Reviewerly/exHarmony huggingface dataset:
A Survey on the Metaverse Aspects and Opportunities in Education

Technological evolution has brought forth the Metaverse, a virtual environment that challenges the boundary between physical and digital reality. This work explores its potential impact on education, delving into scientific publications that elucidate its applications in teaching and learning. The Metaverse presents a third space and emerges as a pertinent medium for communication, collaboration, and exploration, with the potential to reshape education. This study aims to comprehend how the Metaverse can enhance the educational experience of teaching and learning while identifying associated challenges and potentialities. Through an analysis of specialized literature, it probes the ways in which this technology can effectively foster immersive approaches in education. With a focus on perspectives offered by scientific publications, the study strives to examine the potential applications of the Metaverse in the educational context, seeking not only to elevate the educational experience but also to identify the inherent opportunities and challenges inherent in this approach.
Trust But Verify - Blockchain-Empowered Decentralized Authentication Schema on the Metaverse: A Self-Sovereign Identity Approach

With the advent of virtual reality (VR) and online gaming, the decentralized Metaverse has gained significant traction. However, challenges pertaining to identity management and trust have emerged as critical concerns within the decentralized Metaverse. To address these issues, we present an extended proposal building upon previous research, incorporating the principles of Self-Sovereign Identity (SSI) and leveraging Web 3.0 technology. In this paper, we introduce a comprehensive architecture consisting of (1) a mobile wallet provider decentralized application (dApp), (2) a Virtual Environment (VE), and (3) a backend system implemented on Ethereum Smart Contract. We outline the requirements for a decentralized Metaverse and demonstrate how our proposed architecture fulfills these requirements. Moreover, we provide insights into the implementation details of our solution. Lastly, we present a use case scenario to highlight the practical implications of our approach. By integrating SSI principles and Web 3.0 technology, our framework contributes to enhancing the security and trustworthiness of the decentralized Metaverse, fostering a more immersive and secure online environment.
On the Feasibility of Zero-Trust Architecture in Assuring Security in Metaverse

This article explores the implementation of Zero-Trust Architecture (ZTA) on Metaverse platforms using Blockchain technology. It assesses the effectiveness of this approach in enhancing security, dependability, and reliability of Metaverse environments. To determine if a Blockchain-based ZTA can offer a more secure and trustworthy Metaverse environment for users and service providers, it analyzes security and trust concerns in conventional Metaverse platforms and underscores potential benefits of ZTA implementation. The article proposes a ZTA design that integrates blockchain's unique features to ensure secure and transparent user-application interactions on Metaverse platforms. The findings aim to offer valuable insights into the feasibility and effectiveness of applying Blockchain-based ZTA on Metaverse platforms. This study contributes to the growing understanding of Blockchain technology and its applications in virtual environments
VR2CARE: An Age-Friendly Ecosystem for Physical Activity, Rehabilitation, and Social Interaction

The VR2Care project aims to create age-friendly virtual environments fostering interactive technologies for promoting physical activity, rehabilitation, and social interaction. Smart living environments have intelligent interfaces in a single or multi-user context. The VR2Care ecosystem combines virtual reality technologies for supervised exercise with natural interaction techniques, enhancing social virtualization with gamification aspects in the practice of physical activity in a metaverse context. This paper presents the infrastructure and the high-level functional architecture of the VR2Care ecosystem which provides the necessary tools to construct the communication bridges to bring together people sharing common interests, objectives, or ambitions.
Do We Trust Embodied Agents who Look Like us?

With the rise of automation and the metaverse, there's a notable increase in the deployment of embodied agents (EAs). These embodied agents uniquely interact with human counterparts as team members rather than just acting as tools. A limited number of research studies have attempted to examine how such agents in a computer-mediated environment within virtual reality (VR) environment. Previous research has determined attributing anthropomorphism is one of the most common designs associated with human-agent collaboration that significantly impacts the level of trust and acceptance of embodied agents. While acceptance of agent deployment is strongly determined by the level of trust humans place in the agents in a computer-mediated environment, it is critical to also understand how these human users remit trust toward embodied agents. Humans tend to trust others that portray similar physical elements to themselves. Our study therefore examined whether a human would exhibit higher trusting behavior in an embodied agent with a virtual depiction designed to have similar features. To validate this hypothesis, participants engaged in a coin entrustment game with agents varying in resemblance to human facial features. Our results demonstrate that there is a strong relationship between similarity and trust in the VR context, which suggests that users are more likely to trust EAs that have similar facial features as themselves visually. In addition, these findings offer important implications for future embodied agent design. Lastly, we identify some research gaps and propose future research directions beyond the current state of the field.
Utilizing Data Strategy Framework for Retail Business in the Metaverse

