Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Shadowing for Listening: Learners’ Initial Attitudinal Motivation

Shadowing has become an effective technique for improving listening skills. However, the research has focused exclusively on the technical aspects of shadowing and less focused on learners’ psychological status. Therefore, this study quantitatively explores the relationship between the effectiveness of shadowing and learners’ psychological aspects: the effectiveness of pre- and post-shadowing on listening comprehension skills and how learners’ initial attitudinal motivation toward learning English is related to their listening improvement through shadowing training. The participants were 47 Korean university students from different academic departments. After eight shadowing training sessions, the students’ scores for listening comprehension skills improved with statistically significant differences, and there were significant mean differences in the listening improvement depending on the participant’s initial attitudinal motivation level. By demonstrating the outcome of shadowing for listening according to learners’ level of initial attitudinal motivation toward learning English, this paper provides educational implications and suggestions for shadowing training. (Chungnam National University)
Pengaruh Minat Baca Terhadap Kemampuan Menulis Narasi Pada Siswa Kelas V SD Negeri 101874 Desa Tumpatan Nibung

This research aims to ; knowing the students' reading interest, knowing the students' narrative writing skills, knowing the effect of reading interest on the narrative writing skills of the fifth graders of SD Negeri 101874 Tumpatan Nibung.&#x0D; This research is a quantitative research. The population of this research is 17 students. Sampling using total random sampling. The variables studied were interest in reading and writing narratives. Data collection techniques using agket and tests. The validation test uses the Product Moment formula, while the reliability test uses the Alpha formula. Descriptive analysis by presenting data through tables, while parametric analysis through product moment correlation techniques.&#x0D; Based on the results of research and discussion, it can be concluded that, "1) students' reading interest is categorized as good, which is 47.1%, 2) students' writing skills are in good category, which is 35.3%, and 3) there is a positive and significant influence. between reading interest and narrative writing skills of fifth graders at SD Negeri 101874 Tumpatan Nibung Village. This is indicated by the value of rcount&gt;rtable (0.555&gt;0.227). &#x0D; &#x0D;
The Handy IR Manager: A Toolkit for Recruitment, Intake, and Promotion

Drawing on our own experience, the Scholarly Communications team at the University of Arkansas, Fayetteville, presents successful strategies for recruitment, intake, and promotion of institutional repository (IR) collections. We discuss how to identify potential collections that align with your goals, how to market repository services, and how to foster collaboration with IR partners. Next, we show you our process for ensuring metadata is complete and accurate in order to enhance discoverability and increase usage statistics, which document your collection's impact. Social media promotion also improves discoverability and usage; we explain how to select appropriate platforms, manage accounts efficiently, and create engaging posts. Most important, we introduce our toolkit with templates, guidelines, and forms that you are welcome to use and adapt.
The Development of Android-Based Mobile Learning for Indonesian History Course at Vocational High School

This study aimed to develop android-based mobile learning for Indonesian History course at SMK YIS Martapura. This is a Research and Development (R&amp;D) using the Alessi &amp; Trollip model. Data collection techniques used questionnaire while research data were analyzed using the percentage formula. From the validation results, media experts scored got 88.69 percent belong to "Good" category. Instructional design experts scored is 82.0 percent which is belong to "Good" category. Material experts scored got 89.9 percent, belong to "Good" category. In one-to-one evaluation with the number of respondents is 3 students got an average result of 86.38 percent which is belong to "Very Good" category. Then a small group evaluation was carried out with 6 students as respondents got an average result of 86.73 percent belong to "Very Good" category. Furthermore, field test was carried out with a total of 28 students with an average result of 86.78 percent in the "Very Good" category. Field trial score showed that there is an increase of post-test. From the results of expert evaluations and field trials, it can be concluded that android-based mobile learning for Indonesian History subject at Vocational Schools show a good level of feasibility and can be accepted as one of the learning media that can be used in classroom learning.
Towards Digital Creativity: The Effect of Canva Application on the Creativity of Prospective Teacher Leaders

The development of the digital creativity world has transformed the education paradigm, and aspiring teacher leaders are not left behind. In an increasingly connected world, the graphic design application Canva has become a revolutionary force that sparks their creativity. The aim of this research is to explore the influence of the Canva application on the creativity of teacher leaders. This study employed a quantitative approach with a survey method. The subjects of this research were participants of the 7th cohort of prospective teacher leaders in Bojonegoro Regency, with a population of 125 participants. A sample of 52 participants was selected using purposive sampling technique. The instrument used was a Likert-scale questionnaire. The research findings indicate that the use of the Canva application has a positive impact on the creativity of prospective teacher leaders. Based on the results of this study, it is recommended to utilize the Canva application as an effective and efficient solution to enhance the creativity of prospective teacher leaders in creating instructional content.
Akuntansi yang Bagaimana yang Mampu Meredam Sifat Materialistik?

