OpenRaiser/Intern-Atlas huggingface dataset:
A Federated Filtering Framework for Internet of Medical Things
Venue: arXiv cs.NI 2019
Year: 2019

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (${To{l_F}}$) and the local filtering parameter ($δ$); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95\% of the communication cost while maintaining reasonable data privacy and low latency.
Joint Caching and Resource Allocation in D2D-Assisted Wireless HetNet
Venue: arXiv cs.NI 2019
Year: 2019

5G networks are required to provide very fast and reliable communications while dealing with the increase of users traffic. In Heterogeneous Networks (HetNets) assisted with Device-to-Device (D2D) communication, traffic can be offloaded to Small Base Stations or to users to improve the network's successful data delivery rate. In this paper, we aim at maximizing the average number of files that are successfully delivered to users, by jointly optimizing caching placement and channel allocation in cache-enabled D2D-assisted HetNets. At first, an analytical upper-bound on the average content delivery delay is derived. Then, the joint optimization problem is formulated. The non-convexity of the problem is alleviated, and the optimal solution is determined. Due to the high time complexity of the obtained solution, a low-complex sub-optimal approach is proposed. Numerical results illustrate the efficacy of the proposed solutions and compare them to conventional approaches. Finally, by investigating the impact of key parameters, e.g. power, caching capacity, QoS requirements, etc., guidelines to design these networks are obtained.
On Maximizing Task Throughput in IoT-enabled 5G Networks under Latency and Bandwidth Constraints
Venue: arXiv cs.NI 2019
Year: 2019

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory testbed and comparing with the state-of-the-art algorithm.
Precoded Cluster Hopping in Multi-Beam High Throughput Satellite Systems
Venue: arXiv cs.IT 2019
Year: 2019

Beam-Hopping (BH) and precoding are two trending technologies for the satellite community. While BH enables flexibility to adapt the offered capacity to the heterogeneous demand, precoding aims at boosting the spectral efficiency. In this paper, we consider a high throughput satellite (HTS) system that employs BH in conjunction with precoding. In particular, we propose the concept of Cluster-Hopping (CH) that seamlessly combines the BH and precoding paradigms and utilize their individual competencies. The cluster is defined as a set of adjacent beams that are simultaneously illuminated. In addition, we propose an efficient time-space illumination pattern design, where we determine the set of clusters that can be illuminated simultaneously at each hopping event along with the illumination duration. We model the CH time-space illumination pattern design as an integer programming problem which can be efficiently solved. Supporting results based on numerical simulations are provided which validate the effectiveness of the proposed CH concept and time-space illumination pattern design.
Charging control of electric vehicles using contextual bandits considering the electrical distribution grid
Venue: arXiv cs.OH 2019
Year: 2019

With the proliferation of electric vehicles, the electrical distribution grids are more prone to overloads. In this paper, we study an intelligent pricing and power control mechanism based on contextual bandits to provide incentives for distributing charging load and preventing network failure. The presented work combines the microscopic mobility simulator SUMO with electric network simulator SIMONA and thus produces reliable electrical distribution load values. Our experiments are carefully conducted under realistic conditions and reveal that conditional bandit learning outperforms context-free reinforcement learning algorithms and our approach is suitable for the given problem. As reinforcement learning algorithms can be adapted rapidly to include new information we assume these to be suitable as part of a holistic traffic control scenario.
Most vital segment barriers
Venue: arXiv cs.CG 2019
Year: 2019

We study continuous analogues of "vitality" for discrete network flows/paths, and consider problems related to placing segment barriers that have highest impact on a flow/path in a polygonal domain. This extends the graph-theoretic notion of "most vital arcs" for flows/paths to geometric environments. We give hardness results and efficient algorithms for various versions of the problem, (almost) completely separating hard and polynomially-solvable cases.
Matlab vs. OpenCV: A Comparative Study of Different Machine Learning Algorithms
Venue: arXiv cs.LG 2019
Year: 2019

