Reviewerly/exHarmony huggingface dataset:
A Graph based Approach for Keyword Extraction from Documents

This paper presents an unsupervised approach for keyword extraction from a document. Summarizing a research paper is not an easy task, it is time-consuming. For making summarization of documents much easier keywords are used. Keyword helps in summarizing a large amount of textual data. Keyword extraction is a process in which a set of words are selected that gives the context of the whole document. Here, we are proposing a graph-based model approach for extracting keywords from a research paper. The proposing method is a combination of Rapid Automatic Keyword Extraction (RAKE) algorithm and Keyword Extraction using Collective Node Weight (KECNW), in which candidate keywords are extracted using RAKE algorithm and selection of keywords using KECNW model. The final output of this methods are keywords extracted from a document with their rank.
Kaleidoscope: A Crowdsourcing Testing Tool for Web Quality of Experience

Today's webpages development cycle consists of constant iterations with the goal to improve user retention, time spent on site, and overall quality of experience. Big companies like Google, Facebook, Amazon, etc. invest a lot of time and money to perform online testing. The prohibitive costs of these approaches are an entry barrier for smaller players. Further, the lack of a substantial user-base can be problematic to ensure statistical significance within a reasonable duration. In this paper we propose Kaleidoscope, an automated tool to evaluate Web features at a large scale, quickly, accurately, and at a reasonable price. Kaleidoscope can test two crucial user-perceived Web features - the style and page loading. As far as we know, it is the first testing tool to replay page loading by controlling visual changes on a webpage. Kaleidoscope allows to concurrently load a webpage in two versions (e.g., different fonts, with vs without ads) that are shown to a participant side-by-side. Further, Kaleidoscope also allows a participant to interact with each webpage version and provide feedback, e.g., respond to a questionnaire previously prepared by an "experimenter". Kaleidoscope supports both voluntary and paid testers from FigureEight, a popular crowdsourcing platform. Using hundreds of FigureEight testers, we validate that Kaleidoscope matches the accuracy of trusted in-lab tests while providing results about 12x faster (and arguably at a lower cost) than A/B testing. Finally, we showcase how to use Kaleidoscope's page loading feature to study the user-perceived page load time (uPLT) of a webpage.
Separability gap and large-deviation entanglement criterion

For a given Hamiltonian $H$ on a multipartite quantum system, one is interested in finding the energy $E_0$ of its ground state. In the separability approximation, arising as a natural consequence of measurement in a separable basis, one looks for the minimal expectation value $\lambda_{\rm min}^{\otimes}$ of $H$ among all product states. For several concrete model Hamiltonians, we investigate the difference $\lambda_{\rm min}^{\otimes}-E_0$, called separability gap, which vanishes if the ground state has a product structure. In the generic case of a random Hermitian matrix of the Gaussian orthogonal ensemble, we find explicit bounds for the size of the gap which depend on the number of subsystems and hold with probability one. This implies an effective entanglement criterion applicable for any multipartite quantum system: If an expectation value of a typical observable among a given state is sufficiently distant from the average value, the state is almost surely entangled.
Construction of Causality Event Evolutionary Graph of Aviation Accident

Previous research on causality analysis of civil aviation accident was mainly based on probability and statistics, lacking of the analysis based on the whole process of accident occurrence. The process and method of constructing the causality event evolutionary graph of aviation accidents was proposed, which can be used to causality analysis. The authors apply the approach to the dataset consisting of the world aviation safety accident investigation report. On the basis of effectively extraction of causal event pairs, causality chains for each accident was generated. And then, mapping it with the domain ontology to construct causality event evolutionary graph, which provides the data and method to civil aviation accident's comprehensive analysis and scene reappearance.
Classification of Smart Contract Bugs Using the NIST Bugs Framework

