OpenRaiser/Intern-Atlas huggingface dataset:
Positioning services of a travel agency in social networks
Venue: arXiv cs.SI 2019
Year: 2019

In this paper the methods of forming a travel company customer base by means of social networks are observed. These methods are made to involve web-users of the social networks (VK.com and Facebook) for positioning of the service of the travel agency "New Europe" on the Internet. The methods of applying the maintenance activities and interests of web-users are also used. So, the main method of information exchanging in modern network society is on-line social networks. The rapid development and improvement of such information and communication technologies is a key factor in the positioning of the travel agency brand in the global information space. The absence of time and space restrictions and the speed of spreading of the information among an aim audience of social networks create all the conditions for effective popularization of the travel agency "New Europe" and its service in the Internet.
Delta-OMA (D-OMA): A New Method for Massive Multiple Access in 6G
Venue: arXiv cs.NI 2019
Year: 2019

A new multiple access method, namely, delta-orthogonal multiple access (D-OMA) is introduced for massive access in future generation 6G cellular networks. D-OMA is based on the concept of distributed large coordinated multipoint transmission-enabled non-orthogonal multiple access (NOMA) using partially overlapping sub-bands for NOMA clusters. Performance of this scheme is demonstrated in terms of outage capacity for different degrees of overlapping of NOMA sub-bands. D-OMA can also be used for enhanced security provisioning in both uplink and downlink wireless access networks. Practical implementation issues and open challenges for optimizing D-OMA are also discussed.
The Power of One-State Turing Machines
Venue: arXiv cs.FL 2019
Year: 2019

At first glance, one-state Turing machines are very weak: the halting problem for them is decidable, and, without memory, they cannot even accept a simple one element language such as $L = \{ 1 \}$ . Nevertheless it has been showed that a one-state Turing machine can accept non regular languages. We extend such result and prove that they can also recognize non context-free languages, so for some tasks they are more powerful than pushdown automata.
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go
Venue: arXiv cs.AI 2019
Year: 2019

This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart machine cooperative learning system on game of Go. The genetic fuzzy markup language (GFML)-based Robot Agent can work on various kinds of robots, including Palro, Pepper, and TMUs robots. We use the parameters of FAIR open source Darkforest and OpenGo AI bots to construct the knowledge base of Open Go Darkforest (OGD) cloud platform for student learning on the Internet. In addition, we adopt the data from AlphaGo Master sixty online games as the training data to construct the knowledge base and rule base of the co-learning system. First, the Darkforest predicts the win rate based on various simulation numbers and matching rates for each game on OGD platform, then the win rate of OpenGo is as the final desired output. The experimental results show that the proposed approach can improve knowledge base and rule base of the prediction ability based on Darkforest and OpenGo AI bot with various simulation numbers.
DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
Venue: arXiv cs.CV 2019
Year: 2019

As the foundation of driverless vehicle and intelligent robots, Simultaneous Localization and Mapping(SLAM) has attracted much attention these days. However, non-geometric modules of traditional SLAM algorithms are limited by data association tasks and have become a bottleneck preventing the development of SLAM. To deal with such problems, many researchers seek to Deep Learning for help. But most of these studies are limited to virtual datasets or specific environments, and even sacrifice efficiency for accuracy. Thus, they are not practical enough. We propose DF-SLAM system that uses deep local feature descriptors obtained by the neural network as a substitute for traditional hand-made features. Experimental results demonstrate its improvements in efficiency and stability. DF-SLAM outperforms popular traditional SLAM systems in various scenes, including challenging scenes with intense illumination changes. Its versatility and mobility fit well into the need for exploring new environments. Since we adopt a shallow network to extract local descriptors and remain others the same as original SLAM systems, our DF-SLAM can still run in real-time on GPU.
Coexistence of Age and Throughput Optimizing Networks: A Game Theoretic Approach
Venue: arXiv cs.GT 2019
Year: 2019

