Reviewerly/exHarmony huggingface dataset:
Construction of a 5G Wireless Semantic Web-Assisted English Digital Learning Resource Query System

Based on the 5G wireless semantic network, this paper realizes the construction of the English digital learning resources query system, providing the query and management functions for English digital learning resources, and realizes the integration of functions and resources on the digital learning system and mobile learning system. The platform is put into practical application, and data analysis is conducted to conclude how this system is used and whether it is conducive to improving students’ interest in learning English and their English proficiency. We propose a quality algorithm for 5G wireless networks, which not only captures the characteristics of 5G wireless networks such as local broadcast and node movement but also combines quality predicates as guards with broadcast reception actions, which are used to portray that a node can continue to execute subsequent processes only when the node reception actions satisfy certain conditions, which works together with the node default value mechanism to reduce the impact caused by unreliable links. The query content is interpreted and supplemented at the semantic level by using synonym expansion, and the knowledge base is used to filter the synonym expansion and further expand the synonym expansion query based on knowledge structure, to tap the implicit knowledge points related to the query, and to fuse the two extensions to realize the knowledge-based query expansion. We use 5G wireless semantic web-related technologies to build an English digital learning resource query system with complete functions and good experience across terminals. Through the questionnaire survey and data analysis of the users of this system, it is found that the system is well used and effectively enhances students’ interest in learning English and improves learning efficiency. By applying the experimental comparison method, it is known that this system has improved students’ English proficiency to a certain extent.
FedVoting: A Cross-Silo Boosting Tree Construction Method for Privacy-Preserving Long-Term Human Mobility Prediction

The prediction of human mobility can facilitate resolving many kinds of urban problems, such as reducing traffic congestion, and promote commercial activities, such as targeted advertising. However, the requisite personal GPS data face privacy issues. Related organizations can only collect limited data and they experience difficulties in sharing them. These data are in "isolated islands" and cannot collectively contribute to improving the performance of applications. Thus, the method of federated learning (FL) can be adopted, in which multiple entities collaborate to train a collective model with their raw data stored locally and, therefore, not exchanged or transferred. However, to predict long-term human mobility, the performance and practicality would be impaired if only some models were simply combined with FL, due to the irregularity and complexity of long-term mobility data. Therefore, we explored the optimized construction method based on the high-efficient gradient-boosting decision tree (GBDT) model with FL and propose the novel federated voting (FedVoting) mechanism, which aggregates the ensemble of differential privacy (DP)-protected GBDTs by the multiple training, cross-validation and voting processes to generate the optimal model and can achieve both good performance and privacy protection. The experiments show the great accuracy in long-term predictions of special event attendance and point-of-interest visits. Compared with training the model independently for each silo (organization) and state-of-art baselines, the FedVoting method achieves a significant accuracy improvement, almost comparable to the centralized training, at a negligible expense of privacy exposure.
Detecting Credit Card Fraud using Machine Learning

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&amp;LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&amp;LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.
Effect of a Full Immersive Virtual Reality Intervention on Selective Attention in Children

The purpose of this research was to investigate the impact of two educational interventions, a program based on Full Immersive Virtual Reality games and a Typical Training program, in Selective Attention (SA) of children aged 8-10 years old.Forty-eight girls of elementary school from the city of Komotini, in Greece, participated in this study.They were randomly divided into three individual groups of 16 children each, one Control Group (CG) and two experimental groups.The CG did not receive any structured SA training program, while the two experimental groups attended a program focused on improving SA for 6 weeks, twice a week for 24 min, each time.The participants of FIVE group attended a full immersive program in Playstation4 VR and the participants of TT group attended a typical training to the gym.Before, after and one -month after the interventions SA was estimated using Super Lab, recording the number and the RT of correct answers of relevant and irrelevant questions to volleyball.Two-way analyses of variance with repeated measures were conducted to determine the effect of training program groups and measurements across number and RT of correct answers on SA performance.Analysis of the data illustrated that the post-test SA scores and the one -month retention test SA scores were remarkably greater than pre-test SA scores for both experimental groups and not for the CG.In conclusion exergames VR are effective tools for improving SA such as typical training.
Special Issue on “Machine Learning/Deep Learning in Medical Image Processing”

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL) [...]
Study on Improvement of Lightning Damage Detection Model for Wind Turbine Blade

