Reviewerly/exHarmony huggingface dataset:
Use of Natural Media for Early Children's Early Writing Skills

This study aims to evaluate the use of natural media in improving initial writing skills in group a aged 4-5 years. Natural material media was chosen as the focus of the research because of its potential to provide diverse sensory experiences and stimulate the development of children's motor skills. The research method used was a quasi-experiment with a control group and a treatment group. The treatment group received intervention using natural media, while the control group received conventional learning. The data collection instrument consisted of an initial writing ability test, observation of children's involvement, and interviews with teachers. The research results were analyzed using descriptive statistics and tests of differences between groups. The findings of this research indicate that the use of natural media has a positive impact on the early writing abilities of children aged 4-5 years. Children in the treatment group showed significant improvements in aspects of fine motor skills and eye-hand coordination, while the control group showed more limited improvements. The implications of the results of this research can provide recommendations to educators and parents to consider using natural media to support the development of children's early writing skills. In addition, this research can be a basis for further research in the context of using natural media for early childhood learning.
Fcaformer: Forward Cross Attention in Hybrid Vision Transformer

Currently, one main research line in designing a more efficient vision transformer is reducing the computational cost of self attention modules by adopting sparse attention or using local attention windows. In contrast, we propose a different approach that aims to improve the performance of transformer-based architectures by densifying the attention pattern. Specifically, we proposed forward cross attention for hybrid vision transformer (FcaFormer), where tokens from previous blocks in the same stage are secondary used. To achieve this, the FcaFormer leverages two innovative components: learnable scale factors (LSFs) and a token merge and enhancement module (TME). The LSFs enable efficient processing of cross tokens, while the TME generates representative cross tokens. By integrating these components, the proposed FcaFormer enhances the interactions of tokens across blocks with potentially different semantics, and encourages more information flows to the lower levels. Based on the forward cross attention (Fca), we have designed a series of FcaFormer models that achieve the best trade-off between model size, computational cost, memory cost, and accuracy. For example, without the need for knowledge distillation to strengthen training, our FcaFormer achieves 83.1% top-1 accuracy on Imagenet with only 16.3 million parameters and about 3.6 billion MACs. This saves almost half of the parameters and a few computational costs while achieving 0.7% higher accuracy compared to distilled EfficientFormer. Code is available at https://github.com/hkzhang-git/FcaFormer
A pragmatic study of incivility in Trump-Biden first American political debate

This study pragmatically investigates incivility in the first Trump-Biden American political debate. This topic is chosen for its significant negative impact on both individuals and society as a whole, particularly in the political arena. The current study endeavors to achieve the following aims:(1) Specifying the types of incivility used in the Trump-Biden first American political debate, (2) Identifying the prevailing functions of incivility in the chosen data, (3) Finding out the pragmatic strategies employed by the two rivals. To achieve these aims, the following procedures are adopted: (1) Presenting a literature review about the pragmatics of incivility, (2) Adopting an eclectic model to analyze the chosen data pragmatically, and (3) Analyzing the data both qualitatively and quantitatively to come up with some conclusions. The fundamental conclusion of the current study is that debaters in American political debates employ three types of incivility, namely, invectives and ridicule, hyperbole and distortion, and emotionality and histrionics. These three types are pragmatically realized through different pragmatic strategies, such as speech acts, non-observance of maxims, impoliteness strategies, and argumentative appeals.
Demonization of virtual reality in modern media culture

This study aimed to examine the demonization of virtual reality in modern media culture, with a focus on understanding the reasons behind the negative portrayal of virtual reality technologies. The purpose of this article was to identify and analyze the prevalent narratives and discourses surrounding virtual reality in contemporary media culture, and to explore the underlying factors contributing to its demonization. In this research, a qualitative study design was employed, utilizing content analysis of media articles, contemporary literary works, and scientific viewpoints from futurologists and researchers who study virtual reality technology and mass media. To identify recurring themes and patterns in virtual reality presentations, a thematic analysis approach was used. The analysis revealed that virtual reality was often portrayed negatively in media culture, with its potential risks and drawbacks emphasized over its benefits and possibilities. The findings also highlighted recurring themes, such as concerns about the impact of virtual reality on mental health, disconnection from reality, and ethical dilemmas. The demonized portrayal of virtual reality in modern media culture is influential for virtual reality developers, policymakers, consumers, and media representatives, such as fiction, creative, and fantasy writers, journalists, critics, and other stakeholders. It is crucial to understand the reasons behind this negative portrayal to develop strategies for addressing concerns, promoting responsible use of virtual reality, and fostering a more balanced and nuanced understanding of its potential benefits and challenges.
Eye tracking for affective computing in virtual reality healthcare applications

