Reviewerly/exHarmony huggingface dataset:
A Passenger Safety Status Detection Method for Rail Transit Stations Based on Machine Learning

To settle the problems related to passenger safety in rail transit stations, improve the management of areas lack of security service, and implement the intelligent detection of passenger flow safety status in subway stations, we have established a passenger safety status detection system for rail transit stations based on machine learning for target detection, passenger safety status detection, terminal data visualization. Human pose estimation based on YOLOv5 and Alpha Pose enables intelligent detection and optimization of the adjustment model through training of passenger behavior data sets. A non-open terminal visualization platform is established to detect the safety status in each monitored area in real time, and provide real-time safety status information and corresponding areas for station staff passengers, so that managers can timely confirm and process passengers with abnormal behaviors. The abnormal behavior data of passengers in rail transit stations are statistically analyzed to evaluate the safety status of passenger flow in stations and provide data support for improving safety management level. The research results will help to improve the intelligent level of passenger safety management in rail transit stations, which is of great significance to ensure the safe operation of urban rail transit.
Expert System

This chapter is concerned with expert system for forecasting. It comprises of expert system architecture and its characteristic features. The entire procedure of formulation of an Expert system right from knowledge acquisition through design to implementation, testing and maintenance are described. Practical exercises are included.
Recognition of Remote and Small Intrusion Targets Around High-Speed Railway Based on Deep Learning Method

With the rapid development of high-speed railway, its operation management and traffic safety are becoming more and more important. Foreign matter intrusion around high-speed railway needs to be timely prevented and identified. The current intrusion target recognition system still has the problems of high miss rate and false detection rate for the recognition of small intrusion targets beyond 100m. In this paper, aiming at this practical problem, combined with the deep learning target detection algorithm and the requirements of high-speed railway perimeter prevention and control, a system with better intrusion target recognition effect is designed to make up for the shortcomings of current high-speed railway perimeter video monitoring. Based on the actual video monitoring picture of high-speed railway, this experiment establishes a data set of far and small intrusion target recognition of high-speed railway perimeter, and establishes a complete evaluation standard system. Through the selection of algorithm model, it improves the ability of far and small intrusion target recognition of high-speed railway perimeter, and combines the algorithm to establish a far and small intrusion target recognition system of high-speed railway perimeter.
State of Health Prediction of Lithium Battery Based on Extreme Learning Machine Optimized by Genetic Algorithm

Accurate prediction of the capacity of lithium battery is of great significance for the failure prediction and health management. At present, the prediction accuracy of machine learning cannot meet the demand, so this paper proposes a prediction model based on genetic algorithm (GA) to modify extreme learning machine (ELM). Firstly, the characteristic quantities related to degradation of battery are extracted from the cycle life test; then, the aging characteristic parameters of the battery are analyzed using the correlation coefficients; finally, a GA-ELM neural network model is established and the input weights and implicit layer bias of the extreme learning machine are optimized using the genetic algorithm. The proposed model is validated with the experimentally obtained battery data and compared with a single ELM neural network model prediction method. The results show that: the method proposed in this paper can effectively predict the state of health (SOH) with better prediction accuracy than the single ELM model.
Charging Pile Sharing Scheme Based on Blockchain Technology

With the continuous promotion and application of new energy vehicles, the demand for charging piles is increasing. In various types of charging piles, the special charging piles of the business circle and private charging piles are idle for a certain period of time, so with the help of block chain technology, a charging pile sharing scheme based on block chain technology is proposed, which creates a convenient, safe and reliable sharing platform on the charging pile sharing system by virtue of the advantages about block chain decentralization, openness, independence, security and anonymity. Under the supervision of the government, users who hold charging piles can apply to join private charging piles to make a certain profit. Besides, we can also make full use of the functions of all types of charging piles, providing more safe and diverse charging schemes for electric vehicles customers. In this way, we can not only give full play to the role of all types of charging piles, but also provide more and more guaranteed services for electric vehicles customers to achieve a mutually beneficial and win-win situation.
Blockchain Use Possibilities: A Systematic Literature Review

Blockchain and tourism have the potential to become a very advantageous combination, as this technology can provide security and transparency at certain key points. The blockchain can make the transfer and storage of this information easier and more secure, since the responsibility is shared by an entire network. The same goes for payments abroad, which increases the level of trust between all parties. This study aims to make a comparison between these two countries such as Spain and Poland with a significant growth of this technology in tourism. The results of the paper show that blockchain technology is still in an initial phase of its life and that there is a consensus regarding its potential; however, it is still very difficult to know how far it can go and how it will affect tourism.
Research on Similarity Recognition of Face Images Based on Data Dimensionality Reduction

