Reviewerly/exHarmony huggingface dataset:
PERANCANGAN APLIKASI MONITORING TERHADAP KINERJA MURID SEKOLAH DASAR DAN MENENGAH PERTAMA

The process of exchanging information between schools and parents can affect the process of children's educational performance, especially the role of the family, because the family is the person who influences the child's development. However, not a few parents are busy at work, especially career parents who have children so that a good communication process can be neglected. This is a concern for the author to bridge the exchange of information between schools, children and parents so that there is no distance that can lead to errors or information about children at school so that parents can continue to monitor the development of children's education using the platform that the author created anytime and anywhere. The purpose of this research is to help link information about children's development at school not to stop or stop in the school environment, but parents can also participate in knowing the activities their children are doing at school by monitoring their children's performance or performance at school through a platform that can be accessed easily. especially for students at the elementary level, namely elementary and junior high schools because basic education is a general indicator that can be used to see the level of intelligence of the nation. The method that the author uses to carry out system analysis and design is application design using the System Development Life Cycle (SDLC). The results of this research are monitoring applications that are made to make it easier for the school to carry out teaching and learning activities as well as parents who monitor the development and activities of their children at school. It can facilitate access to exchange of information from school to parents and vice versa so that there are no gaps or miss information with parents and easily see the number of students, value recap, and student spp payment reports. parents are also very helpful because they can easily monitor their child's performance at school, both academic and non-academic, parents can also consult with relevant teachers if their child has problems at school and parents can also see the daily agenda and will also get notifications about the status of tuition payments.
Using ICT to research maternal, newborn and child health during the COVID-19 pandemic in Nigeria

Research remains vital to realizing the Sustainable Development Goals (SDGs) (Fayomi et al., -2018), however it may be hampered by unexpected crises. Reducing global maternal mortality to less than 70 per 100,000 live births, neonatal mortality to 12 per 1,000 live births and under-five mortality rates to 25 per 1,000 live births are key SDG targets for 2030 (WHO, -2018). Improving access to quality maternal, newborn and child health (MNCH) services is central to achieving these targets. Despite progress in reducing maternal and neonatal mortality, Nigeria still contributes significantly to the global burden of maternal deaths, accounting for the highest proportion of stillbirths, pregnancy-related deaths and neonatal mortalities worldwide. In 2017, Nigeria accounted for almost a quarter of maternal deaths globally –​ the highest of any country that year (World Bank, -2019).
<i>CSoS</i> ECR outstanding research article award 2023 winner

"CSoS ECR outstanding research article award 2023 winner." Critical Studies on Security, 11(2), p. 63
The Impact of Hybrid Learning on Learning Outcomes: The Mediating Effect of Learning Utilizing ChatGPT and Case-Based Learning

Objectives This study aimed to examine whether hybrid learning has an impact on learning outcome, and to confirm whether learning utilizing chatGPT and case-based learning have a partial mediating effect in the process.&#x0D; Methods To do this, a mediation model analysis was conducted through the responses of 79 learners at University A in Daejeon. The study employed STATA 17 version to conduct basic statistical analysis, correlation analysis, as well as partial mediation analysis and significance testing using the khb analysis method.&#x0D; Results According to the analysis, hybrid learning had a positive effect on learning outcome. In addition, hybrid learning was found to has a positive effect on learning outcome through learning utilizing ChatGPT. However, no significant a partial mediating effect was observed between hybrid learning and learning outcome through case-based learning.&#x0D; Conclusions The results of this study support previous research by confirming the effectiveness of hybrid learning in positively influencing learning outcome. It is also significant in examining and confirming the partial mediating effects of learning through the use of ChatGPT and case-based learning on learning outcomes, which have been proposed as solutions for learning in the post-COVID era.
Differences of coaching competency according to clusters of mothers’ media literacy