Scientific Computing relies on executing computer algorithms coded in some programming languages. Given a particular available hardware, algorithms speed is a crucial factor. There are many scientific computing environments used to code such algorithms. Matlab is one of the most tremendously successful and widespread scientific computing environments that is rich of toolboxes, libraries, and data visualization tools. OpenCV is a (C++)-based library written primarily for Computer Vision and its related areas. This paper presents a comparative study using 20 different real datasets to compare the speed of Matlab and OpenCV for some Machine Learning algorithms. Although Matlab is more convenient in developing and data presentation, OpenCV is much faster in execution, where the speed ratio reaches more than 80 in some cases. The best of two worlds can be achieved by exploring using Matlab or similar environments to select the most successful algorithm; then, implementing the selected algorithm using OpenCV or similar environments to gain a speed factor.
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM
Venue: arXiv cs.DC 2019
Year: 2019

Understanding the bottlenecks in implementing stochastic gradient descent (SGD)-based distributed support vector machines (SVM) algorithm is important in training larger data sets. The communication time to do the model synchronization across the parallel processes is the main bottleneck that causes inefficiency in the training process. The model synchronization is directly affected by the mini-batch size of data processed before the global synchronization. In producing an efficient distributed model, the communication time in training model synchronization has to be as minimum as possible while retaining a high testing accuracy. The effect from model synchronization frequency over the convergence of the algorithm and accuracy of the generated model must be well understood to design an efficient distributed model. In this research, we identify the bottlenecks in model synchronization in parallel stochastic gradient descent (PSGD)-based SVM algorithm with respect to the training model synchronization frequency (MSF). Our research shows that by optimizing the MSF in the data sets that we used, a reduction of 98\% in communication time can be gained (16x - 24x speed up) with respect to high-frequency model synchronization. The training model optimization discussed in this paper guarantees a higher accuracy than the sequential algorithm along with faster convergence.
Seamless Scene Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. As additional contribution we review the panoptic metric and propose an alternative that overcomes its limitations when evaluating non-instance categories. Our proposed network architecture yields state-of-the-art results on three challenging street-level datasets, i.e. Cityscapes, Indian Driving Dataset and Mapillary Vistas.
Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian
Venue: arXiv cs.RO 2019
Year: 2019

Coordinated dual-arm manipulation tasks can be broadly characterized as possessing absolute and relative motion components. Relative motion tasks, in particular, are inherently redundant in the way they can be distributed between end-effectors. In this work, we analyse cooperative manipulation in terms of the asymmetric resolution of relative motion tasks. We discuss how existing approaches enable the asymmetric execution of a relative motion task, and show how an asymmetric relative motion space can be defined. We leverage this result to propose an extended relative Jacobian to model the cooperative system, which allows a user to set a concrete degree of asymmetry in the task execution. This is achieved without the need for prescribing an absolute motion target. Instead, the absolute motion remains available as a functional redundancy to the system. We illustrate the properties of our proposed Jacobian through numerical simulations of a novel differential Inverse Kinematics algorithm.
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
Venue: arXiv cs.CV 2019
Year: 2019

Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: 1) lack of aligned training pairs and 2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images. To synthesize diverse outputs, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Our model takes the encoded content features extracted from a given input and attribute vectors sampled from the attribute space to synthesize diverse outputs at test time. To handle unpaired training data, we introduce a cross-cycle consistency loss based on disentangled representations. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. For quantitative evaluations, we measure realism with user study and Fréchet inception distance, and measure diversity with the perceptual distance metric, Jensen-Shannon divergence, and number of statistically-different bins.
RedisGraph GraphBLAS Enabled Graph Database
Venue: arXiv cs.DB 2019
Year: 2019

