OpenRaiser/Intern-Atlas huggingface dataset:
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
Venue: arXiv cs.LG 2018
Year: 2018

DNN is presenting human-level performance for many complex intelligent tasks in real-world applications. However, it also introduces ever-increasing security concerns. For example, the emerging adversarial attacks indicate that even very small and often imperceptible adversarial input perturbations can easily mislead the cognitive function of deep learning systems (DLS). Existing DNN adversarial studies are narrowly performed on the ideal software-level DNN models with a focus on single uncertainty factor, i.e. input perturbations, however, the impact of DNN model reshaping on adversarial attacks, which is introduced by various hardware-favorable techniques such as hash-based weight compression during modern DNN hardware implementation, has never been discussed. In this work, we for the first time investigate the multi-factor adversarial attack problem in practical model optimized deep learning systems by jointly considering the DNN model-reshaping (e.g. HashNet based deep compression) and the input perturbations. We first augment adversarial example generating method dedicated to the compressed DNN models by incorporating the software-based approaches and mathematical modeled DNN reshaping. We then conduct a comprehensive robustness and vulnerability analysis of deep compressed DNN models under derived adversarial attacks. A defense technique named "gradient inhibition" is further developed to ease the generating of adversarial examples thus to effectively mitigate adversarial attacks towards both software and hardware-oriented DNNs. Simulation results show that "gradient inhibition" can decrease the average success rate of adversarial attacks from 87.99% to 4.77% (from 86.74% to 4.64%) on MNIST (CIFAR-10) benchmark with marginal accuracy degradation across various DNNs.
Cyber Physical Systems: Prospects and Challenges
Venue: arXiv cs.CY 2018
Year: 2018

Cyber physical systems CPSs embodies the conception as well as the implementation of the integration of the state-of-art technologies in sensing, communication, computing, and control. Such systems incorporate new trends such as cloud computing, mobile computing, mobile sensing, new modes of communications, wearables, etc. In this article we give an exposition of the architecture of a typical CPS system and the prospects of such systems in the development of the modern world. We illustrate the three major challenges faced by a CPS system: the need for rigorous numerical computation, the limitation of the current wireless communication bandwidth, and the computation/storage limitation by mobility and energy consumption. We address each one of these exposing the current techniques devised to solve each one of them.
Interference Cancellation and Iterative Detection for Orthogonal Time Frequency Space Modulation
Venue: arXiv cs.IT 2018
Year: 2018

The recently proposed orthogonal time frequency space (OTFS) modulation technique was shown to provide significant error performance advantages over orthogonal frequency division multiplexing (OFDM) in Doppler channels. In this paper, we derive the explicit input-output relation describing OTFS modulation and demodulation (mod/demod) for delay-Doppler channels. We analyze the interferences and develop a novel low-complexity yet efficient message passing (MP) algorithm for joint interference cancellation (IC) and symbol detection. The proposed MP algorithm can effectively compensate for a wide range of channel Doppler spreads.
A Security Credential Management System for V2X Communications
Venue: arXiv cs.CR 2018
Year: 2018

The US Department of Transportation (USDOT) issued a proposed rule on January 12th, 2017 to mandate vehicle-to-vehicle (V2V) safety communications in light vehicles in the US. Cybersecurity and privacy are major challenges for such a deployment. The authors present a Security Credential Management System (SCMS) for vehicle-to-everything (V2X) communications in this paper, which has been developed by the Crash Avoidance Metrics Partners LLC (CAMP) under a Cooperative Agreement with the USDOT. This system design is currently transitioning from research to Proof-of-Concept, and is a leading candidate to support the establishment of a nationwide Public Key Infrastructure (PKI) for V2X security. It issues digital certificates to participating vehicles and infrastructure nodes for trustworthy communications among them, which is necessary for safety and mobility applications that are based on V2X communications. The main design goal is to provide both security and privacy to the largest extent reasonable and possible. To achieve a reasonable level of privacy in this context, vehicles are issued pseudonym certificates, and the generation and provisioning of those certificates are divided among multiple organizations. Given the large number of pseudonym certificates per vehicle, one of the main challenges is to facilitate efficient revocation of misbehaving or malfunctioning vehicles, while preserving privacy against attacks from insiders. The proposed SCMS supports all identified V2X use-cases and certificate types necessary for V2X communication security. This paper is based upon work supported by the USDOT. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the Authors ("we") and do not necessarily reflect the view of the USDOT.
Advancing System Performance with Redundancy: From Biological to Artificial Designs
Venue: arXiv cs.IT 2018
Year: 2018

Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular and nervous system; yet its function has not been fully understood. The conventional interpretation of redundancy is that it serves as a fault-tolerance mechanism, which leads to redundancy's de facto application in man-made systems for reliability enhancement. On the contrary, our previous works have demonstrated an example where redundancy can be engineered solely for enhancing other aspects of the system, namely accuracy and precision. This design was inspired by the binocular structure of the human vision which we believe may share a similar operation. In this paper, we present a unified theory describing how such utilization of redundancy is feasible through two complementary mechanisms: representational redundancy (RPR) and entangled redundancy (ETR). Besides the previous works, we point out two additional examples where our new understanding of redundancy can be applied to justify a system's superior performance. One is the human musculoskeletal system (HMS) - a biological instance, and one is the deep residual neural network (ResNet) - an artificial counterpart. We envision that our theory would provide a framework for the future development of bio-inspired redundant artificial systems as well as assist the studies of the fundamental mechanisms governing various biological processes.
Input-Aware Auto-Tuning of Compute-Bound HPC Kernels
Venue: arXiv cs.DC 2018
Year: 2018

Efficient implementations of HPC applications for parallel architectures generally rely on external software packages (e.g., BLAS, LAPACK, CUDNN). While these libraries provide highly optimized routines for certain characteristics of inputs (e.g., square matrices), they generally do not retain optimal performance across the wide range of problems encountered in practice. In this paper, we present an input-aware auto-tuning framework for matrix multiplications and convolutions, ISAAC, which uses predictive modeling techniques to drive highly parameterized PTX code templates towards not only hardware-, but also application-specific kernels. Numerical experiments on the NVIDIA Maxwell and Pascal architectures show up to 3x performance gains over both cuBLAS and cuDNN after only a few hours of auto-tuning.
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Venue: arXiv cs.CV 2018
Year: 2018

We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potential for other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.
Strongly connected components-Algorithm for finding the strongly connected components of a graph
Venue: arXiv cs.DS 2018
Year: 2018

A directed graph G (V, E) is strongly connected if and only if, for a pair of vertices X and Y from V, there exists a path from X to Y and a path from Y to X. In Computer Science, the partition of a graph in strongly connected components is represented by the partition of all vertices from the graph, so that for any two vertices, X and Y, from the same partition, there exists a path from X to Y and a path from Y to X and for any two vertices, U and V, from different partition, the property is not met. The algorithm presented below is meant to find the partition of a given graph in strongly connected components in O (numberOfNodes + numberOfEdges * log* (numberOfNodes)), where log* function stands for iterated logarithm.
"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study
Venue: arXiv cs.IT 2018
Year: 2018

Recent works investigated the generalization properties in deep neural networks (DNNs) by studying the Information Bottleneck in DNNs. However, the mea- surement of the mutual information (MI) is often inaccurate due to the density estimation. To address this issue, we propose to measure the dependency instead of MI between layers in DNNs. Specifically, we propose to use Hilbert-Schmidt Independence Criterion (HSIC) as the dependency measure, which can measure the dependence of two random variables without estimating probability densities. Moreover, HSIC is a special case of the Squared-loss Mutual Information (SMI). In the experiment, we empirically evaluate the generalization property using HSIC in both the reconstruction and prediction auto-encoding (AE) architectures.
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization
Venue: arXiv cs.SD 2018
Year: 2018

Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF) algorithm for supervised speech blind source separation (BSS). Optimal transport allows us to design and leverage a cost between short-time Fourier transform (STFT) spectrogram frequencies, which takes into account how humans perceive sound. We give empirical evidence that using our proposed optimal transport NMF leads to perceptually better results than Euclidean NMF, for both isolated voice reconstruction and BSS tasks. Finally, we demonstrate how to use optimal transport for cross domain sound processing tasks, where frequencies represented in the input spectrograms may be different from one spectrogram to another.
Genomics as a Service: a Joint Computing and Networking Perspective
Venue: arXiv cs.DC 2018
Year: 2018

