OpenRaiser/Intern-Atlas huggingface dataset:
Reachability Analysis for Spatial Concurrent Constraint Systems with Extrusion
Venue: arXiv cs.LO 2018
Year: 2018

Spatial concurrent constraint programming (SCCP) is an algebraic model of spatial modalities in constrained-based process calculi; it can be used to reason about spatial information distributed among the agents of a system. This work presents an executable rewriting logic semantics of SCCP with extrusion (i.e., process mobility) that uses rewriting modulo SMT, a novel technique that combines the power of term rewriting, matching algorithms, and SMT-solving. In this setting, constraints are encoded as formulas in a theory with a satisfaction relation decided by an SMT solver, while the topology of the spatial hierarchy is encoded as part of the term structure of symbolic states. By being executable, the rewriting logic specification offers support for the inherent symbolic and challenging task of reachability analysis in the constrained-based model. The approach is illustrated with examples about the automatic verification of fault-tolerance, consistency, and privacy in distributed spatial and hierarchical systems.
My camera can see through fences: A deep learning approach for image de-fencing
Venue: arXiv cs.CV 2018
Year: 2018

In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. However, sometimes the amateur photographer is hindered by fences in the scene which have to be removed after the image has been captured. Conventional approaches to image de-fencing suffer from inaccurate and non-robust fence detection apart from being limited to processing images of only static occluded scenes. In this paper, we propose a semi-automated de-fencing algorithm using a video of the dynamic scene. We use convolutional neural networks for detecting fence pixels. We provide qualitative as well as quantitative comparison results with existing lattice detection algorithms on the existing PSU NRT data set and a proposed challenging fenced image dataset. The inverse problem of fence removal is solved using split Bregman technique assuming total variation of the de-fenced image as the regularization constraint.
Closed Walk Sampler: An Efficient Method for Estimating Eigenvalues of Large Graphs
Venue: arXiv cs.SI 2018
Year: 2018

Eigenvalues of a graph are of high interest in graph analytics for Big Data due to their relevance to many important properties of the graph including network resilience, community detection and the speed of viral propagation. Accurate computation of eigenvalues of extremely large graphs is usually not feasible due to the prohibitive computational and storage costs and also because full access to many social network graphs is often restricted to most researchers. In this paper, we present a series of new sampling algorithms which solve both of the above-mentioned problems and estimate the two largest eigenvalues of a large graph efficiently and with high accuracy. Unlike previous methods which try to extract a subgraph with the most influential nodes, our algorithms sample only a small portion of the large graph via a simple random walk, and arrive at estimates of the two largest eigenvalues by estimating the number of closed walks of a certain length. Our experimental results using real graphs show that our algorithms are substantially faster while also achieving significantly better accuracy on most graphs than the current state-of-the-art algorithms.
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
Venue: arXiv cs.NE 2018
Year: 2018

Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models' computational powers for solving algorithmic problems. We define the complexity and difficulty of a number sequence prediction task with the structure of the smallest automaton that can generate the sequence. We suggest two types of number sequence prediction problems: the number-level and the digit-level problems. The number-level problems format sequences as 2-dimensional grids of digits and the digit-level problems provide a single digit input per a time step. The complexity of a number-level sequence prediction can be defined with the depth of an equivalent combinatorial logic, and the complexity of a digit-level sequence prediction can be defined with an equivalent state automaton for the generation rule. Experiments with number-level sequences suggest that CNN models are capable of learning the compound operations of sequence generation rules, but the depths of the compound operations are limited. For the digit-level problems, simple GRU and LSTM models can solve some problems with the complexity of finite state automata. Memory augmented models such as Stack-RNN, Attention, and Neural Turing Machines can solve the reverse-order task which has the complexity of simple pushdown automaton. However, all of above cannot solve general Fibonacci, Arithmetic or Geometric sequence generation problems that represent the complexity of queue automata or Turing machines. The results show that our number sequence prediction problems effectively evaluate machine learning models' computational capabilities.
Free-rider Episode Screening via Dual Partition Model
Venue: arXiv cs.DB 2018
Year: 2018

