Reviewerly/exHarmony huggingface dataset:
Dynamic Security Exchange Scheduling Model for Business Workflow Based on Queuing Theory in Cloud Computing

With the rapid development of e-business, large volume of business processes need to be handled in a constrained time. There is always a security issue related to on-time completion in many applications in the economic fields. So, how to effectively manage and organize business processes became very important. By using cloud computing, instance-intensive processes can be handled more effectively by applying just-right virtual machines. Hence, the management of cloud resources became an important issue that many researchers focus on to fully utilize the advantage of cloud. In this paper, we mainly discuss the queuing theory and put forward our novel dynamic process scheduling model based on queuing theory, which is named M / G / k / l -<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>P</mml:mi></mml:math> for business processes. This model can solve the issue of allocating appropriate number of cloud resources based on the number of tasks and execution stages to ensure whether the numbers of cloud resources are sufficient and adequate or not, which can improve the security issue for business process. The service discipline in our model can provide a dynamic process by setting different priorities to improve the experience of users. Evaluations prove that the queuing model of M / G / k / l -<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>P</mml:mi></mml:math> can work very well for business workflow scheduling.
The argument style in research article discussions to support research findings in language studies

The argument quality in a research article discussion (henceforth RAD) determines the quality of the article as a whole, and therefore this section must be written as convincingly as possible. However, authors in different disciplines such as Language Studies (LS) may address this section in a different argument style. This study is aimed at investigating how Indonesian writers in LS support their findings in their RADs. There were 40 RADs taken from four different Indonesian journals in LS; the articles were chosen from the latest volumes of the nationally accredited journals. This study used a genre analysis method in which the major source of data is a collection of texts as a product of language activity. The results show that the majority of Indonesian writers use Style 1 (interpreting the research findings or suggesting what the research findings mean), Style 2 (explaining or elaborating the research findings), Style-3 (stating the possible cause/s of the research findings), and Style 4 (illustrating or exemplifying the research findings) to support their research findings. However, unlike international authors, Indonesian writers do not relate their research findings with those of previous related studies; if they do so, it is to confirm the interpretation or explanation of their research results. The results of this study may help Indonesian authors in LS improve the quality of their RADs especially when writing articles in English to be submitted to an international journal.
Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment

Facial age estimation is of interest due to its potential to be applied in many real-life situations. However, recent age estimation efforts do not consider juveniles. Consequently, we introduce a juvenile age detection scheme called LaGMO, which focuses on the juvenile aging cues of facial shape and appearance. LaGMO is a combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM). Inspired by the formation of words from morphemes, we obtained facial appearance features comprising facial shape and wrinkle texture and represented them as terms that described the age of the face. By leveraging the implicit ordinal relationship between the frequencies of the terms in the face, TF-IGM was used to compute the weights of the terms. From these weights, we built a matrix that corresponds to the possibilities of the face belonging to the age. Next, we reduced the reference matrix according to the juvenile age range (0–17 years) and avoided the exhaustive search through the entire training set. LaGMO detects the age by the projection of an unlabeled face image onto the reference matrix; the value of the projection depicts the higher probability of the image belonging to the age. With Mean Absolute Error (MAE) of 89% on the Face and Gesture Recognition Research Network (FG-NET) dataset, our proposal demonstrated superior performance in juvenile age estimation.
Quality of life assessment after acute myocardial infarction

Objective: to evaluate the quality of life after acute myocardial infarction, highlighting the differences by sex. Methods: analytical, observational study with 273 patients. For collection, a multidimensional assessment instrument was used for heart attacks. Data collected in a cardiology referral hospital, in which the t Student test was applied for analysis. Results: the majority were male (67.0%),with a mean age of 63.6 years, with a family history of heart attack (64.5%). The average quality of life score was 0.45,with 1.00 being the worst score. Dependence (0.82) and physical activity (0.50) were the worst assessed domains; and side effects (0.27) and diet (0.36), the best. The female gender had the worst averages (0.52, p&lt;0.05), with emphasis on physical activity (0.58), emotion (0.49) and insecurity (0.44). Conclusion: impairments in quality of life were found after myocardial infarction, in which the dependency domain was the most compromised. The female sex had worse scores.
Digital Economy

The digital economy is growing rapidly across the world, albeit at an uneven pace that transcends different countries. In most countries, however, the digital economy is constantly evolving. This article seeks to explore the possible role of the telecoms regulator within the context of a digital economy in Nigeria. The article starts by providing an overview of digital economy by defining this concept as well as its components. Furthermore, the article provides some examples of emerging technologies, which appears to propel the digital economy and some inherent challenges. The article then discusses the readiness of the Nigerian economy in adopting digital economy which involves broadband coverage, data protection, and interoperability of digital platforms. Finally, the implications for the regulator within the context of having a digital economy is considered and practical recommendations provided.
Construction of Evaluation System and Monitoring System of Physical Education Teaching Quality in Colleges and Universities