This paper examines opportunities and challenges associated with using eye tracking as a sensory system for affective computing in extended reality (XR) environments. Affective computing is a rapidly growing field that aims to develop computing systems capable of recognizing, interpreting, and responding to human emotions. Eye tracking has several potential benefits for improving the detection of emotions, including its ability to unobtrusively monitor affective states in real time, and its sensitivity to a variety of affective states. This paper introduces affective computing, explains eye tracking methodologies, and describes how eye tracking can be used to detect attentional and affective states, realizing concepts such as virtual exposure therapy, and adaptive virtual reality. The paper discusses several potential applications of eye tracking for affective computing in healthcare settings.
Important Quantum Gates for Quantum Algorithms of Travelling Salesman Problem

This paper proposes $B\left(\displaystyle \frac{1}{k}\right)$ gates as important gates to generate W state for the quantum approximate optimization algorithm (QAOA) to tackle the popular optimization problem known as the traveling salesman problem (TSP). We also provide a simple proof for the $B\left(\displaystyle \frac{1}{k}\right)$ gates followed by the corresponding quantum circuit. We present the Ising Hamiltonian tailored specifically for the TSP, including its associated constraints. We perform a performance comparison for QAOA with the conventional state initialization method involving the Hadamard gate and QAOA with state initialization using the W state. We found that the performance of QAOA is significantly influenced by two key factors of (i) the number of p layers and (ii) the chosen state initialization method. Our findings highlight that the generation and integration of W state initialization results in a substantial reduction in the search space for QAOA, consequently reducing the required number of p layers for the search process.
基於漢字本位視角的日本漢字異體字分類及定名 ——以日本漢文古辭書《倭名類聚抄》爲例

This article delves into the development of Japanese Chinese character variations, and how they fit into the broader study of Chinese characters. By examining the differences between early Japanese kanji and modern Japanese writing systems, as well as the varying approaches of Chinese and Japanese scholars in studying Japanese kanji, we can propose a more fitting classification and naming method better suited to studying Chinese characters. To that end, we take the Wamyō Ruiju Shō (倭名類聚抄) as an example, exhaustively sorting out the situation of variant characters in the manuscript across different eras, and referring to other ancient Chinese dictionaries from the same period. This article introduces concepts like “inherited variants” and “Japan saved variants” to make studying these characters more comprehensive.
PNEUMONIA PREDICTION USING CONVOLUTIONAL NEURAL NETWORK

Pneumonia is condition which our lungs become inflamed due to infection from viruses, bacteria, or fungi. Pneumonia can affect anyone, both adults and children. Because of this, prevention of pneumonia is important. Prevention can be done by the process of maintain our immunity and lungs. In this study, had been done classify pneumonia based on X-ray images. This study using X-ray images dataset with total data is 5840 images in .jpg extensions. With a total number of images from training data is 5216 images and number of images from the test data is 624 images. The dataset that used in this research has 2 main classes, namely class normal and pneumonia. Normal class indicates that the X-Ray results are not detected with pneumonia. While the pneumonia class indicates that the processed X-Ray results are diagnose affected by pneumonia. The purpose of this research is building model that can be used to classify pneumonia based on X-Ray images. The classification process carried out in this study uses the Convolutional Neural Network method. The purpose of using the CNN method in the classification process of this research is because, in the process, CNN can extract features automatically and independently, so that the data provided does not need to be preprocessing first, but the data still produces good extraction features and can provide accurate classification results. The results from the testing process is carried out to run or perform in the pneumonia classification process, the CNN model built obtained a classification test accuracy of 87.82051205635071%.
HARDWARE DESIGN OF THE TOUCHLESS HAND CODE AND CONVOLUTIONAL NEURAL NETWORKS - BASED AUTOMATIC DOOR SECURITY SYSTEM

The spread of viruses and bacteria through touching door surfaces is essential in maintaining public hygiene and health. In this context, a hand-coded touchless automatic door hardware design has been developed to reduce the spread of diseases through touch. This research aims to create a plan that includes interface development and hardware design to open and close doors automatically without contact. In this research, the automatic door hardware response is tested based on the numeric input from the hand code represented by the numeric database. The input and output control is connected to Python's graphical user interface (GUI). The GUI system design involves tools to connect the Python programming language and the Arduino microcontroller. Based on the experimental results, the hardware design of the automatic door security system based on hand code and Convolutional Neural Networks functions appropriately.
The Ways of Thinking of Intellectuals in the 19th Century based on a Book of People: With a Focus on Jinbeolhuigosokpyeon

