Reviewerly/exHarmony huggingface dataset:
Sweeter than Honey: Are Gmail accounts associated with greater rewards at a higher risk of hijacking?

Objectives. This study investigates the effect of advertised rewards in credential leaks on the likelihood and speed of account hijacking.Methods. In an online field experiment, we created 176 honey Gmail accounts and randomly assigned them to eight different posts containing account credential leaks. We used a 2×2 experimental design, manipulating two key variables within the post titles: thenumber of accounts (5K or 1.5M) and the promise of access to additional platforms (absent or present). We then monitored the accounts for any subsequent activity.Results. Our findings indicate that the promise of access to additional platforms increased the likelihood and speed of an attempted access. Only 12 accounts were fully accessed, however, because most hijackers did not complete the second-factor authentication (2FA) process required for gaining full access. It seems that the 2FA acted as a deterrent to complete Gmail account hijacking.Conclusions. The study aligns with the rational choice perspective of crime, showing that the prospect of greater rewards leads to more attempted account accesses.
Pengaruh Kecerdasan Emosional dan Motivasi Belajar Siswa terhadap Prestasi Belajar IPS

In understanding and absorbing social studies lessons, each child is different. This difference is what makes the achievements of each child also different. Factors that determine student achievement are learning motivation and emotional intelligence. Students who have motivation to learn will have the self-awareness to manage their emotions so they are able to study seriously. The aim of this research is to find out whether there is an influence of emotional intelligence and learning motivation on students' social studies learning achievement, especially at private junior high schools in East Jakarta. The research was conducted quantitatively using a survey method with multiple regression analysis techniques. Collecting data by distributing questionnaires and multiple choice item tests. The population came from class VIII students at private junior high schools in East Jakarta with a sample of 90 students for the 2022/2023 academic year. The results of the research concluded that there was an influence of emotional intelligence and student learning motivation together on students' social studies learning achievement with a contribution of 40.6% as well as an influence of emotional intelligence on learning achievement of 12.67%, and the influence of students' learning motivation on social studies learning achievement amounting to 13.5%.
Frames

HTML frames are treated as independent pages, which is not a good web design practice. In fact, in HTML5, the <frame> tag was deprecated. As a result, few modern websites use frames nowadays. However, there are quite a number of sites that use iFrames. iFrames (inline frames) are a HTML document embedded in another HTML document (hence, inline). You can think of them as pages within a page.
MODES OF PERCEPTION OF PROJECTIONAL IMAGES IN THEATRE: CASE OF TYPOLOGICAL ANALYSIS

The research presents a new insight regarding the ways of studying projectional images in theatre. The research elucidates some effects of using projectional surfaces and devices in performances and enriches a set of procedures to fix the perceptional modes according to projections in stage space. The theoretical and methodological framework employed in the research includes a range of ideas at the intersection of theatre studies, semiotics and visual studies. This combination is intended to give an understanding of the projectional image as a unit of stage practices that is non-translatable due to its specific mediality. Research optics employed in the paper is aimed at depicting the communicative value of projectional image as an expressive mean through the description of its role in the structure of a performative text (performance). The study arrives at a typological grid that is suggested to be a productive analytical tool. The analytical grid covers different modes of the presence of projectional image and its role in the structure of a performative text (performance). The present research deals with the version of a typological grid and focuses on the critical understanding of typology as a methodological procedure.
Analytical study for some of the basic skills of Al-Talaba Football Club for the season 2022-2023

The aim of the research is to identify the level of performance of the Al-Talaba Sports Club players in football for some basic skills, in addition to the extent of the impact of the performance of those skills on the club’s results. The research sample included all Al-Talaba Sports Club football players participating in the 2022-2023 football season for ten matches. The researcher reached several results, perhaps the most prominent of which is a general weakness in the level of players in performing the basic skills investigated, which naturally indicates the weakness of the training program, in addition to its lack of discipline and lack of focus on the physical aspect, which is the basis for enabling the player to perform the skills assigned to him.
Advanced Bayesian-Inspired Multilayer Effective Parameter Determination Method for Automated ANN Model Generation of Microwave Components

