OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistically Shaped Multi-Level Coding with Polar Codes for Fading Channels
Venue: arXiv cs.IT 2018
Year: 2018

A probabilistic shaping method for multi-level coding (MLC) is presented, where the transmitted symbols are forced to have a shaped non-uniform distribution. It is shown that shaping only a single bit-level suffices to compensate for most of the shaping loss on the fading channels. A polar code based implementation of the proposed scheme is presented, where shaping is performed by using a precoder at the transmitter without increasing the decoding complexity. Simulation results show that performance improvements can be obtained compared to BICM- and MLC-based polar coding without shaping on Rayleigh fading channels.
Crack Detection Using Enhanced Thresholding on UAV based Collected Images
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings.
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning
Venue: arXiv cs.LG 2018
Year: 2018

A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incrementally include new useful features into the hypothesis. This can be particularly important when dealing with a very large number of potential features like, for instance, in relational learning and rule extraction. A game theoretical analysis is used to demonstrate the convergence of the algorithm. Furthermore, leveraging on the natural analogy between features and rules, the resulting models can be easily interpreted by humans. An extensive set of experiments on classification tasks shows the effectiveness of the proposed method in terms of interpretability and feature selection quality, with accuracy at the state-of-the-art.
An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering
Venue: arXiv cs.LG 2018
Year: 2018

The power of randomized algorithms in numerical methods have led to fast solutions which use the Singular Value Decomposition (SVD) as a core routine. However, given the large data size of modern and the modest runtime of SVD, most practical algorithms would require some form of approximation, such as sketching, when running SVD. While these approximation methods satisfy many theoretical guarantees, we provide the first algorithmic implementations for sketch-and-solve SVD problems on real-world, large-scale datasets. We provide a comprehensive empirical evaluation of these algorithms and provide guidelines on how to ensure accurate deployment to real-world data. As an application of sketched SVD, we present Sketched Leverage Score Ordering, a technique for determining the ordering of data in the training of neural networks. Our technique is based on the distributed computation of leverage scores using random projections. These computed leverage scores provide a flexible and efficient method to determine the optimal ordering of training data without manual intervention or annotations. We present empirical results on an extensive set of experiments across image classification, language sentiment analysis, and multi-modal sentiment analysis. Our method is faster compared to standard randomized projection algorithms and shows improvements in convergence and results.
Carving Parameterized Unit Tests
Venue: arXiv cs.SE 2018
Year: 2018

We present a method to automatically extract ("carve") parameterized unit tests from system executions. The unit tests execute the same functions as the system tests they are carved from, but can do so much faster as they call functions directly; furthermore, being parameterized, they can execute the functions with a large variety of randomly selected input values. If a unit-level test fails, we lift it to the system level to ensure the failure can be reproduced there. Our method thus allows to focus testing efforts on selected modules while still avoiding false alarms: In our experiments, running parameterized unit tests for individual functions was, on average, 30~times faster than running the system tests they were carved from.
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

In recent years, deep neural networks (DNNs) have been applied to various machine leaning tasks, including image recognition, speech recognition, and machine translation. However, large DNN models are needed to achieve state-of-the-art performance, exceeding the capabilities of edge devices. Model reduction is thus needed for practical use. In this paper, we point out that deep learning automatically induces group sparsity of weights, in which all weights connected to an output channel (node) are zero, when training DNNs under the following three conditions: (1) rectified-linear-unit (ReLU) activations, (2) an $L_2$-regularized objective function, and (3) the Adam optimizer. Next, we analyze this behavior both theoretically and experimentally, and propose a simple model reduction method: eliminate the zero weights after training the DNN. In experiments on MNIST and CIFAR-10 datasets, we demonstrate the sparsity with various training setups. Finally, we show that our method can efficiently reduce the model size and performs well relative to methods that use a sparsity-inducing regularizer.
Toward Intelligent Network Optimization in Wireless Networking: An Auto-learning Framework
Venue: arXiv cs.NI 2018
Year: 2018