&lt;span lang="EN-US"&gt;The discovery of materialistic phenomena among accounting students is a form of the bad reality of conventional accounting education. This study aims to find out how accounting can reduce these characteristics in accounting students. Research using qualitative methods using grounded theory Methods of data collection through interviews, observation, and literature study to produce a theory. The results of the study show that sharia accounting, which is oriented towards immaterial aspects such as love and spirituality, is finally able to bring selfawareness and reduce the materialistic nature that has been embedded previously. In order to make it happen, non-mainstream sharia accounting topics are needed, which address the developing field of sharia accounting but have not yet been accommodated by a professional association. Through the appointment of this topic, it is also proven that science is not selfish because it does not stop at students but also talks back to the surrounding environment. &lt;/span&gt;
Optimization Approaches for Higher Education Management in the Era of Big Data

With the rapid development of the world economy, big data has emerged, representing the world's innovative development and widely recognized by society and the public. Especially in the era of computer and internet, information data is an important component of cultural production and economic growth, and it is applied in daily production in the form of new production factors. Education management is crucial in the development of universities. In the context of big data, the problems of education management in universities are becoming increasingly apparent, and there is an urgent need to optimize and reform education management.In the era of big data, the education management of college students is at an unprecedented height. How to apply big data technology to the education management of college students has become an urgent task for current universities. Based on this, this article analyzes the optimization approaches for university education management in the era of big data from the following aspects.
ジェネリックスキルを用いたディプロマ・ポリシー到達度の可視化

Diploma policies stipulate the requirements for each student to successfully gain knowledge and skills through university education. Thus, it is important to visualize learning outcomes to measure students’ abilities. In this study, we examined the annual changes in the level of achievement regarding diploma policy using the PROG test, which can evaluate literacy and competency as generic skills. Components of generic skills in the PROG test were assigned individually to items of diploma policy, and the efficacy of diploma policy was evaluated. The achievement of diploma policy was approximately from 50-70 %. Furthermore, generic skills were able to describe in detail the students’ abilities acquired as learning outcomes. These results suggest that generic skills can be useful assessment indicators for visualizing the achievement of the diploma policy in university education.
USE OF CELLULAR AUTOMATA ALGORITHMS IN MINIMUM PATH SEARCH PROBLEMS

The paper considers the use of cellular automata to improve the search for the shortest path between objects with obstacles on the way. The proposed algorithm is based on a cellular automaton, namely the game "Life). The rules for constructing the structure of the cellular automaton require changes, depending on the requirements of the user. The article presents a possible variant of finding the shortest path with the help of a cellular automaton using the example of a game. The aim of the game is to reach a certain predetermined cell of the playing field for a minimum number of steps. The problem has many analogues and is used in various fields of science from ordinary games to fixedmilitary strategies.The considered existing analogues of shortest path search agglomerations have a number of drawbacks. These include working only with the structure of graphs, finding the shortest path from only one vertex and others. There is also a drawback that is inherent in all methods - they always try to move in the direction of the goal, even if it is the wrong path. These disadvantages lead to an increase in the time of implementation of the algorithm, stuck characters in the corners and between obstacles.In the above algorithm, the field is considered as a field element of a cellular automaton. In the first step, obstacles are randomly generated, which do not appear throughout the game. We define a cell as an object that moves according to a certain set of rules that depend on the filling of neighboring cells.If there is an obstacle at a given position, then depending on the volume occupied by the figure, we assign a value of 1 or 0 in the characteristic matrix of the corresponding dimension. The rules of movement are determined by the presence of an obstacle, how much this obstacle fills the space (is it possible to move further along the cell, or is it necessary to bypass the obstacles).With this algorithm, the above disadvantages of the algorithm disappear, and the speed of execution is significantly reduced.Keywords: minimum path, cellular automaton, rules for constructing cellular automata, algorithms for finding the minimum distance.
H2020 Lasting - current research information systems solutions in the waterborne transport

