Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Mehrnezhad Informatics2019 6-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:What is this sensor and does this app need access to it?|What is this sensor and does this app need access to it?]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Mobile sensors have already proven to be helpful in different aspects of people’s everyday lives such as fitness, gaming, navigation, etc. However, illegitimate access to these sensors results in a malicious program running with an exploit path. While users are benefiting from richer and more personalized apps, the growing number of sensors introduces new security and privacy risks to end-users and makes the task of sensor management more complex. In this paper, we first discuss the issues around the security and privacy of mobile sensors. We investigate the available sensors on mainstream mobile devices and study the permission policies that Android, iOS and mobile web browsers offer for them. Second, we reflect on the results of two workshops that we organized on mobile sensor security. In these workshops, the participants were introduced to mobile sensors by working with sensor-enabled apps. We evaluated the risk levels perceived by the participants for these sensors after they understood the functionalities of these sensors. The results showed that knowing sensors by working with sensor-enabled apps would not immediately improve the users’ security inference of the actual risks of these sensors. However, other factors such as the prior general knowledge about these sensors and their risks had a strong impact on the users’ perception. ('''[[Journal:What is this sensor and does this app need access to it?|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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