This paper offers an analysis of people recorded in Jinbeolhuigosokpyeon with statistical methods. The book has 1,026 people recorded in it. Judging from the percentage of people allocated to each catalog, the author showed the highest interest in people versed in poetry, calligraphy &amp; paintings and Samgyoguryu. The author was also interested in eccentric, remarkable, and unusual people with special talent. A majority of the people recorded in the book lived in the 16th or 17th century with only six active in the 19th century. There was considerable information about people in addition to many books of people that he could consult in the second half of Joseon, but only a few people attracted his attention. In the book, 614 men had their social classes confirmed. Of them, 453(73.8%), 108(17.6%), and 53(8.6%) were from the Yangban, Jungin, and Sangcheon class, respectively. The book recorded people in the Yangban class 4.2 and 8.5 times more than people in the Jungin and Sangcheon class, respectively. Women appeared across diverse catalogs in the book. Some of them were recorded in the book for their talent in feng shui, singing, and the ability to predict a future, but most of them faithfully practiced the virtues of Confucian society including loyalty, faithfulness, chastity, fidelity, and filial piety. Many previous papers that analyzed books of people reported that the intellectuals of the times acknowledged various types of workers and changed their perceptions of chastity that forced unconditional sacrifices on women. It was also pointed out that they were different from their predecessors in that they recognized the occupational ethics and artistic values of Wihang people, but there is a need to examine if these ways of thinking shared by the intellectuals were not found in the intellectuals before 19th century. The remarkable people from the Jungin and Sangcheon class in Jinbeolhuigosokpyeon also appear in books written by intellectuals before the 19th century. It is because remarkable people naturally attract attention regardless of period and social class. This culture of records was not exclusive to the intellectuals of the 19th century. It has a strong aspect derived from the long-enduring tradition, and it is difficult to say that it was a new change. The dynamics of the 19th-century society is acknowledged, but it does not prove the modernity of the century immediately. When one tries to examine the modernity of the Korean people while being buried in the definitions of modernity sorted out based on Western society, it may rather pose an obstacle to the reconstruction of the 19th-century Korean society experienced by the ancestors.
Investigating the Developer’s Perceptions of Unit Testing and Its Practice

Unit testing is an important and largely disseminated software engineering activity, contributing to the prevention of defects in source code elements before running more comprehensive tests. However, developers typically perform unit tests ad-hoc, lacking in coverage and quality. Consequently, the unit testing practice remains informal and strongly influenced by developers’ initiative. We report in this paper an opinion survey on the developers’ perceptions of unit testing and its practice. We conducted the survey with 32 developers from two Brazilian companies. The survey findings revealed that developers from both companies tend to share positive beliefs about the relevance of unit testing. However, they also tend to share negative perceptions about its practice in their working environments. Besides, we found a positive moderate correlation between the developers’ motivation and their perceptions about the training, frequency, and time allocated for unit testing. The survey results reveal the dissonance between the developers’ beliefs about unit testing and their practice, influenced by issues in their working environments. This dissonance may lead to a lack of commitment and internal conflicts.
Tecnica allusiva e retorica della variazione in <i>AL </i>720a R<sup>2</sup>

The stylistic and formal analysis of AL 720a R2, which summarizes the content of the entire Virgilian corpus in seventeen monosticha, highlights the allusive link with the Virgilian hypotext, and the interaction and emulation with the broad tradition of the argumenta Vergiliana, but also with the texts that represent the fortune of Virgil in antiquity, with a process that transmits a school reworking. The analysis enables reconstructing v. 15 – marked by crux by Riese2 – which in the transmitted form shows metric and meaning difficulties.
Temperature Compensation Algorithm of Tunneling Magnetoresistance Sensors Based on DOA-RBF

The elements in tunneling magnetoresistance (TMR) sensors are composed of the metal material, whose resistivity is affected by temperature. Therefore, zero temperature drift of the Wheatstone bridge sensor is highly dependent on temperature. The consistency of TMR sensors” outputs at different temperatures in practical can be improved through the temperature compensation, reducing the dependence on temperature. A new temperature compensation algorithm for TMR sensors based on DOA-RBF is proposed in this paper. Four specific stages are used to complete the hyperparameter optimization of the RBF neural network. By adjusting the parameters of the Lévy flight function, the iteration speed of hyperparameter optimization process can be improved. It is shown that DOA-RBF has basically converged to the optimal solution after 7 iterations in MATLAB. After compensation, zero temperature coefficient of the sensor is 8.66ppm/ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">○</sup> C, and the sensitivity temperature coefficient is 33.6ppm/ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">○</sup> C. These two coefficients have an order of magnitude lower than that before compensation. DOA-RBF has the characteristics of shorter optimization time. In this paper, the temperature compensation model is verified by Verilog. The sensitivity temperature coefficient after circuit compensation is kept in the same order of magnitude as that in MATLAB. The method proposed improves the temperature stability of TMR and has good practical application value.
A Doctors Behavior Aware and Domain Knowledge Driven Model for Medical Report Generation

