Reviewerly/exHarmony huggingface dataset:
Two-dimensional image zooming by adaptive Sinc-based technique

This article reports a novel adaptive Sinc-based zooming technique. The theoretical basis of the zooming technique is the Whittaker–Shannon interpolation formula and the Nyquist–Shannon sampling theorem. The technique can be used to zoom out, zoom in, and reconstruct two-dimensional images with no zoom. The technique introduces a novel concept called pixel-local-scaled k-space, which is calculated as follows. The k-space magnitude and the bandwidth of the image to zoom are calculated. The bandwidth of the frequency components of the image to zoom is estimated by the difference between the maximum and the minimum numerical value of the k-space magnitude. The ratio between the standardised k-space magnitude of the image and the bandwidth is calculated pixel-by-pixel, and is called pixel-local-scaled k-space. The signal is reconstructed using a finite convolution between image pixel intensities and Sinc functions. The zooming factor is consequential to the numerical difference between the pixel-local-scaled k-space and the sampling rate. The novelty of this zooming technique is to be adaptive because the pixel-local-scaled k-space is a pixel-by-pixel map, that is, an image with the same number of pixels as the departing image. Adaptive Sinc-based zooming is validated through the comparison with non-adaptive Sinc-based zooming, and its smoothing effect is compared to the smoothing of a variety of image space and z-space filters. The adaptive Sinc-based zooming technique achieves image zooming reconstruction using sampling rates within an extended range versus the range allowed by non-adaptive Sinc-based zooming.
Preliminary Evaluation of an Osteopathic Manipulative Treatment to Prevent Motion Sickness.

INTRODUCTION: Motion sickness affecting military pilots and aircrew can impact flight safety and, if severe, can lead to disqualification from flight status. However, due to the common adverse effects of motion sickness pharmaceuticals (e.g., drowsiness), medication options are severely limited. The purpose of this study was to explore the potential utility of a nonpharmaceutical method for motion sickness prevention, specifically an osteopathic manipulative technique (OMT).METHODS:A novel OMT protocol for the reduction of motion sickness symptoms and severity was evaluated using a sham-controlled, counterbalanced, between-subjects study design. The independent variable was OMT treatment administered prior to the motion sickness-inducing procedure (rotating chair). The primary dependent measures were total and subscale scores from the Motion Sickness Assessment Questionnaire.RESULTS:The OMT treatment group experienced significantly fewer gastrointestinal (mean scores postprocedure, treatment M = 20.42, sham M = 41.67) and sopite-related (mean scores postprocedure, treatment M = 12.81, sham M = 20.68) symptoms than the sham group while controlling for motion sickness susceptibility. There were no differences between groups with respect to peripheral and central symptoms.DISCUSSION:The results suggest that the treatment may prevent gastrointestinal (nausea) and sopite-related symptoms (sleepiness). These preliminary findings support further exploration of OMT for the prevention of motion sickness. A more precise evaluation of the mechanism of action is needed. Additionally, the duration of the effects needs to be investigated to determine the usefulness of this technique in training and operational settings.Thomas VA, Kelley AM, Lee A, Fotopoulos T, Boggs J, Campbell J. Preliminary evaluation of an osteopathic manipulative treatment to prevent motion sickness. Aerosp Med Hum Perform. 2023; 94(12):934-938.
A Framework for AR Treasure Hunt Design

This paper proposes a framework for the development of Treasure Hunt games using Augmented Reality for educational purposes. The Framework is based on markers and allows the insertion of clues in the form of a simple instruction or an objective question with multiple choices along with 3D models anchored on the marker. Our main contribution was the development of a method for editing the game directly on the display device, without the need to adjust the content in a desktop editor and with low complexity of use. Overall, the framework was able to create Treasure Hunt applications in an easy and intuitive way.
Smart Glove for Sign Language Translation

Sign language is a vital mode of communication for deaf people, yet it presents a significant barrier when interacting with those who do not understand it. The advent of technology has paved the way for innovative solutions to bridge this communication gap. This abstract explores the development and implications of a smart glove designed for sign language translation (SLT). The primary aim of this study is to create a wearable device, the Smart Glove, capable of recognizing and translating sign language gestures into text or speech. Key objectives include designing a lightweight and ergonomic glove prototype, developing machine learning algorithms for sign language recognition, implementing real-time translation capabilities, evaluating the glove's accuracy and usability, and assessing the potential impact on facilitating communication for deaf people. The Smart Glove utilizes only one sensor, flex sensors, to capture hand movements and gestures. These data inputs are processed through a custom-built machine learning model trained on a comprehensive sign language dataset. Preliminary results indicate a high accuracy rate in recognizing sign language gestures, with an average recognition rate of over 90% across a diverse set of signs. While challenges such as expanding gesture recognition and refining translation algorithms remain, this technology offers a promising solution to break down communication barriers and enhance the quality of life for those who rely on sign language.
An Analysis Study of a Series of Korean Language Textbooks: Reflecting on Elements of Multicultural Education