Throughout the document, an attempt will be made to contextualize the Research Management System concept, the type of architecture and the tools that could currently be interesting to be used by the waterborne sector, by a Top-Down analysis. Special emphasis will be put on the information needs, the form of the data, the typology and importance of each data for projects, patents, publications, papers, events and conferences. A bottom-up analysis will be conducted where the different data sources and project search engines currently available will be analysed in depth. Finally, a benchmarking of Research Information Management System (RIMS) recommendations will be made and conclusions as well as recommendations for the adoption of solutions and their later maintenance will be drawn.
Cryptocurrency Price Prediction Model Based on Sentiment Analysis and Social Influence

Cryptocurrency as an alternative method of payment that acts both as a type of currency and as a virtual accounting system has always been of interest to investors. Since the public sentiment of a society about cryptocurrencies can affect the cryptocurrencies’ prices, a machine learning model based on sentiment analysis has been proposed to forecast the future prices of cryptocurrencies such as Bitcoin, Ethereum, EOS, Cardano, and Ripple using machine learning models that are suitable for time series data analysis to reduce the risk of investing in this market. It was shown that by applying weights to the sentiment scores of tweets according to the influence factor of the individuals, the accuracy of the prediction will increase and a significant difference between the accuracy scores was observed using the LSTM model according to the MAPE indicator ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> =0.045). Also, a hybrid model is proposed based on the combination of features extracted from the texts by one of the dictionary-based text analysis models and the feature of weighted sentiment scores. It was shown that our proposed hybrid model outperformed the other models in predicting the prices of Ethereum, EOS, and Cardano according to the MSE indicator. Also, our proposed model based on weighted sentiment scores according to the influence factor of the Twitterers outperformed the other models in the prediction of the future prices of Bitcoin and Ripple, which indicates that the increase in the number of features will not always lead to an increase in the accuracy of our prediction models.
Source-Data-Free Cross-Domain Knowledge Transfer for Semantic Segmentation

This paper proposes a method for transferring knowledge of semantic segmentation from a labeled source domain to an unlabeled target domain without using the source-domain data. Such a problem is called source-data-free domain adaptation, in which a pre-trained source-domain model and the unlabeled target-domain data are used to transfer the label knowledge across the domains. Like most previous methods, our method uses pseudo labels for distilling and transferring the source-domain knowledge. On the basis of the pseudo-label learning, our method improves the domain adaptation performance in two innovative ways: (1) reducing the domain differences by source-data-free style transfer and (2) exploring the style diversity within the target domain by style modification. To this end, we introduce two additional modules: (1) an inter-domain style transfer module which aligns the feature statistics of the source and target domains before producing the pseudo labels thereby improving the pseudo labels' accuracy, and (2) an intra-domain style modification module which modifies the image styles within the target domain for learning intra-domain style-invariant features. Our method with the two modules outperforms previous source-data-free domain adaptation methods in two commonly used benchmarks. Moreover, our method is well compatible with the previous methods for further improvement.
An efficient federated learning framework deployed in resource-constrained IoV: User selection and learning time optimization schemes

In this article, an efficient federated learning (FL) Framework in the Internet of Vehicles (IoV) is studied. In the considered model, vehicle users implement an FL algorithm by training their local FL models and sending their models to a base station (BS) that generates a global FL model through the model aggregation. Since each user owns data samples with diverse sizes and different quality, it is necessary for the BS to select the proper participating users to acquire a better global model. Meanwhile, considering the high computational overhead of existing selection methods based on the gradient, the lightweight user selection scheme based on the loss decay is proposed. Due to the limited wireless bandwidth, the BS needs to select an suitable subset of users to implement the FL algorithm. Moreover, the vehicle users' computing resource that can be used for FL training is usually limited in the IoV when other multiple tasks are required to be executed. The local model training and model parameter transmission of FL will have significant effects on the latency of FL. To address this issue, the joint communication and computing optimization problem is formulated whose objective is to minimize the FL delay in the resource-constrained system. To solve the complex nonconvex problem, an algorithm based on the concave-convex procedure (CCCP) is proposed, which can achieve superior performance in the small-scale and delay-insensitive FL system. Due to the fact that the convergence rate of CCCP method is too slow in a large-scale FL system, this method is not suitable for delay-sensitive applications. To solve this issue, a block coordinate descent algorithm based on the one-step projected gradient method is proposed to decrease the complexity of the solution at the cost of light performance degrading. Simulations are conducted and numerical results show the good performance of the proposed methods.
Review classification based on machine learning: classifying game user reviews

