Reviewerly/exHarmony huggingface dataset:
Adaptive Deep Feature Fusion for Continuous Authentication With Data Augmentation

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive Deep Feature Fusion scheme for effective feature representation, and a transformer-based GAN for Data Augmentation, by leveraging smartphone built-in sensors of the accelerometer, gyroscope and magnetometer. Given the normalized sensor data, ADFFDA utilizes the transformer-based GAN consisting of a transformer-based generator and a CNN-based discriminator to augment the training data for CNN training. With the augmented data and the especially-designed CNN based on the ghost module and ghost bottleneck, ADFFDA extracts deep features from the three sensors by the trained CNN, and exploits an adaptive-weighted concatenation method to adaptively fuse the CNN-extracted features. Based on the fused features, ADFFDA authenticates users by using the one-class SVM (OC-SVM) classifier. We evaluate the authentication performance of ADFFDA in terms of the efficiency of the transformer-based GAN, GAN-based data augmentation, CNN architecture, adaptive-weighted feature fusion, OC-SVM classifier, and security analysis. The experimental results show that ADFFDA obtains the best authentication performance w.r.t representative approaches, by achieving a mean equal error rate of 0.01%.
Understanding artificial intelligence experience: A customer perspective

The engagement between customers and brands is being transformed by artificial intelligence (AI). However, there has been little study into AI-powered customer experiences; hence, this research aims to examine how the incorporation of AI in purchasing might result in a better AI-powered customer experience. This research will develop a conceptual model based on the service quality model and trust-commitment theory. Further to this, an online questionnaire was distributed to individuals who had utilised an AI-powered service provided by a particular brand, and consequently, a total of 354 responses were analysed using Structural Equation Modelling (SEM). The results that were deduced from the responses demonstrated that relationship commitment has begun to substantially impact AI-powered customer experiences. In addition to this, the results also revealed that perceived sacrifice and trust both play an important role in mediating the impacts of perceived convenience, personalisation, and AI-powered service quality. This finding contributes to the previous literature by highlighting the mediating impacts of perceived sacrifice and trust, as well as the significant influence of relationship commitment on AI-powered customer experience. Furthermore, the research poses significant implications for merchants who use AI in services provided to their customers.
Emotional communication by service robots: a research agenda

Purpose Many service industries are facing severe labor shortages. As a result, service providers are turning to new sources of labor, such as service robots. Critics however often point out that service robots lack emotional communication capabilities without which they cannot be expected to truly replace human employees and fill the emerging labor market gaps. Here, a research agenda for the investigation of the role of emotional communication by service robots and its effects on customers and their service experience are laid out. This paper aims to propose that research in this area will further understanding of how service robots can add value to service frontlines, engage customers, increasingly replace service employees and ultimately help overcome pressing labor shortages. Design/methodology/approach A research agenda structured around the three-step emotional communication process (i.e. read, decide and express) and the four emotional communication strategies crucial for service interactions (i.e. mimicking, alleviating, infusing and preventing) are conceptualized. Findings Three contributions are made. First, the importance of emotional communication by service robots during service interactions is highlighted. Second, interdisciplinary research priorities and opportunities in this emerging field are mapped out. Third, a theoretical structure to connect the findings of future studies is provided. Originality/value Service research investigating the role and implications of emotional communication by service robots is scarce. A research agenda to guide the exploration of this crucial, yet underresearched component of customer-robot service interactions is structured and mapped out.
Responding to the Escalating Cybersecurity Threat to Health Care

Interview with Dr. Eric Perakslis on cybersecurity threats to patients and health care systems. (08:47)Download The cyberthreat to health care has never been greater. Fortunately, clinicians can protect themselves and their patients from the worst of these attacks, in part by means of precautions and preparation.
Immersive learning technology for ensuring quality education: Ukrainian university case

The article considers the problem of using immersive learning in the educational and scientific activities of the university. Literature survey revealed that there is a need for an integrated approach for introduction of immersive learning at the university. It involves the creation of a specialized laboratory of virtual and augmented reality with appropriate technical equipment, introduction of immersive learning methodology in university educational programs, development of software and hardware solutions for immersive learning, and research on the immersive learning effectiveness. We present the description of a specialized university department acting as a developer of software products for immersive learning. We show original developments in the field of immersive education for exact sciences and arts and humanities students. The article describes products that are designed to fulfill the third university mission: to ensure the citizens well-being. We propose "immersive institute" model which can be implemented both at the level of the university in general and at the level of its educational and scientific departments.
Spectral denoising based on Hilbert–Huang transform combined with F-test

