Reviewerly/exHarmony huggingface dataset:
FEATURES OF IMPROVING THE QUALIFICATIONS OF NATURAL EDUCATION TEACHERS IN THE CONTEXT OF THE DEVELOPMENT OF STEM EDUCATION

During the development of digital technologies, education must correspond to the trends of the development of society and have an anticipatory character. The introduction of STEM education into the educational process provides students with the formation of soft skills, critical thinking, the ability to work in a team, etc. However, the problem of improving the qualifications of teachers, who ensure the implementation of the state standard of basic secondary education in terms of the implementation of STEM education, is developed rather superficially and fragmentarily. Therefore, the purpose of the article is to highlight the features of the professional development of science education teachers in the context of the development of STEM education, to pay attention to the factors that hinder the effectiveness of the implementation of the specified direction, and to pay attention to the possibilities of the postgraduate education system in the part that will help to eliminate the identified obstacles. Teachers see STEM education as an element of a certain technology and link it quite closely with the development of natural sciences. The features of improving the qualifications of science education teachers in the context of the development of STEM education are considered, the factors that stand in the way of the introduction of STEM education are identified, and ways to solve the problem of the development of STEM education. The modern stage of development of the system of advanced training of science teachers in postgraduate education is characterized by a simultaneous rethinking of basic values in the content, forms and methods of teacher improvement. Effective methods of managing the educational process are innovative, democratic methods that have a dynamic structure of the content and forms of organization of the educational process, with an emphasis on assimilation and generalization of the innovative experience of teachers. Modern forms of professional development (trainings, workshops, master classes, author's creative workshops) are able to ensure a high intensity of learning, fundamentality and systematicity in the assimilation of educational information, a more thorough disclosure of certain issues of science education is achieved, and students are given an idea of the essence of the STEM-oriented approach , the specifics of the use of STEM technologies in the organization of the educational process.
Crime Classification Using GRU and 1CNN Techniques

Criminal activity is a widespread social problem that affects a nation's standard of living, rate of economic development, and international standing. Numerous crimes have proliferated worldwide in recent years. It has a variety of effects on people of all types. Classification of crime is necessary to create a safe society. Many researchers classify the crimes using machine learning techniques with a common dataset, which presents numerous computational opportunities and challenges. Using the learning capabilities of several deep learning architectures, we are trying to classify the criminal activity from the real-time dataset of particular city. In the proposed work, Gated Recurrent Unit (GRU) and Convolutional Neural Network (1CNN) deep learning architecture are used for crime classification. We have conducted performance comparison for classifying the crime with real-time dataset. The effectiveness of the aforementioned deep learning models was assessed by Training and Validation Loss curve. It was discovered that 1CNN models achieve better result compared to GRU. 1CNN produced 97% accuracy.
Be´zierCE: Low-Light Image Enhancement via Zero-Reference Be´zier Curve Estimation

Due to problems such as the shooting light, viewing angle, and camera equipment, low-light images with low contrast, color distortion, high noise, and unclear details can be seen regularly in real scenes. These low-light images will not only affect our observation but will also greatly affect the performance of computer vision processing algorithms. Low-light image enhancement technology can help to improve the quality of images and make them more applicable to fields such as computer vision, machine learning, and artificial intelligence. In this paper, we propose a novel method to enhance images through Bézier curve estimation. We estimate the pixel-level Bézier curve by training a deep neural network (BCE-Net) to adjust the dynamic range of a given image. Based on the good properties of the Bézier curve, in that it is smooth, continuous, and differentiable everywhere, low-light image enhancement through Bézier curve mapping is effective. The advantages of BCE-Net’s brevity and zero-reference make it generalizable to other low-light conditions. Extensive experiments show that our method outperforms existing methods both qualitatively and quantitatively.
THE USE OF ANIMATED SHORT FILM MEDIA SPEAKING CLASS AT AN INDONESIAN JUNIOR HIGH SCHOOL: CHALLENGES AND STUDENT'S RESPONSES

This study aims to describe the use of animated short films, the challenges faced by the teacher and the students’ response towards its implementation in Speaking class. This research employed descriptive qualitative design and the data were taken through interviews and observations. The participants of this study were 14 students from grade 9 and 1 teacher who were interviewed and observed. The result of this study showed: 1) The procedures of applying media, there are: showing playing the short film media, checking student’s understanding, repeating material explanation, giving assignments related to the media and checking student’s work and conducting evaluations related to the showing of the film. 2) teacher’s challenges in preparing animated short films, there are: getting relevant animated short films, finding interesting and easy understanding animation and determining the right duration, then teacher’s challenges in applying media are: student’s lack of enthusiasm for the material, teacher’s difficulties in time management and infrastructure problems. 3) Students perceive that the media used by the teacher gave positive responses to speaking skills such as increasing pronunciation, making learning enjoyable, stimulating English speaking skills and facilitating understanding of the material.
EFEKTIVITAS MEDIA FLIPBOOK BERBASIS MODEL STAD TERHADAP HASIL BELAJAR SISWA KELAS V