One of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the possible high support of the inside noise events, such free-rider episodes may have abnormally high support that they cannot be filtered by frequency based framework. An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently. In this paper, we take more complex subepisodes into consideration and develop a novel partition model named EDP for free-rider episode filtering from a given set of episodes. It combines (1) a dual partition strategy which divides an episode to an underlying real pattern and potential noises; (2) a novel definition of the expected support of a free-rider episode based on the proposed partition strategy. We can deem the episode interesting if the observed support is substantially higher than the expected support estimated by our model. The experiments on synthetic and real-world datasets demonstrate EDP can effectively filter free-rider episodes compared with existing state-of-the-arts.
Learning to Multitask
Venue: arXiv cs.LG 2018
Year: 2018

Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given multitask problem, we propose a learning framework called learning to multitask (L2MT). To achieve the goal, L2MT exploits historical multitask experience which is organized as a training set consists of several tuples, each of which contains a multitask problem with multiple tasks, a multitask model, and the relative test error. Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation. Given a new multitask problem, the estimation function is used to identify a suitable multitask model. Experiments on benchmark datasets show the effectiveness of the proposed L2MT framework.
Physical and Mechatronic Security, Technologies and Future Trends for Vehicular Environment
Venue: arXiv cs.CR 2018
Year: 2018

Cloning spare parts and entities of mass products is an old and serious unsolved problem for the automotive industry. The economic losses in addition to a loss of know-how and IP theft as well as security and safety threats are huge in all dimensions. This presentation gives an overview of the traditional state of the art on producing clone resistant electronic units in the last two decades. A survey is attempting to demonstrate the techniques so far known as Physically Unclonable Functions PUFs showing their advantages and drawbacks. The necessity for fabricating mechatronic-security in the vehicular environment is emerging to become a vital requirement for new automotive security regulations (legal regulations) in the near future. The automotive industry is facing a challenge to produce low-cost and highly safe and secure networked automotive systems. The emerging networked smart traffic environment is offering new safety services and creating at the same time new needs and threats in a highly networked world. There is a crying need for automotive security that approaches the level of the robust biological security for cars as dominating mobility actors in the modern smart life environment. Possible emerging technologies allowing embedding practical mechatronic-security modules as a low-cost digital alternative are presented. Such digital clone-resistant mechatronic-units (as Electronic Control Units ECUs) may serve as smart security anchors for the automotive environment in the near future. First promising initial results are also presented.
Latent Space Non-Linear Statistics
Venue: arXiv cs.CV 2018
Year: 2018

Given data, deep generative models, such as variational autoencoders (VAE) and generative adversarial networks (GAN), train a lower dimensional latent representation of the data space. The linear Euclidean geometry of data space pulls back to a nonlinear Riemannian geometry on the latent space. The latent space thus provides a low-dimensional nonlinear representation of data and classical linear statistical techniques are no longer applicable. In this paper we show how statistics of data in their latent space representation can be performed using techniques from the field of nonlinear manifold statistics. Nonlinear manifold statistics provide generalizations of Euclidean statistical notions including means, principal component analysis, and maximum likelihood fits of parametric probability distributions. We develop new techniques for maximum likelihood inference in latent space, and adress the computational complexity of using geometric algorithms with high-dimensional data by training a separate neural network to approximate the Riemannian metric and cometric tensor capturing the shape of the learned data manifold.
Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data
Venue: arXiv cs.OH 2018
Year: 2018

By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology and adaptation of CAV, it is more critical to develop the universal evaluation method and the testing standard which could evaluate the impacts on energy consumption and environmental pollution of CAV fairly, especially under the various traffic conditions. In this paper, we proposed a new method and framework to evaluate the energy efficiency and emission of the vehicle based on the unsupervised learning methods. Both the real-world driving data of the evaluated vehicle and the large naturalistic driving dataset are used to perform the driving primitive analysis and coupling. Then the linear weighted estimation method could be used to calculate the testing result of the evaluated vehicle. The results show that this method can successfully identify the typical driving primitives. The couples of the driving primitives from the evaluated vehicle and the typical driving primitives from the large real-world driving dataset coincide with each other very well. This new method could enhance the standard development of the energy efficiency and emission testing of CAV and other off-cycle credits.
Learning a face space for experiments on human identity
Venue: arXiv cs.CV 2018
Year: 2018

Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images. Meeting these requirements is an exacting task, and existing models of human identity and appearance are often unworkably abstract, artificial, uncanny, or biased. Here, we use a variational autoencoder with an autoregressive decoder to learn a face space from a uniquely diverse dataset of portraits that control much of the variation irrelevant to human identity and appearance. Our method generates photorealistic portraits of fictive identities with a smooth, navigable latent space. We validate our model's alignment with human sensitivities by introducing a psychophysical Turing test for images, which humans mostly fail. Lastly, we demonstrate an initial application of our model to the problem of fast search in mental space to obtain detailed "police sketches" in a small number of trials.
The UN Parallel Corpus Annotated for Translation Direction
Venue: arXiv cs.CL 2018
Year: 2018

This work distinguishes between translated and original text in the UN protocol corpus. By modeling the problem as classification problem, we can achieve up to 95% classification accuracy. We begin by deriving a parallel corpus for different language-pairs annotated for translation direction, and then classify the data by using various feature extraction methods. We compare the different methods as well as the ability to distinguish between translated and original texts in the different languages. The annotated corpus is publicly available.
CIAO: Cache Interference-Aware Throughput-Oriented Architecture and Scheduling for GPUs
Venue: arXiv cs.AR 2018
Year: 2018

A modern GPU aims to simultaneously execute more warps for higher Thread-Level Parallelism (TLP) and performance. When generating many memory requests, however, warps contend for limited cache space and thrash cache, which in turn severely degrades performance. To reduce such cache thrashing, we may adopt cache locality-aware warp scheduling which gives higher execution priority to warps with higher potential of data locality. However, we observe that warps with high potential of data locality often incurs far more cache thrashing or interference than warps with low potential of data locality. Consequently, cache locality-aware warp scheduling may undesirably increase cache interference and/or unnecessarily decrease TLP. In this paper, we propose Cache Interference-Aware throughput-Oriented (CIAO) on-chip memory architecture and warp scheduling which exploit unused shared memory space and take insight opposite to cache locality-aware warp scheduling. Specifically, CIAO on-chip memory architecture can adaptively redirect memory requests of severely interfering warps to unused shared memory space to isolate memory requests of these interfering warps from those of interfered warps. If these interfering warps still incur severe cache interference, CIAO warp scheduling then begins to selectively throttle execution of these interfering warps. Our experiment shows that CIAO can offer 54% higher performance than prior cache locality-aware scheduling at a small chip cost.
Effects of Memristors on Fully Differential Transimpedance Amplifier Performance
Venue: arXiv cs.ET 2018
Year: 2018

The progress of the Internet of Things(IoT) technologies and applications requires the efficient low power circuits and architectures to maintain and improve the performance of the increasingly growing data processing systems. Memristive circuits and substitution of energy-consuming devices with memristors is a promising solution to reduce on-chip area and power dissipation of the architectures. In this paper, we proposed a CMOS-memristive fully differential transimpedance amplifier and assess the impact of memristors on the amplifier performance. The fully differential amplifiers were simulated using 180nm CMOS technology and have 5.3-23MHz bandwidths and 2.3-5.7k$Ω$ transimpedance gains with a 1pF load. We compare the memristor based amplifier with conventional architecture. The gain, frequency response, linear range, power consumption, area, total harmonic distortion and performance variations with temperature are reported.
STS Classification with Dual-stream CNN
Venue: arXiv cs.CV 2018
Year: 2018

The structured time series (STS) classification problem requires the modeling of interweaved spatiotemporal dependency. most previous STS classification methods model the spatial and temporal dependencies independently. Due to the complexity of the STS data, we argue that a desirable STS classification method should be a holistic framework that can be made as adaptive and flexible as possible. This motivates us to design a deep neural network with such merits. Inspired by the dual-stream hypothesis in neural science, we propose a novel dual-stream framework for modeling the interweaved spatiotemporal dependency, and develop a convolutional neural network within this framework that aims to achieve high adaptability and flexibility in STS configurations from various diagonals, i.e., sequential order, dependency range and features. The proposed architecture is highly modularized and scalable, making it easy to be adapted to specific tasks. The effectiveness of our model is demonstrated through experiments on synthetic data as well as benchmark datasets for skeleton based activity recognition.
Distributed Approximation of Minimum $k$-edge-connected Spanning Subgraphs
Venue: arXiv cs.DS 2018
Year: 2018

