Reviewerly/exHarmony huggingface dataset:
Topic Detection for Online Course Feedback Using LDA

In an online course, student feedback is used widely in order to enhance the quality of teaching and learning process by improving the teacher-student relationship. If a lecturer wants to get a summary of these comments, the lecturer has to manually read and summarize all these comments. However, dealing with a very large number of comments is difficult. In this paper, we proposed an approach for topic detection for online course feedback by adopting Latent Dirichlet Allocation (LDA). The course feedback from the website of Coursera (i.e., Machine Learning course) is used to demonstrate the effectiveness of our approach.
Application of Blockchain Technology to Prevent The Potential Of Plagiarism in Scientific Publication

Blockchain is an emerging technology that has many potential applications. The blockchain contains a certain and verifiable record of every single transaction ever made. In this paper, we introduce the application for the prevention of potential plagiarism based on decentralized architecture and public-key cryptosystem, such that no need for trusted third party. We use SHA-256 as hash function and Elliptic Curve as digital signature algorithm. The results show that any attempt to plagiarize a submitted paper will violate the rules. The transmission of a paper is also encrypted through the use of complex cryptographic principles and security algorithms such that nobody can see or alter the paper. Even the reviewer is unable to change the paper because by doing such action the blockchain will report the violation.
A Novel Bidirectional LSTM and Attention Mechanism based Neural Network for Answer Selection in Community Question Answering

Deep learning models have been shown to have great advantages in answer selection tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN), have been demonstrated to be effective. However, the traditional RNN-based models still suffer from limitations such as 1) high-dimensional data representation in natural language processing and 2) biased attentive weights for subsequent words in traditional time series models. In this study, a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism. The proposed model is able to generate the more effective question-answer pair representation. Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model. Specifically, we achieve a maximum improvement of 3.8% over the classical LSTM model in terms of mean average precision.
Method of bringing locally varying images into coincidence in video capillaroscopy

This paper presents the results of a study of a method for estimating and compensating mutual displacements of locally varying images of a capillary network when a video sequence is being recorded in order to determine the flow rate of erythrocytes in the capillaries of a nail bed. A method is proposed for bringing images into coincidence in a video sequence, based on the use of a set of reference frames, relative to which the displacement of each image is estimated. It is shown that the proposed method reduces the (subpixel) errors in image alignment, such as local image deformations due to the nature of living biological tissue. Experimental estimates are obtained for the image-alignment error, which equalled 0.11 pixel when 30 reference frames were used. The proposed method has the advantages that it is highly noise resistant and has high computational efficiency in solving problems of video capillaroscopy.
When can Wasserstein GANs minimize Wasserstein Distance

Generative Adversarial Networks (GANs) are widely used models to learn complex real-world distributions. In GANs, the training of the generator usually stops when the discriminator can no longer distinguish the generator's output from the set of training examples. A central question of GANs is that when the training stops, whether the generated distribution is actually close to the target distribution. Previously, it was found that such closeness can only be achieved when there is a strict capacity trade-off between the generator and discriminator: Neither of the two models can be too powerful than the other. In this paper, we established one of the first theoretical results in explaining this trade-off. We show that when the generator is a class of two-layer neural networks, then it is necessary and sufficient for the discriminator to be a one-layer network with ReLU-type activation functions. With this trade-off, using polynomially many training examples, when the training stops, the generator will indeed output a distribution that is inverse-polynomially close to the target. Our result also sheds light on how GANs training can find such a generator efficiently.
Analysis of simulation methods for fractional Brownian motion in the problems of intelligent systems design

Statistical modeling algorithms are often used to develop intelligent systems for solving complex problems. When solving problems in the field of economics, financial mathematics, designing of telecommunication networks, models of fractional Brownian motion can be used. In this article different methods of statistical simulation of fractional Brownian motion (FBM) with accuracy and reliability are investigated. The methods are based on the spectral presentation of the FBM, Dzhaparidze and Zanten expansion, Kahrunen transform and on the stationary increments. The developed methods are used for modeling of self-similar traffic and loading process of telecommunication networks.
Optimal cropping for input images used in a convolutional neural network for ultrasonic diagnosis of liver tumors

