Many programming bugs can lead to privilege escalation, which is a major security concern. However, there are times when the concern proves to be a false positive.In a previous paper, "An Approach to Analyzing the Windows and Linux Security Models", a set of metrics was proposed to assess risks quantitatively [1]. However, with the risk quantified, there is still not a clearly defined way of distinguishing between the true and false positives on the continuum of security risks. An effective method needs to be developed to solve this problem.In this paper, a new set of qualitative metrics is proposed in order to draw a correct conclusion on the criticality of a privilege escalation case. This set of qualitative metrics works more effectively to answer this question. Two cases are examined to demonstrate how this set of qualitative metrics works. Through a comparison of these two cases, it is demonstrated that the question of true or false positive to privilege escalation can be answered correctly. Therefore, this is an effective solution in solving this different type of problems.
The study focused on the significance of facial expressions in pigs as a mode of communication for assessing their emotions, physical status, and intentions. To address the challenges of recognizing facial expressions due to the simple facial muscle group structure of pigs, a novel pig facial expression recognition model called CReToNeXt-YOLOv5 was proposed. Several improvements were made to enhance the accuracy and detection ability of the model. Firstly, the CIOU loss function was replaced with the EIOU loss function to optimize the training model and achieve more accurate regression. This change improved the overall performance of the model. Secondly, the model was equipped with the Coordinate Attention mechanism, which improved its sensitivity to expression features, making it more effective in recognizing facial expressions. Lastly, the CReToNeXt module was integrated into the model to enhance its detection capability for subtle expressions. The results demonstrated the effectiveness of the CReToNeXt-YOLOv5 model. It achieved a mean average an mAP of 89.4%, showing a significant improvement of 6.7% compared to the original YOLOv5 model. Overall, the experimental results confirmed the effectiveness of the optimized YOLOv5 model, CReToNeXt-YOLOv5, in accurately recognizing facial expressions in pigs.
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