Reviewerly/exHarmony huggingface dataset:
Design of Virtual Scenes for Revolutionary Education Based on VR Technology

With today’s technological advancements and the ongoing advancement of information technology, VR virtual reality technology has entered more people’s homes. This study focuses on the creation of virtual sceneries using virtual reality technology, which allows individuals to explore revolutionary education virtual scenes online without leaving their homes using computers, smartphones, and other smart mobile devices. Virtual reality technology can provide in-depth experience functions as well as considerably enhance the visual experience and knowledge acquisition efficiency, better representing the role of online tour + learning. The goal of this project is to merge virtual reality technology with actual museum artifacts as well as non-material cultural assets in order to create an online museum browsing service for visitors and the general public. It is a significant step forward in the development of the Internet+ in the field of cultural education. The panorama museum, on the other hand, is well suited to offering extra educational services for students because it is new knowledge-bearing form, as well as its complete enjoyment and interest to children and young students, particularly college students. Giving further education services to students and providing an online tour of the museum. Furthermore, it has the potential to gain excellent and beneficial cultural knowledge via supervision and education.
Smart Door Locking System Using IoT—A Security for Railway Engine Pilots

Many IoT devices are connected together via gateways in smart door locking systems for railway locomotives. The importance of gateways in smart systems cannot be emphasized, yet due to their centralized structure, they are vulnerable to a variety of security concerns, including integrity, certification, and availability. In this article, a blockchain based smart door locking system for railway engine with gateway network to address these security vulnerabilities is discussed. This smart door integrated with the network, guards against the gateway attacks from misuse by terrorists to operate and collapse railway engine without proper authentication. Sensors and actuators are connected and communicated in a dispersed manner in the smart door lock setup for railway engines with limited resources. Because the information is sensitive, one of the issues in this area is data storage and transfer. The fingerprint images are processed using boundary detection algorithms and artificial neural networks (ANN) for feature extraction. With cloud computing technologies, the retrieved feature data may be evaluated and monitored in real time. Identity is one of the thread models for railway engines’ IoT-based smart door locking systems. Finally, this paper highlights block chain technology, which has the ability to solve IoT application security issues by utilizing fingerprint recognition for authentication. This system can also be extended for development as a smartphone app. The network using Ethereum blockchain technology is used and was tested for industry standards and security, in terms of response time and accuracy. The proposed system can offer a safe and secure locking of the smart door in railway engines to prevent accidents. Some possible solutions were also suggested to tackle the security and privacy problems in IoT based on block chain to illustrate how block chain helps to IoT.
Credit Card Defaulters Prediction Using Unsupervised Features

Banks take multiple risks while providing credit cards and loans to their customers. A customer becomes a credit card defaulter when they fail to pay their credit amount within the due time. Risk minimization is possible when banks are able to predict the probability of whether a customer becomes a defaulter or not. This paper proposes an enhanced machine learning model for predicting default customers, using unsupervised features from the Gaussian mixture model and K-means clustering. The method has been tested against various classification algorithms like gradient boosting, AdaBoost, SVM, linear discriminant analysis, and logistic regression. Experimental results showed that the performance of unsupervised features models had been significantly improved by the proposed methodology. Comparing the performance measures indicated that the gradient boosting machine outperformed other models with accuracy and recall of 88% and 89%, respectively.
A Comparative Study of a New Customized Bert for Sentiment Analysis

This paper presents a new customized Bert method based sentiment analysis classification. The blank space removal method, stop word removal, and stemming methods were used in the data pre-processing step. Then tokens are extracted in tokenization step. Standard lion algorithm is used to optimize the weight and bias of the proposed Bert algorithm. Many comparisons were done in order to analyze the performance of the new proposed Bert model. Results obtained showed that the proposed model outperforms other classical models in term of accuracy and performance other metrics.
D2CDIM:DID-Based Decentralized Cross-Domain Identity Management with Privacy-Preservation and Sybil-Resistance