In the minimum $k$-edge-connected spanning subgraph ($k$-ECSS) problem the goal is to find the minimum weight subgraph resistant to up to $k-1$ edge failures. This is a central problem in network design, and a natural generalization of the minimum spanning tree (MST) problem. While the MST problem has been studied extensively by the distributed computing community, for $k \geq 2$ less is known in the distributed setting. In this paper, we present fast randomized distributed approximation algorithms for $k$-ECSS in the CONGEST model. Our first contribution is an $\widetilde{O}(D + \sqrt{n})$-round $O(\log{n})$-approximation for 2-ECSS, for a graph with $n$ vertices and diameter $D$. The time complexity of our algorithm is almost tight and almost matches the time complexity of the MST problem. For larger constant values of $k$ we give an $\widetilde{O}(n)$-round $O(\log{n})$-approximation. Additionally, in the special case of unweighted 3-ECSS we show how to improve the time complexity to $O(D \log^3{n})$ rounds. All our results significantly improve the time complexity of previous algorithms.
A VEST of the Pseudoinverse Learning Algorithm
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we briefly review the basic scheme of the pseudoinverse learning (PIL) algorithm and present some discussions on the PIL, as well as its variants. The PIL algorithm, first presented in 1995, is a non-gradient descent and non-iterative learning algorithm for multi-layer neural networks and has several advantages compared with gradient descent based algorithms. Some new viewpoints to PIL algorithm are presented, and several common pitfalls in practical implementation of the neural network learning task are also addressed. In addition, we show that so called extreme learning machine is a Variant crEated by Simple name alTernation (VEST) of the PIL algorithm for single hidden layer feedforward neural networks.
Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic features of the input and filter the non-semantic perturbation. FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping between the high-dimensional data space and the low-dimensional semantic space; also mutual information is applied to disentangle the semantically meaningful features. After the bidirectional mapping, the adversarial data can be reconstructed to denoised data, which could be fed into any pre-trained classifier. We empirically show the quality of reconstruction images and the effectiveness of defense.
Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification
Venue: arXiv cs.LG 2018
Year: 2018

Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic multi-kernel approach has been proposed to solve the OCC problem, which is simply a convex combination of different kernels with equal weights. This paper proposes a Localized Multiple Kernel learning approach for Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is assigned locally. Proposed LMKAD approach adapts the weight for each kernel using a gating function. The parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process. We present the empirical results of the performance of LMKAD on 25 benchmark datasets from various disciplines. This performance is evaluated against existing Multi Kernel Anomaly Detection (MKAD) algorithm, and four other existing kernel-based one-class classifiers to showcase the credibility of our approach. Our algorithm achieves significantly better Gmean scores while using a lesser number of support vectors compared to MKAD. Friedman test is also performed to verify the statistical significance of the results claimed in this paper.
Unsupervised Deep Context Prediction for Background Foreground Separation
Venue: arXiv cs.CV 2018
Year: 2018

In many advanced video based applications background modeling is a pre-processing step to eliminate redundant data, for instance in tracking or video surveillance applications. Over the past years background subtraction is usually based on low level or hand-crafted features such as raw color components, gradients, or local binary patterns. The background subtraction algorithms performance suffer in the presence of various challenges such as dynamic backgrounds, photometric variations, camera jitters, and shadows. To handle these challenges for the purpose of accurate background modeling we propose a unified framework based on the algorithm of image inpainting. It is an unsupervised visual feature learning hybrid Generative Adversarial algorithm based on context prediction. We have also presented the solution of random region inpainting by the fusion of center region inpaiting and random region inpainting with the help of poisson blending technique. Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations. The comparison of our proposed method with 12 state-of-the-art methods shows its stability in the application of background estimation and foreground detection.