OpenRaiser/Intern-Atlas huggingface dataset:
Blockchain-based Lightweight Authentication Mechanism for Vehicular Fog Infrastructure
Venue: arXiv cs.NI 2019
Year: 2019

With the increasing development of advanced communication technologies, vehicles are becoming smarter and more connected. Due to the tremendous growth of various vehicular applications, a huge amount of data is generated through advanced on-board devices and is deemed critical to improve driving safety and enhance vehicular services. However, cloud based models often fall short in applications where latency and mobility are critical. In order to fully realize the potential of vehicular networks, the challenges of efficient communication and computation need to be addressed. In this direction, vehicular fog computing (VFC) has emerged which extends the concept of fog computing to conventional vehicular networks. It is a geographically distributed paradigm that has the potential to conduct time-critical and data-intensive tasks by pushing intelligence (i.e. computing resources, storage, and application services) in the vicinity of end vehicles. However secure and reliable transmission are of significant importance in highly-mobile vehicular networks in order to ensure the optimal Quality of Service (QoS). In this direction, several authentication mechanisms have been proposed in the literature but most of them are found unfit due to absence of decentralization, anonymity, and trust characteristics. Thus, an effective cross-datacenter authentication and key-exchange scheme based on blockchain and elliptic curve cryptography (ECC) is proposed in this paper. Here, the distributed ledger of blockchain is used for maintaining the network information while the highly secure ECC is employed for mutual authentication between vehicles and road side units (RSUs). Additionally, the proposed scheme is lightweight and scalable for the considered VFC setup. The performance evaluation results against the existing state-of-the-art reveal that the proposed scheme accomplishes enhanced security features.
Person Identification with Visual Summary for a Safe Access to a Smart Home
Venue: arXiv cs.CV 2019
Year: 2019

SafeAccess is an integrated system designed to provide easier and safer access to a smart home for people with or without disabilities. The system is designed to enhance safety and promote the independence of people with disability (i.e., visually impaired). The key functionality of the system includes the detection and identification of human and generating contextual visual summary from the real-time video streams obtained from the cameras placed in strategic locations around the house. In addition, the system classifies human into groups (i.e. friends/families/caregiver versus intruders/burglars/unknown). These features allow the user to grant/deny remote access to the premises or ability to call emergency services. In this paper, we focus on designing a prototype system for the smart home and building a robust recognition engine that meets the system criteria and addresses speed, accuracy, deployment and environmental challenges under a wide variety of practical and real-life situations. To interact with the system, we implemented a dialog enabled interface to create a personalized profile using face images or video of friend/families/caregiver. To improve computational efficiency, we apply change detection to filter out frames and use Faster-RCNN to detect the human presence and extract faces using Multitask Cascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP/FaceNet to identify a person and groups by matching extracted faces with the profile. SafeAccess sends a visual summary to the users with an MMS containing a person's name if any match found or as "Unknown", scene image, facial description, and contextual information. SafeAccess identifies friends/families/caregiver versus intruders/unknown with an average F-score 0.97 and generates a visual summary from 10 classes with an average accuracy of 98.01%.
Biomechanical modeling and computer simulation of the brain during neurosurgery
Venue: arXiv cs.CE 2019
Year: 2019

Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and it's collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach, and describe the brain deformation in mechanical terms (such as displacements, strains and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the Total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
Incorrect implementations of the Floyd--Warshall algorithm give correct solutions after three repeats
Venue: arXiv cs.DS 2019
Year: 2019

The Floyd--Warshall algorithm is a well-known algorithm for the all-pairs shortest path problem that is simply implemented by triply nested loops. In this study, we show that the incorrect implementations of the Floyd--Warshall algorithm that misorder the triply nested loops give correct solutions if these are repeated three times.
The Impact of Systematic Edits in History Slicing
Venue: arXiv cs.SE 2019
Year: 2019

While extracting a subset of a commit history, specifying the necessary portion is a time-consuming task for developers. Several commit-based history slicing techniques have been proposed to identify dependencies between commits and to extract a related set of commits using a specific commit as a slicing criterion. However, the resulting subset of commits become large if commits for systematic edits whose changes do not depend on each other exist. We empirically investigated the impact of systematic edits on history slicing. In this study, commits in which systematic edits were detected are split between each file so that unnecessary dependencies between commits are eliminated. In several histories of open source systems, the size of history slices was reduced by 13.3-57.2% on average after splitting the commits for systematic edits.
Cooperation Techniques for A Cellular Internet of Unmanned Aerial Vehicles
Venue: arXiv cs.IT 2019
Year: 2019

