Reviewerly/exHarmony huggingface dataset:
Intelligent Recommendation System for Early Childhood Learning Platform Based on Big Data and Machine Learning Algorithm

Due to the low cognitive ability of children, the intelligent recommendation system should assist children in learning with the assistance of parents. Therefore, the intelligent recommendation system needs to evaluate the ability of users (children learners and their parents), and assign learning content with suitable difficulty Match with the user’s ability, assist the user to learn efficiently, and gradually cultivate the user’s learning interest and habits, and can be a useful supplement to the user’s regular learning. This paper uses JAVA EE language to develop an intelligent recommendation system for children’s learning platform based on Hadoop big data framework and machine learning algorithms. Learning platform with intelligent recommendation function. By analyzing the cognitive ability of children, and using the collaborative recommendation algorithm to recommend learning resources suitable for this age stage for children, in order to improve learning efficiency and cultivate children’s learning interest from an early age, at the same time, the resource library in the learning platform has more Practical value.
C Language Programming System Based on Data Analysis Algorithm

C language is the most popular computer language at home and abroad. Because it has the advantages of high-level languages and many features of low-level languages, it is especially suitable for writing system software, so it is very popular and widely used. The purpose of this paper is to study C programming system based on data analysis algorithm. The characteristics of C language, the concept of XML technology and the specific functions to be researched are analyzed and designed in detail, the functions of each part are outlined and designed, and the key algorithms are initially analyzed and set, propose and implement the time complexity calculation method of C functions. The method proposed in this paper can be used to calculate the complexity of C functions. The results of the comparison method are consistent with the comparison information obtained after the actual operation, and statistical charts, pie charts and other forms are introduced to display.
Application of Computer Vision Technology Based on Neural Network in Path Planning

Path planning (PP) is a hot topic in the field of robotics, and neural networks (NNs) and computer vision technologies are widely used in the PP of robots. In this paper, the application of robot PP is classified, compared and analyzed, the autonomous dynamic obstacle avoidance of the robot in the dynamic obstacle environment is realized, and the particle swarm algorithm is used to find the global optimal path. In this paper, a fuzzy optimization algorithm for PP of visual robots is proposed. The algorithm is based on predictive control rolling optimization, and expresses the system optimization goals and constraints by membership, so as to realize the multi-objective optimization problem. Simulation experiments show that the proposed algorithm is effective. The experimental results show that compared with the traditional PP of the robot based on NN, the NN can find the shortest path for the robot in the dynamic avoidance of obstacles, which greatly optimizes the avoidance time. Therefore, it is very valuable to use NNs and computer vision technology to study PP.
Leveraging Digitalization for Improving Energy Efficiency

Energy efficiency holds a lot of promise for boosting economic growth while also reducing greenhouse gas emissions. Digital technologies help to improve energy efficiency by providing efficiency, analytics, and enhanced control. Some of the most likely effects of digitalization include innovation, new business strategies and models, and energy-efficient products and services. Digital transformation can help build smart energy-efficient homes, smart buildings, smart energy systems, smart cities, and low-carbon systems that can lead to significant energy savings. This chapter explores the potential for leveraging digitalization to maximize energy efficiency and the impact of digitalization in achieving energy optimization as well as sustainable development goals. The study aims to identify key areas of focus, such as energy efficiency strategies, technological advancements, and the potential for energy savings and reducing carbon emissions as a result of these developments. Examples of real-life situations are included in the study in order to highlight the key policy initiatives and research efforts. The research provides qualitative perspectives based on a comprehensive literature review and an understanding of insights into key aspects of the current situation, the progress of digital transformation, challenges, future direction, and suggestions for achieving sustainable energy efficiency.
Single and Multi-objective Genetic Programming Methods for Prediction Intervals

A PI is the range of values in which the real target value of a supervised learning task is expected to fall into, and it should combine two contrasting properties: to be as narrow as possible, and to include as many data observations as possible. This article presents an study on modelling Prediction Intervals (PI) with two Genetic Programming (GP) methods. The first proposed GP method is called CWC-GP, and it evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure. This measure is the Coverage Width-based Criterion (CWC), which combines the width and the probability coverage of the PI. The second proposed GP method is called LUBE-GP, and it evolves independently the lower and upper boundaries of the PI. This method applies a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied with the Direct and the Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the results pave the way to further investigations.
Artificial Intelligence and Extended Reality in Luxury Fashion Retail: Analysis and Reflection