Artificial neural networks (ANNs) have revolutionized microwave computer-aided design by leveraging machine-learning techniques to tackle complex problems. One critical aspect of this process is the automatic modeling of the neural network structure. The conventional approach, which involves qualitative adjustments to prevent underfitting and overfitting, often leads to inefficiencies when the initial structure significantly deviates from the optimal one. This article presents a groundbreaking approach to address this issue, proposing an automatic modeling algorithm for neural networks. This algorithm employs Bayesian theory to optimize the ANN model structure. Based on the Bayesian theory, the formula for calculating effective parameters in a multihidden layer neural network is derived, allowing the initial structure to adopt any form and permitting efficient, quantitative adjustments. This innovative approach enables the computation of effective parameters under any given structure. A higher maximum number of effective parameters in multihidden layer ANN has been obtained compared to single-hidden layer ANN, thus improving modeling accuracy. Compared with the existing Bayesian-based automated ANN model generation methods, the proposed approach significantly enhances both modeling accuracy and speed. The effectiveness of this method is verified through the application of three microwave components.
Evaluating the Use of Satellite Data and Machine Learning Models for PV Performance Monitoring

Understanding and quantifying the performance of PV plants is crucial for predicting energy production, optimizing maintenance activities to cost-effectively increase the energy production, and improving future designs and construction. PV plant performance data and weather inputs along with models to generate expected power are the basis of performance analysis. This work evaluates the accuracy of expected power models across sources of weather input data and model types. Variations include free and commercial satellite-based and ground-based weather data. For PV plant models machine learning models are compared with physics-based and simple regression models. Additionally, the sensitivity of the resulting performance loss rate (PLR) calculation to different weather inputs is studied.
Phishing Detection Using Deep Learning and Machine Learning Algorithms: Comparative Analysis

Phishing attacks continue to pose a significant threat to online security, with attackers using increasingly sophisticated techniques to trick users into divulging sensitive information. In this paper, we compare the performance of two different Deep Learning (DL) models with three Machine Learning (ML) algorithms in detecting phishing attacks. The DL models include a combined model of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), as well as a Multilayer Perceptron (MLP) model. Furthermore, the ML algorithms consist of Gradient Boosting Classifier (GBC), Logistic Regression (LR), and Naive Bayes (NB). By using a public dataset of more than 10,000 websites, our performance evaluation demonstrated that the combined DL model of CNN and LSTM outperformed all of the other models and algorithms used in this study, with an accuracy of 93.1%. On the other hand, the least-performing algorithm was NB, attaining a low accuracy of 66.0%.
Bell-like sound as a Feature of S. V. Rachmaninov’s Artistic Style

Rachmaninoff is one of those composers whose works show the influence of the ringing of bells on a grand scale. This is not only the “Bells” “lying on the surface”, but incorporation of the most prosperous tradition of the bell ringing. Despite this, there is no comprehensive work dedicated to this topic. The Russian historiography of this problem consists of sporadic and digressed observations in academic papers on entirely different issues or limited conclusions from the analyses focused on the single Rachmaninov’s work. Moreover, these publications often have errors, indicating that the authors are unfamiliar with the art of the bell ringing. The article presents an overview of the role of the bell ringing in the entire legacy of S. V. Rachmaninov and highlights significant issues related to this topic, which are worth independent research.
DETERMINING STUDENT GRADUATION BASED ON SCHOOL LOCATION USING GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION

Faculty of Mathematics and Natural Sciences (FMIPA) is one of the Faculties in Tanjungpura University with 9 Undergraduate Programs (S1). Based on the graduation data of the 2014 batch of FMIPA students, the number of students who did not complete their studies was 131 students or 29% of the total 445 students and 187 schools in Indonesia. If the study period of students can be predicted early, the study program can provide advice or recommendations so that students can complete their studies in/exactly 8 semesters. This study aims to determine the model for analyzing the factors that influence the graduation of FMIPA students using GWLR. Geographically Weighted Logistic Regression (GWLR) is a developing logistic regression model applied to spatial data. This model is used to predict data with binary dependent variables that consider the location characteristics of each observation. The units of observation in this study are the school location of 455 students spread across Indonesia. The variables used in this study were sourced from the Academic and Student Affairs Bureau UNTAN and divided into dependent variables (Y) and independent variables (X), i.e. Gender, college selection, Accreditation, School Type, School Location, and Name of Study Program. The dependent variable analyzed is the graduated status of FMIPA UNTAN students, i.e. completed and not completed their studies. The results showed that gender and the name of the study program are factors that affect the graduation of FMIPA UNTAN 2014 students with a classification accuracy of 72.6%.
Bibliometric Analysis of the Academic Development Process of the "Metaverse" in the World and Turkey

