Reviewerly/exHarmony huggingface dataset:
A Comprehensive Survey on Sharding in Blockchains

Blockchain technology has been widely used in many fields, such as smart cities, smart health care, and smart manufacturing, due to its anonymity, decentralization, and tamper resistance in peer-to-peer (P2P) networks. However, poor scalability has severely affected the widespread adoption of traditional blockchain technology in high-throughput and low-latency applications. Therefore, based on the three-layer architecture, this study presents a variety of solutions to improve the scalability of the blockchain. As the scale of the network expands, one of the most practical ways to achieve horizontal scalability is sharding, where the network is divided into multiple subnetworks to avoid repeated communication overhead, storage, and calculations. This study provides a systematic and comprehensive introduction to blockchain sharding, along with a detailed comparison and evaluation for primarily considered sharding mechanisms. We also provide the detailed calculations and then analyze the characteristics of existing solutions along with our insights.
Justification for Adopting Qualitative Research Method, Research Approaches, Sampling Strategy, Sample Size, Interview Method, Saturation, and Data Analysis

Justifying the adoption of the qualitative research method to satisfy the examiners (for thesis) and reviewers (for journal articles) is a challenging task for researchers in business, management, marketing, tourism, hospitality and albeit in social science domain. The difficulty continues in establishing the justification for selecting qualitative research approaches, sample strategy, sample size, data collection methods (i.e. interview methods), saturation, and data analysis. In this guide, we aim to ground brief justifications for researchers and guidance on how to justify the section of qualitative research method in thesis and journal articles. This study also provides brief justification on selecting specific qualitative research approaches, sampling strategies, sample size, interviews, and data analysis methods. Furthermore, this study provides a glimpse of justification regarding when and how to reach saturation point in qualitative research. Keywords: Qualitative research method, Research approaches, Sampling strategy, Sample size, Interview method, Saturation, and Qualitative data analysis (QDA)
Are Data-Driven Personas Considered Harmful?

In this work, we build on research on data-driven personas to present what might be “wrong with them”. From wrong assumptions by the client and wrong applications of methods to imbalanced, messy, or superficial data; a lack of communication regarding how these personas are created; and issues with usability, there are a plethora of issues that plague data-driven personas. We conclude by contemplating whether data-driven personas are even worthwhile and, if they are, then what are some of the immediate remedies required from the human-computer interaction community to make data-driven personas a viable tool for user understanding.
Congestion Risk Propagation Model Based on Multi-Layer Time-Varying Network

To quantify the responses of drivers to traffic information and the congestion evacuation effect on the basis of traffic guidance information, a multilayer network congestion risk propagation model of urban roads was built to analyse the influence of the advanced traveller information system (ATIS) penetration rate, group behaviours of drivers, and traveller flow distribution features on the traffic congestion risk propagation of urban roads.Meanwhile, the dynamic evolutionary characteristics of group behaviours of drivers in a road network under the guidance of traffic information were analysed with the microscopic Markov chain approach (MMCA).A simulation analysis of the artery network in the fourth ring of Beijing was also carried out.Results demonstrated that the influence of traffic information and drivers' information reinforcement psychology on congestion risk propagation depend on the aggregation effect caused by traffic information.Increasing the ATIS market penetration rate and drivers' acceptance of information is beneficial to relieve traffic congestion as long as the drivers' aggregation effect is within a critical range.
User-Centered Virtual Reality Environments to Support the Educational Needs of Children With ADHD in the COVID-19 Pandemic

The use of technology to face the challenges in daily life is something that is increasingly needed and in complicated times like the one we live in today where there is a health contingency (COVID-19) that prevents people from exposing themselves to each other and restricts physical contact, virtual reality can be an alternative that allows the transmission of knowledge in an immersive and interactive way in various fields.This work proposes the use of virtual reality environments as an alternative to support the learning process in children with special educational needs such as Attention Deficit and Hyperactivity Disorder (ADHD) and other associated disorders that occur in basic education.These proposed virtual reality environments are designed under a user-centered approach and their contents are in accordance with expert therapeutic guidelines.As a result of this proposal, a case study is presented in which the user experience is evaluated through the use of an interactive environment to support the special educational needs of elementary school children attending an educational institution in Mexico.
Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

In view of the fact that it is difficult for existing algorithms to identify the movements of a player in an accurate way, this paper puts forward an artificial intelligence (AI) motion model on the basis of the deep learning neural network instruction set architecture (ISA). Firstly, a mobile neural network (MNN) inference engine was utilized to create a new AI sports project-side intelligent practice model. Under this model, a movement can be segmented into a series of decomposition movements, which are recognized and judged separately for the purpose of measuring the entire movement. In order to test its feasibility, the study compares the MNN inference engine with the traditional reasoning engine in terms of their algorithmic capabilities and compares the results obtained through this algorithm and traditional online motion app. Research shows that, in the MNN of the AI sports project proposed in this paper, the datasets of action recognition exceed the results of other inference engines, characterized by lightweight, high performance, and accessibility. Research also demonstrates that the AI sports project model can adapt to the needs of sports projects with a variety of themes and improve the accuracy of movement recognition details.
Happiness in the Workplace

