Reviewerly/exHarmony huggingface dataset:
Teaching Social-Emotional Learning with Immersive Virtual Technology: Exploratory Considerations

Virtual Reality (VR) and Immersive Virtual Environments (IVEs) are increasingly becoming employed in the classroom to facilitate embodied forms of experiential learning in sensorially rich contexts. This chapter presents the findings of a study conducted with 15-year-old students in a Singapore school. The study evaluated the effectiveness of IVEs as a novel pedagogical approach to the teaching of social and emotional competencies, in the context of Character and Citizenship Education; it sought to ascertain if the affordances of VR and IVEs—immersion, presence and embodiment—when accompanied by real-world narratives would facilitate greater empathy, perspective-taking and responsible decision-making. Students were divided into three treatment conditions: IVEs, “pen-and-paper” mental simulation and video-viewing, and each treatment contained a problem scenario that involved an ethical dilemma young people in Singapore today face. A quasi-experimental, pre-test post-test, non-equivalent group design was employed and the study adopted a mixed-method approach to data collection. The findings show how IVEs can effectively facilitate perspective-taking and empathy, and this is due to its ability to immerse the user in the fictional space of the narrative, thereby encouraging a deeper sense of presence and embodiment.
Digital Image Compression

To eliminate redundant data and highlight useful information, it is generally necessary to compress the image. The typical compression process can be divided into two categories: lossless compression, in which redundancy is eliminated based on the information entropy theory, and lossy compression, which is based on the theory of the rate distortion function, with the minimum amount of data stored to retain the maximum possible amount of useful information while minimizing engineering errors.
Generating a Suitable Hash Function Using Sudoku for Blockchain Network

Sudoku, a number-placement, logic-based puzzle, not only treats our minds but also secures our data in today’s digitized world by its uniqueness. Nowadays, Blockchain technology is ruling a significant part of the financial market. This gigantic Blockchain technology represents a transaction’s digital ledger which is mainly duplicated and distributed throughout the entire network on the Blockchain. Our work’s main objective is to enhance Blockchain technology’s security. Our method proposed a new concept by making changes in generating the hash function of SHA256 by embedding mind cracking Sudoku puzzle. This work adds extra security to blockchain technology. This method gives superior protection against brute force attacks as well as pre-image and collision attacks compared to SHA256 without increasing complexity. Those added characteristics make it quite impossible to change, hack, break, or cheat the system by using records of transaction processes by generating the strongest hash function.
SmartGP: A Framework for a Two-Factor Graphical Password Authentication Using Smart Devices

Authentication procedures are employed to verify and validate user’s identification on various smart devices and a variety of digital apps. Alphanumeric and Personal Identification Number (PIN)-based passwords are easy to remember and replicate. However, they are vulnerable to different types of attacks. Alternative to traditional passwords, Graphical Passwords (GP) have gained popularity as they are harder to crack, more user-friendly, and offer more password space. On the other hand, Two-Factor Authentication (2FA) provides an extra layer of protection to the system by requiring additional login credentials. Nowadays, 2FA is widely used and gaining popularity. This paper introduces a novel and reliable user authentication method for Android Smartphones, namely SmartGP. It is deployed on Smartwatch which plays as the second factor during authentication. SmartGP is a robust, usable, and secure 2FA graphic al authentication system with an average accuracy of 94.99% and an average login time of 24.19 s.
Can Generalised Divergences Help for Invariant Neural Networks?

We consider a framework including multiple augmentation regularisation by generalised divergences to induce invariance for non-group transformations during training of convolutional neural networks. Experiments on supervised classification of images at different scales not considered during training illustrate that our proposed method performs better than classical data augmentation.
Finding trustworthy neighbors: Graph aided federated learning for few-shot industrial fault diagnosis with data heterogeneity

Federated fault diagnosis has drawn increased attention recently, which makes use of datasets from different clients with data privacy. However, data distribution varies across clients since facilities often work under different conditions, and thus clients may have the few-shot problem on different fault type. Personalized federated learning (PFL) methods have been proposed to analyze client relationship related with data distribution and establishes multiple models suitable for different clients. Nevertheless, such relationship analysis adopts a single description, which may be disturbed by clients with the few-shot problem and cannot be adapted to new clients with new dataset and unknown client relationships. In this paper, a graph aided federated learning method with a few-shot node inhibition mechanism (GAFL) is proposed to solve the problem by finding trustworthy neighbors for all clients. Collaboration graphs are designed in pair-wise and category-wise levels to describe client relationships so that the varied data distribution under different fault type could be distinguished. After proper initialization, graphs and personalized models are trained alternately with the designed few-shot node inhibition mechanism, where information flow from neighbors with the few-shot problem are controlled and less trusted during the training process. Moreover, an incremental learning strategy is designed to address new clients, in which graphs and model updates are restricted to a small range, so that old models are less interfered. The proposed GAFL is evaluated on the real-world datasets in load bearings, demonstrating the superiority of the proposed method for few-shot fault diagnosis problem with data heterogeneity.
Dilemmas experienced by teachers in adapting to the role of facilitator in the STEAM classroom

