Reviewerly/exHarmony huggingface dataset:
An examination of the research studies on augmented reality use in preschool education: a bibliometric mapping analysis

AbstractAbstractThis study explores the research trends regarding augmented reality (AR) use in preschool education through a bibliometric mapping analysis. The researchers included 85 publications from Web of Science databases for the analysis. VOSViewer program was utilized to conduct the keyword and term analyses. The keyword analysis’ results indicated that the publications mainly focus on AR use in preschool education with a limited concentration on motivation, knowledge, and English language learning. The term analysis results revealed four main themes which are the possible use of AR applications on children, the design process of AR, the children’s AR experiences, and the used technology. Based on the results, the implications for preschool education and recommendations for the use of AR applications are discussed.Keywords: Preschool educationearly childhoodpreschooleraugmented realitybibliometric mapping analysisvisualization Additional informationNotes on contributorsHalil KayadumanHalil Kayaduman is an Assoc. Prof. Dr. in Distance Education Application and Research Center at Inonu University, Turkey. He earned his doctoral degree from the Department of Computer Education and Instructional Technology at Middle East Technical University, Turkey. His primary research interests fall primarily in the areas of teaching and learning in open and distance education environments, learning analytics, technology adoption in education, design and use of technology-enhanced learning environments, and human-computer interaction.Mehmet SağlamMehmet Saglam is a faculty member in the Department of Child Development at Inonu University, Turkey. He earned his doctoral degree from the Department of Child Development at Ankara University, Turkey. His main research interests are technology integration in preschool education, technology addiction in children, autistic children, child rights, and children’s drawings.
Development of precise forgery detection algorithms in digital radiography images using convolution neural network

The widespread availability of forged image software necessitates the integrity verification of digital images in industrial and medical applications. Because of image manipulation, detecting small tampering and duplicated forgery from digital radiography (gamma and x-ray) images has become a research challenge, Two essential approaches are proposed for forgery detection from digital radiography images. A precise forgery detection approach with pretrained deep convolution neural networks (CNN) is conducted. Alexnet, Resnet-18 and VGG-19 are three pretrained networks for features extraction. Artificial neural network (ANN) and multiclass support vector machine (MSVM) classifiers are applied for classifying the extracted features into authentic or forged. The second suggested approach depends on Haralick and Zoning extractors. These extracted features are trained and tested using the K-nearest neighbors (KNN) classifier. The suggested approaches are investigated using several manipulated industrial (gamma welding images) and medical (spine images) datasets images. Besides, these approaches are tested with several color benchmark dataset images. The results are verified using a variety of evaluation metrics. The approaches are validated through comparison with published work and high agreements are demonstrated. For digital radiography images, Alexnet pretrained network with MSVM, Resnet-18 pretrained network with ANN and Haralick extractor with KNN achieve the highest accuracy and assessment metrics. It is observed that the performance of pretrained CNN outperforms that of conventional classification algorithms in respect of accuracy with computational time. The developed approaches allow for the precise detection of forgery regions in x-ray and gamma radiographic images as well as digital images.
Lossy SU(1,1) interferometers in the single-photon-pair regime

Abstract The success of quantum technologies is intimately connected to the possibility of using them in real-world applications. This requires the system to be comprehensively modeled including various relevant experimental parameters. To this aim, in this paper, we study the performance of lossy SU(1,1) interferometers in the single-photon pair regime, posing particular attention to the different amount of information contained in the measurement of single counts and of coincidences at the output of the interferometer. To this aim, we derive the classical Fisher information (FI) of both single and coincidence events, and study it as a function of the internal and external losses of the system. Our analysis shows that, in the absence of external losses, the FI of the coincidence events is always higher or equal than the one of single events. On the other hand, in the presence of external losses, the FI of the singles can increase above the one of the coincidences. Moreover, our analysis shows that coincidence measurement can be exploited to partially mitigate the effect of internal losses in the absence of external losses. Finally, comparing SU(1,1) and classical SU(2) interferometers, we find that the former can outperform the classical systems when the internal losses are above 50%.
Exploring automated object detection methods for manholes using classical computer vision and deep learning

