Reviewerly/exHarmony huggingface dataset:
The Adoption of Component-Based Architecture in the Development of E-Learning Website Interface

The problem that front-end web developers often face is writing program code repeatedly on the same component so that the program code becomes less efficient and flexible, and also complicating the maintenance process. In this study, the development of an e-learning website interface was carried out using a component-based architecture. The application of component-based architecture aims to make program code on a component reusable, flexible, and simplify the maintenance process. This research will use the Vue.js framework to implement component-based architectures. The results of the website interface program code using Vue.js will be tested using automated testing with a unit test using Jest. Automated testing can provide fast feedback and allow developers to find code errors after they have been created. Testing with unit testing using Jest will produce a code coverage matrix that is used to state parts of the software code that are not tested. The matrix indicates that there are sufficient tests of program code, resulting in a rarity of software bugs and providing confidence for developers to modify existing code. The results of this study show that the development of an e-learning web interface by implementing a component-based architecture using Vue.js can make program code reusable, efficient, flexible and easy to maintain.
Application of Behavior Driven Development for Validation Testing (School Information System)

Before the software is handed over to the user, testing is needed to ensure the features created are in accordance with the needs, then validation testing is carried out. In this study, the validation test process will be carried out using the Behavior Driven Development (BDD) method by defining requirements into step by step scenarios according to user behavior towards the software. In this testing process, automated testing that is integrated with BDD will also be made using the Katalon Studio tools. The results of this study indicate that the implementation of BDD can be carried out in school information systems. The most visible impact is the description of requirements that is much more focused and detailed.
A Genetic Algorithm Based Solution of Hungarian Team Orienteering Competitions

In this paper, a special team orienteering problem is presented where a set of control points should be distributed among the members of the team while a course with fixed order of controls is need to be completed too. The aim of the team is to minimize the maximum route length of the members. In this paper, a simple genetic algorithm is proposed to solve this special team orienteering problem. The performance of the algorithm is evaluated on real competition examples and the results are compared to the solutions given by the real winners of the race.
User Interface Design Pattern Selection and Implementation for Designing a Web-Based Transaction and Goods Record-Keeping Application for Micro, Small and Medium Enterprises

The smooth running of Micro, Small and Medium Enterprises (MSMEs) by external factors such as assistance from the government and internal factors such as business management, one of which is recording. Record-keeping system integrated with database management system can create more powerful information system. Information systems can simplify the recording process by providing calculations that are able to store business data for the long term. Currently, the record-keeping information system is available in the form of a website-based application. Based on interviews, the use of these applications by MSMEs is still minimal. The lack of understanding of the MSMEs owners on the interface elements used and the way these elements are presented in these recording applications causes the minimal use. Therefore, this research proposed a web-based record-keeping application for transactions and goods with a goal to facilitate interaction and communication with MSMEs. User Centered Design method provides a more in-depth knowledge about user needs and preferences than other design methods. In business, record-keeping involves interconnected data such as the amount of goods sold in a transaction. Therefore, this research uses design patterns to be able to facilitate interaction and communication between MSMEs owners and record-keeping application for transactions and goods. The test results show that the protoype fulfills the usability goals with high user score.
Robust Sign Language Interpreter for Deaf Persons

The present study focuses on a category of people who suffer from disorders of hearing and/or speaking and communicating orally with others, they are deaf and usually have difficulties in learning how to write and read text. That is why sign language has emerged as an effective alternative to expressing their thoughts, and that most people (who hear) do not know sign language, in this research, the development of an application design on a mobile phone that works without the use of the internet, to translate the Iraqi sign language through the development of the system to include copying text messages and converting them into sign language. Also, in the event that the word does not exist in the database, a note will be displayed to the recipient explaining that the apparent translation is an alphabetic translation and not a real translation, as well as adding a photo capture icon to take a picture for any text to be translated. As a result of applying the system to a number of volunteers, according to a questionnaire that was conducted for them, the system was 100 % efficient and accurate. It was programmed in Java under the Android environment, and the application interfaces were designed using the Extensible Markup Language (XML) Then it was implemented and tested under the environment of the Android operating system version (9.0). The Blender program was used to design images that represent words, letters and sign language numbers.
Factors Affecting Information Assurance for Big Data