Objectives The purposes of this study were to classified the type of media literacy of mothers, and to verify whether there is any difference in mothers’ coaching competency.&#x0D; Methods For this purpose, a survey was conducted with 293 mothers with children aged 3, 4, and 5. Cluster analysis using the sub-scales of media literacy was conducted. Then, the differences in coaching competency according to the clusters were analyzed by one-way ANOVA and Scheffé.&#x0D; Results As for the clusters of mothers' media literacy, four clusters were found: ‘social communication’, ‘limited communication’, ‘factual understanding’, and ‘critical understanding’. As a result of ANOVA, ‘social communication group’ showed significantly higher scores than ‘factual understanding group’. but It was found that different sub-factors of coaching competency also occurred depending on the characteristics of the mother group.&#x0D; Conclusions The results of this study suggest that it is necessary to provide a high level of media literacy education for mothers instead of simply functional media literacy education for creating a healthy media environment at home.
The Effect of Entrepreneurial Capability on Entrepreneurial Behavior: Focused on the Mediating Effects of Entrepreneurial Education and Entrepreneurial Experience·Experience on Entrepreneurial Activities

Objectives This study aimed to examine entrepreneurial capability has an impact on entrepreneurial behavior, and to confirm whether entrepreneurial education and entrepreneurial experience·experience on entrepreneurial activities have a mediating effect in the process.&#x0D; Methods Entrepreneurial capability was assumed as an independent variable, entrepreneurial behavior was assumed as a dependent variable, and entrepreneurial education and entrepreneurial experience·experience on entrepreneurial activities as mediating variables. To do this, a mediation model analysis was conducted through the responses of 297 learners from 14 universities who performed the KDB entrepreneurial education program conducted by the Korea Entrepreneurship Foundation.&#x0D; Results According to the analysis, entrepreneurial capability had a positive effect on entrepreneurial behavior. In addition, entrepreneurial capability was found to have a positive effect on entrepreneurial behavior, through entrepreneurial education. However, no significant mediating effect was observed between entrepreneurial capability and entrepreneurial behavior through entrepreneurial experience·experience on entrepreneurial activities.&#x0D; Conclusions The results of this study support previous research by confirming the effectiveness of entrepreneurial capability in positively influencing entrepreneurial behavior. Furthermore, it is significant to examine and confirm the mediating effects of entrepreneurial education and entrepreneurial experience·experience on entrepreneurial activities on the relationship between entrepreneurial capability and entrepreneurial behavior. The identification of entrepreneurial education as a mediating variable in the process where the effects of entrepreneurial capability manifest holds academic and practical significance.
Improving online-based practical teaching competency of preservie science teachers through the learning assistant program

Objectives Learning Assistant (LA) program was developed for pre-service science teachers to improve their online-based practical teaching competency to conduct online science classes.&#x0D; Methods The online-based teaching competency is defined as instructional design, material development, instructional execution and instructional reflection competency, in order to improve it, LA training course consists of four stages: PCK, planning, practice, and reflection. LA supported course is a science liberal arts class in which learning assistants practice teaching, and consists of the stages of concept introduction, concept understanding, and concept development. The ‘practice’ stage of the LA training course and the ‘concept development’ stage of the LA supported course intersect. 13 students from 2nd-4th grades majoring in physics education and environment education participated as learning assistants, and 24 students from 1st-3rd graders from various majors participated as learners. LA conducted a modified RTOP test based on online. After that, the change in online- based practical teaching capabilities before and after application of the program was analyzed through the corresponding sample t-test.&#x0D; Results Since both courses were conducted online for one semester, learning assistants can increase the effectiveness of learning at online teaching context such as online experiment, online evaluation, and online discussion guidance, after learning about PCK and making plans to apply them, they actually helped with learning. As a result of evaluation of the learning assistant's performance using RTOP, it was confirmed that the online-based practical teaching competency improved as the learning assistance experience was accumulated.&#x0D; Conclusions This result is significant in that it suggests a new direction in the way of nurturing online teaching competency in the college curriculum for teacher training.
The Effect of University Entrepreneurial Education on College Students' Entrepreneurial Capability: Focused on the mediating effects of Instructor Satisfaction and Entrepreneurial Education Experiences

