Deteksi Kesamaan Teks Jawaban pada Sistem Test Essay Online dengan Pendekatan Neural Network

E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.

Lightweight, Secure, Similar-Document Retrieval over Encrypted Data

Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that have been outsourced to remote servers. None of the existing solutions can be described as lightweight techniques that are compatible with lightweight client implementation, and this deficiency can limit the effectiveness of these systems. For instance, the discovery of similarity between two conferences or journals must maintain the privacy of the submitted papers in a lightweight manner to ensure that the security and application requirements for limited-resource devices are fulfilled. This paper considers the problem of lightweight similarity detection between document sets while preserving the privacy of the material. The proposed solution permits documents to be compared without disclosing the content to untrusted servers. The fingerprint set for each document is determined in an efficient manner, also developing an inverted index that uses the whole set of fingerprints. Before being uploaded to the untrusted server, this index is secured by the Paillier cryptosystem. This study develops a secure, yet efficient method for scalable encrypted document comparison. To evaluate the computational performance of this method, this paper carries out several comparative assessments against other major approaches.

Image Processing Methods for Gesture-Based Robot Control

In this paper we discuss some image processing methods that can be used for motion recognition of human body parts such as hands or arms in order to interact with robots. This interaction is usually associated to gesture-based control. The considered image processing methods have been experienced for feature recognition in applications involving human robot interaction. They are namely: Sequential Similarity Detection Algorithm (SSDA), an appearance-based approach that uses image databases to model objects, and Kanade-Lucas-Tomasi (KLT) algorithm which is usually used for feature tracking. We illustrate the gesture-based interaction by using KLT algorithm. We discuss the adaptation of each of these methods to the context of gesture-based robot interaction and some of their related issues.

Research on Restoration Algorithm of Tomb Murals Based on Sequential Similarity Detection

Considering the problems of fuzzy repair and low pixel similarity matching in the repair of existing tomb murals, we propose a novel tomb mural repair algorithm based on sequential similarity detection in this paper. First, we determine the gradient value of tomb mural through second-order Gaussian Laplace operator in LOG edge detection and then reduce the noise in the edge of tomb mural to process a smooth edge of tomb mural. Further, we set the mathematical model to obtain the edge features of tomb murals. To calculate the average gray level of foreground and background under a specific threshold, we use the maximum interclass variance method, which considers the influence of small cracks on the edge of tomb murals and separates the cracks through a connected domain labelling algorithm and open and close operations to complete the edge threshold segmentation. In addition, we use the priority calculation function to determine the damaged tomb mural area, calculate the gradient factor of edge information, obtain the information entropy of different angles, determine the priority of tomb mural image repair, detect the similarity of tomb mural repair pixels with the help of sequential similarity, and complete the tomb mural repair. Experimental results show that our model can effectively repair the edges of the tomb murals and can achieve a high pixel similarity matching degree.

Initial Suspicion on Detecting Code Plagiarism and Collusion in Academia: Case Study of Algorithm and Data Structure Courses

In engineering education, some assessments require the students to submit program code, and since that code might be a result of plagiarism or collusion, a similarity detection tool is often used to filter excessively similar programs. To improve the scalability of such a tool, it is suggested to initially suspect some programs and only compare those programs to others (instead of exhaustively compare all programs one another). This paper compares the ef-fectiveness of two common techniques to raise such initial suspicion: focusing on the submissions of smart students (as they are likely to be copied), or the submissions of slow-paced students (since those students are likely to breach academic integrity to get higher assessment mark). Our study shows that the latter statistically outperforms the former by 13% in terms of precision; slow-paced students are likely to be the perpetrators, but they fail to get the submissions of smart students.