Open, broken, and improperly closed manholes can pose problems for autonomous vehicles and thus need to be included in obstacle avoidance and lane-changing algorithms. In this work, we propose and compare multiple approaches for manhole localization and classification like classical computer vision, convolutional neural networks like YOLOv3 and YOLOv3-Tiny, and vision transformers like YOLOS and ViT. These are analyzed for speed, computational complexity, and accuracy in order to determine the model that can be used with autonomous vehicles. In addition, we propose a size detection pipeline using classical computer vision to determine the size of the hole in an improperly closed manhole with respect to the manhole itself. The evaluation of the data showed that convolutional neural networks are currently better for this task, but vision transformers seem promising.
An interactive opposition matrix and regulatory gene fusion strategy for evolutionary multitasking

Evolutionary multitasking algorithms (EMT) study how to solve multiple optimization tasks simultaneously by evolutionary computation, and investigate how knowledge sharing can accelerate the convergence of individual tasks, meaning that useful knowledge gained in solving one task can be used to solve other tasks. However, as the evolutionary search continues, the learnability among tasks may decrease, leading to a decrease in the efficiency of knowledge transfer and affecting the population evolution. To solve this problem, a new multifactorial evolutionary algorithm (MFEA-VOM) is proposed in this paper, which applies to three strategies, namely, implicit conversion strategy, opposition matrix strategy, and regulatory gene fusion strategy. The implicit conversion strategy is applied to minimize the threat of negative knowledge migration and reduce the impact caused by negative knowledge migration. The proposed opposition matrix strategy explores more unknown areas of the population and improves the exploration ability of the population by further exploring and utilizing the unified search space, transforming the parent individuals into an appropriate task through mapping relationships, and reducing the gap between tasks. The proposed regulatory gene fusion strategy is applied to the reproduction of individuals to produce better individuals applicable to the task, submitting the efficiency of knowledge transfer. Through a comprehensive experimental analysis of the EMT optimization problem, the experimental results demonstrate the better performance of MFEA-VOM compared to other EMT algorithms.
Correlation between resource-generating powers of quantum gates

We analyze the optimal basis for generating the maximum relative entropy of quantum coherence by an arbitrary gate on a two-qubit system. The optimal basis is not unique, and the high quantum coherence generating gates are also typically high entanglement generating ones and vice versa. However, the profile of the relative frequencies of Haar random unitaries generating different amounts of entanglement for a fixed amount of quantum coherence is different from the one in which the roles of entanglement and quantum coherence are reversed, although both follow the beta distribution.
Challenges and Advantages of Location-Based Services in Tourism Industries

Applications that deal with all services connected to location-based information are known as location-based services (LBSs). This idea is focused on the localization of all elements related to a tourist location, including people, services, amenities, and attractions. LBSs have emerged as a new mode of promotion with the growth of mobile communication. One of the key and quickly evolving trends in mobile marketing is location-based marketing. Technology for location-based marketing is evolving quickly, creating amazing new options. LBSs have many advantages but also some challenges. This chapter investigates the challenges like security and safety, privacy, unusual access to the location, competing for attention, and advantages like enhancing the experience for customers, obtaining thorough information about consumer behaviour, and boosting your marketing initiatives of location-based services in tourism industries.
AI Chatbot to Assist Students Applying APA (7th Edition) Style in Their Academic Writing in a Polytechnic in Singapore

This chapter focuses on a pilot project that utilized an artificial intelligence chatbot named the APABot to help 24 students from the Diploma in Psychology Studies apply APA (7th edition) style in their 400- to 500-word essay. This writing task was a requirement for Communication and Information Literacy, a fundamental subject for all first-year students in Temasek Polytechnic, Singapore. The four research questions were as follows: 1) How did the students respond to the APABot? 2) What strategies did the students use to learn from the APABot? 3) How did the APABot assist students to comply with the APA (7th edition) conventions in their writing? and 4) What were the characteristics of successful self-regulators using the APABot? Data from user logs, the number and types of errors, and a survey provided insights to the above questions.
Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?

