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Abstract

Jalal al-Din Rumi (1207–1273), a 13th-century Persian Islamic poet, is considered one of the greatest Sufi mystic poets in the history of humanity. His legacy has survived many centuries and crossed the threshold of languages through the door of translation, enabling his oeuvre to access other cultures. Rumi composed his poems predominantly in Persian (Farsi), some poems in Arabic, a few poems in Turkish, and fewer in Greek, as in his magnum opus, Divan-e Shams-e Tabrizi, (which was named after his spiritual teacher). Many of his Persian works have been translated into English and other European languages over the past few centuries, and these translations have played a paramount role in introducing Rumi and Sufism to Western audiences. Not surprisingly, Rumi loves Arabic, and he has a special place in his heart for this Semitic tongue. However, despite the importance of Arabic in Rumi‘s writing, his Arabic poems are seldom translated or analyzed, unlike Persian poems, which are extensively translated and examined. Hence, the paucity of translating Arabic poems of Rumi into English and their analysis is quite curious. Only a few scholars have tried their hands at translating and examining the Arabic poems of Rumi most notably Franklin Lewis. This essay sheds light on the significance of Arabic in Rumi‘s writings, and it then examines Nesreen Akhtarkhavari‘s and Anthony Lee‘s translation of Love Is My Savior: The Arabic Poems of Rumi (2016). It also seeks to address how the power of creativity in translation is crucial in rendering or reconciling the aesthetic characteristics of Rumi‘s Arabic poetic expression into English.

A new robust blind image watermarking method based on lifting wavelet transform (LWT), and Schur decomposition in combination with discrete cosine transform (DCT), and entropy analysis for copyright protection of multimedia information is introduced in this paper. At first, the 2-level LWT is applied to the original image to improve the invisibility of the watermarking method and then the high-frequency sub-band of 2-level LWT is decomposed by DCT. Then, the DCT coefficients are divided into 4×4 non-overlapping blocks. After that, Schur decomposition applies to each selected block, while the first row, the first column element of the upper triangular matrix is used to embed the watermark. For evaluation, the invisibility and robustness of the proposed watermarking method, the peak signal to noise ratio (PSNR), and normalized cross-correlation (NC) are used to measure the quality and the ability of the proposed watermarking method to be robust against signal processing operations and geometric attacks. Experimental results have demonstrated that the proposed method is achieved a very good tradeoff between invisibility and robustness. The comparisons with other scheme have shown that the proposed digital watermarking scheme has a superior performance in terms of invisibility than other. 

Abstract:

In this research study, we investigate the effectiveness of a proposed automated framework that leverages AI algorithms to improve the accuracy, scalability, and efficiency of data analysis. We compare the performance of the proposed framework to traditional data analysis methods in terms of accuracy, time and effort required for data analysis, and scalability. We conducted a questionnaire-based survey to collect data on the application of the proposed framework and traditional data analysis methods in real-world scenarios. We used statistical analyses, including t-tests and regression analyses, to analyze the data and draw conclusions. Our findings indicate that the proposed automated framework outperforms traditional data analysis methods in terms of accuracy, reduces the time and effort required for data analysis, and has better scalability for processing large datasets in real-time. Our results suggest that the proposed framework offers significant benefits for organizations seeking to improve their data analysis processes. However, we also note the importance of considering the costs and benefits of implementing the framework, as well as any ethical or legal implications. Further research is recommended to confirm the findings and to investigate the effectiveness of the proposed framework in different contexts and with different datasets.