ABSTRACT&#x0D; &#x0D; This research is a quantitative research that aims to determine the effectiveness of the STAD Model-Based Flipbook media on the learning outcomes of grade V students. The population of this study is grade V students of SD Negeri Oebali for the 2022/2023 school year, totaling 11 people, using saturated sampling techniques so that all populations are used as research samples. This study used observation sheets and tests as a type of instrument with data collection techniques such as observation, tests, and document studies. Based on the results of the normality test of learning outcomes from the pretest-posttest, a significant value of 0.984 &gt; 0.05 was obtained so that the data was normally distributed, the homogeneity test results showed (sig) Levene's of 0.055 &gt; 0.05 so that the data obtained were homogeneous. The results of hypothesis testing, using paired sample t-test show that the value of sig. (2-tailed) from pretest and posttest values of 0.000 &lt; 0.05 thus it can be concluded that flipbook media based on the STAD model is effective in improving student learning outcomes.&#x0D; &#x0D; Keywords: learning outcomes, media flipbook, STAD model&#x0D;
Integrating View Magnification into an Augmented Reality Head-Mounted Display to Support Surgery

Medical applications are a good use case for augmented reality (AR) technology as the overlay of virtual content on patients supports the visualization of body components, pre-surgery scans and real-time biometric data. Thus, there is increasing use of AR for medical training, surgery planning and tele-mentoring with growing access to affordable AR hardware. Also AR is now being used in actual surgeries as the technology can provide hands free interaction and line-of-sight rendering of virtual content to support surgeons. However, the magnification of surgery sites is often needed and AR hardware cameras are typically only used for environmental tracking or the real-time recording/streaming of the current user’s view. Access to a dedicated magnification view is not readily available from commercial AR headsets. This paper identifies several challenges to adding an external camera to provide magnification functionality to be integrated into an AR scene. A proposed system is described with an overview of ongoing and future work.
The Acceptability of a Multisensory VR Game for Older Adults

Current game designs for digital interventions addressing the decline of visual function, cognitive ability, and physical function during aging process mostly focus on cognitive and physical sensory, while ignoring the importance of vision sensory for older adults. We developed a game that combines the three sensory channels (i.e., cognitive psychology paradigms, full-body movement, and dynamic visual acuity exercises) in the VR system environment. Our long-term goal is to use a VR game system which offers effective digital health interventions for older adults, as the first step we conducted an experiment for testing the acceptability of the game system among older adults. Results showed that older adults could accept our game system. This work broadens the vision of VR game design. Our work also contributes to multi-domain research that combines the domains of cognitive psychology and HCI.
Experimental results on nonlinear distortion compensation using photonic reservoir computing with a single set of weights for different wavelengths

Abstract Photonics-based computing approaches in combination with wavelength division multiplexing offer a potential solution to modern data and bandwidth needs. This paper experimentally takes an important step towards wavelength division multiplexing in an integrated waveguide-based photonic reservoir computing platform by using a single set of readout weights for up to at least 3 ITU-T channels to efficiently scale the data bandwidth when processing a nonlinear signal equalization task on a 28 Gbps modulated on-off keying signal. Using multiple-wavelength training, we obtain bit error rates well below that of the $$1.5 \times \,10^{-2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.5</mml:mn> <mml:mo>×</mml:mo> <mml:mspace /> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> forward error correction limit at high fiber input powers of 18 dBm, which result in high nonlinear distortion. The results of the reservoir chip are compared to a tapped delay line filter and clearly show that the system performs nonlinear equalization. This was achieved using only limited post processing which in future work can be implemented in optical hardware as well.
An image deblurring method using improved U-Net model based on multilayer fusion and attention mechanism