Objectives This study aimed to examine whether university entrepreneurial education has an impact on entrepreneurial capability, and to confirm whether instructor satisfaction and entrepreneurial education experiences have a partial mediating effect in the process.&#x0D; Methods To do this, a mediation model analysis was conducted through the responses of 297 learners from 14 universities who performed the KDB entrepreneurial education program conducted by the Korea Entrepreneurship Foundation. The study employed STATA 17 version to conduct basic statistical analysis, correlation analysis, as well as partial mediation analysis and significance testing using the khb analysis method. University entrepreneurial education was assumed as an independent variable, entrepreneurial capability was assumed as a dependent variable, and instructor satisfaction and entrepreneurial education experiences as mediating variables.&#x0D; Results According to the analysis, university entrepreneurial education had a positive effect on entrepreneurial capability. In addition, entrepreneurial capability was found to have a positive effect on entrepreneurial capability through instructor satisfaction. However, no significant a partial mediating effect was observed between entrepreneurial education and entrepreneurial behavior through entrepreneurial experience. The study results confirmed the need for university entrepreneurial education to enhance entrepreneurial capability. It was found that in order to maximize the effectiveness of university entrepreneurial education, the abilities and qualities of instructors who deliver the education are crucial. The study also revealed that such educational approaches contribute to the improvement of entrepreneurial capability.&#x0D; Conclusions The results indicating that university entrepreneurial education has a positive impact on entrepreneurial capability is significant in confirming the effectiveness and necessity of university entrepreneurial education. Additionally, the study's contribution lies in verifying and elucidating the partial mediating effects of instructor satisfaction and entrepreneurial education experience on the relationship between university entrepreneurial education and entrepreneurial capability. By examining whether instructor satisfaction mediates the effects of entrepreneurial education on entrepreneurial capability, the study provides detailed insights into how the effectiveness of entrepreneurial education translates into improved entrepreneurial capability. Furthermore, the study holds academic and practical significance by highlighting the role of instructor satisfaction as an intermediate variable in the process where the effects of university entrepreneurial education manifest.
The Association Between Syntactic Complexity and Proficiency Level in Korean EFL Learner Writings: An Automated Analysis with TAASSC

Recent applications of natural language processing technologies to learner corpus analysis have begun to shed new light on syntactic development in second language (L2) written production. The present study aims to further our understanding of syntactic complexity development in Korean EFL learners by analyzing their writings with an automated tool for syntactic feature extraction. Stepwise linear regression is used to determine unique predictors of proficiency level among the clause and noun phrase complexity indices extracted from 560 argumentative essays sampled from the Yonsei English Learner Corpus. The results suggest that syntactic complexity increases at various levels of specificity both in clauses and noun phrases along with proficiency levels, and that features reflecting noun phrase complexity are overall stronger predictors of proficiency level than clause complexity features. These findings may help researchers to present a more fine-grained description of L2 syntactic development and teachers to make better informed decisions on grammar instruction tailored to learners’ current level of writing proficiency.
Control of the flow of light in the non-adiabatic regime

We demonstrate that coupled waveguides systems can be operated in the so called non-adiabatic regime. In this regime, light can undergo transitions between different steady states in a manner analogue to the transition of an electron between energy levels. This approach contrasts with the previous concepts in integrated photonics that rely exclusively on the adiabatic control of the flow of light. We show in particular that abrupt changes of the control parameters are not required to create such non-adiabatic transitions. The constraint for adiabaticity being lifted, light can now be manipulated optimally. And the analogy between the time evolution of a quantum system and the propagation of light in a waveguides array can be pushed one step further.
Path Planning Algorithm Based on RRT-A<sup>*</sup> in 3D Terrain

Efficient and safe global path planning for vehicles is a key issue in the field of navigation. However, in large-scale maps, traditional A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithms have the problem of long search times, which seriously affects planning efficiency. This article proposes a global planning algorithm called RRT-A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> based on large-scale maps, considering multiple path costs, and designing dynamic step size adjustment. The results show that the algorithm proposed in this article has much shorter planning time than A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , the time consumption is close to RRT and has lower path cost than RRT algorithms. Therefore, it can greatly improve the efficiency of path planning in large-scale maps.
A Collision-Free Pedestrian-Following System for Mobile Robots Base on Path Planning

In order to overcome the problems of collision avoidance and the pedestrian target loss in pedestrian-following system. This paper proposes a Collision-Free pedestrian following approach with a multi-sensor fusion-based pedestrian detector module and a path planning module. The pedestrian detector module use extended Kalman filter(EKF) to fuse a camera and a 3D LiDAR data for the target pedestrian positioning and perceive obstacles.for the path planning module, In order to track pedestrians in real time and collision-free, a global path planner and a local path planner will work simultaneously. Overall, we conducted experiments on pedestrian following in different environmental conditions to evaluate the performance of ours proposed approach.
RescueAid: Smartphone-Aided Situational Awareness For Emergency Response

