Reviewerly/exHarmony huggingface dataset:
A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation, electrical security, and other aspects. Accordingly, this study proposes a distributed power trading scheme based on blockchain and AI. To protect the legitimate rights and interests of consumers and producers, credibility is used as an indicator to restrict untrustworthy behavior. Simultaneously, the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency, and a weighted communication tree construction algorithm is designed to achieve superior data forwarding. Finally, AI sensors are set up in power equipment to detect electricity generation and transmission, which alert users when security hazards occur, such as thunderstorms or typhoons. The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.
Augmented reality

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Virtual reality

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Mobile learning

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EVALUATION OF DIGITAL CUSTOMER EXPERIENCE AND CUSTOMER LOYALTY WHEN BUYING GOODS ONLINE

The article presents the results of a statistical study of the dynamics of the development of digital skills of the Russian population, as well as studies the trends of changes in their involvement in the digital space when searching and buying goods in real time. The features of the development of digital skills and consumer preferences in the online purchase of goods by representatives of four main generations are investigated: Baby Boom, X, Y, Z. The influence of digital technologies on the change of the consumer value chain has been studied. An integrated system of determinants for assessing online customer loyalty has been developed, which includes six determinants and allows for a comprehensive assessment of the level of customer loyalty during online purchase of goods and to determine directions for improving communication with customers. The methodological tools for the integral assessment of the level of online customer loyalty are given, which allows identifying the level of such loyalty, as well as evaluating the effectiveness of online sales organization from the consumer’s perspective and determining ways to improve interaction with him in the digital environment. A model of the formation of an omnichannel marketing strategy is obtained, which takes in to account integrated marketing channels available to the consumer, covers the full cycle of sales funnel management, takes in to account the segment of the target audience and allows effective interaction with the consumer in the digital environment.
Semi-Supervised Machine Learning Method for Predicting Observed Individual Risk Preference Using Gallup Data

Risk and uncertainty are vital for making economic decision. An individual's propensity to make riskier decisions depends on various circumstances. Most studies on general and financial risk preference focus on analyzing the correlation between the sociodemographic and economic factors, using ordinary least-square and supervised machine learning. This article aims to investigate the effects of social and economic covariates on an individual's willingness to take general risks (observed) using the GPS and Gallup datasets. Based on the available observed risk-taking data for one year, we propose a semi-supervised machine learning-based approach that can efficiently predict the observed risk index for those countries/individuals for years when the observed risk-taking index was not collected. We find that linear models are insufficient to capture certain patterns, and non-linear models, such as random forest regression, can obtain lower error than those reported in past literature. We also find that subjective well-being influences risk-taking behavior.
Analysis of Network Traffic Measurements using Machine Learning

In recent times, an exponential increase in internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning.In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models
Quantum advantage of Monte Carlo option pricing

Abstract Quantum computers have the potential to provide quadratic speedup for Monte Carlo methods currently used in various classical applications. In this work, we examine the advantage of quantum computers for financial option pricing with the Monte Carlo method. Systematic and statistical errors are handled in a joint framework, and a relationship to quantum gate error is established. New metrics are introduced for the assessment of quantum advantage based on sample count and optimized error handling. We implement and analyze a Fourier series based approach and demonstrate its benefit over the more traditional rescaling method in function approximation. Our numerical calculations reveal the unpredictable nature of systematic errors, making consistent quantum advantage difficult with current quantum hardware. Our results indicate that very low noise levels, a two-qubit gate error rate below 10 −6 , are necessary for the quantum method to outperform the classical one, but a low number of logical qubits (ca. 20) may be sufficient to see quantum advantage already.
Content Moderation on Social Media: Does It Matter Who and Why Moderates Hate Speech?