In wireless communication systems (WCSs), the network optimization problems (NOPs) play an important role in maximizing system performances by setting appropriate network configurations. When dealing with NOPs by using conventional optimization methodologies, there exist the following three problems: human intervention, model invalid, and high computation complexity. As such, in this article we propose an auto-learning framework (ALF) to achieve intelligent and automatic network optimization by using machine learning (ML) techniques. We review the basic concepts of ML techniques, and propose their rudimentary employment models in WCSs, including automatic model construction, experience replay, efficient trial-and-error, RL-driven gaming, complexity reduction, and solution recommendation. We hope these proposals can provide new insights and motivations in future researches for dealing with NOPs in WCSs by using ML techniques.
Specializing Underdetermined Action Descriptions Through Plan Projection
Venue: arXiv cs.RO 2018
Year: 2018

Plan execution on real robots in realistic environments is underdetermined and often leads to failures. The choice of action parameterization is crucial for task success. By thinking ahead of time with the fast plan projection mechanism proposed in this paper, a general plan can be specialized towards the environment and task at hand by choosing action parameterizations that are predicted to lead to successful execution. For finding causal relationships between action parameterizations and task success, we provide the robot with means for plan introspection and propose a systematic and hierarchical plan structure to support that. We evaluate our approach by showing how a PR2 robot, when equipped with the proposed system, is able to choose action parameterizations that increase task execution success rates and overall performance of fetch and deliver actions in a real world setting.
A Novel Large-scale Ordinal Regression Model
Venue: arXiv cs.LG 2018
Year: 2018

Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels. Recent years, people share their opinions and sentimental judgments conveniently with social networks and E-Commerce so that plentiful large-scale OR problems arise. However, few studies have focused on this kind of problems. Nonparallel Support Vector Ordinal Regression (NPSVOR) is a SVM-based OR model, which learns a hyperplane for each rank by solving a series of independent sub-optimization problems and then ensembles those learned hyperplanes to predict. The previous studies are focused on its nonlinear case and got a competitive testing performance, but its training is time consuming, particularly for large-scale data. In this paper, we consider NPSVOR's linear case and design an efficient training method based on the dual coordinate descent method (DCD). To utilize the order information among labels in prediction, a new prediction function is also proposed. Extensive contrast experiments on the text OR datasets indicate that the carefully implemented DCD is very suitable for training large data.
Detecting GAN-generated Imagery using Color Cues
Venue: arXiv cs.CV 2018
Year: 2018

Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent work has shown that GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation, and show that the network's treatment of color is markedly different from a real camera in two ways. We further show that these two cues can be used to distinguish GAN-generated imagery from camera imagery, demonstrating effective discrimination between GAN imagery and real camera images used to train the GAN.
A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we explore how to computationally characterize subsurface geological structures presented in seismic volumes using texture attributes. For this purpose, we conduct a comparative study of typical texture attributes presented in the image processing literature. We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i.e., seismic volume labeling. For this application, a data volume is automatically segmented into various structures, each assigned with its corresponding label. If the labels are assigned with reasonable accuracy, such volume labeling will help initiate an interpretation process in a more effective manner. Our investigation proves the feasibility of accomplishing this task using texture attributes. Through the study, we also identify advantages and disadvantages associated with each attribute.
Analog Signal Processing Using Stochastic Magnets
Venue: arXiv cs.ET 2018
Year: 2018

We present a low barrier magnet based compact hardware unit for analog stochastic neurons and demonstrate its use as a building-block for neuromorphic hardware. By coupling circular magnetic tunnel junctions (MTJs) with a CMOS based analog buffer, we show that these units can act as leaky-integrate-and fire (LIF) neurons, a model of biological neural networks particularly suited for temporal inferencing and pattern recognition. We demonstrate examples of temporal sequence learning, processing, and prediction tasks in real time, as a proof of concept demonstration of scalable and adaptive signal-processors. Efficient non von-Neumann hardware implementation of such processors can open up a pathway for integration of hardware based cognition in a wide variety of emerging systems such as IoT, industrial controls, bio- and photo-sensors, and Unmanned Autonomous Vehicles.
Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences
Venue: arXiv cs.IR 2018
Year: 2018

