Reviewerly/exHarmony huggingface dataset:
Technological Roadmap of the Future Trend of Metaverse based on IoT, Blockchain, and AI Techniques in Metaverse Education

Metaverse is defined as a collection of technology gadgets and metaverse connected to IoT, Blockchain, Artificial Intelligence, and all the other tech industries including educational. In particular, the big success of the metaverse in the recent era is in the educational sector, metaverse resolved many difficulties in the education domain. Especially, in the situation of covid 19 people continued there all the educational things virtually using metaverse technologies. At this moment it was only a sector that was not hampered by the reason of covid 19, everyone did their educational work online instead of offline. Metaverse uses artificial intelligence and IoT technology to build a digital virtual world where you can safely and freely engage in social and educational activities that transcend the limits of the real world, and the application of these latest technologies will be expedited. In this paper, we are going to introduce the future of the metaverse, including its history, explanation, and shared features. Then, we will describe what technologies the metaverse is using and metaverse potentiality in education, the metaverse in education is clearly defined, and a detailed framework of the metaverse in education is proposed, along with in-depth discussions of its features.
Can language representation models think in bets?

In recent years, transformer-based language representation models (LRMs) have achieved state-of-the-art results on difficult natural language understanding problems, such as question answering and text summarization. As these models are integrated into real-world applications, evaluating their ability to make rational decisions is an important research agenda, with practical ramifications. This article investigates LRMs' rational decision-making ability through a carefully designed set of decision-making benchmarks and experiments. Inspired by classic work in cognitive science, we model the decision-making problem as a bet. We then investigate an LRM's ability to choose outcomes that have optimal, or at minimum, positive expected gain. Through a robust body of experiments on four established LRMs, we show that a model is able to 'think in bets' if it is first fine-tuned on bet questions with an identical structure. Modifying the bet question's structure, while still retaining its fundamental characteristics, decreases an LRM's performance by more than 25%, on average, although absolute performance remains well above random. LRMs are also found to be more rational when selecting outcomes with non-negative expected gain, rather than optimal or strictly positive expected gain. Our results suggest that LRMs could potentially be applied to tasks that rely on cognitive decision-making skills, but that more research is necessary before these models can robustly make rational decisions.
TVSR‐OR: Tile‐based 360‐degree video streaming over real time streaming protocol with optimized read

Abstract Among video application use cases and scenarios, 360‐degree video applications gradually become significant. The state‐of‐the‐art solution for 360‐degree video streaming is field of view (FOV) transmission, whose quality of experience highly depends on system processing speed and network latency. The extant 360‐degree video FOV transmission profiles transmit multiple video tile streams to the client over either Hypertext Transfer Protocol (HTTP) or Real Time Streaming Protocol (RTSP). The client needs to merge all different tile streams after receiving them, which may cause a data synchronization problem of waiting for each tile to sync in the presents of packet losses. Therefore, we propose a system to select and merge tile streams on the Content Delivery Network (CDN) server and transmit the merged stream to the client. During the streaming service, the client can send a viewport switch request through RTSP signaling, and the server will subsequently deliver the merged video stream of the new viewport to the client after receiving the request. Also, to reduce disk overhead from parallel reads, we optimize the system with a file prefetch strategy to reduce unorganized, random, and parallel reads into serialized reads of large data trunks. We evaluate its performance through user request simulation experiments. Compared with the extant transmission solution, our system provides slightly lower video transmission latency, lower central processing unit (CPU) usage, and better disk performance under heavy service load.
A study of faults in the Superior province of Ontario and Quebec using the random forest machine learning algorithm: Spatial relationship to gold mines

This paper uses the random forest (RF) algorithm to produce two maps of orogenic gold prospectivity (MPM) across the entire Superior geologic province of Ontario and Quebec, Canada. One MPM of this entire study area is based on a compilation of mapped faults while the other is based on a manual interpretation of lineaments from airborne magnetic data. In addition, we have created four MPMs over the Abitibi region as the MPM map of the entire Superior is characterized by a mapping bias due to more intense mapping of faults over the Abitibi portion of the study area with respect to the rest of the Superior province. There are three RF maps generated over the Abitibi region based on faults, fault density and fault intersection density, faults + magnetic data and faults + gravity + magnetic data. To reduce the effect of the fault mapping bias, we developed a fourth map based on a knowledge driven (weighted sum technique). Statistically the best MPM is based on the faults, gravity and magnetic data. The major predictors of gold are NW-SE, NE-SW, EW trending faults, fault intersection density between EW and NW trending faults and to a lesser extent fault density and a magnetic vertical gradient image. In addition, we compare two greenstone belts in three dimensions with respect to their potential for gold exploration. We conclude that the Larder Lake greenstone belt is more fertile with respect to gold mineralization than the Swayze greenstone belt due to deeper penetrating faults which have greater potential for tapping gold mineralized fluid from the mantle or lower crust. Our final MPM map has identified prospective areas that contain known gold mines as well as areas that do not contain any known gold mines. These prospective areas may be prime for orogenic gold exploration.
Numerical and experimental investigation of a dispersive optoelectronic oscillator for chaotic time-delay signature suppression

