Reviewerly/exHarmony huggingface dataset:
Mobile Application for Online Pharmacy: A-Pharma App

The global spread of diseases has impacted people negatively causing them to be worried about their health which in turn makes them visit the physicians or pharmacies frequently. Online pharmacies are one of the technologies that will be in high demand in the future. Though there are online pharmacy apps available, these apps could not provide the facility to check the availability of medicines in different pharmacies. As an impact, users seem to visit physically and check various stores, which is time consuming. To overcome this inconvenience, this study has proposed to develop a mobile app, A-Pharma App which is an online medical centre. Once the data has been acquired regarding the online pharmacy applications using survey and interview, the results obtained have been utilized to build the features of the proposed application. The presented application provides consultation with the pharmacist and people can get their medicines delivered at their doorstep. The application attempts to satisfy the user's requirements regarding their health concern to a great extent. Furthermore, this paper determines that the proposed A-Pharma App provides all the features necessary for an online pharmacy compared to the other similarly existing apps.
Deep Neural Network Architecture for Face Mask Detection Against COVID-19 Pandemic Using Pre-trained Exception Network

The coronavirus disease (COVID-19) is an infectious disease caused by coronavirus. The COVID-19 virus spreads mostly through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, so it is important to practice respiratory etiquette. The COVID-19 is spreading our community in a faster manner, stay safe by taking some simple precautions, such as physical distancing, wearing a mask, keeping rooms well ventilated, avoiding crowds, and cleaning hands. The appropriate use of wearing a mask is a normal part of our life. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel severe acute respiratory syndrome coronavirus. Genetic variants of SARS-CoV-2 have been emerging and circulating around the world throughout the COVID-19 pandemic. To minimize the risk of transmissions, the use of face masks or coverings has been recommended in public settings. Many countries and local jurisdictions encourage or mandate the use of face masks by members of the public to limit the spread of the virus. Masks are also strongly recommended for those who may have been infected and those taking care of someone who may have the disease. In this paper, novel face mask detection on masked face data set is done by using pretrained Xception, deep learning with depth wise separable convolution. The proposed method classifies from the given face image, mask is worn or not. The proposed method is tested and validated using the face mask data set obtained from Kaggle. This data set contains about 503 face images with mask and 503 images without mask. The experimental results show that the proposed face mask detection method significantly dominates other compared pretrained models. The results of the receiver operating characteristic curve and area under curve justify the relevance of the better results in favor of the proposed method.
Preliminary Design and Implementation Discussion of Low Latency Live Video Web Streaming Engine

The threat of COVID-19 maximizes the demands for live video streaming. How to provide a video streaming service with low latency becomes one urgent and necessary research issue. This paper would survey and discuss the critical factors to impact transmission latency from the perspective of software implementation of a web streaming engine. Based on the initial experiment, the proposed live video web streaming engine can achieve under 3-second latency.
Proposal of ultra-high-resolution video delivery system in edge-cloud environment

In this study, we propose a real-time video delivery system that can deliver video streams by dynamically adjusting the video rate to the customer environment to deliver the video while maintaining high quality. We also describe the implementation method of a dynamic rate-conversion system using edge computers and the IP multicast method.
Development Structure Light Projection Technique for Reconstruction 3D Shape Object

Non-contact scanning technology structured light projection has become an important choice in the field of engineering. It provides potential feasibility in terms of speed data acquisition, and precision of measurements. The proposed system of structured light scanning works by projecting different grayscale lines patterns and capturing the light once it falls on the scene using a camera and projector devices. That information from captured images that contained distortion lines patterns was used to reconstruct 3D geometry. Measurement of plane displacement objects is an objective of this application. In this study, the plane object was axially punched load varying with intervals 0.5 mm to 30 mm in displacement. The results of the displacement from the punch load were recorded and saved in the format of the image. Experimental and simulation results on the plane displacements of the object test shown onto 3D topographic histogram. The proposed scanning system still has an error of 0.005 mm. However, it has the potential feasibility to reconstruct 3D shape objects and offer the plane measurement displacement.
Cryptocurrencies, Big Techs, central bank digital currencies and the changing role of banks in the payment industry: old wine in new bottles?