Feature dimensionality reduction can remove redundant and useless information in data and improve the accuracy of pattern recognition. Based on this, a face image recognition algorithm based on singular value decomposition is proposed. Firstly, the feature extracted data is decomposed by SVD, and the top N values with the largest feature are retained, and the sample data after feature reduction is obtained. Then, through calculation, the category corresponding to the maximum similarity is determined, and the similarity recognition of face images based on data dimensionality reduction is realized. Through experiments on the face image data set, the experimental results show that the algorithm has a good effect.
Continuously Non-malleable Codes from Authenticated Encryptions in 2-Split-State Model

Tampering attack is the act of deliberately modifying the codeword to produce another codeword of a related message. The main application is to find out the original message from the codeword. Non-malleable codes are introduced to protect the message from such attack. Any tampering attack performed on the message encoded by non-malleable codes, guarantee that output is either completely unrelated or original message. It is useful mainly in the situation when privacy and integrity of the message is important rather than correctness. Unfortunately, standard version of non-malleable codes are used for one-time tampering attack. In literature, we show that it is possible to construct non-malleable codes from authenticated encryptions. But, such construction does not provide security when an adversary tampers the codeword more than once. Later, continuously non-malleable codes are constructed where an attacker can tamper the message for polynomial number of times. In this work, we propose a construction of continuously non-malleable code from authenticated encryption in 2-split-state model. Our construction provides security against polynomial number of tampering attacks and non-malleability property is preserved. The security of proposed continuously non-malleable code reduces to the security of underlying leakage resilient storage when tampering experiment triggers self-destruct.
Introduction to Artificial and Emotional Intelligences

Since the concept of human intelligence was induced nearly two centuries ago as cognitive abilities to learn, understand and apply and reason, intelligence has been viewed as a key factor of humanised evolutions manifested in micro, meso and macro levels.
Information Visualization (InfoVis) in the Decision Process

This work studies the importance of information visualization and visual analysis, in the decision process, in the context of marketing and its tools. Researchers, marketing strategies, brand positioning, financial analysis, and many other needs, have goals for a better understanding and analysis of data and information for understanding, growth, productivity, and innovation. Consumers and citizens analyze publicly and visually available data such as product specifications and price, via the web, websites, blogs, and online communities to choose the products they will be able to buy, when deciding to answer questions, voting through likes or other qualitative data, forms of voting and the search for related information. InfoVis and visual analytics technologies begin to provide an effective vision for the decisions, to deal with behavior analysis, general and specific patterns, data irregularities, lapses in information, and trends felt by users in society, in their intervention in social media, and in spaces physical and virtual.
Optimizing Marketing Through Web Scraping

The objective of this research was to develop an indexed database on electrical appliances marketed in the city of Ibarra, Ecuador, using web scraping in order to optimize the search time of consumers for these products. It is an applied research, where the survey technique was applied to determine the marketers and the products to be considered. The results of the survey led to directing the study in the marketers Todohogar, Almacenes Japon and La Ganga and to the products: televisions, kitchens and refrigerators. The web scraping process contemplated three phases: search and inspection of the web page, data extraction and cleaning and transformation and storage of final data, for which the Python program and the Request and Beautiful Soup libraries were used. The Data Access Object (DAO) design pattern was used to separate the logic layer from data access, and the connection to the MySQL database was defined with the Python programming language using the mysql.connector library. For the automatic and indexed storage of the data extracted and refined from the pages of the marketers, some databases called product table, characteristic table, brand table were built. As a result, there is a web page that makes use of the web scraping program developed to show the user the summarized and updated information of the three products offered by each of the marketers analyzed.
Intrusion Detection Using Federated Learning

In the evolving world, the drastic expansion of the internet and the use of smart devices demands a change in existing infrastructure. These rapid changes in the structural level also open new dimensions that are susceptible to cyber-attacks. One of the efficient methods to tackle these situations is to apply intelligence to the systems and detect abnormal behaviours. As privacy plays a vital role, here Federated Learning method is used to detect cyber attacks by analysing the data logs and compared with the non-federated learning techniques on the same data. It is very evident from the experiment that Federated Learning is very effective in detecting these attacks by preserving the privacy of the victim organisations/systems.
Visual Multi Scene Intelligent Car Service Platform Based on Internet of Things Technology