The emergence of the metaverse has brought about a significant shift in the retail industry as businesses strive to couple from the physical and digital worlds. However, this transition presents challenges in managing the massive and rapidly streaming data originating from the metaverse. In this paper, we explore the business opportunities that the metaverse offers for retail businesses and shed light on the ways to utilize data strategy in metaverse retailing. Our research analyzes the potential metaverse scenarios and the characteristics of metaverse data. We identify these scenarios into three key categories: Decoupled, Semi-coupled, and Tightly coupled. In particular, we highlight the importance of robust data strategies for organizations to effectively handle metaverse data and unlock success in this new realm. As a result, we propose a comprehensive data strategy framework that empowers retail businesses to effectively manage metaverse data, leverage valuable data insights, and make informed decisions at the rapid pace of the metaverse, enabling them to stay competitive in this evolving digital realm.
Optimized Kalman Filter using Genetic Algorithm for IoT Sensor

Accurate sensor readings have an important role in ensuring the reliability of data for IoT devices. However, sensor measurements are susceptible to noise, leading to unreliable data. This research paper addresses the issue of sensor reading errors and proposes a solution using Kalman Filter to mitigate the impact of noise. The study focuses on implementing the widely known Kalman Filter, a filtering method, that can be used to reduce the influence of noise on sensor readings. Moreover, for optimizing the parameters of Kalman Filter, Genetic Algorithm is employed. The optimized parameters, denoted as Q = 9.9 and R = 0.1, represent the covariance matrices for process and measurement noise, respectively. In order to demonstrate practicality, this research utilizes the ESP32 and Analog-to-Digital Converter for collecting sensor data. Experimental results validate the effectiveness of the proposed approach. The optimized Kalman Filter can reduce the MSE caused by noise to 74.102. It indicates a noteworthy enhancement in sensor reading accuracy. This achievement holds potential for IoT applications where reliable and precise sensor data is of utmost importance. It can be concluded that by combining the Kalman Filter, Genetic Algorithm, this study contributes to the development of robust noise reduction mechanisms in IoT systems. The findings of this research offer promising prospects for enhancing the overall quality of sensor data in many IoT applications.
Cybersecurity Threats and Vulnerabilities in the Metaverse

The metaverse, an immersive virtual reality environment, has gained significant attention recently. As this digital realm expands, so do the associated cybersecurity threats and vulnerabilities. This paper explores the challenges posed by cyber threats within the metaverse, focusing on three key areas: data privacy and personal information, virtual asset security, and social engineering attacks. We highlight the importance of robust cybersecurity measures and proactive strategies to safeguard users' integrity, privacy, and virtual assets in the metaverse by examining real case studies and examples.
Augmenting Education

This study evaluates the cognitive effects of an interactive Augmented Reality (AR) learning application, displayed on a Head Mounted Display (HMD). The experiment includes 11 participants and employs both subjective and objective assessment methods. To gather subjective feedback, a User Experience Questionnaire (UXQ) is utilized as a self-reporting assessment tool. Additionally, objective assessment is conducted through the analysis of electroencephalogram (EEG) signal recordings. The results indicate that the participants experienced high levels of immersion, attention, positive emotion, and demonstrated a strong adoption of the AR technology.
Visual Guidance in Interactive Virtual Reality*

Interactive virtual reality (IVR) allows users to move freely in the virtual environment and use the controller to interact with virtual objects to trigger plots or events. In some situations, such as interactive VR in an exhibition, the time for users to experience the VR work is limited. Therefore, some guidance is needed to help users experience the VR work smoothly. In this paper, we present a design space that describes the visual guidance with the following two dimensions: “where the guidance is rendered” and “how the guidance behaves.” Then we conducted a user study to compare the effectiveness of different guidance designs in IVR. The results show that the design of “User-Target-Linked with Adaptive” performs best in most situations, while other designs may perform poorly in certain situations. We present our opinions and provide perspectives on interactive virtual reality design.
Exploring Immersive Learning in the Metaverse: A Prototype for Interactive Virtual Classroom

Is a metaverse classroom as exciting as it sounds? This paper presents the design and implementation of a prototype metaverse classroom system that utilizes virtual reality (VR) and augmented reality (AR) technologies to enhance the educational experience. The system aims to provide students with personalized, immersive, and engaging learning experiences in a virtual environment. The development process involved researching virtual learning environments, using Unity as the primary platform, and incorporating the Mona engine for seamless interaction. Technical performance testing was conducted using Mona, with future plans for user evaluations. The paper discusses the benefits of the metaverse classroom system, such as enhanced student engagement and collaboration, as well as its limitations and potential improvements. It also explores future research directions in metaverse-based education, highlighting the importance of continued development and research to fully realize the metaverse's potential in revolutionizing education.
Explainable AI for the Metaverse: A Short Survey