The central position of banks in the payment system in the current hierarchical monetary system has been challenged first by the emergence of a large crypto-ecosystem following the Bitcoin invention, second by the entry of Big Techs who, relying on their vast network of users, wish to offer their own payment services and third, with the possible introduction of Central Banks Digital Currencies (CBDC). This chapter aims to discuss the consequences of those digital innovations on the changing role of banks in the payment industry. As we will show, those technological innovations re-actualizes some typical tensions between centralization and fractionation of money, as well as historical debates on the efficient monetary reform. We show that, more than cryptocurrencies, Big Techs seem to be able to contest the structure of payment systems, even though they show willingness to comply with the regulatory framework instead of strongly disrupting the payment system. Cryptocurrencies are more likely to be adopted in countries with very weak or even failed state. CBDC can be analyzed as a way to counter the rise of BigTechs and cryptocurrencies and thus keep the hierarchical banking system alive but a the cost of changing the banks' business model, strengthening their control and changing the way monetary policy is implemented. Although cryptocurrencies increase monetary competition and fractionation of payment systems in the same spirit as Hayek's proposals of denationalization of money, in contrast, depending on the design of CBDC, it could be a resurgence of the 100% money scheme or the Tobin's (1985) deposited currency proposal.
Development of a Linear Scale Consensus Method of the Blockchain System

The research highlights the development of a new and reliable blockchain system consensus method focused on applying a linearly scalable consensus mechanism. The new system is based on an analysis of available consensus mechanisms, sharding, and distributed randomness generation. The proposed approach allows the development of a blockchain with the following advantages: full scalability, security, energy efficiency, and fast consensus. Compared to known methods, the proposed shard option performs network connection and transaction verification and shows the current state of the blockchain. The decision threshold is low enough to allow small validators to participate in the network and receive appropriate rewards. The sharding process runs safely by applying a distributed randomness generation process that is unpredictable unbiased. In order to prevent adaptive Byzantine malicious validators, a continuous network reboot process is provided. The developed approach is resistant to fluctuations in the number of validators. It does not set a lower limit against the number of validators in every fragment, as in other solutions such as Zilliqa. Instead, an adaptive Proof-of-Stake-based model was adopted so that attackers can never occupy more than one-third of the shares that vote in a single shard that makes it reliable. The methods for creating the proposed blockchain improve available mechanisms for the blockchain functioning and have practical value for application in various digital economy sectors.
Study of Vulnerabilities in the Cryptography Algorithms

The authors demonstrate the vulnerability of particular algorithms using the principles of timing attacks. RSA and Diffie-Hellman are the algorithms used and comparative study was done on them. In each approach, the private keys are extracted by analysing time differences in various scenarios while modifying specific parameters of the private key. The authors have identified crucial points of difference that can be exploited to decipher the private keys. The authors also attempt to exploit the Elliptical Curve Cryptography technique through a timing attack.
Exploring The Challenges of Language Learning Strategies in Learning The English Language

Students' mastery of the English language is a major concern despite the various strategies implemented whether at primary and secondary levels or in higher institutions.The purpose of the current research is to examine the challenges encountered in instructional learning activities when learners are required to apply the language learning strategies in learning English.The goal of the research is to identify the barriers and formulate solutions that can be implemented to achieve the learning goals.This qualitative research approach was conducted by gathering data from commerce students in a polytechnic through a semistructured interview.Based on the thematic analysis conducted, findings have shown that the students are highly burdened by anxiety in their attempt to integrate LLS into learning the English language and lack motivation.The implication of this study bridges a gap in knowledge regarding the challenges in learning the English language by providing insights into ways in which the use of LLS in polytechnics can be improved.Therefore, educators should understand the underlying problems that may contribute to the reduction in morale for a better language learning process.
A hybrid adaptive approach for instance transfer learning with dynamic and imbalanced data

Machine learning has demonstrated success in clinical risk prediction modeling with complex electronic health record (EHR) data. However, the evolving nature of clinical practices can dynamically change the underlying data distribution over time, leading to model performance drift. Adopting an outdated model is potentially risky and may result in unintentional losses. In this paper, we propose a novel Hybrid Adaptive Boosting approach (HA-Boost) for transfer learning. HA-Boost is characterized by the domain similarity-based and class imbalance-based adaptation mechanisms, which simultaneously address two critical limitations of the classical TrAdaBoost algorithm. We validated HA-Boost in predicting hospital-acquired acute kidney injury using real-world longitudinal EHRs data. The experiment results demonstrate that HA-Boost stably outperforms the competing baselines in terms of both Area Under Receiver Operating Characteristic and Area Under Precision-Recall Curve across a 7-year time span. This study has confirmed the effectiveness of transfer learning as a superior model updating approach in a dynamic environment.
Gendering products and services: from design to brand