In this study, a bibliometric analysis was carried out to reveal the development process of academic studies on Metaverse in the world and in Turkey. The aim of the analysis is to examine the development process of the Metaverse concept in the academic world and to guide future research and studies on this subject. In the bibliometric analysis process, relevant publications were searched in Web of Science (WoS) and Scopus databases using the keyword "Metaverse" on 30.05.2023 and a total of 2201 publications were obtained from both databases between 1995 and 2022. The publications were analysed according to research areas, years, countries, institutions, keywords and number of citations. VOSviewer programme was used for keyword analysis. It was observed that the most studies on metaverse were conducted in the field of computer science in 2022. It was determined that the most publications were made at Sabancı University in our country and at the Chinese Academy of Sciences in the world. It can be said that the most cited publications on metaverse are the study of Davis et al. in 2009 and the study of Dionisio et al. in 2013.
The Identification Of Self-Assessment To Develop Students’ Writing Skill

Many students still didn’t understand how to write properly and correctly, there are also many obstacles’ students face when writing, one of which is how to arrange sentences in making stories, and many don't know that writing is also one of the 4 abilities that are often used by many people. The writer brought this self-assessment method to the SMP Santo Petrus Medan to be used as research material for students. The purpose of this method is for students to know what obstacles make students late in understanding how to write well. After the self-assessment method was taught in class which would be used as research material by the writer, the writer found that in the final test students almost all students understood what the self-assessment method was, and in the description of the data it can be seen that the number of students in the experimental class was 27 students and in the control class as many as 27 students. From the experimental class scores, the average pre-test was 62.70 and the post-test was 79.70. The median for the pre-test was 62.00 and for the post-test was 80.00. The lowest score for the pre-test was 50 and the highest was 76. For the post-test, 70 was obtained for the lowest score and 90 for the highest score. It can be concluded that the lowest and highest scores on the post-test are higher than the pre-test.
Mitigating Algorithmic Bias on Facial Expression Recognition

Biased datasets are ubiquitous and present a challenge for machine learning. For a number of categories on a dataset that are equally important but some are sparse and others are common, the learning algorithms will favor the ones with more presence. The problem of biased datasets is especially sensitive when dealing with minority people groups. How can we, from biased data, generate algorithms that treat every person equally? This work explores one way to mitigate bias using a debiasing variational autoencoder with experiments on facial expression recognition.
Cycle structure of Mallows permutation model with the $L^1$ distance

Introduced by Mallows as a ranking model in statistics, Mallows permutation model is a class of non-uniform probability distributions on the symmetric group $S_n$. The model depends on a distance metric on $S_n$ and a scale parameter $\beta$. In this paper, we take the distance metric to be the $L^1$ distance (also known as Spearman's footrule in the statistics literature), and investigate the cycle structure of random permutations drawn from Mallows permutation model with the $L^1$ distance. We focus on the parameter regime where $\beta>0$. We show that the expected length of the cycle containing a given point is of order $\min\{\max\{\beta^{-2},1\},n\}$, and the expected diameter of the cycle containing a given point is of order $\min\{e^{-2\beta}\max\{\beta^{-2},1\}, n-1\}$. Moreover, when $\beta\ll n^{-1\slash 2}$, the sorted cycle lengths (in descending order) normalized by $n$ converge in distribution to the Poisson-Dirichlet law with parameter $1$. The proofs of the results rely on the hit and run algorithm, a Markov chain for sampling from the model.
Fast classification of drones and birds with an LSTM network applied to 1D phase data

This study investigates a new type of drone classifier based on Long Short-Term Memory (LSTM) networks. As a real-time surveillance system, the classification time of a drone detection radar is crucial. The motivation for this work is to develop a classification framework which has low latency in terms of data processing for the algorithm input. Theoretical modeling of a rotary wing drone and a bird wing flapping returns were done first to exhibit the difference in the patterns of the respective phase progressions. Then, 94 GHz experimental trial data containing 4800 sequences of drones, birds, noise and clutter were used to create a diverse training dataset of 1D phase data for supervised learning. A stacked LSTM network with tuned hyperparameters was generated to reduce the possible overfitting from a simple LSTM model. Validation accuracy of 98.1% was achieved for 2-class classification of drone and non-drone. Further performance assessment was then done with 30 unseen test data, where the network was able to correctly classify all the sequences. It is ascertained that this method can be ~10 times faster than a spectrogram based classification model, which requires additional Fast Fourier Transform (FFT) operations.
Borukh’s Metals. On the 85th Anniversary of Boris A. Shteinberg’s Birth

This article is devoted to the creative work of the nonconformist artist Borukh (Boris Arckadyevich Shteinberg, 1938–2003). His legacy has hardly been studied, and the information about him is mainly found in catalogs, memoirs of friends and colleagues, and exhibition explications. Meanwhile, the work of this artist is a highlight of Russian underground art. Borukh was a multi-talented master: a writer, painter and graphic artist, author of collages and assemblages. The emphasis in the article is on the artist’s original metal works. They represent an important chapter in his creative biography, standing out both against the background of the late Soviet and Russian art and in a wider context. Having a creative relationship with the works of such masters of object art as, for example, Cornell, Arman, and Cesar, Borukh’s metals illustrate the commonality of searches in the art of the second half of the 20th century.
Aplikasi Pembelajaran Bahasa Inggris Untuk Anak Usia Dini Berbasis Android