Blockchain technology has recently emerged as the primary platform for the transfer of digital currency. This technology, which has been heralded as a revolutionary tool to facilitate the transfer of funds between participating parties, is still in its infancy and should be subjected to thorough scrutiny. In recent years, researchers have attempted to uncover a litany of bugs embedded within these distributed systems; however, there does not yet exist a formal and standardized method for their classification. In this paper, we present the first formal classifications of known bugs in smart contract systems using NIST's Bugs Framework and propose two new classes: Distributed System Protocol (DSP) and Distributed System Resource Management (DRM).
Stain Style Transfer Using Transitive Adversarial Networks

Digitized pathological diagnosis has been in increasing demand recently. It is well known that color information is critical to the automatic and visual analysis of pathological slides. However, the color variations due to various factors not only have negative impact on pathologist’s diagnosis, but also will reduce the robustness of the algorithms. The factors that cause the color differences are not only in the process of making the slices, but also in the process of digitization. Different strategies have been proposed to alleviate the color variations. Most of such techniques rely on collecting color statistics to perform color matching across images and highly dependent on a reference template slide. Since the pathological slides between hospitals are usually unpaired, these methods do not yield good matching results. In this work, we propose a novel network that we refer to as Transitive Adversarial Networks (TAN) to transfer the color information among slides from different hospitals or centers. It is not necessary for an expert to pick a representative reference slide in the proposed TAN method. We compare the proposed method with the state-of-the-art methods quantitatively and qualitatively. Compared with the state-of-the-art methods, our method yields an improvement of 0.87 dB in terms of PSNR, demonstrating the effectiveness of the proposed TAN method in stain style transfer.
A reflected feature space for CART

Summary We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.
A Moment Ratio Bound for Polynomials and Some Extremal Properties of Krawchouk Polynomials and Hamming Spheres

Let p ≥ 2. We improve the bound ||f|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> / ||f|| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ≤ (p-1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s/2</sup> for a polynomial f of degree s on the boolean cube {0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , which comes from hypercontractivity, replacing the right hand side of this inequality by an explicit bivariate function of p and s, which is smaller than (p-1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s/2</sup> for any p > 2 and s > 0. We show the new bound to be tight, within a smaller order factor, for the Krawchouk polynomial of degree s. This implies several nearly-extremal properties of Krawchouk polynomials and Hamming spheres (equivalently, Hamming balls). In particular, Krawchouk polynomials have (almost) the heaviest tails among all polynomials of the same degree and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The Hamming spheres have the following approximate edge-isoperimetric property: For all 1 ≤ s ≤ n/2, and for all even distances 0 ≤ i ≤ 2s(n-s) / n, the Hamming sphere of radius s contains, up to a multiplicative factor of O(i), as many pairs of points at distance i as possible, among sets of the same size (there is a similar, but slightly weaker and somewhat more complicated claim for all distances). This also implies that Hamming spheres are (almost) stablest with respect to noise among sets of the same size. In coding theory terms this means that a Hamming sphere (equivalently a Hamming ball) has the maximal probability of undetected error, among all binary codes of the same rate. We also describe a family of hypercontractive inequalities for functions on {0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , which improve on the `usual' “ q → 2” inequality by taking into account the concentration of a function (expressed as the ratio between its <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> norms), and which are nearly tight for characteristic functions of Hamming spheres. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This has to be interpreted with some care.
Analysis of Experiments on Statistical and Neural Parsing for a Morphologically Rich and Free Word Order Language Urdu

This article presents an analysis of experiments with statistical and neural parsing techniques for Urdu, a widely spoken South Asian language. We demonstrate state of the art constituency parsing results for an Urdu treebank. Urdu is a morphologically rich and is characterized by free word order. Language representation (e.g. input type, lemmatization, word clusters), part of speech tag set, phrase labels and the size of a training corpus are crucial for parsing such languages. In this article, probabilistic context-free grammars, data-oriented parsing, and recursive neural network based models have been experimented with several linguistic features which show improvements in the parsing results. Features include syntactic sub-categorization of POS tags, empirically learned horizontal and vertical markovizations and lexical head words. These features enable dependency information for case markers and add phrasal and lexical context to the parse trees. The data-oriented parsing and recursive neural network model give an f-score of 87.1 by considering gold POS tags in the test set, on textual input, they show a performance with f-scores of 83.4 and 84.2, respectively. To overcome the issue of data sparsity due to the morphological richness, lemmatization and unsupervised word clustering have been performed. A treebank should cover most probable word orders of the language so that models can learn various orders accurately. To analyze the order coverage of the treebank and learning capability of different parsers, a test set has been prepared conditioning different word orders. This test set is evaluated with the best performing parsing models and with gold POS tags, f-scores are above 90 and on textual input, the average f-score is 87.6.
Performance Analysis of Face Recognition using Eigenface Approach

