Reviewerly/exHarmony huggingface dataset:
Analysis of Dependence of Grey Wolf Optimizer To Shift-Transformations and its Shift-Invariant Methods Adaptively Controlling the Search Areas

As a metaheuristic method for the continuous optimization problem, the grey wolf optimizer (GWO) has attracted much attention from researchers because the method is reported to be superior to other methods according to many studies. However, some works show that the GWO is too specialized only to problems having the zero-optimal solution, which can lead a significant deterioration of the efficiency for other problems. Thus, an invariant GWO, GWO-R, to the shift-transformation was proposed by using reference points to overcome the drawback.In this paper, first, we theoretically prove the shift-dependence of the GWO in the coordinate representation of the problem, which is the underlying cause of the over-specialization of the GWO, and we experimentally analyze the property by using a larger number of problems in more detail. Secondly, we propose a modification of GWO-R, GWO-SR, which can adjust more reasonably its search areas than GWO-R to improve the intensification of the search in the final stages. Moreover, we modify GWO-SR, which is called GWO-AS, by adding two methods: an adjustment technique the size of the search area for each grey wolf and a mutation process to enhance the diversity of the search. Finally, we show advantages of two proposed GWOs by comparing them with other metaheuristic methods.
Discussion of Quantum Consensus Algorithms

Leader election is a crucial process in many areas such as cloud computing, distributed systems, task orchestration, and blockchain. Oftentimes, in a distributed system, the network needs to choose a leader, which would be responsible for synchronization between different processors, data storage, information distribution, and more.
PERBEDAAN COMPUTER VISION SYNDROME BERDASARKAN SCREEN TIME DALAM PEMBELAJARAN DARING PADA MAHASISWA PSSIKPN FK UNIVERSITAS UDAYANA

The impact of the Covid-19 pandemic on education has caused the learning process to be conducted online. Online learning is carried out by all students in various educational institutions, including nursing students. The students who undergo online learning can spend hours in front of a laptop, computer, or gadget, therefore they are at risk of developing eye problems such as Computer Vision Syndrome (CVS). The purpose of this study was to determine the difference between CVS based on screen time in online learning for Udayana University nursing students. This study implemented a comparative descriptive design with repeated measurements three times on the CVS variable. The data were collected by screen time questionnaire and CVS complaints with measurements for two weeks. The respondents in this study were 161 nursing students who were selected by the stratified random sampling method. The results showed that there was a significant difference in CVS on day 1, 5, and 10 with p = 0.000. The conclusion of this study is that there is a significant difference in CVS on days 1, 5, and 10 in which there is an increase in scores on CVS on days 1, 5, and 10.
Students' Strategies to Overcome The Level of Language Anxiety in Speaking Performance at UPT SMP Negeri 13 Gresik

The objectives of this research to find out the factors caused the students’ anxiety in speaking performance and to find out the students’ strategies to overcome the level of language anxiety in speaking performance. The research applied a qualitative descriptive as research method. It was conducted in UPT SMPN 13 Gresik. The data were gathered by questionaire, observations and interview. While, technique and data source triangulation were used to validate data. The results of this study indicate that not all levels of language anxiety are experienced by students.
Exploring Trends and Gaps in Teaching Linguistics Research among Undergraduate EFL Students: A Bibliometric Analysis

The importance of linguistics is not just for EFL teachers to assist in mastering the concept of teaching. However, the understanding of linguistics should be started with the introductory for EFL Students as a fundamental course to help them build their understanding regarding what they are studying including the trends and gaps of the study in that context. This study aims to analyse trends and gaps in studies regarding teaching linguistics as a subject taught to EFL Students at the university level. This qualitative research applied bibliometrics analysis by collecting relevant studies taken from Publish or Perish and manage to collect 599 articles related to teaching linguistics from Google Scholar, Scopus, and Web of Science databases ranging from 2018-2022; by applying exclusion and inclusion criteria, 367 articles were selected to be analysed using VOSviewer. The result showed the terms for the classroom, technology, foreign language, type, attitude, and literature are the most frequent and mostly discussed by the researchers. While in terms of solution, exploration, contribution, learning process, perception, difficulty, and text seems still gaining less attention compare the others. Therefore, these spots can be seen as research topic opportunity for future researchers to finding the niche. These result implies and also highlights that there are still many interesting topic to be explored regarding the scope of linguistics in language learning.
Linguistic Communication and Its Role in Developing Language Skills "Listening Skill As A Model"

