OpenRaiser/Intern-Atlas huggingface dataset:
Characterization of Brain Cortical Morphology Using Localized Topology-Encoding Graphs
Venue: arXiv cs.CV 2018
Year: 2018

The human brain cortical layer has a convoluted morphology that is unique to each individual. Characterization of the cortical morphology is necessary in longitudinal studies of structural brain change, as well as in discriminating individuals in health and disease. A method for encoding the cortical morphology in the form of a graph is presented. The design of graphs that encode the global cerebral hemisphere cortices as well as localized cortical regions is proposed. Spectral features of these graphs are then studied and proposed as descriptors of cortical morphology. As proof-of-concept of their applicability in characterizing cortical morphology, the descriptors are studied in the context of discriminating individuals based on their sex.
G-SMOTE: A GMM-based synthetic minority oversampling technique for imbalanced learning
Venue: arXiv cs.LG 2018
Year: 2018

Imbalanced Learning is an important learning algorithm for the classification models, which have enjoyed much popularity on many applications. Typically, imbalanced learning algorithms can be partitioned into two types, i.e., data level approaches and algorithm level approaches. In this paper, the focus is to develop a robust synthetic minority oversampling technique which falls the umbrella of data level approaches. On one hand, we proposed a method to generate synthetic samples in a high dimensional feature space, instead of a linear sampling space. On the other hand, in the proposed imbalanced learning framework, Gaussian Mixture Model is employed to distinguish the outliers from minority class instances and filter out the synthetic majority class instances. Last and more importantly, an adaptive optimization method is proposed to optimize these parameters in sampling process. By doing so, an effectiveness and efficiency imbalanced learning framework is developed.
LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color
Venue: arXiv cs.CV 2018
Year: 2018

Designing a logo is a long, complicated, and expensive process for any designer. However, recent advancements in generative algorithms provide models that could offer a possible solution. Logos are multi-modal, have very few categorical properties, and do not have a continuous latent space. Yet, conditional generative adversarial networks can be used to generate logos that could help designers in their creative process. We propose LoGAN: an improved auxiliary classifier Wasserstein generative adversarial neural network (with gradient penalty) that is able to generate logos conditioned on twelve different colors. In 768 generated instances (12 classes and 64 logos per class), when looking at the most prominent color, the conditional generation part of the model has an overall precision and recall of 0.8 and 0.7 respectively. LoGAN's results offer a first glance at how artificial intelligence can be used to assist designers in their creative process and open promising future directions, such as including more descriptive labels which will provide a more exhaustive and easy-to-use system.
Deep Learning Scooping Motion using Bilateral Teleoperations
Venue: arXiv cs.RO 2018
Year: 2018

We present bilateral teleoperation system for task learning and robot motion generation. Our system includes a bilateral teleoperation platform and a deep learning software. The deep learning software refers to human demonstration using the bilateral teleoperation platform to collect visual images and robotic encoder values. It leverages the datasets of images and robotic encoder information to learn about the inter-modal correspondence between visual images and robot motion. In detail, the deep learning software uses a combination of Deep Convolutional Auto-Encoders (DCAE) over image regions, and Recurrent Neural Network with Long Short-Term Memory units (LSTM-RNN) over robot motor angles, to learn motion taught be human teleoperation. The learnt models are used to predict new motion trajectories for similar tasks. Experimental results show that our system has the adaptivity to generate motion for similar scooping tasks. Detailed analysis is performed based on failure cases of the experimental results. Some insights about the cans and cannots of the system are summarized.
Effective extractive summarization using frequency-filtered entity relationship graphs
Venue: arXiv cs.CL 2018
Year: 2018

Word frequency-based methods for extractive summarization are easy to implement and yield reasonable results across languages. However, they have significant limitations - they ignore the role of context, they offer uneven coverage of topics in a document, and sometimes are disjointed and hard to read. We use a simple premise from linguistic typology - that English sentences are complete descriptors of potential interactions between entities, usually in the order subject-verb-object - to address a subset of these difficulties. We have developed a hybrid model of extractive summarization that combines word-frequency based keyword identification with information from automatically generated entity relationship graphs to select sentences for summaries. Comparative evaluation with word-frequency and topic word-based methods shows that the proposed method is competitive by conventional ROUGE standards, and yields moderately more informative summaries on average, as assessed by a large panel (N=94) of human raters.
Reduced order modeling of subsurface multiphase flow models using deep residual recurrent neural networks
Venue: arXiv cs.CE 2018
Year: 2018