Unmanned aerial vehicles (UAVs) are powerful Internet-of-Things components to provide sensing and communications in the air due to their advantages in mobility and flexibility. As aerial users, UAVs are envisioned to support various sensing applications in the next generation cellular systems, which have been studied by the Third Generation Partnership Project (3GPP). However, the Quality-of-Services (QoS) of the cellular link between the UAV and the base station may not be guaranteed when UAVs are at the cell edge or experiencing deep fading. In this article, we first introduce the non-cooperative cellular Internet of UAVs. Then we propose a cooperative sense-and-send protocol, in which a UAV can upload sensory data with the help of a UAV relay, to provide a better communication QoS for the sensing tasks. Key techniques including trajectory design and radio resource management that support the cooperative cellular Internet of UAVs are presented in detail. Finally, the extended cooperative cellular Internet of UAVs is discussed for QoS improvement with some open issues, such as massive multiple-input multiple-output systems, millimeter-wave, and cognitive communications.
Hierarchical method for cataract grading based on retinal images using improved Haar wavelet
Venue: arXiv cs.CV 2019
Year: 2019

Cataracts, which are lenticular opacities that may occur at different lens locations, are the leading cause of visual impairment worldwide. Accurate and timely diagnosis can improve the quality of life of cataract patients. In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed. To obtain more appropriate features for the automatic grading, the Haar wavelet is improved according to the characteristics of retinal images. Retinal images of non-cataract, as well as mild, moderate, and severe cataracts, are automatically recognized using the improved Haar wavelet. A hierarchical strategy is used to transform the four-class classification problem into three adjacent two-class classification problems. Three sets of two-class classifiers based on a neural network are trained individually and integrated together to establish a complete classification system. The accuracies of the two-class classification (cataract and non-cataract) and four-class classification are 94.83% and 85.98%, respectively. The performance analysis demonstrates that the improved Haar wavelet feature achieves higher accuracy than the original Haar wavelet feature, and the fusion of three sets of two-class classifiers is superior to a simple four-class classifier. The discussion indicates that the retinal image-based method offers significant potential for cataract detection.
SurFi: Detecting Surveillance Camera Looping Attacks with Wi-Fi Channel State Information (Extended Version)
Venue: arXiv cs.CR 2019
Year: 2019

The proliferation of surveillance cameras has greatly improved the physical security of many security-critical properties including buildings, stores, and homes. However, recent surveillance camera looping attacks demonstrate new security threats - adversaries can replay a seemingly benign video feed of a place of interest while trespassing or stealing valuables without getting caught. Unfortunately, such attacks are extremely difficult to detect in real-time due to cost and implementation constraints. In this paper, we propose SurFi to detect these attacks in real-time by utilizing commonly available Wi-Fi signals. In particular, we leverage that channel state information (CSI) from Wi-Fi signals also perceives human activities in the place of interest in addition to surveillance cameras. SurFi processes and correlates the live video feeds and the Wi-Fi CSI signals to detect any mismatches that would identify the presence of the surveillance camera looping attacks. SurFi does not require the deployment of additional infrastructure because Wi-Fi transceivers are easily found in the urban indoor environment. We design and implement the SurFi system and evaluate its effectiveness in detecting surveillance camera looping attacks. Our evaluation demonstrates that SurFi effectively identifies attacks with up to an attack detection accuracy of 98.8% and 0.1% false positive rate
Fast Bayesian Restoration of Poisson Corrupted Images with INLA
Venue: arXiv cs.CV 2019
Year: 2019

Photon-limited images are often seen in fields such as medical imaging. Although the number of collected photons on an image sensor statistically follows Poisson distribution, this type of noise is intractable, unlike Gaussian noise. In this study, we propose a Bayesian restoration method of Poisson corrupted image using Integrated Nested Laplace Approximation (INLA), which is a computational method to evaluate marginalized posterior distributions of latent Gaussian models (LGMs). When the original image can be regarded as ICAR (intrinsic conditional auto-regressive) model reasonably, our method performs very faster than well-known ones such as loopy belief propagation-based method and Markov chain Monte Carlo (MCMC) without decreasing the accuracy.
Context-Aware Misbehavior Detection Scheme for Vehicular Ad Hoc Networks using Sequential Analysis of the Temporal and Spatial Correlation of the Cooperative Awareness Messages
Venue: arXiv cs.CY 2019
Year: 2019

