Jinmyeong Lee, S. Yoon, Beopyeon Kim, Hunyeong Kwon
Data is a strategic asset for digital transformation. National innovation based on data has become a matter of global competition and survival. For pursuing a national innovation, it is important that the governance clearly defines the role and responsibility to lead innovation at the national level. In this regard, a national chief data officer (CDO) system has emerged recently as a new paradigm for national data innovation, mainly in the United States, the United Kingdom, and South Korea. This study employs a comparative approach to explaining the trends and common features of the national CDO system. The focal point of analysis is the legal base of CDO system, organization and governance, the required capability and authority of CDO, and its hiring process. Summing up, the study shows that an organization-wide awareness of the benefits of data innovation, a powerful authority to lead and coordinate regarding agencies, and a competent supporting organization are crucial to the successful operation of a CDO system.
{"title":"Considerations for the Effective National CDO Policy","authors":"Jinmyeong Lee, S. Yoon, Beopyeon Kim, Hunyeong Kwon","doi":"10.4018/ijbdia.315767","DOIUrl":"https://doi.org/10.4018/ijbdia.315767","url":null,"abstract":"Data is a strategic asset for digital transformation. National innovation based on data has become a matter of global competition and survival. For pursuing a national innovation, it is important that the governance clearly defines the role and responsibility to lead innovation at the national level. In this regard, a national chief data officer (CDO) system has emerged recently as a new paradigm for national data innovation, mainly in the United States, the United Kingdom, and South Korea. This study employs a comparative approach to explaining the trends and common features of the national CDO system. The focal point of analysis is the legal base of CDO system, organization and governance, the required capability and authority of CDO, and its hiring process. Summing up, the study shows that an organization-wide awareness of the benefits of data innovation, a powerful authority to lead and coordinate regarding agencies, and a competent supporting organization are crucial to the successful operation of a CDO system.","PeriodicalId":272065,"journal":{"name":"International Journal of Big Data Intelligence and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121341275","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}
In this paper, the authors have proposed a computationally efficient, robust, and lightweight system for gait recognition. The proposed system contains two main stages: In the first stage, a classification network identifies optical flow corners in the normalized silhouette and calculates the distances traveled in every viewpoint which is further used by a regression model to identify the viewing angle. In the second stage, a feature extraction network computes the Gait Energy Image (GEI) for every viewpoint and then uses Principal Component Analysis (PCA) to extract low dimensional feature vectors from these GEI images. Finally, a multi-layer perceptron model is trained using the extracted principal components for every viewing angle. The performance of a system is comprehensively evaluated on the CASIA B and OULP gait dataset. The experimental results demonstrate the superior performance of a proposed system in viewing angle classification (100% accuracy), gait recognition (100% accuracy in normal walk), computational efficiency, robustness to clothing, and viewing angle variation.
{"title":"A LIGHTWEIGHT SYSTEM TOWARDS VIEWING ANGLE AND CLOTHING VARIATION IN GAIT RECOGNITION","authors":"","doi":"10.4018/ijbdia.287616","DOIUrl":"https://doi.org/10.4018/ijbdia.287616","url":null,"abstract":"In this paper, the authors have proposed a computationally efficient, robust, and lightweight system for gait recognition. The proposed system contains two main stages: In the first stage, a classification network identifies optical flow corners in the normalized silhouette and calculates the distances traveled in every viewpoint which is further used by a regression model to identify the viewing angle. In the second stage, a feature extraction network computes the Gait Energy Image (GEI) for every viewpoint and then uses Principal Component Analysis (PCA) to extract low dimensional feature vectors from these GEI images. Finally, a multi-layer perceptron model is trained using the extracted principal components for every viewing angle. The performance of a system is comprehensively evaluated on the CASIA B and OULP gait dataset. The experimental results demonstrate the superior performance of a proposed system in viewing angle classification (100% accuracy), gait recognition (100% accuracy in normal walk), computational efficiency, robustness to clothing, and viewing angle variation.","PeriodicalId":272065,"journal":{"name":"International Journal of Big Data Intelligence and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128176379","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}
The focus of this work is on detecting and classifying attacks in network traffic using a binary as well as multi-class machine learning classifier, Random Forest, in a distributed Big Data environment using Apache Spark. The classifier is tested using the UNSW-NB15 dataset. Major problems in these types of datasets include high dimensionality and imbalanced data. To address the issue of high dimensionality, both Information Gain as well as Principal Components Analysis (PCA) were applied before training and testing the data using Random Forest in Apache Spark. Binary as well as multi-class Random Forest classifiers were compared in a distributed environment, with and without using PCA, using various number of Spark cores and Random Forest trees, in terms of performance time and statistical measures. The highest accuracy was obtained by the binary classifier at 99.94%, using 8 cores and 30 trees. This study obtained higher accuracy and lower FAR rates than previously achieved, with low testing times.
{"title":"Classifying UNSW-NB15 Network Traffic in the Big Data Framework using Random Forest in Spark","authors":"","doi":"10.4018/ijbdia.287617","DOIUrl":"https://doi.org/10.4018/ijbdia.287617","url":null,"abstract":"The focus of this work is on detecting and classifying attacks in network traffic using a binary as well as multi-class machine learning classifier, Random Forest, in a distributed Big Data environment using Apache Spark. The classifier is tested using the UNSW-NB15 dataset. Major problems in these types of datasets include high dimensionality and imbalanced data. To address the issue of high dimensionality, both Information Gain as well as Principal Components Analysis (PCA) were applied before training and testing the data using Random Forest in Apache Spark. Binary as well as multi-class Random Forest classifiers were compared in a distributed environment, with and without using PCA, using various number of Spark cores and Random Forest trees, in terms of performance time and statistical measures. The highest accuracy was obtained by the binary classifier at 99.94%, using 8 cores and 30 trees. This study obtained higher accuracy and lower FAR rates than previously achieved, with low testing times.","PeriodicalId":272065,"journal":{"name":"International Journal of Big Data Intelligence and Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131938389","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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