Recently, the United Arab Emirates (UAE) government has worked towards establishing a national program for happiness and wellbeing. Workplace-related happiness has become a topic of interest in today’s employment world. Administrators need to look at the ingredients within the recipe to address happiness and happiness in an organization. However, in the Middle East Region, there is still the absence of an empirical Islamic happiness scale which also encapsulates the spiritual and emotional wellness element, employee happiness and performance in one scale. Also, empirical studies on employee spiritual and emotional happiness at work are still lacking. Thus, the researcher will explore these ideas in greater detail This paper reviews what is known about the definition, causes and consequences of happiness at work, The interest in happiness has also extended to workplace experiences. drawing also on insights from the expanding positive psychology literature on happiness in general. Happiness at work includes, but is far more than, job satisfaction. A comprehensive measure of individual-level happiness might include work engagement, job satisfaction, and effective organizational commitment. We will be focusing on happiness Importance and Impact of Happiness at Work by highlighting the Advantages &amp; Disadvantage
Federated Learning-Based Privacy-Preserving and Security: Survey

Traditional machine learning (ML) algorithms need to collect a large mount of users&#x0027; data for model training, which result in privacy leak and &#x201C;data islands&#x201D; problems emerge in endlessly. In order to solve the above problems, federated learning (FL) has emerged as an outstanding tool. FL is widely used for the sixth generation mobile network (6G) communications, artificial intelligence, and privacy-preserving applications. This article starts from the concept of FL, introduces the research status of FL algorithms and privacy-preserving technology, and further explains some of the current applications and future challenges. Although the FL has brought dawn, it still faces many challenges in terms of enhancing privacy-preserving and training model security. Communication overhead is a problem in the encryption process; the noise threshold of different scenarios needs to be solved in the process of noise handling; how to identify malicious attackers, and reduce malicious attacks is also a worth noticing challenge in modeling process.
Digital image forgery detection under complex lighting using Phong reflection model

Image manipulation is transformed into a big issue for data integrity. The use of advanced imaging technology expends the regularity of multimedia forgeries. To detect such forgeries, some effective forgery identification methods are proposed to estimate the 3D lighting fingerprints by making certain suppositions related to the surface and reflection model. Incident lighting dispersal in a scene provides a physics-based key for exposing image exploitations. The proposed technique is more relaxed for multiple light source-based forgery detection. Also, the novelty of the technique is that it considers heterogeneous image surfaces, nearby surface geometry, and texture information to assess the lighting environment. The Phong reflection model is implemented to estimate the structural profiles. The method works in two phases. In the first phase, pre-processing over selected patches is performed followed by angle and error estimations in the second phase. To identify such forgeries, elevation angles ϑ concerning mounted light sources are estimated. The value of the elevation angle is computed for various patches of the image. The proposed method is relevant for a wide range of objects present in the image, i.e., not only limited to head positions, etc. To build up and validate the technique, comprehensive testing on the synthetic image dataset is done. Synthetic images are designed under different intensity light sources. Experimental results demonstrate a better forgery detection accuracy compared with other state-of-the-art methods in this domain. Also, the proposed technique is tested for generalized forged images. A comparative analysis with other related techniques is done to validate the proposed method using a synthetic image dataset. The results are pertinent to all type of images, and an error threshold is limited to 8 degrees with respect to the illuminant position.
Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

Aiming at the problems of low image data retrieval accuracy and slow retrieval speed in the existing image database retrieval algorithms, this paper designs a clothing image database retrieval algorithm based on wavelet transform. Firstly, it represents the color consistency vector of clothing image, reflects the composition and distribution of image color through color histogram, quantifies the visual features of clothing image, aggregates them into a fixed size representation vector, and uses the Fair Value (FV) model to complete the collection of clothing image data. Then, the size of the clothing image is adjusted by using the size transformation technology, and the clothing pattern is divided into four moments with the same size. On this basis, the clothing image is discretized with the help of Hu invariant moment to complete the preprocessing of clothing image data. Finally, the generating function of wavelet transform is determined, and a cluster of functions is obtained through translation and expansion. The wavelet filter is decomposed into basic modules, and then, the wavelet transform is studied step by step. The clothing image data are regarded as a signal, split, predicted, and updated and input into the wavelet model, and the retrieval research of clothing image database is completed. The experimental results show that the design of the retrieval algorithm is reasonable, the retrieval data accuracy is high, and the retrieval speed is fast.
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting

Movie Recommender System is widely applied in commercial environments such as NetFlix and Tubi. Classic recommender models utilize technologies such as collaborative filtering, learning to rank, matrix factorization and deep learning models to achieve lower marketing expenses and higher revenues. However, audience of movies have different ratings of the same movie in different contexts. Important movie watching contexts include audience mood, location, weather, etc. Tobe able to take advantage of contextual information is of great benefit to recommender builders. However, popular techniques such as tensor factorization consumes an impractical amount of storage, which greatly reduces its feasibility in real world environment. In this paper, we take advantage of the MatMat framework, which factorizes matrices by matrix fitting to build a context-aware movie recommender system that is superior to classic matrix factorization and comparable in the fairness metric.
Efficient Image Super-Resolution via Self-Calibrated Feature Fuse

Recently, many super-resolution reconstruction (SR) feedforward networks based on deep learning have been proposed. These networks enable the reconstructed images to achieve convincing results. However, due to a large amount of computation and parameters, SR technology is greatly limited in devices with limited computing power. To trade-off the network performance and network parameters. In this paper, we propose the efficient image super-resolution network via Self-Calibrated Feature Fuse, named SCFFN, by constructing the self-calibrated feature fuse block (SCFFB). Specifically, to recover the high-frequency detail information of the image as much as possible, we propose SCFFB by self-transformation and self-fusion of features. In addition, to accelerate the network training while reducing the computational complexity of the network, we employ an attention mechanism to elaborate the reconstruction part of the network, called U-SCA. Compared with the existing transposed convolution, it can greatly reduce the computation burden of the network without reducing the reconstruction effect. We have conducted full quantitative and qualitative experiments on public datasets, and the experimental results show that the network achieves comparable performance to other networks, while we only need fewer parameters and computational resources.
Prospects of Blockchain in Contract and Property

Abstract Recurrent difficulties are delaying what for the time being are still modest applications of blockchain. This paper identifies what value this new technology adds to the contractual and property processes, exploring its potential and analyzing the main difficulties it is facing. Paying particular attention to the distinction between contract (personal or in personam ) rights and property (real or in rem ) rights, it first examines the difficulties for trading contract rights through blockchain-based applications, mainly those to complete contracts ex ante without relying on third-party enforcers. Second, it explores the difficulties faced by blockchain to enable trade in property rights.
The Impact of Knowledge Management Process on Job Satisfaction and Employee Retention

Knowledge management has become a vital element in today's extremely competitive, uncertain, and quickly changing environment. The method of acquiring expertise is knowledge management. In the highly competitive, volatile, and rapidly evolving market climate, knowledge management has become crucial. The goal line of this study is to measure the effect of knowledge management processes (acquisition, share, codification, creation, and retention of knowledge) on job satisfaction. Further, we examine the affiliation between job satisfaction and employee retention. A theoretical model is suggested based on connecting knowledge management processes, job satisfaction, and employee retention. The results of survey data gathered from 32pharmaceutical and chemical companies listed in the Dhaka Stock Exchange in Bangladesh are empirically checked with PLS-SEM (Structural Equation Modeling-Partially Least Square). The study finds that knowledge management processes have a significant and positive impact on job satisfaction. Also, job satisfaction is positively associated and highly significant with employee retention. The findings generated from this study would be a policy dialog to the human resource department, regulatory bodies, academicians, and policy-makers.
Introduction to Virtualization in Cloud Computing

Everything about today’s everyday life is digitally interactive. The Internet offers access to a huge amount of information. The Internet is the world’s largest network for gaining free access to data from everywhere at any time. A vast volume of data must be stored and accessed through the Internet. The cloud is the answer to this dilemma. Cloud has massive data storage, so it demands the optimization of cloud resources along with customer expectations. Optimization can be achieved by increasing the performance in terms of various factors: Disk performance, throughput, queries response of customer, availability and capacity. This paper presents the cloud virtualization that is used to optimize the cloud services. It is the main sustaining technology in the cloud environment. Virtualization is accomplished with the help of virtualization software. This software separates a real calculating unit into several virtual ones; any device can be simply used and handled. Ideal computing resources can be allocated and managed easily with the help of available virtualization software; thus, virtualization speeds up the throughput in cloud computing. This chapter discusses the classes of virtualization and its effect and how to optimize the cloud computing.
An Experiment Study on Federated Learning Testbed

Shen, Cheng Xue, WanliWhile Internet of Things (IoT) can benefit from machine learning by outsourcing model training on the cloud, user data exposure to an untrusted cloud service provider can pose threat to user privacy. Recently, federated learning is proposed as an approach for privacy-preserving machine learning (PPML) for the IoT, while its practicability remains unclear. This work presents the evaluation on efficiency and privacy performance of a readily available federated learning framework based on PySyft, a Python library for distributed deep learning. It is observed that training speed of the framework is significantly slower that of the centralized approach due to communication overhead. Meanwhile, the framework bears some vulnerability to potential man-in-the-middle attacks at network level. The report serves as a starting point for PPML performance analysis and suggests the future direction for PPML framework development.
Apatite Eu/Y-Ce discrimination diagram: A big data based approach for provenance classification