(1) dilemmas experienced by teachers adapting to the role of ‘facilitator’ in a student-centered, technology-supported, STEAM learning environment; (2) strategies they implemented to address them; and (3) students' responses. Data sources included interviews and video-ethnographic, classroom observations with 29 teachers and 325 students, from 20 schools across the United States. Using thematic coding, we identified eight dilemmas and three types of strategies facilitators used to address them. We discuss students' responses to strategies. Findings have implications for understanding how teachers might best facilitate STEAM learning activities and for our understanding of how teachers adapt to educational change.
Optimizing the surface of orthohedra with virtual reality in primary school

Despite its importance for mathematics, science and technology, the conceptualization and calculation of volumes and surfaces of geometric solids is a source of difficulties, both in primary and secondary school. Immersive virtual reality (IVR) is a powerful resource to overcome these difficulties and promote learning with understanding that enables students to go beyond current curricular contents. This paper presents a design-research study in 6th grade of primary school, comprising three cycles, that allowed schoolchildren aged 11-12 to tackle a final challenge: the optimization of the surface area of orthohedra of a given volume. The design of the cycles, their implementation and the results obtained are described. Reflections are made on the benefits and drawbacks involved in using IVR in the classroom, and on the methodological strategies that enabled the students to successfully overcome the challenge posed.
2D Cast Shadow Generation in E-commerce Image Using UNet Vision Transformer

in e-commerce, the visual presentation of products plays a crucial role in attracting customers and driving sales. One important aspect of product imagery is the generation of realistic cast shadows, which adds depth and enhances the overall visual appeal. In this paper, we propose the algorithm for 2D cast shadow generation in e-commerce using version of the Soft Shadow Network (SSN) modified by UNet Vision. The proposed model can handle complex lighting scenarios and produce realistic shadows with varying degrees of opacity. The UNet Vision Transformer helps to extract and encode the features of the object and its lighting conditions to generate the shadow maps. The model is employed to refine the generated shadows and ensure their consistency with the product images. The obtained results demonstrate that the use of the transformer has a positive effect on the quality of the trained network's prediction. The technology is integrated into the system developed by us which aim is to process the images and prepare e-commerce catalogue.
Computation of Green's function by local variational quantum compilation

Computation of the Green's function is crucial to study the properties of quantum many-body systems such as strongly correlated systems. Although the high-precision calculation of the Green's function is a notoriously challenging task on classical computers, the development of quantum computers may enable us to compute the Green's function with high accuracy even for classically-intractable large-scale systems. Here, we propose an efficient method to compute the real-time Green's function based on the local variational quantum compilation (LVQC) algorithm, which simulates the time evolution of a large-scale quantum system using a low-depth quantum circuit constructed through optimization on a smaller-size subsystem. Our method requires shallow quantum circuits to calculate the Green's function and can be utilized on both near-term noisy intermediate-scale and long-term fault-tolerant quantum computers depending on the computational resources we have. We perform a numerical simulation of the Green's function for the one- and two-dimensional Fermi-Hubbard model up to $4\ifmmode\times\else\texttimes\fi{}4$ sites lattice (32 qubits) and demonstrate the validity of our protocol compared to a standard method based on the Trotter decomposition. We finally present a detailed estimation of the gate count for the large-scale Fermi-Hubbard model, which also illustrates the advantage of our method over the Trotter decomposition.
Detection of Malicious Requests to Protect Web Applications and DNS Servers Against SQL Injection Using Machine Learning

According to the Open Web Application Security Project (OWASP), SQL injection is one of the top ten threats associated with web security issues. This type of attack takes advantage of web application vulnerabilities to cause substantial damages such as financial loss and privacy exposure. In addition, it uses the Domain Name System (DNS) recursion process to extract information stored within the DNS-targeted databases. In this paper, Response Policy Zones (RPZ) technique will be applied to protect DNS systems against data exfiltration. On the other hand, the machine learning algorithm AdaBoost will be used to detect this type of attacks. Our proposed tool will allow Web Application Firewall (WAF) systems to automatically recognize and distinguish between normal and malicious SQL queries. Our results have shown improved accuracy score and reduced false and positives alarm rates, and our tool is tested against well-known SQL injection benchmark datasets.
Research on Image Lightweight Binocular Ranging

