Reviewerly/exHarmony huggingface dataset:
Evaluating Usability of AR-based Learning Applications for Children Using SUS and Heuristic Evaluation

Usability, the key quality attributes of any software applications, needs to assess and ensure to design and develop any Augmented Reality(AR) based learning applications for children. Though a number of studies have been conducted focusing on AR-based learning applications, a less attention has been paid to its usability issues. Therefore, the purpose of this article is to propose a set of heuristics to evaluate the usability of AR-based learning applications for children; and to assess the usability evaluation of AR learning applications for children. To attain these objectives, firstly, a set of heuristics was proposed through a literature survey and divided the heuristics into four modules through an affinity diagram approach. Secondly, three selected AR-based learning applications were evaluated using the proposed set of heuristics and also using the System Usability Scale (SUS) approach. Finally, a light-weighted comparison was made between the findings of these two evaluations. As outcomes, a few usability problems and a good number of SUS scores (88.75) were found in 360ED Alphabet AR application, while the Magic Book AR showed a number of usability problems and a lower SUS score (19.375). The research also showed that both methods provide similar kind of results; which indicates that the proposed heuristics are effective and applicable to evaluate the usability of AR-based learning applications for children.
Impact of Covid-19 on Employee Satisfaction and Trust with Focus on Working from Home

Due to the Covid-19 pandemic, companies were forced to send employees into home office. This presented major challenges for many companies. In addition, this meant a change of habits, both for employees and managers. The paper focuses on the question how this circumstance has an influence on employee satisfaction. Therefore, a theoretically based empirical study was conducted. A conceptual model was created, and hypotheses were formulated for this purpose based on a structured literature review. Isolation, communication, and trust (from supervisors' and employees' perspective) were identified as influencing factors that were examined in this study. It could be shown that the biggest influencing factors are isolation and supervisor trust. The influence of communication on employee satisfaction was confirmed, too. For an outlook, linear regression was conducted.
Organizational Learning from Cybersecurity Performance: Effects on Cybersecurity Investment Decisions

Abstract IS literature has identified various economic, performance, and environmental factors affecting cybersecurity investment decisions. However, economic modeling approaches dominate, and research on cybersecurity performance as an antecedent to investments has taken a backseat. Neglecting the role of performance indicators ignores real-world concerns driving actual cybersecurity investment decision-making. We investigate two critical aspects of cybersecurity performance: breach costs and breach identification source, as antecedents to cybersecurity investment decisions. We use organizational learning to theorize how performance feedback from these two aspects of cybersecurity breaches influences subsequent investment decisions. Using firm-level data on 722 firms in the UK, we find that higher breach costs are more likely to elicit increases in cybersecurity investments. This relationship is further strengthened if a third party identifies the breach instead of the focal firm. We contribute to the literature on cybersecurity investments and incident response. The findings stress the need for firms to analyze aspects of their cybersecurity performance and use them as feedback for investment decisions, making these decisions data-driven and based on firm-specific needs.
Research on a Traffic Sign Recognition Method under Small Sample Conditions

Traffic signs are updated quickly, and there image acquisition and labeling work requires a lot of manpower and material resources, so it is difficult to provide a large number of training samples for high-precision recognition. Aiming at this problem, a traffic sign recognition method based on FSOD (few-shot object learning) is proposed. This method adjusts the backbone network of the original model and introduces dropout, which improves the detection accuracy and reduces the risk of overfitting. Secondly, an RPN (region proposal network) with improved attention mechanism is proposed to generate more accurate target candidate boxes by selectively enhancing some features. Finally, the FPN (feature pyramid network) is introduced for multi-scale feature extraction, and the feature map with higher semantic information but lower resolution is merged with the feature map with higher resolution but weaker semantic information, which further improves the detection accuracy. Compared with the baseline model, the improved algorithm improves the 5-way 3-shot and 5-way 5-shot tasks by 4.27% and 1.64%, respectively. We apply the model structure to the PASCAL VOC dataset. The results show that this method is superior to some current few-shot object detection algorithms.
CNN2D-SENet-Based Prospecting Prediction Method: A Case Study from the Cu Deposits in the Zhunuo Mineral Concentrate Area in Tibet

