":"IoT Product Design for User Experience and Technological Innovation in Virtual Reality Environments","authors":"Hao Zhang","doi":"10.4108/eetsis.5833","DOIUrl":"https://doi.org/10.4108/eetsis.5833","url":null,"abstract":"INTRODUCTION: The rapid development of virtual reality technology and the Internet of Things (IoT) has provided new possibilities for user experience, and a variety of new products have emerged, especially in the field of painting, where the combination of these two provides a new platform for innovative artistic expression.OBJECTIVES: This study takes IoT products in the art field as an example to analyze the user experience in virtual reality environments and the impact of technological innovations on IoT products, as well as to explore the potentials and limitations of this emerging form of products and forms of painting.METHODS: In this study, the author constructed a virtual reality painting environment, utilized IoT technology to collect data from the user's painting process, and combined quantitative and qualitative analysis methods to assess user experience and technological innovation comprehensively.RESULTS: In the virtual reality environment, the user experience was significantly improved, and the users were more immersed in the painting process and felt more robust creativity and expression. Meanwhile, the application of Internet of Things (IoT) technology also provides more possibilities for drawing; for example, using smartpens makes the drawing process more smooth and natural.CONCLUSION: IoT painting with user experience and technological innovation in a virtual reality environment can provide a new creative platform for artists and bring a richer artistic experience to the audience, showing the feasibility and broad prospect of IoT products based on a virtual reality environment. ","PeriodicalId":502678,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141022192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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