Reviewerly/exHarmony huggingface dataset:
The outlook of learning through metaverse technology from the perspective of teachers in the science education

As a personal avatar, Metaverse can be very effective in science, technology, engineering and mathematics (STEM) classrooms such as science classes that are practical and experimental. In this article, the aim is to report a study related to the perception of teachers and their attitudes towards the use of metaverse tools in teaching elementary science classes in Iranian education system. The study uses qualitative content analysis as well as quantitative analysis represented by descriptive statistics. The former includes of semi-structured interviews with 28 samples from two groups of pre-service teachers (inexperienced) and in-service teachers (experienced), which participants were given the opportunity to express their perceptions of Metaverse tools through interviews. The latter comprises a survey was designed to get their attitudes towards the potential use of Metaverse technology. The results showed that the nature of inexperienced ‘digital native’ is in line with the metaverse world and this group had relatively high confidence in using Metaverse in their teaching. Generating these new ideas requires a degree of experience that pre-service teachers do not have. However, this gap can be bridged through a group of experienced teachers who can use their experience to help inexperienced teachers understand how such tools can be integrated into practice.
Intelligent System for Identification and Classification of Pneumonia using Machine Learning

The current study introduces an advanced system that utilizes machine learning methodologies to accurately detect and categorize pneumonia cases from chest X-ray images. The utilized system employs the VGG16 model as the foundational architecture for the classifier. It also utilises transfer learning techniques to adjust the model to the merged dataset of chest X-ray images that have been labelled. The model has been enhanced with the utilisation of Grad-CAM, a methodology that offers interpretability for the model's predictions through visual explanations. The training methodology employed involves mini-batch stochastic gradient descent, with a batch size of 8, for a total of 200 epochs. The evaluation of the system's performance is conducted by means of the utilisation of the test dataset. The findings indicate that the system that was created has the ability to effectively and precisely detect and categorise pneumonia based on chest X-ray images. This suggests that the system has the potential to be a valuable tool in the medical field for prompt diagnosis and treatment.
Credit Card Fraud Detection Using Machine Learning

A credit card is a credit payment facility provided by banks or similar financial organizations that allow the card holders to borrow money to pay for products and services from merchants accepting credit cards. Since everything is now done electronically, there is a risk of card misuse and loss of funds for account holders. It is essential that the credit card companies should be able to recognize fraudulent credit card transactions so that the customers are not charged for products which they did not purchase. These types of problems can be solved using data science and machine learning techniques. This can be done by modeling the dataset using machine learning algorithms combined with credit card fraud detection. The essential key to machine learning algorithms can be the historical credit card transactions with data of those found to be fraudulent. The first step is to evaluate and pre-process the data before feeding the machine learning algorithm to the credit card dataset to determine algorithm parameters and calculate performance metrics. The pattern can be then used to detect whether a new transaction is legitimate or not. The goal is to determine the occurrence of fraudulent transaction.
Facial Matching and Reconstruction Techniques in Identification of Missing Person Using Deep Learning

The number of missing person cases has dramatically increased nowadays, leaving loved ones with a lot of unanswered questions. Police inquiries and public announcements are two regularly used traditional methods for locating missing persons, although they frequently fall short, especially over time. Artificial intelligence (AI) is gaining popularity and could be used to enhance the search process. This study offers a revolutionary approach for solving the unsolved cases of missing individuals by using AI-based facial matching and face reconstruction approaches. The proposed method successfully uses the ORL (Olivetti Research Laboratory) Dataset's Support Vector Machine (SVM) classifier to reach an outstanding accuracy of 93% by combining face landmarks and machine learning algorithms. Additionally, a 3D face reconstruction method based on Convolutional Neural Networks (CNN) trained on the varied 300-W dataset achieves a high accuracy of 90%. These results demonstrate the potential of AI and deep learning models for improving missing person identification. The proposed approach offers a viable option that aids in providing closure to the impacted families, making a significant contribution to the field and reducing crimes in the future.
Attention-Based Transformer Thermal Infrared Tracker

