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All Journal Educalitra: English Education, Linguistics, and Literature Journal Scientific Journal of Engineering Research
Ur Rahman, Atta
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Humanities Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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Exploring Translation Strategies: A Content Analysis of the English Translation of the Holy Quran Ur Rahman, Atta
Educalitra: English Education, Linguistics, and Literature Journal Vol. 2 No. 2 (2023)
Publisher : English Language Education Study Program, Faculty of Social, Economics, and Humanities, University of Nahdlatul Ulama Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.8198304

Abstract

This study aims to diagnose culture-specific items (CSIs) in the English translation of the Holy Quran and the strategies used to translate the CSIs. Translating CSIs can be demanding because such items have specific meanings in the culture and language in which they exist but not necessarily in others. Considering this fact, the present study investigates the strategies used in the translation of culture-specific items in the English translation of the Holy Quran. To achieve these goals, the descriptive approach is applied to the analysis of the translation of specific items of culture. The descriptive analysis is also used to investigate the translation strategies used therein. The findings of this study indicate that the use of the cultural equivalent strategy was the most frequently applied strategy. The second most frequently applied translation strategies were the descriptive equivalent and thorough translation, followed by transference, functional equivalent, componential analysis, synonymy, modulation, and notes. The results show that the translation strategies are helpful (especially the target-oriented ones) in different ways to convey the meaning of the text from Arabic into English.
LSKD: Lightweight Self-Knowledge Distillation Framework for Fast and Robust Crowd Counting Raza, Muhammad; Ling, Miaogen; Ur Rahman, Atta; Pallewatta, Pandula; Hersi, Aboubakar Abdinur; Beruwalage, Shehan Maxwell; Kannangara, Deshan Sachintha
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.436

Abstract

Crowd counting plays an important role in the surveillance of the safety of the people, traffic, and intelligent surveillance systems. However, the exact density estimations remain hard to achieve in highly congested scenes due to the tough occlusion, large-scale variance, and complicated background. Although the recent deep-learning methods have high performance, several of them do not need computationally efficient underlying backbone networks, and rather, they employ an external teacher-student distillation architecture, which can limit their use in resource-constrained applications. To avoid this problem, we introduce LSKD, a lightweight self-knowledge distillation network that is density map regression-specific. Unlike other conventional teacher-dependent processes, LSKD can also independently carry out internal multi-level feature alignment within a single small network that is not in need of an external teacher model. The structure integrates a Feature Matching Block (FMB) and a Context Fusion (CoFuse) block to enhance the hierarchical match of features and global awareness of context. The large experiments demonstrate that LSKD obtain competitive performance using the number of parameters as 2.65 million and GFLOPs as 10.23. Particularly, it has 63.17 MAE on ShanghaiTech Part A, 8.94 on ShanghaiTech Part B, 143.7 on UCF-QNRF, and 223.88 on UCF-CC-50, which is a good ratio between the accuracy and the efficiency of the calculations. Such results indicate that LSKD has an implementable and efficient solution to the real-time counting of crowds at the edge devices.
Co-Authors Beruwalage, Shehan Maxwell Hersi, Aboubakar Abdinur Kannangara, Deshan Sachintha Ling, Miaogen Pallewatta, Pandula Raza, Muhammad