We present a reduced order modeling (ROM) technique for subsurface multi-phase flow problems building on the recently introduced deep residual recurrent neural network (DR-RNN) [1]. DR-RNN is a physics aware recurrent neural network for modeling the evolution of dynamical systems. The DR-RNN architecture is inspired by iterative update techniques of line search methods where a fixed number of layers are stacked together to minimize the residual (or reduced residual) of the physical model under consideration. In this manuscript, we combine DR-RNN with proper orthogonal decomposition (POD) and discrete empirical interpolation method (DEIM) to reduce the computational complexity associated with high-fidelity numerical simulations. In the presented formulation, POD is used to construct an optimal set of reduced basis functions and DEIM is employed to evaluate the nonlinear terms independent of the full-order model size. We demonstrate the proposed reduced model on two uncertainty quantification test cases using Monte-Carlo simulation of subsurface flow with random permeability field. The obtained results demonstrate that DR-RNN combined with POD-DEIM provides an accurate and stable reduced model with a fixed computational budget that is much less than the computational cost of standard POD-Galerkin reduced model combined with DEIM for nonlinear dynamical systems.
Software Rejuvenation for Secure Tracking Control
Venue: arXiv cs.SY 2018
Year: 2018

Software rejuvenation protects cyber-physical systems (CSPs) against cyber attacks on the run-time code by periodically refreshing the system with an uncorrupted software image. The system is vulnerable to attacks when it is communicating with other agents. Security is guaranteed during the software refresh and re-initialization by turning off all communication. Although the effectiveness of software rejuvenation has been demonstrated for some simple systems, many problems need to be addressed to make it viable for real applications. This paper expands the scope of CPS applications for which software rejuvenation can be implemented by introducing architectural and algorithmic features to support trajectory tracking. Following each software refresh, while communication is still off, a safety controller is executed to assure the system state is within a sufficiently small neighborhood of the current point on the reference trajectory. Communication is then re-established and the reference trajectory tracking control is resumed. A protected, verified hypervisor manages the software rejuvenation sequence and delivers trusted reference trajectory points, which may be received from untrusted communication, but are verified using an authentication process. We present the approach to designing the tracking and safety controllers and timing parameters and demonstrate the secure tracking control for a 6 DOF quadrotor using the PX4 jMAVSim quadrotor simulator. The concluding section discusses directions for further research.
Design of Software Rejuvenation for CPS Security Using Invariant Sets
Venue: arXiv cs.SY 2018
Year: 2018

Software rejuvenation has been proposed as a strategy to protect cyber-physical systems (CSPs) against unanticipated and undetectable cyber attacks. The basic idea is to refresh the system periodically with a secure and trusted copy of the online software so as to eliminate all effects of malicious modifications to the run-time code and data. Following each software refresh a safety controller assures the CPS is driven to a safe state before returning to the mission control mode when the CPS is again vulnerable attacks. This paper considers software rejuvenation design from a control-theoretic perspective. Invariant sets for the Lyapunov function for the safety controller are used to derive bounds on the time that the CPS can operate in mission control mode before the software must be refreshed and the maximum time the safety controller will require to bring the CPS to a safe operating state. With these results it can be guaranteed that the CPS will remain safe under cyber attacks against the run-time system and will be able to execute missions successfully if the attacks are not persistent. The general approach is illustrated using simulation of the nonlinear dynamics of a quadrotor system. The concluding section discusses directions for further research.
Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering
Venue: arXiv cs.PF 2018
Year: 2018

Automatic compiler phase selection/ordering has traditionally been focused on CPUs and, to a lesser extent, FPGAs. We present experiments regarding compiler phase ordering specialization of OpenCL kernels targeting a GPU. We use iterative exploration to specialize LLVM phase orders on 15 OpenCL benchmarks to an NVIDIA GPU. We analyze the generated NVIDIA PTX code for the various versions to identify the main causes of the most significant improvements and present results of a set of experiments that demonstrate the importance of using specific phase orders. Using specialized compiler phase orders, we were able to achieve geometric mean improvements of 1.54x (up to 5.48x) and 1.65x (up to 5.7x) over PTX generated by the NVIDIA CUDA compiler from CUDA versions of the same kernels, and over execution of the OpenCL kernels compiled from source with the NVIDIA OpenCL driver, respectively. We also evaluate the use of code-features in the OpenCL kernels. More specifically, we evaluate an approach that achieves geometric mean improvements of 1.49x and 1.56x over the same OpenCL baseline, by using the compiler sequences of the 1 or 3 most similar benchmarks, respectively.
On the Real Stability Radius of Sparse Systems
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we study robust stability of sparse LTI systems using the stability radius (SR) as a robustness measure. We consider real perturbations with an arbitrary and pre-specified sparsity pattern of the system matrix and measure their size using the Frobenius norm. We formulate the SR problem as an equality-constrained minimization problem. Using the Lagrangian method for optimization, we characterize the optimality conditions of the SR problem, thereby revealing the relation between an optimal perturbation and the eigenvectors of an optimally perturbed system. Further, we use the Sylvester equation based parametrization to develop a penalty based gradient/Newton descent algorithm which converges to the local minima of the optimization problem. Finally, we illustrate how our framework provides structural insights into the robust stability of sparse networks.
Inverse reinforcement learning for video games
Venue: arXiv cs.LG 2018
Year: 2018

