The "Internet of Things" is changing the way companies operate and consumers behave. Th... more The "Internet of Things" is changing the way companies operate and consumers behave. Therefore, it is essential to capture trends in "Internet of Things". This paper proposes IoTViz, a visual analytics tool for analyzing "Internet of Things" news on social media. The principal aim of IoTViz is to observe the dynamic behavior of topics along with their proximity to other dimensions such as user comments and ratings in multiple coordinated views. IoTViz provides an interactive exploration of the IoT topics and supports of a range of interactive features, such as linking and filtering, allowing users to narrow down events of interest quickly. It is interesting to filter and visualize IoT news regarding the individual organization, e.g., user opinions/ratings regarding a company or its products.
Soil is an essential element of life, and soil properties are crucial in analyzing soil health. R... more Soil is an essential element of life, and soil properties are crucial in analyzing soil health. Recent developments of proximal sensor technologies, such as portable X-ray fluorescence (pXRF) spectroscopy or visible and nearinfrared (Vis-NIR) spectroscopy, offer rapid and non-destructive alternatives for quantifying data from soil profiles. While the data collection time using these technologies decreases significantly, the subsequent analysis remains time-consuming, and current analysis solutions only provide basic visualizations. Furthermore, the use of collected data from proximal sensors to predict high-level soil properties has garnered worldwide attention in the past decade, owing to its convenience. Therefore, this paper discusses the objectives for software solutions in this area, consolidated from interviewing 102 stakeholders. Following these requirements, data visualizers work closely with soil scientists to propose a set of interactive visualizations for analyzing soil profiles using pXRF data. These interactive visualizations receive positive feedback from the domain experts. This project also explores various machine learning and deep learning approaches to predict soil properties from spectral data. This work then proposes a deep learning model called RDNet that achieves state-of-the-art results in predicting pH H2O and pH KCl from Vis-NIR spectra acquired from a set of globally distributed soil samples.
2018 IEEE International Conference on Big Data (Big Data)
Cyber attacks cause great damage to our national security, ranging from individual internet user ... more Cyber attacks cause great damage to our national security, ranging from individual internet user to biggest governmental/industrial organizations, such as Equifax (Data Breach 145.5 Million Accounts, reported in July 2017) or Uber (Data Breach 57 Million Records, reported in November 2017). The cyber assault has significantly increased in breadth and depth. This paper introduces CVExplorer, a novel interactive system for visualizing cybersecurity threats reported in the National Vulnerability Database. The proposed system aims to work as a reporting and alerting tool that can help enhance the security against cyber attacks can potentially reduce network vulnerabilities. The CVExplorer system containing multiple linked views allows users to visualize the relationships of various dimensions in the large number of vulnerability reports, such as types and levels of vulnerability, vendors, and products. The CVExplorer provides an intuitive interface and supports a range of interactive features, such as filtering and ordering by vulnerability severity ratings, allowing users to narrow down topics of interest quickly. To demonstrate the effectiveness of the proposed system, we demonstrate the CVExplorer on two case studies of Common Vulnerabilities and Exposures retrieved from the National Vulnerability Database.
Soil is an essential element of life, and soil properties are crucial in analyzing soil health. R... more Soil is an essential element of life, and soil properties are crucial in analyzing soil health. Recent developments of proximal sensor technologies, such as portable X-ray fluorescence (pXRF) spectroscopy or visible and nearinfrared (Vis-NIR) spectroscopy, offer rapid and non-destructive alternatives for quantifying data from soil profiles. While the data collection time using these technologies decreases significantly, the subsequent analysis remains time-consuming, and current analysis solutions only provide basic visualizations. Furthermore, the use of collected data from proximal sensors to predict high-level soil properties has garnered worldwide attention in the past decade, owing to its convenience. Therefore, this paper discusses the objectives for software solutions in this area, consolidated from interviewing 102 stakeholders. Following these requirements, data visualizers work closely with soil scientists to propose a set of interactive visualizations for analyzing soil profiles using pXRF data. These interactive visualizations receive positive feedback from the domain experts. This project also explores various machine learning and deep learning approaches to predict soil properties from spectral data. This work then proposes a deep learning model called RDNet that achieves state-of-the-art results in predicting pH H2O and pH KCl from Vis-NIR spectra acquired from a set of globally distributed soil samples.
