Reviewerly/exHarmony huggingface dataset:
The Influence of Hr Information Systems, Competence, and Work Discipline on Work Productivity Post-Merger Bank Syariah Indonesia

ABSTRACT Research Aims: HR information systems, competencies, and work discipline are the independent variables while work productivity is the dependent variable used. Methodology: The case studies that became the research population were employees of Bank Syariah Indonesia Mataram Pejanggik 1 Branch Office. This research is a quantitative research with saturated sampling technique used in determining the sample. The distribution of respondents obtained was 40 filled out questionnaires. Data analysis method with IBM SPSS 26 because it uses multiple linear regression analysis. Research Findings: The results of this study were obtained simultaneously from the three variables influencing employee work productivity. Meanwhile, if partially obtained the results of information system variables, and work discipline have a positive and significant effect on work productivity. But the competency variable partially has no positive and significant effect on employee work productivity. The results of this study are that Bank Syariah Indonesia Pejanggik 1 Branch has implemented an HR information system that is accurate and clear, as well as good work discipline, even though there are still employees who are not competent in their fields.
Dominantnost muških vrijednosti i izbjegavanje neizvjesnosti kao instrumenti razvoja kulturne inteligencije studenata

This paper shows how students' perception on the dominance of masculinity values and uncertainty avoidance characteristics relates with the level of students' cultural intelligence. In order to examine the phenomena, the research was conducted on a sample of 146 exchange programme students (ERASMUS exchange programme). The results indicate a positive and significant correlation between the variables uncertainty avoidance and determinants of cultural intelligence. Furthermore, the obtained results indicate a negative correlation between students' perception on dominance of masculinity values and metacognitive and motivational cultural intelligence, while relationships with cognitive and behavioural cultural intelligence were statistically non-significant. The results of this study can help teachers, researchers, and economic and social policy makers to build educational models that will help students develop an awareness of cultural disparities. As a result, individuals will be able to focus their efforts on developing and shaping the individual mental dimensions necessary for successful adaptation to the international environment.
Industry Revolution 4.0: Rapid Growth of Technology May Affect Job Security in Auditing Profession: A Concept Paper

Auditing in IR 4.0 presents several challenges due to the changing nature of the business environment and the increasing complexity of organizational systems.One of the main challenges of auditing in IR 4.0 is the need for auditors to have a deep understanding of the emerging technologies that are being used by organizations.This includes the ability to analyze data generated by these technologies, understand how they are integrated into business processes, and identify potential risks associated with their use.IR 4.0 brings disruption with much more automated and technologically complex processes.The industry is entering the so-called "new generation," and keeping up with it is the most difficult challenge.Auditing firms need to consider technology investment so as to keep their competitiveness.IR 4.0 has severely impacted the labour market, including audit firms.To safeguard their profession, auditors will need to embrace the automation of current auditing procedures.This paper investigates factors that influence the job security of auditors due to the rapid growth of technology in the IR 4.0 era through an article review.Three factors are discussed which are technological, knowledge and skills, and environmental.With these insights, recommendations are proposed for auditors to be prepared for and well-suited to the IR 4.0 industry.
Special Issue “Advance in Machine Learning”

Machine learning has increasingly become the bridge between theoretical knowledge and practical applications, transforming countless aspects of modern life [...]
Learners’ Attendance and Reading Performance During Face-to-Face and Online Learning: Basis for Policy Enhancement

The capability to read is vital to learning which leads to advancement toward higher literacy skills. Online learning became a way to ensure learners attended their classes virtually during the pandemic. Online learning modality affects the student’s reading performance and attendance compared to face-to-face learning. This research used a quantitative research design using a survey method with 84 respondents from select public high schools. It was found that the students have better attendance and reading performance in a face-to-face learning set-up compared to online learning. Also, the income of parents monthly caused the difference in the class attendance and reading performance of the learners. Also, a significant relationship exists between the reading performance of students and their attendance to face–to–face learning. Hence, face-to- face learning tends to be more beneficial to students when it comes to maintaining class attendance and improving the learner’s reading abilities because they can have more direct assistance from their teachers.
Pragmatic Functions of Emoji on Chinese Social Media