In recent years there have been many studies on computer-aided diagnosis (CAD) using convolutional neural networks (CNNs). For CAD of a tumor, data are generally obtained by cropping a region of interest (ROI), including a tumor, in an image. However, ultrasonic diagnosis also uses information from around a tumor. Therefore, in CAD using ultrasound images, diagnostic accuracy could be improved by using a ROI that includes the periphery of the tumor. In this study, we examined how much of the surrounding area should be included in a ROI for a CNN using ultrasound images of liver tumors. We used the ratio between the maximum diameter of the tumor and the ROI size as the index for ROI cropping. Our results show that the diagnostic accuracy was maximized when this index is 0.6. Therefore, optimal ROI cropping is important in CNNs for ultrasonic diagnosis.
A Novel Smooth Variable Structure Smoother for Robust Estimation

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.
Task design and assignment of full-text generation on mass Chinese historical archives in digital humanities

Purpose The purpose of this paper is to explore the task design and assignment of full-text generation on mass Chinese historical archives (CHAs) by crowdsourcing, with special attention paid to how to best divide full-text generation tasks into smaller ones assigned to crowdsourced volunteers and to improve the digitization of mass CHAs and the data-oriented processing of the digital humanities. Design/methodology/approach This paper starts from the complexities of character recognition of mass CHAs, takes Sheng Xuanhuai archives crowdsourcing project of Shanghai Library as a case study, and makes use of the theories of archival science, including diplomatics of Chinese archival documents, and the historical approach of Chinese archival traditions as the theoretical basis and analysis methods. The results are generated through the comprehensive research. Findings This paper points out that volunteer tasks of full-text generation include transcription, punctuation, proofreading, metadata description, segmentation, and attribute annotation in digital humanities and provides a metadata element set for volunteers to use in creating or revising metadata descriptions and also provides an attribute tag set. The two sets can be used across the humanities to construct overall observations about texts and the archives of which they are a part. Along these lines, this paper presents significant insights for application in outlining the principles, methods, activities, and procedures of crowdsourced full-text generation for mass CHAs. Originality/value This study is the first to explore and identify the effective design and allocation of tasks for crowdsourced volunteers completing full-text generation on CHAs in digital humanities.
An online Bayesian approach to change-point detection for categorical data

Change-point detection for categorical data has wide applications in many fields. Existing methods either are distribution-free, not utilizing categorical information sufficiently, or have limited performance when there exists “rare events” (events that occur with low frequency). In this paper, we propose a Bayesian change-point detection model for categorical data based on Dirichlet-multinomial mixtures. Because of the introduction of prior information, our method performs well for the existence of “rare events”. An online parameter estimation procedure and an online detection strategy are then designed to adapt to data streams. Monte Carlo simulations discuss the power of the proposed method and show advantages compared with existing algorithms. Applications in biomedical research, document analysis, health news case study and location monitoring indicate practical values of our method.
Towards the Use of Generative Adversarial Neural Networks to Attack Online Resources

The role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide services to third parties. Cost effectiveness is a direct consequence of a correct estimation of resource usage, to be able to define a budget and estimate the right price to put own services on the market. Attacks that overload resources with non legitimate requests, being them explicit attacks or just malicious, non harmful resource engagements, may push the use of Cloud resources beyond estimation, causing additional costs, or unexpected energy usage, or a lower overall quality of services, so intrusion detection devices or firewalls are set to avoid undesired accesses. We propose the use of Generative Adversarial Neural Networks (GANs) to setup a method for shaping request based attacks capable of reaching resources beyond defenses. The approach is studied by using a publicly available traffic data set, to test the concept and demonstrate its potential applications.
Selection on Various Traditional Image Encryption Techniques: A Study

With the increase in social media platforms, an increasing amount of images are transferred through the internet. In many of these platforms, images are not encrypted in real-time. As was with data, images may too contain a lot of important and/or personal information that cannot be risked to be shared with the other users of the internet. There are several traditional techniques present for image encryption such as AES, DES, Blowfish, RSA, etc. This paper is intended to provide a survey on image encryption techniques. In this paper, we have discussed a complete encryption technique which will decrease the computation time of encryption algorithm in real-time. The study of pseudorandom number generators is presented, wherein the number generated for a particular random key may be repeated for another random key. A slightly modified version of one of such previously existing pseudorandom number generators will be used with the RSA encryption algorithm that will provide stability in the encryption process.
Use of QEMSCAN® to characterize oxidized REE ore from the Bear Lodge carbonatite, Wyoming, USA