There have been many reports of damage to wind turbine blades caused by lightning strikes in Japan. In some of these cases, the blades struck by lightning continue to rotate, causing more serious secondary damage. To prevent such accidents, it is a requirement that a lightning detection system is installed on the wind turbine in areas where winter lightning occurs in Japan. This immediately stops the wind turbine if the system detects a lightning strike. Normally, these wind turbines are restarted after confirming soundness of the blade through visual inspection. However, it is often difficult to confirm the soundness of the blade visually for reasons such as bad weather. This process prolongs the time taken to restart, and it is one of the causes that reduces the availability of the wind turbines. In this research, we constructed a damage detection model for wind turbine blades using machine learning based on SCADA system data and, thereby, considered whether the technology automatically confirms the soundness of wind turbine blades.
ROLE OF SOCIAL SUPPORT TOWARD STUDENT ACADEMIC SELF-EFFICACY IN ONLINE LEARNING DURING PANDEMIC

The Covid-19 pandemic has changed many government policies. One of them is distance learning policies implemented by affected countries. However, distance-learning or online learning program has received a negative response due to the lack of preparation and socialization. Therefore parents, teachers, and students find it difficult to adapt. Students feel doubtful and anxious about success in online learning programs. At the same time, students need high self-efficacy to advance in learning and get the desired results. Therefore, this study aims to describe the description of student self-efficacy in online learning during the pandemics. The research method used is a qualitative approach. The primary data in library studies are compiled through various search pages for scientific journals, books, and government regulations. The study results show that not all students' academic self-efficacy during distance learning is high. Some students are in the lower middle category. In addition, social support has a strong role in increasing students' academic self-efficacy. Thus, this research can be used as an evaluation material in education and a reference in creating policies.
Map Evaluation under COVID-19 restrictions: A new visual approach based on think aloud interviews

Abstract. With the beginning of the COVID-19 pandemic, the execution of eye-tracking user studies in indoor environments was no longer possible, and remote and contactless substitutes are needed. With this paper, we want to introduce an alternative method to eye tracking, completely feasible under COVID-19 restrictions. Our main technique are think aloud interviews, where participants constantly verbalize their thoughts as they move through a test. We record the screen and the mouse movements during the interviews, and analyse both the statements and the mouse positions afterwards. With this information, we can encode the approximate map position of the user’s attention for each second of the interview. This allows us to use the same visual methods as for eye-tracking studies, like attention maps or trajectory maps. We implement our method conducting a user study with 21 participants to identify user behaviour while solving high-level interpretation tasks, and with the results of this study, we can show that or new method provides a useful substitute for eye-tracking user studies.
Designing A Virtual Jakarta Tourism Application On Android Os And Virtual Reality Tools

The background of this research is to make a prototype of a Jakarta virtual tourism application system using Android OS and using virtual reality tools. The method used by this panel is to use a literature review and create an application prototype that will be applied to the making of a virtual Jakarta tourism program. The results of this study are the application of virtual reality tools on the Jakarta virtual tourism application which aims to help the tourism sector affected by the COVID-19 pandemic and the manufacture of application prototypes that use the Android OS. The limitation of this research lies in making an application prototype using the Android OS which is not perfect and has not produced a real application. This research is very useful for the development of the tourism sector during the COVID-19 pandemic and in the future, so that tourists can choose to travel online or offline.
Trajectory optimization with obstacles avoidance via strong duality equivalent and hp‐pseudospectral sequential convex programming

Abstract A trajectory optimization algorithm is developed in this article to compute a trajectory that avoids obstacles for an unmanned aerial vehicle and minimize the load factor. Quadratic and polyhedron obstacles are both considered in this article. To satisfy the safety distance requirements in engineering practice, a distance‐based obstacle avoidance constraint is formulated, which restricts the minimum distance that a vehicle can approach the obstacle. Strong duality equivalent converts the constraints into equivalent forms without introducing binary variables. Subsequently, the trajectory optimization problem is numerically solved via a hp‐pseudospectral sequential convex programming method, which iteratively solves a sequence of second‐order cone programming subproblems until convergence is attained. Furthermore, a modified trust‐region constraint and a customized nominal trajectory update rule are proposed to accelerate the convergence of the algorithm. A group of simulations are performed to demonstrate that the algorithm is computationally efficient and capable of generating a trajectory that satisfies safety distance requirements, preventing conservativeness in the trajectory, reducing the incidence of intersample constraints violation.
Publishing trends on research data management in Sub-Saharan Africa: A bibliometrics analysis