Most of the current decentralized cross-domain identity management frameworks are based on blockchain. In these schemes, the identity providers (IDPs) in different trust domains connect through blockchain technology. However, a single IDP in the domain is still at the risk of being compromised or corrupted. What’s more, users may create redundant identities for different services but can not dominate all their identities completely. Therefore, we propose a cross-domain identity management system based on the concept of the Decentralized Identifier standard. It provides full ownership and management of identity for users. In order to reduce the burden of private key management caused by redundant identities, we present a deduplication scheme to limit the number of one user’s DIDs. It can also resist the Sybil attack by real-life identity credential. Besides that, we improve the authentication efficiency by dynamic accumulator and preserve the identity privacy by the anonymous credential and zero knowledge in the cross-domain authentication scheme.
Research on Optimization of YOLOv3 Pedestrian Detection Algorithm

Although pedestrian detection has been developed for many years, there are still some deficiencies exist. In this paper, a pedestrian detection algorithm based on improved YOLOv3 is proposed by combining PANet, ECA attention mechanism and other structures. The improved algorithm further enhances the ability of feature extraction and enhances the ability of network learning. The detection accuracy of the improved algorithm on INRIA pedestrian dataset is 3.54% higher than that of the original YOLOv3, which can show better performance in pedestrian detection and pedestrian detection. Therefore, the proposed model has strong applicability and robustness.
Wine Tourism Next-Gen: A Case Study of a Virtual Reality Implementation in a Wine Cooperative in France

This chapter discusses the planning and implementation of a virtual reality (VR) project by a small wine cooperative in the south of France. As competition in the wine industry continually increases, wine stakeholders are always looking for new and more efficient ways to attract and retain customers. Although VR-based solutions are increasingly used in retailing for marketing purposes, only very few operators in the wine industry have started using VR to enhance customer experiences and satisfaction. This research is based on an illustrative case with the Labastide Wine Cooperative. Our data collection was formalised through participating observations. In addition to these observations, one-on-one semi structured interviews with key internal players within the organisation were also conducted. The chapter starts by presenting the concept of the VR project idea and the technology solution that was adopted for maximising the technology while making it compatible with the cooperative’s needs. The chapter continues by discussing the obstacles that the wine cooperative had to face during the technology implementation, which include: the implementation of the VR technology, the development of the resources used for the VR, and the use of VR by consumers. Finally, the chapter concludes by providing suggestions on how the wine tourism industry can better use VR in order to attract more clients, create a new network strategy, gain a competitive advantage, and face the current and any future health crises.
Challenges and Future Scopes in Current Applications of Deep Learning in Human Cancer Diagnostics

In cancer diagnostics, deep learning has proven to be astonishingly accurate in evaluating images. Cancer—a complex and multifaceted disease—has thousands of genetic and epigenetic changes. Deep learning is used to detect genetic abnormalities in cancer diagnosis and therapy. The Artificial Intelligence (AI) machine learning acts as a human brain to process data, recognize images, objects and languages, and improve drug development. Artificial neural networks (ANNs) are used to process data for cancer detection. This chapter brings out how AI-based assistance could help oncologists to give an accurate therapy by combining biology and AI. Machine Learning is a subset of AI in which algorithms are built on the basis of neural networks. Deep learning uses an ANN to process data, including medical images, to mimic human neural architecture. In order to decode the molecular start of cancer, clinical oncology research is now more focused on understanding the intricate biological architecture of cancer cell proliferation. Generalize AI, super AI, and tight AI are the three types of AI. With the evolution of AI technology, attempts have been made to construct robots that can perceive biological changes by pulling real-time data and comparing it to data from a population pool for accurate clinical interpretation of cancer test results.
Distributed Learning based on Asynchronized Discriminator GAN for remote sensing image segmentation

Remote sensing images are usually distributed in different departments and contain private information, so they normally cannot be available publicly. However, it is a trend to jointly use remote sensing images from different departments, because it normally enables the model to capture more information and remote sensing image analysis based on deep learning generally requires lots of training data. To address the above problem, in this paper, we apply a distributed asynchronized discriminator GAN framework (DGAN) to jointly learn remote sensing images from different client nodes. The DGAN is composed of multiple distributed discriminators and a central generator, and only the synthetic remote sensing images generated by the DGAN are used to train a semantic segmentation model. Based on DGAN, we establish an experimental platform composed of multiple different hosts, which adopts socket and multi-process technology to realize asynchronous communication between hosts, and visualize the training and testing process. During DGAN training, instead of original remote sensing images or convolutional network model information, only synthetic images, losses and labeled images are exchanged between nodes. Therefore, the DGAN well protects the privacy and security of the original remote sensing images. We verify the performance of the DGAN on three remote sensing image datasets (City-OSM, WHU and Kaggle Ship). In the experiments, we take different distributions of remote sensing images in client nodes into consideration. The experiments show that the DGAN has a great capacity for distributed remote sensing image learning without sharing the original remote sensing images or the convolutional network model. Moreover, compared with a centralized GAN trained on all remote sensing images collected from all client nodes, the DGAN can achieve almost the same performance in semantic segmentation tasks for remote sensing images.
Utility Tokens