Retailing service is facing a rapid evolution through the incorporation of Extended Reality (XR) technologies and Artificial Intelligence (AI) algorithms. Within this umbrella, one can consider all real-and-virtual combined environments and interactions generated by computer, including Augmented Reality (AR), Mixed Reality (MR) and Virtual Reality (VR). This chapter brings together research on the experience of luxury fashion consumer retail and AI and XR technologies to identify critical gaps and open avenues for future research. It aims to analyse the incorporation and the potential evolution of AI and XR technologies in luxury fashion retail. The chapter presents the main theoretical concepts and the luxury fashion consumption cycle and offers a discuss about the human and non-human rights, privacy, and ethics at three levels: mega, micro, and nano. Also, the chapter contributes with a discussion about the rights, privacy and ethics involving humans, hybrid humans, and non-humans.
Smart Extended Reality in the Metaverse-Tailing: The Rise of New Retail Landscape

This chapter aims to investigate the rise of Metaverse by considering the merge of interactive technologies (such as augmented and virtual reality and artificial intelligence) into a new retail landscape. The research flow of this chapter is guided by three theories able to identify and clarify how a smart Extended Reality in the Metaverse-Tailing can be considered as the rise of new retail environment. First, the affordance theory of technology will help to understand the possibility of obtaining value from specific technology, deriving from the Metaverse world. Secondly, the lens of regulatory engagement theory is helpful to understand the positive engagement of people during their experience in a specific object or environment thanks to the exploitation of augmented and virtual reality in a Metaverse world. In line with the regulatory engagement theory, this chapter aims to underline the possible way of involvement in the Metaverse during a shopping journey and exploiting potentialities deriving from Augmented Reality, Virtual Reality and Artificial Intelligence, such as interaction, immersion, inspiration and satisfaction. Finally, the self-determination theory identifies the possible intrinsic or extrinsic motivation that leads people to experience retailing in the metaverse-tailing as a fusion of several advanced technologies.
Exergames, Artificial Intelligence and Augmented Reality: Connections to Body and Sensorial Experiences

This chapter introduces Augmented Reality (AR) as one of the most prominent technologies promoting immersive and sensorial experiences. The research aims to describe how Artificial Intelligence (AI) has been used through game engines in complex feedback systems (Deep Learning), as well as customizing efficient models to engage and monitor human interaction in an immersive way in activities such as health and well-being. It was possible to observe that different fields such as military training, education, cultural exhibition, and many others, are applying activities and developing many interactions mediated through AR. In addition, with the rise of wearables, it is clear there is a growing need to expand mechanisms that make the experience more realistic and interactive. One of the possible proposals would be the expansion of immersive environments with AR projection. Another growing area is entertainment, bringing new connections with Virtual Reality (VR) in the newest generation of exergames. The intention is to connect body movements with games through the sensorial immersion of AR, empowering new possibilities for the evolution of video games. This chapter presents a review of literature with the initial experiences involving AR and exergames through Deep Learning and to discuss the possible future of this emerging connection of sensoriality and body movements, supported by the AR tools and devices.
Mixed Reality and Deep Learning: Augmenting Visual Information Using Generative Adversarial Networks

This chapter summarizes the theory of Generative Adversarial Networks (GANs), overviews the evolution of GAN methods that have either been used in published Augmented Reality (AR) research or have a strong potential for future AR work, and presents interesting use cases of GANs in AR. GAN is one of the most innovative deep learning techniques. Since it was first introduced in 2014, many variations have been proposed. Recent implementations can create photo-realistic high-fidelity visual content that is virtually indiscernible from real-world images. In AR, photo-realism is crucial for the user to perceive the composed reality as belonging to the same world. The composition of virtual objects into the real-world background must be done in a plausible way, with semantically-aware object placement, consistent illumination and convincingly cast shadows. Various GAN architectures were proposed that address one or another of these challenges. The discussed methods are categorized into three distinct groups that correspond to unrelated bodies of research: GANs for image composition, AR face filters, and 3D model generators.
Augmented Galatea for Physical Pygmalion: A Phenomenological Approach to Intimacy in VTubers in the East Asia Region