The Bear Lodge REE deposit is located in northeastern Wyoming. The Eocene carbonatite dyke and stockwork system intrudes trachytic-phonolitic rocks that contain multiple diatremes. The original magmatic characteristics of the REE-enriched carbonatite complex are strongly overprinted by carbo-hydrothermal and later supergene fluids. These fluids redistributed the REEs and created high variability in the ore mineral assemblage, as well as compositional variability within individual REE mineral species. The REE ore minerals at Bear Lodge can be classified into four types: 1. fluorocarbonates (bastnaesite, parisite, synchysite), 2. phosphates (monazite, xenotime, florencite, rhabdophane, churchite), 3. cerianite, and 4. ancylite. These minerals vary greatly in abundance, grain size, and morphology. REE distribution is heterogeneous throughout the deposit. Variations within a given REE mineral in terms of Ce depletion, Th content, degree of heavier REE enrichment, etc., create difficulties in the initial definition of discrete mineral species (i.e., by X-ray spectra) and their resultant species identification protocols for use in automated mineralogy (QEMSCANⓇ in this study). Prevalent submicron-scale supergene mineralization result in hybrid spectra from multiple phases. Iterative work in reconciling QEMSCANⓇ data interpretation with bulk assay, XRD, SEM, and optical petrography data allows for refinement of the protocols to quantify for both ore and gangue minerals. Use of automated mineralogy in the development of complex deposits requires rigorous review of these identification criteria in order to achieve results that can be applied with confidence to resolve mineral processing issues.
SQL Injection Attacks Predictive Analytics Using Supervised Machine Learning Techniques

Structured Query Language Injection Attack (SQLIA) is one of the most prevalent cyber attacks against web-based application vulnerabilities; that are manipulated through injection techniques to gain access to restricted data, bypass authentication mechanisms, and execute unauthorized data manipulation language.There are several solutions and approaches for identification and prevention of SQLIA, such as Cryptography, Extensible Markup Language (XML), Pattern Matching, Parsing and Machine Learning.Machine Learning (ML) approach has been found to be profound for SQLIA mitigation, which is implemented through defensive coding approach.Machine Learning Approach requires a lot of data for efficient model training with capability for using several attack patterns.ML approach can be used to mitigate a very hard blind SQL injection attack.An experimental analysis was performed in Waikato Environment for Knowledge Analysis on Logistic Regression (LRN), Stochastic Gradient Descent (SDG), Sequential Minimal Optimization (SMO), Bayes Network (BNK), Instance Based Learner (IBK), Multilayer Perceptron (MLP), Naive Bayes (NBS), and J48.Hold-Out (70%) and 10-fold Cross Validation evaluation techniques were used to evaluate the performance of the supervised learning classification algorithms to choose the best algorithm.The results of Cross Validation technique showed that SMO, IBK and J48 had Accuracy of 99.982%, 99.995% and 99.999% respectively; while Hold-Out technique showed that SMO, IBK and J48 had Accuracy of 99.986 %, 99.989 % and 100 respectively.On the other hand, in Cross Validation technique SMO, IBK and J48 had time to build model value of 10.15sec, 0.06sec, and 14.12sec respectively while in Hold-Out technique SMO, IBK and J48 had time to build model value of 9.71sec, 0.16sec and 14.28sec respectively.From the findings, IBK had the minimum time to build model in Cross Validation technique in addition to better performance in Accuracy, Sensitivity as well as Specificity and was chosen as the classifier for SQLIA detection and prevention.Therefore, beyond Accuracy, other performance evaluation metrics are critical for optimal algorithm selection for predictive analytics.
A Rub-impact Recognition Method Based on Improved Convolutional Neural Network

Based on the theory of modal acoustic emission (AE), when the convolutional neural network (CNN) is used to identify rotor rub-impact faults, the training data has a small sample size, and the AE sound segment belongs to a si... | Find, read and cite all the research you need on Tech Science Press
Activation Adaptation in Neural Networks