When doctors write a medical report, they first focus on key regions in the image, which usually contain abnormal information. Then, they write the report based on their experience and professional knowledge. Some existing medical report generation methods design corresponding schemes to model this process, but they still have some drawbacks. Some of them adopt the attention map to model the doctors’ image-reading behavior, which requires additional cropping and feature extraction operations, bringing many extra computations. Other methods manually construct knowledge graphs to model the doctors’ domain knowledge, which requires manual construction for different disease domains and has less generalization. Therefore, We propose DEKG, a doctors behavior aware and domain knowledge driven model, to improve the quality of generated reports by modeling the fixed pattern of doctors when writing reports. Specifically, feature clustering is utilized to model the doctors’ image-reading behavior that only brings fewer computations, and then we capture key regions with possible abnormalities. We also design a method to automatically construct domain knowledge graphs, which can quickly accomplish the construction process for different disease domains without human intervention. Extensive experiments on datasets from different disease domains demonstrate that DEKG achieves competitive results with state-of-the-art methods and can generate high-quality reports.
Writing Narrative Essays Using the Problem-Based Learning Model Assisted by Series Drawings in Elementary Schools

Students' low ability to write narrative essays is caused by several problems in the learning process. These problems include teachers not using appropriate learning models in implementing learning. Teachers also do not use appropriate learning media. Apart from that, learning is still teacher-centered which causes students to be less actively involved in learning. This research aims to improve students' ability to write narrative essays using the Problem-Based Learning (PBL) model assisted by a series of pictures. This research is Classroom Action Research (CAR) involving 18 elementary school students. The approach used in this research is a mixed method approach or a qualitative and quantitative approach. The research was carried out in 2 cycles with data collection techniques in the form of document analysis, observation, and tests. The results of the research showed an increase in: a) assessment of teaching modules in cycle I 82.14% (Good) to 92.85% (Very Good) in cycle II, b) observations on the teacher aspect of cycle I 75% (Good) to 85% (Good) in cycle II, while implementation in the student aspect of cycle I was 75% (Good) to 85% (Very Good) in cycle II, c) the assessment of students in cycle I obtained an average of 69.10 and cycle II with an average of 80.10. Based on the research results, it shows that there has been an increase in students' writing skills using the Problem-Based Learning (PBL) model assisted by a series of images.
Knowledge-Enhanced Difference-Aware Clinical Time Series Representation Learning for Diagnosis Prediction

Predicting future health status based on historical patient visits is one of the essential tasks in healthcare. Many existing approaches attempt to enhance the representation learning capability of models by incorporating relevant medical knowledge, but their effectiveness is severely affected by the incompleteness and noise of the knowledge graphs. Moreover, due to the inability to capture temporal features at a fine-grained level, most existing methods also have limitations in learning the temporal development of patients’ health status. To address these issues, we propose a Knowledge-Enhanced Difference-Aware clinical time series representation learning model (KEDA) for diagnosis prediction. In this model, we first combine the medical ontology graph and co-occurrence graph, and use hierarchical graph convolution and contrastive learning methods to enhance the semantic representation of medical entities. After that, a task-specific difference-aware temporal module is designed to improve the accuracy of patient representation, which adds two novel gated units in the original GRU to fuse multi-type clinical information based on the relationship between different types of data and prediction tasks and capture fine-grained temporal evolution of patient health status. We validate our model on two publicly available datasets, and the experimental results demonstrate that KEDA outperforms the state-of-the-art methods.
Application of Machine Learning and Parallel Computing to Search for Hypersurfaces Containing Data in Non-Linear Spaces

To apply machine learning in practice, you often need to process a large amount of data and this takes a lot of time. The use of parallel computing and other types of parallelism can significantly speed up calculations, sometimes even without changing the algorithm itself. This paper explores the use of machine learning and parallel computing to search for hypersurfaces containing data in non-linear spaces. Since the transfer of data between the CPU and GPU is very speed- limited, it is important to be able to generate geometry directly on the GPU. Using various vertex arrays makes it easy to transfer a large amount of various vertex attributes (such as coordinates, normal/binormal, color, texture coordinates, etc.) directly to the GPU and quickly render data. The main advantages of using CUDA for parallel computing are its simplicity and flexibility.