Aiming at the problems of low speed and accuracy of traditional binocular ranging methods, this paper discusses the influence of a filtering sampling preprocessed lightweight image of binocular ranging. Firstly, the image to be detected is processed with different levels of Gaussian filtering and up-down sampling, and different levels of preprocessed images are obtained. Secondly, based on YOLOv5 object detection and recognition, MATLAB binocular calibration and stereo rectification are carried out, and then SGBM and BM stereo matching algorithms are respectively combined to realize the binocular ranging of the two methods. Finally, the relationship between binocular ranging range, speed, and accuracy is analyzed under the condition of image preprocessing. The results show that the image preprocessed by one upsampling and downsampling can achieve a simultaneous improvement in the speed and accuracy of the range measurement.
An Intelligent Method for Credit Card Fraud Detection using Improved CNN and Extreme Learning Machine

Credit card fraud has been on the rise in recent years. Criminals are taking advantage of the public by pretending to be legitimate businesses or individuals in order to steal their money. So, it is essential to combat this type of fraud. This research study has developed a novel method for spotting suspicious credit card transactions. The proposed approach may provide most of the necessary information to spot illegal or fraudulent financial transactions. As technology advances at an unprecedented rate, it becomes more difficult to monitor the illegal transactions and money transfers. With the recent developments in machine learning, artificial intelligence, and other critical areas of IT, it is now possible to automate this process and save huge amounts of labor involve in recognizing credit card fraud. After receiving an input image, it is preprocessed and features are retrieved by using principal component analysis, and then the data is used for training the CNN-ELM model. The proposed method achieves a higher accuracy (about 98.7%) than other methods like CNN and ELM.
Compositional inverse Gaussian models with applications in compositional data analysis with possible zero observations

AbstractCompositional data (CoDa) often appear in various fields such as biology, medicine, geology, chemistry, economics, ecology and sociology. Although existing Dirichlet and related models are frequently employed in CoDa analysis, sometimes they may provide unsatisfactory performances in modelling CoDa as shown in our first real data example. First, this paper develops a multivariate compositional inverse Gaussian (CIG) model as a new tool for analysing CoDa. By incorporating the stochastic representation (SR), the expectation–maximization (EM) algorithm (aided by a one-step gradient descent algorithm) can be established to solve the parameter estimation for the proposed distribution (model). Next, zero observations may be often encountered in the real CoDa analysis. Therefore, the second aim of this paper is to propose a new model (called as ZCIG model) through a novel mixture SR based on both the CIG random vector and a so-called zero-truncated product Bernoulli random vector to model CoDa with zeros. Corresponding statistical inference methods are also developed for both cases without/with covariates. Two real data sets are analysed to illustrate the proposed statistical methods by comparing the proposed CIG and ZCIG models with existing Dirichlet and logistic-normal models.Keywords: Compositional inverse Gaussian distributionEM algorithminverse Gaussian distributionzero-truncated product Bernoulli distributionMathematics Subject Classifications: 62F1062H12 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingPengyi Liu research was supported by a grant from the National Natural Science Foundation of China (No. 12201549) and the Fundamental Research Funds of Yunnan, China (No. 202301AU070085). Guo-Liang Tian research was supported by a grant from the National Natural Science Foundation of China (No. 12171225). Kam Chuen Yuen research was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU17306220). Chi Zhang research was supported by a grant from the National Natural Science Foundation of China (No. 11801380).
ReliaMatch: Semi-Supervised Classification with Reliable Match

Deep learning has been widely used in various tasks such as computer vision, natural language processing, predictive analysis, and recommendation systems in the past decade. However, practical scenarios often lack labeled data, posing challenges for traditional supervised methods. Semi-supervised classification methods address this by leveraging both labeled and unlabeled data to enhance model performance, but they face challenges in effectively utilizing unlabeled data and distinguishing reliable information from unreliable sources. This paper introduced ReliaMatch, a semi-supervised classification method that addresses these challenges by using a confidence threshold. It incorporates a curriculum learning stage, feature filtering, and pseudo-label filtering to improve classification accuracy and reliability. The feature filtering module eliminates ambiguous semantic features by comparing labeled and unlabeled data in the feature space. The pseudo-label filtering module removes unreliable pseudo-labels with low confidence, enhancing algorithm reliability. ReliaMatch employs a curriculum learning training mode, gradually increasing training dataset difficulty by combining selected samples and pseudo-labels with labeled data. This supervised approach enhances classification performance. Experimental results show that ReliaMatch effectively overcomes challenges associated with the underutilization of unlabeled data and the introduction of error information, outperforming the pseudo-label strategy in semi-supervised classification.
Hypothesis testing procedure for binary and multi‐class <i>F</i><sub>1</sub>‐scores in the paired design