Energy costs represent a significant share of the total cost of ownership in high-performance computing systems. Using a unique data set collected by massive sensor networks in a petascale national supercomputing center, we first present an explanatory model to identify key factors affecting energy consumption in supercomputing. Our analytic results show that workload distribution among the nodes has significant effects and could effectively be leveraged to improve energy efficiency. We then establish the high model performance using in-sample and out-of-sample analyses and develop prescriptive models for energy-optimal runtime workload management. We present four dynamic resource management methodologies (packing, load balancing, threshold-based switching, and energy optimization), model their application at two levels (within-rack and cross-rack resource allocation), and explore runtime resource redistribution policies for jobs under the computational steering and comparatively evaluate strategies that use computational steering with those that do not. Our experimental results lead to a threshold strategy that yields near-optimal energy efficiency under all workload conditions. We further calibrate the energy-optimal resource allocations over the full range of workloads and present a bi-criteria evaluation to consider energy consumption and job performance tradeoffs. We conclude with implementation guidelines and policy insights into energy-efficient computing resource management in large supercomputing centers.
A BIBLIOMETRIC ANALYSIS OF THE EFFECTS OF THE COVID-19 PANDEMIC ON STUDENTS

The global impact of the Covid-19 pandemic has generated major changes in educational processes and universities’ activities, affecting both the private life and academic activity of students. All these developments have prompted academic scholars to shift their focus to the study these unprecedented phenomena. This research provides a bibliometric examination of the personal and academic repercussions of the COVID 19 pandemic for students. To assess how these implications have been reflected in academic works published so far, a search of the Scopus database for the keywords "Covid*" and "students*" yielded 15,641 scholarly publications published between 2019 and 2022. This bibliometric research presents an analysis of the evolution of the works in time, the main geographical areas, their classification by fields and the most cited works, and finally, the analysis of keywords and the strength of the links between the terms.
AMERICAN SIGN LANGUAGE RECOGNITION AND CONVERTER

This research paper proposes an innovative approach to develop an application that translates American Sign Language (ASL) gestures into text or speech using Convolutional Neural Networks (CNN).The proposed application is intended to overcome the communication barriers by providing real-time recognition and conversion of ASL gestures.The use of deep learning and natural language processing techniques and the accessibility of webcams offer a promising solution to enhance the accessibility of sign language recognition and conversion to a wider audience.In addition to sign recognition, the application will also provide a userfriendly interface.
SEmHuS: a semantically embedded humanitarian space

Humanitarian crises are unpredictable and complex environments, in which access to basic services and infrastructures is not adequately available. Computing in a humanitarian crisis environment is different from any other environment. In humanitarian environments the accessibility to electricity, internet, and qualified human resources is usually limited. Hence, advanced computing technologies in such an environment are hard to deploy and implement. Moreover, time and resources in those environments are also limited and devoted for life-saving activities, which makes computing technologies among the lowest priorities for those who operate there. In humanitarian crises, interests and preferences of decision-makers are driven by their original languages, cultures, education, religions, and political affiliations. Hence, decision-making in such environments is usually hard and slow because it solely depends on human capacity in absence of proper computing techniques. In this research, we are interested in overcoming the above challenges by involving machines in humanitarian response. This work proposes and evaluates a text classification and embedding technique to transform historical humanitarian records from human-oriented into a machine-oriented structure (in a vector space). This technique allows machines to extract humanitarian knowledge and use it to answer questions and classify documents. Having machines involved in those tasks helps decision-makers in speeding up humanitarian response, reducing its cost, saving lives, and easing human suffering.The online version contains supplementary material available at 10.1186/s41018-023-00135-4.
Symbolic expression generation <i>via</i> variational auto-encoder

There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation via variational autoencoder (VAE). We suggest using a VAE to generate mathematical expressions, and our training strategy forces generated formulas to fit a given dataset. Our framework allows encoding apriori knowledge of the formulas into fast-check predicates that speed up the optimization process. We compare our method to modern symbolic regression benchmarks and show that our method outperforms the competitors under noisy conditions. The recovery rate of SEGVAE is 65% on the Ngyuen dataset with a noise level of 10%, which is better than the previously reported SOTA by 20%. We demonstrate that this value depends on the dataset and can be even higher.
A container deployment strategy for server clusters with different resource types