The CNN-based tracker achieves excellent performance, but there are few researches on tracking TIRsingle target. TIR images have blurred outlines, lack color and texture information, so it is difficult to extract strong feature information and distinguish interference when using an RGB tracker to directly track TIR targets, which will lead to poor tracking robustness. We suggest a TIR tracker based on a Siamese tracker and a Transformer to address these issues. Specifically, we improved a feature extraction network with channel attention. Based on Transformer, we propose submodules named CA. We use Transformer as a matching module between the feature map of the template and the search region. The CA module realizes the mutual enhancement of the two path information, finds the feature dependence of the template and the search region, which provides rich context information. And we used the LSOTB-TIR evaluation dataset to verify its performance, which results that our tracker having advanced performance.
Secure Sharing of university Data Using Hyperledger Fabric and IPFS system

the universities across the country rely on sharing data, often sharing student information with those outside the institutions and the university often sharing the data with the her providers the services and the projects. While the general rule from education records cannot be disclosed a anyone. In this paper we proposed hyperledger fabric in same university for connected all faculty and knowledge sharing with we use the InterPlanetary File System (IPFS) for the storage of tendering document.
Heritage Tourism and Religious Identity: A Deep Neural Network-Based Deductive Classification Approach

This paper is concerned with the classification of Southeast Asian temples with the deep neural network with various images of temples. Two of the most popular Buddhist temples in Thailand are Wat Prakaew and Wat Po. The stunning temples in Thailand draw tens of thousands of visitors every year. Sadly, many tourists to the temple sometimes cannot tell whether their vacation photographs were taken at Wat Prakaew or Wat Po. In this study, we use deep learning and computer vision to identify whether a given photograph was shot at Wat Prakaew or Wat Po. The idea is to use a deep neural network to create a pattern that makes it easy to classify images. The work shows better classification results compared to other machine-learning techniques. The proposed techniques attain 96.33% validation accuracy.
Artificial Intelligence Enabled Identification and Mapping of Criminals Using Live Footage on Google Maps

As we have all observed, as technology advances, there is a risk that if it falls into the wrong hands, the results could be disastrous. Criminals today are becoming more cunning and leaving no traces or evidence behind, which makes it more challenging for law enforcement to apprehend them. Additionally, because they were not alerted in a timely manner, the police arrive at the location after the crime has been committed. Therefore, these contemporary issues demand contemporary answers. Nowadays, CCTVs are put at the majority of the nation's locations. The CCTV footage can be utilized to identify criminals and suspects, as well as to alert or notify the authorities of the criminals and suspects at the appropriate moment. In this paper, a Criminal recognition system is developed to detect and map the location of criminals and has been implemented in a computer simulation. The police will receive an email message from this system with the name of the criminal who was identified, the time, and the location of the criminal. This system will be able to recognize faces. Additionally, a map of all the areas where the criminal has been seen on CCTV will be given to the authorities. So that the authorities would have all the tools prepared in a timely manner in an orderly manner, enabling them to apprehend criminals more quickly.
Semi-Automatic Ontology Development Framework for Building Energy Data Management

With the ongoing digital transformation and multi-domain interaction occurring in the buildings, a huge amount of heterogeneous data is generated and stored on a daily basis. To take advantage of the gathered data and help better decision makings, suitable methods are needed to meet the demand for building operations and reinvestment planning. Ontology, which provides not only the vocabulary of a certain domain but also the relationship between each other has been used in multiple engineering fields to manage heterogeneous data. A plethora of ontology development methodologies have been developed in the last decade, whereas those methods are still really time-consuming and in a low degree of automation. In this paper, we approach the problem by first presenting a semi-automatic ontology development framework that integrates existing automatic ontology tools and reuses existing ontology and data model. Based on this framework, we create a building energy management ontology and evaluate the data coverage of several real-life data sets.
A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