With the development of the game industry, the maturity of online game sales platforms, and the increasing complexity of game software itself, game companies need to analyze massive amounts of user reviews to understand the hidden defects of the game and the direction of future iterations. Manually reading game reviews is a labor-intensive and time-consuming task, as the number of reviews can go up to several thousand per day. Automatically classifying these game reviews will help alleviate this issue, but traditional classifiers will need a large number of labeled instances for training. In this paper, we propose and implement an approach that combines transfer learning (BERT), unsupervised learning, and active learning to classify game reviews using only a small amount of labeled instances. We found that our approach obtains 88.8% classification accuracy with only 100 labeled training instances. Our implementation can be extended to handle different types of new games by using a small amount of extra labeled instances and manual work.
Deep learning based signal detector for OFDM systems

In this paper, we propose a novel deep learning (DL)-based receiver design for orthogonal frequency division multiplexing (OFDM) systems. The entire process of channel estimation, equalization, and signal detection is replaced by a neural network (NN), and hence, the detector is called a NN detector (N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D). First, an OFDM signal model is established. We analyze both temporal and spectral characteristics of OFDM signals, which are the motivation for DL. Then, the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory (Bi-LSTM) NN. Especially, a discriminator (F) is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain (OCG), which can greatly improve the performance of the detector. Finally, the trained N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D is used for online recovery of OFDM symbols. The performance of the proposed N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D is analyzed theoretically in terms of bit error rate (BER) by Monte Carlo simulation under different parameter scenarios. The simulation results demonstrate that the BER of N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D is obviously lower than other algorithms, especially at high signal-to-noise ratios (SNRs). Meanwhile, the proposed N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D is robust to the fluctuation of parameter values.
Universality and Approximation Bounds for Echo State Networks With Random Weights

We study the uniform approximation of echo state networks (ESNs) with randomly generated internal weights. These models, in which only the readout weights are optimized during training, have made empirical success in learning dynamical systems. Recent results showed that ESNs with ReLU activation are universal. In this article, we give an alternative construction and prove that the universality holds for general activation functions. Specifically, our main result shows that, under certain condition on the activation function, there exists a sampling procedure for the internal weights so that the ESN can approximate any continuous casual time-invariant operators with high probability. In particular, for ReLU activation, we give explicit construction for these sampling procedures. We also quantify the approximation error of the constructed ReLU ESNs for sufficiently regular operators.
SOSNet: Real-Time Small Object Segmentation via Hierarchical Decoding and Example Mining

Real-time semantic segmentation plays an important role in auto vehicles. However, most real-time small object segmentation methods fail to obtain satisfactory performance on small objects, such as cars and sign symbols, since the large objects usually tend to devote more to the segmentation result. To solve this issue, we propose an efficient and effective architecture, termed small objects segmentation network (SOSNet), to improve the segmentation performance of small objects. The SOSNet works from two perspectives: methodology and data. Specifically, with the former, we propose a dual-branch hierarchical decoder (DBHD) which is viewed as a small-object sensitive segmentation head. The DBHD consists of a top segmentation head that predicts whether the pixels belong to a small object class and a bottom one that estimates the pixel class. In this situation, the latent correlation among small objects can be fully explored. With the latter, we propose a small object example mining (SOEM) algorithm for balancing examples between small objects and large objects automatically. The core idea of the proposed SOEM is that most of the hard examples on small-object classes are reserved for training while most of the easy examples on large-object classes are banned. Experiments on three commonly used datasets show that the proposed SOSNet architecture greatly improves the accuracy compared to the existing real-time semantic segmentation methods while keeping efficiency. The code will be available at https://github.com/StuLiu/SOSNet.