Student affairs, which enhance student growth and development, have been playing more and more important roles at institutions of higher education. An efficient and automated management system for student affairs can ...
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Student affairs, which enhance student growth and development, have been playing more and more important roles at institutions of higher education. An efficient and automated management system for student affairs can advance student learning and development;foster community engagement;promote diversity, inclusion and respect;and empower students to thrive. With the rapid development of the Internet technology, more and more management systems for student affairs have been designed and implemented. However, few of them can provide efficient and automated services of student affairs. In this paper, we design an efficient and automated management system for student affairs at institutions of higher education. We divide the management system into four functional modules, i.e. login module, student module, practitioner(teacher) module and administrator module. Based on three-tier B/S architecture, we use Microsoft *** and SQL Server to implement the system.
With the increase in popularity of Portable Document Format(PDF) documents and increasing vulnerability of PDF users, effective detection of malicious PDF documents has become as a more and more significant issue. In ...
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With the increase in popularity of Portable Document Format(PDF) documents and increasing vulnerability of PDF users, effective detection of malicious PDF documents has become as a more and more significant issue. In this paper, we proposed a way to detect malicious PDF files by using semi-supervise learning method. Compare with previous studies, this method not only improve detection accuracy and generalization ability by combining with three different classifiers, but also effectively utilize the abundant unlabeled PDF files to retrain classifiers and update module by selecting the "useful" files from unlabeled test set.
Recently, deep convolutional neural networks(CNNs) have made great achievements, whether taken as features extractor or classifier, in particular for very high resolution(VHR) images classification task which is a key...
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Recently, deep convolutional neural networks(CNNs) have made great achievements, whether taken as features extractor or classifier, in particular for very high resolution(VHR) images classification task which is a key point in the remote sensing field. This work aims to improve the VHR image classification accuracy by exploiting the fusion of two pre-trained deep convolutional neural network models. In this paper, we propose to concatenate the features extracted from the last convolutional layer of each pre-trained deep convolutional neural network to get a long features vector which is fed into a fullyconnected layer and then perform a fine-tuning for a VHR image dataset classification. The experimental results are promising since they show that the fusion of two deep CNNs achieves better accuracy for the classification compared to the individual CNN models on the same dataset.
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