Vehicular ad hoc Networks (VANETs) are emerged mainly to improve road safety, traffic efficiency, and passenger comfort. The performance of most VANET applications relies on the availability of accurate and recent mobility-information, shared by neighboring vehicles. However, sharing false mobility information can disrupt any potential VANET application. Misbehavior detection is an important security component. However, existing misbehavior detection solutions lack considering the high dynamicity of vehicular context which leads to low detection accuracy and high false alarms. The use of predefined and static security thresholds are the main drawbacks of the existing solutions. In this paper, a context-aware misbehavior detection scheme (CAMDS) is proposed using sequential analysis of temporal and spatial properties of mobility information. A dynamic context reference is constructed online and timely updated using statistical techniques. Firstly, the Kalman filter algorithm is used to track the mobility information received from neighboring vehicles. Then, the innovation errors of the Kalman filter are utilized to construct a temporal consistency assessment model for each vehicle using Box-plot. Then, the Hampel filter is used to construct a spatial consistency assessment model that represents the context reference model. Similarly, plausibility assessment reference models are built online and timely updated using the Hampel filter and by utilizing the consistency assessment reference model of neighboring information. Finally, a message is classified as suspicious if its consistency and plausibility scores deviate much from the context reference model. The proposed context-aware scheme achieved a 73% reduction in false Alarm rate while it achieves a 37% improvement in the detection rate. This proves the effectiveness of the proposed scheme compared with the existing static solutions.
Neural Vector Conceptualization for Word Vector Space Interpretation
Venue: arXiv cs.CL 2019
Year: 2019

Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, we train a neural model to conceptualize word vectors, which means that it activates higher order concepts it recognizes in a given vector. Contrary to prior approaches, our model operates in the original vector space and is capable of learning non-linear relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.
NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles
Venue: arXiv cs.CY 2019
Year: 2019

In this paper, we present a dataset of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. We incorporate ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust. The NELA-GT-2018 dataset can be found at https://doi.org/10.7910/DVN/ULHLCB.
A Review of Critical Infrastructure Protection Approaches: Improving Security through Responsiveness to the Dynamic Modelling Landscape
Venue: arXiv cs.CR 2019
Year: 2019

As new technologies such as the Internet of Things (IoT) are integrated into Critical National Infrastructures (CNI), new cybersecurity threats emerge that require specific security solutions. Approaches used for analysis include the modelling and simulation of critical infrastructure systems using attributes, functionalities, operations, and behaviours to support various security analysis viewpoints, recognising and appropriately managing associated security risks. With several critical infrastructure protection approaches available, the question of how to effectively model the complex behaviour of interconnected CNI elements and to configure their protection as a system-of-systems remains a challenge. Using a systematic review approach, existing critical infrastructure protection approaches (tools and techniques) are examined to determine their suitability given trends like IoT, and effective security modelling and analysis issues. It is found that empirical-based, agent-based, system dynamics-based, and network-based modelling are more commonly applied than economic-based and equation-based techniques, and empirical-based modelling is the most widely used. The energy and transportation critical infrastructure sectors reflect the most responsive sectors, and no one Critical Infrastructure Protection (CIP) approach - tool, technique, methodology or framework -- provides a fit-for-all capacity for all-round attribute modelling and simulation of security risks. Typically, deciding factors for CIP choices to adopt are often dominated by trade-offs between complexity of use and popularity of approach, as well as between specificity and generality of application in sectors.
Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation
Venue: arXiv cs.RO 2019
Year: 2019

Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm manipulation skills in the rapidly changing, dynamic and unpredictable real-world environments where they have to operate. Given the expertise of humans in conducting these activities, it is natural to study humans' motions to use the resulting knowledge in robotic control. With this in mind, this work leverages human knowledge to formulate a more general, real-time, and less task-specific framework for dual-arm manipulation. The proposed framework is evaluated on the iCub humanoid robot and several synthetic experiments, by conducting a dual-arm pick-and-place task of a parcel in the presence of unexpected obstacles. Results suggest the suitability of the method towards robust and generalisable dual-arm manipulation.
Oriented coloring on recursively defined digraphs
Venue: arXiv cs.DS 2019
Year: 2019