This chapter aims to analyze the effects on the intimacy of introducing VTubers in the East Asia region from a phenomenological perspective. Digital technologies are pervasive, and they are becoming intimate in many different ways. There are dating apps dominated by algorithms and artificial intelligence generated to be digital companions of the users. Much attention has been given to the creation of virtual characters who can be controlled by artificial intelligence for their potential use in Augmented Reality (AR) and Metaverse. China is one of the predominant countries implementing digital technologies within society in order to shape a novel way of living in the world. Chinese virtual characters are used by millions of people in their daily activities. For example, VTubers are virtual avatars created by an entertainment company to be used for social media. The virtual characters generated in Augmented Reality and usable in Metaverse can have deep repercussions on who we are and how we live our intimacy by becoming intimate with us. This chapter focuses on the implication and the use of such virtual characters for developing intimacy with the users through phenomenology and post-phenomenology. The chapter is divided into three parts. The first part considers the introduction of VTubers into the Chinese cultural context and the impact these characters have on Chinese daily life (by relating the societal and Augmented Reality applications). The second part focuses on phenomenology to highlight the links binding people together and to show how people are shaped through this intimate connection and exposure to others. The third part intertwines VTubers in China with the findings highlighted in phenomenology to show how virtual characters can generate different subjectivities through their actions in daily contexts.
The Calibration of Inspection Data on Juvenile Theft Cases in 5G Context and IOT Age

In the context of 5g and IOT age, big data has many functions, and the core function of legal big data is to make predictions. However, there is a problem of data transmission errors, and all erroneous data signals cannot be ruled out. This article mainly takes juvenile theft cases as the starting point, and discusses the role of big data in the statistics of legal cases and the possibility of data transmission errors that may occur, and in the context of big data, for juvenile theft. Recidivism rates were studied and practical recommendations were made. Unlike other articles, this article integrates the Internet of Things into judicial issues, aiming to better serve the law through technology.
A Cross-Domain Authentication Scheme Based Master-Slave Chain in Edge Computing

As an emerging technology, edge computing can solve the problem of limited computing resources of IoT devices under the premise of lower latency. However, the existing mobile edge computing architecture cannot well solve the security problem of the identity authentication of the terminal device. In particular, the cross-domain authentication of the device cannot be completed efficiently when the device is switched between different IoT domains. To address these challenging issues, in this article, a lightweight edge computing cross-domain identity authentication scheme which combines edge computing with blockchain based on master-slave chain is proposed. The scheme uses the consortium blockchain as the master chain, that is, a decentralized authentication platform, realizes cross-domain authentication when the device switches domains, and can also solve the single point of failure problem of traditional authentication. The slave chain is maintained by edge computing nodes and terminal devices in each domain. When devices in the domain are mutually authenticated, the efficiency of authentication can be improved. During authentication, ring signature technology is used to ensure the security of the system, and at the same time, it can effectively save the storage space of the blockchain. Finally, the performance evaluation and security analysis of this scheme have been carried out to prove the safety and effectiveness of our scheme.
Using HTC Vive to Design a Virtual Reality Simulation Environment on Radiography

This study aims to design and develop a virtual reality situational simulation system for the radiation oncology unit in a hospital. This study applies HTC Vive, Autodesk 3ds Max and Unity 3D to develop a virtual reality simulation model, followed by programming in the C# language and Visual Studio compiler to create a virtual scene for the stereotactic radiography imaging verification process. The result of this study provides a prototype system.
Give Them a Second Chance? Prediction of Recurrent Financial Intermediary Misconduct

Financial intermediary misconduct represents a major threat for financial markets. Of particular concern is recurrent misconduct, where intermediaries harm investors for their own benefit. This not only impairs affected investors but also decreases trust and participation of investors in financial markets resulting in reduced possibilities for retirement savings and inefficiencies regarding the allocation of funds to the real economy. To solve this societal challenge, recurrent misconduct needs to be prevented. Based on a comprehensive data set, we develop predictive models to identify brokers that repeatedly commit misconduct. In line with existing theories, we show that the disciplinary history of brokers together with the linguistic style and the content of brokers’ comments to allegations provide valuable features for predictive models. Our results contribute to the literature on financial misconduct and automated fraud detection. They are valuable for investors and regulators alike assisting them to identify and prevent recurrent financial intermediary misconduct.
Validation Check of Optimization Method using Monte Carlo Method for Multi-objective Function

