Reviewerly/exHarmony huggingface dataset:
Troll Culture in Bangladesh: A Study on social media

Bangladesh's rapid proliferation of online communication platforms has given rise to a pervasive and impactful phenomenon, troll culture.This paper is written on the characteristics of troll culture in our country as online trolling become a new trend in our country, especially on social networking sites. It aims to explore the characteristics of troll culture in the country, shedding light on its various manifestations, the underlying behavioral patterns, and the consequential societal impact.Troll culture has become an integral part of online communication inBangladesh. This study aims to explore the different types of trolls in Bangladesh, their behavior, and the impact of troll culture on society. Social Media has revolutionized the way people communicate, behave, and interact within this virtual landscape people connect with others.These days online trolling gains momentum, particularly on social networking sites, and its nuances become imperative for both academic discourse and societal well-being.The researcher adopted a mixed-methods design incorporating both quantitative and qualitative data collection and analysis techniques. The quantitative aspect involves the use of surveys and data analytics to quantify the prevalence of trolling and its various forms.On the qualitative part content analysis is utilized to gain a nuanced understanding of troll behavior.Utilized some random sampling techniques to ensure a representative dataset and conducted frequency analysis to quantify the prevalence of trolling and identify patterns. The findings reveal that there are various types of trolls in Bangladesh, including political trolls, social media trolls, and cyberbullies. The study also found that troll behavior is mainly driven by group identity, anonymity, and the desire for attention and validation. This paper aims to provide a comprehensive overview of troll culture by examining both overt and covert scenariosand their implications for the cultural fabric of Bangladesh
Exploring the Work Values, Self-Leadership, and Leadership Styles of Millennials

Because millennials are now dominating the workplace accounting for a significant share in the labor force, their influence on the workforce is inevitable. This study determined the millennials' leadership style, self-leadership strategies, and work values. The study involved 202 randomly selected millennial employees from six organizations located in a major city and province of Southern Philippines. Structural equation modeling (SEM) was employed to confirm the hypothesized model. Findings revealed that the millennial employees' practice of both transformational and transactional leadership styles is promising and shows their capability to be directive and empowering in any situation. The structural model showed a strong influence of self-leadership on leadership style, sending a powerful message to organizations about the urgency of enhancing further the millennial employees' self-leadership. The model also revealed that self-leadership was strongly associated with work values, encouraging organizations to enhance self-influence considering the high values of self-transcendence and openness to change. Findings suggest that strong self-leadership skill leads to greater transformational and transactional leadership styles, suggesting that high awareness of oneself leads to understanding others better. Keywords: Millennials, work values, self-leadership, leadership styles
Professional formation in initial education from the perspective of the pedagogy of love

It is proposed to strengthen the teaching performance of the Initial Education teachers of the Pujilí Occident C10 Educational Circuit, through the use of strategies from the Pedagogy of Love. This research had a qualitative - quantitative approach, at an explanatory level, in addition the technique of focus group. On the other hand, the quantitative study was applied in the execution of structured interviews aimed at school authorities and teachers, as well as the analysis carried out when comparing the results of the pre-test and the post-test. In the study, the teachers’ lack of knowledge about this teaching modality was determined and therefore the proposal “Educating with Love” was designed, framed in workshops that provide them with the necessary knowledge to apply them in classes, with activities for socio-affective development. of the students, additionally a digital instruction manual was designed to carry out future workshops.
CREDIT CARD FRAUD PREDICTION FOR BANKS USING ABNORMALITY AND REGRESSION ALGORITHM WITH WEBAPP

Credit card fraud poses a significant challenge for banks, as conventional fraud detection methods often fall short in identifying fraudulent transactions. This paper introduces a credit card fraud prediction system that harnesses the power of machine learning abnormality and regression algorithms, complemented by a user-friendly web application. The system aims to forecast fraudulent transactions by analysing historical data. Python programming language, along with widely adopted opensource libraries such as Scikit-learn, Pandas, and Flask, forms the foundation of its implementation. The web application enables users to interact with the system, input transaction data, and obtain predictions. Empirical findings substantiate the system's exceptional accuracy in detecting fraudulent transactions, rendering it a valuable asset for banks seeking to fortify their fraud detection capabilities. Additionally, the system's adaptability allows for customization, facilitating integration with other systems and the integration of supplementary security features. In conclusion, the credit card fraud prediction system proposed in this paper offers an efficient and precise solution for banks combatting credit card fraud.
Topic Discovery in Scientific Literature