The very development of markets can be gendered and in Chapter 4 we take a closer look at the gendering of products and services in tandem with considering the intersection of new technologies and marketing, both offline and online. Of course, technology in general is an especially gendered field and the unequal gender relations that shape its design and development have long been documented. This carries over to the online environment where a misogynistic culture in male-dominated internet communities flourishes alongside the pornification of culture.
Factors influencing well-being of special education teachers in Ho Chi Minh City, Vietnam

Increasing numbers of special education teachers are leaving jobs across different cultures and countries, which leads to teachers' well-being has gotten more attention over the past decades.This study aims to investigate the levels of well-being and explore the factors influencing well-being in special education teachers in Vietnam.Special education teachers (N = 81) from three special education schools and a special education Centre in Vietnam were involved in the current study.Descriptive analysis, Pearson correlation, and multiple linear regression were used to examine the levels of well-being and determine whether life satisfaction, job satisfaction, self-compassion, and salary and benefits influencing the well-being levels among teachers.The results show a positive correlation between well-being and job satisfaction, life satisfaction, self-compassion, and salary and benefits.Additionally, multiple regression analysis reveals that life satisfaction, self-compassion, and salary and benefits influence well-being levels positively, meaning that teachers with increased life satisfaction, self-compassion, and salary and benefits have higher levels of well-being.The present findings contribute to the general discussion about the nature of well-being and the influential factors in teachers' well-being.It is highly suggested that organizations/schools provide appropriate policies and increase the special education teachers' well-being in Ho Chi Minh City, Vietnam.
Image communication management in a modern library

The article raises an important and urgent problem of forming the image of a modern library. In recent years, the issue of the effectiveness of image communications and the management of this process has been particularly acute. In this context, the system of additional professional training is important, where modern library specialists receive knowledge about the current state of the PR industry, image technologies and other important and urgent problems of image formation. The offered master classes help to study the technique of image communication, the elements of image policy management in the library and information sphere.
The Extent of Using Lean Accounting Tools and their Impact on the Achievement of a Competitive Advantage in the Jordanian Commercial Banks

This study aimed to explore the extent of using lean accounting tools and their impact on the achievement of a competitive advantage in the Jordanian commercial banks. To meet the goals of this study, the researcher designed a questionnaire. The questionnaire forms were passed to Jordanian commercial banks that were listed on Amman stock exchange before the period 31/12/2020. Those banks are thirteen banks. A purposive sample was chosen. It consists from 78 administrators working in Jordanian commercial banks.The researcher found that the extent of using lean accounting tools in those banks is high. It was found that the extent of exerting effort by those banks to achieve a competitive advantage is high. It was found that using lean accounting tools jointly has an impact on achieving a competitive advantage in those banks. In the light of the study’s results, the researcher offered several recommendations. They recommend providing more attention to the extent of using lean accounting tools by the management of those banks. That should be done to benefit from the merits of implementing them and seek achieving a competitive advantage.
Further discussion on the complexity of synonymic interchange of characters in the Chu bamboo slips

The synonymic interchange of characters in the Chu bamboo slips could be combined with other language phenomena, such as jiajie (假借), showing complexity as follows: one character was synonymously interchanged as another character, and then it was borrowed as the loan graph of a third character, which was same in pronunciation as the other character, or one character was borrowed as the loan graph of another character, and then it was synonymously interchanged as a third character, which is the same as or close in meaning to the other character. After one character was synonymously interchanged as another character, the interchanged character could be used as the phonetic component to construct new characters.
A Cloud Storage

The goal of this research is to describe how cloud storage works and to show the main benefits, drawbacks, and enterprise solutions for businesses. To begin, the investigation delves into cloud computing by explaining its fundamental concepts. Second, the layers of the cloud computing architecture are depicted, with a bias toward Infrastructure as a Service (IaaS), which includes the cloud storage architecture. Third, the study introduces the concept of Storage as a Service (SaaS), which provides clients or customers with cloud storage rather than requiring them to purchase physical storage. Fourth, the benefits and drawbacks of using cloud storage are discussed. Finally, this paper investigates some of the market's enterprise solutions for business, such as Amazon Simple Storage.
Gait Phase Detection in Walking and Stairs Using Machine Learning

