Reviewerly/exHarmony huggingface dataset:
Implementation of the Naïve Bayes Method to Determine Student Interest in Gaming Laptops

The development of the times resulted in the development of technology to date. With the existence of technology, many people have used technology to help their daily activities. In this study, the author will discuss the technology that is often used by students to help them with their assignments, namely laptops. Laptop is a technology that has been widely used by students, teachers and the public. Having a laptop can make things easier. Until now, each laptop brand continues to develop their laptop production laptops with good specifications. Until now, almost all laptop brands have made gaming laptops that are actually intended for people-people who play games. But with good specifications, gaming laptops can also be used for daily activities. With an attractive design and good specifications, of course you can attract student and public interest in gaming laptops. Therefore the authors made a study of student interest in gaming laptops. With good design and specifications on gaming laptops, the author aims to classify the number of students who are interested and not interested in gaming laptops. The classification will be carried out using the Naïve Bayes method with the number of sample data used as many as 100 student data in data mining. The classification results obtained were 55 students (55% representation) interested in gaming laptops and 45 students (45% representation) had no interest in gaming laptops. The results show that not all students are interested in gaming laptops, even though they have laptops design and great specs.
Information Visualization in Strategic Decisions: Impact of Cognitive Integration and Overconfidence

Information visualization generally improves information acquisition and processing in decision-making. However, empirical research fails to provide conclusive support for this effect in strategic decisions and points toward the need to include moderating effects. Building on extant visualization research in cognitive psychology, we introduce cognitive integration as a boundary condition for information visualization to support strategic decision-making. We argue that only managers with high cognitive integration benefit from complex visualized information. We further posit that a reduction in decision overconfidence mediates the positive effect of information visualization on decision quality for managers with high cognitive integration. A vignette-based decision experiment involving 65 managers supports our hypotheses. Our paper contributes to strategic decision-making research by clarifying the roles of information visualization and cognitive integration as antecedents of effective decision-making. It also specifies the suitability of information visualization for communicating complex information in practice.
PENGARUH LABEL HALAL TERHADAP KEPUTUSAN PEMBELIAN PRODUK KOSMETIK WARDAH

Cosmetics is one of the products offered to meet the secondary needs and desires of consumers, in order to appear more beautiful and attractive. Because the majority of the population in Indonesia adheres to the Islamic religion with a Muslim population reaching 86.9%, therefore certainty about product halalness is an important thing to pay attention to. The development of halal cosmetics in Indonesia has grown quite significantly. This is marked by the emergence of cosmetic companies with various brands. The cosmetic brand that is quite well-known in the community is Wardah. This study aims to determine how the influence of the halal label on the decision to purchase Wardah cosmetic products. In this study the authors used the label theory according to Philip Kotler and Garry Armstrong (2010) and the purchasing decision theory according to Tjiptono (2011). The method used in this study is a quantitative method by distributing questionnaires. Subjects in this study used a sample of 86 respondents. Sources of data used in this study are primary and secondary data. The data analysis method used in this study is the data normality test, data linearity test, simple regression test and t test using IBM SPSS 22. The results of this study indicate that the Halal Label (X) influences Purchase Decision (Y). The results in the test can be seen from the results of the simple linear regression test, it can be seen that the results of the significance of t are 0.000 &lt;0.05 so that H0 is rejected and Ha is accepted. This shows that the Halal Label variable (X) has a significant effect on Purchase Decision (Y). And it can also be seen from the t count of 14.517 which means the t count is greater than the t table of 1.992.
Gamified Cyber-Crime Monitoring and Control Framework in a Computer Network Environment

Recent advancements in cybercrime are continually emerging, with the estimated damages to the global economy reaching the billion dollar mark. In the past, people acting alone or in small groups were the main perpetrators of cybercrime. Complex cybercriminal networks are now bringing people from all over the world together in real time to commit crimes on a never-before-seen scale. Game theory gives a formal vocabulary for the description and study of interacting situations in which a number of "entities," known as players, take actions that have an effect on one another. The field of cyber security could benefit from problem-solving techniques based on games theory to protect assets. In this article, we suggest a conceptual framework for a system for monitoring and controlling cybercrime.
A Pragmatic Analysis of Machine Translation Techniques for Preserving the Authenticity of the Sanskrit Language