Recommendation systems have an important place to help online users in the internet society. Recommendation Systems in computer science are of very practical use these days in various aspects of the Internet portals, such as social networks, and library websites. There are several approaches to implement recommendation systems, Latent Dirichlet Allocation (LDA) is one the popular techniques in Topic Modeling. Recently, researchers have proposed many approaches based on Recommendation Systems and LDA. According to importance of the subject, in this paper we discover the trends of the topics and find relationship between LDA topics and Scholar-Context-documents. In fact, We apply probabilistic topic modeling based on Gibbs sampling algorithms for a semantic mining from six conference publications in computer science from DBLP dataset. According to our experimental results, our semantic framework can be effective to help organizations to better organize these conferences and cover future research topics.
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that even being trained on synthetic data, the proposed system performs well on real-world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.
Structure Selection of Polynomial NARX Models using Two Dimensional (2D) Particle Swarms
Venue: arXiv cs.SY 2018
Year: 2018

The present study applies a novel two-dimensional learning framework (2D-UPSO) based on particle swarms for structure selection of polynomial nonlinear auto-regressive with exogenous inputs (NARX) models. This learning approach explicitly incorporates the information about the cardinality (i.e., the number of terms) into the structure selection process. Initially, the effectiveness of the proposed approach was compared against the classical genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO is superior. Further, since the performance of any meta-heuristic search algorithm is critically dependent on the choice of the fitness function, the efficacy of the proposed approach was investigated using two distinct information theoretic criteria such as Akaike and Bayesian information criterion. The robustness of this approach against various levels of measurement noise is also studied. Simulation results on various nonlinear systems demonstrate that the proposed algorithm could accurately determine the structure of the polynomial NARX model even under the influence of measurement noise.
DAC: Data-free Automatic Acceleration of Convolutional Networks
Venue: arXiv cs.CV 2018
Year: 2018

Deploying a deep learning model on mobile/IoT devices is a challenging task. The difficulty lies in the trade-off between computation speed and accuracy. A complex deep learning model with high accuracy runs slowly on resource-limited devices, while a light-weight model that runs much faster loses accuracy. In this paper, we propose a novel decomposition method, namely DAC, that is capable of factorizing an ordinary convolutional layer into two layers with much fewer parameters. DAC computes the corresponding weights for the newly generated layers directly from the weights of the original convolutional layer. Thus, no training (or fine-tuning) or any data is needed. The experimental results show that DAC reduces a large number of floating-point operations (FLOPs) while maintaining high accuracy of a pre-trained model. If 2% accuracy drop is acceptable, DAC saves 53% FLOPs of VGG16 image classification model on ImageNet dataset, 29% FLOPS of SSD300 object detection model on PASCAL VOC2007 dataset, and 46% FLOPS of a multi-person pose estimation model on Microsoft COCO dataset. Compared to other existing decomposition methods, DAC achieves better performance.
Towards Modernising Data Collection and Archive for the Tor Network
Venue: arXiv cs.OH 2018
Year: 2018

CollecTor is developed by Tor Project's Metrics Team for the purpose of archiving data relating to the public Tor network and applications developed by Tor Project. This report distills the requirements for a prototype modernized replacement of the CollecTor service, and evaluates frameworks and libraries that are available to reduce code maintenance costs for the CollecTor service.
Multi-Object Tracking with Interacting Vehicles and Road Map Information
Venue: arXiv cs.RO 2018
Year: 2018

In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations.
Safety Practice and its Practitioners: Exploring a Diverse Profession
Venue: arXiv cs.SE 2018
Year: 2018