Chaos generation from a novel single-loop dispersive optoelectronic oscillator (OEO) with a broadband chirped fiber Bragg grating (CFBG) is numerically and experimentally investigated. The CFBG has much broader bandwidth than the chaotic dynamics such that its dispersion effect rather than filtering effect dominates the reflection. The proposed dispersive OEO exhibits chaotic dynamics when sufficient feedback strength is guaranteed. Suppression of chaotic time-delay signature (TDS) is observed as the feedback strength increases. The TDS can be further suppressed as the amount of grating dispersion increases. Without compromising bandwidth performance, our proposed system extends the parameter space of chaos, enhances the robustness to modulator bias variation, and improves TDS suppression by at least five times comparing to the classical OEO. Experimental results qualitatively agree well with numerical simulations. In addition, the advantage of dispersive OEO is further verified by experimentally demonstrating random bit generation with tunable rate up to 160 Gbps.
Leading Role of E-Learning and Blockchain towards Privacy and Security Management: A Study of Electronics Manufacturing Firms

The success of businesses is now mostly dependent on e-learning methods as these methods are a rapidly growing innovative technology. Blockchain technology has also been considered to have the ability to change businesses. Therefore, this research aims to explore the direct influence of e-learning on the effectiveness of privacy and security in electronics manufacturing. This study also examines the considerable mediating role of the adoption of blockchain technology between e-learning and privacy and security. Furthermore, the current research investigates how digital orientation moderates the association between e-learning and privacy and security. For the collection of data, the cross-sectional research design and random sampling technique were used, and data were gathered from employees of electronics manufacturing firms in Pakistan through questionnaires. The working response rate of the study was 70%. The findings proved that e-learning plays a considerable role in boosting the privacy and security of electronics manufacturers. The results also demonstrate that the adoption of blockchain technology mediates and digital orientation moderates the link between e-learning and privacy and security. This study adds to the better understanding of management by presenting the significant role of e-learning and blockchain technology in improving the efficiency of privacy and security for electronics manufacturing firms.
Multi-threshold image segmentation based on an improved differential evolution: Case study of thyroid papillary carcinoma

The scholarly world has demonstrated an immense enthusiasm for medical image segmentation due to its intricate nature and critical role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, due to its simplicity and straightforwardness. This paper presents an improved Differential Evolution (DE) algorithm called AGDE, which is based on MTIS and was used to evaluate its high capability at IEEE CEC 2017. Comparisons with classical and advanced algorithms were conducted as part of the experiments. An AGDE-based multi-threshold image segmentation method utilizing a non-local mean 2D histogram in combination with Rényi's entropy was applied to segment images from the Berkeley Segmentation Datasets 500 (BSDS500) and microscopic images of thyroid papillary carcinoma (TPC). The experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation.
Augmented Reality in Medical Education: A Mixed Methods Feasibility Study

Augmented reality (AR) is a novel technology with many applications in medical education. Perhaps one of the most beneficial potential applications is to enable better clinical access for students; however, there is limited research into this use. The purpose of this mixed-methods feasibility study was to evaluate the applicability and acceptability of AR in undergraduate and early postgraduate medical education.Single-group quasi-experimental study design was developed for critical care-themed simulation teaching delivered using Microsoft HoloLens (Microsoft Corporation, Redmond, Washington, United States). Post-test questionnaires were completed including a validated adapted immersive experience questionnaire (AIEQ) and an abridged intrinsic motivation inventory (AIMI). The AIMI focused on the domains of 'interest and enjoyment', and 'value and usefulness'. Following the teaching, focus group interviews with thematic analysis were conducted to evaluate participants' experiences with AR.All 15 participants (100%) completed the AIEQ and AIMI. Co-located airway teaching (i.e., the demonstrator and participants were placed in the same AR environment) was reported as having a moderate level of user immersion (median 72) and a high level of user enjoyment and value (median 52). Thematic analysis revealed four key themes: visual conceptualization for learning, accessibility, varied immersion, and future application. Conclusions: Remote simulation for the management of airways in critical care was found to be acceptable and afforded a high level of enjoyment and value. Similarly, this was reflected in the thematic analysis. However, immersion was rated variably in both AIEQ and thematic analysis. The challenges identified with the application of AR included technical infrastructure and patient consent. AR-enabled education benefits are relevant to a number of clinical teaching areas.
A semisupervised autoencoder-based method for anomaly detection in cutting tools