Artificial intelligence (AI) has been increasingly integrated into content moderation to detect and remove hate speech on social media. An online experiment (N = 478) was conducted to examine how moderation agents (AI vs. human vs. human–AI collaboration) and removal explanations (with vs. without) affect users' perceptions and acceptance of removal decisions for hate speech targeting social groups with certain characteristics, such as religion or sexual orientation. The results showed that individuals exhibit consistent levels of perceived trustworthiness and acceptance of removal decisions regardless of the type of moderation agent. When explanations for the content takedown were provided, removal decisions made jointly by humans and AI were perceived as more trustworthy than the same decisions made by humans alone, which increased users' willingness to accept the verdict. However, this moderated mediation effect was only significant when Muslims, not homosexuals, were the target of hate speech.
RFID Based For Biometric Voting Machine

The Internet of Things (IoT) is a current era that permits digital devices, software, sensors, vehicles, and home appliances to interconnect with networks and transfer data without human or computer intervention. However, the current electronic voting machines lack recent security measures that allow voters to verify their identity before casting their vote, which can lead to false voters casting duplicate or fake votes. To address this issue, a proposed system utilizes RFID and IoT to enhance security mechanisms in electronic voting machines. The system replaces the traditional voter ID with an active RFID tag that the system can scan and match with collected fingerprints. To verify their identity, the voter must scan their RFID tag and confirm their identity with their fingerprints. The voting machine includes an active reading device (reader) to read data on the RFID tag and a fingerprint scanner to capture fingerprints. If the fingerprints match against the database, the individual can cast their vote. In addition, the voting machine includes an LCD display to show the voter's relevant information from the database. This approach can help prevent illegal voting or impersonation since fingerprints are unique to each individual. This paper describes the layout and operation of a Smart Electronic Voting Machine the usage of Arduino UNO, RFID, to improve the election process by preventing electoral fraud and ensuring safety, security, reliability, and seamless conduct of elections in the country. The proposed solution involves the device communicating with the RFID tag. When the voter scans their RFID card, the controller verifies the ID, and if it matches, the LCD displays the result and enables the voter to cast their vote for the corresponding party. It is essential to ensure the secure implementation of the system.
Numerical framework for semi-device-independent quantum random-number generators

Quantum random-number generation (QRNG) is one of the most widely applied branches in quantum cryptography. Among all QRNG schemes, semi-device-independent (semi-DI) QRNG is quite promising, achieving high randomness generation rates with few assumptions on the devices. For the central task of a QRNG study---security analysis---numerical approaches become popular for its generality to various semi-DI QRNG schemes. Here we formulate a numerical framework for the finite-size security of general semi-DI QRNGs, which gives a secure lower bound of the finite-size randomness generation rate against general attacks. We consider a simple example of an optical semi-DI QRNG as an application of our framework.
Debt Financing, Information Sharing, and Profitability: Evidence from Listed Firms from an Emerging Economy

This study investigates how credit information sharing conditions debt financing to boost the profitability of 20 listed enterprises on the Ghana Stock Exchange between 2003 and 2013. We employ robust least squares and simultaneous bootstrapping models in a panel setting. Our findings show that the impact of debt financing on profitability increases when it is subject to information sharing and takes the shape of short, long, and total debts. In the worst-case situation, contingent debt financing reduces the negative impact of debt financing on profitability. Therefore, authorities must adopt laws and legislation that deepen, widen, and strengthen credit information sharing to offset the negative impact of information asymmetry on loan financing and business profitability.
Small and Medium Businesses Readiness towards Cyberattacks in Saudi Arabia

While large companies have the capability and resources to protect their systems from the increasing number of cyberattacks, little is known regarding the recent cybersecurity preparedness of Small and Medium Businesses (SMBs). The purpose of this study is to examine the factors that impact the SMB's readiness towards cyberattacks, this research used the identified constructs, the SMB's readiness toward cyberattacks, government regulations and support, cyberattacks, awareness of cybersecurity-threat, and information technology risk management that were established in prior research. The findings demonstrated an overall good model. In addition, the results showed that government regulations and support, awareness of cybersecurity-Threat, and information technology risk management have a significant impact on the SMB's readiness toward cyberattack. Furthermore, government regulations and support have a significant impact on cyberattacks and awareness of cybersecurity-threat. These findings can be used to facilitate future studies and improve the existing understanding of how SMBs can be prepared to reduce the incidence and effect of cyberattacks.
Image Noise Removal using Multiple Weights in AWGN Environments