Intelligent prospecting and prediction are important research foci in the field of mineral resource exploration. To solve the problem of the performance degradation of deep convolutional neural networks, enhancing the attention to target information and suppressing unnecessary feature information, this paper proposes a new prospecting prediction method based on a two-dimensional convolutional neural network (CNN2D). This method mainly uses known Cu deposits as the positive sample labels, adopts the sliding window method for data enhancement, and uses the window area as a unit to extract spatial variation features. It is important to supplement squeeze-and-excitation networks (SENets) to add an attention mechanism to the channel dimension, assign a weight value to each feature layer, and finally make prospecting predictions by matching the features of the known deposit window area and the features of the unknown window area. This method allows the neural network to focus on certain characteristic channels and realizes prospecting prediction in the case where there are few known deposits so that the deep learning method can be more effectively used for the prospecting prediction of mineralization. Based on geological data, geochemical exploration data of water system sediments, and aeromagnetic data, and via this method, this study carried out prospecting prediction of Cu deposits in the Zhunuo area of Tibet and predicted 12 favorable Cu prospecting prediction areas. Combined with previous research results and field exploration, the predicted result is consistent with the established mineralization and prospecting pattern and has good prospects for Cu deposit prospecting.
Improved deep learning method for accurate flow field reconstruction from sparse data

The analysis of flow field is essential for the fluid dynamics and the internal mechanism of hydraulic machinery. The use of deep learning technology has revolutionized the field of fluid dynamics by not only reducing computational power and time, but also improving the quality of experimental measurements and numerical simulations. However, certain numerical computing errors can lead to inaccurate simulation results with inadequate data. Fortunately, deep learning technology can accurately reconstruct distorted results, making flow mechanism research more convenient. In this study, a novel super-resolution convolutional neural network with a u-shaped architecture (SRUnet) model was developed by combining the benefits of super-resolution convolutional neural network (SRCNN) and convolutional neural network with the u-shaped architecture (U-net) to address the challenge of reconstructing turbulence flow field images with low resolution. The SRUnet model was validated with small datasets and was shown to deliver high-precision results when applied to reconstruct the velocity flow field of the two-dimensional (2D) cylinder wake flow. Furthermore, the SRUnet model's training and testing performance surpasses that of SRCNN and U-net models, producing more under-resolved details. When compared to the quality of the velocity flow field reconstruction of SRCNN and U-net models with the same hyper-parameter values, the SRUnet model produced 5.35% and 14.22% higher average values of the peak signal-to-noise ratio (PSNR) when low-resolution velocity field images were used as model inputs for the testing dataset reconstruction. Additionally, the average values of the structural similarity index metric (SSIM) were 1.09% and 3.33% higher than those of SRCNN and U-net models. When super-low resolution flow field images were used as model inputs for the testing dataset reconstruction, the average values of the PSNR metric were 8.31% and 2.76% higher than those of SRCNN and U-net models, respectively. The average values of the SSIM metric were also 5.81% and 2.25% higher than those of SRCNN and U-net models, respectively.
From motivational experience to creative writing: A motivational AR-based learning approach to promoting Chinese writing performance and positive writing behaviours

The ability to write is one of the key literacies required by students in relation to 21st century skills and is an emphasis of early education. Although the ability to write is a vital foundation for further education, due to the lack of authentic experience and limited instructional methods, cultivating writing ability in the early stage of education is still a significant challenge for students in elementary schools. Therefore, the majority of students are not able to actively engage in the process of learning to write. To address these problems, this study proposes a motivational AR-based (MAR) learning approach to support students in learning to write, which not only provides an authentic learning environment, but also aims to motivate students' active writing. Its effects were verified through a quasi-experiment. A total of 47 elementary school students from China were invited to participate, and were assigned to either an experimental group (EG) which was exposed to the MAR approach or the control group (CG) which was exposed to the motivational learning approach in a conventional environment (MC). The results showed that the proposed MAR learning approach improved the students' writing performance in terms of feature descriptiveness and thinking innovation. It can be found that, compared with the CG, the achievement gaps between the low- and high-engagement students were much larger in the EG. In addition, the sequential pattern analysis results showed that students who learned with the MAR learning approach concentrated more on the process of observation than those who learned with the MC learning approach, and they tended to exhibit less distracted behaviours. To sum up, the proposed MAR learning approach was effective in terms of facilitating elementary school students’ writing education. The main contribution of our study is that it provides evidence for the effectiveness of the proposed MAR learning approach and opens up opportunities for future studies which aim to further explore its impacts in different designs of writing learning activities.
Attentional prototype inference for few-shot segmentation