Due to the influence of uncontrollable factors such as the environment and instruments, noise is unavoidable in a spectral signal, which may affect the spectral resolution and analysis result. In the present work, a novel spectral denoising method is developed based on the Hilbert-Huang transform (HHT) and F-test. In this approach, the original spectral signal is first decomposed by empirical mode decomposition (EMD). A series of intrinsic mode functions (IMFs) and a residual (r) are obtained. Then, the Hilbert transform (HT) is performed on each IMF and r to calculate their instantaneous frequencies. The mean and standard deviation of instantaneous frequencies are calculated to further illustrate the IMF frequency information. Third, the F-test is used to determine the cut-off point between noise frequency components and non-noise ones. Finally, the denoising signal is reconstructed by adding the IMF components after the cut-off point. Artificially chemical noised signal, X-ray diffraction (XRD) spectrum, and X-ray photoelectron spectrum (XPS) are used to validate the performance of the method in terms of the signal-to-noise ratio (SNR). The results show that the method provides superior denoising capabilities compared with Savitzky-Golay (SG) smoothing.
XAI based model evaluation by applying domain knowledge

Artificial intelligence(AI) is used in decision support systems which learn and perceive features as a function of the number of layers and the weights computed during training. Due to their inherent black box nature, it is insufficient to consider accuracy, precision and recall as metrices for evaluating a model's performance. Domain knowledge is also essential to identify features that are significant by the model to arrive at its decision. In this paper, we consider a use case of face mask recognition to explain the application and benefits of XAI. Eight models used to solve the face mask recognition problem were selected. GradCAM Explainable AI (XAI) is used to explain the state-of-art models. Models that were selecting incorrect features were eliminated even though, they had a high accuracy. Domain knowledge relevant to face mask recognition viz., facial feature importance is applied to identify the model that picked the most appropriate features to arrive at the decision. We demonstrate that models with high accuracies need not be necessarily select the right features. In applications requiring rapid deployment, this method can act as a deciding factor in shortlisting models with a guarantee that the models are looking at the right features for arriving at the classification. Furthermore, the outcomes of the model can be explained to the user enhancing their confidence on the AI model being deployed in the field.
Stimulation of Language Skills for Early Childhood through the Picture Story Method

The article analyzed the implementation of learning using the storytelling method through image media and analyzed the obstacles faced in the use of storytelling methods with picture media in developing motivational language skills in early childhood. This research was conducted at the Kindergarten of Sultan Agung Nglanjaran Sardonoharjo Ngaglik Sleman Yogyakarta. The method used the case study method. The research data sources consisted of informants including school principals, class teachers, and students, supported by other sources in the form of documents. Collection data techniques used observation, interviews and documentation. The data analysis is focused during the field process along with data collection. The results found that the development of language skills of students in class B2 elementary school consists of 23 students. There were 40% or 10 students that develop in achieving the indicators, and 60% of children have developed very well in achieving the specified indicators, where as many as 20 students out of 23 students have achieved very good development indicators, and 3 students have started developing.
Competencies and needs of nurse educators and clinical mentors for teaching in the digital age – a multi-institutional, cross-sectional study

Abstract Background The impact of technology and digitalization on health care systems will transform the nursing profession worldwide. Nurses need digital competencies to integrate new technology in their professional activities. Nurse educators play a crucial role in promoting the acquisition of digital competences and therefore need to be digitally competent themselves. Research on digital competencies of nursing educators is scarce but suggests lack of digital knowledge and skills and support needs. Although digitalization is to be seen as a global process, regional contexts need to be taken into account, such as pre-existing competencies, local conditions, and individual needs. Thus, it remains unclear which competencies nurse educators possess and which support needs they have. Aim of this study was to assess nurse educators’ and clinical mentors’ digital competencies and explore their needs and requirements concerning the digital aspects of their pedagogy and teaching activities in Germany. Methods A descriptive exploratory study with a cross-sectional design was conducted. Participants were identified using a convenience sampling approach. Data were collected during July and September 2020 using a standardized self-reported questionnaire that was developed specifically for this study. The questionnaire was provided in a paper and online format and participants could decide which format to use. It contained open- and closed-ended questions. Data were analyzed using descriptive and content analysis. Additionally, explorative subgroup analyses based on job designation, age, and gender were performed. Reporting of this study adhered to the STROBE checklist. Results A total of 169 educating nurses participated in the survey. The respondents considered themselves as digitally competent and showed a positive attitude towards the integration of digital technology in their teaching activities. Their perceived preparedness to integrate digital technology into teaching and training varied. Almost all respondents (98%) declared a need for further training and seemed motivated to participate in corresponding educational events. There were some indications for differences in competencies or needs between subgroups. Conclusions Educating nurses appear to possess basic digital competencies but there is a need to support their professional development in terms of new technologies. Findings can be used as a basis for developing supportive interventions. Further qualitative investigations could inform the design and content of such interventions.
A YOLO Detector Providing Fast and Accurate Pupil Center Estimation using Regions Surrounding a Pupil

