Reviewerly/exHarmony huggingface dataset:
Teaching Vocabulary Through Morphological Analysis At The Second Year Students Of MTs PPUW Benteng

The objective of the research is to find whether or not the use of morphological analysis can improve the student vocabularu mastery.The research was applied quasi-experimental design . It involved two groups, The experimental group and the control group. The population of this research was the second year students in academic year 2014-2015 of MTS PPUW Benteng. It consist of 7 classes. The total number of population is178 students, The researcher choesestwo classes as simple .Finally, The researcher selected VIII/D as the experimental group and VIII/E as the control group. VIII.D consist of 16 students and VIII.E consist of 16 students so the total sample of the research is 32 students The researcher applied morphological analysis on the sxperimental group and for the contol group the student tought using conventional technique . There were two Thenques of data collecting applied in this study , They were test . The test is used to compare students vocabulary achievements,The application of morphological analysis anhanced the students ability to vocabulary mastery in the second year students of MTS PPUW Benteng . The achievement in vocabulary mastery of the students who applied morphological analysis and those who did not apply morphological analysis had sighnficant difference . It was oroved by the mean score in post test of experimental group was bigher than the mean score of control groupn (79.37-66.87) and the t,test value on post test was bigher than t-table(3.363&gt;2.042)Based on the data above the researcher cocluded that using morphological analysis increase students vocabulary mastery.
Blockchain Technology: A Game Changer for Smart Healthcare Systems

Blockchain technology enables reliable, secure, traceable, distributed, and fault-tolerant data storage. With the emergence of smart contracts (programs that run above the blockchain), the technology is no longer used only for financial purposes but also for more complex applications. This chapter aims to present the fundamental characteristics of blockchain and how it works. We show the main use cases of blockchain and examples of their use focused on the health area. Also, we discuss the application scenarios and the challenges to be overcome for the implementation and deployment of the technology.
The choice of toys by early childhood children

The article deals with the problem of choosing toys by children aged 0-48 months. It represents how children's situational involuntary interest in toys gradually transforms into cognitive interest. The latter determines the young children’s choices in various spheres of their life and, in particular, in choosing among the most accessible and feasible items – toys. The children’s cognitive interest in the choice of toys has been studied in the object and toy environment. A comparative analysis of children's choices among three types of the most common toys was carried out. It has been proved that figurative-interactive toys are the most popular among young children, figurative toys are in the second place, and collapsible toys are in the third place. The functional features of toys that encourage children to choose them have been revealed: sound, mobility, colour brightness. The more such features a toy has, the more often children choose it, the longer and more meaningful they play with it. It has been found that young children choose toy according to their psycho-physical, sensory capabilities and skills to act with it.
A study on the relationship between English teacher’s teaching styles and elementary school students’ English learning competence

The study aims to verify the impact exerted by task-oriented and relationship-oriented&#x0D; teaching styles adopted by English teachers on the cognitive, motivational, and&#x0D; behavioral proficiencies of elementary school students learning English. To this end,&#x0D; online questionnaires with questions about the English teachers’ teaching styles as&#x0D; well as their own English learning competencies were distributed and collected from&#x0D; after-school programs of public schools and private educational institutions and 262&#x0D; students in grades 4 to 6 across six provinces in Korea. The major findings are as&#x0D; follows: First, both task-oriented and relationship-oriented styles influenced students’&#x0D; learning competencies significantly. However, the impact of the task-oriented teaching&#x0D; style was relatively stronger on cognitive competence than that of the&#x0D; relationship-oriented style. Next, the relationship-oriented style significantly influenced&#x0D; motivational and behavioral competence, while the task-oriented style had none.&#x0D; Therefore, this study suggests that English teachers should exercise a&#x0D; relationship-oriented teaching style to enhance students’ English learning competencies.&#x0D; However, teachers should prioritize task-oriented teaching for students who lack&#x0D; cognitive competence. Moreover, to effectively demonstrate teaching styles, teachers&#x0D; should adapt their instructional approaches based on the educational situations at hand&#x0D; and the individual characteristics of their students.
LCSH and Environmental Science