With the progress of society and science, various fields have achieved unprecedented development. Various research directions and problems have blossomed, and a huge amount of scientific literature has emerged. Scientific literature contains rich “knowledge”, e.g., research hotspots and topics. If those “knowledge” can be obtained from scientific literature, it would be of great practical significance to both government and researchers. The existing methods generally obtain “knowledge” by analyzing the semantics of scientific literature, which is complex and time-consuming. In this paper, we aim to explore new methods of research hot word extraction and research topic discovery from the perspective of network. Firstly, the word network is constructed based on the text of scientific literature. Next, a research hot word extraction method based on node centrality and a structural topic discovery method are proposed on the word network. Then, the consistency between structural topics and semantic topics is explored. Finally, the proposed methods are experimentally verified on a real dataset. The experimental results show that the proposed centrality based hot word extraction method can effectively extract research hot words, and the topics obtained by the structural topic discovery method are consistent with the semantic topics in some cases, providing a new way to textual knowledge discovery.
An E-learning recommender system using switch hybridization

A recommender system is developed to improve the efficient performance of new learners using stereotype filtering and collaborative filtering.The paper aimed at adopting a model that follows a classification of recommendation based on information sources that are being used, such as user profile and user-item profile to make accurate recommendation to new learners.The system undertakes stereotype filtering because it is less sensitive to the new learner.In stereotype filtering, recommendations are issued to new user by first identifying the category they belongs to and locating preferences of other users in the same category.The proposed system combines both recommendation techniques in order to gain better performance and address the shortcomings of each.The result showed that the satisfaction level are based on learner academic Information.Privacy issues should be explored in gaining learner's loyalty as well as frequent feedback.Hence, there is need for developing additional facilities to preserve privacy information and at the same time be able to use them for accurate recommendation.
Cluster Based Segmentation using K-Means Algorithm

In this paper we present about Data Mining and brief detail about the clustering algorithm which is used to cluster categorical data. The algorithm called K means. The K means algorithm is capable of clustering large data sets. Clustering is used to measure the difference between objects by identifying the distance between the pair of objects. These measures include the Euclidean, Minkowski and Manhattan distance. In this paper we used Euclidean distance to measure the distance between objects. Segmentation is within our overall customer database, which ones have something in common. We need to find the groups of people, understand them and make some commercial value from the different groups
Pre-implementation Method Name Prediction for Object-oriented Programming

Method naming is a challenging development task in object-oriented programming. In recent years, several research efforts have been undertaken to provide automated tool support for assisting developers in this task. In general, literature approaches assume the availability of method implementation to infer its name. Methods, however, are usually named before their implementations. In this work, we fill the gap in the literature about method name prediction by developing an approach that predicts the names of all methods to be implemented within a class. Our work considers the class name as the input: The overall intuition is that classes with semantically similar names tend to provide similar functionalities, and hence similar method names. We first conduct a large-scale empirical analysis on 258K+ classes from real-world projects to validate our hypotheses. Then, we propose a hybrid big code-driven approach, Mario , to predict method names based on the class name: We combine a deep learning model with heuristics summarized from code analysis. Extensive experiments on 22K+ classes yielded promising results: compared to the state-of-the-art code2seq model (which leverages method implementation data), our approach achieves comparable results in terms of F-score at token-level prediction; our approach, additionally, outperforms code2seq in prediction at the name level. We further show that our approach significantly outperforms several other baselines.
Challenges and Opportunities in Open Research: Webinar Commentary