Deep reinforcement learning achieves superhuman performance in a range of video game environments, but requires that a designer manually specify a reward function. It is often easier to provide demonstrations of a target behavior than to design a reward function describing that behavior. Inverse reinforcement learning (IRL) algorithms can infer a reward from demonstrations in low-dimensional continuous control environments, but there has been little work on applying IRL to high-dimensional video games. In our CNN-AIRL baseline, we modify the state-of-the-art adversarial IRL (AIRL) algorithm to use CNNs for the generator and discriminator. To stabilize training, we normalize the reward and increase the size of the discriminator training dataset. We additionally learn a low-dimensional state representation using a novel autoencoder architecture tuned for video game environments. This embedding is used as input to the reward network, improving the sample efficiency of expert demonstrations. Our method achieves high-level performance on the simple Catcher video game, substantially outperforming the CNN-AIRL baseline. We also score points on the Enduro Atari racing game, but do not match expert performance, highlighting the need for further work.
Continual Classification Learning Using Generative Models
Venue: arXiv cs.LG 2018
Year: 2018

Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on previously learned tasks when tasks are presented one at a time. This problem is called catastrophic forgetting. In this work, we propose a classification model that learns continuously from sequentially observed tasks, while preventing catastrophic forgetting. We build on the lifelong generative capabilities of [10] and extend it to the classification setting by deriving a new variational bound on the joint log likelihood, $\log p(x; y)$.
Alleviating Patch Overfitting with Automatic Test Generation: A Study of Feasibility and Effectiveness for the Nopol Repair System
Venue: arXiv cs.SE 2018
Year: 2018

Among the many different kinds of program repair techniques, one widely studied family of techniques is called test suite based repair. However, test suites are in essence input-output specifications and are thus typically inadequate for completely specifying the expected behavior of the program under repair. Consequently, the patches generated by test suite based repair techniques can just overfit to the used test suite, and fail to generalize to other tests. We deeply analyze the overfitting problem in program repair and give a classification of this problem. This classification will help the community to better understand and design techniques to defeat the overfitting problem. We further propose and evaluate an approach called UnsatGuided, which aims to alleviate the overfitting problem for synthesis-based repair techniques with automatic test case generation. The approach uses additional automatically generated tests to strengthen the repair constraint used by synthesis-based repair techniques. We analyze the effectiveness of UnsatGuided: 1) analytically with respect to alleviating two different kinds of overfitting issues; 2) empirically based on an experiment over the 224 bugs of the Defects4J repository. The main result is that automatic test generation is effective in alleviating one kind of overfitting issue--regression introduction, but due to oracle problem, has minimal positive impact on alleviating the other kind of overfitting issue--incomplete fixing.
Minsum $k$-Sink Problem on Path Networks
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problem of locating a set of $k$ sinks on a path network with general edge capacities that minimizes the sum of the evacuation times of all evacuees. We first present an $O(kn\log^4n)$ time algorithm when the edge capacities are non-uniform, where $n$ is the number of vertices. We then present an $O(kn\log^3 n)$ time algorithm when the edge capacities are uniform. We also present an $O(n\log n)$ time algorithm for the special case where $k=1$ and the edge capacities are non-uniform.
Lower Bounds for Oblivious Data Structures
Venue: arXiv cs.DS 2018
Year: 2018

An oblivious data structure is a data structure where the memory access patterns reveals no information about the operations performed on it. Such data structures were introduced by Wang et al. [ACM SIGSAC'14] and are intended for situations where one wishes to store the data structure at an untrusted server. One way to obtain an oblivious data structure is simply to run a classic data structure on an oblivious RAM (ORAM). Until very recently, this resulted in an overhead of $ω(\lg n)$ for the most natural setting of parameters. Moreover, a recent lower bound for ORAMs by Larsen and Nielsen [CRYPTO'18] show that they always incur an overhead of at least $Ω(\lg n)$ if used in a black box manner. To circumvent the $ω(\lg n)$ overhead, researchers have instead studied classic data structure problems more directly and have obtained efficient solutions for many such problems such as stacks, queues, deques, priority queues and search trees. However, none of these data structures process operations faster than $Θ(\lg n)$, leaving open the question of whether even faster solutions exist. In this paper, we rule out this possibility by proving $Ω(\lg n)$ lower bounds for oblivious stacks, queues, deques, priority queues and search trees.
Law and Adversarial Machine Learning
Venue: arXiv cs.LG 2018
Year: 2018