This paper is concerned with the multi-objective optimization of MOST, a new optimization method proposed by the author. Optimization techniques have become essential tools for efficient and rational engineering design and development. To meet this need, many optimization methods have been proposed and tested for practical use. In engineering design, there are often multiple objective functions, each with a trade-off. Therefore, multi-objective optimization techniques are essential. The optimization method MOST proposed by the author is highly convergent and has been applied to the optimization of neural networks and verified in previous papers. In this paper, we apply MOST to each of multi-objective functions and find the optimal value for each. Furthermore, MOST is applied to minimize the sum of the error of each objective function and its optimal value. This approach yields a Pareto solution. The improved MOST was applied to the benchmark problem of Zitzler et al., and compared with existing methods. As a result, MOST was able to reproduce the theoretical Pareto solution.
A Brief Review on Right to Recall Voting System Based on Performance Using Machine Learning and Blockchain Technology

Voting is an important part of a democracy’s political life cycle. A democracy is defined by citizens voting freely, fairly, and after due consideration. Citizens of the country vote based on candidates’ pledges or incumbents’ performance, as well as a perception formed from information provided by political parties, media houses, and social media and election manifesto. The electorate, who returned a midway result, had placed their trust in the candidate in vain. In this case, the electorate has no choice but to wait until the next election to see if the incumbent can be ousted from office. To address this inadequacy, remedies have been provided in some of the democracies in the form of the right to recall and No Confidence Motion (NCM), by a re-election or replacement of one elected representative with other for the same position in the government house. To address these issues, we propose a stable right to recall e-voting system in place of our traditional signature campaign based on blockchain and machine learning concepts. The measure intends to hold elected governments more accountable for pledges made to the country’s voters. The power to recall gives the electorates of an elected representative the ability to withdraw or replace their mandate before the elected person’s customary term ends. Here mean while you can say that the voters have a power to de-elect their representatives from the government through a direct vote. For the purpose of protecting the integrity and security of votes, we deploy blockchain technology, as well as a model of machine learning, to detect infiltration into voting data centers and electronic polling locations. Using the principles of personal blockchain and public blockchain, we offer a new paradigm for blockchain technology. With the proposed blockchain-based electronic voting system, users will have greater transparency, better treasury management, and greater confidence. The system will also prohibit unauthorized access to the data exchange network.
Machine Vision Communication System Based on Computer Intelligent Algorithm

Machine vision communication is an important application direction of artificial intelligence, and its research value in this wave of scientific research is self-evident. Technologies such as image detection and image segmentation have gradually matured, and image restoration technology has also made some progress. The purpose of this paper is to study the design of machine vision communication system based on computer intelligence algorithm. Combined with the current design and development trend of robot vision system software, analyze the functional requirements and performance requirements of the vision system software, so as to choose a suitable development platform. Design the overall software framework and formulate a software development scheme based on the performance of the development platform and processor. Finally, build a development environment on the development platform. The full variational model image inpainting algorithm is used to repair the pixels of the image. The running results show that the inpainting result of the algorithm is ideal, and the running time of 1000 iterations is 5.6 ms reasonable and feasible.
Sentiment Analysis and Its Applications in Recommender Systems

E-commerce and social network make sentiment analysis be more important to businessmen. This is a process to detect opinion in text which is positive or negative. This research focuses on doing sentiment analysis and applies it to recommender system. Doing sentiment analysis, we apply both feature extraction with TF-IDF and word embedding to train the model by deep learning and machine learning. Based on sentiment analysis results, we predict the ratings number of the comments by Collaborative Filtering techniques to make recommendations to users.
A Systematic Review on Sentiment Analysis for the Depression Detection During COVID-19 Pandemic

Various social media platforms like Facebook, Twitter, etc., are the powerful tools to express sentiments and emotions across the globe. Sentiment analysis and its evaluation are used to reveal the positive or negative opinions associated with an individual. In this paper, we have tried to study the sentiment analysis based on their count, year wise, country wise, university wise, and keyword wise progression to understand the depth of the study in the field of sentiment analysis. Results show that sentiment analysis is not a new field, and authors are contributing to this field since 2008. Collaboration among different countries and universities has also seen during our study. In 2019, maximum contributions are received. Further, this study shows that 200 keywords with 149 unique and 18 as repeated are used by different author. Authors from 65 universities with 40 universities listed in Times Higher Education ranking 2020 are observed with highest number of authors from India.