This study aims to analyse the incorporation of multicultural education (ME) elements in the content and objectives of “Standard Korean”, a textbook series for primary students in the 2017 Korean curriculum. The criteria for content analysis of ME include rules/etiquette; intercultural understanding; utilisation of culture as a tool; Korean culture-centred; and others. The textbook strongly emphasises the understanding of the cultural background of Korea as a key content element. Activity analysis was conducted based on three criteria derived from content analysis: comprehension, experiential learning, and internalisation. The activities predominantly leaned towards a comprehensive aspect. To better address the cognitive development levels of the learners, including communication and experiential activities should be further incorporated. The reflective aspects of ME in “Standard Korean” appear to fall short of adopting a predominantly intercultural perspective overall. Korean language textbooks should evolve into customized resources capable of accommodating the diverse cultures of a variety of learners.
A Study on the Education of Applied Music and Web Literacy

Based on surveys of students majoring in applied music at a four-year university and consultations with professors engaged in applied music, this study was conducted to understand the demand and necessity of 'web literacy' education in 'applied music major education'. This study examined how much 'web media/platform' such as music streaming platform, YouTube, and SNS actually play a role in music learning and listening to college students majoring in applied music, and also explored the awareness of the web's status as a 'music presentation and distribution' media as a production subject of music content. Additionally, the study investigated how applied music professors understand the necessity and demand of education related to 'web literacy' of students in the educational field. As a result of the study, it was confirmed that both the educational providers and recipients recognize the proactive role of web media throughout the entire process, including music education, listening, presentation, and distribution, that education related to these aspects of web media should be introduced and expanded to keep pace with the evolving landscape. This study reveals that there is a widely shared recognition of the substantial need for the expansion of education and the introduction of curriculum related to 'web literacy'.
The Impact of Non-verbal Communication on Educational Satisfaction and Class Attitude in Skin Care Practical Classes at Beauty Colleges

This study was conducted to find out the effect of non-verbal communication on educational satisfaction and instructional attitude of skin care practical classes at Beauty University. From June 20 to July 30, 2023, a survey was conducted on third and fourth graders attending the College of Beauty, and a total of 220 copies were analyzed using the SPSS WIN 21.0 program. As a result of the study, non-verbal communication was found to have a positive correlation in body language, similar language, and physical appearance factors. In addition, it was found that nonverbal communication of skin care practical class instructors affects educational satisfaction and class attitude. Among them, body language factors using movements such as hand gestures, gestures, and nodding were found to have the greatest influence. Therefore, instructors need to properly utilize and research and develop nonverbal communication factors in order to increase satisfaction with skin beauty practical classes and pursue correct instructional attitudes.
Dovetail Usage Prediction Model for Resource-Efficient Virtual Machine Placement in Cloud Computing Environment

As IT services have migrated to the cloud, efficient resource management in cloud computing environments has become an important issue. Consequently, research has been conducted on virtual machine placement(VMP), which can increase resource efficiency without the need for additional equipment in data centers. This paper proposes the use of a usage prediction model as a method for selecting and deploying hosts suitable for virtual machine placement. The dovetail usage prediction model, which improves the shortcomings of the existing usage prediction models, measures indicators such as CPU, disk, and memory usage of virtual machines running on hosts and extracts features using a deep learning model by converting them into time series data. By utilizing this approach in virtual machine placement, hosts can be used efficiently while ensuring appropriate load balancing of the virtual machines.
Cultural and philosophical analysis of theatrical art in Kazakhstan

This article considers the ways of professional evelopment of kazakh directing in the era of globalization, having studied the artistic horizons of modern theatrical art from a new point of view. The directions of continuity of the theatre sphere, which is an artistic and aesthetic phenomenon in the culture of our state, from ancient times to the present day are considered. The national character of the country's theatrical directing is considered the trends of its development in the context of cultural and philosophical contexts. Theatrical art is a spiritual, social, artistic and aesthetic beginning for the nation from analysis of the current trends in the development of theatrical directing in Kazakhstan, determining its existence and essence. The role of the theatre industry is determined in a globalizing society in the preservation and development of the image and image of the nation. Scientific and theoretical analysis of the creative heritage of outstanding national directors will be carried out. The purpose of this study is to determine the role of theatrical art for the socio – cultural space of modern Kazakhstan, to determine the role of theatrical amateurs in the formation of modern kazakh culture, to identify interaction with other elements of the socio-cultural system.
Understanding Container Isolation