Research data management is an umbrella term used to describe activities related to the creation, organisation, structuring, naming, backing up, storage, conservation, and sharing of research data as well as all actions that guarantee security of research data. As is often the case, researchers from Sub-Saharan Africa are lagging behind their counterparts in developed countries in embracing the best practices of research data management. One of the factors to which this slow pace of adoption of research data management could be attributed, is inadequate research on the subject. The purpose of this paper is to analyse the quantity, quality, visibility and authorship of publications on research data management in Sub-Saharan Africa. Bibliometrics approaches were used to analyse publications on research data management from, and on, Sub-Saharan Africa which are currently indexed in Google Scholar. The index was chosen because it is free and is reputed to have liberal selection criteria which do not favour, or discriminate, any discipline or geographic regions. Data was retrieved from Google Scholar using Harzing’s “Publish or Perish” software and analysed using VOSviewer. The findings of the study revealed that the quantity, quality, visibility and authorship collaboration of scholarly publications on research data management in Sub-Saharan Africa is low. The findings may be used by libraries and research institutions in Sub-Saharan Africa to develop and promote best practices in research data management as a means of enhancing their research output and impact.
Faculty readiness for a digital education model: A self-assessment from health sciences educators

During the COVID-19 pandemic, the continuity of education across the world is being supported through e-learning. Healthcare programs especially, require continuing patient-centered training to benefit individuals and society. The objective of this study was to assess the faculty members’ skills to continue educational services through a digital education model at the beginning of the lockdown. The methodology consisted in a quantitative approach descriptive and cross-sectional design. The instrument was a survey with two sections: (1) self-assessment and (2) self-ranking based on the digital education model. The results for 497 participants indicated higher scores on active learning and web conference digital skills. Evaluation is still a competence required to be reinforced by the faculty to incorporate a full transition into online learning. There seems to be an agreement about faculty readiness to implement different active learning strategies, despite being in a distance education model. Implications for practice: University leaders welcomed the self-assessment that faculty members performed on digital skills as it allowed them to adapt the training programs and designate staff teams to support the educators once classes restarted. Educators implementing a digital education model should consider a planned and structured educational solution that is beyond the distance between learners and teachers, but an engaging environment for learning incorporating different technologies and active pedagogies.
A Design of Multi-Purpose Image-Based QR Code

A multi-purpose image-based QR code is designed in this paper. There are four purposes for the generated image-based QR code. In the first purpose, the basic image-based QR code with the look of a host image is with an ingenious layout to be identified easier. In the second one, a saliency region detection method is adopted for enhancing the quality of the image-based QR code. In the third one, the host image is embedded into the image-based QR code for further access to the host image; Finally, the visual cryptography-based watermarking method is applied to the host image embedded image-based QR code. In the case that the specific users need verification from the image-based QR code, the binary verified image can be retrieved when the public share is available. The experimental results demonstrate that the generated image-based QR code not only looked better than some previous works but also had high quality host image embedded and identification ability.
Learning doubly stochastic and nearly idempotent affinity matrix for graph-based clustering

In graph-based clustering, a relevant affinity matrix is crucial for good results. Double stochasticity of the affinity matrix has been shown to be an important condition, both in theory and in practice. In this paper, we emphasize idempotency as another key condition. In fact, a theorem from Sinkhorn, R. (1968) allows us to exhibit the bijective relationship between the set of doubly stochastic and idempotent matrices of order n (modulo permutation of rows and columns) on the one hand, and the set of possible partitions of a set of n objects on the other hand. Thereby, both properties are necessary and sufficient conditions for properly modeling the clustering or graph partitioning tasks using matrices. Yet, this leads to a NP-hard discrete optimization problem. In this context, our main contribution is the introduction of a new relaxed model that efficiently learns a double stochastic and nearly idempotent affinity matrix for graph-based clustering. Our approach leverages existing properties between doubly stochastic and idempotent matrices on the one hand, and their associated Laplacian matrices on the other hand. The resulting optimization problem is bi-convex and can be addressed by an Alternating Direction Method of Multipliers scheme. Furthermore, our model requires less parameters to set in contrast to most of recent works. The experimental results we obtained using several real-world benchmarks, exhibit the interest of our method and the importance of taking into account idempotency in graph-based clustering.
Anyuak Language Named Entity Recognition Using Deep Learning Approach

