Reviewerly/exHarmony huggingface dataset:
Can Metaverse Retail lead to purchase intentions among the youth? A Stimulus-Organism-Response theory perspective

This study investigated the intention of youngsters to experiment with metaverse during their shopping experience. The study combined the Uses and Gratification Theory and Stimulus-Organism-Response theory to develop the conceptual model. The model was validated through an empirical study involving youngsters (N=270). The results show that the retail experience and metaverse application characteristics significantly impacted the organism components of hedonic and social gratification. However, features of retail experience characteristics and metaverse applications did not impact utilitarian gratification. Hedonic and social gratification significantly impacted the youngsters' intention to experiment metaverse during their shopping experience. Utilitarian gratification did not impact their shopping intentions, enabled through the metaverse experience. The study elucidates the implications for theory and retail practice benefiting consumers, metaverse retail app designers and influences retailers' marketing strategy.
Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation

When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimize that bias by testing the predictions of one's belief against objective data. But interpreting visualized data is a complex perceptual and cognitive process. Through two crowdsourced experiments, we demonstrate that supposedly objective assessments of the strength of a correlational relationship can be influenced by how strongly a viewer believes in the existence of that relationship. Participants viewed scatterplots depicting a relationship between meaningful variable pairs (e.g., number of environmental regulations and air quality) and estimated their correlations. They also estimated the correlation of the same scatterplots labeled instead with generic 'X' and 'Y' axes. In a separate section, they also reported how strongly they believed there to be a correlation between the meaningful variable pairs. Participants estimated correlations more accurately when they viewed scatterplots labeled with generic axes compared to scatterplots labeled with meaningful variable pairs. Furthermore, when viewers believed that two variables should have a strong relationship, they overestimated correlations between those variables by an r-value of about 0.1. When they believed that the variables should be unrelated, they underestimated the correlations by an r-value of about 0.1. While data visualizations are typically thought to present objective truths to the viewer, these results suggest that existing personal beliefs can bias even objective statistical values people extract from data.
Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&amp;E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&amp;E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.
FlowNL: Asking the Flow Data in Natural Languages

Flow visualization is essentially a tool to answer domain experts' questions about flow fields using rendered images. Static flow visualization approaches require domain experts to raise their questions to visualization experts, who develop specific techniques to extract and visualize the flow structures of interest. Interactive visualization approaches allow domain experts to ask the system directly through the visual analytic interface, which provides flexibility to support various tasks. However, in practice, the visual analytic interface may require extra learning effort, which often discourages domain experts and limits its usage in real-world scenarios. In this paper, we propose FlowNL, a novel interactive system with a natural language interface. FlowNL allows users to manipulate the flow visualization system using plain English, which greatly reduces the learning effort. We develop a natural language parser to interpret user intention and translate textual input into a declarative language. We design the declarative language as an intermediate layer between the natural language and the programming language specifically for flow visualization. The declarative language provides selection and composition rules to derive relatively complicated flow structures from primitive objects that encode various kinds of information about scalar fields, flow patterns, regions of interest, connectivities, etc. We demonstrate the effectiveness of FlowNL using multiple usage scenarios and an empirical evaluation.
Pixle: a fast and effective black-box attack based on rearranging pixels

Recent research has found that neural networks are vulnerable to several types of adversarial attacks, where the input samples are modified in such a way that the model produces a wrong prediction that misclassifies the adversarial sample. In this paper we focus on black-box adversarial attacks, that can be performed without knowing the inner structure of the attacked model, nor the training procedure, and we propose a novel attack that is capable of correctly attacking a high percentage of samples by rearranging a small number of pixels within the attacked image. We demonstrate that our attack works on a large number of datasets and models, that it requires a small number of iterations, and that the distance between the original sample and the adversarial one is negligible to the human eye.
Multi-Task Transformer with Adaptive Cross-Entropy Loss for Multi-Dialect Speech Recognition