Machine learning-based activity and gait phase recognition algorithms are used in powered motion assistive devices to inform control of motorized components. The objective of this study was to develop a supervised multiclass classifier to simultaneously detect activity and gait phase (stance, swing) in real-world walking, stair ascent, and stair descent using inertial measurement data from the thigh and shank. The intended use of this algorithm was for control of a motion assistive device local to the knee. Using data from 80 participants, two decision trees and five long short-term memory (LSTM) models that each used different feature sets were initially tested and evaluated using a novel performance metric: proportion of perfectly classified strides (PPCS). Based on the PPCS of these initial models, five additional posthoc LSTM models were tested. Separate models were developed to classify (i) both activity and gait phase simultaneously (one model predicting six states), and (ii) activity-specific models (three individual binary classifiers predicting stance/swing phases). The superior activity-specific model had an accuracy of 98.0% and PPCS of 55.7%. The superior six-phase model used filtered inertial measurement data as its features and a median filter on its predictions and had an accuracy of 92.1% and PPCS of 22.9%. Pooling stance and swing phases from all activities and treating this model as a binary classifier, this model had an accuracy of 97.1%, which may be acceptable for real-world lower limb exoskeleton control if only stance and swing gait phases must be detected. Keywords: machine learning, deep learning, inertial measurement unit, activity recognition, gait.
Computational Learning Models of Scikit-Learn for Automatic People Identification Integrated in a GUI

This paper presents the analysis and weighting of the computational learning methods of classifiers of logistic regression, perceptron, Ridge and Passive-Aggressive from the Scikit-Learn library for a GUI-integrated automatic person identification system. Datasets for 10 classes are created using optimized cascade classifier techniques and the performance of the classifiers are trained and compared at hardware and software levels. The best performance was presented by the Ridge classifier together with the logistic regression classifier with 98.5 % in accuracy, being these also the best performers in accuracy per class with 98.52 %. At the hardware level, the Ridge classifier had the best performance, requiring only 6.9 % of CPU, 0.3166 seconds for training, and 216.2 MB of memory. The method and classifiers considered are efficient in a way that allows their replicability in access control systems on processors, microprocessors and embedded systems, and are shown to be an alternative to conventional face recognition detection methods based on deep learning.
A Novel Deep Learning Framework with Stacked Bi-Directional Long-Short Term Memory and Cnn with Enhanced Adaptive Moment Optimizer and Error Correction Strategy for Predicting Time-Series Data

Over the last decade, time-series forecasting has gained enormous importance as it facilitates intelligent decision-making activities. Deep learning models have shown immense confidence in modelling and predicting forecasting patterns. However, owing to the complex non-linear patterns and substantial disparities in the randomness of the characteristics of diverse categories of real-world time series data, it becomes apparently challenging for a specific deep learning model to be effective and robust at the same time. In this research, with an objective to fill this disparity, a novel stacked deep learning framework is proposed and implemented to attain precise time-series forecasting and capture the non-linearity inherent in the time-series data. In the stacked-based deep learning model, a bi-directional long-short term memory network is combined with a convolutional 1D and a max-pooling-1D layer in each stack. To increase the forecasting accuracy and the speed of convergence, we have proposed and developed the enhanced version of the adaptive moment estimation. We have recommended a dynamic error correction method as well to improve the model’s performance. Lastly, the generalization capability, prediction performance, and efficacy of the model are substantiated using four diverse real-world time-series data (air quality, PM 2.5 , electricity price and securities finance). The experiment results establish the pre-eminence of the proposed model over existing models in terms of various metrics, which are standard deviation, mean square error and mean absolute error by quite a margin. The proposed model’s mean absolute error for air quality data is 0.0163 over 0.0337, which is about ~ 52% improvement. For electricity price data, the proposed model has shown improvements of ~40% error in mean squared error. Likewise, the proposed model improved the mean square error for the PM2.5 dataset by ~37% . The proposed enhanced adaptive moment estimation and error correction method have improvised the performance and accuracy of the model overall. The proposed model proves to be excellent in the generalization capabilities as well as it performs better in multi-indigenous domain data sets