This paper provides a global picture about the deployment of networked processing services for genomic data sets. Many current research make an extensive use genomic data, which are massive and rapidly increasing over time. They are typically stored in remote databases, accessible by using Internet. For this reason, a significant issue for effectively handling genomic data through data networks consists of the available network services. A first contribution of this paper consists of identifying the still unexploited features of genomic data that could allow optimizing their networked management. The second and main contribution of this survey consists of a methodological classification of computing and networking alternatives which can be used to offer what we call the Genomic-as-a-Service (GaaS) paradigm. In more detail, we analyze the main genomic processing applications, and classify not only the main computing alternatives to run genomics workflows in either a local machine or a distributed cloud environment, but also the main software technologies available to develop genomic processing services. Since an analysis encompassing only the computing aspects would provide only a partial view of the issues for deploying GaaS system, we present also the main networking technologies that are available to efficiently support a GaaS solution. We first focus on existing service platforms, and analyze them in terms of service features, such as scalability, flexibility, and efficiency. Then, we present a taxonomy for both wide area and datacenter network technologies that may fit the GaaS requirements. It emerges that virtualization, both in computing and networking, is the key for a successful large-scale exploitation of genomic data, by pushing ahead the adoption of the GaaS paradigm. Finally, the paper illustrates a short and long-term vision on future research challenges in the field.
Discrepancy-based Evolutionary Diversity Optimization
Venue: arXiv cs.NE 2018
Year: 2018

Diversity plays a crucial role in evolutionary computation. While diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence, the use of evolutionary algorithms to obtain a diverse set of solutions has gained increasing attention in recent years. Diversity optimization in terms of features on the underlying problem allows to obtain a better understanding of possible solutions to the problem at hand and can be used for algorithm selection when dealing with combinatorial optimization problems such as the Traveling Salesperson Problem. We explore the use of the star-discrepancy measure to guide the diversity optimization process of an evolutionary algorithm. In our experimental investigations, we consider our discrepancy-based diversity optimization approaches for evolving diverse sets of images as well as instances of the Traveling Salesperson problem where a local search is not able to find near optimal solutions. Our experimental investigations comparing three diversity optimization approaches show that a discrepancy-based diversity optimization approach using a tie-breaking rule based on weighted differences to surrounding feature points provides the best results in terms of the star discrepancy measure.
Point systems in Games for Health: A bibliometric scoping study
Venue: arXiv cs.CY 2018
Year: 2018

Very few details about point systems used in games for health are reported in scientific literature. To shed some light on this topic a bibliometric study, analyzing the papers containing terms related to games for health and point systems was performed and a mini taxonomy was derived. The search string game* AND health AND (point* OR score) AND system* in a Scopus bibliographic database was used to produce the corpus. We limited the search to articles, reviews and conference papers written in English and to topics related to medical, health and social subjects. The corpus papers abstracts and titles were analysed by VOSviewer and a scientific landscape was generated. The search resulted in a corpus consisting of 354 papers. The derived taxonomy contains three objects; video games, serious games and educational games. The biblimetric mapping and taxonomy revealed some interesting conclusions: (1) the video games have mostly negative effects on health, (2) the serious games might have both a direct positive health effects on users and also indirect effects by improved competencies of health professionals, and (3) the research is concerned not only to computer based educational games, but also to traditional table games and sporting games. Based on the derived taxonomy we can conclude that point systems should reward physical activity and healthy living style and punish sedentary activities.
Grammar-based Compression of Unranked Trees
Venue: arXiv cs.DS 2018
Year: 2018

We introduce forest straight-line programs (FSLPs) as a compressed representation of unranked ordered node-labelled trees. FSLPs are based on the operations of forest algebra and generalize tree straight-line programs. We compare the succinctness of FSLPs with two other compression schemes for unranked trees: top dags and tree straight-line programs of first-child/next sibling encodings. Efficient translations between these formalisms are provided. Finally, we show that equality of unranked trees in the setting where certain symbols are associative or commutative can be tested in polynomial time. This generalizes previous results for testing isomorphism of compressed unordered ranked trees.
Novel weak form quadrature elements for non-classical higher order beam and plate theories
Venue: arXiv cs.CE 2018
Year: 2018