RedisGraph is a Redis module developed by Redis Labs to add graph database functionality to the Redis database. RedisGraph represents connected data as adjacency matrices. By representing the data as sparse matrices and employing the power of GraphBLAS (a highly optimized library for sparse matrix operations), RedisGraph delivers a fast and efficient way to store, manage and process graphs. Initial benchmarks indicate that RedisGraph is significantly faster than comparable graph databases.
Single Image 3D Hand Reconstruction with Mesh Convolutions
Venue: arXiv cs.CV 2019
Year: 2019

Monocular 3D reconstruction of deformable objects, such as human body parts, has been typically approached by predicting parameters of heavyweight linear models. In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes. The prior on 3D hand shapes is learned by training an autoencoder with intrinsic graph convolutions performed in the spectral domain. The pre-trained decoder acts as a non-linear statistical deformable model. The latent parameters that reconstruct the shape and articulated pose of hands in the image are predicted using an image encoder. We show that our system reconstructs plausible meshes and operates in real-time. We evaluate the quality of the mesh reconstructions produced by the decoder on a new dataset and show latent space interpolation results. Our code, data, and models will be made publicly available.
PAL: A Wearable Platform for Real-time, Personalized and Context-Aware Health and Cognition Support
Venue: arXiv cs.HC 2019
Year: 2019

Personalized Active Learner (PAL) is a wearable system for real-time, personalized, and context-aware health and cognition support. PAL's system consists of a wearable device, mobile app, cloud database, data visualization web app, and machine learning server. PAL's wearable device uses multi-modal sensors (camera, microphone, heart-rate) with on-device machine learning and open-ear audio output to provide real-time and context-aware cognitive, behavioral and psychological interventions. PAL also allows users to track the long-term correlations between their activities and physiological states to make well-informed lifestyle decisions. In this paper, we present and open-source PAL's system so that people can use it for health and cognition support applications. We also open-source three fully-developed example applications using PAL for face-based memory augmentation, contextual language learning, and heart-rate-based psychological support. PAL's flexible, modular and extensible platform combines trends in data-driven medicine, mobile psychology, and cognitive enhancement to support data-driven and empowering health and cognition applications.
A Feature-Oriented Corpus for Understanding, Evaluating and Improving Fuzz Testing
Venue: arXiv cs.SE 2019
Year: 2019

Fuzzing is a promising technique for detecting security vulnerabilities. Newly developed fuzzers are typically evaluated in terms of the number of bugs found on vulnerable programs/binaries. However,existing corpora usually do not capture the features that prevent fuzzers from finding bugs, leading to ambiguous conclusions on the pros and cons of the fuzzers evaluated. A typical example is that Driller detects more bugs than AFL, but its evaluation cannot establish if the advancement of Driller stems from the concolic execution or not, since, for example, its ability in resolving a dataset`s magic values is unclear. In this paper, we propose to address the above problem by generating corpora based on search-hampering features. As a proof-of-concept, we have designed FEData, a prototype corpus that currently focuses on four search-hampering features to generate vulnerable programs for fuzz testing. Unlike existing corpora that can only answer "how", FEData can also further answer "why" by exposing (or understanding) the reasons for the identified weaknesses in a fuzzer. The "why" information serves as the key to the improvement of fuzzers.
Job Edge-Fog Interconnection Network Creation Game in Internet of Things
Venue: arXiv cs.DC 2019
Year: 2019

This is the first paper to address the topology structure of Job Edge-Fog interconnection network in the perspective of network creation game. A two level network creation game model is given, in which the first level is similar to the traditional network creation game with total length objective to other nodes. The second level adopts two types of cost functions, one is created based on the Jackson-Wolinsky type of distance based utility, another is created based on the Network-Only Cost in the IoT literature. We show the performance of this two level game (Price of Anarchy). This work discloses how the selfish strategies of each individual device can influence the global topology structure of the job edge-fog interconnection network and provides theoretical foundations of the IoT infrastructure construction. A significant advantage of this framework is that it can avoid solving the traditional expensive and impractical quadratic assignment problem, which was the typical framework to study this task. Furthermore, it can control the systematic performance based only on one or two cost parameters of the job edge-fog networks, independently and in a distributed way.
A Type System for First-Class Layers with Inheritance, Subtyping, and Swapping
Venue: arXiv cs.PL 2019
Year: 2019