Interesting learning methods can increase children's interest in learning, especially young children. Learning methods using applications on computers, tablets and smartphones are starting to be created according to user needs. One of the Android-based applications that will be created is an English learning application for early childhood. This application consists of a main page which contains material choices, a material page which contains illustrated objects, and a quiz page which is a form of evaluation to train children's memory. The research method for making this application uses the System Development Life Cycle (SDLC). Starting with planning an overview of the application, analysis, design using Unified Modeling Language (UML), designing the application interface, to creating the application using Android Studio. Testing of the applications that have been created is carried out on several Android-based smartphone devices. The results achieved are that this Android application is able to function well and can be used as an English learning medium for young children.
PEMBUATAN SMARTCONTRACT APLIKASI FUNDRAISING BERBASIS BLOCKCHAIN

Fundraising activities have become a common method to achieve various objectives, including charitable activities and gathering funds from investors for companies. However, cases of fund misuse often occur, leading to inappropriate use of funds or even unfair loss. Therefore, an innovative approach is needed to enhance trust and transparency in the fundraising process. In this study, the use of smart contracts on a blockchain network will be employed as a solution to ensure the security and integrity of each transaction in fundraising activities. By utilizing smart contracts, all transactions in fundraising activities can be verified by all users on the blockchain network. Transaction data recorded on the blockchain is permanent and tamper-proof, preventing any manipulation or fund misuse. Leveraging smart contracts and blockchain technology, fundraising activities can be conducted more efficiently, transparently, and securely. This approach helps improve trust among involved parties and reduces the risk of fund misuse
Analisis Kesalahan Morfologi Penerjemahan Arab–Indonesia pada ChatGPT

The research focuses on analyzing translation errors in Arabic-Indonesian translations generated by Chat GPT in the field of morphology. The study's objectives are to: 1) Determine whether there are translation errors at the morphological level using the Chat GPT translation system and 2) Identify the areas where translation errors occur in morphology. The research utilizes purposive sampling as the data collection method. Qualitative data is analyzed through a literature review using content analysis techniques. The study's findings reveal the presence of translation errors in Arabic-Indonesian translations generated by Chat GPT at the morphological level. The errors identified include: 1) translating passive verbs into active verbs, 2) translating with the addition of morphemes in the target language, 3) translating verbs into nouns, 4) translating nouns into passive verbs, 5) selecting target language vocabulary that deviates from the source language, and 6) translating words that do not require translation.
Kepuasan Pengguna Sistem Informasi Rekam Medis Elektronik RSUD Tugurejo

Evaluation of user satisfaction of Electronic Medical Record (EMR) system users can be considered by hospitals in developing EMR systems. Tugurejo Hospital has implemented EMR both outpatient and inpatient. Quantitative research is conducted to determine user satisfaction. The respondents were 67 users of EMR from doctors and nurses. EMR user satisfaction according to Delone and McLean's IT/IS Success Model theory was analyzed using SMART-PLS to determine how strong the influence of system quality, information quality and service quality on EMR user satisfaction. The results showed that user satisfaction was influenced by system quality (T-stat = 2.96), information quality (T-stat = 1.66), service quality (T-stat = 1.84). There is a mutual influence between system quality, information quality and service quality on user satisfaction with strong categories (R-square = 0.748).
Printing on the Archives of Sultan Syarif Kasim II

This article reviews the printers listed in the archives left by Sultan Syarif Kasim II (SSK II). SSK II has many archives that are currently stored in the palace of the Siak Sultanate. Part of the archive is handwritten, typed and printed. Types of printed manuscripts are quite interesting to discuss because there are names of printing companies. In the 19-20 centuries, the Colonial Government controlled the printing sector. They dominated the development of the print world and exercised strict supervision. The research results showed that there were seven printers in the archives of Sultan Syarif Kasim II, including Batavia Landsdrukkerij, De unie Weltevreden, Stoomsnelpersdrukkerij van H. M. Van Dorp &amp; Co. Batavia, Typ. Courant Deli, Typ. Drukkerij Batak, Typ. Limbago Pajakoemboeh, and Typ. Varekamp &amp; Co., Medan. The link between the Siak Sultanate and all publications and contents of documents shows that all government activities must be under supervision and permits are required in the implementation process.