Abstract The method of deploying microservices based on container technology is widely used in cloud environments. This method can realize the rapid deployment of microservices and improve the resource utilization of cloud datacenters. However, resource allocation and deployment of container‐based microservices are key issues. With the continuous growth of computing‐ and storage‐intensive services, it is necessary to consider the deployment of microservices of different business types. This study establishes a multi‐objective optimization problem model with the similarity between containers and servers, load balance of clusters, and reliability of microservice execution as the optimization objectives. An improved artificial fish swarm algorithm is proposed for the container deployment of computing‐ and storage‐intensive microservices. The comprehensive experimental results show that, compared with the existing deployment strategies, the matching degree between the container and server, cluster load balance value, service execution reliability, and other performance parameters are improved while shortening the running time of the algorithm. In addition, under the constraint of load balancing, the resource utilization of the computing and storage server clusters is improved.
Automated climate prediction using pelican optimization based hybrid deep belief network for Smart Agriculture

A significant part of the economies of several nations throughout the world, including India, where agriculture accounts for 16% of the total economy. Due to their dynamic and turbulent natures, climate prediction in this region is the most difficult because statistical methodologies can't provide accurate predictions. Typically, weather forecasts were made using extraordinarily complex physics techniques that took into account various atmospheric conditions over a long time. These circumstances were usually unstable as a result of weather system disturbances, which led to the development of techniques for making unreliable predictions. In this study, an Automated Climate Prediction for Smart Agriculture is developed utilizing a Pelican Optimization-based Hybrid Deep Belief Network (ACP-POHDBN). The purpose of the ACP-POHDBN approach is to identify the appropriate meteorological conditions. The acp-POHDBN method uses the test data to calculate the appropriate weather conditions. Pre-processing, prediction, and hyperparameter tuning are the three key steps that make up the proposed ACP-POHDBN approach. The min-max normalization procedure is the primary procedure used by the ACP-POHDBN model to convert the meteorological data into a standard format. In addition to pre-processing data, the Deep Belief Network (DBN) model is used to forecast weather conditions. Finally, the DBN method's hyperparameters (epoch, batch size, and learning rate) may be properly tuned using the POA-based hyperparameter tuning technique. The development of a Pelican Optimization Algorithm (POA)-based hyperparameter optimizer for the process of predicting the climate demonstrates the originality of the study. The proposed algorithm over other recent approaches with maximum accuracy of 95.03%, sensitivity of 95.03%, specificity of 95.03%, and F-score of 95.03%.
Improving named entity correctness of abstractive summarization by generative negative sampling

The problem of factual incorrectness in machine-generated abstractive summarization has received widespread attention in the past few years. Although large-scale neural models show excellent capability in generating fluent and coherent summaries, they still struggle with factual inconsistency, in which the named entity incorrectness is the most frequent and notable one, especially for the character-based languages, such as Chinese. Since abstractive summaries are mostly generated character by character in Chinese, the problem of hallucinated entities is more severe than that of other word-based languages. In this paper, we propose CC-Gens, a novel approach for Correctness Checking based on a Generative negative sampling strategy. Considering that the problem is due to the uncertain nature of the language generation process, we leverage fine-tuned generative language models, i.e., UniLMv2 and mT5, to generate summaries with incorrect entities, thereby constructing a synthetic binary classification dataset for the factuality discriminative model. We propose three strategies: Entity Sampling, Sequence Sampling, and Cloze Sampling, to generate summaries with incorrect entities. With such strategies, the negative samples are much more similar in nature to the output of the neural summarization model. We then train a BERT-based discriminator to identify factually incorrect machine-generated summaries with entity errors based on these generative negative samples. We further propose a novel PU learning algorithm to improve the performance of our approach by iteratively training the discriminator to select high confident negative samples from the unlabeled model generated summaries to replace the former artificially constructed ones. By generating multiple candidate summaries and selecting the one with the highest factual correctness score among them, our approach can significantly reduce the probability that an output summary contains factual errors. According to a comprehensive evaluation, the CC-Gens we proposed outperforms previous works in identifying faithless summaries as well as providing faithful ones.