Containers have become a popular method of deploying and managing applications due to their lightweight nature, scalability, and portability. However, as container technologies evolve, it is important to understand new implications for performance and resource isolation. In this paper, we compare the performance of the runc, crun, and runsc container runtimes standalone and in parallel. We found that all runtimes struggled to isolate identical memory operations of concurrent containers. Runsc had higher overhead and lower performance than runc or crun, but less performance loss when scaling to 40 concurrent containers.
Design of Multi-Channel High-Speed and High-Precision Digital Receiver Based on Multi-FPGA

Aiming at the radar signal processor for signal acquisition speed and multi-channel amplitude and phase consistency has high requirements, through the use of high-performance and high sampling rate ADC (analogue-to-digital converter) chip to achieve the SAR echo signal acquisition, with a number of high-performance FPGA to complete the real-time processing of data and FPGA inter-chip data transmission, and finally unified in a FPGA data group frame through the multiplexed TLK2711 interface to send to the digital transmission subsystem. TLK2711 interface sent to the digital transmission subsystem, designed and implemented a multi-FPGA multi-channel high-speed high-precision data processor design. The data processor up to 1.6Gsps sampling rate and 12-bit RESOLUTION RATIO synchronous acquisition of 8-channel signals, two FPGAs can be processed separately 4-channel data to achieve DBF synthesis, and through the SRIO interface to 10Gbps rate uniformly forwarded to the third FPGA. Experiments show that the designed data processor has a high sampling rate and sampling accuracy, and can achieve multi-channel data acquisition and real-time processing at high speed and high precision.
SAR Image Quality Enhancement Algorithm

After the radiometric calibration of SAR images, the brightness of the pixel units reflects the size of the microwave scattering capacity of the features. However, there are still some inherent defects in SAR images that need to be remedied, otherwise it will cause difficulties in the subsequent image interpretation. In this paper, we will analyse the mechanism of coherent spot suppression, its impact on millimetre band, and applicable suppression algorithms from the perspective of coherent spot suppression.
Secure Storage and Access Control for Personal and EEG Data using Blockchain

This paper introduces a blockchain-based solution for secure and efficient management of personal and EEG data for research subjects. Our approach separates but interlinks the data to ensure privacy and integrity, with role-based access controls implemented via a Solidity smart contract on the Ethereum blockchain. We integrate the principles of operational excellence to enhance traditional information security methodologies, focusing on a more comprehensive understanding and functionality of data management. Our method offers a secure, efficient, and user-friendly data management system, drawing stakeholders closer to essential professional practices and upholding the values of privacy and integrity.
A State-of-the-Art Survey on Local Training Methods in Federated Learning

Federated learning is a machine learning paradigm that enables the training of machine learning models on decentralised data while significantly reducing the amount of communication required. This paper comprehensively overviews the five generations of local training methods in federated learning. We discuss the key contributions of each generation and the challenges that remain to be addressed. The first generation of local training methods proposed in the early 2010s was based on heuristic approaches. These methods had no theoretical guarantees, but they were often effective in practice. The second generation of local training methods, which was proposed in the late 2010s, was based on the assumption that the data on the different clients was homogeneous. This assumption allowed for the development of more theoretical guarantees for local training methods. The third generation of local training methods, which was proposed in the early 2020s, relaxed the assumption of homogeneous data and allowed for heterogeneous data. This made local training methods more applicable to real-world scenarios. The fourth generation of local training methods, which was proposed in the mid-2020s, further improved the convergence rate of local training methods. This enabled training machine learning models on federated learning with less communication and less computation. The fifth generation of local training methods, which is still under development, is exploring the use of advanced techniques such as acceleration and quantisation to improve the efficiency of local training methods further. We believe that the five generations of local training methods have made significant progress in the field of federated learning. We believe that the future of federated learning is bright, and we are excited to see what the next generation of local training methods will bring. Research on federated learning with adaptive communication has the potential to improve the efficiency and effectiveness of this machine learning method, which could be a topic for further research.
Architecture of a Serverless Cloud Application

In this article we present our initial work on defining a generic architecture of a serverless cloud application, built according to the Function-as-a-Service Paradigm. In our previous publications [10], [11] we have described the original application on which we base our work, the transformation of its legacy form into a set of serverless functions, and some basic concepts of data access. Here we describe our further progress. While working on moving the application into serverless cloud, we had to solve several problems, one of them being data access. By adhering to the serverless cloud concept, we have created a basic framework, and have been able to define an architecture, which is generic to the whole class of serverless cloud applications. The architecture is not complete so far, and further work will be required to add to it additional components.
Fuzzy Rule-based Estimation of Students’ Skills