Context-Oriented Programming (COP) is a programming paradigm to encourage modularization of context-dependent software. Key features of COP are layers---modules to describe context-dependent behavioral variations of a software system---and their dynamic activation, which can modify the behavior of multiple objects that have already been instantiated. Typechecking programs written in a COP language is difficult because the activation of a layer can even change objects' interfaces. Inoue et al. have informally discussed how to make JCop, an extension of Java for COP by Appeltauer et al., type-safe. In this article, we formalize a small COP language called ContextFJ$_{<:}$ with its operational semantics and type system and show its type soundness. The language models main features of the type-safe version of JCop, including dynamically activated first-class layers, inheritance of layer definitions, layer subtyping, and layer swapping.
Automated building image extraction from 360° panoramas for postdisaster evaluation
Venue: arXiv cs.CV 2019
Year: 2019

After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. However, in many cases, the images collected are captured without sufficient spatial context. When damage is severe, it may be quite difficult to even recognize the building. Accessing images of the pre-disaster condition of those buildings is required to accurately identify the cause of the failure or the actual loss in the building. Here, to address this issue, we develop a method to automatically extract pre-event building images from 360o panorama images (panoramas). By providing a geotagged image collected near the target building as the input, panoramas close to the input image location are automatically downloaded through street view services (e.g., Google or Bing in the United States). By computing the geometric relationship between the panoramas and the target building, the most suitable projection direction for each panorama is identified to generate high-quality 2D images of the building. Region-based convolutional neural networks are exploited to recognize the building within those 2D images. Several panoramas are used so that the detected building images provide various viewpoints of the building. To demonstrate the capability of the technique, we consider residential buildings in Holiday Beach, Texas, the United States which experienced significant devastation in Hurricane Harvey in 2017. Using geotagged images gathered during actual post-disaster building reconnaissance missions, we verify the method by successfully extracting residential building images from Google Street View images, which were captured before the event.
An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter
Venue: arXiv cs.SI 2019
Year: 2019

Pathogenic Social Media (PSM) accounts such as terrorist supporter accounts and fake news writers have the capability of spreading disinformation to viral proportions. Early detection of PSM accounts is crucial as they are likely to be key users to make malicious information "viral". In this paper, we adopt the causal inference framework along with graph-based metrics in order to distinguish PSMs from normal users within a short time of their activities. We propose both supervised and semi-supervised approaches without taking the network information and content into account. Results on a real-world dataset from Twitter accentuates the advantage of our proposed frameworks. We show our approach achieves 0.28 improvement in F1 score over existing approaches with the precision of 0.90 and F1 score of 0.63.
Deep Multi-Sensor Lane Detection
Venue: arXiv cs.CV 2019
Year: 2019

Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. To address this issue, we propose a novel deep neural network that takes advantage of both LiDAR and camera sensors and produces very accurate estimates directly in 3D space. We demonstrate the performance of our approach on both highways and in cities, and show very accurate estimates in complex scenarios such as heavy traffic (which produces occlusion), fork, merges and intersections.
Detecting Pathogenic Social Media Accounts without Content or Network Structure
Venue: arXiv cs.SI 2019
Year: 2019

The spread of harmful mis-information in social media is a pressing problem. We refer accounts that have the capability of spreading such information to viral proportions as "Pathogenic Social Media" accounts. These accounts include terrorist supporters accounts, water armies, and fake news writers. We introduce an unsupervised causality-based framework that also leverages label propagation. This approach identifies these users without using network structure, cascade path information, content and user's information. We show our approach obtains higher precision (0.75) in identifying Pathogenic Social Media accounts in comparison with random (precision of 0.11) and existing bot detection (precision of 0.16) methods.