OpenRaiser/Intern-Atlas huggingface dataset:
Blockchain for 5G and Beyond Networks: A State of the Art Survey
Venue: arXiv cs.NI 2019
Year: 2019

The fifth generation (5G) wireless networks are on the way to be deployed around the world. The 5G technologies target to support diverse vertical applications by connecting heterogeneous devices and machines with drastic improvements in terms of high quality of service, increased network capacity and enhanced system throughput. Despite all these advantages that 5G will bring about, there are still major challenges to be addressed, including decentralization, transparency, risks of data interoperability, network privacy and security vulnerabilities. Blockchain can offer innovative solutions to effectively solve the challenges in 5G networks. Driven by the dramatically increased capacities of the 5G networks and the recent breakthroughs in the blockchain technology, blockchain-based 5G services are expected to witness a rapid development and bring substantial benefits to future society. In this paper, we provide a state-of-art survey on the integration of blockchain with 5G networks and beyond. Our key focus is on the discussions on the potential of blockchain for enabling key 5G technologies, including cloud/edge computing, Software Defined Networks, Network Function Virtualization, Network Slicing, and D2D communications. We then explore the opportunities of blockchain to important 5G services, ranging from spectrum management, network virtualization, resource management to interference management, federated learning, privacy and security provision. The recent advances in the applications of blockchain in 5G Internet of Things are also surveyed in various domains, i.e. smart healthcare, smart city, smart transportation, smart grid and UAVs. The main findings derived from the survey are then summarized, and possible research challenges with open issues are also identified. Lastly, we complete this survey by shedding new light on future directions of research on this newly emerging area.
Securing Fleets of Consumer Drones at Low Cost
Venue: arXiv cs.CR 2019
Year: 2019

In recent years, the use and suitability of drones for many applications, including surveillance, search and rescue, research, agriculture and civil engineering, has greatly increased due to their improved affordability and improved functionality. However, low-cost consumer drones are rarely designed to work in fleets, which limits the applications for which business, research and individuals may deploy such drones. Proprietary, commercial and bespoke options are available at higher cost and existing solutions providing fleet functionality have limited security, if any, which excludes their use for sensitive applications. In this paper, we discuss the repurposing of consumer off-the-shelf (COTS) drones for use in secured fleets and provide the design, implementation and evaluation of a complete approach for creating end-to-end secured fleets of consumer drones (SFCD). We present a protocol for securing communications within fleets whilst employing more efficient symmetric key cryptography throughout to reduce the impact of our security on the limited and resource-constrained COTS drones, exploiting the characteristics of a fleet with an online and central ground control station, which may act as a key distribution centre. The protocol allows an arbitrary number of channels to be established to authenticate and optionally encrypt real-time data transmitted on these channels. We also discuss routing in fleets, as well as the control and monitoring of them, to allow SFCD to be fully deployed; providing an extensive and thorough solution. Our experimental evaluation confirms the suitability of low-cost consumer drones for use in SFCD, with flight time impacted by only 9.9% and worst-case bandwidth of 4.7Mibit/s.
BioNet: Infusing Biomarker Prior into Global-to-Local Network for Choroid Segmentation in Optical Coherence Tomography Images
Venue: arXiv cs.CV 2019
Year: 2019

Choroid is the vascular layer of the eye, which is directly related to the incidence and severity of many ocular diseases. Optical Coherence Tomography (OCT) is capable of imaging both the cross-sectional view of retina and choroid, but the segmentation of the choroid region is challenging because of the fuzzy choroid-sclera interface (CSI). In this paper, we propose a biomarker infused global-to-local network (BioNet) for choroid segmentation, which segments the choroid with higher credibility and robustness. Firstly, our method trains a biomarker prediction network to learn the features of the biomarker. Then a global multi-layers segmentation module is applied to segment the OCT image into 12 layers. Finally, the global multi-layered result and the original OCT image are fed into a local choroid segmentation module to segment the choroid region with the biomarker infused as regularizer. We conducted comparison experiments with the state-of-the-art methods on a dataset (named AROD). The experimental results demonstrate the superiority of our method with $90.77\%$ Dice-index and 6.23 pixels Average-unsigned-surface-detection-error, etc.
Array Languages Make Neural Networks Fast
Venue: arXiv cs.PL 2019
Year: 2019