Emojis, expressive symbols on social media, gained popularity due to its convenience and versatility. This paper explores the pragmatic functions of four specific emojis (doge, smile, funny face, love) from comments on Bilibili and Weibo. A corpus of 229 comments is created through manual collection and crawler, and annotated according to the intext functions of the emojis. The results show that the four emojis are mainly used as “external tonal complements” and used for friendliness or joking, which can be explained by the renderings of the emojis. Furthermore, the friendly-looking love emoji and smile emoji also have occasional uses for sarcasm. Emojis used mainly for joking have softening uses, while emojis used mainly for friendliness are found to have sarcastic uses instead of softening uses. A possible cause of these negative uses is the censorship in Chinese social media. The research serves as a basis for Chinese emoji uses research of greater scale and provides insight for sentiment analysis with emojis.
The Hybrid Neuro-Fuzzy Models' Most General Intelligent Architectures

Hybrid systems of the fuzzy logic and neural networks, are widely spread in real world problems with high effectivenessand versatility for different kinds of applications. The state description of unknown plant by using mathematical models, sometimes, isdifficult to obtain. The fuzzy logic systems with their ability of tackling imprecise knowledges, and neural networks with theiradvantages of establishing a relationship between the inputs and the outputs of the system, are represented as qualified tools for systemsof unknown plant. Furthermore, the hybrid systems which utilize the features of the fuzzy logic and Neural networks has been employedfor better characteristics. Whilst, there are several different architectures of the neuro-fuzzy system proposed in literature, this articlecome out to highlight the common known architectures of how these techniques fuse together to build an enhanced system that cancomplement the lack of each method individually and improve the system performance over all.
Literature and Data-driven Learning

This article aims to examine how and why we can use digital storytelling (DST) to teach literature in the upper secondary classroom, and makes use of Mark Haddon’s complex, multimodal novel The Curious Incident of the Dog in the Night-Time (2004) as an example. DST is a powerful tool for the 21st century classroom, with the potential to help pupils to become empathetic, creative, inquisitive and critical thinkers. When applied to literature, DST allows pupils to continue the literary text in creative ways by making their own digital story using a basic video editing application such as iMovie or Microsoft Bilder. Such literature-based DST (LDST) distinguishes itself from ordinary DST use in school. The values, qualities and peculiarities of literature offer an imaginative point of departure for new ways of using digital media in school. The Curious Incident is an exceptionally appropriate novel to use with DST, because the novel and the method share numerous features, not least multimodal ones. The narrative structures and aesthetic dimensions of the novel overlap with characteristics of DST. Furthermore, the many hermeneutic dimensions of Haddon’s novel can also be explored by the imaginative possibilities of DST. The definitions and interrelations between literature, text and modality are discussed throughout the article with reference to both philosophers and school didacticians. LDST is remarkable in the way it encourages pupils to sympathise, to interpret, to think critically and to develop their own imaginative ability and language skills. This article argues that LDST is useful in upper secondary education by examining the corresponding educational qualities and possibilities of Haddon’s multimodal novel and DST, and by drawing upon the new education reform in Norway articulated in the Core curriculum of 2020 and the Curriculum in English. Furthermore, the article concludes with the presentation, analysis and assessment of a pilot LDST-project conducted in an upper secondary school in Troms County as part of an MA lector thesis at UiT The Arctic University Norway. Thus, this article’s wider aim is to bring new research perspectives to English education, with a special focus on the use of literature and active forms of learning.
Inter-Annotator Agreement for the Factual Status of Predicates in the TAGFACT Corpus

This paper reports on a study of the inter-annotation agreement to assess the manual annotation of the TAGFACT Gold Standard corpus. This corpus has been created as part of a larger project (TAGFACT), whose final objective is to automatize the classification of the factual status of events in a corpus of Spanish journalistic texts. In our study, six annotators labeled a corpus using the four levels of linguistic description proposed in our project to extract factual information. Each one of these levels has been assessed independently. As expected, the more fine-grained the classification is, the more problematic the annotation. This study identifies some of the most important differences and discusses the main problems encountered to obtain full agreement. We use Cohenâs Kappa to measure inter-annotation agreement as well as descriptive statistical analysis.
Pelatihan Pengelolaan Taman Baca dan Penyuluhan Jajanan Anak yang Sehat dan Aman

This community service activity was carried out in the society-based library blok E, Pasir Angin village, Cileungsi, Jawa Barat. This activity aimed to increase the knowledge and skills of society-based library administrators in managing their society-based library. Furthermore, a coloring competition for kids and counseling on healthy and safe snacks was also held which aimed to increase public knowledge and at the same time encourage public interest in visiting this society-based library. The training activities were carried out using the discussion method and practice on how to classify books, make catalog cards, book call numbers, and the membership lists of society-based libraries. Counseling activity was carried out using lecture and discussion methods. Evaluation of the training activity was carried out using the interview method, while the counseling activity was evaluated using a questionnaire instrument. The result be achieved from this activity is that the administrators can manage the society-based library well so that they are able to foster public interest in visiting and at the same time stimulate public interest in reading.
Sign Language Recognition and Response via Virtual Reality