Objectives: This study aims about the development of Anyuak language named entity recognition of its first kind.NER is a fundamental sub task in natural language processing and the high accuracy competence in NER system marks the effectiveness of the downstream tasks.Anyuak language named entity recognition concern is addressed by using a long short-term memory model to categorize tokens into predefined classes.Methods: A long short-term memory is used to model the NER for Anyuak language to detect and classify words into five predefined classes: Person, Time, Organization, Location, and Others (non-named entity words).Because of feature selection plays a vital role in long short-term memory framework, the experiment in this work were conducted to discover most suitable features for Anyuak NER tagging task.Findings: When we evaluated the experiment in cross-validation, we achieved a promising result of precision, recall, and F1-measure values of 98%, 90, and 94% respectively.From the experimental result, it is possible to determine that tag context, word features, part of speech tags, suffixes and prefixes are significant features in named entity recognition and classification for Anyuak language.Novelty: Finally we have contributed a new architecture for Anyuak NER which uses automatically features for Anyuak named entity recognition which are not dependent on other NLP tasks.We proved that deep learning models can be extended, trained and can work for Anuak languages.
Dense Semantic Forecasting in Video by Joint Regression of Features and Feature Motion

Dense semantic forecasting anticipates future events in the video by inferring pixel-level semantics of an unobserved future image. We present a novel approach that is applicable to various single-frame architectures and tasks. Our approach consists of two modules. The feature-to-motion (F2M) module forecasts a dense deformation field that warps past features into their future positions. The feature-to-feature (F2F) module regresses the future features directly and is, therefore, able to account for emergent scenery. The compound F2MF model decouples the effects of motion from the effects of novelty in a task-agnostic manner. We aim to apply F2MF forecasting to the most subsampled and the most abstract representation of the desired single-frame model. Our design takes advantage of deformable convolutions and spatial correlation coefficients across neighboring time instants. We perform experiments on three dense prediction tasks: semantic segmentation, instance-level segmentation, and panoptic segmentation. The results reveal state-of-the-art forecasting accuracy across three dense prediction tasks.
Effects of Power Distance on Organizational Commitment: A Study on Maritime Faculty Students

This study investigates the effect of power distance on the organizational commitment of maritime faculty students.The hypotheses developed in this context were analyzed using the structural equation modeling method based on data collected from 406 participants.The results from one-dimensional (1D) evaluation indicated that organizational power distance had a positive impact on the organizational commitment levels of maritime faculty students.However, there are several studies in the literature that show that this effect may lead to inconsistent results in 1D examination.These conflicting results necessitate a multidimensional analysis of the research.Evaluation of the sub-dimensions included in the study revealed that: (1) acceptance of power had positive effects on calculative and moral commitment, (2) justification of power on moral commitment, and (3) acquiescence of power on calculative, alienative, and moral commitment.
Intelligent High Payload Audio Watermarking Algorithm Using Colour Image in DWT-SVD Domain

Abstract Copyright protection and ownership verification of digital audio tracks have become increasingly important to be enabled by digital watermarking techniques. A novel high payload intelligent audio watermarking scheme with RGB color watermark image is proposed in this paper. The color watermark image is encrypted using Arnold chaotic map and passed through an adaptive scaling filter to scale the image to match the required payload. The encoding process is performed on the scaled encrypted version of the watermark image. A portion of the audio signal is used to embed a synchronization code and the other one is decomposed into short frames. These frames are processed with a two-level discrete wavelet transform (DWT), followed by a singular value decomposition (SVD) process on the approximation coefficients. The encoded watermark is inserted into the diagonal matrix using quantization index modulation (QIM). The inverse process of SVD and DWT is applied to obtain the marked audio signal. Blind extraction of the hidden information from the marked audio signal is performed in the reverse order of the embedding process. Experiments show that security, high payload, transparency and imperceptibility of the algorithm are satisfied. The robustness against several kinds of audio signal processing attacks is shown. Performance evaluation tests with SNR, BER, and FSIM are conducted.