When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks are more likely to result in liability than others. We end with a call for action to ML researchers to invest in transparent benchmarks of attacks and defenses; architect ML systems with forensics in mind and finally, think more about adversarial machine learning in the context of civil liberties. The paper is targeted towards ML researchers who have no legal background.
Cicero: Multi-Turn, Contextual Argumentation for Accurate Crowdsourcing
Venue: arXiv cs.HC 2018
Year: 2018

Traditional approaches for ensuring high quality crowdwork have failed to achieve high-accuracy on difficult problems. Aggregating redundant answers often fails on the hardest problems when the majority is confused. Argumentation has been shown to be effective in mitigating these drawbacks. However, existing argumentation systems only support limited interactions and show workers general justifications, not context-specific arguments targeted to their reasoning. This paper presents Cicero, a new workflow that improves crowd accuracy on difficult tasks by engaging workers in multi-turn, contextual discussions through real-time, synchronous argumentation. Our experiments show that compared to previous argumentation systems which only improve the average individual worker accuracy by 6.8 percentage points on the Relation Extraction domain, our workflow achieves 16.7 percentage point improvement. Furthermore, previous argumentation approaches don't apply to tasks with many possible answers; in contrast, Cicero works well in these cases, raising accuracy from 66.7% to 98.8% on the Codenames domain.
Towards Delay-Tolerant Flexible Data Access Control for Smart Grid with Renewable Energy Resources
Venue: arXiv cs.CR 2018
Year: 2018

In the Smart Grid with Renewable Energy Resources (RERs), the Residential Units (RUs) with Distributed Energy Resources (DERs) are considered to be both power consumers and suppliers. Specifically, RUs with excessive renewable generations can trade with the utility in deficit of power supplies for mutual benefits. It causes two challenging issues. First, the trading data of RUs is quite sensitive, which should be only accessed by authorized users with fine-grained policies. Second, the behaviors of the RUs to generate trading data are spontaneous and unpredictable, then the problem is how to guarantee system efficiency and delay tolerance simultaneously. In this paper, we propose a delay-tolerant flexible data access control scheme based on Key Policy Attribute Based Encryption (KP-ABE) for Smart Grid with Renewable Energy Resources (RERs). We adopt the secret sharing scheme (SSS) to realize a flexible access control with encryption delay tolerance. Furthermore, there is no central trusted server to perform the encryption/decryption. We reduce the computation cost on RUs and operators via a semi-trusted model. The analysis shows that the proposed scheme can meet the data security requirement of the Smart Grid with RERs, and it also has less cost compared with other popular models.
Word Embedding based Edit Distance
Venue: arXiv cs.CL 2018
Year: 2018

Text similarity calculation is a fundamental problem in natural language processing and related fields. In recent years, deep neural networks have been developed to perform the task and high performances have been achieved. The neural networks are usually trained with labeled data in supervised learning, and creation of labeled data is usually very costly. In this short paper, we address unsupervised learning for text similarity calculation. We propose a new method called Word Embedding based Edit Distance (WED), which incorporates word embedding into edit distance. Experiments on three benchmark datasets show WED outperforms state-of-the-art unsupervised methods including edit distance, TF-IDF based cosine, word embedding based cosine, Jaccard index, etc.
The Logoscope: a Semi-Automatic Tool for Detecting and Documenting French New Words
Venue: arXiv cs.CL 2018
Year: 2018

In this article we present the design and implementation of the Logoscope, the first tool especially developed to detect new words of the French language, to document them and allow a public access through a web interface. This semi-automatic tool collects new words daily by browsing the online versions of French well known newspapers such as Le Monde, Le Figaro, L'Equipe, Libération, La Croix, Les Échos. In contrast to other existing tools essentially dedicated to dictionary development, the Logoscope attempts to give a more complete account of the context in which the new words occur. In addition to the commonly given morpho-syntactic information it also provides information about the textual and discursive contexts of the word creation; in particular, it automatically determines the (journalistic) topics of the text containing the new word. In this article we first give a general overview of the developed tool. We then describe the approach taken, we discuss the linguistic background which guided our design decisions and present the computational methods we used to implement it.