In modern medicine, medical tests are used for various purposes including diagnosis, disease screening, prognosis, and risk prediction. To quantify the performance of the binary medical test, we often use sensitivity, specificity, and negative and positive predictive values as measures. Additionally, the F1$$ {F}_1 $$ -score, which is defined as the harmonic mean of precision (positive predictive value) and recall (sensitivity), has come to be used in the medical field due to its favorable characteristics. The F1$$ {F}_1 $$ -score has been extended for multi-class classification, and two types of F1$$ {F}_1 $$ -scores have been proposed for multi-class classification: a micro-averaged F1$$ {F}_1 $$ -score and a macro-averaged F1$$ {F}_1 $$ -score. The micro-averaged F1$$ {F}_1 $$ -score pools per-sample classifications across classes and then calculates the overall F1$$ {F}_1 $$ -score, whereas the macro-averaged F1$$ {F}_1 $$ -score computes an arithmetic mean of the F1$$ {F}_1 $$ -scores for each class. Additionally, Sokolova and Lapalme 1$$ {}^1 $$ gave an alternative definition of the macro-averaged F1$$ {F}_1 $$ -score as the harmonic mean of the arithmetic means of the precision and recall over classes. Although some statistical methods of inference for binary and multi-class F1$$ {F}_1 $$ -scores have been proposed, the methodology development of hypothesis testing procedure for them has not been fully progressing yet. Therefore, we aim to develop hypothesis testing procedure for comparing two F1$$ {F}_1 $$ -scores in paired study design based on the large sample multivariate central limit theorem.
Approach to provide interpretability in machine learning models for image classification

Abstract One of the main reasons why machine learning (ML) methods are not yet widely used in productive business processes is the lack of confidence in the results of an ML model. To improve the situation, interpretability methods may be used, which provide insight into the internal structure of an ML model, and criteria, based on which the model makes a certain prediction. This paper aims to consider the state of the art in interpretability methods and apply the selected methods to an industrial use case. Two methods, called LIME and SHAP, were selected from the literature and next implemented in the use case for image classification using a convolutional neural network. The research methodology consists of three parts, the first is the literature analysis, followed by the practical implementation of an ML model for image classification and the subsequent application of the interpretability methods, and the third part is a multi-criteria comparison of selected LIME and SHAP methods. This work enables companies to select the most effective interpretability method according to their use case and also to increase companies’ motivation for using ML.
Pedestrian multi-object tracking based on YOLOv7 and BoT-SORT

As a crucial component in the realm of computer vision, multi-object tracking has garnered widespread application in areas such as autonomous driving, smart transportation, and surveillance technology.Based on YOLOv7 and BoT-SORT algorithms, this paper followed TBD (track by detection) framework. First, YOLOv7 served as the detector tasked with identifying the target and specifying its location within the frame. Then, the BoT-SORT algorithm was used to track the target based on the target detection results. The results are then tested on the MOT20 dataset. The experimental data show that on MOT20 data set, the accuracy of multi-object tracking reaches 69.36%, and the accuracy of multi-object tracking reaches 79.11%, which effectively solves the problems of target occlusion and frequent identity switching. The experiment proves that it still has good tracking effect under dim lighting and crowded complex scenes. It can achieve better tracking for the non-rigid target of pedestrians, and has higher accuracy than YOLOv7-DeepSORT and YOLOv7-ByteTrack.
Research on collaborative filtering recommendation algorithm with improved K-means clustering

In response to the issues of data sparsity, limited scalability, and low recommendation accuracy faced by traditional collaborative filtering algorithms used for personalized user recommendations in the face of increasing data volumes, an optimized collaborative filtering algorithm is proposed that addresses these problems by improving the initial clustering center and incorporating Weighted Slop One filling. This algorithm first uses user similarity as the basis for clustering, and utilizes the Weighted Slope One algorithm to predict the user-rating matrix. Then, the generated matrix is clustered using the AP-MMD-K-means algorithm to obtain a set of similar users. Finally, nearest neighbor search is performed on the target user within their corresponding similar set to generate the recommendation results. Comparative experimental results show that this algorithm improved both recommendation performance and accuracy compared to traditional recommendation algorithms. This algorithm effectively alleviates the problem of data sparsity and improves scalability, significantly improving recommendation accuracy.