Real-time monitoring applications have Internet-of-Things (IoT) devices sense and communicate information (status updates) to a monitoring facility. Such applications desire the status updates available at the monitor to be fresh and would like to minimize the age of delivered updates. Networks of such devices may share wireless spectrum with WiFi networks. Often, they use a CSMA/CA based medium access similar to WiFi. However, unlike them, a WiFi network would like to provide high throughputs for its users. We model the coexistence of such networks as a repeated game with two players, an age optimizing network (AON) and a throughput optimizing network (TON), where an AON aims to minimize the age of updates and a TON seeks to maximize throughput. We define the stage game, parameterized by the average age of the AON at the beginning of the stage, and derive its mixed strategy Nash equilibrium (MSNE). We study the evolution of the equilibrium strategies over time, when players play the MSNE in each stage, and the resulting average discounted payoffs of the networks. It turns out that it is more favorable for a TON to share spectrum with an AON in comparison to sharing with another TON. The key to this lies in the MSNE strategy of the AON that occasionally refrains all its nodes from transmitting during a stage. Such stages allow the TON competition free access to the medium.
Ego-motion Sensor for Unmanned Aerial Vehicles Based on a Single-Board Computer
Venue: arXiv cs.CV 2019
Year: 2019

This paper describes the design and implementation of a ground-related odometry sensor suitable for micro aerial vehicles. The sensor is based on a ground-facing camera and a single-board Linux-based embedded computer with a multimedia System on a Chip (SoC). The SoC features a hardware video encoder which is used to estimate the optical flow online. The optical flow is then used in combination with a distance sensor to estimate the vehicle's velocity. The proposed sensor is compared to a similar existing solution and evaluated in both indoor and outdoor environments.
Securing Manufacturing Intelligence for the Industrial Internet of Things
Venue: arXiv cs.CR 2019
Year: 2019

Widespread interest in the emerging area of predictive analytics is driving industries such as manufacturing to explore new approaches to the collection and management of data provided from Industrial Internet of Things (IIoT) devices. Often, analytics processing for Business Intelligence (BI) is an intensive task, and it also presents both an opportunity for competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual Property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a Unified Threat Management (UTM) model is simpler to maintain and has less potential vulnerabilities than a distributed security model. Conversely, a distributed model of security out-performs the UTM model and offers more scope for the use of existing hardware resources. In conclusion, a hybrid security model is proposed where security controls are segregated into a multi-cloud architecture.
Most memory efficient distributed super points detection on core networks
Venue: arXiv cs.DC 2019
Year: 2019

The super point, a host which communicates with lots of others, is a kind of special hosts gotten great focus. Mining super point at the edge of a network is the foundation of many network research fields. In this paper, we proposed the most memory efficient super points detection scheme. This scheme contains a super points reconstruction algorithm called short estimator and a super points filter algorithm called long estimator. Short estimator gives a super points candidate list using thousands of bytes memory and long estimator improves the accuracy of detection result using millions of bytes memory. Combining short estimator and long estimator, our scheme acquires the highest accuracy using the smallest memory than other algorithms. There is no data conflict and floating operation in our scheme. This ensures that our scheme is suitable for parallel running and we deploy our scheme on a common GPU to accelerate processing speed. We also describe how to extend our algorithm to sliding time. Experiments on several real-world core network traffics show that our algorithm acquires the highest accuracy with only consuming littler than one-fifth memory of other algorithms.
Risk analysis beyond vulnerability and resilience - characterizing the defensibility of critical systems
Venue: arXiv cs.CR 2019
Year: 2019

