OpenRaiser/Intern-Atlas huggingface dataset:
Modelling and Simulation of Fog and Edge Computing Environments using iFogSim Toolkit
Venue: arXiv cs.DC 2018
Year: 2018

Resource management in Fog computing is very complicated as it engages significant number of diverse and resource constraint Fog nodes to meet computational demand of IoT-enabled systems in distributed manner. Its integration with Cloud triggers further difficulties in combined resource management. Different sensing frequency of IoT devices, distributed application structure and their coordination also influence resource management in Fog computing environment. For advancement of Fog and its resource management, the necessity of extensive research in beyond question. To develop and evaluate different ideas and resource management policies, empirical analysis on Fog environment is the key. Since Fog computing environment incorporates IoT devices, Fog nodes and Cloud datacenters along with huge amount of IoT-data and distributed applications, real-world implementation of Fog environment for research will be very costly. Moreover, modification of any entity in real-world Fog environment will be tedious. In this circumstance, simulation of Fog computing environment can be very helpful. Simulation toolkits not only provide frameworks to design customized experiment environment but also assist in repeatable evaluation. In this chapter we focus on delivering a tutorial on iFogSim toolkit for modelling and simulating Fog computing environments. We believe this chapter will offer researchers a simplified way to apply iFogSim in their research works.
Automatically Annotating Articles Towards Opening and Reusing Transparent Peer Reviews
Venue: arXiv cs.IR 2018
Year: 2018

An increasing number of scientific publications are created in open and transparent peer review models: a submission is published first, and then reviewers are invited, or a submission is reviewed in a closed environment but then these reviews are published with the final article, or combinations of these. Reasons for open peer review include giving better credit to reviewers and enabling readers to better appraise the quality of a publication. In most cases, the full, unstructured text of an open review is published next to the full, unstructured text of the article reviewed. This approach prevents human readers from getting a quick impression of the quality of parts of an article, and it does not easily support secondary exploitation, e.g., for scientometrics on reviews. While document formats have been proposed for publishing structured articles including reviews, integrated tool support for entire open peer review workflows resulting in such documents is still scarce. We present AR-Annotator, the Automatic Article and Review Annotator which employs a semantic information model of an article and its reviews, using semantic markup and unique identifiers for all entities of interest. The fine-grained article structure is not only exposed to authors and reviewers but also preserved in the published version. We publish articles and their reviews in a Linked Data representation and thus maximize their reusability by third-party applications. We demonstrate this reusability by running quality-related queries against the structured representation of articles and their reviews.
Brief survey of Mobility Analyses based on Mobile Phone Datasets
Venue: arXiv cs.SI 2018
Year: 2018

This is a brief survey of the research performed by Grandata Labs in collaboration with numerous academic groups around the world on the topic of human mobility. A driving theme in these projects is to use and improve Data Science techniques to understand mobility, as it can be observed through the lens of mobile phone datasets. We describe applications of mobility analyses for urban planning, prediction of data traffic usage, building delay tolerant networks, generating epidemiologic risk maps and measuring the predictability of human mobility.
A System for Automated Image Editing from Natural Language Commands
Venue: arXiv cs.CL 2018
Year: 2018

This work presents the task of modifying images in an image editing program using natural language written commands. We utilize a corpus of over 6000 image edit text requests to alter real world images collected via crowdsourcing. A novel framework composed of actions and entities to map a user's natural language request to executable commands in an image editing program is described. We resolve previously labeled annotator disagreement through a voting process and complete annotation of the corpus. We experimented with different machine learning models and found that the LSTM, the SVM, and the bidirectional LSTM-CRF joint models are the best to detect image editing actions and associated entities in a given utterance.
Blockchain based secure data handover scheme in non-orthogonal multiple access
Venue: arXiv cs.NI 2018
Year: 2018