Detecting anomalies such as breakage and excessive wear of cutting tools in the machining process is crucial to prevent damage and improve productivity. Data-driven anomaly detection (AD) methods suffer from limited availability of anomaly samples, which is ineluctable in practice owing to strict reliability restrictions. Therefore, we propose a semisupervised AD approach in which only failure-free samples are required to establish an AD model. The key strategy is to learn the characteristics of failure-free samples using an improved autoencoder (AE) and discern observations by deviations from the characteristics. We rebuild the loss function of AE to impel the model to learn the common characteristics in latent space. We propose a factor that reflects the anomaly degree as the decision-making function to implement AD. The proposed approach is verified on an experimental cutting tool breakage dataset and a public cutting tool wear dataset. The experimental results demonstrate the validity of the proposed approach. The comparisons with conventional methods substantiate that the proposed approach outperforms existing AD methods.
MuKGB-CRS: Guarantee privacy and authenticity of cross-domain recommendation via multi-feature knowledge graph integrated blockchain

In distributed cross-domain recommendation systems, current privacy protection techniques are ineffective at verifying data correctness and existing authenticity techniques have limitations in privacy protection. This poses a conflict between security and credibility. This paper designs a trusted cross-domain recommendation model based on multi-feature knowledge graph (MuKG) and blockchain to boost recommendation accuracy, data credibility and security. To the best of our knowledge, MuKG is proposed, for the first time, to realize a unified representation of multi-source heterogeneous data in a secure manner. Under this model, federated learning integrated with blockchain is used to implement a co-trust mechanism for distributed learning while guaranteeing the credibility of data through blockchain traceability. Our model can guarantee both data security and authenticity with no need of generalization for privacy protection and coordination of centralized servers for distributed controls. Experiments are performed on the two classic datasets, MovieLens and Amazon, with different sparsity. The results have shown that MuKG improves the recommendation accuracy by 1.5 % and diversity by 18 % while ensuring data security and credibility in sparse data. We also verify that different data characteristics have different influence on the recommendation results.
Imaginaries and problematisations: A heuristic lens in the age of artificial intelligence in education

Abstract This paper suggests that artificial intelligence in education (AIEd) can be fruitfully analysed as ‘policies frozen in silicon’. This means that they exist as both materialised and proposed problematisations (problem representations with corresponding solutions). As a theoretical and analytical response, this paper puts forward a heuristic lens that can provide insights into how AI technologies (or advocated AI technologies) function as proposed solutions to certain problematisations based on various imaginaries about how education and learning are best performed or supported. The combined reading of imaginaries and problematisations can thereby aid in our understanding of why and how visions of learning and education are framed in relation to AIEd developments. The overall ambition is to advance theoretical and analytical approaches towards an educational system which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology‐augmented) education in society. Practitioner notes What is already known about this topic Artificial intelligence in education (AIEd) is repeatedly presented as a solution for a range of educational ‘problems’. This means that such ‘solutions’ must also frame certain aspects as ‘problems’. Such problems and ‘solutions’ (problematisations) also exist within certain imaginaries of the present times and of the future, where these problematisations are presented as particularly significant and acute, and promoting specific anticipations of learning and ideals of education. What this paper adds An exposition of problematisations in educational settings. An exposition of educational imaginaries. A heuristic lens for understanding the ‘present’ and ‘future’ in a particular imaginary as entangled in, and dependent on, a certain ‘past’. Implications for practice and/or policy The approach presented in this paper provides a heuristic lens for examining how AI technologies (or advocated AI technologies) function as proposed solutions to problematisations based on imaginaries about how education and learning are best performed or supported. This aids our understanding of how and why certain visions of learning and education are framed in relation to AIEd developments (real or imagined). It also advances theoretical and analytical approaches towards an educational system, which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology‐augmented) education in society.
Adaptive fringe projection algorithm for image saturation suppression

