Pub Date : 2022-04-08DOI: 10.4108/eai.8-4-2022.173800
Haiyan Yu, Jihong Wang
{"title":"RETRACTED: Modified dark channel prior based on multi-scale Retinex for power image defogging [EAI Endorsed Scal Inf Syst (2022), Online First]","authors":"Haiyan Yu, Jihong Wang","doi":"10.4108/eai.8-4-2022.173800","DOIUrl":"https://doi.org/10.4108/eai.8-4-2022.173800","url":null,"abstract":"","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79735352","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}
Pub Date : 2022-04-08DOI: 10.4108/eai.8-4-2022.173786
Xiaobin Guo
{"title":"RETRACTED: An automatic scoring method for Chinese-English spoken translation based on attention LSTM [EAI Endorsed Scal Inf Syst (2022), Online First]","authors":"Xiaobin Guo","doi":"10.4108/eai.8-4-2022.173786","DOIUrl":"https://doi.org/10.4108/eai.8-4-2022.173786","url":null,"abstract":"","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86330804","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}
Pub Date : 2022-04-08DOI: 10.4108/eai.8-4-2022.173797
Dianhuai Shen, X. Jiang, Lin Teng
{"title":"RETRACTED: A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement [EAI Endorsed Scal Inf Syst (2022), Online First]","authors":"Dianhuai Shen, X. Jiang, Lin Teng","doi":"10.4108/eai.8-4-2022.173797","DOIUrl":"https://doi.org/10.4108/eai.8-4-2022.173797","url":null,"abstract":"","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86441246","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}
Pub Date : 2022-04-04DOI: 10.4108/eai.4-4-2022.173780
D. Patil, T. Pattewar
Attackers continually foster new endeavours and attack strategies meant to keep away from safeguards. Many attacks have an effect on other malware or social engineering to collect consumer credentials that grant them get access to network and data. A network intrusion detection system (NIDS) is essential for network safety because it empowers to understand and react to malicious traffic. In this paper, we propose a feature selection and majority voting based solutions for detecting intrusions. A multi-model intrusion detection system is designed using Majority Voting approach. Our proposed approach was tested on a NSL-KDD benchmark dataset. The experimental results show that models based on Majority Voting and Chi-square features selection method achieved the best accuracy of 99.50% with error-rate of 0.501%, FPR of 0.005 and FNR of 0.005 using only 14 features.
{"title":"Majority Voting and Feature Selection Based Network Intrusion Detection System","authors":"D. Patil, T. Pattewar","doi":"10.4108/eai.4-4-2022.173780","DOIUrl":"https://doi.org/10.4108/eai.4-4-2022.173780","url":null,"abstract":"Attackers continually foster new endeavours and attack strategies meant to keep away from safeguards. Many attacks have an effect on other malware or social engineering to collect consumer credentials that grant them get access to network and data. A network intrusion detection system (NIDS) is essential for network safety because it empowers to understand and react to malicious traffic. In this paper, we propose a feature selection and majority voting based solutions for detecting intrusions. A multi-model intrusion detection system is designed using Majority Voting approach. Our proposed approach was tested on a NSL-KDD benchmark dataset. The experimental results show that models based on Majority Voting and Chi-square features selection method achieved the best accuracy of 99.50% with error-rate of 0.501%, FPR of 0.005 and FNR of 0.005 using only 14 features.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74869487","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}
Pub Date : 2022-04-04DOI: 10.4108/eai.4-4-2022.173782
Raziya Siddiqui, N. Khan, S. Ahmad
Strategic management of information technology (IT) requires the attention provided to internal and external organizational factors. This paper discusses different enabling factors that allow strategic management of IT, making advances not only for using the approaches independently as well as in using them in a corresponding and adaptive way. A questionnaire-based survey and in-depth discussions were performed with 40 primary stakeholders to assess the relevance of enabling factors. Using available resources-based analysis, enabling factors were defined in four different categories: organizational, business, technological, and operational assessment. Subsequently, these four enabling factors were prioritized using the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision analysis method. Finally, technological assessments were given high priority on the basis of the findings to allow more successful strategic IT management.