Machine Translation has been a field of study for over six decades, but it has acquired substantial prominence in the last decade as processing capacity in personal computers has increased. The purpose of this paper is to discuss the usage of Sanskrit as a source, target, or supporting language in various Machine Translation systems. To investigate Machine Translation, researchers use a variety of strategies, including corpus-based, direct, and rule-based approaches. The primary goal of employing Sanskrit in Machine Translation is to evaluate its appropriateness, lexicon, and performance when proper Machine Translation methods are used. The research examines various modelling strategies for developing a machine translation system, specifically Statistical and Neural Machine Translation, in order to bridge the gap between Sanskrit and its current successor, Hindi. Interpretations are formed in Statistical Machine Translation by matching words from the source and target languages with statistical models and bilingual text corpora to learn parameters. Neural Machine Translation, on the other hand, uses an artificial neural network to predict the likelihood of a word sequence, frequently modelling entire phrases within a single integrated model. Neural Machine Translation is implemented using an encoder-decoder architecture with an attention mechanism. One of the most significant contributions of this paper is the use of different data sources, data collecting, and scraping to create a complete dataset. According to the study's findings, Neural Machine Translation outperforms the Statistical Machine Translation modelling technique. Furthermore, the paper examines the distinctive qualities of the Sanskrit language as well as the difficulties encountered by researchers in digesting Sanskrit while constructing the machine translation system. This study investigates the use of Sanskrit in Machine Translation and analyses several modelling methods, such as Statistical and Neural Machine Translation. The paper emphasizes the advantages of Neural Machine Translation and discusses the unique characteristics and challenges of the Sanskrit language in machine translation development.
REST APIs

You’ve just completed building a web page frontend in Rust. But the web is massive, in content type, technologies used, and raw quantity. At the time of writing this book there are over 1.8 billion websites on the World Wide Web. And if you look at job boards for software developers, web developers take up a large proportion of the open positions. Since you’ve seen how Rust can replace JavaScript frameworks in the frontend, the next obvious question is what can it do on the backend (i.e., server side)? There are already many established programming languages for building backend applications: Java, PHP, Python, Ruby, Node.js, and Go, to name a few of the popular ones. This chapter shows how Rust can be similarly used to create traditional backend web applications.
Introduction

An operation on source data that is often helpful and even necessary is mathematical transformation. Such transformations are ubiquitous in signal processing and signal (audio, image, video, scientific data) compression. They help reduce algorithmic complexity and computation time, increase the efficiency of digital processing in signal estimation and restoration, and substantially reduce transmission rate and storage requirements. This introductory chapter describes the methodology that is invoked to realize these benefits of mathematical transformations.
Dual Correlation Network for Efficient Video Semantic Segmentation

Video data bring a big challenge to semantic segmentation due to the large volume of data and strong inter-frame redundancy. In this paper, we propose a dual local and global correlation network tailored for efficient video semantic segmentation. It consists of three modules: 1) a local attention based module, which measures correlation and achieves feature aggregation in a local region between key frame and non-key frame; 2) a consistent constraint module, which considers long-range correlation among pixels from a global view for promoting intra-frame semantic consistency of non-key frame; and 3) a key frame decision module, which selects key frames adaptively based on the ability of feature transferring. Extensive experiments on the Cityscapes and Camvid video datasets demonstrate that our proposed method could reduce inference time significantly while maintaining high accuracy. The implementation is available at https://github.com/An01168/DCNVSS.
Software Bug Prediction Using Reward-Based Weighted Majority Voting Ensemble Technique

An accurate prediction of bugs in software projects can help in improving software projects' quality. A simple majority voting (SMV) ensemble is an effective technique for bug prediction. SMV combines the results of base classifiers (BCs) based on the majority voting of class. All the stand-alone BCs do not perform equally well, yet all the BCs in SMV are given equal weights. Therefore, in order to improve the performance of SMV, BCs should be assigned different weights. Therefore, here, we propose a novel reward-based weighted majority voting (WMV) ensemble technique to build a bug prediction model. In WMV, the performance of each classifier in the ensemble is evaluated; then, a reward-based mechanism is used to calculate the weights of each classifier. When a BC predicts the correct class of an instance, then a reward is provided, but no punishment is given for wrong prediction. A BC will get higher weight in an ensemble that predicts more instances correctly. Naive Bayes, support vector machine, K-nearest neighbor, random forest, and C5.0 heterogeneous algorithms are used as BCs in the WMV ensemble. WMV outperforms aforesaid BCs, SMV, and also majority of state-of-the-art techniques published recently in terms of accuracy, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F$</tex-math></inline-formula> -measure, and Matthew's correlation coefficient.
Perencanaan Keuangan (Rapbs) Rencana Anggaran Pendapatan dan Belanja Sekolah dalam Pengembangan Sekolah di SMK Al Washliyah 9 Perbaungan