Modern machine learning frameworks are complex: they are typically organised in multiple layers each of which is written in a different language and they depend on a number of external libraries, but at their core they mainly consist of tensor operations. As array-oriented languages provide perfect abstractions to implement tensor operations, we consider a minimalistic machine learning framework that is shallowly embedded in an array-oriented language and we study its productivity and performance. We do this by implementing a state of the art Convolutional Neural Network (CNN) and compare it against implementations in TensorFlow and PyTorch --- two state of the art industrial-strength frameworks. It turns out that our implementation is 2 and 3 times faster, even after fine-tuning the TensorFlow and PyTorch to our hardware --- a 64-core GPU-accelerated machine. The size of all three CNN specifications is the same, about 150 lines of code. Our mini framework is 150 lines of highly reusable hardware-agnostic code that does not depend on external libraries. The compiler for a host array language automatically generates parallel code for a chosen architecture. The key to such a balance between performance and portability lies in the design of the array language; in particular, the ability to express rank-polymorphic operations concisely, yet being able to do optimisations across them. This design builds on very few assumptions, and it is readily transferable to other contexts offering a clean approach to high-performance machine learning.
Two Birds with One Stone: Investigating Invertible Neural Networks for Inverse Problems in Morphology
Venue: arXiv cs.CL 2019
Year: 2019

Most problems in natural language processing can be approximated as inverse problems such as analysis and generation at variety of levels from morphological (e.g., cat+Plural <-> cats) to semantic (e.g., (call + 1 2) <-> "Calculate one plus two."). Although the tasks in both directions are closely related, general approach in the field has been to design separate models specific for each task. However, having one shared model for both tasks, would help the researchers exploit the common knowledge among these problems with reduced time and memory requirements. We investigate a specific class of neural networks, called Invertible Neural Networks (INNs) (Ardizzone et al. 2019) that enable simultaneous optimization in both directions, hence allow addressing of inverse problems via a single model. In this study, we investigate INNs on morphological problems casted as inverse problems. We apply INNs to various morphological tasks with varying ambiguity and show that they provide competitive performance in both directions. We show that they are able to recover the morphological input parameters, i.e., predicting the lemma (e.g., cat) or the morphological tags (e.g., Plural) when run in the reverse direction, without any significant performance drop in the forward direction, i.e., predicting the surface form (e.g., cats).
Metamorphic Security Testing for Web Systems
Venue: arXiv cs.SE 2019
Year: 2019

Security testing verifies that the data and the resources of software systems are protected from attackers. Unfortunately, it suffers from the oracle problem, which refers to the challenge, given an input for a system, of distinguishing correct from incorrect behavior. In many situations where potential vulnerabilities are tested, a test oracle may not exist, or it might be impractical due to the many inputs for which specific oracles have to be defined. In this paper, we propose a metamorphic testing approach that alleviates the oracle problem in security testing. It enables engineers to specify metamorphic relations (MRs) that capture security properties of the system. Such MRs are then used to automate testing and detect vulnerabilities. We provide a catalog of 22 system-agnostic MRs to automate security testing in Web systems. Our approach targets 39% of the OWASP security testing activities not automated by state-of-the-art techniques. It automatically detected 10 out of 12 vulnerabilities affecting two widely used systems, one commercial and the other open source (Jenkins).
Traffic map prediction using UNet based deep convolutional neural network
Venue: arXiv cs.LG 2019
Year: 2019

This paper describes our UNet based deep convolutional neural network approach on the Traffic4cast challenge 2019. Challenges task is to predict future traffic flow volume, heading and speed on high resolution whole city map. We used UNet based deep convolutional neural network to train predictive model for the short term traffic forecast. On each convolution block, layers are densely connected with subsequent layers like a DenseNet. Trained and evaluated on the real world data set collected from three distinct cities in the world, our method achieved best performance in this challenge.
differint: A Python Package for Numerical Fractional Calculus
Venue: arXiv cs.MS 2019
Year: 2019

Fractional calculus has become widely studied and applied to physical problems in recent years. As a result, many methods for the numerical computation of fractional derivatives and integrals have been defined. However, these algorithms are often programmed in an ad hoc manner, requiring researchers to implement and debug their own code. This work introduces the \textit{differint} software package, which offers a single repository for multiple numerical algorithms for the computation of fractional derivatives and integrals. This package is coded in the open-source Python programming language. The Grünwald-Letnikov, improved Grünwald-Letnikov, and Riemann-Liouville algorithms from the fractional calculus are included in this package. The algorithms presented are computed from their descriptions found in [2]. This work concludes with suggestions for the application of the \textit{differint} software package.
Training Deep SLAM on Single Frames
Venue: arXiv cs.CV 2019
Year: 2019