Virtual reality, augmented reality, and immersive technologies have advanced rapidly, giving rise to the concept of the metaverse. As users delve into these virtual environments, it becomes crucial to understand the decision-making processes of intelligent systems within the metaverse. Explainable AI (XAI) provides a framework for interpreting and understanding the outcomes of artificial intelligence, making it an essential component for ensuring transparency, trust, and user engagement within the metaverse. This paper aims to explore the fusion of XAI in the context of the metaverse, including key enabling technologies, the impact of XAI on metaverse applications, integration challenges, and future directions.
Transforming Affordable Virtual Reality Headsets into Effective Learning Environments

Virtual Reality (VR) has emerged as a powerful tool for enhancing educational experiences by providing simulated environments that overcome physical limitations. This paper explores the transformation of affordable Virtual Reality technology, specifically the Meta Quest 2 headset, into an inclusive standalone learning environment. The official Meta store currently lacks popular 2D apps and Google Play services, while existing 3D apps are mostly game-focused or task-specific. Switching between 3D apps and keyboard navigation is complex. Leveraging and integrating tools like SideQuest, Termux, VS Code Server, and PRoot-based Linux distributions, we overcome these limitations, crafting a dedicated learning-friendly VR environment on the standalone Meta Quest 2 headset. We adopt a user personabased scenario approach to identify potential use cases and VR requirements in the educational context. We then devise an experimental setup through thorough experimentation with the technologies mentioned earlier. We also conduct testing on our experimental setup and collect feedback from participants to examine the efficiency and usability of our standalone VR learning environment. The findings of this study contribute to the evolution of VR-based educational systems, enhancing learning experiences and broadening the scope of students' educational journeys. Although our research is based on qualitative results from a small group of participants, we believe that our study design, methodologies, tools, and deployment are significant for future related studies. Therefore we invite educators and researchers to replicate or modify our experiment in their classroom settings and engage in a scientific dialogue with us, further driving the development and refinement of affordable and inclusive VR-based education.
Exploration of Educational Possibilities of Extended Reality(XR) in Higher Education Society

Extended Reality (XR) encompasses a spectrum of technologies that go beyond the conventional realms of Virtual Reality (VR) and Augmented Reality (AR). Recently gaining momentum, XR has begun influencing various sectors, notably education. It allows students to immerse themselves in virtual environments, engage with interactive simulations, and cooperate with peers in real time. Such hands-on experiences can enrich learning, potentially enhancing memory retention and understanding of the subject matter. The following study investigates the potential effectiveness of Extended Reality (XR) in Higher Education Society.
Terahertz spintronics: New insights into magnetic phenomena and their application in terahertz photonics

By applying terahertz time-domain techniques to spintronic nanostructures, new insights into the ultrafast dynamics of electron spins can be gained. Relevant applications such as the spintronic generation and detection of terahertz electromagnetic fields emerge.
VMD-based methods for denoising terahertz signals obtained from biological tissue

We examine methods for denoising THz signals. The main method is based on the variational mode decomposition (VMD) followed by one of several thresholding techniques. We present some preliminary results for the denoising of a signal obtained from a biological tissue under low-noise condition and to which various amounts of synthetic noise are added. The study shows the potential of VMD-based methods in characterizing biological samples and, in particular, human breast cancer tissue.
Normalizing Flow-Based Industrial Complex Background Anomaly Detection

This paper proposes a novel approach called cross-scale with attention normalizing flow (CSA-Flow) enhanced with channel-attention (CA) and self-attention (SA) modules for high-speed railway anomaly detection in complex industrial backgrounds to reduce the manual workload of the primary maintenance of high-speed electric multiple units. Detecting defects in industrial environments, characterized by intricate backgrounds and unclear subjects, poses significant challenges. To address this, CSA-Flow introduces a channel feature extraction module that combining the pretrained convolutional neural network models with a CA module for feature extraction, capturing information at different scales, and uses the SA module to capture more contextual information by its larger receptive field. The performance evaluation of CSA-Flow on the MVTec-AD dataset demonstrates an impressive area under the receiver operating characteristic curve (AUROC) score of 98.7%, with an equally remarkable score of 98.4% across all object classes. To further assess the effectiveness of CSA-Flow in complex background scenarios, we introduce a dedicated dataset, specifically designed for high-speed rail braking devices (HSRBDs). The experimental results establish the superiority of CSA-Flow over current state-of-the-art approaches in terms of both AUROC score and recall score, validating its exceptional capability for detecting anomalies in industrial complex backgrounds.
Development of data labeling techniques for terahertz image-based AI cancer diagnosis