This demonstration paper presents the RescueAid platform, that leverages the collective intelligence of edge devices by using spatio-temporal data readily available on modern smartphones (i.e., camera, GPS, and inertial sensor measurements) to deliver situational awareness during emergency response operations. Within the RescueAid mobile application, popular machine learning (ML) frameworks are deployed with pretrained models for object detection and image segmentation and used to automatically annotate and tag collected images from the field. RescueAid will be demonstrated in two modes: (i) Interactive mode, where attendees will be able to walk around outside the conference venue carrying RescueAid-enabled smartphones to generate sample data that will be automatically shared and displayed on the web platform interface and (ii) Trace-driven mode, where attendees will have the opportunity to visualize on the platform’s user interface the location traces and associated images collected during a real field exercise.
Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses

Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://github.com/JunbongJang/contour-tracking/
Chaotic Time Series Prediction of Multi-Dimensional Transformer Based on Bionic Reconfigurable Three-Dimensional Scanning Robot

With the rapid development of modern science and technology, especially with the emergence and widespread application of computer technology, it has become possible to use computers for predicting chaotic phenomena. There are still some problems in predicting chaotic time series, such as weak robustness, low accuracy, and poor generalization ability. Therefore, this article proposes a new chaos time series prediction algorithm based on the multi-dimensional transformer (DTM) algorithm. This algorithm mainly achieves chaos time series prediction through end-to-end data correlation prediction. Experimental results show that the accuracy of this algorithm reaches 93.227%, which is significantly higher than that of SVM, LSTM and ESN algorithms. Moreover, it has stronger robustness and better generalization performance.
DEVELOPMENT OF NEURAL NETWORK APPLICATIONS OF BRAIN-COMPUTER INTERACTION BASED ON THE STABLE DIFFUSION MODEL

This paper investigates neurotechnologies for developing braincomputer interaction (BCI) based on the generative deep learning Stable Diffusion model. An algorithm for modeling BCI is proposed, its training and testing on artificial data are described. The results are encouraging for researchers and can be used in various areas of BCI, such as distance learning, remote medicine, etc.
Single/Multi-Source Black-Box Domain Adaption for Sensor Time Series Data

Unsupervised domain adaption (UDA), which transfers knowledge from a labeled source domain to an unlabeled target domain, has attracted tremendous attention in many machine learning applications. Recently, there have been attempts to apply domain adaption for sensor time series data, such as human activity recognition and gesture recognition. However, existing methods suffer from some drawbacks that hinder further performance improvement. They often require access to source data or source models during training, which is unavailable in some fields because of privacy protection and storage limit. Typically, the source domains may only provide an application programming interface (API) for the target domain to call. On the other hand, current UDA methods have not considered the temporal consistency and low-signal-to-noise ratio (SNR) of sensor time series. To address the challenges, this article presents a black-box domain adaption framework for sensor time series data (B2TSDA). First, we propose a single/multi-source teacher-student learning framework to distill the knowledge from the source domains to a customized target model. Then we design a new temporal consistency loss by combining an adaptive mask method and dynamic threshold method to maintain consistent temporal information and balance the learning difficulties of different classes. For the multisource black-box domain adaption, we further propose a Shapley-enhanced method to determine the contribution of each source domain. Experimental results on both single-source and multisource domain adaption show that our framework has superior performance compared to other black-box UDA methods.
Watermark Based Sensor Data Protection System for Wireless Sensor Network

Most of the sensor nodes in the Internet of Things (IoT) and home automation systems (HAS) applications are designed to be low-cost and energy efficient. These devices are expected to be lightweight, and flexible with low maintenance. These criteria limit the computational and communications capabilities of such systems whereas system protection and data authentication have always been limitation factors due to security cost complexity.In this paper, we investigate and propose a simple, lightweight, and cost-effective watermark security method for Wireless sensor networks (WSNs). It is an improved version of our previous research on the metadata-enhanced cross-watermarking approach. The original watermark generator has been extended to achieve cipher block chaining integrating with side-channel watermarking. The watermark bits are hidden in the transmitting channel properties. The experimental results and security analysis prove this scheme, provide data authenticity and transmission integrity without additional security payload.