The visual multi scene intelligent car service platform based on Internet of things technology is different from any previous machine idea. It is specially designed to serve an industry or a field. What we want to achieve here is a multi scene service state, and switching scenes without switching machines (only switching conversion method). At the same time, in order to meet this requirement, the traditional centralized design scheme can not be used to arrange all tasks in the machine. Therefore, the hardware level is separated from the software level for step-by-step processing, which will not only reduce the pressure of the hardware machine, but also improve its processing efficiency. Further, its processing capacity is no longer limited by the underlying hardware, but determined by the performance of the upper computer, After having a very powerful upper computer, it can realize the control mode of multi lower computer management. At the same time, it can adopt the scheme of time-sharing processing to deal with the multi lower computer, so as to meet its various performance requirements. In this way, we can complete the service mode of more scenarios under the current technical force, and only adopt a machine mode, a scalable and scalable smart car service platform whose efficiency can be improved at any time.
Discovery of Rare Itemsets Using Hyper-Linked Data Structure: A Parallel Approach

Pattern mining has been more important in the solution of various data mining jobs over the years. The extraction of common patterns was the primary focus of pattern mining research for a long period of time, with the mining of rare patterns being neglected. Rare pattern mining is becoming more popular as researchers recognize the importance of rare patterns. The hyper-linked data structure is suitable to store sparse data set in the main memory and enables dynamic adjustment of links during the mining process using recursion. However, a sequential approach to discovering rare patterns from a large dataset is inefficient. Hence a CUDA-based parallel algorithm has been implemented to discover rare itemsets. The algorithm is tested using dense and sparse datasets on a GPU. The GPU initialization time affects the time taken to discover rare itemsets. The time taken to transfer data between CPU and GPU is significantly large and the parallel implementation of an algorithm with a recursive approach is unsuitable.
Single-Equation Econometric Model

The first chapter is devoted to econometric models in the form of one stochastic equation. Linear, product, and limited dependent variable models were presented. In this chapter all stages of building a single-equation econometric model are discussed. These are: specification, estimation of parameters, verification, and exploitation possibilities. Basic methods of estimation of structural parameters and parameters of the stochastic structure of the model are presented. Basic statistical measures determining the quality of an empirical econometric model are also discussed.
Augmented Reality: Toward a Research Agenda for Studying the Impact of Its Presence Dimensions on Consumer Behavior

Augmented reality (AR) virtual try-ons (VTO) have emerged as an important decision-making tool because of the highly realistic experience. For instance, AR enables users to virtually try-on sunglasses by placing the virtual product on their face. Research increasingly emphasizes the importance of spatial presence in the realistic AR experience. However, prior research on AR presence remains scant and overlooks social and self-presence. To fill this gap, we review literature on presence in the context of prior immersive technologies and propose a future research agenda on the impact of AR presence dimensions on product-relevant outcomes. This article starts by presenting AR spatial presence definition and proposing definitions for AR social and self-presence by drawing parallels between AR apps features and the presence dimensions of prior immersive shopping technologies. Thereafter, our review uncovers how each presence dimension leads to positive consumer outcomes. Then, we propose a research agenda for future studies of AR presence in marketing that outlines the need for a multidimensional perspective of presence to help uncover their unique impact on consumer responses. In addition, future research should investigate which contextual factors (marketing channels, for instance, in store and online as well as the types of products displayed in AR for instance makeup and sunglasses) might explain differences in the outcomes of presence. Our study has several limitations as it only considers the type of presence dimensions relevant to current AR-VTO experiences.
Leveraging Artificial Intelligence for Customer Satisfaction and Loyalty

Do customers prefer employee service or artificial intelligence (AI) powered services? This chapter provides answers to this question by proposing AI-powered applications as service and discusses how employee and AI services influence overall service quality and customer response, particularly customer satisfaction and loyalty which is related to business profitability and sustainability. The chapter begins with reviewing the relevant literature on the link of service quality to customer satisfaction and loyalty then hypothesises an association of AI service quality with customer response. To provide a better insight into this association, employee service quality is included as a comparison. An empirical study in the hotel sector is undertaken to test the proposed relationships. The methodology, results and discussion of the case study findings are elaborated subsequently. Implications of these finding are highlighted and conclude this chapter.
Comparative Study of Sentiment Analysis on Cyber Security Related Multi-sourced Data in Social Media Platforms