Non-orthogonal multiple access (NOMA) with successive interference cancellation receiver is considered as one of the most potent multiple access techniques to be adopted in future wireless communication networks. Data security in the NOMA transmission scheme is on much attention drawing issue. Blockchain is a distributed peer-to-peer network enables a way of protecting information from unauthorized access, tempering etc. By utilizing encryption techniques of blockchain, a secured data communication scheme using blockchain in NOMA is proposed in this paper. A two-phase encryption technique with key generation using different parameter is proposed. In the first-phase data is encrypted by imposing users' public key and in the second phase, a private key of the base station (BS) is engaged for encryption. Finally, the superiority of the proposed scheme over existing scheme is proven through a comparative study based on the different features.
Enhancing Physical Layer Security for NOMA Transmission in mmWave Drone Networks
Venue: arXiv cs.IT 2018
Year: 2018

Physical layer security (PLS) is critically important for emerging wireless communication networks to maintain the confidentiality of the information of legitimate users. In this paper, we investigate enhancing PLS in an unmanned aerial vehicle (UAV) based communication network where a UAV acting as an aerial base station (BS) provides coverage in a densely packed user area (such as a stadium or a concert area). In particular, non-orthogonal multiple access (NOMA) together with highly-directional multi-antenna transmission techniques in mmWave frequency bands are utilized for improving spectral efficiency. In order to achieve PLS against potential eavesdropper attacks, we introduce a protected zone around the user region. However, limited resource availability refrain protected zone being extended to cover the entire eavesdropper region. Hence, we propose an approach to optimize the protected zone shape (for fixed area) at each UAV-BS hovering altitude. The associated secrecy performance is evaluated considering the secrecy outage and sum secrecy rates. Numerical results reveal the importance of protected zone shape optimization at each altitude to maximize NOMA secrecy rates.
Distributed Communication-aware Motion Planning for Networked Mobile Robots under Formal Specifications
Venue: arXiv cs.SY 2018
Year: 2018

Control and communication are often tightly coupled in motion planning of networked mobile robots, due to the fact that robotic motions will affect the overall communication quality, and the quality of service (QoS) of the communication among the robots will in turn affect their coordination performance. In this paper, we propose a control theoretical motion planning framework for a team of networked mobile robots in order to accomplish high-level spatial and temporal motion objectives while optimizing communication QoS. Desired motion specifications are formulated as Signal Temporal Logic (STL), whereas the communication performances to be optimized are captured by recently proposed Spatial Temporal Reach and Escape Logic (STREL) formulas. Both the STL and STREL specifications are encoded as mixed integer linear constraints posed on the system and/or environment state variables of the mobile robot network, where satisfactory control strategies can be computed by exploiting a distributed model predictive control (MPC) approach. To the best of the authors' knowledge, we are the first to study controller synthesis for STREL specifications. A two-layer hierarchical MPC procedure is proposed to efficiently solve the problem, whose soundness and completeness are formally ensured. The effectiveness of the proposed framework is validated by simulation examples.
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation
Venue: arXiv cs.CL 2018
Year: 2018

A natural language interface (NLI) to structured query is intriguing due to its wide industrial applications and high economical values. In this work, we tackle the problem of domain adaptation for NLI with limited data on target domain. Two important approaches are considered: (a) effective general-knowledge-learning on source domain semantic parsing, and (b) data augmentation on target domain. We present a Structured Query Inference Network (SQIN) to enhance learning for domain adaptation, by separating schema information from NL and decoding SQL in a more structural-aware manner; we also propose a GAN-based augmentation technique (AugmentGAN) to mitigate the issue of lacking target domain data. We report solid results on GeoQuery, Overnight, and WikiSQL to demonstrate state-of-the-art performances for both in-domain and domain-transfer tasks.
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
Venue: arXiv cs.IR 2018
Year: 2018

In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several additions have been proposed for extending such models in order to handle specific problems or data. Two such extensions are 1.) modeling of inter-session relations for catching long term dependencies over user sessions, and 2.) modeling temporal aspects of user-item interactions. The former allows the session-based recommendation to utilize extended session history and inter-session information when providing new recommendations. The latter has been used to both provide state-of-the-art predictions for when the user will return to the service and also for improving recommendations. In this work we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction. The model consists of a Hierarchical RNN for the inter-session and intra-session items recommendation extended with a Point Process model for the time-gaps between the sessions. The experimental results indicate that the proposed model improves recommendations significantly on two datasets over a strong baseline, while simultaneously improving return-time predictions over a baseline return-time prediction model.
BSGD-TV: A parallel algorithm solving total variation constrained image reconstruction problems
Venue: arXiv cs.IT 2018
Year: 2018