磷灰石广泛分布于火成岩、沉积岩和变质岩中，是一种常见的、包含丰富微量元素的副矿物。磷灰石晶格可容纳丰富的微量元素，且因其形成的物理化学条件不同会表现出差异明显的微量元素特征。利用磷灰石微量元素特征可以追踪物质来源和演化。现在常用的方法是利用磷灰石的微量元素绘制二元判别图解，经典判别图解包括Sr-Y、Sr-Mn、Y-（Eu/Eu<sup>*</sup>）和（Ce/Yb）<sub>N</sub>-REE图解。随着微区测试技术发展，磷灰石微量元素数据日渐丰富，同时由于磷灰石化学成分的复杂性，传统图解已逐渐无法有效利用这些数据所携带的信息，进而无法准确判别其生成环境。建立能准确判别磷灰石物源的新型判别图解故而迫切。近年来，磷灰石微量元素数据的大量积累，为运用以大数据为依托，准确判别磷灰石物源奠定了数据基础。本研究将大数据技术与地球化学数据相结合，共收集整理了1925个代表性磷灰石测试点的微量元素数据，对富碱性火成岩、超镁铁质岩石、镁铁质火成岩、长英质花岗岩、中-低级变质岩、高级变质岩六种类型中磷灰石微量元素数据进行穷举端元处理，共获得7140个磷灰石物源判别图解端元组合，在轮廓系数限定下，进一步有效筛选并提取出能判别磷灰石物源类型的最优图解端元。本文构建了Eu/Y-Ce磷灰石判别新图解，相较于之前的磷灰石判别图解，其涵盖了更全面的物源类型，可以更准确地判别源区类型。;Apatite is a common and important accessory mineral in rocks. Apatite comprises rich various trace elements, which are formed in different apatite-generation environments. Constructing apatite-associated trace element discrimination diagrams is a classic and efficient technique for the investigation of apatite provenance. The typical examples include the diagrams of Sr-Y, Sr-Mn, Y-(Eu/Eu<sup>*</sup>) and (Ce/Yb)<sub>N</sub>-REE. However, the current discrimination diagrams cannot precisely distinguish apatite types due to the massive increase of apatite trace elements that are detected by the developing microscale analyses, which therefore cannot precisely predict the apatite-formation environment. Fortunately, the significant increase of apatite trace element data advances our understanding in apatite provenance by using the big data analysis technique. In this study, we applied the big data study for the collected 1925 published apatite trace element data, analyzing six apatite-associated rock types, which are alkali-rich igneous rocks, ultramafic rocks, mafic igneous rocks, felsic granitoids, low- and medium-grade metamorphic rocks and high-grade metamorphic rocks. The yield 7140 apatite-associated discrimination diagrams were further detailed evaluated by introducing the Silhouette coefficient. By the process, we proposed an Eu/Y-Ce discrimination diagram to distinguish apatite.
Subjective Employment Obstacle of College Students and Its Predictor Model Based on Deep Learning

With the development of higher education in full swing, the number of college students in China is increasing, the employment pressure of college students is increasing, and the employment situation in universities is not optimistic. Subjective career obstacles are obstacles that individuals may encounter when they perceive themselves according to their own conditions and surrounding environmental factors based on their future career pursuit and goals. In this paper, it is of practical significance to use the employment confidence index of college students to analyze and predict their employment confidence. Based on DL (deep learning), a model GM-BPNN (Gray model-BP neural network) of subjective employment obstacles of college students and its predictive factors is proposed. Initially, the employment data of a university are collected and normalized. Then, GM and BPNN are used to model and predict the number of college students’ employment from different angles. Finally, the weights of the prediction results of GM and BPNN are determined, and the final prediction results of the number of college students’ employment are obtained by weighting. The results show that the relative error of the combined model is smaller and the accuracy is higher.
PENGUATAN LITERASI, NUMERASI, DAN ADAPTASI TEKNOLOGI PADA PEMBELAJARAN DI SEKOLAH

Strengthening literacy, numeracy, and technological adaptation are important efforts to improve the quality of human resources in the Digital Age and the Era of Disruption. Indonesia is very far behind other countries in this capacity. The government as well as related parties (Schools, Universities, Education Quality Assurance Institutions, and Parents) must participate in strengthening literacy, numeracy, and technology adaptation for students and educators in Indonesia. One of the activities is to integrate literacy, numeracy, and technology activities in learning either at school or at home.