Utility tokens are digital currencies that serve as the only accepted means of payment for services and products provided through a blockchain-based platform. They finance the development of their product or service, reward and incentivize early adopters and network promoters, align economic incentives between supply, demand, and the marketplace, and enhance network effects among all participants. Their tokenomic design consists of the rules and regulations governing a token’s issuance, distribution, allocation, and potential destruction. The chapter describes utility tokens, compares them with other types of cryptoassets, and discusses their value creation process and role in network economics. It also reviews common tokenomic designs, discusses different regulatory approaches, and provides examples of current utility token applications in decentralized applications such as decentralized finance and virtual reality platforms (metaverses).
A COMPARATIVE STUDY OF THE EFFECTS OF PEER FEEDBACK IN THE ANALYSIS OF DYNAMIC SYSTEMS ON STUDENTS OF ELECTRICAL TECHNOLOGY

Background: The COVID-19 arrived by surprise, and in a very short time, it has changed social habits. The educational environment has not been immune to these changes, and in a short time, it has had to modify its training strategy. A generalized strategy has been distance training through the Internet. However, in many cases, the classroom was moved to a digital platform without considering its effectiveness. One of the links of the training is the assessment. A scheme of formative evaluation in line with distance education is the co-evaluation by peers. These schemes allow feedbacking the processes from results, at the same time that habits are modified, and the motivation is stimulated. Aims: This study has the purpose of evaluating the effects of the evaluation by peers as an active strategy in the transitory model of distance education of undergraduate students in the Universidad Distrital Francisco José de Caldas. The objective is to determine its utility and effects in forming the students in two academic spaces. Methods: These courses are taught in the last semesters of the Technology in Electricity program. The study addresses a quasi-experimental scheme under which two equivalent populations are taken. The first group comprises 31 students from the first semester of 2020, who operated as a control group, and a second group is made up of 34 students from the second semester of 2020 who formed the peer feedback group. As an instrument, the study used classic written tests. Results and Discussion: The data was collected through surveys and were analyzed both quantitatively and qualitatively. The conclusions of the study establish that the technique of peer evaluation is useful for strengthening some aspects of the training process, particularly for consolidation of concepts and motivation towards autonomous study. Conclusions: The comparative results indicate that the strategy has a positive impact in terms of academic performance, but also shows some level of overload in students, a topic not explored, and that raises the need for further study
Fair classification via domain adaptation: A dual adversarial learning approach

Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stake applications. Recent research on fair classifiers has drawn significant attention to developing effective algorithms to achieve fairness and good classification performance. Despite the great success of these fairness-aware machine learning models, most of the existing models require sensitive attributes to pre-process the data, regularize the model learning or post-process the prediction to have fair predictions. However, sensitive attributes are often incomplete or even unavailable due to privacy, legal or regulation restrictions. Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes. Thus, it is important to exploit auxiliary information from a similar domain to help improve fair classification in the target domain. Therefore, in this paper, we study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can learn to adapt the sensitive attributes from a source domain for fair classification in the target domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model for fair classification, even when no sensitive attributes are available in the target domain.
Collaborative Private Classifiers Construction

Cyber-physical systems (CPS) are smart computer systems that control or monitor machines through computer-based algorithms, which are vulnerable to both cyber and physical threats. Similar to the growing number of applications, CPS also employ classification algorithms as a tool for data analysis and continuous monitoring of the system. While the utility of data is significantly important in building an accurate and efficient classifier, a free access to original (raw) format of data is a crucial challenge due to privacy constraints. Therefore, it is tremendously important to train classifiers in a private setting in which the privacy of individuals is protected, while data remains still practically useful for building the model. In this chapter, we investigate the application of three privacy preserving models, namely anonymization, Differential Privacy (DP), and cryptography, to privatize data and evaluate the performance of two popular classifiers, Naïve Bayes and Support Vector Machine (SVM) over the protected data. Their performances are compared in terms of accuracy, training construction costs on the same data and in the same private environment. Finally, comprehensive findings on constructing the privacy preserved classifiers are outlined. The attack models against the training data and against the private classifier models are also discussed.
Cheating your apps: Black‐box adversarial attacks on deep learning apps