The main aim of this project is to provide a voice to voiceless people with the help of Python and Unity prototype systems. Aforesaid project work focuses on the problem of gesture recognition in real time that sign language is used by the community of deaf people. The problem addressed is based on Digital image processing using color segmentation, Skin detection, Image Segmentation, Image Filtering, and Template Matching techniques. This system recognizes gestures of ISL(Indian Sign Language) including the alphabet and a subset of its words.
Bullet Id: A Virtual Bullet Identification for Forensic Ballistics Students

As school is a public place where the crowd is unavoidable and children are vulnerable, there is a need to strengthen policy in terms of the delivery of instruction - to provide opportunities for online learning platforms. Numerous innovative programs have been proposed by the different learning sectors in the Philippines. The Commission on Higher Education suggested strengthening online platforms and blended learning such as but not limited to google classroom, messenger, zoom, Edmodo, Facebook, and YouTube. This study was conducted using the Agile Software Development Methodology. The Bullet ID is a promising educational tool that will help both instructors and students in their learning activities, especially in the field of forensic ballistics. This new learning technology will bridge the gap between face-to-face and online classes. The main feature of this system is that it is an offline-based mobile application which made it different from the other forensic ballistic application developed by other researchers which are computer-based software application that requires an internet connection.
Performance analysis of feature selection and classification in Big Data Information extraction

Purpose: Information extraction from big data is improved by either reducing the number of features in a data set or selecting features using intelligent data analysis. Generally, big data sets are complex to process using traditional approaches. Feature selection is highly essential in big data information extraction because it chooses the subset of features that influence the final classification. Reducing the number of selected features in the data leads to enhanced accuracy and efficiency of data extraction with other attributes used in the mathematical model. This work aims to improve the performance of the classifier using an enhanced binary bat algorithm-based effective feature selection model. formulated to enhance accuracy, efficiency of data extraction with other attributes. An enhanced binary bat algorithm (EBBA) proposed to solve the mentioned problem using local optimization and global optimization factor which improves the performance of optimization. Experiment carried out with different datasets selected to test effective performance of proposed algorithm and demonstrated performance is better with other algorithms. Design: The purpose of this paper is to provide, an effective feature selection model for big data information extraction. An enhanced binary bat algorithm has been proposed to improve attribute selection using local optimization and global optimization methods. Classification of multisource data using selected features using labeled approach. Particular Information extraction for multi view multi label (PIMM) approach is compared with EBBA algorithm. Further to enhance effectiveness of shared and specific information in big data [3] by setting the delta and omega factors in order to fuse different information from different view point, Online analysis of relevance with any redundancy analysis also been incorporated. Findings: All the experiments were carried out with different datasets on the number of iterations and fitness of the attributes to validate the effective performance of the proposed algorithm. Experimental results and graphs show that the proposed methodology improves the overall performance of optimization using PIMM models. Originality: A feature selection model based on the binary bat algorithm has been the focus of this paper. Subset selection and feature ranking are the two important methods used in this approach. Experiments were conducted on datasets to analyze the patterns in the number of iterations and fitness of the attributes over selection. The improvement in feature selection leads to better classification accuracy of the proposed model compared to other nature inspired techniques.
Generative Adversarial Networks for Video Summarization Based on Key-frame Selection

Video summarization based on generative adversarial networks (GANs) has been shown to easily produce more realistic results. However, most summary videos are composed of multiple key components. If the selection of some video frames changes during the training process, the information carried by these frames may not be reasonably reflected in the identification results. In this paper, we propose a video summarization method based on selecting keyframes over GANs. The novelty of the proposed method is the discriminator not only identifies the completeness of the video, but also takes into account the value judgment of the candidate keyframes, thus enabling the influence of keyframes on the result value. Given GANs are mainly designed to generate continuous real values, it is generally challenging to generate discrete symbol sequences during the summarization process directly. However, if the generated sample is based on discrete symbols, the slight guidance change of the discrimination network may be meaningless. To better use the advantages of GANs, the study also adopts the video summarization optimization method of GANs under a collaborative reinforcement learning strategy. Experimental results show the proposed method gets a significant summarization effect and character compared with the existing cutting-edge methods.
FORECASTING CATEGORY IN PUBLIC SCHOOL STAFF STUDENTS AND SPENDING IN 2018 WITH COMPARISON METHOD