We propose a parallel reconstruction algorithm to solve large scale TV constrained linear inverse problems. We provide a convergence proof and show numerically that our method is significantly faster than the main competitor, block ADMM.
Regularized Fuzzy Neural Networks to Aid Effort Forecasting in the Construction and Software Development
Venue: arXiv cs.AI 2018
Year: 2018

Predicting the time to build software is a very complex task for software engineering managers. There are complex factors that can directly interfere with the productivity of the development team. Factors directly related to the complexity of the system to be developed drastically change the time necessary for the completion of the works with the software factories. This work proposes the use of a hybrid system based on artificial neural networks and fuzzy systems to assist in the construction of an expert system based on rules to support in the prediction of hours destined to the development of software according to the complexity of the elements present in the same. The set of fuzzy rules obtained by the system helps the management and control of software development by providing a base of interpretable estimates based on fuzzy rules. The model was submitted to tests on a real database, and its results were promissory in the construction of an aid mechanism in the predictability of the software construction.
Estimating 6D Pose From Localizing Designated Surface Keypoints
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image. The core of our approach is that we first designate a set of surface points on target object model as keypoints and then train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can recover the 6D pose according to the 2D-3D relationship of keypoints. Different from recent state-of-the-art CNN-based approaches that rely on a time-consuming post-processing procedure, our method can achieve competitive accuracy without any refinement after pose prediction. Meanwhile, we obtain a 30% relative improvement in terms of ADD accuracy among methods without using refinement. Moreover, we succeed in handling heavy occlusion by selecting the most confident keypoints to recover the 6D pose. For the sake of reproducibility, we will make our code and models publicly available soon.
Discrete-Time Distributed Observers over Jointly Connected Switching Networks and an Application
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we first establish an exponential stability result for a class of linear switched systems and then apply this result to show the existence of the distributed observer for a discrete-time leader system over jointly connected switching networks. A special case of this result leads to the solution of a leader-following consensus problem of multiple discrete-time double-integrator systems over jointly connected switching networks. Then, we further develop the adaptive distributed observer for the discrete-time leader system over jointly connected switching networks, which has the advantage over the distributed observer in that it does not require that every follower know the system matrix of the leader system. As an application of the discrete-time distributed observer, we will solve the cooperative output regulation problem of a discrete-time linear multi-agent system over jointly connected switching networks. A leader-following formation problem of mobile robots will be used to illustrate our design. This problem cannot be handled by any existing approach.
Continuous User Authentication by Contactless Wireless Sensing
Venue: arXiv cs.CR 2018
Year: 2018

This paper presents BodyPIN, which is a continuous user authentication system by contactless wireless sensing using commodity Wi-Fi. BodyPIN can track the current user's legal identity throughout a computer system's execution. In case the authentication fails, the consequent accesses will be denied to protect the system. The recent rich wireless-based user identification designs cannot be applied to BodyPIN directly, because they identify a user's various activities, rather than the user herself. The enforced to be performed activities can thus interrupt the user's operations on the system, highly inconvenient and not user-friendly. In this paper, we leverage the bio-electromagnetics domain human model for quantifying the impact of human body on the bypassing Wi-Fi signals and deriving the component that indicates a user's identity. Then we extract suitable Wi-Fi signal features to fully represent such an identity component, based on which we fulfill the continuous user authentication design. We implement a BodyPIN prototype by commodity Wi-Fi NICs without any extra or dedicated wireless hardware. We show that BodyPIN achieves promising authentication performances, which is also lightweight and robust under various practical settings.
A novel lightweight hardware-assisted static instrumentation approach for ARM SoC using debug components
Venue: arXiv cs.CR 2018
Year: 2018