Coloring is one of the most famous problems in graph theory. The coloring problem on undirected graphs has been well studied, whereas there are very few results for coloring problems on directed graphs. An oriented k-coloring of an oriented graph G=(V,A) is a partition of the vertex set V into k independent sets such that all the arcs linking two of these subsets have the same direction. The oriented chromatic number of an oriented graph G is the smallest k such that G allows an oriented k-coloring. Deciding whether an acyclic digraph allows an oriented 4-coloring is NP-hard. It follows, that finding the chromatic number of an oriented graph is an NP-hard problem. This motivates to consider the problem on oriented co-graphs. After giving several characterizations for this graph class, we show a linear time algorithm which computes an optimal oriented coloring for an oriented co-graph. We further prove how the oriented chromatic number can be computed for the disjoint union and order composition from the oriented chromatic number of the involved oriented co-graphs. It turns out that within oriented co-graphs the oriented chromatic number is equal to the length of a longest oriented path plus one. We also show that the graph isomorphism problem on oriented co-graphs can be solved in linear time.
Contrastive Predictive Coding Based Feature for Automatic Speaker Verification
Venue: arXiv cs.CL 2019
Year: 2019

This thesis describes our ongoing work on Contrastive Predictive Coding (CPC) features for speaker verification. CPC is a recently proposed representation learning framework based on predictive coding and noise contrastive estimation. We focus on incorporating CPC features into the standard automatic speaker verification systems, and we present our methods, experiments, and analysis. This thesis also details necessary background knowledge in past and recent work on automatic speaker verification systems, conventional speech features, and the motivation and techniques behind CPC.
Improving Noise Tolerance of Mixed-Signal Neural Networks
Venue: arXiv cs.ET 2019
Year: 2019

Mixed-signal hardware accelerators for deep learning achieve orders of magnitude better power efficiency than their digital counterparts. In the ultra-low power consumption regime, limited signal precision inherent to analog computation becomes a challenge. We perform a case study of a 6-layer convolutional neural network running on a mixed-signal accelerator and evaluate its sensitivity to hardware specific noise. We apply various methods to improve noise robustness of the network and demonstrate an effective way to optimize useful signal ranges through adaptive signal clipping. The resulting model is robust enough to achieve 80.2% classification accuracy on CIFAR-10 dataset with just 1.4 mW power budget, while 6 mW budget allows us to achieve 87.1% accuracy, which is within 1% of the software baseline. For comparison, the unoptimized version of the same model achieves only 67.7% accuracy at 1.4 mW and 78.6% at 6 mW.
Data Disclosure under Perfect Sample Privacy
Venue: arXiv cs.IT 2019
Year: 2019

Perfect data privacy seems to be in fundamental opposition to the economical and scientific opportunities associated with extensive data exchange. Defying this intuition, this paper develops a framework that allows the disclosure of collective properties of datasets without compromising the privacy of individual data samples. We present an algorithm to build an optimal disclosure strategy/mapping, and discuss it fundamental limits on finite and asymptotically large datasets. Furthermore, we present explicit expressions to the asymptotic performance of this scheme in some scenarios, and study cases where our approach attains maximal efficiency. We finally discuss suboptimal schemes to provide sample privacy guarantees to large datasets with a reduced computational cost.
Empirical Evaluations of Preprocessing Parameters' Impact on Predictive Coding's Effectiveness
Venue: arXiv cs.IR 2019
Year: 2019

Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review. Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by subject-matter experts. Many predictive coding tools require users to rely on static preprocessing parameters and a single machine learning algorithm to develop the predictive model. Little research has been published discussing the impact preprocessing parameters and learning algorithms have on the effectiveness of the technology. A deeper dive into the generation of a predictive model shows that the settings and algorithm can have a strong effect on the accuracy and efficacy of a predictive coding tool. Understanding how these input parameters affect the output will empower legal teams with the information they need to implement predictive coding as efficiently and effectively as possible. This paper outlines different preprocessing parameters and algorithms as applied to multiple real-world data sets to understand the influence of various approaches.