Design and Build an Assessment Platform by Inserting Moodle-Based Cryptographic Methods

The use of digital platforms in the learning process is increasing, especially in the context of assessment activities. In this context, it is essential to realize that digital platforms can be subject to attack or fraud by irresponsible parties. It is due to the presence of sensitive data and/or restricted to a limited number of authorized persons. Therefore, the protection of data and its security in using digital platforms is very important. To enhance this layer of security in data protection, cryptographic methods play a crucial role in maintaining information security. By applying cryptographic methods to learning platforms for assessment purposes, we can increase the security and integrity of the data involved in the assessment process. This research aims to produce a plugin that can be used on a Moodle-based Learning Management System (LMS). This plugin will provide an additional activity in the form of an assessment activity with an essay exam type. When this plugin is used, all questions and answers will be encrypted into text that is difficult to understand by unauthorized parties when an attack attempt occurs. In this way, the learning platform for assessment purposes can safeguard and protect data from access by irresponsible parties

End-To-End Evaluation of Deep Learning Architectures for Off-Line Handwriting Writer Identification: A Comparative Study

Identifying writers using their handwriting is particularly challenging for a machine, given that a person’s writing can serve as their distinguishing characteristic. The process of identification using handcrafted features has shown promising results, but the intra-class variability between authors still needs further development. Almost all computer vision-related tasks use Deep learning (DL) nowadays, and as a result, researchers are developing many DL architectures with their respective methods. In addition, feature extraction, usually accomplished using handcrafted algorithms, can now be automatically conducted using convolutional neural networks. With the various developments of the DL method, it is necessary to evaluate the suitable DL for the problem we are aiming at, namely the classification of writer identification. This comparative study evaluated several DL architectures such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet end-to-end to examine their advantages to offline handwriting for writer identification problems with IAM and CVL databases. Each architecture compared its respective process to the training and validation metrics accuracy, demonstrating that ResNet50 DL had the highest train accuracy of 98.86%. However, Xception DL performed slightly better due to the convergence gap for validation accuracy compared to all the other architectures, which were 21.79% and 15.12% for IAM and CVL. Also, the smallest gap of convergence between training and validation accuracy for the IAM and CVL datasets were 19.13% and 16.49%, respectively. The results of these findings serve as the basis for DL architecture selection and open up overfitting problems for future work