At present, most multi-dialect speech recognition models are based on a hard-parameter-sharing multi-task structure, which makes it difficult to reveal how one task contributes to others. In addition, in order to balance multi-task learning, the weights of the multi-task objective function need to be manually adjusted. This makes multi-task learning very difficult and costly because it requires constantly trying various combinations of weights to determine the optimal task weights. In this paper, we propose a multi-dialect acoustic model that combines soft-parameter-sharing multi-task learning with Transformer, and introduce several auxiliary cross-attentions to enable the auxiliary task (dialect ID recognition) to provide dialect information for the multi-dialect speech recognition task. Furthermore, we use the adaptive cross-entropy loss function as the multi-task objective function, which automatically balances the learning of the multi-task model according to the loss proportion of each task during the training process. Therefore, the optimal weight combination can be found without any manual intervention. Finally, for the two tasks of multi-dialect (including low-resource dialect) speech recognition and dialect ID recognition, the experimental results show that, compared with single-dialect Transformer, single-task multi-dialect Transformer, and multi-task Transformer with hard parameter sharing, our method significantly reduces the average syllable error rate of Tibetan multi-dialect speech recognition and the character error rate of Chinese multi-dialect speech recognition.
Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss

In this paper, we present the multi-stage attentive network (MSAN), an efficient and good generalization performance convolutional neural network (CNN) architecture for motion deblurring. We build a multi-stage encoder-decoder network with self-attention and use the binary cross-entropy loss to train our model. In MSAN, there are two core designs. First, we introduce a new attention-based end-to-end method on top of multi-stage networks, which applies group convolution to the self-attention module, effectively reducing the computing cost and improving the model's adaptability to different blurred images. Secondly, we propose using binary cross-entropy loss instead of pixel loss to optimize our model to minimize the over-smoothing impact of pixel loss while maintaining a good deblurring effect. We conduct extensive experiments on several deblurring datasets to evaluate the performance of our solution for deblurring. Our MSAN achieves superior performance while also generalizing and compares well with state-of-the-art methods.
Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks

The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors.
Applying the metaverse

Business, entertainment, and fashion companies already are staking their claims in this enhanced virtual world.
PassengXR: A Low Cost Platform for Any-Car, Multi-User, Motion-Based Passenger XR Experiences

We present PassengXR, an open-source toolkit for creating passenger eXtended Reality (XR) experiences in Unity. XR allows travellers to move beyond the physical limitations of in-vehicle displays, rendering immersive virtual content based on - or ignoring - vehicle motion. There are considerable technical challenges to using headsets in moving environments: maintaining the forward bearing of IMU-based headsets; conflicts between optical and inertial tracking of inside-out headsets; obtaining vehicle telemetry; and the high cost of design given the necessity of testing in-car. As a consequence, existing vehicular XR research typically relies on controlled, simple routes to compensate. PassengXR is a cost-effective open-source in-car passenger XR solution. We provide a reference set of COTS hardware that enables the broadcasting of vehicle telemetry to multiple headsets. Our software toolkit then provides support to correct vehicle-headset alignment, and then create a variety of passenger XR experiences, including: vehicle-locked content; motion- and location-based content; and co-located multi-passenger applications. PassengXR also supports the recording and playback of vehicle telemetry, assisting offline design without resorting to costly in-car testing. Through an evaluation-by-demonstration, we show how our platform can assist practitioners in producing novel, multi-user passenger XR experiences.
Blockchain technology: An overview

In 2009, Bitcoin was launched as peer-to-peer electronic cash. Bitcoin inspired the launch of several other cryptocurrencies. Bitcoin set the stage for a new field called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blockchain</i> , which is a special case of distributed ledger technology (DLT). In 2015, programmable blockchain/DLT projects, such as Ethereum, Hyperledger Fabric, and R3 Corda, were launched. Over the last decade, blockchain/DLT has evolved to encompass a collection of distributed computer network architectures implementing various data structures, decentralized consensus protocols, and economic incentive models. In this rapidly emerging field, some remarkable innovations, experimentations, and developments have been carried out by start-ups, enterprises, and governments. This article will highlight various current and future applications of this technology.
Image restoration algorithm incorporating methods to remove noise and blurring from positron emission tomography imaging for Alzheimer's disease diagnosis

The aim of this study was to design an image restoration algorithm that combined denoising and deblurring and to confirm its applicability in positron emission tomography (PET) images of patients with Alzheimer's disease (AD). PET images of patients with AD obtained using 18F-AV-45, which have a lot of noise, and 18F-FDG, which have a lot of blurring, were available in the Alzheimer's Disease Neuroimaging Initiative open dataset. The proposed framework performed image restoration incorporating blind deconvolution after noise reduction using a non-local means (NLM) approach to improve the PET image quality. We found that the coefficient of variation result after denoising and deblurring of the 18F-AV-45 image was improved 1.34 times compared to that for the degraded image. In addition, the profile result of the 18F-FDG PET image of patients with AD, which had a relatively large amount of blurring, showed a gentle shape when deblurring was performed after denoising. The overall no-reference-based evaluation results showed different results according to the degree of noise and blurring in the PET images. In conclusion, the applicability of the deconvolution deblurring algorithm to AD PET images after NLM denoising processing was demonstrated in this study.
Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks

Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony.
Making Common Fund data more findable: catalyzing a data ecosystem

Abstract The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs’ Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.
Female chatbots are helpful, male chatbots are competent?