A common problem in risk analysis is to characterize the overall security of a system of valuable assets (e.g., government buildings or communication hubs), and to suggest measures to mitigate any hazards or security threats. Currently, analysts typically rely on a combination of indices, such as resilience, robustness, redundancy, security, and vulnerability. However, these indices are not by themselves sufficient as a guide to action; for example, while it is possible to develop policies to decrease vulnerability, such policies may not always be cost-effective. Motivated by this gap, we propose a new index, defensibility. A system is considered defensible to the extent that a modest investment can significantly reduce the damage from an attack or disruption. To compare systems whose performance is not readily commensurable (e.g., the electrical grid vs. the water-distribution network, both of which are critical, but which provide distinct types of services), we defined defensibility as a dimensionless index. After defining defensibility quantitatively, we illustrate how the defensibility of a system depends on factors such as the defender and attacker asset valuations, the nature of the threat (whether intelligent and adaptive, or random), and the levels of attack and defense strengths and provide analytical results that support the observations arising from the above illustrations. Overall, we argue that the defensibility of a system is an important dimension to consider when evaluating potential defensive investments, and that it can be applied in a variety of different contexts.
Coded Caching in Fog-RAN: b-Matching Approach
Venue: arXiv cs.IT 2019
Year: 2019

Fog radio access network (Fog-RAN), which pushes the caching and computing capabilities to the network edge, is capable of efficiently delivering contents to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum b-matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of O(M + N) is also proposed, where M is the number of users and N is the number of Fog-APs. Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region (OER) and diversity-multiplexing region (DMR). Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching, but also provides a systematic way to evaluate and optimize the performance of Fog-RANs.
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning
Venue: arXiv cs.CY 2019
Year: 2019

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.
SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing
Venue: arXiv cs.CV 2019
Year: 2019

Recently, the advancement in industrial automation and high-speed printing has raised numerous challenges related to the printing quality inspection of final products. This paper proposes a machine vision based technique to assess the printing quality of text on industrial objects. The assessment is based on three quality defects such as text misalignment, varying printing shades, and misprinted text. The proposed scheme performs the quality inspection through stochastic assessment technique based on the second-order statistics of printing. First: the text-containing area on printed product is identified through image processing techniques. Second: the alignment testing of the identified text-containing area is performed. Third: optical character recognition is performed to divide the text into different small boxes and only the intensity value of each text-containing box is taken as a random variable and second-order statistics are estimated to determine the varying printing defects in the text under one, two and three sigma thresholds. Fourth: the K-Nearest Neighbors based supervised machine learning is performed to provide the stochastic process for misprinted text detection. Finally, the technique is deployed on an industrial image for the printing quality assessment with varying values of n and m. The results have shown that the proposed SAML-QC technique can perform real-time automated inspection for industrial printing.
Improving the data quality in the research information systems
Venue: arXiv cs.DB 2019
Year: 2019

In order to introduce an integrated research information system, this will provide scientific institutions with the necessary information on research activities and research results in assured quality. Since data collection, duplication, missing values, incorrect formatting, inconsistencies, etc. can arise in the collection of research data in different research information systems, which can have a wide range of negative effects on data quality, the subject of data quality should be treated with better results. This paper examines the data quality problems in research information systems and presents the new techniques that enable organizations to improve their quality of research information.
Multiple Graph Adversarial Learning
Venue: arXiv cs.CV 2019
Year: 2019

Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming with multiple graphs, and it is non-trivial to adapt GCNs to deal with data representation with multiple graph structures. One main challenge for multi-graph representation is how to exploit both structure information of each individual graph and correlation information across multiple graphs simultaneously. In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning. MGAL aims to learn an optimal structure-invariant and consistent representation for multiple graphs in a common subspace via a novel adversarial learning framework, which thus incorporates both structure information of intra-graph and correlation information of inter-graphs simultaneously. Based on MGAL, we then provide a unified network for semi-supervised learning task. Promising experimental results demonstrate the effectiveness of MGAL model.
Solve For Shortest Paths Problem Within Logarithm Runtime
Venue: arXiv cs.DS 2019
Year: 2019