This paper aims to address few-shot segmentation. While existing prototype-based methods have achieved considerable success, they suffer from uncertainty and ambiguity caused by limited labeled examples. In this work, we propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. The probabilistic modeling of the prototype enhances the model's generalization ability by handling the inherent uncertainty caused by limited data and intra-class variations of objects. To further enhance the model, we introduce a local latent variable to represent the attention map of each query image, which enables the model to attend to foreground objects while suppressing the background. The optimization of the proposed model is formulated as a variational Bayesian inference problem, which is established by amortized inference networks. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods. We also provide comprehensive analyses and ablation studies to gain insight into the effectiveness of our method for few-shot segmentation.
Performing arts metaverse: The effect of perceived distance and subjective experience

As a new paradigm for experiencing virtual worlds, metaverses are beginning to gain traction as a new way to enjoy the performing arts. Additionally, as its feasibility as a new market for the distribution of performing arts has been confirmed, there have been various attempts to better enable audiences to experience performing arts in a metaverse. However, metaverse performing arts still require several improvements for active user participation and better enjoyment. It is also necessary to understand the barriers that people perceive in experiencing metaverse performing arts. Based on the construal level theory, this study explores and identifies the causal relationship in factors that influence viewing metaverse performing arts, such as the user's psychological distance, objective distance, subjective experience, and resistance. The results demonstrate that artistic distance is a significant factor that strongly affects resistance to viewing performances. Spatial, social, economic distance, and subject experience also impact resistance. The results also uncover the issues that must be discussed to vitalize metaverse-based performing arts.
Error feedback method (EFM) based dimension synthesis optimisation for four-bar linkage mechanism

In this study, we propose an error-feedback method (EFM) for constraint handling of the path-generation problem, which replaces the traditional penalty-functional method. Moreover, it proportionally redistributes solutions that do not satisfy the constraint conditions, such that each offspring can effectively participate in the iterative process and avoid additional computing resource wastage. To test the performance, the EFM, penalty, and self-adaptive–penalty-functional methods were applied to heuristic algorithms, such as the differential evolution (DE), teaching learning-based optimisation (TLBO), whale optimisation algorithm (WOA), and gaining–sharing knowledge (GSK). Three four-bar mechanisms of dimensional synthesis and one path-generation problem of a pickup manipulator was considered as examples. The numerical results showed that the algorithm processed by the new constraint-processing method exhibited improved performance compared to other methods.
Algorithmic probing: Prompting offensive Google results and their moderation

Google results have been scrutinized over the years for what they privilege, be it the surface web, the powerful, optimized webpages, the personalized and/or their own properties. For some time now, another type of Google returns also has been the source of attention: the offensive result. The following revisits a selection of offensive and other problematic results found by journalists and researchers alike. In a technique termed ‘algorithmic probing’, the prompting queries are re-run to study what has come of these results in Google Web and Image Search but mainly in Google Autocompletion. The question concerns a different kind of privileging – Google's hierarchy of concerns – or the extent to which certain categories as well as languages are moderated and others less so. In all, it was found that Google heavily moderates religion, ethnicities and sexualities (albeit with gaps) but leaves alone stereotypes of gendered professions as well as ageism. It also moderates to a greater degree in English compared to southern European and Balkan languages. The article concludes with a discussion of the stakes of Google's moderation, including its uneven coverage.
Quantum Image Encryption Based on Quantum DNA Codec and Pixel-Level Scrambling

In order to increase the security and robustness of quantum images, this study combined the quantum DNA codec with quantum Hilbert scrambling to offer an enhanced quantum image encryption technique. Initially, to accomplish pixel-level diffusion and create enough key space for the picture, a quantum DNA codec was created to encode and decode the pixel color information of the quantum image using its special biological properties. Second, we used quantum Hilbert scrambling to muddle the image position data in order to double the encryption effect. In order to enhance the encryption effect, the altered picture was then employed as a key matrix in a quantum XOR operation with the original image. The inverse transformation of the encryption procedure may be used to decrypt the picture since all the quantum operations employed in this research are reversible. The two-dimensional optical image encryption technique presented in this study may significantly strengthen the anti-attack of quantum picture, according to experimental simulation and result analysis. The correlation chart demonstrates that the average information entropy of the RGB three channels is more than 7.999, the average NPCR and UACI are respectively 99.61% and 33.42%, and the peak value of the ciphertext picture histogram is uniform. It offers more security and robustness than earlier algorithms and can withstand statistical analysis and differential assaults.
Semi-Supervised Specific Emitter Identification via Dual Consistency Regularization