Eye-tracking technology has many useful applications, including Virtual Reality (VR) devices, Augmented Reality (AR) devices, and assistive technology. The main objective of eye-tracking technology is to detect eye position and track eye movements. It is possible to determine the eye position when the pupil center is detected. In this paper, a deep learning-based approach to the detection of pupil centers from webcam images is presented. As opposed to all previous approaches to object detection based on training the detector with objects to be detected, our object detector was trained with both the region surrounding a pupil and the region between an eye and the region surrounding a pupil. The latter set of regions has been found to increase the overall detection accuracy. A novel post-processing algorithm is also presented to estimate the pupil center from all the detected regions. To achieve real-time performance, we have adopted the tiny architecture of YOLOv3, which has 23 layers and can be executed without requiring a GPU accelerator. To train the detectors, different variations of regions covering a pupil and an eye were used, as well as expanding regions surrounding a pupil and an eye. The PUPPIE dataset was used as the primary input for training the detector. The setting with the best detection accuracy was applied to all publicly available datasets: I2Head, MPIIGaze, and U2Eyes. In terms of accuracy, the results indicate that pupil center estimation is comparable to the state-of-the-art approach. It achieves pupil center estimation errors below the size of a constricted pupil in more than 98.24% of images. Furthermore, the detection time is 2.8 times faster than the state-of-the-art approach. Doi: 10.28991/ESJ-2022-06-05-05 Full Text: PDF
Blended learning in the context of digitalization informatization

The realities of digitalization require changes in strategies for choosing educational technologies. The modern educational process is not possible without the use of digital technologies. Digital technologies have led to the arising and development of blended learning. However, its effectiveness is determined not only by technology. The human factor receives special attention in this direction. Analysis of the World Development Report 2016: Digital Dividends allows us to identify digital competence as a necessary condition for the successful use of digital technologies, and hence blended learning. Learning interactions designing in the process of implementing blended learning requires timely diagnosis of the level of digital competence. A popular tool for this is the Digital Competence Framework for Citizens. To clarify the peculiarities of its use was made an analysis of the experimental implementation results of blended learning in the industrial training in sewing for intended masters. During the research, it was revealed that the most important digital competence areas for the variable learning establishment in the training of future professionals are Information and data literacy, Communication and collaboration and Problem solving. In addition, competence for area Problem solving conduce to increase the level of competence for all other areas. The level of digital competence of the subjects mainly coincide to the characteristics of basic and secondary levels. The obtained data clarified the reasons for the difficulties, decrease motivation and cognitive activity that occur among students using distance courses-resources learning designed for blended learning. Thus, the use of the Digital Competence Framework for Citizens at the initial stage of implementing blended learning can make a rational choice of strategies for combining face-to-face and distance learning technologies.
Experimental investigation of wave-particle duality relations in asymmetric beam interference

Abstract Wave-particle duality relations are fundamental for quantum physics. Previous experimental studies of duality relations mainly focus on the quadratic relation D 2 + V 2 ≤ 1, based on symmetric beam interference, while a linear form of the duality relation, predicated earlier theoretically, has never been experimentally tested. In addition, the difference between the quadratic form and the linear form has not been explored yet. In this work, with a designed asymmetric beam interference and by utilizing the polarization degree of freedom of the photon as a which-way detector, we experimentally confirm both forms of the duality relations. The results show that more path information is obtained in the quadratic case. Our findings reveal the difference between the two duality relations and have fundamental implications in better understanding these important duality relations.
Cloud technologies and learning analytics: web application for PISA results analysis and visualization

This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.
Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt's index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.
Quantum expectation-value estimation by computational basis sampling

Measuring expectation values of observables is an essential ingredient in variational quantum algorithms. A practical obstacle is the necessity of a large number of measurements for statistical convergence to meet requirements of precision, such as chemical accuracy in the application to quantum chemistry computations. Here we propose an algorithm to estimate the expectation value based on its approximate expression as a weighted sum of classically tractable matrix elements with some modulation, where the weight and modulation factors are evaluated by sampling appropriately prepared quantum states in the computational basis on quantum computers. Each of those states is prepared by applying a unitary transformation consisting of at most $N$ CNOT gates, where $N$ is the number of qubits, to a target quantum state whose expectation value is evaluated. Our algorithm is expected to require fewer measurements than conventional methods for a required statistical precision of the expectation value when the target quantum state is concentrated in particular computational basis states. We provide numerical comparisons of our method with existing ones for measuring electronic ground-state energies (expectation values of electronic Hamiltonians for the lowest-energy states) of various small molecules. Numerical results show that our method can reduce the numbers of measurements to obtain the ground-state energies for a targeted precision by several orders of magnitudes for molecules whose ground states are concentrated. Our results provide another route to measure expectation values of observables, which could accelerate the variational quantum algorithms.