One of the key issues in engineering higher education is the choice of modern teaching-learning methods. The main goal of this study is to investigate the effects of system theory, system-engineering and project-based approach on engineering students’ skills. It is necessary to accurately and objectively define their skills, before and after their education in order to describe the growth of students’ competences. However, most people, including teachers tend to have some subjectivity that can be modelled by fuzzy set theory. This study investigates the possibility and methodology of using fuzzy rule-based estimation of students’ skills
Effect of noise on the robustness of MobileNetV2+U-Net semantic segmentation model for MRI images

This study aims to evaluate effect of noise on the robustness of semantic segmentation models for Magnetic Resonance Imaging (MRI) head images with tumor. We implemented the MobileNetV2+U-Net architectural model. We tested the segmentation model with Gaussian and Poisson noises in various levels. The addition of noise was performed five iterations with a variance of 0.01 each iteration. We carried out evaluations by examining the segmentation results, loss function values, accuracy and dice score. Based on the results, increase in noise affects model performance. Evaluation using loss function shows that graph instability is influenced by the noise level. The accuracy results on the highest and lowest validation data were 99.47% and 98.99% for Gaussian noise and 99.64% and 99.5% for Poisson noise. Apart from that, the highest and lowest dice scores were 82.80% and 69.18% for Gaussian noise and 87.83% and 83.10% for Poisson noise. We recommend training the segmentation model using noisy data so that the model can adapt to noisy images.
Generalization Evaluation of a Nonlinear Auto-Regressive Neural Network for PON Technologies

Building robust models with high generalization capability that can efficiently solve problems in a wide range of situations is essential to promote the development of enabling technologies. In this context, this paper evaluates the generalization capacity of a nonlinear autoregressive with external input neural network (NARXNET) for a passive optical network (PON). We adopted this type of neural network, given its applicability for nonlinear filtering processes, in which the target output is a noise-free version of the input signal. The built NARXNET was assessed with eight extra distinct experimental data sets demonstrating average RMSE and R2 values of 0.295 and 0.961, respectively, with a high generalization capability. Furthermore, NARXNET showed a fast training time of 240 seconds and an improved eye diagram for on-off keying (OOK) modulation format on a PON system.
Anomaly Detection for Numerical Literals in Knowledge Graphs: A Short Review of Approaches

Anomaly Detection is an important problem that has been well-studied within diverse research areas and application domains. However, within the field of Semantic Web and Knowledge Graphs, anomaly detection has been relatively overlooked. Additionally, the existing literature on anomaly detection over Knowledge Graphs lacks proper organization and poses challenges for new researchers seeking a comprehensive understanding. In light of these gaps, this paper aims to offer a well-structured and comprehensive overview of the existing research conducted on anomaly detection over Knowledge Graphs. In this overview, we review the quality metrics of KGs and discuss the possible errors which may occur in different parts of the RDF data. Additionally, we outline a generic conceptual framework for the execution pipeline of Anomaly Detection over KGs. Moreover, we study the anomaly detection techniques, along with their variants, and present key assumptions, to differentiate between normal and anomalous behavior. Finally, we outline open issues in research and challenges encountered while adopting anomaly detection techniques for KGs.
Side-Channel Based Device Authentication in Wireless Charging: An Experimental Study

As wireless technologies increasingly occupy the digital landscape, wireless charging services for smartphones and related accessories have taken a leap forward. In this context, the ability to authenticate and distinguish devices has become a paramount necessity for maintaining consistent service quality and user satisfaction. In this study, we implement an efficient and cost-effective side-channel based device authentication system. Our approach primarily focused on harvesting side-channel data from Qi wireless charger voltage measurements, thus developing a procedure that is not confined to a specific protocol. To implement this authentication framework, we developed a model that classifies devices by continuously monitoring voltage fluctuations in a wireless charging environment, and learns from these observations. We conducted validation experiments using four products from two different smartphone manufacturers. Our results show that the manufacturer of a mobile device can be identified with an exceptional 100% accuracy by relying solely on the voltage readings obtained from the Qi wireless charger. Moreover, our method can distinguish among four devices with different models with an accuracy of 92%. By adopting such a side-channel based device authentication system, service providers can mitigate the risks associated with unauthorized access by unauthenticated users. Our groundbreaking findings may also contribute to the development of a robust and scalable architecture for device classification and authentication in wireless charging environments.