OpenRaiser/Intern-Atlas huggingface dataset:
Checking Robustness Against Snapshot Isolation
Venue: arXiv cs.LO 2019
Year: 2019

Transactional access to databases is an important abstraction allowing programmers to consider blocks of actions (transactions) as executing in isolation. The strongest consistency model is {\em serializability}, which ensures the atomicity abstraction of transactions executing over a sequentially consistent memory. Since ensuring serializability carries a significant penalty on availability, modern databases provide weaker consistency models, one of the most prominent being \emph{snapshot isolation}. In general, the correctness of a program relying on serializable transactions may be broken when using weaker models. However, certain programs may also be insensitive to consistency relaxations, i.e., all their properties holding under serializability are preserved even when they are executed over a weak consistent database and without additional synchronization. In this paper, we address the issue of verifying if a given program is {\em robust against snapshot isolation}, i.e., all its behaviors are serializable even if it is executed over a database ensuring snapshot isolation. We show that this verification problem is polynomial time reducible to a state reachability problem in transactional programs over a sequentially consistent shared memory. This reduction opens the door to the reuse of the classic verification technology for reasoning about weakly-consistent programs. In particular, we show that it can be used to derive a proof technique based on Lipton's reduction theory that allows to prove programs robust.
Parallel memory-efficient all-at-once algorithms for the sparse matrix triple products in multigrid methods
Venue: arXiv cs.DC 2019
Year: 2019

Multilevel/multigrid methods is one of the most popular approaches for solving a large sparse linear system of equations, typically, arising from the discretization of partial differential equations. One critical step in the multilevel/multigrid methods is to form coarse matrices through a sequence of sparse matrix triple products. A commonly used approach for the triple products explicitly involves two steps, and during each step a sparse matrix-matrix multiplication is employed. This approach works well for many applications with a good computational efficiency, but it has a high memory overhead since some auxiliary matrices need to be temporarily stored for accomplishing the calculations. In this work, we propose two new algorithms that construct a coarse matrix with taking one pass through the input matrices without involving any auxiliary matrices for saving memory. The new approaches are referred to as "all-at-once" and "merged all-at-once", and the traditional method is denoted as "two-step". The all-at-once and the merged all-at-once algorithms are implemented based on hash tables in PETSc as part of this work with a careful consideration on the performance in terms of the compute time and the memory usage. We numerically show that the proposed algorithms and their implementations are perfectly scalable in both the compute time and the memory usage with up to 32,768 processor cores for a model problem with 27 billions of unknowns. The scalability is also demonstrated for a realistic neutron transport problem with over 2 billion unknowns on a supercomputer with 10,000 processor cores. Compared with the traditional two-step method, the all-at-once and the merged all-at-once algorithms consume much less memory for both the model problem and the realistic neutron transport problem meanwhile they are able to maintain the computational efficiency.
Spatially Constrained Spectral Clustering Algorithms for Region Delineation
Venue: arXiv cs.LG 2019
Year: 2019

Regionalization is the task of dividing up a landscape into homogeneous patches with similar properties. Although this task has a wide range of applications, it has two notable challenges. First, it is assumed that the resulting regions are both homogeneous and spatially contiguous. Second, it is well-recognized that landscapes are hierarchical such that fine-scale regions are nested wholly within broader-scale regions. To address these two challenges, first, we develop a spatially constrained spectral clustering framework for region delineation that incorporates the tradeoff between region homogeneity and spatial contiguity. The framework uses a flexible, truncated exponential kernel to represent the spatial contiguity constraints, which is integrated with the landscape feature similarity matrix for region delineation. To address the second challenge, we extend the framework to create fine-scale regions that are nested within broader-scaled regions using a greedy, recursive bisection approach. We present a case study of a terrestrial ecology data set in the United States that compares the proposed framework with several baseline methods for regionalization. Experimental results suggest that the proposed framework for regionalization outperforms the baseline methods, especially in terms of balancing region contiguity and homogeneity, as well as creating regions of more similar size, which is often a desired trait of regions.
A Universal Approximation Result for Difference of log-sum-exp Neural Networks
Venue: arXiv cs.NE 2019
Year: 2019