信息技术(IT)的战略管理需要关注内部和外部组织因素。本文讨论了允许IT战略管理的不同启用因素，不仅在独立使用这些方法以及以相应和自适应的方式使用它们方面取得了进展。对40个主要利益攸关方进行了基于问卷的调查和深入讨论，以评估促成因素的相关性。使用可用的基于资源的分析，将启用因素定义为四个不同的类别:组织、业务、技术和操作评估。随后，采用模糊TOPSIS (Similarity to Ideal Solution)多准则决策分析法对这4个使能因素进行排序。最后，根据调查结果给予技术评估以高度优先级，以允许更成功的战略IT管理。
{"title":"A Fuzzy TOPSIS Based Analysis to Prioritize Enabling Factors for Strategic Information Technology Management","authors":"Raziya Siddiqui, N. Khan, S. Ahmad","doi":"10.4108/eai.4-4-2022.173782","DOIUrl":"https://doi.org/10.4108/eai.4-4-2022.173782","url":null,"abstract":"Strategic management of information technology (IT) requires the attention provided to internal and external organizational factors. This paper discusses different enabling factors that allow strategic management of IT, making advances not only for using the approaches independently as well as in using them in a corresponding and adaptive way. A questionnaire-based survey and in-depth discussions were performed with 40 primary stakeholders to assess the relevance of enabling factors. Using available resources-based analysis, enabling factors were defined in four different categories: organizational, business, technological, and operational assessment. Subsequently, these four enabling factors were prioritized using the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision analysis method. Finally, technological assessments were given high priority on the basis of the findings to allow more successful strategic IT management.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79332629","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}
Pub Date : 2022-04-04DOI: 10.4108/eai.4-4-2022.173781
N. Venkateswaran, S. Prabaharan
As of late mobile ad hoc networks (MANETs) have turned into a very popular explore the theme. By giving interchanges without a fixed infrastructure MANETs are an appealing innovation for some applications, for ex, reassigning tasks, strategic activities, nature observing, meetings, & so forth. This paper proposes the use of a neuro Deep learning wireless intrusion detection system that distinguishes the attacks in MANETs. Executing security is a hard task in MANET due to its immutable vulnerabilities. Deep learning gives extra security to such systems and the proposed framework comprises a hybrid conspiracy that joins the determination and abnormality-based methodologies. Executing the partial IDS utilizing neuro Deep learning improves the identification rate in MANETs. The proposed plan utilizes deep neural networks and a cross breed neural system. It demonstrates that Recurrent neural networks can successfully improve the identification and diminish the rate of false caution and failure.
{"title":"An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks","authors":"N. Venkateswaran, S. Prabaharan","doi":"10.4108/eai.4-4-2022.173781","DOIUrl":"https://doi.org/10.4108/eai.4-4-2022.173781","url":null,"abstract":"As of late mobile ad hoc networks (MANETs) have turned into a very popular explore the theme. By giving interchanges without a fixed infrastructure MANETs are an appealing innovation for some applications, for ex, reassigning tasks, strategic activities, nature observing, meetings, & so forth. This paper proposes the use of a neuro Deep learning wireless intrusion detection system that distinguishes the attacks in MANETs. Executing security is a hard task in MANET due to its immutable vulnerabilities. Deep learning gives extra security to such systems and the proposed framework comprises a hybrid conspiracy that joins the determination and abnormality-based methodologies. Executing the partial IDS utilizing neuro Deep learning improves the identification rate in MANETs. The proposed plan utilizes deep neural networks and a cross breed neural system. It demonstrates that Recurrent neural networks can successfully improve the identification and diminish the rate of false caution and failure.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84981617","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}
Pub Date : 2022-03-02DOI: 10.4108/eai.2-3-2022.173548
Fadi Mohsen, C. Zwart, D. Karastoyanova, G. Gaydadjiev
In an e ff ort to examine the spread of large-scale cyber attacks, researchers have created various taxonomies. These taxonomies are purposefully built to facilitate the understanding and the comparison of these attacks , and hence counter their spread. Yet, existing taxonomies focus mainly on the technical aspects of the attacks, with little or no information about how to defend against them. As such, the aim of this work is to extend existing taxonomies by incorporating new features pertaining the defense strategy, scale, and others. We will compare the proposed taxonomy with existing state of the art taxonomies. We also present the analysis of 174 large cyber security attacks based on our taxonomy. Finally, we present a web tool that we developed to allow researchers to explore exiting data sets of attacks and contribute new ones. We are convinced that our work will allow researchers gain deeper insights into emerging attacks by facilitating their categorization, sharing and analysis, which results in boosting the defense e ff orts against cyber attack.