Eigenface is an algorithm in the principal component analysis (PCA) that is used to recognize faces. Eigenface used to reduce dimensionality and find the best vector for distributing the facial image in the facial space. This method has been widely used and implemented in various previous researches to recognize human face images. Not only to detect human faces under normal conditions, but PCA has also been proven to be able to properly recognize images in various expressions. It can even recognize facial images with various challenges such as detecting faces after plastic surgery and combining them with facial image reconstruction techniques. This research aims to examine the performance of the PCA-Eigenface method to recognize human face images from several databases that have their own challenges, such as the lack of illumination of facial images, significant variations in expression and the use of accessories such as glasses. The recognizable accuracy is quite varied, from 100% to 67% in each database with and with an average recognition of more than 85%.
Privacy-Preserving Techniques for Big Data Analysis in Cloud

Big Data is termed as the enormous collection of large datasets that cannot be processed by using the traditional computational technique. Big data consists of different formats like structured data and unstructured data; it is not efficient in terms of response time to perform analysis of big data using existing traditional RDBMS techniques. The Big data possess many challenges because of several properties like variety, value, variability, volume, visualization, velocity and veracity. Big data is not allowed to store on one machine due to its collection of enormous datasets so it requires a new distributed platform to analyze and store such huge amount of datasets for future prediction and decision making. Hadoop provides the framework for distributed data processing to analyze and store this large amount of data using commodity hardware. Cloud computing can process the distributed queries across multiple datasets with the use of Hadoop. Big data is stored in the cloud computing environment for processing and analyzing the enormous datasets. Because of this paradigm, many new challenges for securing user's sensitive data, privacy-preserving mechanisms are used in untrusted cloud servers. Privacy-preserving mechanism cryptographically enforced the security to the private data. In this paper, to provide data confidentiality and integrity, three techniques Homomorphic encryption, order-preserving encryption schemes and Attribute-based encryption are described. These techniques can preferably use in the cloud to provide data privacy and also preferred for big data to maintain the efficiency and scalability of large datasets for decision making.
A LSTM-Based Method for Comprehension and Evaluation of Network Security Situation

Neural networks have been used in situation awareness and anomaly detection. However, most existing applications are based on fully connected neural network that is not suitable enough for processing raw situation data generated by networks. Generally, situation data can be organized and comprehended by time series because network attacks and states change over time. It's a gradual process and there are logical associations between different stages. Considering above factors, we propose a neural network structure based on LSTM to establish time correlation between situation data. Improve LSTM with cross entropy function, rectified linear unit and appropriate layer stacking. Experiments prove that the improved LSTM neural network has better discriminative performance for current network security situation. The LSTM-based method enables an efficient comprehension and evaluation of network security situation.
Multiple Channel with Crash Fault Tolerant Consensus Blockchain Network: A Case Study of Vegetables Supplier Supply Chain