The Shortest Paths Problem (SPP) is no longer unresolved. Just for a large scalar of instance on this problem, even we cannot know if an algorithm achieves the computing. Those cutting-edge methods are still in the low performance. If we go to a strategy the best-first-search to deal with computing, it is awkward that the technical barrier from another field: the database, which with the capable of Online Oriented. In this paper, we will introduce such a synthesis to solve for SPP which comprises various modules therein including such database leads to finish the task in a logarithm runtime. Through experiments taken on three typical instances on mega-scalar data for transaction in a common laptop, we show off a totally robust, tractable and practical applicability for other projects.
Debugging Frame Semantic Role Labeling
Venue: arXiv cs.CL 2019
Year: 2019

We propose a quantitative and qualitative analysis of the performances of statistical models for frame semantic structure extraction. We report on a replication study on FrameNet 1.7 data and show that preprocessing toolkits play a major role in argument identification performances, observing gains similar in their order of magnitude to those reported by recent models for frame semantic parsing. We report on the robustness of a recent statistical classifier for frame semantic parsing to lexical configurations of predicate-argument structures, relying on an artificially augmented dataset generated using a rule-based algorithm combining valence pattern matching and lexical substitution. We prove that syntactic pre-processing plays a major role in the performances of statistical classifiers to argument identification, and discuss the core reasons of syntactic mismatch between dependency parsers output and FrameNet syntactic formalism. Finally, we suggest new leads for improving statistical models for frame semantic parsing, including joint syntax-semantic parsing relying on FrameNet syntactic formalism, latent classes inference via split-and-merge algorithms and neural network architectures relying on rich input representations of words.
Analytics-Driven Digital Platform for Regional Growth and Development: A Case Study from Norway
Venue: arXiv cs.CY 2019
Year: 2019

In this paper, we present the growth barometer (Vekstbarometer in Norwegian), which is a digital platform that provides the development trends in the regional context in a visual and user-friendly way. The platform is developed to use open data from different sources that is presented mainly in five main groups: goals, premises or prerequisites for growth, industries, growth, and expectations. Furthermore, it also helps to improve decision-making and transparency, as well as provide new knowledge for research and society. The platform uses sensitive and non-sensitive open data. In contrast to other similar digital platforms from Norway, where the data is presented as raw data or with basic level of presentations, our platform is advantageous since it provides a range of options for visualization that makes the statistics more comprehensive.
What Can Machine Learning Teach Us about Communications?
Venue: arXiv cs.IT 2019
Year: 2019

Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well. In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier.
Sparse Graph Codes for Non-adaptive Quantitative Group Testing
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers the problem of Quantitative Group Testing (QGT). Consider a set of $N$ items among which $K$ items are defective. The QGT problem is to identify (all or a sufficiently large fraction of) the defective items, where the result of a test reveals the number of defective items in the tested group. In this work, we propose a non-adaptive QGT algorithm using sparse graph codes over bi-regular bipartite graphs with left-degree $\ell$ and right degree $r$ and binary $t$-error-correcting BCH codes. The proposed scheme provides exact recovery with probabilistic guarantee, i.e. recovers all the defective items with high probability. In particular, we show that for the sub-linear regime where $\frac{K}{N}$ vanishes as $K,N\rightarrow\infty$, the proposed algorithm requires at most ${m=c(t)K\left(t\log_2\left(\frac{\ell N}{c(t)K}+1\right)+1\right)+1}$ tests to recover all the defective items with probability approaching one as ${K,N\rightarrow\infty}$, where $c(t)$ depends only on $t$. The results of our theoretical analysis reveal that the minimum number of required tests is achieved by $t=2$. The encoding and decoding of the proposed algorithm for any $t\leq 4$ have the computational complexity of $\mathcal{O}(K\log^2 \frac{N}{K})$ and $\mathcal{O}(K\log \frac{N}{K})$, respectively. Our simulation results also show that the proposed algorithm significantly outperforms a non-adaptive semi-quantitative group testing algorithm recently proposed by Abdalla \emph{et al.} in terms of the required number of tests for identifying all the defective items with high probability.