The environmental sciences are characterized by their boundless interdisciplinarity and cannot be discussed independently from other scientific fields such as ecology, engineering, and climatology. The complex nature of the discipline leads to challenges in placing it within a controlled vocabulary such as the Library of Congress Subject Headings (LCSH). However, the placement of a term within a thesaurus hierarchy has potential repercussions for the discoverability of materials assigned that subject heading. As the environmental sciences are rapidly expanding due to global climate change, accurate representation of this discipline within a widely used vocabulary is crucial. In this paper we employ a visual mind mapping technique to examine how the environmental sciences are represented by codified language within the LCSH, then complete a domain analysis of the field to determine how environmental science represents itself. In comparing these two analyses we determine that the LCSH subject headings do not capture the interdisciplinary nature of the field in two primary ways; the term Environmental sciences is not sufficiently connected to the terms representing other major scientific subjects essential for a foundational understanding of environmental science, and key forward-looking topics of concern to environmental scientists such as Climatology are not in direct relationships with Environmental sciences. Correcting these issues is an important task, as ensuring that researchers are able to access a full range of environmental science materials will aid in finding sustainable climate solutions for our planet.
A temporal continuity clustering algorithm for spatial-temporal data based on GMM and KNN

With the development of technology and the further exploration of space, the acquisition of numerous spatial-temporal data has become possible. How to use data mining technology, especially use clustering method, to analyze and derive information from spatial-temporal data to more effectively cope with real-world problems, has become an important issue. Based on the spatialtemporal temperature dataset covering 42 latitude and longitude points in one year, a new clustering algorithm combining GMM and KNN is developed to solve the problem of non-continuous time for within-cluster points. Then, this paper applies this clustering algorithm to the datasets of other time points in the same year and to the datasets of the subsequent year to explore the transferability of the algorithm and the availability of the classification results. The application results show that the proposed clustering algorithm can achieve temporal clustering of spatial-temporal data based on temperature while ensuring the temporal continuity of within-cluster points and the availability of classification results.
A method for military entity relation extraction based on two-level attention mechanism

To address the challenge of limited training data for entity relation extraction in the military domain, training instances within this domain are automatically generated employing the distant supervision method. Subsequently, to mitigate the problem of noise in corpus construction, a two-level attention model for entity relation extraction (RE) is introduced. Following the bidirectional gated recurrent unit (BI-GRU) network, instance-level and word-level attention mechanisms are incorporated. This allows the model to initially capture bidirectional semantic information of the training instances through BI-GRU. Subsequently, it employs word-level attention to identify crucial words within each training instance, and introduces instance-level attention across multiple training instances, emphasizing the key examples. Experiments conducted on the automatically constructed training corpus in the military domain illustrate the model&apos;s effective entity relation extraction capabilities. Furthermore, it mitigates the influence of noise data introduced through distant supervision, thereby enhancing the accuracy of relation extraction.
The relevance of financial inclusion on social problems, a causality demonstration

This study aims to contribute to the literature by assessing the causal relation among financial inclusion and social variables. Using Generalized Method of Moments and Directed Acyclic Graphs, on data for 92 countries for the year 2019, from the World Bank, we can prove that variables like number of automated teller machines and commercial bank branches (financial inclusion variables), have an impact on child mortality and education (social variables). This study´s limitations include the availability of data for every country, on this manner, we recommend a panel data analysis where we can incorporate time series analysis for the available data set, and other econometric techniques like Vector Autoregressive and Cointegration. To our knowledge, this is the first study to use the technique of Directed Acyclic Graphs to estimate causality in any research related with financial inclusion.
BlinkLinMulT: Transformer-Based Eye Blink Detection

This work presents BlinkLinMulT, a transformer-based framework for eye blink detection. While most existing approaches rely on frame-wise eye state classification, recent advancements in transformer-based sequence models have not been explored in the blink detection literature. Our approach effectively combines low- and high-level feature sequences with linear complexity cross-modal attention mechanisms and addresses challenges such as lighting changes and a wide range of head poses. Our work is the first to leverage the transformer architecture for blink presence detection and eye state recognition while successfully implementing an efficient fusion of input features. In our experiments, we utilized several publicly available benchmark datasets (CEW, ZJU, MRL Eye, RT-BENE, EyeBlink8, Researcher’s Night, and TalkingFace) to extensively show the state-of-the-art performance and generalization capability of our trained model. We hope the proposed method can serve as a new baseline for further research.
Knowledge representation learning method based on background information and adaptive weight measurement