{"title":"A Taxonomy for Large-Scale Cyber Security Attacks","authors":"Fadi Mohsen, C. Zwart, D. Karastoyanova, G. Gaydadjiev","doi":"10.4108/eai.2-3-2022.173548","DOIUrl":"https://doi.org/10.4108/eai.2-3-2022.173548","url":null,"abstract":"In an e ff ort to examine the spread of large-scale cyber attacks, researchers have created various taxonomies. These taxonomies are purposefully built to facilitate the understanding and the comparison of these attacks , and hence counter their spread. Yet, existing taxonomies focus mainly on the technical aspects of the attacks, with little or no information about how to defend against them. As such, the aim of this work is to extend existing taxonomies by incorporating new features pertaining the defense strategy, scale, and others. We will compare the proposed taxonomy with existing state of the art taxonomies. We also present the analysis of 174 large cyber security attacks based on our taxonomy. Finally, we present a web tool that we developed to allow researchers to explore exiting data sets of attacks and contribute new ones. We are convinced that our work will allow researchers gain deeper insights into emerging attacks by facilitating their categorization, sharing and analysis, which results in boosting the defense e ff orts against cyber attack.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72478753","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}
Pub Date : 2022-02-17DOI: 10.4108/eai.17-2-2022.173456
Yangzi He, Jiawen Zhu, Weina Fu
INTRODUCTION: "Improving the employment rate of college students" directly affects the stability of the country and society and the healthy development of the industry market. The traditional graduate employment rate model only predicts the future employment rate based on changes in historical employment data in previous years. OBJECTIVES: Quantify the employment factors and solve the employment problems in colleges and universities in a targeted manner. METHODS: We construct a credible employment prediction model for college graduates based on LightGBM. RESULTS: We use the model to predict the employment status of students and obtain the special importance which is important to employment of college students . CONCLUSION: The final result shows that our Model performs well in the two indicators of accuracy and model quality.
{"title":"A credible predictive model for employment of college graduates based on LightGBM","authors":"Yangzi He, Jiawen Zhu, Weina Fu","doi":"10.4108/eai.17-2-2022.173456","DOIUrl":"https://doi.org/10.4108/eai.17-2-2022.173456","url":null,"abstract":"INTRODUCTION: \"Improving the employment rate of college students\" directly affects the stability of the country and society and the healthy development of the industry market. The traditional graduate employment rate model only predicts the future employment rate based on changes in historical employment data in previous years. OBJECTIVES: Quantify the employment factors and solve the employment problems in colleges and universities in a targeted manner. METHODS: We construct a credible employment prediction model for college graduates based on LightGBM. RESULTS: We use the model to predict the employment status of students and obtain the special importance which is important to employment of college students . CONCLUSION: The final result shows that our Model performs well in the two indicators of accuracy and model quality.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87357755","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}
Pub Date : 2022-02-11DOI: 10.4108/eai.11-2-2022.173451
K. Liang, Xiao-qing Cai, Lu Hui
Aiming at the problems of poor evaluation effect and long system response time in the existing project cost course quality evaluation system, a project cost education course quality evaluation system based on data clustering is designed. The data acquisition module of infrastructure layer is used to collect the quality evaluation data of engineering cost education course, and the collected data is transmitted to the upper computer by can communication module. The processor control module in the upper computer transmits the data to the course quality evaluation module, and the processor control module selects 32-bit fixed-point chip TMS320F2812; After receiving the data, the course quality evaluation module uses the fuzzy matter-element proximity clustering evaluation method in data mining to evaluate the quality of engineering cost education courses. The evaluation results are transmitted to the application layer for users to use, and the evaluation results are displayed to users through the display interface of the display layer to complete the system design. The experimental results show that the proposed system can complete the quality evaluation of engineering cost education course, the response time of system evaluation is controlled within 400ms, and the response efficiency of the system is improved.