The draft School Revenue and Expenditure Budget (RAPBS) is carried out through a process of managing school funds, of course involving various parties such as the school principal who regulates financial nets and their management is carried out by treasurers and other components that assist the process of implementing school finances. The purpose of this study was to analyze the implementation of financial planning (RAPBS) school income and expenditure budget plans in school development. This research was carried out with a qualitative approach using descriptive methods. Data collection techniques are observation, interviews and documentation as well as literature studies and group discussions. The results of this research are that the acceptance component of SMK Al Washliyah 9 Perbaungan is optimized by using educational funding sources, namely the existence of regular BOS funds sourced from APBN revenues. Reporting on the accountability of the RAPBS is carried out only to the extent of the school principal. And will be conveyed at the meeting with the parties involved in it. The principal submits a report, especially regarding school financial receipts and expenditures. Evaluation is carried out every quarter or per semester. The conclusion from this study is that good use of the RAPBS has made school development better, including: character education, human resource development and the use of technology.&#x0D; Keywords: RAPBS; Accountability; Development
Faktor yang Mempengaruhi Prestasi Belajar Siswa pada Pembelajaran PKN di MIS Fajar Shiddiq Kota Medan Marelan

This study aims to: describe the factors that influence student achievement, determine student learning outcomes. This type of research is qualitative, namely to determine how to find, collect, process and analyze data from the research. The sample of this research was fourth grade students at MIS FAJAR SHIDDIQ, totaling 32 people. Data collection methods used are observation, interviews and documentation studies. Data on factors that influence learning outcomes are collected using observation sheets and interview guidelines. The results of this study at MIS FAJAR SHIDDIQ show that the factors that influence student achievement in grade IV students are influenced by internal (inside) and external (external) factors. Internal factors include physical factors and psychological factors. Physical factors such as state of health, state of the body. And psychological factors such as attention, interest, talent, and readiness. While external factors, namely school factors such as curriculum, teaching methods, interaction between students, discipline at school, learning tools, the condition of the building, and the library. However, in this study centered on internal factors or within the student.&#x0D; Keywords: PPKN, Learning Achievement, Factors
Enhanced contrastive learning with multi-aspect information for recommender systems

In recent years, graph neural networks (GNNs), as the state-of-the-art technology have advanced the solutions of recommender systems (RSs). However, the representations of nodes obtained from GNN-based models are always suboptimal when facing sparse interaction data. Contrastive learning on graphs has been proven to be effective in alleviating the influence of data sparsity. Most related works perform contrastive learning through self-discrimination. As there is extensive homophily among nodes in RSs, we consider it irrational to ignore the self-supervised signal from neighboring nodes, which is crucial for optimizing the embedding representation of nodes. To address these issues, we propose a model named enhanced contrastive learning with multiaspect information (ECMI) for RSs, which aims to capture the self-supervised signals from both the nodes themselves and their neighbors. Specifically, we conducted contrastive learning in a two-layer space separately. We consider structurally connected homophilic node pairs as positive samples in explicit space while introducing a social relationship network and an item entity network in implicit space. These two networks can be combined with the interaction network to conduct a two-channel training procedure. This procedure can build more effective contrastive learning by exploring the similar potential neighbors of nodes. We conduct experiments on four public datasets, and the results consistently validate the effectiveness of the ECMI.
A Comprehensive Design Framework for Multi-disciplinary Cyber Security Education

This paper presents an innovative approach to teaching soft skills in cyber security. It highlights the importance of integrating soft skills, such as critical thinking, problem solving, communication, and empathy, alongside technical skills. The COLTRANE framework is introduced as a tool for educators to enhance the teaching of soft skills. The framework involves several phases, including identifying relevant soft skills, establishing learning objectives, and designing conducive learning environments. By employing this framework, learners are exposed to real-life challenges that necessitate the integration of technical and soft skills. This approach ensures that learners not only gain technical knowledge but also develop the capacity to apply it in practical situations, fostering effective communication, collaboration, and empathy. By utilizing the COLTRANE framework, educators can better prepare their students for future careers in cyber security, where complex problem-solving and effective teamwork are essential.
Development of Image Series Media in Text Descriptive to Improve English Speaking Skills