현대사회는 다양한 분야에서 자동화와 인공지능 기술이 사용되고 있으며, 객체를 인식하거나 분류하기 위해 카메라와 센서를 사용하여 영상 데이터를 취득하고 있다. 하지만 영상 데이터를 취득되는 과정에서 센서의 결함이나 외부의 신호에 의해 잡음이 발생할 수 있으며, 잡음에 훼손된 영상은 시스템에 악영향을 미치기 때문에 전처리 과정에서 잡음제거를 사용하고 있다. 본 논문에서는 AWGN 제거과정에서 발생하는 블러링 현상을 최소화하기 위해 다중가중치를 사용한 필터 알고리즘을 제안하였다. 제안한 알고리즘은 중심마스크와 매칭마스크를 사용하여 두 영역을 비교하였으며, 구조적 특징에 의한 가중치, 화소값 비교에 의한 가중치와 화소 분포에 의한 가중치를 각각 구하였다. 필터의 출력은 세 가중치를 매칭영역의 화소값에 컨벌루션하여 계산하였으며, 제안하는 알고리즘의 잡음제거 성능을 분석하기 위해 시뮬레이션을 진행하였다. In today’s society, automation and artificial intelligence technology are used in various fields, and image data is acquired using cameras and sensors to recognize or classify objects. However, noise can be caused by sensor defects or external signals in the process of acquiring image data, and noise removal is used in the preprocessing process because images damaged by noise adversely affect the system. This study proposes a filter algorithm using composite weights to minimize blurring that occurs in the process of removing AWGN. The proposed algorithm compares two regions using a central mask and a matching mask, and obtains weight by structural features, weight by pixel value comparison, and weight by pixel distribution, respectively. The output of the filter was calculated by convolutioning three weights to the pixel values of the matching area. Simulations were conducted to analyze the noise removal performance of the proposed algorithm.
Mineable Fast Cryptocurrency KIITCOIN using SCRYPT Algorithm

KIITCOIN is a peer-to-peer coin that enables fast and near-zero cost payments worldwide. KIITCOIN is an open-source cryptocurrency project. It provides a fully decentralized global payment network. With a secured network, KIITCOIN provides a swift transaction acknowledgement time and more efficient storage than the other currencies. The main challenge for this decentralized system is double-spending, to tackle this we used a peer-to-peer network. All transactions are done on the network, and their timestamps are hashed into the current chain of proof-of-work and these cannot be altered without restarting the chain. In addition to providing proof of the sequence of events witnessed, the longest chain shows that it originated from the most powerful pool of processors. It will be possible to outpace attackers as long as more than half of the CPU power is controlled by nodes not cooperating to attack the network. A minimal structure is required for the network itself. The Mining reward of KIITCOIN is set to 100 coins per block mined. We know that after every 210,000 blocks, bitcoin generation is halved. Similarly, for every 1051200 blocks mined, KIITCOIN generation will be halved. The maximum coin supply will be 1000 million KIITCOIN, roughly 47.5 times more than Bitcoin. With Scrypt proof of work and fast retargeting times, KIITCOIN difficulty will retarget every 3.5 days.
Recommendation System for Movies Using Improved version of SOM with Hybrid Filtering Methods

Many industries uses recommendation systems (RS) to iden-tify product recommendations when users actively participate on e-commerce sites. Recently, massive growth in both goods and consumers has faced serious challenges. Numerous websites present the consumer with numerous options at once, creating a lot of confusion. In addition, finding the right product or active user is an essential part of RS. Products are already recommended based on consumer preferences and sociodemo-graphic trends. A hybrid-action-related recommendation based on K-Nearest Neighbor Similarity (HAR-KNN) combines the ease of hybrid filtering with the development of feature vectors to improve the user behaviour matrix. To categorize attributes, it uses both quality and quantity classifiers. Additionally, the proposed methodology overcomes shortcomings in earlier approaches to evaluating user preference for goods and feature analysis. The SOM AND KNN classification technique has been approved for the purpose of locating information about user behavior online and in real time for a specific user group containing a large amount of data in re-lation to the commonalities among many users and target users. A test result is evaluated by using highly predictive metrics such as Precision (P), Recall (R), F, as well as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE).INDEX TERMS Recommendation System (RS), User behaviour data, Hy-brid filtering, KNN, behavioural matrix.
Fixing Liabilities for Technology in Cyber Crimes – A Critical Analysis

