This paper provides a survey of methods and tools for automated code-reuse exploit generation. Such exploits use code that is already contained in a vulnerable program… The code-reuse approach allows one to exploit vulnerabilities in the presence of operating system protection that prohibits data memory execution. This paper contains a description of various code-reuse methods: return-to-libc attack, return-oriented programming, jump-oriented programming, and others. We define fundamental terms: gadget, gadget frame, gadget catalog. Moreover, we show that, in fact, a gadget […]

This paper is dedicated to simulation of so-called trapping-effect observed in Troitsk nu-mass experiment. The effect is caused by magnetic trapping of decay electrons in the windowless gaseous tritium source and the gradual of evaporation of those electrons… As a result, alongside regular tritium beta-spectrum electrons, we see additional electrons that are initially trapped in the source and escape it with changed energy. The spectrum of evaporated electrons is quite peculiar and could not be directly measured in the experiment. […]

We introduce a randomized algorithm, namely {tt rchol}, to construct an approximate Cholesky factorization for a given sparse Laplacian matrix (a.k.a., graph Laplacian). The (exact) Cholesky factorization for the matrix introduces a clique in the associated graph after eliminating every row/column… By randomization, {tt rchol} samples a subset of the edges in the clique. We prove {tt rchol} is breakdown free and apply it to solving linear systems with symmetric diagonally-dominant matrices. In addition, we parallelize {tt rchol} based on […]

The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patches. The resulting patch representations are combined back together into the image space and aggregated hierarchically using multiple MPS blocks per layer to obtain the final decision rules… In this work, we propose a non-patch based modification to LoTeNet that performs one MPS operation per layer, instead of several patch-level operations. The spatial information in the input […]

In the real world, data streams are ubiquitous — think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time… This is challenging because (1) streams often have high dimensionality, and (2) the data characteristics may change over time. Existing approaches tend to focus on only one aspect, either high dimensionality or the specifics of the streaming setting. For static data, a common approach to deal with high dimensionality […]

Understanding the per-layer learning dynamics of deep neural networks is of significant interest as it may provide insights into how neural networks learn and the potential for better training regimens. We investigate learning in Deep Convolutional Neural Networks (CNNs) by measuring the relative weight change of layers while training… Several interesting trends emerge in a variety of CNN architectures across various computer vision classification tasks, including the overall increase in relative weight change of later layers as compared to earlier […]

Segmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To increase robustness and performance of segmentation methods this study combines automatic segmentation and assessment of segmentation uncertainty in CMRI to detect image regions containing local segmentation failures… Three state-of-the-art convolutional neural networks (CNN) were trained to automatically segment cardiac anatomical structures and obtain two measures of predictive uncertainty: entropy and a measure derived by MC-dropout. Thereafter, using […]

We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging, and utterance segmentation in a simple deep recurrent setting. We show that these tasks provide positive inductive biases to each other with the optimal contribution of each one relying on the severity of the noise from the task… Our live multi-task model outperforms similar individual tasks, delivers competitive performance, and is beneficial for […]

As news and social media exhibit an increasing amount of manipulative polarized content, detecting such propaganda has received attention as a new task for content analysis. Prior work has focused on supervised learning with training data from the same domain… However, as propaganda can be subtle and keeps evolving, manual identification and proper labeling are very demanding. As a consequence, training data is a major bottleneck. In this paper, we tackle this bottleneck and present an approach to leverage cross-domain […]

Deep learning classifiers are assisting humans in making decisions and hence the user’s trust in these models is of paramount importance. Trust is often a function of constant behavior… From an AI model perspective it means given the same input the user would expect the same output, especially for correct outputs, or in other words consistently correct outputs. This paper studies a model behavior in the context of periodic retraining of deployed models where the outputs from successive generations of […]