We show that a neural network whose output is obtained as the difference of the outputs of two feedforward networks with exponential activation function in the hidden layer and logarithmic activation function in the output node (LSE networks) is a smooth universal approximator of continuous functions over convex, compact sets. By using a logarithmic transform, this class of networks maps to a family of subtraction-free ratios of generalized posynomials, which we also show to be universal approximators of positive functions over log-convex, compact subsets of the positive orthant. The main advantage of Difference-LSE networks with respect to classical feedforward neural networks is that, after a standard training phase, they provide surrogate models for design that possess a specific difference-of-convex-functions form, which makes them optimizable via relatively efficient numerical methods. In particular, by adapting an existing difference-of-convex algorithm to these models, we obtain an algorithm for performing effective optimization-based design. We illustrate the proposed approach by applying it to data-driven design of a diet for a patient with type-2 diabetes.
Coding theory for noiseless channels realized by anonymous oblivious mobile robots
Venue: arXiv cs.DC 2019
Year: 2019

We propose an information transmission scheme by a swarm of anonymous oblivious mobile robots on a graph. The swarm of robots travel from a sender vertex to a receiver vertex to transmit a symbol generated at the sender. The codeword for a symbol is a pair of an initial configuration at the sender and a set of terminal configurations at the receiver. The set of such codewords forms a code. We analyze the performance of the proposed scheme in terms of its code size and transmission delay. We first demonstrate that a lower bound of the transmission delay depends on the size of the swarm, and the code size is upper bounded by an exponent of the size of the swarm. We then give two algorithms for a swarm of a fixed size. The first algorithm realizes a near optimal code size with a large transmission delay. The second algorithm realizes an optimal transmission delay with a smaller code size. We then consider information transmission by swarms of different sizes and present upper bounds of the expected swarm size by the two algorithms. We also present lower bounds by Shannon's lemma and noiseless coding theorem.
Contrast Enhancement of Medical X-Ray Image Using Morphological Operators with Optimal Structuring Element
Venue: arXiv cs.CV 2019
Year: 2019

To guide surgical and medical treatment X-ray images have been used by physicians in every modern healthcare organization and hospitals. Doctor's evaluation process and disease identification in the area of skeletal system can be performed in a faster and efficient way with the help of X-ray imaging technique as they can depict bone structure painlessly. This paper presents an efficient contrast enhancement technique using morphological operators which will help to visualize important bone segments and soft tissues more clearly. Top-hat and Bottom-hat transform are utilized to enhance the image where gradient magnitude value is calculated for automatically selecting the structuring element (SE) size. Experimental evaluation on different x-ray imaging databases shows the effectiveness of our method which also produces comparatively better output against some existing image enhancement techniques.
Dynamic Searchable Symmetric Encryption Schemes Supporting Range Queries with Forward/Backward Privacy
Venue: arXiv cs.CR 2019
Year: 2019

Dynamic searchable symmetric encryption (DSSE) is a useful cryptographic tool in encrypted cloud storage. However, it has been reported that DSSE usually suffers from file-injection attacks and content leak of deleted documents. To mitigate these attacks, forward privacy and backward privacy have been proposed. Nevertheless, the existing forward/backward-private DSSE schemes can only support single keyword queries. To address this problem, in this paper, we propose two DSSE schemes supporting range queries. One is forward-private and supports a large number of documents. The other can achieve backward privacy, while it can only support a limited number of documents. Finally, we also give the security proofs of the proposed DSSE schemes in the random oracle model.
Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach
Venue: arXiv cs.AI 2019
Year: 2019

In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases.
VGG Fine-tuning for Cooking State Recognition
Venue: arXiv cs.CV 2019
Year: 2019

An important task that domestic robots need to achieve is the recognition of states of food ingredients so they can continue their cooking actions. This project focuses on a fine-tuning algorithm for the VGG (Visual Geometry Group) architecture of deep convolutional neural networks (CNN) for object recognition. The algorithm aims to identify eleven different ingredient cooking states for an image dataset. The original VGG model was adjusted and trained to properly classify the food states. The model was initialized with Imagenet weights. Different experiments were carried out in order to find the model parameters that provided the best performance. The accuracy achieved for the validation set was 76.7% and for the test set 76.6% after changing several parameters of the VGG model.
Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment
Venue: arXiv cs.CV 2019
Year: 2019