In conventional optimization problems, it is assumed that all relevant parametric constraints remain stationary. In contrast, optimization problems encountered in practical applications are dynamic and supervened by uncertainties. The research community has evinced a keen interest in multi-population approaches combined with nature-inspired algorithms to manage dynamic optimization problems efficiently. Applying multi-population approaches to solve dynamic optimization problems engenders specific vital issues, such as reproducing sub-populations in new environments influenced by archival information. Moreover, over-partitioning the population may lead to aberrant utilization of computational resources among the sub-populations. These impediments are addressed using the proposed hybrid multi-population reinitialization strategy, which is a combination of distributed differential evolution algorithmic framework and re-initialization strategy. This scheme relies on simple reinitialization to surmount the dynamism. This framework was assessed on different instances in a moving peak benchmark problem, a proven benchmarking function in the domain of dynamic optimization. Furthermore, this study encompasses a comparative and statistical analysis to validate the effectiveness of the proposed approach in comparison to cutting-edge algorithms in solving dynamic optimization problems efficiently. The experimental results consistently show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hybrid multi-population reinitialization strategy</i> outperforms conventional Differential Evolution algorithms across various parameter configurations. This hybrid multi-population reinitialization strategy showcases its effectiveness in the successful handling of increased shift lengths and number of peaks, which are pivotal parameters in solving moving peak benchmark function.
Optimization Strategy of College Students’ Education Management Based on Smart Cloud Platform Teaching

With the passage of time and social changes, the form of education is also changing step by step. In just a few decades, information technology has developed by leaps and bounds, and digital education has not yet been widely promoted. Intelligent education cloud platforms based on big data, Internet of things, cloud computing, and artificial intelligence have begun to emerge. The research on the "smart campus" cloud platform is conducive to improving the utilization rate of existing hardware equipment in colleges and universities and is conducive in improving the level of teaching software deployment. At the same time, this research also provides a new idea for the research in the field of cloud security. While cloud computing brings convenience to teaching work, it also brings new problems to system security. At present, virtualization technology is still in the ascendant stage in the construction of "smart campus" in colleges and universities and is gradually applied to cloud computing service products. At present, there are many cases about the construction of teaching resource platform, but most of them are modified from the early resource management system, which has strong coupling of single system, insufficient functions of collecting, processing, searching, sharing, and reusing resources, and weak application support ability for related business systems. Under this social background, this paper studies the teaching process management system for intelligent classroom.
Robust Manga Page Colorization via Coloring Latent Space

Manga (Japanese comics) are commonly drawn with black ink on paper. Colorization of manga pages can enrich the visual content and provide a better reading experience. However, the existing colorization approaches are not sufficiently robust. In this paper, we propose a two-stage approach for manga page colorization that supports sampling and color modification with color hints. In the first step, we employ the Pixel2Style2Pixel architecture to map the black-and-white manga image into the latent space of StyleGAN pretrained on the highly blurred colored manga images that we call Coloring Latent Space. The latent vector is automatically or manually modified and fed into the StyleGAN synthesis network to generate a coloring draft that sets the overall color distribution for the image. In the second step, heavy Pix2Pix-like conditional GAN fuses the information from the coloring draft and user-defined color hints and generates the final high-quality coloring. Our method partially overcomes the multimodality of the considered problem and generates diverse but consistent colorings without user input. The visual comparison, the quantitative evaluation with Frechet Inception Distance, and the qualitative evaluation via Mean Opinion Score exhibit the superiority of our approach over the existing state-of-the-art manga pages colorization method.
An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach

There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents:•A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection.•And Harris Hawk optimization with Bi-LSTM for social bot prediction.•Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset.
SPAR: An efficient self-attention network using Switching Partition Strategy for skeleton-based action recognition

