School of Information Technology and Engineering

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http://etd.aau.edu.et/handle/123456789/460

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Browsing School of Information Technology and Engineering by Subject "BERT"

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    Enhancing Neural Machine Translation Through Incorporation of Unsupervised Language Understanding and Generation Techniques: The Case of English-Afaan Oromo Translation
    (2024-05) Chala Bekabil; Fantahun Bogale (PhD)
    Breaking down language barriers is a paramount pursuit in the realm of Artificial Intelligence. Machine Translation (MT), a domain within Natural Language Processing (NLP), holds the potential to bridge linguistic gaps and foster global communication. Enhancing cross-cultural communication through MT will be realized only if we succeed in developing accurate and adaptable techniques which in turn demands adequate availability of linguistic resources. Unluckily, under-resourced languages face challenges due to limited linguistic resources and sparse parallel data. Previous studies tried to solve this problem by using monolingual pre-training techniques. However, such studies solely rely on either Language Understanding (LU) or Language Generation (LG) techniques resulting in skewed translation. This study aims to enhance translation outcomes beyond the capabilities of previous studies by marrying the concepts of LU and LG and hence boosting the quality of MT in both directions. Our proposed model, the BERT-GPT incorporated Transformer, combines SOTA language models, BERT and GPT, trained on monolingual data into the original Transformer model and demonstrates substantial improvements. Experimental results shows that translation quality leaps forward, as evidenced by a significant increase in the BLEU score reaching 42.09, from the baseline score of 35.75 for English to Afaan Oromo translation, and 44.51 from the baseline score of 40.35 for Afaan Oromo to English translation on test dataset. Notably, our model unveils a deep understanding of Afaan Oromo’s linguistic nuances, resulting in translations that are precise, contextually appropriate, and faithful to the original intent. By leveraging the power of unsupervised pre-training and incorporation of unsupervised LU and LG techniques to the transformer model, we pave the way for enhanced cross-cultural communication, advanced understanding and inclusivity in our interconnected world.
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    Investigating Malicious Capabilities of Android Malwares that Utilize Accessibility Services
    (Addis Ababa University, 2025-02) Tekeste Fekadu; Fitsum Assamnew (PhD)
    The Android accessibility service provides a range of powerful capabilities. These include observing user actions, reading on-screen content, and executing actions on behalf of the user. Although these features are designed to enhance the user experience for individuals with disabilities, they introduce design vulnerabilities that make the accessibility service susceptible to malicious exploitation. This research investigates how Android malware leverages accessibility services for malicious purposes. By analyzing a dataset of malicious applications, we identified common patterns of accessibility service abuse and developed a machine learning-based detection approach using TinyBERT and XGBoost models. We first manually compiled a base dataset of 134 accessibility service event patterns comprising source and sink API calls. These patterns were labeled according to specific malicious functionalities: BlockAccess, ManipulateUI, and ContentEavesdrop. To address data limitations, we generated callgraph from 121 malware samples using Flow- Droid taint analysis and applied agglomerative clustering and fuzzy matching, ultimately expanding the dataset size to 1,497 patterns. Our classification experiments compared the performance of TinyBERT, a transformer-based model, and XGBoost, a gradient-boosted decision tree model, in classifying malicious functionalities. Results show TinyBERT’s outstanding performance, achieving an accuracy of 97.7% and an F1 score of 97.6% over ten-fold cross-validation, compared to XGBoost’s 90.4% accuracy and 90.0% F1 score. This study demonstrates the potential of transformer-based models in capturing sequential dependencies and contextual characteristics in API call patterns, enabling robust detection of accessibility service misuse. Our findings contribute a novel approach to detecting malicious behavior in Android malware and a valuable dataset that may aid similar research.