For three-dimensional (3D) measurement of the products with specular reflection, a new adaptive fringe projection algorithm is proposed in this paper. We introduce a new algorithm to generate adaptive intensity coefficient template based on two additional images. Since the adaptive intensity coefficient template is on the camera image, it needs to be transformed to the projector image. To this end, we establish the pixel mapping between the projector image and the camera image based on two additional fringe sequences, and propose a new pixel mapping correction algorithm for intensity saturation surface based on linear interpolation. Next, adaptive projection fringes can be obtained based on pixel mapping and adaptive intensity coefficient template. Experiments conform that the proposed algorithm can more effectively correct the measurement error caused by intensity saturation than the existing state-of-the-art algorithms. In addition, compared with the existing state-of-the-art algorithms, it requires less additional projection fringe patterns, which greatly improves the measurement efficiency. The proposed algorithm is unique in that it proposes a novel adaptive intensity coefficient template generation algorithm and a pixel mapping correction algorithm based on linear interpolation, which reduces the saturation-induced error and improves the measurement efficiency.
Self-paced ensemble for constructing an efficient robust high-performance classification model for detecting mineralization anomalies from geochemical exploration data

Given a base classifier such as the support vector classifier, the self-training algorithm can be used to build a high-performance classification model to detect mineralization anomalies from geochemical exploration data. However, the established classification model has poor robustness. To solve this problem, the self-paced ensemble algorithm was adopted to establish an efficient robust high-performance model for detecting mineralization geochemical anomalies. The self-paced ensemble algorithm can efficiently build a robust classification model based on a base classifier such as the support vector classifier, decision tree classifier, k-nearest neighbor classifier, gradient boosting classifier and multilayer perceptron. To illustrate the superiority of the self-paced ensemble algorithm, a case study for molybdenum mineralization anomaly detection was carried out in the Molidawa area, Inner Mongolia, China. The self-paced ensemble algorithm and self-training algorithm were used to build classification models based on decision tree classifier to detect molybdenum mineralization anomalies from stream sediment survey data. Each algorithm was repeated five times, each time using the same set of parameters to initialize the algorithm. Thus, five classification models were established for each algorithm. The precision-recall curve (PRC) and area under the precision-recall curve (AUPRC) were used to evaluate the performance of the classification models in geochemical exploration. The results show that compared with the PRCs of the five classification models established by the self-training algorithm, the PRCs of those established by the self-paced ensemble algorithm coincide with each other and are closer to the upper right corner of the precision-recall space. The AUPRCs of the five classification models established by the self-paced ensemble algorithm are all 0.3538, and the AUPRCs of those established by the self-training algorithm are between 0.006624 and 0.02941, much lower than the value 0.3538. In addition, the time of the self-paced ensemble algorithm for geochemical data modeling ranges from 44.84 to 47.05 s, and the time of the self-training algorithm for geochemical data modeling ranges from 51.97 to 59.46 s. Therefore, the data-modeling efficiency, robustness and classification performance of the model established by the self-paced ensemble algorithm are better than those of the model established by the self-training algorithm in detecting molybdenum mineralization anomalies. It can be concluded that the self-paced ensemble algorithm is one of effective tools to establish an efficient robust high-performance classification model for mineralization anomaly detection.
The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review

Social media data are rapidly evolving and accessible, which presents opportunities for research. Data science techniques, such as sentiment or emotion analysis which analyse textual emotion, provide an opportunity to gather insight from social media. This paper describes a systematic scoping review of interdisciplinary evidence to explore how sentiment or emotion analysis methods alongside other data science methods have been used to examine nutrition, food and cooking social media content. A PRISMA search strategy was used to search nine electronic databases in November 2020 and January 2022. Of 7325 studies identified, thirty-six studies were selected from seventeen countries, and content was analysed thematically and summarised in an evidence table. Studies were published between 2014 and 2022 and used data from seven different social media platforms (Twitter, YouTube, Instagram, Reddit, Pinterest, Sina Weibo and mixed platforms). Five themes of research were identified: dietary patterns, cooking and recipes, diet and health, public health and nutrition and food in general. Papers developed a sentiment or emotion analysis tool or used available open-source tools. Accuracy to predict sentiment ranged from 33·33% (open-source engine) to 98·53% (engine developed for the study). The average proportion of sentiment was 38·8% positive, 46·6% neutral and 28·0% negative. Additional data science techniques used included topic modelling and network analysis. Future research requires optimising data extraction processes from social media platforms, the use of interdisciplinary teams to develop suitable and accurate methods for the subject and the use of complementary methods to gather deeper insights into these complex data.