With the deepening of the reform of the educational system, the increasing number of students enrolled in Colleges and universities, and the decrease of the total teaching hours, how to grasp the quality of teaching and cultivate a group of qualified physical education teachers who can really meet the needs of the society is an urgent problem to be solved in Colleges and departments of physical education in Colleges and universities. Through relevant literature and investigation, this paper analyses the problems existing in the teaching quality evaluation of Physical Education Specialty in Colleges and universities, and puts forward the implementation of teaching quality monitoring system to promote the teaching quality of physical education in Colleges and universities.
Analysis of click-stream data to predict STEM careers from student usage of an intelligent tutoring system

In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young students to enroll in STEM college majors. However, enrolling in a STEM major requires specific skills in math and science that are learned in schools. Hopefully, institutions are adopting educational software that collects data from the students' usage. This gathered data will serve to conduct analysis and detect students' behaviors, predict their performances and their eventual college enrollment. As we will outline in this paper, we used data collected from the students' usage of an Intelligent Tutoring System to predict whether they would pursue a career in STEM-related fields. We conducted different types of analysis called and approach. The problem- based approach focused on evaluating students' actions based on the problems they solved. Likewise, in the skill-based approach we evaluated their usage based on the skills they had practiced. Furthermore, we investigated whether comparing students' features with those of their peer schoolmates can improve the prediction models in both the skill-based and the problem-based approaches. The experimental re- sults showed that the skill-based approach with school aggregation achieved the best results with regard to a combination of two metrics which are the Area Under the Receiver Operating Characteristic Curve (AUC) and the Root Mean Squared Error (RMSE).
On stability and associative recall of memories in attractor neural networks

Attractor neural networks such as the Hopfield model can be used to model associative memory. An efficient associative memory should be able to store a large number of patterns which must all be stable. We study in detail the meaning and definition of stability of network states. We reexamine the meanings of retrieval, recognition and recall and assign precise mathematical meanings to each of these terms. We also examine the relation between them and how they relate to memory capacity of the network. We have shown earlier in this journal that orthogonalization scheme provides an effective way of overcoming catastrophic interference that limits the memory capacity of the Hopfield model. It is not immediately apparent whether the improvement made by orthgonalization affects the processes of retrieval, recognition and recall equally. We show that this influence occurs to different degrees and hence affects the relations between them. We then show that the conditions for pattern stability can be split into a necessary condition (recognition) and a sufficient one (recall). We interpret in cognitive terms the information being stored in the Hopfield model and also after it is orthogonalized. We also study the alterations in the network dynamics of the Hopfield network upon the introduction of orthogonalization, and their effects on the efficiency of the network as an associative memory.
Prediction modelling to personalise care for gestational diabetes

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Multi-Label Multi-Class Action Recognition With Deep Spatio-Temporal Layers Based on Temporal Gaussian Mixtures

Current action recognition studies enjoy the benefits of two neural network branches, spatial and temporal. This work aims to extend the previous work by introducing a fusion of spatial and temporal branches to provide superior action recognition capability toward multi-label multi-class classification problems. In this paper, we propose three fusion models with different fusion strategies. We first build several efficient temporal Gaussian mixture (TGM) layers to form spatial and temporal branches to learn a set of features. In addition to these branches, we introduce a new deep spatio-temporal branch consisting of a series of TGM layers to learn the features that emerged from the existing branches. Each branch produces a temporal-aware feature that assists the model in understanding the underlying action in a video. To verify the performance of our proposed models, we performed extensive experiments using the well-known MultiTHUMOS benchmarking dataset. The results demonstrate the importance of our proposed deep fusion mechanism, contributing to the overall score while keeping the number of parameters small.
The image inpainting algorithm used on multi-scale generative adversarial networks and neighbourhood

Various problems existed in the image inpainting algorithms, which can’t meet people's requirements visually. Aiming at the defects of the existing image inpainting algorithms, such as low accuracy, poor visual consistency, and unstable training, an improved image inpainting algorithm used on a multi-scale generative adversarial network (GAN) and neighbourhood model have been proposed in the paper. The proposed algorithm mainly improves the network structure of the discriminator, and introduces a multi-scale discriminator based on the global discriminator and the local discriminator. The multi-scale discriminators were trained on images of different resolutions. Discriminators of different scales have different receptive fields, which can guide the generator to generate more global image views and finer details. Aiming at the problem of gradient disappearance or gradient explosion that often occurs in GAN training, the method of WGAN (Wasserstein GAN) has been used to simulate the sample data distribution using EM distance. The proposed model has been trained and tested on the CelebA, ImageNet, and Place2. The experimental results show that compared with the previous algorithm model, the proposed algorithm improves the accuracy of image inpainting and can generate more realistic repairing images, and it is suitable for many types of images.
LiDAR-Camera Fusion for 3D Object Detection

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset.
Cyber-Bullying and Cyber-Harassment Detection Using Supervised Machine Learning Techniques in Arabic Social Media Contents