We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation.
Skin Cancer Recognition using Deep Residual Network
Venue: arXiv cs.CV 2019
Year: 2019

The advances in technology have enabled people to access internet from every part of the world. But to date, access to healthcare in remote areas is sparse. This proposed solution aims to bridge the gap between specialist doctors and patients. This prototype will be able to detect skin cancer from an image captured by the phone or any other camera. The network is deployed on cloud server-side processing for an even more accurate result. The Deep Residual learning model has been used for predicting the probability of cancer for server side The ResNet has three parametric layers. Each layer has Convolutional Neural Network, Batch Normalization, Maxpool and ReLU. Currently the model achieves an accuracy of 77% on the ISIC - 2017 challenge.
An Efficient Pre-processing Method to Eliminate Adversarial Effects
Venue: arXiv cs.CV 2019
Year: 2019

Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated by imposing subtle perturbations to inputs that lead a model to predict incorrect outputs. Currently, a large number of researches on defending adversarial examples pay little attention to the real-world applications, either with high computational complexity or poor defensive effects. Motivated by this observation, we develop an efficient preprocessing method to defend adversarial images. Specifically, before an adversarial example is fed into the model, we perform two image transformations: WebP compression, which is utilized to remove the small adversarial noises. Flip operation, which flips the image once along one side of the image to destroy the specific structure of adversarial perturbations. Finally, a de-perturbed sample is obtained and can be correctly classified by DNNs. Experimental results on ImageNet show that our method outperforms the state-of-the-art defense methods. It can effectively defend adversarial attacks while ensure only very small accuracy drop on normal images.
Fast Computing the Algebraic Degree of Boolean Functions
Venue: arXiv cs.DM 2019
Year: 2019

Here we consider an approach for fast computing the algebraic degree of Boolean functions. It combines fast computing the ANF (known as ANF transform) and thereafter the algebraic degree by using the weight-lexicographic order (WLO) of the vectors of the $n$-dimensional Boolean cube. Byte-wise and bitwise versions of a search based on the WLO and their implementations are discussed. They are compared with the usual exhaustive search applied in computing the algebraic degree. For Boolean functions of $n$ variables, the bitwise implementation of the search by WLO has total time complexity $O(n.2^n)$. When such a function is given by its truth table vector and its algebraic degree is computed by the bitwise versions of the algorithms discussed, the total time complexity is $Θ((9n-2).2^{n-7})=Θ(n.2^n)$. All algorithms discussed have time complexities of the same type, but with big differences in the constants hidden in the $Θ$-notation. The experimental results after numerous tests confirm the theoretical results - the running times of the bitwise implementation are dozens of times better than the running times of the byte-wise algorithms.
Activity Recognition and Prediction in Real Homes
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.
Automata Terms in a Lazy WSkS Decision Procedure (Technical Report)
Venue: arXiv cs.LO 2019
Year: 2019

We propose a lazy decision procedure for the logic WSkS. It builds a term-based symbolic representation of the state space of the tree automaton (TA) constructed by the classical WSkS decision procedure. The classical decision procedure transforms the symbolic representation into a TA via a bottom-up traversal and then tests its language non-emptiness, which corresponds to satisfiability of the formula. On the other hand, we start evaluating the representation from the top, construct the state space on the fly, and utilize opportunities to prune away parts of the state space irrelevant to the language emptiness test. In order to do so, we needed to extend the notion of language terms (denoting language derivatives) used in our previous procedure for the linear fragment of the logic (the so-called WS1S) into automata terms. We implemented our decision procedure and identified classes of formulae on which our prototype implementation is significantly faster than the classical procedure implemented in the Mona tool.
Semi-Lagrangian Subgrid Reconstruction for Advection-Dominant Multiscale Problems
Venue: arXiv cs.CE 2019
Year: 2019