Based on Lagrange and Hermite interpolation two novel versions of weak form quadrature element are proposed for a non-classical Euler-Bernoulli beam theory. By extending these concept two new plate elements are formulated using Lagrange-Lagrange and mixed Lagrange-Hermite interpolations for a non-classical Kirchhoff plate theory. The non-classical theories are governed by sixth order partial differential equation and have deflection, slope and curvature as de- grees of freedom. A novel and generalize way is proposed herein to implement these degrees of freedom in a simple and efficient manner. A new procedure to compute the modified weighting coefficient matri- ces for beam and plate elements is presented. The proposed elements have displacement as the only degree of freedom in the element do- main and displacement, slope and curvature at the boundaries. The Gauss-Lobatto-Legender quadrature points are considered as element nodes and also used for numerical integration of the element matrices. The framework for computing the stiffness matrices at the integra- tion points is analogous to the conventional finite element method. Numerical examples on free vibration analysis of gradient beams and plates are presented to demonstrate the efficiency and accuracy of the proposed elements.
Tools and resources for Romanian text-to-speech and speech-to-text applications
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we introduce a set of resources and tools aimed at providing support for natural language processing, text-to-speech synthesis and speech recognition for Romanian. While the tools are general purpose and can be used for any language (we successfully trained our system for more than 50 languages and participated in the Universal Dependencies Shared Task), the resources are only relevant for Romanian language processing.
System Identification via Polynomial Transformation Method
Venue: arXiv cs.SY 2018
Year: 2018

We propose a method based on minimum-variance polynomial approximation to extract system poles from a data set of samples of the impulse response of a linear system. The method is capable of handling the problem under general conditions of sampling and noise characteristics. The superiority of the proposed method is demonstrated by statistical comparison of its performance with the performances of two exiting methods in the special case of uniform sampling.
Integrating AADL and FMI to Extend Virtual Integration Capability
Venue: arXiv cs.SY 2018
Year: 2018

Virtual Integration Capability is paramount to perform early validation of Cyber Physical Systems. The objective is to guide the systems engineer so as to ensure that the system under design meets multiple criteria through high-fidelity simulation. In this paper, we present an integration scheme that leverages the FMI (Functional Mock-Up interface) standard and the AADL architecture description language. Their combination allows for validation of systems combining embedded platform captured by the AADL, and FMI components that represent physical elements, either mechanical parts, or the environment. We present one approach, and demonstrator case studies.
IBeaconMap: Automated Indoor Space Representation for Beacon-Based Wayfinding
Venue: arXiv cs.HC 2018
Year: 2018

Traditionally, there have been few options for navigational aids for the blind and visually impaired (BVI) in large indoor spaces. Some recent indoor navigation systems allow users equipped with smartphones to interact with low cost Bluetoothbased beacons deployed strategically within the indoor space of interest to navigate their surroundings. A major challenge in deploying such beacon-based navigation systems is the need to employ a time and labor-expensive beacon planning process to identify potential beacon placement locations and arrive at a topological structure representing the indoor space. This work presents a technique called IBeaconMap for creating such topological structures to use with beacon-based navigation that only needs the floor plans of the indoor spaces of interest. IBeaconMap employs a combination of computer vision and machine learning techniques to arrive at the required set of beacon locations and a weighted connectivity graph (with directional orientations) for subsequent navigational needs. Evaluations show IBeaconMap to be both fast and reasonably accurate, potentially proving to be an essential tool to be utilized before mass deployments of beacon-based indoor wayfinding systems of the future.
MPC-Inspired Neural Network Policies for Sequential Decision Making
Venue: arXiv cs.LG 2018
Year: 2018

In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making. We introduce an extension to the DAgger algorithm for training such policies and show how they have improved training performance and generalization capabilities. We take advantage of this extension to show scalable and efficient training of complex planning policy architectures in continuous state and action spaces. We provide an extensive comparison of neural network policies by considering feed forward policies, recurrent policies, and recurrent policies with planning structure inspired by the Path Integral control framework. Our results suggest that MPC-type recurrent policies have better robustness to disturbances and modeling error.
ISEC: Iterative over-Segmentation via Edge Clustering
Venue: arXiv cs.CV 2018
Year: 2018

Several image pattern recognition tasks rely on superpixel generation as a fundamental step. Image analysis based on superpixels facilitates domain-specific applications, also speeding up the overall processing time of the task. Recent superpixel methods have been designed to fit boundary adherence, usually regulating the size and shape of each superpixel in order to mitigate the occurrence of undersegmentation failures. Superpixel regularity and compactness sometimes imposes an excessive number of segments in the image, which ultimately decreases the efficiency of the final segmentation, specially in video segmentation. We propose here a novel method to generate superpixels, called iterative over-segmentation via edge clustering (ISEC), which addresses the over-segmentation problem from a different perspective in contrast to recent state-of-the-art approaches. ISEC iteratively clusters edges extracted from the image objects, providing adaptive superpixels in size, shape and quantity, while preserving suitable adherence to the real object boundaries. All this is achieved at a very low computational cost. Experiments show that ISEC stands out from existing methods, meeting a favorable balance between segmentation stability and accurate representation of motion discontinuities, which are features specially suitable to video segmentation.