Most of hardware-assisted solutions for software security, program monitoring, and event-checking approaches require instrumentation of the target software, an operation which can be performed using an SBI (Static Binary Instrumentation) or a DBI (Dynamic Binary Instrumentation) framework. Hardware-assisted instrumentation can use one of these two solutions to instrument data to a memory-mapped register. Both these approaches require an in-depth knowledge of frameworks and an important amount of software modifications in order to instrument a whole application. This work proposes a novel way to instrument an application with minor modifications, at the source code level, taking advantage of underlying hardware debug components such as CS (CoreSight) components available on Xilinx Zynq SoCs. As an example, the instrumentation approach proposed in this work is used to detect a double free security attack. Furthermore, it is evaluated in terms of runtime and area overhead. Results show that the proposed solution takes 30 $μ$s on average to instrument an instruction while the optimized version only takes 0.014 us which is ten times better than usual memory-mapped register solutions used in existing works.
Impact of Sentiment Detection to Recognize Toxic and Subversive Online Comments
Venue: arXiv cs.CL 2018
Year: 2018

The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection against a subversive user.
GANtruth - an unpaired image-to-image translation method for driving scenarios
Venue: arXiv cs.CV 2018
Year: 2018

Synthetic image translation has significant potentials in autonomous transportation systems. That is due to the expense of data collection and annotation as well as the unmanageable diversity of real-words situations. The main issue with unpaired image-to-image translation is the ill-posed nature of the problem. In this work, we propose a novel method for constraining the output space of unpaired image-to-image translation. We make the assumption that the environment of the source domain is known (e.g. synthetically generated), and we propose to explicitly enforce preservation of the ground-truth labels on the translated images. We experiment on preserving ground-truth information such as semantic segmentation, disparity, and instance segmentation. We show significant evidence that our method achieves improved performance over the state-of-the-art model of UNIT for translating images from SYNTHIA to Cityscapes. The generated images are perceived as more realistic in human surveys and outperforms UNIT when used in a domain adaptation scenario for semantic segmentation.
Exploiting Data Sensitivity on Partitioned Data
Venue: arXiv cs.DB 2018
Year: 2018

Several researchers have proposed solutions for secure data outsourcing on the public clouds based on encryption, secret-sharing, and trusted hardware. Existing approaches, however, exhibit many limitations including high computational complexity, imperfect security, and information leakage. This chapter describes an emerging trend in secure data processing that recognizes that an entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome some of the limitations of existing encryption-based approaches. In particular, data and computation can be partitioned into sensitive or non-sensitive datasets - sensitive data can either be encrypted prior to outsourcing or stored/processed locally on trusted servers. The non-sensitive dataset, on the other hand, can be outsourced and processed in the cleartext. While partitioned computing can bring new efficiencies since it does not incur (expensive) encrypted data processing costs on non-sensitive data, it can lead to information leakage. We study partitioned computing in two contexts - first, in the context of the hybrid cloud where local resources are integrated with public cloud resources to form an effective and secure storage and computational platform for enterprise data. In the hybrid cloud, sensitive data is stored on the private cloud to prevent leakage and a computation is partitioned between private and public clouds. Care must be taken that the public cloud cannot infer any information about sensitive data from inter-cloud data access during query processing. We then consider partitioned computing in a public cloud only setting, where sensitive data is encrypted before outsourcing. We formally define a partitioned security criterion that any approach to partitioned computing on public clouds must ensure in order to not introduce any new vulnerabilities to the existing secure solution.
A Formal Method for Mapping Software Engineering Practices to Essence
Venue: arXiv cs.SE 2018
Year: 2018

Essence Framework EF aims at addressing the core problems of software engineering SE and its practices
Graph based Question Answering System
Venue: arXiv cs.CL 2018
Year: 2018

In today's digital age in the dawning era of big data analytics it is not the information but the linking of information through entities and actions which defines the discourse. Any textual data either available on the Internet off off-line (like newspaper data, Wikipedia dump, etc) is basically connect information which cannot be treated isolated for its wholesome semantics. There is a need for an automated retrieval process with proper information extraction to structure the data for relevant and fast text analytics. The first big challenge is the conversion of unstructured textual data to structured data. Unlike other databases, graph databases handle relationships and connections elegantly. Our project aims at developing a graph-based information extraction and retrieval system.