System safety refers to a diverse engineering discipline assessing and improving various aspects of safety in socio-technical systems and their software-intensive sub-systems. While system safety has been a vital area of applied research for many decades, its practice and practitioners seem empirically still not well studied. Beyond mainly anecdotal evidence (interviews, on-line discussions), incident reports, and surveys, we are missing open, large-scale, and long-term investigations that promote knowledge transfer and research validation. We explore means for work safety practitioners rely on, factors influencing their performance, and their perception of their role in the system life cycle. Along with that we examine observations from previous research. We build a construct of safety practice, collect data for this construct using an on-line survey, summarise and interpret the collected data, and investigate several hypotheses based on the previous observations. We analyse and present the responses of 124 practitioners in safety-critical system and software projects. Aside from other findings, our data suggests that safety decision making mainly depends on expert opinion and project memory, lacks evidence that safety is typically a cost-benefit question, does not exhibit the prejudice that formal methods are not beneficial, leaves it unclear as to whether or not standards and methods have become inadequate, and indicates that safety is not typically confused with reliability. Additionally, we contribute a research design directing towards explanatory empirical studies of safety practice. Empirical research of safety practice is still in an early stage, bearing the risk of undesirable mismatches of the state of the art and the state of practice. However, this situation offers great opportunities for research.
The Query Complexity of a Permutation-Based Variant of Mastermind
Venue: arXiv cs.DS 2018
Year: 2018

We study the query complexity of a permutation-based variant of the guessing game Mastermind. In this variant, the secret is a pair $(z,π)$ which consists of a binary string $z \in \{0,1\}^n$ and a permutation $π$ of $[n]$. The secret must be unveiled by asking queries of the form $x \in \{0,1\}^n$. For each such query, we are returned the score \[ f_{z,π}(x):= \max \{ i \in [0..n]\mid \forall j \leq i: z_{π(j)} = x_{π(j)}\}\,;\] i.e., the score of $x$ is the length of the longest common prefix of $x$ and $z$ with respect to the order imposed by $π$. The goal is to minimize the number of queries needed to identify $(z,π)$. This problem originates from the study of black-box optimization heuristics, where it is known as the \textsc{LeadingOnes} problem. In this work, we prove matching upper and lower bounds for the deterministic and randomized query complexity of this game, which are $Θ(n \log n)$ and $Θ(n \log \log n)$, respectively.
Automated CFI Policy Assessment with Reckon
Venue: arXiv cs.CR 2018
Year: 2018

Protecting programs against control-flow hijacking attacks recently has become an arms race between defenders and attackers. While certain defenses, e.g., \textit{Control Flow Integrity} (CFI), restrict the targets of indirect control-flow transfers through static and dynamic analysis, attackers could search the program for available gadgets that fall into the legitimate target sets to bypass the defenses. There are several tools helping both attackers in developing exploits and analysts in strengthening their defenses. Yet, these tools fail to adequately (1) model the deployed defenses, (2) compare them in a head-to-head way, and (3) use program semantic information to help craft the attack and the countermeasures. Control Flow Integrity (CFI) has proved to be one of the promising defenses against control flow hijacks and tons of efforts have been made to improve CFI in various ways in the past decade. However, there is a lack of a systematic assessment of the existing CFI defenses. In this paper, we present Reckon, a static source code analysis tool for assessing state-of-the-art static CFI defenses, by first precisely modeling them and then evaluating them in a unified framework. Reckon helps determine the level of security offered by different CFI defenses, and find usable code gadgets even after the CFI defenses were applied, thus providing an important step towards successful exploits and stronger defenses. We have used Reckon to assess eight state-of-the-art static CFI defenses on real-world programs such as Google's Chrome and Apache Httpd. Reckon provides precise measurements of the residual attack surfaces, and accordingly ranks CFI policies against each other. It also successfully paves the way to construct code reuse attacks and to eliminate the remaining attack surface, by disclosing calltargets under one of the most restrictive CFI defenses.