Education is one of the efforts to develop capabilities and shape the character and civilization of a dignified nation. Increasingly expensive education costs require parents to prepare more maturely. Therefore, the purpose of this study is to show forecasting of rill costs per student in 2018 using the Timeseries Public School Staff, Students, and Spending 1970-2017 data. In this study, the ARIMA and the SVM model are used to forecast rill costs per student in 2018. ARIMA is one of the simplest models and the most widely used method. This model will be compared using machine learning, namely SVM. From the experimental results that compare the performance of SVM and ARIMA. In the calculation above, the MAPE results from the Arima and SVM models are 0.142 and 0.0029. Whereas for RMSE the results of the ARIMA calculations obtained results of 36.625, while for SVM the RMSE results obtained were 7.470. The results of the study concluded that the SVM model has a better performance in predicting crude oil prices compared to the ARIMA method.
USE OF ONE-TO-ONE IPAD IN PRIMARY SCHOOL TO TEACH THE CONCEPT OF ENERGY IN PHYSICS: A CASE STUDY

In recent years, technology has become an increasingly important tool in education. The use of technology in primary education has become increasingly popular in recent years, and the iPad has emerged as one of the most versatile and powerful tools for enhancing learning. This paper will focus on the impact of one-to-one iPad use in primary schools on teaching the concept of energy in physics. A total of 25 students in the 3rd grade from a primary school in Greece participated in the study, using iPads during their physics lessons. This study’s results showed that iPads significantly improved the student’s understanding of energy in physics. The students who used iPads had a more profound understanding of the concept, showed greater engagement and motivation and demonstrated a deeper understanding of the material.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0031/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Effects of Teacher Burnout on their Credibility

This study determined which domain of teacher burnout best influenced the credibility of teachers in Davao de Oro Division, Region XI, Philippines.The study employed the quantitative-descriptive approach using correlational technique.The respondents were the 215 public elementary teachers who were selected using the simple random sampling procedure.The Mean, Pearson r, and Regression Analysis were the statistical tools used in this study.Moreover, adapted survey questionnaires were used to measure the levels of teacher burnout and teacher credibility.The result showed that the level of teacher burnout was moderate and teacher credibility was low.Further, data showed that there was a significant negative relationship between teacher burnout and their credibility and that overall teacher burnout influenced the credibility of teachers.Among the domains of teacher burnout, depersonalization, and personal accomplishment best influenced the credibility of teachers.Likewise, between the two domains, personal accomplishment best influenced teacher credibility.
A Penetration Method for UAV Based on Distributed Reinforcement Learning and Demonstrations

The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in modern warfare. Enhancing UAV’s ability of autonomous penetration through machine learning has become a research hotspot. However, the current generation of autonomous penetration strategies for UAVs faces the problem of excessive sample demand. To reduce the sample demand, this paper proposes a combination policy learning (CPL) algorithm that combines distributed reinforcement learning and demonstrations. Innovatively, the action of the CPL algorithm is jointly determined by the initial policy obtained from demonstrations and the target policy in the asynchronous advantage actor-critic network, thus retaining the guiding role of demonstrations in the initial training. In a complex and unknown dynamic environment, 1000 training experiments and 500 test experiments were conducted for the CPL algorithm and related baseline algorithms. The results show that the CPL algorithm has the smallest sample demand, the highest convergence efficiency, and the highest success rate of penetration among all the algorithms, and has strong robustness in dynamic environments.
Appropriation of digital knowledge in the training of Mexican normalist students

Digital knowledge constitutes knowledge, skills and attitudes related to the use of Information and Communication Technologies (ICT) in the educational field. The training of normalist teachers requires, as a graduation profile, to strengthen the development of digital knowledge that allows them to enrich the design, development and implementation of didactic situations and sequences with the use of ICT, not only from an instrumental level, but also from a didactic perspective. This research presents the results of a quantitative, non-experimental, descriptive study, whose objective was to diagnose the appropriation of digital knowledge in the formation of Mexican normalist students. The sample consisted of 505 Mexican normal students from the states of Chihuahua, Hidalgo and Tamaulipas, who were administered a questionnaire with a Likert-type scale with 36 items distributed in 7 dimensions. Among the main findings, digital knowledge was identified with a higher score for the use of messaging services, as well as the management of different types of sizes and fonts; the lowest scores were recorded in data analysis with specialized software and the development of blogs to communicate information.