This study aims to identify linguistic communication and its role in developing language skills “listening skill as a model.” The research included an important problem with educational dimensions in achieving effective linguistic communication between the teacher and his students, and the ability to know the strengths and weaknesses that students suffer from when communicating with others. We have referred to the concept of linguistic communication through which information and ideas are transferred between individuals, considering communication an essential part linked to human life. Without linguistic communication, we cannot achieve any specific goal or goal, in addition we touched on the skill of listening as the first of the important language skills, which is a receptive skill, the role of this skill in the linguistic communication process and the role of the teacher in it, and the steps and methods of teaching this skill, in order to enrich the students’ language from using this skill in the communication process. daily.
Forecast of the distribution of volcanogenic masses on the basis of the analysis of magnetometric and borehole data in the north-western part of the Yevlakh-Agjabedi depression

The article presents the results of new magnetometric planning work carried out with a Geometrics G-856AX (made of USA) magnetometer in the north-western part of the Yevlakh Agjabedi basin and explores the field distribution characteristics of high-intensity local magnetic maxima. Local anomalies of the geomagnetic field in different averaging radii (R = 1.5, 2, 3, 5 km) of the north-western part of the Yevlakh Agjabedi basin were calculated and distribution maps of the field were constructed. As a result, it is shown that volcanic structures are distributed in the zone of maxima identified on the maps of local anomalies in the part of the NW part of the Yevlakh Agjabedi basin. It is assumed that the deep fault zone separated by seismic data in the Naftalan-Godakboz and Duzdag areas will be characterized by magnetic minimums, and intensive magnetic maxima will be determined to the east. In the west, volcanic masses have been found in the zones of high-intensity magnetic maxima identified in the Borsunlu, Dalimammadli, and Hazirahmedli fields. Of these, volcanic roots are believed to be present in the deep fault zone in the Hazirahmedli area, which is characterized by the highest intensity anomaly. Keywords: magnetometric exploration; magnetic field; magnetic maximum; volcanic rocks.
Pengaruh Game Online Mobile Legend Terhadap Kesehatan Mental Peserta Didik

The motivation behind this research is to examine how much influence the online mobile legend game has on the mental health of students at SMPN 1 Kota Baru. This study uses a quantitative methodology with ex-post facto research techniques. This research was conducted at SMPN 1 Kota Baru on May 20 to June 6 2022. The population was all Grade VIII students at SMPN 1 Kota Baru, totaling 427 people. The population in this study was taken using the snowball sampling method, consisting of 30 individuals consisting of 21 men and 9 women. The instrument uses a mobile legend online game questionnaire and a mental health questionnaire. The examination method used in this exploration is a linear regression equation. The consequences of this study show the extent to which sig. &lt;0.05 and a guarantee coefficient of 0.000 which indicates that the effect of playing mobile legend online games on the mental health of students at SMPN 1 Kota Baru is 90.8% while 9.2% is influenced by different variables. The conditions set are = 4,923 + 1,000 X, so that the mental health of students will decrease as the strength of playing online mobile legend games increases by 1,000.
Implementation of Multi-host Podcast-Based Learning Videos in Multimedia Concept Courses

Learning through digital means is a modern method of education that has been influenced by rapid development of communication technology and information in the era of industrial revolution 4.0. In universities, teaching and learning activities have been changed because of the COVID-19 pandemic. Multihost podcasts based on learning videos have emerged as an alternative learning media for online study in Multimedia Concept programs. Based on the results of distributing a questionnaire, the research method used has been a 4D Model with the conclusion that multihost podcast-based learning videos are highly feasible with a percentage rate of 86,8% from the perspective of video display, learning content and understanding. As an effective and efficient learning medium, podcasts have substantial potential for university students.
Media Presentasi Dalam Bentuk Motion Graphic (Studi Kasus: Proses Produksi Detak Kepri Malam Batam Tv)

&#x0D; &#x0D; &#x0D; Apprentices at Batam TV are required to understand the production process at Batam TV, such as the production process for the Detak Kepri Malam, and other programs. The presentation of the material is delivered through presentations using the PowerPoint application and presented by the presenter. The level of visits is very high, taking up Batam TV's time for presentations. The solution offered is the presentation of material in the form of motion graphics. The research method uses RnD, namely making presentation material products in the form of motion graphics using the Villamil-Molina method, then analyzing the application of motion graphic aspects and presentation material aspects using Quasi Experiment Nonequivalent Control Group Design through a Paired sample T-test. The results showed that the media presentation to explain the production process of Detak Kepri Malam in video form was considered effective. The video lasts 2 minutes 30 seconds and is in Mp4 format and is displayed using a prompter in the Batam TV broadcast studio so it doesn't depend on the presenter of the material, and is easy to access via the Batam TV YouTube channel.&#x0D; &#x0D; &#x0D;
THE IMPACT OF BLOCKCHAIN TECHNOLOGY ON CRISIS MANAGEMENT: THE CASE OF COVID-19

