Phishing is the practice of deceiving humans into disclosing sensitive information or inappropriately granting access to a secure system. Unfortunately, there is a severe lack of theoretical models to adequately explain and predict the cognitive dynamics underlying end-user susceptibility to phishing emails. This paper reports findings from an Instance-Based Learning (IBL) model developed to predict human response to emails obtained from a laboratory experiment. Particularly, this work investigates the effectiveness of using established natural language processing methods, such as LSA, GloVe, and BERT, to represent email text within IBL models. We found that using representations that consider contextual meanings assigned by humans could enable IBL agents to predict human response with high accuracy (80%). In addition, we found that traditional NLP methods that capture semantic meanings in natural language may not be effective at representing how people may encode and recall email messages. We discuss the implications of these findings.
Tuberculosis is a chronic respiratory infectious disease that seriously endangers human health. Diagnosis of pulmonary tuberculosis usually depend on the analysis of chest X-rays by radiologists. However, there is a certain misdiagnosis rate with time consuming. Therefore, the purpose of this study is to propose a low-cost and automatic detection method of pulmonary tuberculosis images on chest X-rays to help primary radiologists. A pulmonary tuberculosis classification algorithm based on convolution neural network is proposed, which uses deep learning to classify chest X-ray images. Our method introduces coordinate attention mechanism into convolutional neural network (VGG16), so that the algorithm can capture not only cross-channel information, but also direction sensing and position sensing information, in order to better identify and classify pulmonary tuberculosis images. During the training process, we use the method of transfer learning and freeze network to make the model fit faster. The performance of our method is evaluated on the public dataset of tuberculosis classification of Shenzhen Third Hospital, China. We take the average data through 5-fold cross validation: accuracy= 92.73%, AUC= 97.71%, precision= 92.73%, recall= 92.83%, F1 score = 92.82%. Compared with the existing end-to-end method based on convolutional neural network (CNN), our method is superior to ConvNet, FPN + Faster RCNN and other methods. The comparison results with other methods show that our method has better accuracy, which can help radiologists make auxiliary diagnosis.
Vehicle re-identification (Re-ID) is a critical component of the autonomous driving perception system, and research in this area has accelerated in recent years. However, there is yet no perfect solution to the vehicle re-identification issue associated with the car’s surround-view camera system. Our analysis identifies two significant issues in the aforementioned scenario: (1) It is difficult to identify the same vehicle in many picture frames due to the unique construction of the fisheye camera. (2) The appearance of the same vehicle when seen via the surround vision system’s several cameras is rather different. To overcome these issues, we suggest an integrative vehicle Re-ID solution method. On the one hand, we provide a technique for determining the consistency of the tracking box drift with respect to the target. On the other hand, we combine a Re-ID network based on the attention mechanism with spatial limitations to increase performance in situations involving multiple cameras. Finally, our approach combines state-of-the-art accuracy with real-time performance. We will soon make the source code and annotated fisheye dataset available.
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