We introduce a new framework of numerical multiscale methods for advection-dominated problems motivated by climate sciences. Current numerical multiscale methods (MsFEM) work well on stationary elliptic problems but have difficulties when the model involves dominant lower order terms. Our idea to overcome the assocociated difficulties is a semi-Lagrangian based reconstruction of subgrid variablity into a multiscale basis by solving many local inverse problems. Globally the method looks like a Eulerian method with multiscale stabilized basis. We show example runs in one and two dimensions and a comparison to standard methods to support our ideas and discuss possible extensions to other types of Galerkin methods, higher dimensions and nonlinear problems.
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
Venue: arXiv cs.CV 2019
Year: 2019

This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cloud is turned into a 2D range-image by exploiting the topology of the sensor. This image is then used as input to a U-net. This architecture has already proved its efficiency for the task of semantic segmentation of medical images. We demonstrate how it can also be used for the accurate semantic segmentation of a 3D LiDAR point cloud and how it represents a valid bridge between image processing and 3D point cloud processing. Our model is trained on range-images built from KITTI 3D object detection dataset. Experiments show that RIU-Net, despite being very simple, offers results that are comparable to the state-of-the-art of range-image based methods. Finally, we demonstrate that this architecture is able to operate at 90fps on a single GPU, which enables deployment for real-time segmentation.
Textured Neural Avatars
Venue: arXiv cs.CV 2019
Year: 2019

We present a system for learning full-body neural avatars, i.e. deep networks that produce full-body renderings of a person for varying body pose and camera position. Our system takes the middle path between the classical graphics pipeline and the recent deep learning approaches that generate images of humans using image-to-image translation. In particular, our system estimates an explicit two-dimensional texture map of the model surface. At the same time, it abstains from explicit shape modeling in 3D. Instead, at test time, the system uses a fully-convolutional network to directly map the configuration of body feature points w.r.t. the camera to the 2D texture coordinates of individual pixels in the image frame. We show that such a system is capable of learning to generate realistic renderings while being trained on videos annotated with 3D poses and foreground masks. We also demonstrate that maintaining an explicit texture representation helps our system to achieve better generalization compared to systems that use direct image-to-image translation.
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Recently, Convolutional Neural Networks (CNNs) demonstrate a considerable vulnerability to adversarial attacks, which can be easily misled by adversarial perturbations. With more aggressive methods proposed, adversarial attacks can be also applied to the physical world, causing practical issues to various CNN powered applications. To secure CNNs, adversarial attack detection is considered as the most critical approach. However, most existing works focus on superficial patterns and merely search a particular method to differentiate the adversarial inputs and natural inputs, ignoring the analysis of CNN inner vulnerability. Therefore, they can only target to specific physical adversarial attacks, lacking expected versatility to different attacks. To address this issue, we propose DoPa -- a comprehensive CNN detection methodology for various physical adversarial attacks. By interpreting the CNN's vulnerability, we find that non-semantic adversarial perturbations can activate CNN with significantly abnormal activations and even overwhelm other semantic input patterns' activations. Therefore, we add a self-verification stage to analyze the semantics of distinguished activation patterns, which improves the CNN recognition process. We apply such a detection methodology into both image and audio CNN recognition scenarios. Experiments show that DoPa can achieve an average rate of 90% success for image attack detection and 92% success for audio attack detection. Announcement:[The original DoPa draft on arXiv was modified and submitted to a conference already, while this short abstract was submitted only for a presentation at the KDD 2019 AIoT Workshop.]
Geometry of Deep Convolutional Networks
Venue: arXiv cs.LG 2019
Year: 2019

We give a formal procedure for computing preimages of convolutional network outputs using the dual basis defined from the set of hyperplanes associated with the layers of the network. We point out the special symmetry associated with arrangements of hyperplanes of convolutional networks that take the form of regular multidimensional polyhedral cones. We discuss the efficiency of large number of layers of nested cones that result from incremental small size convolutions in order to give a good compromise between efficient contraction of data to low dimensions and shaping of preimage manifolds. We demonstrate how a specific network flattens a non linear input manifold to an affine output manifold and discuss its relevance to understanding classification properties of deep networks.