On Jan 26, 2023, Ginny Herbert hosted the Council of Science Editors webinar titled “Challenges and Opportunities in Open Research,” during which panelists discussed the concept of open science, the contributions of different scholarly publishing actors, challenges to open research, including consideration of non-academic factors such as Indigenous data sovereignty and protecting vulnerable populations, and to what extent current open research policies have made research more transparent and efficient. Panelists proposed a number of solutions to enhance research transparency and efficiency, such as San Francisco Declaration on Research Assessment (DORA),1 CARE principles for indigenous data governance,2 and initiatives like NASA’s The Year of Open Science3 providing an open research platform, establishing an open access university press, offering expert advice and data science resources, and providing funds for open access publishing. Panelists acknowledged that research and academic publishing have long traditions, and these traditions can be difficult to break. To encourage researchers to adopt open research practices, they posited that institutions must provide support and infrastructure for open research. Additionally, they suggest institutions should profile open research ambassadors, publicize open access articles, run training and engagement programs for early career researchers, and incentivize open research practices through grant funding, awards, and staff promotion criteria. Although there are still challenges to overcome, the panelists highlighted the potential of open research to benefit not just the research community, but society as a whole. Open access has moved from the domain of disruptive technology to an increasingly adopted approach to research dissemination over the […]
Developing English Speaking Materials for Tenth Grade Students of Conversation Program at Madrasah Aliyah (MA) Ali Maksum

The purpose of this study is to develop English learning materials for MA Ali Maksum's Conversational Program. Because the presence of English materials in speaking classes is critical, material development may aid students in learning. Materials that help students improve their skills and motivate them are those that are personalised to the student's situation and background. This research and development (R&amp;D) study employs the ADDIE instructional design model. This research consists of needs analysis, syllabus design, English speaking material development, product validation, product testing, and final product. Interviewees include experienced English teachers, an expert in English materials design, and 10th grade students from MA Ali Maksum's Conversation Program. Student questionnaires, student and teacher interview guides, and validation sheets for expert judgment are among the data collection tools used. The data obtained were analyzed quantitatively and qualitatively. The result showed that the average interval for the English module was 2.26 x 3.24, and material expert validation ranged from 2.80 to 3.40, classifying this module as good and valid with several revisions. Therefore, this English module can be used to motivate students while also assisting them in learning English in a speaking or conversation context.
Copyright Originality in the Digital Space

Long after the United States’ Supreme court Feist decision, the threshold for conceptualizing copyright originality is still unfolding. The predominant doctrines are sweat of the brow, selective creation, and an author’s intellectual creation. Artificial intelligence (AI) and machine learning tools have changed the human creative landscape. The Kingdom of Saudi Arabia (KSA) couched its copyright law in a hybridized regime and positioned it to enhance the creativity of creators. The KSA law included the selective creativity, the author’s intellectual creation, and sweat of the brow approach in conceptualizing copyright originality. This chapter analyzes and explores the KSA copyright creative theories, regimes, and its socioeconomic impact in the digital economy. The chapter further seeks to create a path for further de-escalating the copyright originality threshold to focus on human creators and for effective developmental growth.
A Zoom Tetrad: Applying McLuhan's Laws of Media to Video Conferencing

Based on observations of video conference story slams, this article uses Marshall and Eric McLuhan's "laws of media" to propose a video conference tetrad The article also compares the resulting tetrad to a previous tetrad of video conferencing among mobile knowledge workers The purpose of the analysis is to better understand this medium and how it changes when used in different settings Story slam video conference extends the face and presence, retrieves (brings into new use) text, obsolesces (displaces) in-person performance, and reverses into facelessness and a lack of presence These findings suggest hosts should lean in to the opportunities for connection provided by video conferencing without overextending the medium This tetrad differs from the earlier one, illustrating the agency of humans when interacting with technology.
THE IMPORTANCE OF ADEQUATE PRE-ANALYTICAL MANAGEMENT FOR HISTOLOGICAL AND MOLECULAR EXAMINATION FOR DIAGNOSTIC PURPOSES

Almost all Anatomic Pathology laboratories have experienced that tissue patient who comes to these laboratories without adequate fixation and causes complicated patient diagnosis. In addition, adequate fixation also makes advanced procedures such as immunohistochemical staining or DNA and RNA extraction difficult because protein, DNA, and RNA become damaged due to inadequate fixation. The fixation procedure should be the responsibility of the clinicians, specifically surgeons and team. If this procedure is not carried out appropriately resulting in damage to the tissue due to the natural process, namely autolysis, which the fixation procedure can prevent. This autolysis will damage the tissue. The laboratory process results are not optimal, complicating the evaluation of patient tissues, resulting in difficulty and delay of diagnosis. If further examination is needed, such as immunohistochemistry and other molecular examinations related to the extraction of DNA or RNA, the examination results will also be not optimal.&#x0D; Moreover, patient therapy becomes delayed or difficult. For this reason, we conducted extension activities for health workers, some of whom may be involved in a team of surgeons. Knowledge of pre-analytic procedures can improve the quality of pre-analytical handling, which will improve the quality of tissue andcytology specimens; hence, a precise and optimal diagnosis can be obtained. This will help the clinicians in determining the patient's therapy. Therefore, morbidity and mortality rates of patients can be reduced.
Physiological signal analysis in exercise fatigue detection application based on deep learning