The investigation of image deblurring techniques in dynamic scenes represents a prominent area of research. Recently, deep learning technology has gained extensive traction within the field of image deblurring methodologies. However, such methods often suffer from limited inherent interconnections across various hierarchical levels, resulting in inadequate receptive fields and suboptimal deblurring outcomes. In U-Net, a more adaptable approach is employed, integrating diverse levels of features effectively. Such design not only significantly reduces the number of parameters but also maintains an acceptable accuracy range. Based on such advantages, an improved U-Net model for enhancing the image deblurring effect was proposed in the present study. Firstly, the model structure was designed, incorporating two key components: the MLFF (multilayer feature fusion) module and the DMRFAB (dense multi-receptive field attention block). The aim of these modules is to improve the feature extraction ability. The MLFF module facilitates the integration of feature information across various layers, while the DMRFAB module, enriched with an attention mechanism, extracts crucial and intricate image details, thereby enhancing the overall information extraction process. Finally, in combination with fast Fourier transform, the FRLF (Frequency Reconstruction Loss Function) was proposed. The FRLF obtains the frequency value of the image by reducing the frequency difference. The present experiment results reveal that the proposed method exhibited higher-quality visual effects. Specifically, for the GoPro dataset, the PSNR (peak signal-to-noise ratio) reached 31.53, while the SSIM (structural similarity index) attained a value of 0.948. Additionally, for the Real Blur dataset, the PSNR achieved 31.32, accompanied by an SSIM score of 0.934.
Enhancing Association Utility: Dedicated Knowledge Graph

Gang fraud, the major and primary security issue in online lending services, can be efficiently solved by the data-driven paradigm that is recognized as a promising solution for online lending gang fraud prediction. However, it is challenging that such predictions need to detect evolving and increasingly impalpable fraud patterns based on low-quality data, i.e., very preliminary and coarse applicant information. The technical difficulty mainly stems from two factors: the extreme deficiency of information associations and weakness of data labels. In this work, we mainly address the challenges by enhancing the utility of associations (i.e., recovering missing associations and mining underlying associations) on a knowledge graph. Specifically, we first propose an efficient method of Chinese address disambiguation to recover some critical associations that are broken by the ambiguity of applicant information, e.g., address related information. Then, to mine the implicit associations, we design a novel association representation method, called Adaptive Connected Component Embedding Simplification Scheme (ACCESS), which can adaptively implement embedding for different connected components depending on their sizes. Finally, we adopt the graph clustering algorithms and devised predicting schemes based on the above enhanced associations to predict gang fraud in the case of weakness of data labels. Moreover, we propose a framework called RMCP by integrating the above techniques, which is consists of four steps: Recovering, Mining, Clustering, and Predicting, for efficiently predicting gang fraud. The good performance is validated by the experiments on a real-world dataset from a commercial lending company. Meanwhile, we provide a visual decision support system named LongArms over the RMCP framework.
Underwater Image Restoration System

Abstract: Underwater imaging poses significant challenges due to light absorption, scattering, and color distortion. This paper introduces an innovative underwater image restoration system designed to enhance the visibility and analytical capabilities of underwater imaging. The proposed system employs advanced image processing techniques to address the inherent issues associated with underwater photography. The methodology involves the development of a model that accounts for the optical properties of water, including attenuation and scattering. By leveraging this model, the system corrects color distortions and enhances contrast, leading to improved clarity in underwater images. Additionally, a novel algorithm is employed to reduce the impact of particulate matter, such as suspended sediments, contributing to a clearer representation of the underwater scene. The system incorporates machine learning approaches for adaptive filtering, allowing it to dynamically adjust parameters based on environmental conditions. This adaptability enables the restoration system to perform effectively across a range of underwater scenarios, from clear to turbid waters.
Associations Dynamic Evolution: Evolving Graph Transformer

In online lending services, fraud prediction is an especially critical step to control loss risk and improve processing efficiency. It is definitely challenging that such predictions need to detect evolving and increasingly impalpable fraud patterns. The technical difficulty mainly stems from one factor: evolution of fraud patterns. As a widely recognized method currently, GNNs has attracted much attention from researchers. According to the requirements of the task scenario, graphs can be divided into four categories from two perspectives: static-homogeneous graph, static-heterogeneous graph, dynamic-homogeneous graph, and dynamic-heterogeneous graph. The GNNs on the dynamic-heterogeneous graph is undoubtedly the methods with the highest level and the most generalization. From the perspective of heterogeneity, the past methods mainly used meta-path (the disadvantage is that additional expert knowledge is required), and newer works (such as HGT) abandon meta-path and use meta-relation instead, and set up multiple sets of projections The type of matrix modeling edge. From the perspective of dynamics, the past methods mainly used the sequential combination of GNN+RNN (such as TGCN), but this method is difficult to learn irregular behavior. A new approach is not to train the embedding of the GNN output, but to use the RNN to train the parameters of the GNN (making the model itself have higher generalization), while also reducing the number of parameters that need to be learned. We propose Evolving Graph Transformer (EGT), which has three main improvements. It does not rely on expert knowledge by using the idea of meta-relation to directly model the node pair. In order to better model the diversity of node and edge types in heterogeneous graphs, we set up each type-specific projection matrix so that their distribution can be displayed more realistically. Compared with using RNN to learn the node representation of GNN output, we use RNN to directly evolve the parameters of GNN. This approach effectively performs model adaptation, which focuses on the model itself rather than the node embeddings.
Meningkatkan Hasil Belajar Siswa Kelas II pada Materi Matematika Perkalian Bilangan Cacah dengan Menggunakan Media Gambar di SDM PUU UPPO