The meaning of cybercrime, its concept and types are covered in this research. It further deals with the liability in cybercrimes and whether the liability can be fixed to technology. By the late 1970s, the idea of ‘crime by computer’ had become a serious problem that called for new criminal laws, but technology’s constant growth and continuous penetration into the legal system had not yet had the same destructive impact as it does now. The word ‘cyber’ has a technological connotation. Cybercrime is the term used to describe crimes committed in this digital age. They are non-violent, bloodless crimes committed by computer users who have a solid understanding of how computer systems work and use intricate, technical techniques. There isn’t a single, accepted definition of cybercrime. The most common cybercrimes are click jacking, spyware, email fraud, social media fraud, banking fraud, ransomware assaults, cyber espionage, etc. There are many different sorts of cybercrimes. ‘Cyberspace Liability’ refers to the issues over liabilities incurred when communicating or conducting business online. The existing legal framework does not explicitly hold technology accountable for any cybercrimes that may be committed. The user, service provider, and person are largely responsible. There is currently no law that makes it possible for technology to be held accountable for cybercrime.
Analýza oblastí reedukácie u žiakov s dysgrafiou

This paper presents the most common areas of dysgraphia re-education that special education teachers focus on when working with students with dysgraphia. Early, targeted, and systematic support for each area significantly eliminates problems that impact the writing process. The theoretical part defines the basic terminological concepts that form the basis for the empirical part. The empirical part consists of an analysis of the areas of dysgraphia re-education from the perspective of special education teachers.
Introduction

This chapter acts as an introduction to some of the key terms and concepts that are necessary to understand the subsequent chapters. The terms artificial intelligence (AI), machine learning and deep learning are introduced and their relationship explained. A brief review is provided of some of the key applications of AI in medicine and medical imaging. The role of big data in training effective AI systems is discussed.
Constative experimental study of values dynamics of the competence of personal, social development and learning to learn of primary school students

The article contains experimental research at the acknowledgement stage aimed at evaluating the values of the competence of personal, social development and learning to learn in terms of axiological reference of students, by which we consider a system of desirable values, suitable to be formed in primary education. The pedagogical experiment was organized in grades 1-4, during 2018-2022 on a sample of 68 students from primary education. The experimental approach allowed the detection of the following problems that become impediments in the process of forming the values of the competence of personal, social development and learning to learn: difficulties in expressing empathy, respect for classmates and friendship, inconsistencies in affirmation by assuming responsibility, assertive communication, innovative spirit and autonomy in the school group, which demonstrates the need to design axiological education, at the level of functional strategies for the formation of the axiological referential that represents the competence of personal, social development and learning to learn of primary education students.
IMPACT OF EMPLOYEES RETENTION PRACTICES ON ORGANIZATIONAL COMMITMENT: AN ANALYSIS OF STAR CATEGORY HOTELS OF CHANDIGARH (UT)

The hospitality industry is one of industry that focuses on the quality of human resource. In order to provide the good service, the hospitality industry needs qualified human resources. This is because the hospitality industry fully oriented to customer’s satisfaction. The success of the hospitality industry can be seen from customer’s satisfaction through the services provided by the hotel. However, there is one problems faced by hospitality, there is the employee turnover. The impact that felt because of the employee turnover are disrupting the operations of the hotel and also causing losses for the cost, time and a decrease in production and services. The questions and the purpose of this research are to determine the significant factors of the employee turnover at Hotels of Chandigarh. Referring to the concept of Santhanam et.al (2015). There are six factors about the employee turnover in hospitality industry. Those are recruitment, selection, training, performance appraisal, career growth opportunities, and salary &amp; benefit. After determining the significant factors of the employee turnover at Chandigarh Hotels, the purpose of this research is to reduce employee turnover at Chandigarh Hotels. The researcher distributed the questionnaire to employees of three stars and above category Hotels and obtained 405 respondents. The results of the questionnaire collecting were processed by using SEM.
On the accuracy of open-source and commercial solar forecasting tools

This paper evaluates the performance of some solar forecasting tools that are available publicly over a 24-hour time horizon. These tools include one open-source, namely PVlib-python, and two commercial ones, each with different time resolutions. These tools are evaluated in terms of the forecasted direct normal irradiance (DNI) and global horizontal irradiance (GHI) which were compared to the measurements made locally, under both clear-sky, partly cloudy-sky and cloudy-sky conditions. Standardized metrics, including mean bias error, mean absolute error, mean absolute percent error, and root mean square error are used for comparison purposes. The goal is to determine how accurate these tools are for day-ahead solar forecasting, without resorting to sky imaging or ground-based irradiance monitoring.