Deep learning (DL)-based specific emitter identification (SEI) is a potential physical layer authentication technique for Industrial Internet-of-Things (IIoT) Security, which detects the individual emitter according to its unique signal features resulting from transmitter hardware impairments. The success of DL-based SEI often depends on sufficient training samples and the integrity of samples’ labels. The extensive deployment of wireless devices generates a huge amount of signals, but signals labeling is quite difficult and expensive with the high demand for expertise. In this article, we present an SEI method based on dual consistency regularization (DCR), which enables feature extraction and identification using a few labeled samples and a large number of unlabeled samples. With the help of pseudo labeling, we leverage consistency between the predicted class distribution of weakly augmented unlabeled training samples and that of strongly augmented training unlabeled samples, and consistency between semantic feature distribution of labeled samples and that of pseudo-labeled samples, which takes the unlabeled samples into account to model parameter tuning for a more accurate emitter identification. Extensive numerical results demonstrate that compared with well-known semi-supervised learning-based SEI methods, our method obtains 99.77% identification accuracy on a WiFi data set and 90.10% identification accuracy on an automatic dependent surveillance-broadcast (ADS-B) data set when only 10% of training samples are labeled, and improves the identification accuracy on the WiFi data set and the ADS-B data set by more than 19.07% and 5.30%, respectively. Our codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lovelymimola/DCR-Based-SemiSEI</uri> .
Improving building rooftop segmentation accuracy through the optimization of UNet basic elements and image foreground-background balance

Building rooftop segmentation using deep learning techniques is a popular yet challenging area of research in computer vision and remote sensing image processing. While recent studies have developed various deep learning models, there has been less focus on investigating the basic elements of network architecture and foreground-background balance in sampled images. To address this research gap, this study proposes optimizing the UNet basic elements and image foreground-background balance to improve building rooftop segmentation accuracy. The Inria dataset is used for model training and accuracy evaluation. The results show that the impact of network backbone on segmentation accuracy depends on its ability to extract lower-level features and whether it is pretrained by large amounts of data. This study also finds that residual structures of network backbone can negatively affect performance regardless of the network depth. Among the decoding network upsampling strategies, the proposed super-resolution method achieves the best segmentation results, but does not significantly differ from the commonly used bilinear interpolation and deconvolution methods. Additionally, this study proposes a hybrid loss function that considers area, arithmetic, shape, spectral, and texture discrepancy, which further improves the segmentation performance. Finally, this study highlights the significant impact of image foreground-background balance on building rooftop segmentation and proposes an ensemble model to mitigate image foreground-background imbalance and improve segmentation performance.
Phishing scams detection via temporal graph attention network in Ethereum

Recently, phishing scams have become one of the most serious types of crime involved in Ethereum, the second-largest blockchain-based cryptocurrency platform. The existing phishing scams detection techniques for Ethereum mostly use traditional machine learning or network representation learning to mine the key information from the transaction network and identify phishing addresses. However, these methods typically crop the temporal transaction graph into snapshot sequences or construct temporal random wanderings to model the dynamic evolution of the topology of the transaction graph. In this paper, we propose PDTGA, a method that applies graph representation learning based on temporal graphs attention to improve the effectiveness of phishing scams detection in Ethereum. Specifically, we learn the functional representation of time directly and model the time signal through the interactions between the time encoding function and node features, edge features, and the topology of the graph. We collected a real-world Ethereum phishing scam dataset, containing over 250,000 transaction records between more than 100,000 account addresses, and divided them into three datasets of different sizes. Through data analysis, we first summarized the periodic pattern of Ethereum phishing scam activities. Then we constructed 14 kinds of account node features and 3 kinds of transaction edge features. Experimental evaluations based on the above three datasets demonstrate that PDTGA with 94.78% AUC score and 88.76% recall score outperforms the state-of-the-art methods.