The comparative study investigates cyber security attitudes and behaviour across time using data gathered from social networking platforms such as Reddit and Twitter. Due to the continuously advancing technology in the modern world, enormous amounts of data are produced every second. The chosen field of study seeks to determine the opinions of people on social media posts. The dataset for the proposed work was a multi-source dataset from the comment or post section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were incorporated to perform sentiment analysis on the obtained dataset. The proposed work provides a comparative analysis using various techniques namely ML, DL, and lexicon-based. Naive Bayes Classifier was used as a Machine Learning algorithm, TextBlob is used as the Lexicon-based approach, and the deep-learning algorithm used is BiLSTM. It was observed that BiLSTM model outperforms the other two models with an F1-score of 0.895, accuracy of 0.896, precision of 0.899, and recall of 0.891.
Leverage Emotional and Artificial Intelligences for Employees

Emotional intelligence has been popularising in both scientific and no-scientific communities as a new domain of human intelligence to predict individual and organizational success. Artificial intelligence has permeated the business world as a transformational evolution of utilising machines for human jobs. In view of the controversies over the role of artificial intelligence in replacing human jobs, this chapter discusses how emotional intelligence as human intelligence trumps artificial intelligence as machine intelligence over employee loyalty and performance and how artificial intelligence facilitates the latter. Employee performance is operationalised into internal and external dimensions that capture employees’ task efficiency over both internal and external service encounter with co-workers and customers respectively. A case study with service employees working in the hotel was conducted to verify these claims.
Remote Sensing Image Mosaic Technology Based on Improved SIFT Algorithm

To solve the problem of a large amount of computation and low efficiency of the traditional remote sensing image mosaic algorithm, an Improved SIFT feature matching image mosaic algorithm is proposed. Aiming at the disadvantage of too many dimensions of feature point descriptors in SIFT algorithm, a dimension reduction scheme is proposed. Centered on the feature point, concentric circles are constructed with the radius of 2 pixels, 4 pixels and 6 pixels respectively. The region is divided according to the distance from the feature point. If the distance is close, the weight is high, and if the distance is far, the weight is low. The region is divided into 4, 8, and 16 partitions, so as to generate a 56-dimensional descriptor. Furthermore, the feature points in the image matching are divided into two data sets, namely, maximum and minimum. The best matching point is selected by calculating the distance between the maximum and minimum, which reduces the amount of calculation of image matching and saves the algorithm time. Finally, the weighted smoothing method is used for image mosaic. Experimental tests were carried out on the three situations of remote sensing image scaling, rotation, and gray change. The results show that the proposed algorithm can effectively reduce the calculation amount and accelerate the stitching speed, and the stitching effect is ideal.
A Better MixColumns Matrix to AES Against Differential Fault Analysis Attack

The most widely used block cipher AES, is vulnerable against side channel attacks only. An attacker can mount Differential Fault Analysis attack on AES to find the secret key. In literature, it is shown that AES key can be recovered by forming a system of linear equations with a pair of faulty and fault free ciphertexts with an exhaustive search of $$2^{32}$$ , which is further improved to $$2^{8}$$ . Ghosal et al. strengthens the security of AES to $$2^{84} $$ with an additional diffusion layer. In this work, we propose two different MixColumns like matrices to the AES block cipher that secures the cipher against Differential Fault Analysis attacks. The attack complexity is increased to $$2^{116}$$ with our proposed matrix.
Research and Design of Intelligent Monitoring System for Solar Energy Power Generation Based on GPRS Technology

In order to solve the problems of poor monitoring efficiency and untimely maintenance of traditional solar power generation system, a set of intelligent monitoring and detection system for solar energy power generation based on GPRS technology is designed. The main functional design of the system includes a three-layer architecture of data acquisition and perception layer, master array node network transmission and intelligent monitoring and control center. The main control array node hardware system mainly includes Ethernet communication and GPRS wireless communication hardware circuit. The sensing layer hardware system includes a light radiometer, a backplane and battery temperature sensing circuit, an ambient temperature and humidity sensing circuit, and a peripheral circuit connection design. The software design process includes data acquisition process and data transmission process, and the results of experimental test analysis show that the system can monitor and control the working parameters of the solar energy power generation system in real time, and provide real-time and efficient condition guarantee for the maintenance and repair of the solar energy power generation system.