The "Internet of Things" is changing the way companies operate and consumers behave. Th... more The "Internet of Things" is changing the way companies operate and consumers behave. Therefore, it is essential to capture trends in "Internet of Things". This paper proposes IoTViz, a visual analytics tool for analyzing "Internet of Things" news on social media. The principal aim of IoTViz is to observe the dynamic behavior of topics along with their proximity to other dimensions such as user comments and ratings in multiple coordinated views. IoTViz provides an interactive exploration of the IoT topics and supports of a range of interactive features, such as linking and filtering, allowing users to narrow down events of interest quickly. It is interesting to filter and visualize IoT news regarding the individual organization, e.g., user opinions/ratings regarding a company or its products.
Soil is an essential element of life, and soil properties are crucial in analyzing soil health. R... more Soil is an essential element of life, and soil properties are crucial in analyzing soil health. Recent developments of proximal sensor technologies, such as portable X-ray fluorescence (pXRF) spectroscopy or visible and nearinfrared (Vis-NIR) spectroscopy, offer rapid and non-destructive alternatives for quantifying data from soil profiles. While the data collection time using these technologies decreases significantly, the subsequent analysis remains time-consuming, and current analysis solutions only provide basic visualizations. Furthermore, the use of collected data from proximal sensors to predict high-level soil properties has garnered worldwide attention in the past decade, owing to its convenience. Therefore, this paper discusses the objectives for software solutions in this area, consolidated from interviewing 102 stakeholders. Following these requirements, data visualizers work closely with soil scientists to propose a set of interactive visualizations for analyzing soil profiles using pXRF data. These interactive visualizations receive positive feedback from the domain experts. This project also explores various machine learning and deep learning approaches to predict soil properties from spectral data. This work then proposes a deep learning model called RDNet that achieves state-of-the-art results in predicting pH H2O and pH KCl from Vis-NIR spectra acquired from a set of globally distributed soil samples.
2018 IEEE International Conference on Big Data (Big Data)
Cyber attacks cause great damage to our national security, ranging from individual internet user ... more Cyber attacks cause great damage to our national security, ranging from individual internet user to biggest governmental/industrial organizations, such as Equifax (Data Breach 145.5 Million Accounts, reported in July 2017) or Uber (Data Breach 57 Million Records, reported in November 2017). The cyber assault has significantly increased in breadth and depth. This paper introduces CVExplorer, a novel interactive system for visualizing cybersecurity threats reported in the National Vulnerability Database. The proposed system aims to work as a reporting and alerting tool that can help enhance the security against cyber attacks can potentially reduce network vulnerabilities. The CVExplorer system containing multiple linked views allows users to visualize the relationships of various dimensions in the large number of vulnerability reports, such as types and levels of vulnerability, vendors, and products. The CVExplorer provides an intuitive interface and supports a range of interactive features, such as filtering and ordering by vulnerability severity ratings, allowing users to narrow down topics of interest quickly. To demonstrate the effectiveness of the proposed system, we demonstrate the CVExplorer on two case studies of Common Vulnerabilities and Exposures retrieved from the National Vulnerability Database.
Soil is an essential element of life, and soil properties are crucial in analyzing soil health. R... more Soil is an essential element of life, and soil properties are crucial in analyzing soil health. Recent developments of proximal sensor technologies, such as portable X-ray fluorescence (pXRF) spectroscopy or visible and nearinfrared (Vis-NIR) spectroscopy, offer rapid and non-destructive alternatives for quantifying data from soil profiles. While the data collection time using these technologies decreases significantly, the subsequent analysis remains time-consuming, and current analysis solutions only provide basic visualizations. Furthermore, the use of collected data from proximal sensors to predict high-level soil properties has garnered worldwide attention in the past decade, owing to its convenience. Therefore, this paper discusses the objectives for software solutions in this area, consolidated from interviewing 102 stakeholders. Following these requirements, data visualizers work closely with soil scientists to propose a set of interactive visualizations for analyzing soil profiles using pXRF data. These interactive visualizations receive positive feedback from the domain experts. This project also explores various machine learning and deep learning approaches to predict soil properties from spectral data. This work then proposes a deep learning model called RDNet that achieves state-of-the-art results in predicting pH H2O and pH KCl from Vis-NIR spectra acquired from a set of globally distributed soil samples.
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Papers by Pham Van Vung