Graph convolutional networks (GCN) have become the mainstream in skeleton-based action recognition. For further performance improvement, existing methods propose to utilize self-attention to model long-range features of joints. However, these methods cannot balance accuracy with computational efficiency. In this paper, we propose the Switching Partition Strategy (SPAR) Network that uses the self-attention mechanism for the simultaneous and efficient extraction of spatial–temporal long-range information from the skeleton. We design two partition strategies that reduce the computational cost and improve the efficiency of the computation of self-attention. Extensive experiments are conducted on two large-scale datasets, i.e. NTU RGB+D 60 and NTU RGB+D 120, to evaluate the performance of the proposed SPAR network. The results demonstrate that our method outperforms the state-of-the-art on accuracy as well as computational cost.
An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain

In air traffic management (ATM), voice communications are critical for ensuring the safe and efficient operation of aircraft. The pertinent voice communications—air traffic controller (ATCo) and pilot—are usually transmitted in a single channel, which poses a challenge when developing automatic systems for air traffic management. Speaker clustering is one of the challenges when applying speech processing algorithms to identify and group the same speaker among different speakers. We propose a pipeline that deploys (i) speech activity detection (SAD) to identify speech segments, (ii) an automatic speech recognition system to generate the text for audio segments, (iii) text-based speaker role classification to detect the role of the speaker—ATCo or pilot in our case—and (iv) unsupervised speaker clustering to create a cluster of each individual pilot speaker from the obtained speech utterances. The speech segments obtained by SAD are input into an automatic speech recognition (ASR) engine to generate the automatic English transcripts. The speaker role classification system takes the transcript as input and uses it to determine whether the speech was from the ATCo or the pilot. As the main goal of this project is to group the speakers in pilot communication, only pilot data acquired from the classification system is employed. We present a method for separating the speech parts of pilots into different clusters based on the speaker’s voice using agglomerative hierarchical clustering (AHC). The performance of the speaker role classification and speaker clustering is evaluated on two publicly available datasets: the ATCO2 corpus and the Linguistic Data Consortium Air Traffic Control Corpus (LDC-ATCC). Since the pilots’ real identities are unknown, the ground truth is generated based on logical hypotheses regarding the creation of each dataset, timing information, and the information extracted from associated callsigns. In the case of speaker clustering, the proposed algorithm achieves an accuracy of 70% on the LDC-ATCC dataset and 50% on the more noisy ATCO2 dataset.
Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling

Federated learning (FL) offers a promising solution in edge computing to overcome bandwidth limitations and privacy concerns associated with traditional cloud-based training. However, current FL methods often suffer from transmission delay and excessive communication resource usage. In this paper, we introduce an innovative asynchronous hierarchical FL approach based on bandwidth allocation and client scheduling. Specifically, we propose an efficient algorithm that dynamically assigns clients to edge servers based on client mobility during training and accelerates parameter uploading while taking into account the remaining bandwidth of the edge servers. Our experimental results demonstrate the effectiveness of our approach, particularly in scenarios with frequent client mobility. This research strongly supports the application of FL in edge computing and underscores the crucial role of resource allocation in addressing communication resource constraints and reducing the training time of FL.
A novel model compression method based on joint distillation for deepfake video detection

In recent years, deepfake videos have been abused to create fake news, which threaten the integrity of digital videos. Although existing detection methods leveraged cumbersome neural networks to achieve promising detection performance, they cannot be deployed in resource-constrained scenarios. To overcome this limitation, we propose a novel model compression framework based on joint distillation for deepfake detection, which includes a pre-training stage and a knowledge transfer stage. In the pre-training stage, a teacher network is trained with sufficient labeled samples. Then, in the knowledge transfer stage, a lightweight student network is constructed by considering dimension alignment. To transfer forensics knowledge comprehensively, a joint distillation loss is designed, including cross-entropy loss, knowledge distillation loss, and gradient-guided feature distillation loss. For feature distillation, feature maps from both shallow and deep layers are utilized to calculate channel-wise mean square error weighted by gradient information for transferring knowledge of forensics features adaptively. Besides, a decayed teaching strategy is constructed to adjust the importance of feature distillation, which aims at mitigating the risk of negative transfer. Extensive experiments show that student networks obtained by our model compression method can achieve competitive detection performance and outstanding efficiency by distinctly reducing computational costs compared against state-of-the-art methods.