3 research outputs found
Tandem Mass Spectrometry (MS/MS) for Determination of Architecture of Synthetic Polymers
Tandem Mass Spectrometry (MS/MS) was used to determine the architecture of synthetic polymers and they are difluorene-N3, 3-difluorene-N3 and VPOSS-3-difluorene. VPOSS materials usually exhibit electrochemical properties and the research was performed to determine their accurate structure and to provide evidence of their proper synthesis. The compounds were mixed with a matrix solution, DCTB, and a silver salt to enable proper protonation. The solvent mixture was on a MALDI plate and then put into the mass spectrometer. The mass spectrum generated was analyzed and each fragment was identified to structures with the synthetic polymers via ChemDraw. Thus, trial and error method was used in identifying each fragment peak in the mass spectrum and the final structure of the polymers was verified
Adversarial Robustness of Learning-based Static Malware Classifiers
Malware detection has long been a stage for an ongoing arms race between
malware authors and anti-virus systems. Solutions that utilize machine learning
(ML) gain traction as the scale of this arms race increases. This trend,
however, makes performing attacks directly on ML an attractive prospect for
adversaries. We study this arms race from both perspectives in the context of
MalConv, a popular convolutional neural network-based malware classifier that
operates on raw bytes of files. First, we show that MalConv is vulnerable to
adversarial patch attacks: appending a byte-level patch to malware files
bypasses detection 94.3% of the time. Moreover, we develop a universal
adversarial patch (UAP) attack where a single patch can drop the detection rate
in constant time of any malware file that contains it by 80%. These patches are
effective even being relatively small with respect to the original file size --
between 2%-8%. As a countermeasure, we then perform window ablation that allows
us to apply de-randomized smoothing, a modern certified defense to patch
attacks in vision tasks, to raw files. The resulting `smoothed-MalConv' can
detect over 80% of malware that contains the universal patch and provides
certified robustness up to 66%, outlining a promising step towards robust
malware detection. To our knowledge, we are the first to apply universal
adversarial patch attack and certified defense using ablations on byte level in
the malware field
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
In this paper, we propose a novel contrastive learning based deep learning
framework for patient similarity search using physiological signals. We use a
contrastive learning based approach to learn similar embeddings of patients
with similar physiological signal data. We also introduce a number of neighbor
selection algorithms to determine the patients with the highest similarity on
the generated embeddings. To validate the effectiveness of our framework for
measuring patient similarity, we select the detection of Atrial Fibrillation
(AF) through photoplethysmography (PPG) signals obtained from smartwatch
devices as our case study. We present extensive experimentation of our
framework on a dataset of over 170 individuals and compare the performance of
our framework with other baseline methods on this dataset.Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in
Biology and Medicin