Even though abstracts are short, they require a specific type and formal structure that is accepted by academic discourse communities (San & Tan, 2012). They are governed by a discipline's own conventions i.e. a series of moves that characterize the flow of the discourse and linguistic features. This study aims to identify the rhetorical moves and metadiscourse markers that are deployed in the abstracts of projects that were written by undergraduate students in the English department at the faculty of Arts/Gharyan University. The data consisted of 30 abstracts. Hyland's models (2000; 2005) were adopted in this study. The first model (2000) was used to analyze the rhetorical moves. The second one (2005) was employed to analyze the metadiscourse. The findings revealed that most of the abstracts did not consist of the five rhetorical moves. The introduction and purpose moves were prominent, while the method, result, conclusion, and structure (new move) moves were not prominent. The absence of some of the rhetorical moves in the English abstracts of undergraduate projects might make the project abstract unable to achieve its communicative purposes and will lead to difficulty for readers to understand the content of the abstract. For metadiscourse, the findings showed that interactive metadiscourse was deployed more than interactional metadiscourse. Among these two categories, transitions, frame markers, and endophoric were used more frequently.

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The rapidly growing use and storage of private, sensitive, and personal information across different applications have given rise to the need to restrict access to such information; thus, leading to the development of biometric authentication. Multimodal biometric authentication has improved system accuracy, but it has not been able to overcome all the vulnerabilities of biometric authentication. To reduce the amount of data that is stored or communicated during the authentication process and to ensure the authenticity of the biometric templates, image-watermarking techniques have been used to embed the information in one template over the other. In this paper, a new watermarking method is proposed, based on the Discrete Cosine Transform (DCT) method and the Least Significant Bit (LSB) method. The LSBs of the quantized DCT coefficients of a face image are manipulated according to the values of a binarized fingerprint image. This combination was used to allow the storing and communication of the watermarked images using the popular JPEG format. As the watermark information is not hand-crafted, tamper detection could not be achieved by comparing a static image to the extracted watermark. Thus, a Machine-Learning (ML)-based method was implemented to detect the existence of fingerprint patterns in the watermark. However, as the proposed system used a Convolutional Neural Network (CNN) to measure the similarity between the templates collected from the user and those stored in the model database, tamper detection was already embedded in the same neural network. Experiments were conducted to evaluate the performance of the proposed system. The results show a 98.96% average accuracy, where each prediction took an average processing time of 139.06 ms. The results also showed that the accuracy of tampering detection was 100%. Besides, the size of the files on the disk (or the bandwidth required to communicate the files) was reduced to less than 50% of their original size using the proposed fragile multibiometric watermarking technique.