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DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode., , , , , , , and . IEEE Trans. Software Eng., 49 (10): 4691-4706 (October 2023)Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets., , , , , and . ICMLA, page 1449-1454. IEEE, (2022)A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain., , , , , , and . WWW (Companion Volume), page 260-268. ACM / IW3C2, (2021)JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis., , , , , , , , and . ICSE, page 1232-1244. ACM, (2022)LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish., , , , , , , , and . LREC, page 5080-5089. European Language Resources Association, (2022)On the Suitability of SHAP Explanations for Refining Classifications., , , , , , , , and . ICAART (3), page 395-402. SCITEPRESS, (2022)Evaluating the Impact of Text De-Identification on Downstream NLP Tasks., , , , , , , , and . NoDaLiDa, page 10-16. University of Tartu Library, (2023)Android Malware Detection: Looking beyond Dalvik Bytecode., , , and . ASE Workshops, page 34-39. IEEE, (2021)LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning., , , , , and . NLDB (1), volume 14762 of Lecture Notes in Computer Science, page 62-77. Springer, (2024)The Devil is in the Details: Unwrapping the Cryptojacking Malware Ecosystem on Android., , , , and . SCAM, page 153-163. IEEE, (2022)