Abstract This paper proposes a physiological signal analysis method in exercise fatigue detection application based on deep learning models to provide fast and accurate feedback for the player's physical status and better assist the player to perform exercise. We adopt the deep neural network as backbone model and design following strategies in our proposed method to process and extract features in signals. First, we preprocess the physiological signal, including noise reduction and segmentation. Second, we use a deep learning model to design a feature extraction method, which uses an autoencoder to label and feature the signal. Third, we perform motion fatigue detection on the fused signal features based on a long short‐term memory network model. The results prove that the method proposed has good performance.
Probability and Statistical Inference

An R Companion for the Third Edition of The Fundamentals of Political Science Research offers students a chance to delve into the world of R using real political data sets and statistical analysis techniques directly from Paul M. Kellstedt and Guy D. Whitten's best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using R. Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of R step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in R to produce original statistical analyses for evaluating causal claims. End-of-chapter exercises encourage this innovation by asking students to formulate and evaluate their own hypotheses.
Part Two: Neural Network Controller for Hydrogen-CNG Powered Vehicle

Background: The control system of the vehicle regulates parameters like fuel flow control, vehicle speed control, tracking, etc Objective: The main objective of the paper is to monitor and determine an efficient, and automated control system for an H-CNG-powered vehicle. Using neural networks and machine learning, we would develop an algorithm for the controller to regulate the speed of the car with the help of variables involved during the runtime of the vehicle. Methods: Initially, Generating a dataset with the help of formulation and computation for training. Further, analysing different supervised machine learning algorithms and training the Artificial Neural Network (ANN) using the generated dataset to predict and track the gains of the H-CNG vehicle accurately. Results: Analysis of the gains of the H-CNG vehicle are presented to understand the precision of the trained Neural Network. Conclusion: The final verdict of the paper is that the Neural Network is successful in tracking the gains of the H-CNG vehicle with the help of the dataset presented for training using the Random Forest Regression technique for machine learning.
Online Fake Review Detection Using SVM

Abstract: After the pandemic, our overall life is changing and challenging that’s why our demand is changing now, we are focused on wellness, sustainability, technology, and the gig economy because of these trends we can observe the reflection in the changing desired need and limitation of seller and customer. Many customers can post a review on any website after making a purchase. Whether it’s an online purchase or an offline retail purchase. When customers buy a product online, they check the product reviews. This is very important for today’s e-commerce product decisions. There is a financial gain associated with writing fake reviews, which is why there has been a significant increase in misleading statements about certain product reviews on websites. Misleading reviews are dangerous reviews. Positive product reviews can attract customers and increase sales. Negative product reviews can reduce demand for that product and reduce sales. These misleading reviews are dangerous to your product’s reputation. In this paper, we use machine learning algorithms which are SVM Support vector machines, which are one of the most popular supervised learning algorithms used for both classification and regression problems. However, it is mainly used for machine-learning classification problems
The Impact of Accounting Disclosure through Social Media on Reducing Information Gap: A Field Study in Companies on the Iraqi Stock Exchange

The study aimed to investigate the impact of accounting disclosure through social media on reducing the information gap between investors. The study adopted a descriptive-analytical approach, and the population of the study consisted of 47 companies listed on the Iraqi stock exchange. The study sample consisted of 161 employees working in these companies. The study found that accounting disclosure through social media is important and plays a role in reducing the information gap for users. The field study showed that there is a significant relationship between accounting disclosure through social media and the reduction of the information gap in the Iraqi stock market. The study recommended the disclosure of future expectations regarding opportunities or risks resulting from positive or negative trends in financial and market performance indicators through social media platforms. This is important in providing stakeholders with clearer and more impactful information than that provided through mandatory disclosure. The study also recommended improving the credibility of financial reports in line with market expectations, especially when negative predictions arise regarding the companys future performance.