The social media has provided users with the chance to publish their written and multimedia content and express feelings and emotions about particular subjects via the internet. However, some users have abused these platforms by performing various acts such as Cyber-Bullying and Cyber-Harassment. These phenomena are dangerous and have negative psychological, health, and social effects. Although multiple works have focused on detecting these phenomena on English text, few works studied this phenomenon on Arabic. Moreover, these works used limited number of methods and datasets. Furthermore, there is a lack in Arabic datasets that are concerned with this topic. We propose the use of Machine Learning to detect such negative written acts. We apply various classification algorithms to the dataset, and we use various Arabic Natural Language Processing (NLP) tools. To evaluate the performance of the classifiers, we use Recall, Precision, and F1-Measure. The results show that the Random Forest algorithm yields the highest values of F1-Measure. The same results occurred when no stemming and no stop-word removal are applied. However, when separating datasets into Facebook Posts dataset and Twitter Tweets dataset, SVM gives the highest F1-Measure value. Significant tests were conducted to support our results.
Particle Filter for Randomly Delayed Measurements with Unknown Latency Probability

This paper focuses on developing a particle filter based solution for randomly delayed measurements with an unknown latency probability. A generalized measurement model that includes measurements randomly delayed by an arbitrary but fixed maximum number of time steps along with random packet drops is proposed. Owing to random delays and packet drops in receiving the measurements, the measurement noise sequence becomes correlated. A model for the modified noise is formulated and subsequently its probability density function (pdf) is derived. The recursion equation for the importance weights is developed using pdf of the modified measurement noise in the presence of random delays. Offline and online algorithms for identification of the unknown latency parameter using the maximum likelihood criterion are proposed. Further, this work explores the conditions that ensure the convergence of the proposed particle filter. Finally, three numerical examples, one with a non-stationary growth model and two others with target tracking, are simulated to show the effectiveness and the superiority of the proposed filter over the state-of-the-art.
Smart Contract-based Hierarchical Auction Mechanism for Edge Computing in Blockchain-empowered IoT

Edge computing is a promising paradigm to expand the capability of Internet of Things (IoT) devices by computation offloading. To establish a distributed ledger to provide a secure and trusted environment for the resource allocation between edge servers and IoT devices, the emerging blockchain technology has attracted a lot of attention recently. However, in practice, edge resource allocation in IoT devices often involves multi-layer structures, which poses a challenge due to information incompleteness among different layers. Moreover, how to design a suitable and efficient blockchain framework for hierarchical resource allocation markets is a critical issue. In this paper, we apply blockchain to propose a secure and efficient hierarchical resource allocation framework for edge computing. First, we study the edge computing resource allocation problem in the hierarchical market of IoT devices, in which the IoT devices beyond the coverage of Access Points can participate in the resource allocation through middlemen. To solve the problem, a smart contract-based hierarchical auction mechanism is developed. The edge computing resources allocated in the top market can be continually reallocated to the sub-markets based on the mechanism, which then leads an efficient solution that maximizes the social welfare of the whole participants. Moreover, the mechanism is implemented as a smart contract in the blockchain, which enforces the rule of the hierarchical auction in a non-deniable and automated manner. Finally, the extensive simulations demonstrate the correctness and performance of the proposed mechanism.
Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how &#x201C;good&#x201D; the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.
AREVA: Augmented Reality Voice Assistant for Industrial Maintenance

Within the context of Industry 4.0, AREVA is presented: a Voice Assistant with Augmented Reality visualisations for the support and guidance of operators when carrying out tasks and processes in industrial environments. With the aim of validating its use for the training of new operators, first evaluations were performed by a group of non-expert users who were asked to carry out a maintenance task on a Universal Robot.
Intelligent Character Recognition System Using Convolutional Neural Network

Computational Linguistics involves the techniques of Computer Science which play a vital role in recognizing written or printed characters such as numbers or letters to change them into a form that the computer can use it efficiently.Convolutional Neural Network differs from other approaches by extracting the features automatically.The proposed approach is capable of recognizing characters in a variety of challenging conditions using the Convolutional Neural Network, where traditional character recognition systems fail, notably in the presence of low resolution, substantial blur, low contrast, and other distortions.Intellectual Character Recognition System is an application that uses Convolutional Neural Network (CNN) to recognize the Tamil character dataset accurately developed by HP Labs India.The novelty of this system is that, it recognizes the characters of the Predominant Tamil language.With the help of suitable datasets consisting of the Tamil Scripts, the model is trained efficiently.This work has produced a training accuracy of 99.16% which is far better compared to the traditional approaches.
Nexus between Remittance, Nonperforming Loan, Money Supply, and Financial Volatility: An Application of ARDL

The motivation of the study is to investigate the nexus between Remittance, Nonperforming loan, Money supply, and finical volatility for the period from 1976 to 2017. To do so, study apply unit root test for determining variables order of integration, Lag length criteria test to select optimal lag for empirical test and long-run cointegration test i.e. Autoregressive Distributed Lagged (ARDL). Referring to unit root tests, it is apparent that variables established integration in mixed order i.e. few variables are stationary at level and few variable are at first difference. The results of Bound testing ascertain that the presence of long-run association between financial volatility and selected macro fundaments. Referring to the long-term model estimation, it is palpable that magnitudes from macro fundaments to financial volatility established that money supply in the economy is responsible for financial instability on the other hand, remittance and financial development play critical role in maintaining financial stability in the economy.