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Featured Application: Findings of the paper can be implemented in malware detection applications. Malware is a growing threat to all mobile platforms and hundreds of new malicious applications are being detected every day. At the same time, the development of automated software obfuscation techniques allows for the easy production of new malware variants even by attackers with entry-level programming skills. Such obfuscation techniques can evade the signature-based mechanism implemented in current antimalware technology. This paper presents the results of a study that examined how automated obfuscation techniques affect malicious and benign applications by two widely used malware detection approaches, namely static and dynamic analyses. The research explored 5000 samples of malware and benign programs and evaluated the impact of automated obfuscation on Android applications. The experimental results indicated that (1) up to 73% of the reviewed applications "survived" the automated obfuscation; (2) automated obfuscation reduced the detection ratio to 65–85% depending on the obfuscation method used. These findings call for a more active use of advanced malware detection methods in commonly used antivirus platforms. [ABSTRACT FROM AUTHOR]

Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.

Acute myeloid leukemia (AML) is a form of cancer that affects the hematopoietic precursor cells with lethal effects. We investigated the prospect of using genistein as an effective alternate therapy for AML. A two-cell line model, one possessing the FLT3 gene with the ITD mutation (MV4-11) and the other with the wildtype FLT3 gene (HL-60) has been employed. Our 8-plexed iTRAQ™-based quantitative proteomics analysis together with various functional studies demonstrated that genistein exerts anti-leukemic effects on both the AML cell lines. Genistein treatment on the AML cells showed that the drug arrested the mTOR pathway leading to down-regulation of protein synthesis. Additionally, genistein treatment is found to induce cell death via apoptosis. Contrasting regulatory effects of genistein on the cell cycle of the two cell lines were also identified, with the induction of G2/M phase arrest in HL-60 cells but not in MV4-11 cells. Hence, our study highlights the potent anti-leukemic effect of genistein on AML cells irrespective of their genetic status. This suggests the potential use of genistein as an effective general drug therapy for AML patients.