Blockchain sector has advanced as a recourse can be used in numerous ways by global health tacticians and policy experts to reduce the disastrous impact of coronavirus disease (COVID-19). The COVID-19 pandemic has spawned many blockchain-related ideas and initiatives, but to the best knowledge, no in-depth studies have been performed to uncover and summarize the key features of these technologies. The SARS-CoV-2 pandemic began at the end of 2019 when a new coronavirus was discovered in the form of unknown pneumonia. Our proposed high-level, decentralized architecture facilitates in managing large-scale information and saves the data on a blockchain overseen by authorities. There are considerate difficulties faced if institutions are to regain their trust and have hope again, given today's distrust, last year's fear of the pandemic, and the emergence of technology that enables the production of standard sample.
IMPACT OF DISTANCE LEARNING TECHNOLOGY ON ONLINE EDUCATION PERFORMANCE

Distance learning technologies have become mainstream due to the emergence of coronavirus (COVID-19) and its variants over the last few months. Both companies and institutions are now resorting to using technology for their operations, whether to train their workforce or educate their students. This research has devised full stack distance learning technology platform to enable a factious educational institutions or corporation to enable its usage and effectively demonstrate how technology could be utilized to fully transform learning online. As a result, the functioning of how a university or learning institution could take their learning online away from their physical medium, thereby insulting themselves from the risk of suffering contagion due to the coronavirus while continuing their operations and business as usual.
WITH GAMIFICATION TO COLLABORATIVE LEARNING IN CHEMISTRY LESSONS

Gamification with the use of ICT and with the support of the teacher represents an intense, effective, and creative way of learning. By using it in lessons, it is possible to develop collaborative learning, activate student's prior knowledge and contribute to a better understanding and permanence of knowledge. Cooperative learning gives students the opportunity to explore the learning content more deeply during class and evolve critical thinking, while at the same time having a positive effect on the class climate and the building of close mutual relationships. With both didactic approaches it is possible to create new opportunities for learning, e.g., when the students themselves come up with new problems out of the discussed topic. The aim of the article is to examine how students accept the use of didactic method of gamification in the form of online applications Quizlet live and Socrative space race. In the centre of attention is the impact which gamification and the use of online applications have on students' motivation to learn, and how they simultaneously with collaborative learning contribute to better students’ results, understanding, and permanence of knowledge. Hypotheses on positive impact of gamification and collaborative learning on students' motivation for learning, understanding and students' academic achievements were confirmed by the research. Key words: gamification, collaborative learning, Quizlet live, Socrative space race
Customized Latent Space: Practical Usage of Variational Autoencoder

We propose a new semi-supervised learning method of Variational AutoEncoder (VAE) which yields a customized and explainable latent space by EXplainable encoder Network (EXoN). Customization means a manual design of latent space layout for specific labeled data. To improve the performance of our VAE in a classification task without the loss of performance as a generative model, we employ a new semi-supervised classification method called SCI (Soft-label Consistency Interpolation). The classification loss and the Kullback-Leibler divergence play a crucial role in constructing explainable latent space. The variability of generated samples from our proposed model depends on a specific subspace, called active latent subspace. Our numerical results with MNIST and CIFAR-10 datasets show that EXoN produces an explainable latent space and reduces the cost of investigating representation patterns on the latent space.
Improving Document-Level Neural Machine Translation with Discourse Features

Existing work in neural machine translation (NMT) is commonly restricted to the sentence level, but there is a growing trend toward document-level NMT that incorporates inter-sentential dependencies in order to improve translation quality. In this work, we hypothesize that discourse structure can be leveraged to capture long-range information. We propose a discourse-aware document-level NMT model, Disco2Doc, that utilizes discourse features obtained from a rhetorical structure theory (RST) parser to enhance translation quality and contextual awareness. We also introduce a new, simple, training method, PartialOut, to promote usage of the discourse features. We conduct extensive experiments and analyses on English-German, English-French and English-Russian translations. Our experiments, including the human evaluation, show that discourse structure can indeed significantly improve translation quality as measured by BLEU, as well as contextual awareness measured by accuracy on focused contrastive test sets.
Three-Dimensional Forward-Looking Sonar Interferometry Based on Subpixel Image Registration