Learning-based visual odometry and SLAM methods demonstrate a steady improvement over past years. However, collecting ground truth poses to train these methods is difficult and expensive. This could be resolved by training in an unsupervised mode, but there is still a large gap between performance of unsupervised and supervised methods. In this work, we focus on generating synthetic data for deep learning-based visual odometry and SLAM methods that take optical flow as an input. We produce training data in a form of optical flow that corresponds to arbitrary camera movement between a real frame and a virtual frame. For synthesizing data we use depth maps either produced by a depth sensor or estimated from stereo pair. We train visual odometry model on synthetic data and do not use ground truth poses hence this model can be considered unsupervised. Also it can be classified as monocular as we do not use depth maps on inference. We also propose a simple way to convert any visual odometry model into a SLAM method based on frame matching and graph optimization. We demonstrate that both the synthetically-trained visual odometry model and the proposed SLAM method build upon this model yields state-of-the-art results among unsupervised methods on KITTI dataset and shows promising results on a challenging EuRoC dataset.
Self-Driving Car Steering Angle Prediction Based on Image Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Self-driving vehicles have expanded dramatically over the last few years. Udacity has release a dataset containing, among other data, a set of images with the steering angle captured during driving. The Udacity challenge aimed to predict steering angle based on only the provided images. We explore two different models to perform high quality prediction of steering angles based on images using different deep learning techniques including Transfer Learning, 3D CNN, LSTM and ResNet. If the Udacity challenge was still ongoing, both of our models would have placed in the top ten of all entries.
Solving the Reaction-Diffusion equation based on analytical methods and deep learning algorithm; the Case study of sulfate attack to concrete
Venue: arXiv cs.LG 2019
Year: 2019

The reaction-diffusion equation is one of the cornerstones equations in applied science and engineering. In the present study, a deep neural network has been trained in order to predict the solution of the equation with different coefficients using the numerical solution of this equation and the utility of deep learning. Analytical solution of the Reaction-Diffusion equation also has been conducted by taking advantage of the Danckwerts method. The accuracy of deep learning results was compared with the analytical solutions. In order to decrease the learning time and to find out similar equations solutions, such as pure diffusion and pure reaction, dimensional analysis technique has been performed. It was demonstrated that deep learning can accurately estimate the Partial Differential Equations solutionin the case of the reaction-diffusion equation with a constant coefficient.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Venue: arXiv cs.LG 2019
Year: 2019

Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing. For multi-laboratory scenarios however, certain confounding factors may strongly impede their performance. In this work, we introduce Deep Relevance Regularization, a method of restricting what the neural network can focus on during classification, in order to improve the classification performance. We demonstrate how Deep Relevance Regularization robustifies neural networks against confounding factors on a challenging inter-lab dataset consisting of breast and ovarian carcinoma. We further show that this makes the relevance map -- a way of visualizing the discriminative parts of the mass spectrum -- sparser, thereby making the classifier easier to interpret
MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Manipulating training data leads to robust neural models for MT.
Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS
Venue: arXiv cs.AI 2019
Year: 2019

Recent work on Neural-Symbolic systems that learn the discrete planning model from images has opened a promising direction for expanding the scope of Automated Planning and Scheduling to the raw, noisy data. However, previous work only partially addressed this problem, utilizing the black-box neural model as the successor generator. In this work, we propose Double-Stage Action Model Acquisition (DSAMA), a system that obtains a descriptive PDDL action model with explicit preconditions and effects over the propositional variables unsupervized-learned from images. DSAMA trains a set of Random Forest rule-based classifiers and compiles them into logical formulae in PDDL. While we obtained a competitively accurate PDDL model compared to a black-box model, we observed that the resulting PDDL is too large and complex for the state-of-the-art standard planners such as Fast Downward primarily due to the PDDL-SAS+ translator bottleneck. From this negative result, we argue that this translator bottleneck cannot be addressed just by using a different, existing rule-based learning method, and we point to the potential future directions.
Deep One-bit Compressive Autoencoding
Venue: arXiv cs.LG 2019
Year: 2019

Parameterized mathematical models play a central role in understanding and design of complex information systems. However, they often cannot take into account the intricate interactions innate to such systems. On the contrary, purely data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of interpretability. In this paper, we consider the design of a one-bit compressive autoencoder, and propose a novel hybrid model-based and data-driven methodology that allows us to not only design the sensing matrix for one-bit data acquisition, but also allows for learning the latent-parameters of an iterative optimization algorithm specifically designed for the problem of one-bit sparse signal recovery. Our results demonstrate a significant improvement compared to state-of-the-art model-based algorithms.
deepsing: Generating Sentiment-aware Visual Stories using Cross-modal Music Translation
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we propose a deep learning method for performing attributed-based music-to-image translation. The proposed method is applied for synthesizing visual stories according to the sentiment expressed by songs. The generated images aim to induce the same feelings to the viewers, as the original song does, reinforcing the primary aim of music, i.e., communicating feelings. The process of music-to-image translation poses unique challenges, mainly due to the unstable mapping between the different modalities involved in this process. In this paper, we employ a trainable cross-modal translation method to overcome this limitation, leading to the first, to the best of our knowledge, deep learning method for generating sentiment-aware visual stories. Various aspects of the proposed method are extensively evaluated and discussed using different songs.
Computação Urbana da Teoria à Prática: Fundamentos, Aplicações e Desafios
Venue: arXiv cs.CY 2019
Year: 2019