Blockchain has become one of important technologies because of its capabilities such as transparency and privacy. Many enterprises see these capabilities as a breakthrough to many cases, such as supply chain. This paper discusses a case of a vegetables supplier's problems in term of ledger. Generally, the company need one day to finish the ledger and sometimes there are some information distortion between supplier and client. This paper use hyperledger fabric to build blockchain network that is compatible with the supplier problems. The Blockchain network created with nine channels and crash fault tolerant by using kafka. This blockchain network is tested with two iterations which each iteration consists of 40 rounds and each round contains 3000 transactions. The first iteration throughput average is 27.9 tps with the highest throughput is in round 1 with a throughput of 34.1 tps and the lowest is in round 37 with a throughput of 25.3 tps. The second iteration throughput average is 28.1 tps with the highest throughput is in round 1 with a throughput of 32.8 tps and the lowest is in round 40 with a throughput of 21.3 tps. This performance indicates that multiple channel blockchain network could help the vegetables supplier to improve their ledger capabilities to be less than one day.
Adaptive Cubature Kalman Filter Based on the Expectation-Maximization Algorithm

A cubature Kalman filter is considered to be one of the most useful methods for nonlinear systems.However, when the statistical characteristics of noise are unknown, the estimation accuracy is degraded.Therefore, an adaptive square-root cubature Kalman filter (ASCKF) is designed to handle the unknown noise.The maximum likelihood criterion and expectation-maximization algorithm are employed to adaptively estimate the parameters of unknown noise, thus restraining the disturbance resulting from unknown noise and improving the estimation accuracy.The stability of the proposed algorithm is theoretically analyzed.Finally, simulations are carried out to illustrate that the performance of the ASCKF algorithm is much more reliable than that of a standard square-root cubature Kalman filter.
Codes and Gap Sequences of Hermitian Curves

Hermitian functional and differential codes are AG-codes defined on a Hermitian curve. To ensure good performance, the divisors defining such AG-codes have to be carefully chosen, exploiting the rich combinatorial and algebraic properties of the Hermitian curves. In this paper, the case of differential codes C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω</sub> (D, mT) on the Hermitian curve ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 3 defined over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 6 is worked out where su.yppp(T) := ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 3(F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 2), the set of all F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 2-rational points of ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 3, while D is taken, as usual, to be the sum of the points in the complementary set D = ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 3(F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 6) \ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 3(F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 2). For certain values of m, such codes C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω</sub> (D, mT) have better minimum distance compared with true values of 1-point Hermitian codes. The automorphism group of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> (D, mT), m ≤ q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> - 2, is isomorphic to P GU(3, q).
A multi-period comprehensive evaluation method of construction safety risk based on cloud model

The construction industry has long been seen as a high-risk industry, and the risk evaluation method is the core of safety risk management. Complex construction environments can lead to risk evolution over time, leading to uncertainty in risk assessment. Therefore, it is necessary to establish a r isk evaluation method for multi-period group decision, which can also deal with uncertain information reliably. This study defines the risk evaluation indicators for construction safety and adopts the cloud model to deal with the uncertain information of experts’ evaluations. A cloud-based aggregation algorithm is also employed for group decision. Then, a cloud-based Minkowski distance function is proposed to enhance the ability of TOPSIS to deal with the uncertain information. Finally, an optimization algorithm is used to calculate the multi-period comprehensive evaluation value to define the risk priority. A real case is used for demonstration and the results show that the proposed method can effectively deal with the risk evaluation problem of multi-project, multi-period and group decision with uncertain information.
A Study on Security Considerations in IoT Environment and Data Protection Methodologies for Communication in Cloud Computing

Over recent years, we have witnessed the rapid growth of Internet-connected devices, ranging from very simple sensors to highly complex cloud servers shaping the Internet of Things (IoT). This paper depicts the impact and importance of cloud computing on IoT, as huge bytes of data generated by IoT devices require a proper system to retrieve data. IoT is used in various fields like industrial, medical, automobile and many more applications to ease human lives. As this technology is employed over various applications, the threats to IoT are also increasing. For secure communication in IoT, there is a requirement of various levels of security algorithms (like encryption techniques, etc.). We are working on the analytic exploration of data protection methodologies. Evolution and importance of Lightweight encryption techniques suitable for IoT environment are discussed. This paper also highlights the need for an End to End Encryption in the IoT environment. Signal protocol explained is a security protocol providing applications with end to end encryption. This paper concludes with several directions to future research on security considerations and solutions.