To develop an AI-based cancer diagnosis technology using terahertz medical imaging, it is imperative to obtain accurately labeled pathological tissue-stained images that serve as the ground truth. However, acquiring these reference images presents challenges such as the cooperation of pathology specialists, high-capacity Whole Slide Images (WSI), and accuracy issues. This study addresses these challenges by creating a brain tumor animal model to obtain tissue-stained images of brain tumor samples, and developing an algorithm using a U-Net neural network for automatic cancerous area segmentation. Through data preprocessing and AI training, high-accuracy labeled data can be provided for terahertz-medical imaging-based AI cancer diagnosis.
Shared Experiential History for Encryption Based Peer-To-Peer Authentication

This paper aims to address the challenge of wireless authentication without security server support by proposing a novel dynamic encryption scheme based on the shared experiential history at different layers between communicating peers for enhanced peer-to-peer authentication. By utilizing an initial physical layer observation and less noisy cross-layer attributes over time as shared historical secrets and source of dynamicity, an ever growing historical log is established, enabling dynamic encryption key generation for passive authentication. The proposed peer-to-peer authentication scheme is further enhanced by a channel-aware k-means quantization and mapping method which reduces the effect of observation noise from the physical layer attributes while increasing the efficiency and entropy by finding the optimal k-value. Both mathematical analysis and numerical simulation results are provided to evaluate the scheme’s performance.
Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation

We treat the problem of client selection in a Federated Learning (FL) setup, where the learning objective and the local incentives of the participants are used to formulate a goal-oriented communication problem. Specifically, we incorporate the risk-averse nature of participants and obtain a communication-efficient on-device performance, while relying on feedback from the Parameter Server (PS). A client has to decide its transmission plan on when not to participate in FL. This is based on its intrinsic incentive, which is the value of the trained global model upon participation by this client. Poor updates not only plunge the performance of the global model with added communication cost but also propagate the loss in performance on other participating devices. We cast the relevance of local updates as semantic information for developing local transmission strategies, i.e., making a decision on when to "not transmit". The devices use feedback about the state of the PS and evaluate their contributions in training the learning model in each aggregation period, which eventually lowers the number of occupied connections. Simulation results validate the efficacy of our proposed approach, with up to 1.4× gain in communication links utilization as compared with the baselines.
Analyzing Federated Learning Aggregation and Distributed Personalization Algorithms Towards Understanding Users’ Residential Electric Load Patterns

Privacy concerns arise from sharing electric load data. For instance, this data can be hijacked and deductions can be made about building occupancy. Anonymized data does not fully solve the issue, as there have been several successful attempts of re-identifying individuals from anonymized data. Federated Learning (FL) involves training a global model among several clients at the edge without sharing data, but instead, sharing model weights. Besides training a global model to reduce the universal error, it is important to focus on reducing the local error for each client. Personalization helps improve local model convergence without affecting the global model learning process. Therefore, individuals can have a more accurate understanding of their energy usage behavior. It also reduces the number of FL rounds needed for training, which reduces load on the smart grid edge computing resources, communication network, and therefore reduces costs. Previously, research has only looked at stochastic gradient descent (SGD) and Adam for fine-tuning clients’ local models at the edge in the process of FL. This research simulates different FL environments to explore electric load forecasting using different FL aggregation algorithms at the server, as well as seven local optimizers for FL personalization (SGD, Adam, Adagrad, Adadelta, Adamax, Nadam, and RM-SProp). An analysis of the different local optimizer algorithms is also presented.
A transfer learning approach based on integrated feature extractor for anti-jamming in wireless networks

One of the security issues in a wireless network is jamming attacks, where the jammer causes congestion and significant decrement in the network throughput by obstructing channels and disrupting user signals. Deep reinforcement learning (DRL) models have been adopted to confront the jammer. However, training a DRL model from scratch may take a long time. In this paper, we propose a transfer learning (TL) approach to enable the DRL agent to learn fast in dynamic wireless networks to confront jamming attacks effectively. To make our proposed TL method adaptive to different network environments, we propose a novel method to quantitatively measure the difference between the source and target domains, using an integrated feature extractor. Based on the measured difference, we choose an optimal setting for the TL model. Experiment results show that the proposed TL method can effectively reduce the training time for the DRL model and outperforms other existing TL methods.