The growing of cities has resulted in innumerable technical and managerial challenges for public administrators such as energy consumption, pollution, urban mobility and even supervision of private and public spaces in an appropriate way. Urban Computing emerges as a promising paradigm to solve such challenges, through the extraction of knowledge, from a large amount of heterogeneous data existing in urban space. Moreover, Urban Computing correlates urban sensing, data management, and analysis to provide services that have the potential to improve the quality of life of the citizens of large urban centers. Consider this context, this chapter aims to present the fundamentals of Urban Computing and the steps necessary to develop an application in this area. To achieve this goal, the following questions will be investigated, namely: (i) What are the main research problems of Urban Computing?; (ii) What are the technological challenges for the implementation of services in Urban Computing?; (iii) What are the main methodologies used for the development of services in Urban Computing?; and (iv) What are the representative applications in this field?
Meaning guided video captioning
Venue: arXiv cs.CV 2019
Year: 2019

Current video captioning approaches often suffer from problems of missing objects in the video to be described, while generating captions semantically similar with ground truth sentences. In this paper, we propose a new approach to video captioning that can describe objects detected by object detection, and generate captions having similar meaning with correct captions. Our model relies on S2VT, a sequence-to-sequence model for video captioning. Given a sequence of video frames, the encoding RNN takes a frame as well as detected objects in the frame in order to incorporate the information of the objects in the scene. The following decoding RNN outputs are then fed into an attention layer and then to a decoder for generating captions. The caption is compared with the ground truth by learning metric so that vector representations of generated captions are semantically similar to those of ground truth. Experimental results with the MSDV dataset demonstrate that the performance of the proposed approach is much better than the model without the proposed meaning-guided framework, showing the effectiveness of the proposed model. Code are publicly available at https://github.com/captanlevi/Meaning-guided-video-captioning-.
The Metagenomic Binning Problem: Clustering Markov Sequences
Venue: arXiv cs.IT 2019
Year: 2019

The goal of metagenomics is to study the composition of microbial communities, typically using high-throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and want to cluster them according to their genome of origin. Based on the empirical observation that genomes of different bacterial species can be distinguished based on their tetranucleotide frequencies, we model this task as the problem of clustering N sequences generated by M distinct Markov processes, where M<<N. Utilizing the large-deviation principle for Markov processes, we establish the information-theoretic limit for perfect binning. Specifically, we show that the length of the contigs must scale with the inverse of the Chernoff Information between the two most similar species. Our result also implies that contigs should be binned using the conditional relative entropy as a measure of distance, as opposed to the Euclidean distance often used in practice.
Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
Venue: arXiv cs.LG 2019
Year: 2019

While Bayesian neural networks have many appealing characteristics, current priors do not easily allow users to specify basic properties such as expected lengthscale or amplitude variance. In this work, we introduce Poisson Process Radial Basis Function Networks, a novel prior that is able to encode amplitude stationarity and input-dependent lengthscale. We prove that our novel formulation allows for a decoupled specification of these properties, and that the estimated regression function is consistent as the number of observations tends to infinity. We demonstrate its behavior on synthetic and real examples.
Automatic Layout Generation with Applications in Machine Learning Engine Evaluation
Venue: arXiv cs.LG 2019
Year: 2019

Machine learning-based lithography hotspot detection has been deeply studied recently, from varies feature extraction techniques to efficient learning models. It has been observed that such machine learning-based frameworks are providing satisfactory metal layer hotspot prediction results on known public metal layer benchmarks. In this work, we seek to evaluate how these machine learning-based hotspot detectors generalize to complicated patterns. We first introduce a automatic layout generation tool that can synthesize varies layout patterns given a set of design rules. The tool currently supports both metal layer and via layer generation. As a case study, we conduct hotspot detection on the generated via layer layouts with representative machine learning-based hotspot detectors, which shows that continuous study on model robustness and generality is necessary to prototype and integrate the learning engines in DFM flows. The source code of the layout generation tool will be available at https://github. com/phdyang007/layout-generation.