The material of multiplication of whole number using picture media in class IIB SDM Puu Uppo. Type this research is classroom action research (PTK) which was carried out in two cycles with the steps of implementation planning, observation, and reflection. this research was carried out in class IIB odd semester with a total of 25 students. The resuts of the research shoul that using picture media can improve student learning outcomes in the whole number multiplication material in class IIB. There was an increase in learning outcomes from cycle I to cycle II and shouwed an increase and in the average value and percentage of complication in the classical cycle of 1,68 % with the number of students who completed 17 people and increase in the second cycle of classical completeness of 92 with the number of students who completed 23 students who had reachd 65 according to the specified minimum completion criteria (KKM) value. School the reculd of observation of frake activation from cycle I were 88 and increased in cycle II by 91. Resuld of observation of aktivities in cycle I were 82 and increased in cycle II by 94. Thus, the use of image media can improve student learning outcomes in the mathematical material of multiplication of number
Writing Daily Status On Social Media: Code-Mixing Phenomena At Sekolah Advent Makassar

The purpose of this study was to analyze the using of code mixing on social media at the Elevent Grade of Sekolah Advent Makassar. This research applied descriptive qualitative research. Data collection technique by using an observation and interview. Purposive sampling is the technique to chose the subject. There were 5 Students as subject from 26 students. The data was taken by the screeshot observation to know their type of code mixing on Instagram. The researchers interviewed for the 5 students to know about the students’ reasons using code mixing in writing status. Based on the results of the observation, interviews and recording that have been conducted, students used type of code mixing dominant is insertion in writing daily satus and ther reasons students’ used code mixing in writing daily status are less of English vocabulary and social community. The researcher found that the highest reason of using code mxing by the students is that they have less English vocabulary. This proves that the role of social media and community is very important at this ages and Students’ involvement in every situation to express their languages
MaskWarp: Visuo-Haptic Illusions in Mixed Reality using Real-time Video Inpainting

We present MaskWarp, a novel approach to enable haptic retargeting and other visuo-haptic illusions in video see-through (VST) mixed reality (MR). Virtual reality provides a unique ability to shift where users perceive their virtual hands. However, in VST MR, users can see their real hands through a video feed, breaking the illusion. MaskWarp uses real-time video inpainting to remove the physical hand from the video, which is then replaced with a virtual hand. The virtual hand position can then be warped for visuo-haptic illusions. We use haptic retargeting to demonstrate this technique in an engaging cups and ball game.
The Effects of Self-Integrity on Successful Aging of the Elderly Participating in Literacy Education: Mediating Effects of Social Isolation and Moderated Mediating Effects of Hope

This study was conducted to find a practical plan to increase the successful aging of the elderly who participated in a literacy education program. Specifically, it was designed to identify the relationship between the elderly's sense of self-integrity and successful aging, and to verify whether they have a mediating effect of social isolation and a moderated mediating effect of hope. To conduct this study, a questionnaire was distributed to 280 elderly people aged 65 or older participating in literacy education in some areas of Chungcheongnam-do, and SPSS window version 25.0 and PROCESS macro version 4.1 were used for the data of 260 people, excluding 20 insincere responses. For data analysis, frequency analysis, reliability analysis, correlation analysis, mediating effect analysis, moderating effect analysis, and bootstrap were used. As a result of the study, first, the sense of self-integrity according to the elderly's participation in literacy education had a positive relationship with successful aging. Second, social isolation mediated the relationship between self-integrity and successful aging. Third, in the relationship between self-integrity and successful aging, the mediating effect of social isolation was regulated by hope. The results of this thesis will be used as a model for the successful aging promotion of the elderly who participated in the literacy education program through hope.