Currently, the knowledge representation learning methods are based on knowledge background information and integrated with the calculation related to correlation strength, type matching degree, and multi-part path. However, besides excellent learning effects, their loss functions are simple so that more complex knowledge graphs cannot be represented well. In order to solve these problems, a more flexible knowledge representation learning model is proposed in the paper and the adaptive measure functions are imported to optimize the loss functions in the knowledge representation learning method. The experiment was performed on several baseline data sets and optimal results are well obtained.
Establishing Modern Industrial Colleges and Schools of Future Technology

This chapter elaborates upon the two structural arrangements of China’s engineering education reform, namely the establishment of Modern Industrial Colleges and Schools of Future Technology. Whereas the former prioritizes the application of current information technology and resource innovation to enhance teaching and education quality, the latter focuses on cutting-edge, revolutionary, and disruptive technologies projected to emerge within the next 10–15 years. Their objective is to transcend traditional conventions, constraints, and barriers while fostering change, innovation, and leadership. These institutions aim to cultivate forward-looking, pioneering technology innovators who can spearhead future development and contribute to the pursuit of high-quality development in building a robust higher education nation.
Geochemistry and Petrology: Collaborative Roles in Resource Exploration and Environmental Research

Geochemistry and petrology, as distinct yet interrelated fields within geology, play pivotal roles in understanding Earth’s composition, processes, and history. This paper explores the collaborative synergy between these disciplines and their significance in resource exploration and environmental research. It delves into their fundamental principles, applications, and emerging trends, highlighting successful interdisciplinary projects. Despite communication challenges and funding limitations, the future promises exciting opportunities for innovation and discovery through continued collaboration. As we address pressing global challenges, the partnership between geochemistry and petrology remains vital for a sustainable and resilient future.
Effects of Online Education on Elementary Science Gifted Students’ Systems Thinking, Science Motivation, and Scientific Self-Efficacy during the COVID-19

The main purpose of this study was to investigate the effect of online education on gifted students’ systems thinking, science motivation, and scientific self-efficacy during the COVID-19 pandemic. The subjects were 68 elementary science gifted students in 2020. The education program focusing on 15 different subjects was conducted for 90 hours. To investigate the effect on systems thinking, science motivation, and scientific self-efficacy, the preandpost-test design was used. The results of this study were as follows. First, it was revealed that online gifted education was systematically conducted using the university’s existing LMS system, while also reducing students’ group activities and interactions between both students-students and students-instructor within the classroom. Second, online gifted education was effective in gifted elementary science students’ systems thinking and science motivation, however, the effect size was relatively small. Third, educational effects on students’ systems thinking were affected by the district of residence and previous educational experience. Therefore, gifted education is required in consideration of changes in population structure and regional disparities to cultivate human resources needed in the future fast-changing society.
CI/CD Implementation Application Deployment Process Academic Information System (Case Study Of Paramadina University)

This research discusses the implementation of Continuous Integration/Continuous Delivery (CI/CD) in the deployment process of the Academic Information System application at Paramadina University, where the application development process still uses manual methods. CI/CD is an approach in software development that enables development teams to automatically integrate code, run tests, and deploy applications periodically and continuously. The purpose of this research is to implement CI/CD in the deployment process of Academic Information System applications so that the development process becomes more effective and the quality of the resulting application is better and more adaptable to changes. The method used in this research is the qualitative method by conducting direct observation to collect data. The results of the research show that by implementing the stages of CI/CD, the application deployment process becomes more efficient and can provide a solution to the problems that arise in the previously conventional or manual application deployment process. The results of this research can serve as a guide for development teams and operations who want to adopt CI/CD in the application deployment process, as well as provide insights for researchers and practitioners to optimize the use of CI/CD in software development.
Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning

Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.
Robot Path Planning Research Incorporating Improved A* Algorithm and DWA Algorithm

For the traditional A* algorithm has problems such as long paths, large number of nodes, and the demand for dynamic obstacle cannot be avoided in complex environment. A mobile robot dynamic path avoidance method will be improved to improve the A * algorithm and improve DWA algorithm Two map environments are used for simulation verification. First, the evaluation function and key node selection strategy are optimized for the A* algorithm, and redundant nodes are deleted; then the dynamic obstacle distance evaluation function is added to the DWA algorithm which for the purpose of the obstacle avoidance performance can be enhanced. The results about the improved A* algorithm reduces 12.20% and 58.33% in path length and number of turning points respectively compared with the traditional A* algorithm can be obviously grasped by the simulation experiment; by using the fusion algorithm whose purpose of using arcs instead of the straight lines is to turn more smoothly, and can be closest to the global optimum while avoiding dynamic obstacles to complete the search.
CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis

Computer-aided detection (CADe) of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) is crucial in the early diagnosis of breast cancer. Although convolutional neural network (CNN)-based detection models have achieved excellent performance in medical lesion detection, they are subject to some limitations in MC detection: 1) Most existing models employ the feature pyramid network (FPN) for multi-scale object detection; however, the rough feature sharing between adjacent layers in the FPN may limit the detection ability for small and low-contrast MCs; and 2) the MCs region only accounts for a small part of the annotation box, so the features extracted indiscriminately within the whole box may easily be affected by the background. In this paper, we develop a novel CNN-based CADe method to alleviate the impacts of the above limitations for the accurate and rapid detection of MCs in DBT. The proposed method has two parts: a novel context attention pyramid network (CAPNet) for intra-layer MC detection in two-dimensional (2D) slices and a three-dimensional (3D) aggregation procedure for aggregating 2D intra-layer MCs into a 3D result according to their connectivity in 3D space. The proposed CAPNet is based on an anchor-free and one-stage detection architecture and contains a context feature selection fusion (CFSF) module and a microcalcification response (MCR) branch. The CFSF module can efficiently enrich shallow layers’ features by the complementary selection of local context features, aiming to reduce the missed detection of small and low-contrast MCs. The MCR branch is a one-layer branch parallel to the classification branch, which can alleviate the influence of the background region within the annotation box on feature extraction and enhance the ability of the model to distinguish MCs from normal breast tissue. We performed a comparison experiment on an in-house clinical dataset with 648 DBT volumes, and the proposed method achieved impressive performance with a sensitivity of 91.56 % at 1 false positive per DBT volume (FPs/volume) and 93.51 % at 2 FPs/volume, outperforming other representative detection models. The experimental results indicate that the proposed method is effective in the detection of MCs in DBT. This method can provide objective, accurate, and quick diagnostic suggestions for radiologists, presenting potential clinical value for early breast cancer screening.
Action Research on the Application of Core Concept and Teacher’s Role to Practice Young Child-Centered Education

The purposes of this study is to provide reference materials for teachers who want to practice young child-centered education through exploring what teachers experience in the process of application of core concept and teacher’s role to practice young child-centered education reestablished by Im Hwan-mi and Yoon Sun-young(2022). For this, action research was conducted with seven teachers through focus group interview and non-participant observation. Focus group interview and non-participant observation was done alternately at 1-week intervals at 10 times. The collected data are transcription data, observation journals, journals of teachers and researchers, and other data, and the results were derived after analyzing according to the inductive analysis procedure of Lee Dong-Sung &amp; Kim Young-Chon(2014). As a result of the study, teachers experienced difficulties such as “Awkward to recognize”, “It's difficult to form a relationship”, “I try to plan habitually”, “It's burdensome to model”, and “Awkward to guide”. However, as teachers clearly understood and interpreted the core concepts and teacher’s role, they experienced “New recognition of me and children”, “I'm happy to share my feelings”, “I sympathized with them, and I became fun”. In addition, teachers actively practiced young child-centered education with direction and confidence. Finally, based on the results of the study, key concepts and the teacher's role from another perspective and teacher self-assessment support, teacher education format, progress method, and contents were suggested.