{"title":"Quality evaluation system of engineering cost education curriculum based on data clustering","authors":"K. Liang, Xiao-qing Cai, Lu Hui","doi":"10.4108/eai.11-2-2022.173451","DOIUrl":"https://doi.org/10.4108/eai.11-2-2022.173451","url":null,"abstract":"Aiming at the problems of poor evaluation effect and long system response time in the existing project cost course quality evaluation system, a project cost education course quality evaluation system based on data clustering is designed. The data acquisition module of infrastructure layer is used to collect the quality evaluation data of engineering cost education course, and the collected data is transmitted to the upper computer by can communication module. The processor control module in the upper computer transmits the data to the course quality evaluation module, and the processor control module selects 32-bit fixed-point chip TMS320F2812; After receiving the data, the course quality evaluation module uses the fuzzy matter-element proximity clustering evaluation method in data mining to evaluate the quality of engineering cost education courses. The evaluation results are transmitted to the application layer for users to use, and the evaluation results are displayed to users through the display interface of the display layer to complete the system design. The experimental results show that the proposed system can complete the quality evaluation of engineering cost education course, the response time of system evaluation is controlled within 400ms, and the response efficiency of the system is improved.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85792088","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}
Pub Date : 2022-02-11DOI: 10.4108/eai.11-2-2022.173450
Hua Zhihan, Liang Yuan, Tao Jin
In order to improve the input accuracy and response speed of music training, this paper designs an intelligent assistant system. The architecture is divided into infrastructure layer, data layer, application layer and presentation layer. In the hardware design, the combination of ARM and digital signal processor (DSP) is used to realize the interaction between data analysis and human and interface. In the software design, cepstrum algorithm is used to extract cepstrum features of music signals, linear smoothing algorithm is used to filter, dynamic time warping method is used to match patterns, and radial basis function algorithm is used to output the results. Thus, the overall design of the music-assisted training system is completed. Experimental results show that the signal-to-noise ratio of music signal transmission is more than 14dB, the accuracy is higher than 99.5%, and the response speed of serving 240 users is only 1s. The system has strong operability and good performance of music assistant training.
{"title":"Design of music training assistant system based on artificial intelligence","authors":"Hua Zhihan, Liang Yuan, Tao Jin","doi":"10.4108/eai.11-2-2022.173450","DOIUrl":"https://doi.org/10.4108/eai.11-2-2022.173450","url":null,"abstract":"In order to improve the input accuracy and response speed of music training, this paper designs an intelligent assistant system. The architecture is divided into infrastructure layer, data layer, application layer and presentation layer. In the hardware design, the combination of ARM and digital signal processor (DSP) is used to realize the interaction between data analysis and human and interface. In the software design, cepstrum algorithm is used to extract cepstrum features of music signals, linear smoothing algorithm is used to filter, dynamic time warping method is used to match patterns, and radial basis function algorithm is used to output the results. Thus, the overall design of the music-assisted training system is completed. Experimental results show that the signal-to-noise ratio of music signal transmission is more than 14dB, the accuracy is higher than 99.5%, and the response speed of serving 240 users is only 1s. The system has strong operability and good performance of music assistant training.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85201528","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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