Many neural network architectures rely on the choice of the activation function for each hidden layer. Given the activation function, the neural network is trained over the bias and the weight parameters. The bias catches the center of the activation, and the weights capture the scale. Here we propose to train the network over a shape parameter as well. This view allows each neuron to tune its own activation function and adapt the neuron curvature towards a better prediction. This modification only adds one further equation to the back-propagation for each neuron. Re-formalizing activation functions as CDF generalizes the class of activation function extensively. We aimed at generalizing an extensive class of activation functions to study: i) skewness and ii) smoothness of activation functions. Here we introduce adaptive Gumbel activation function as a bridge between Gumbel and sigmoid. A similar approach is used to invent a smooth version of ReLU. Our comparison with common activation functions suggests different data representation especially in early neural network layers. This adaptation also provides prediction improvement.
Dataless Sharing of Interactive Visualization

Interactive visualization has become a powerful insight-revealing medium. However, the close dependency of interactive visualization on its data inhibits its shareability. Users have to choose between the two extremes of (i) sharing non-interactive dataless formats such as images and videos, or (ii) giving access to their data and software to others with no control over how the data will be used. In this work, we fill the gap between the two extremes and present a new system, called Loom. Loom captures interactive visualizations as standalone dataless objects. Users can interact with Loom objects as if they still have the original software and data that created those visualizations. Yet, Loom objects are completely independent and can therefore be shared online without requiring the data or the visualization software. Loom objects are efficient to store and use, and provide privacy preserving mechanisms. We demonstrate Loom's efficacy with examples of scientific visualization using Paraview, information visualization using Tableau, and journalistic visualization from New York Times.
Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

The traditional human behavior recognition algorithm is easy to ignore the spatial constraint problem of feature blocks, which leads to poor recognition effect and low correct rate.Therefore, we proposed a human motion target recognition algorithm based on Convolution Neural Network (referred to as the ''CNN'') and global constraint block matching.First, key frames of the human motion video were extracted, second, the local feature and global feature of key frames were analyzed, and CNN was used to perform feature fusion, then, according to the result of the feature fusion, a feature block was formed and the closest matching feature block is obtained, using the definition of spatial constraint, we considered the spatial data of human motion in the vertical direction, calculates the spatial constraint weight, and further completes the matching.Finally, the score of matching block and the spatial constraint weight were calculated, and the human motion targets are recognized based on the cumulative score.The experimental results show that the proposed algorithm has a high key frame extraction accuracy of more than 90% and less time consumed in feature fusion, high matching accuracy of feature blocks of more than 80%, and high feature blocks, the F-measure of human behavior recognition is 0.95 on average, and the overall recognition performance is good.
Effect of Parent Selection Methods on Modularity

The effects of various genetic operators and parent selection algorithms on the performance of a genetic programming system on different problems have been well studied. In this paper, we analyze how different selection algorithms influence modularity in the population of evolving programs. In particular, we observe how the number of individuals with some form of modular structure, i.e., the presence of code blocks executed multiple times, changes over generations for various selection algorithms.
payGo: Incentive-Comparable Payment Routing Based on Contract Theory

The increasing use of cryptocurrencies has raised scalability and performance issues in blockchain systems that require global consensus and security assurance for real-time payments.The payment channel network (PCN) is a promising off-chain solution for cryptocurrencies; in these networks, transactions are offloaded from the blockchain and are handled directly using a payment channel with minimum involvement of the blockchain.Routing protocols for PCNs are critical for finding a path-based transaction (PBT) path with low latency and high throughput, and several routing protocols have been proposed to address decentralization, concurrency, and privacy issues.However, incentive mechanisms for the payment service provider (PSP), which are critical for successful path-based transactions, have not been sufficiently studied.Previous routing protocols are proposed only to find a best path in the given budget.In this study, we propose the payGo routing protocol, which not only discovers a feasible path but also derives the optimal incentive for the PSP using contract theory.Furthermore, payGo induces the PSP to make a contract with a counterparty to guarantee payment latency and throughput with a penalty.We implement the payGo protocol by extending the Raiden network and evaluate its performance on a testbed.The results indicate that, in comparison with the conventional linear pricing for latency, payGo can improve about 90% of payer's utility by optimizing latency and incentive.
Graph neural network

In recent years, with the emergence of massive data, graph structure data that can represent complex relationshipsbetween objects has received more and more attention and has brought great challenges to existing algorithms.As a deep topology information can be revealed, graph neural network models have been widely used in many fields such as communication,life sciences, and finance. This paper reviews the basic models, algorithms, applications and recent developments of existing graph neuralnetworks in recent years, and proposes problems for further research.