Abstract Deep learning is a powerful technique to boost application performance in various fields, including face recognition, image classification, natural language understanding, and recommendation system. With the rapid increase in the computing power of mobile devices, developers can embed deep learning models into their apps for building more competitive products with more accurate and faster responses. Although there are several works of adversarial attacks against deep learning models in apps, they all need information about the models' internals (i.e., structures and weights) or need to modify the models. In this paper, we propose an effective black‐box approach by training substitute models to spoof the deep learning systems inside the apps. We evaluate our approach on 10 real‐world deep‐learning apps from Google Play to perform black‐box adversarial attacks. Through the study, we find three factors that can affect the performance of attacks. Our approach can reach a relatively high attack success rate of 66.60% on average. Compared with other adversarial attacks on mobile deep learning models, in terms of the average attack success rates, our approach outperforms its counterparts by 27.63%.
Automatic Detection of Cyberbullying: Racism and Sexism on Twitter

With the increasing number of people more people utilising social media platforms, the production of aggressive language online such as attacks, abuse, and denigration increase. However, the constantly changing and different forms of online language provide difficulties in detecting violent language. Not only is this a difficult undertaking, but it is also an area for research and growth, considering the harm caused by cyber violence to children, women, and victims of racial prejudice, as well as the severity of cyberbullying's consequences. This paper identifies some violent terms and proposes a model for detecting racism and sexism on social media (twitter) based on TextCNN and Word2Vec sentiment analysis achieving 96.9% and 98.4% accuracy.
Moving Object Detection Using Satellite Navigation System

In present-day patterns, knowledge of the video shut circuit for the TV might be fundamental and applicable to the subject of exploration. It is viable with the wide scope of uses, for example, video correspondence and security and surveillance, air terminal, traffic signal, observation exercises at traffic intersections to distinguish blockage, and afterward traffic, and anticipating traffic stream. It is the most unfavorable to the PC is the closet space expected to store this information and recover indistinguishable ones on a case-by-case basis. The proposed work centers around achieving a compelling and productive framework with its insight to keep away from human intercession in distinguishing security dangers. Moving article identification is a staggering impact and powerful exploration point. It becomes applicable to distinguish the real state of the moving item from a given arrangement of video outlines.
Application of Digital Educational Technologies and Means of Intercultural Communication in the Multi-Cultural Educational Environment of Universities

This article describes the application of modern digital technologies in the field of education and means of intercultural communication in the modern multi-cultural conditions of the educational environment in universities. The significance and effectiveness of new technologies in the educational process is noted on the example of the National research Tomsk Polytechnic University. The prospects of using new opportunities in the implementation of cross-cultural communication are considered.
A CNN model based on fuzzy US health insurance dataset

In this paper, we propose an analysis method that converts the existing crisp data into fuzzy data and applies a convolutional neural network (CNN). We confirm that the accuracy of our CNN model based on fuzzy US health insurance dataset is higher than that of the CNN based on the crisp data.
Information Security and Data Management for IoT Smart Healthcare

Information security and data management are crucial in healthcare organizations. Patients’ electronic health records (EHRs) are considered very sensitive and private information globally. It should be stored and accessed safely and securely so that only authorized people can reach it. Adequate service is expected when the healthcare management system can deliver correct information correctly and at the right time. The Internet of things (IoT) technology has been widely enabled recently in healthcare systems. Medical devices are equipped with smart embedded systems that control the operations of the human body. These devices are also connected over the internet to provide medical surveillance and remotely control critical cases. Then, more flexible and comfortable services are provided to the patients during their daily active life. However, these services are vulnerable to intrusion and external attacks. In this work, we aim to investigate the role of information security and data management in IoT-enabled smart healthcare systems. We explore how sensitive health information is protected against cybercrime attacks and the associated risks in healthcare systems. At the same time, we discuss how sensitive information is shared and transmitted among different entities in healthcare firms and units without losing the patient’s privacy.