Speaking skills are seen as a very important skill to be understood by learners of a language. This is related to the function of language as a means of communication which requires its users to be able to use the language in real communication. The implication of this is that teachers should be able to create a learning environment that is able to facilitate learners to practice functional expressions actively and communicatively. Meanwhile, speaking skill is considered as one of the language skills that is used as a benchmark in determining the quality of ability. This research uses the method of methods (SLR). regarding research methods: "Literature" which means it is a critical analysis of the research that is being done on a special topic or in the form of a question to a part of science. helps us in compiling a framework that is in accordance with the theory, findings, and results of previous research in solving the problem formulation in the research that we make. The SLR method is used to identify, review, evaluate and interpret all available research with the topic area of the phenomenon of interest, with specific relevant research questions. With the use of the SLR Method, a systematic review and identification of journals can be carried out. The results of the study are described basically the ability to speak can be influenced by the methods and media provided, the initial stimulus carried out by the teacher will be able to stimulate the child's ability, especially at the psychomotor level, interesting learning media will certainly have an influence on students during the teaching and learning process in the classroom. For this reason, the role of the teacher as a facilitator is expected in providing stimulus to students.
The Importance of High Speed Storage in Deep Learning Training

With the increase of computational power and techniques over the past decades, the use of Deep Learning (DL) algorithms in the biomedical field has grown significantly. One of the remaining challenges to using deep neural networks is the proper tuning of the model's performance beyond its simple accuracy. Therefore, in this work, we implement the combination of the NVIDIA DALI API for high-speed storage access alongside the TensorFlow framework, applied to the image classification task of skin cancer. To that end, we use the VGG16 model, known to perform accurately on skin cancer classification. We compare the performance between the use of CPU, GPU and multi-GPU devices training both in terms of accuracy and runtime performance. These performances are also evaluated on additional models, as a mean for comparison. Our work shows the high importance of model choice and fine tuning tailored to a particular application. Moreover, we show that the use of high-speed storage considerably increases the performance of DL models, in particular when handling images and large databases which may be a significant improvement for larger databases.
Media Pembelajaran Huruf Hijaiyah untuk Siswa Sekolah Dasar berbasis Augmented Reality

The ability of grade 1 students of SD N Ponowaren 02 to recognize and memorize hijaiyah letters that are still lacking needs to be improved. Today's technology can be utilized as a learning media by using smartphones. Especially in the world of education, the use of this technology can facilitate both teachers and students in an innovative, creative, and educational learning process. The purpose of this research is to develop teaching materials that encourage and increase children's interest in learning Hijaiyah letters by utilizing augmented reality technology. In this study using the Research and Development approach method. The method is used to produce certain products and test the effectiveness of the product, then for the product development model using the Multimedia Development Life Cycle, which includes concept, design, material collection, manufacturing process, testing, and product distribution. Observation and questionnaires are used for the data collection process. This research shows how augmented reality technology combined with educational games can improve children's learning efficiency in learning hijiayah letters. Based on the research of learning media game Bijaya (Belajar huruf hijaiyah) shows that the feasibility of media obtains results above 90%. Based on the Likert scale test criteria, this shows that the Bijaya application learning media is very feasible to be developed and utilized.
A Fiscal Perspective on Cryptoassets

In this paper we review the concept of cryptoasset and cryptocurrency and their operation as blockchain technology-based instruments. Despite great success in spreading through financial markets, the risk associated with this type of asset stands out as a factor of concern both for supervisory institutions and for investors. Announced as an alternative to currency in circulation, it is concluded that cryptocurrency has limitations that prevent it from fully assuming the functions for which it was initially proposed. There is little consensus on the taxation of cryptoassets, and there are several simultaneous tax frameworks at a global level, depending largely on the conceptual approach to cryptoassets (asset, commodity, financial instrument, currency). The OECD has a plan in place to standardize the treatment of cryptoassets, but at this stage it is limited to standardizing the methodology for approaching the subject rather than standardizing the interpretation of the concept and tax treatment.
Reengineering for Disambiguation of Trade-related Terms Interpretation

Due to the expansion of world trade transactions such as FTAs, trade-related terms that have been limited to specific traders are now becoming terms that everyone should know. However, the information on trade-related terms provided by portal sites or trade-related organizations is inaccurate or insufficient. In particular, there are cases where the contents of the revision are not reflected. Accordingly, a lot of time may be consumed or incorrect information may be obtained in the process of searching for unknown trade-related terms. The purpose of this study is to present some errors in the information provided on the Internet, review the problems of providing information on trade-related terms, and then propose a reengineering method for them. In addition, the expected effects of such reengineering are also presented in this paper. As a result, learning about trade-related terms is carried out efficiently, and understanding of them can be improved. It will also help train trade experts. It is considered disambiguation of trade-related terminology will act as a major change in the future.