In recent years, three-dimensional forward-looking sonar (3D FLS) has become a research hotspot. The 3D FLS can obtain three-dimensional spatial information of the scene and structure in the field of view in a single ping while obtaining the backscattered image in the field of view. Aiming at the problem of image registration in 3D FLS interferometry, this paper designs the 3D FLS receiving array using the multi-subarray structure and proposes a subpixel image registration algorithm based on a two-dimensional (2D) surface. The homonymous points are selected using the amplitude-phase gradient method, and their neighborhood is extracted to calculate the mutual coherence coefficient matrix to solve a 2D surface to estimate pixel offset and complete the subpixel registration of an FLS interferometry image. The main advantages of the proposed algorithm are that it can reduce the interference of high coherence noise, optimize the step search process, and overcome the influence of interpolation fineness on the registration results, achieving high accuracy and low computational complexity. The effectiveness of the proposed method is verified by the tank and lake experiments using self-developed FLS for detection. The experimental results show that the proposed algorithm can accurately complete seafloor bathymetry and three-dimensional target detection in the field of view. The proposed algorithm can greatly improve the mapping and surveying efficiency of underwater vehicles, and provide more accurate position information for underwater obstacle avoidance and object detection.
How Do Trading Location and Government Direct Intervention Affect Security Price: Evidence from Taiwan's Oil ETFs

The Taiwan government relaxed the liquidation exemption rule for one oil ETF when this ETF faced delisting in the oil price crisis of 2020 due to COVID-19. However, this event attracted even more investors to purchase this ETF, and such strong demand increased its issue size and premium to become the world’s largest ETF in its category. This research first investigates the information content of opening prices, and then adopts the regression model to examine the return comovements of the oil ETFs on the oil and the Taiwan stock markets. The results support that oil ETF returns are more affected by the Taiwan market than the oil market when the Taiwan market is volatile, and their returns fully reflect oil price information only when both markets are stable. Our findings show that government support of the intervened ETF is directly and strongly associated with its premium in the short run, but the effect disappears when the support policy is not extended. This study finally presents that the intervention event attracted new investors, and these participators were mostly retail investors who regarded the intervened ETF as a gamble substitute, for which they lost heavily in the end. Therefore, if the government plans to support the stock market, then it should limit its intervention to blue-chip stocks.
Computing the Charlap-Coley-Robbins modular polynomials

Let $\mathcal{E}$ be an elliptic curve over a field $K$ and $\ell$ a prime. There exists an elliptic curve $\mathcal{E}^*$ related to $\mathcal{E}$ by anisogeny (rational map that is also a group homomorphisms) of degree $\ell$ if and only $\Phi_\ell(X, j(\mathcal{E})) = 0$, where $\Phi_\ell(X, Y)$ is the traditional modular polynomial. Moreover, the modular polynomial gives the coefficients of $\mathcal{E}^*$, together with parameters needed to build the isogeny explicitly. Since the traditional modular polynomial has large coefficients, many families with smaller coefficients can be used instead, as described by Elkies, Atkin and others. In this work, we concentrate on the computation of the family of modular polynomials introduced by Charlap, Coley and Robbins. It has the advantage of giving directly the coefficients of $\mathcal{E}^*$ as roots of these polynomials. We review and adapt the known algorithms to perform the computations of modular polynomials. After describing the use of series computations, we investigate fast algorithms using floating point numbers based on fast numerical evaluation of Eisenstein series. We also explain how to use isogeny volcanoes as an alternative.
Collaborative Bi-Aggregation for Directed Graph Embedding

Directed graph is able to model asymmetric relationships between nodes and research on directed graph embedding is of great significance in downstream graph analysis and inference. Learning source and target embeddings of nodes separately to preserve edge asymmetry has become the dominant approach, but also poses challenge for learning representations of low or even zero in/out degree nodes that are ubiquitous in sparse graphs. In this paper, a collaborative bi-directional aggregation method (COBA) for directed graph embedding is proposed. Firstly, the source and target embeddings of the central node are learned by aggregating from the counterparts of the source and target neighbors, respectively; Secondly, the source/target embeddings of the zero in/out degree central nodes are enhanced by aggregating the counterparts of opposite-directional neighbors (i.e. target/source neighbors); Finally, source and target embeddings of the same node are correlated to achieve collaborative aggregation. Both the feasibility and rationality of the model are theoretically analyzed. Extensive experiments on real-world datasets demonstrate that COBA comprehensively outperforms state-of-the-art methods on multiple tasks and meanwhile validates the effectiveness of proposed aggregation strategies.