Abstract Chatbots have in recent years increasingly been used by organizations to interact with their customers. Interestingly, most of these chatbots are gendered as female, displaying stereotypical notions in their avatars, profile pictures and language. Considering the harmful effects associated with gender-based stereotyping at a societal level—and in particular the detrimental effects to women—it is crucial to understand the effects of such stereotyping when transferred and perpetuated by chatbots. The current study draws on the Stereotype Content Model (SCM) and explores how the warmth (high vs. low) of a chatbot’s language and the chatbot’s assigned gender elicit stereotypes that affect the perceived trust, helpfulness, and competence of the chatbot. In doing so, this study shows how established stereotype theory can be used as a framework for human-machine communication research. Moreover, its results can serve as a foundation to explore ways of mitigating the perpetuation of stereotyping and bring forward a broader discussion on ethical considerations for human-machine communication.
Accurate Passive 3D Polarization Face Reconstruction under Complex Conditions Assisted with Deep Learning

Accurate passive 3D face reconstruction is of great importance with various potential applications. Three-dimensional polarization face reconstruction is a promising approach, but one bothered by serious deformations caused by an ambiguous surface normal. In this study, we propose a learning-based method for passive 3D polarization face reconstruction. It first calculates the surface normal of each microfacet at a pixel level based on the polarization of diffusely reflected light on the face, where no auxiliary equipment, including artificial illumination, is required. Then, the CNN-based 3DMM (convolutional neural network; 3D morphable model) generates a rough depth map of the face with the directly captured polarization image. The map works as an extra constraint to correct the ambiguous surface normal obtained from polarization. An accurate surface normal finally allows for an accurate 3D face reconstruction. Experiments in both indoor and outdoor conditions demonstrate that accurate 3D faces can be well-reconstructed. Moreover, with no auxiliary equipment required, the method ensures a total passive 3D face reconstruction.
2C-Net: integrate image compression and classification via deep neural network

Providing effective support for intelligent vision tasks without image reconstruction can save numerous computational costs in the era of big data. With the help of the Deep Neural Network (DNN), integrating image compression and intelligent vision tasks at a feature representation level becomes a new promising approach. But how to perform non-linear transformation for image compression and extract image patterns for intelligent vision tasks simultaneously within a shared DNN remains an open problem. In this paper, a versatile framework is studied to explore the common feature representations for both image compression and classification. A fully shared latent representation is extracted in a more compact way to support compression and classification task. The General Feature Extraction and Feature-Analytic Classifier are proposed to generate and utilize shared latent representation. Then, the whole framework is joint optimized by considering multiple factors (i.e., rate, quality, and accuracy). Extensive experiments are carried out to validate that the proposals can improve the performance of both learning-based image compression and classification. The results show that the proposed method outperforms the conventional codecs like BPG and JPEG2000 in compression efficiency, while achieving acceptable accuracy on different image classification datasets without image reconstruction.
Minority-prediction-probability-based oversampling technique for imbalanced learning

In this study, we propose an oversampling method called the minority-predictive-probability-based synthetic minority oversampling technique (MPP-SMOTE) for imbalanced learning. First, MPP-SMOTE removes noisy samples from minority classes. Subsequently, it divides minority samples into two types (hard-to-learn and easy-to-learn) by predicting the probability of samples belonging to the minority class. For both sample types, we adopt a divide-and-conquer strategy. We separately calculate the probability of each sample being selected to generate a new synthetic sample. The relative density of a sample in both the majority and minority classes is considered in the method for calculating the selection probability of hard-to-learn samples, and the relative density of a sample in only the minority class is considered in that of easy-to-learn samples. Finally, according to the types and selection probabilities, MPP-SMOTE separately selects samples and generates synthetic samples based on them by using different sample-generation schemes. Experimental results reveal that the proposed method outperforms other oversampling methods in terms of three imbalanced-learning metrics for three common classifiers. According to the results, when a support vector machine classifier is applied, the area under the curve performance of the MPP-SMOTE improves by a factor of 1.44%.