Big Data is a concept used in various sectors today, including the government sector in the Smart Government initiative. With a large amount of structured and unstructured data being managed, information assurance becomes important in adopting Big Data. However, so far, no research has focused on information assurance for Big Data. This paper identified information assurance factors for Big Data. This research used the systematic snapshot mapping approach to examine factors relating to information assurance from the literature related to Big Data from 2011 through 2021. The data extraction process in gathering 15 relevant papers. The findings revealed ten factors influencing the information assurance implementation for Big Data, with the security factor becoming the most concentrated factor with 18 sub-factors. The findings are expected to serve as a foundation for adopting information assurance for Big Data to develop an information assurance framework for Smart Government.
Coverless Message Hiding Technique based on Eye Blinking Activity

Most existing methods for hiding data depend on the features present in each image or frame, so these characteristics will be lost when exposed to any attack. For this reason, in this work, we present a novel method based on an activity in the video. Our approach is to send a secret message using a de Bruijn sequence formed as voluntary blinks. The sender converts a secret text message into binary blocks using a Huffman coding tree and then builds a mapping table based on specified de Bruijn sequence dB(2,5) to create an index key which is needed at the receiver side to recover the secret message fragments, then the sender records a video of a person blinking voluntarily, long and short blinks, with the corresponding $0^{\prime}$s and $1^{\prime}$s in dB(2,5). The receiver reads stego-video frames, then for each frame, face detection and eye localization processes will be applied based on the mediapipe model, then the eye region passes into CNN to determine the state of the eye. Thus, define the type of blink if it is long or short, then the receiver translates long and short blinks into $0^{\prime}$s and $1^{\prime}$s in order to produce the sent dB(2,5). The experiment results showed that the accuracy of extracted secret bits was very high, reaching 100% if blinks were sent without any errors. As well, the experiments showed that the robustness reached 95.2%, which means the proposed method has more robustness against salt and pepper, Gaussian, speckle, cropping, rotation, and translation attacks) than other coverless data hiding techniques.
Resource Constrained, Fast Convergence Training for Violence Detection in Video Streams

This paper addresses the automated identification of violent acts from CCTV video streams using a Deep Learning model under constrained resources. While this process typically involves a powerful setup, it is useful to accelerate the training and get accurate results using more modest computational resources that would bring automatic recognition of violent acts closer to common surveillance resources. Our results provide 94.98% accuracy, on par with the state-of-the-art, but at a fraction of the training time. This translates into lower energy requirements and allows a broader deployment on large scale (urban) autonomous surveillance networks while providing an increased privacy towards citizens and lower chances of abuse from authorities.
Classification of Written and Spoken Arabic Letters based on Deep Learning

This research focuses on automatic Arabic speech recognition (AASR) (Arabic characters) as well as Arabic character handwriting recognition. The Mel Spectrogram is used to calculate the spectral slope coefficient. The major applications of a convolutional neural network (CNN) are feature learning and classification. In automatic speech recognition, CNN has improved its performance (ASR). In addition, it is used to recognize handwritten Arabic letters. Put the CNN model to the test using the Arabic pronunciation set for Arabic letters, and made some tweaks to it by adjusting pitch, speed, dynamic range, introducing noise, and moving forward and backward in time. The greatest accuracy attained while employing (Mel Spectrogram) with CNN was reported to be 99.4%. When compared to earlier studies, the findings show that CNN performed much higher in the AASR. When the same CNN model was tested with handwritten Arabic letters, the outcome was 98.8%.
Involving New Coefficients Conjugate Gradient Method for Restoring Distorted Images

Conjugate gradient methods are one of the most common iterative mathematical techniques of their usefulness in optimizing linear and non-linear systems. In this paper, a new conjugated gradient formula based on a second order Taylor series is proposed and we also use Perry’s conjugacy to derive the coefficient of the new method. The proposed method achieves the property of sufficient descent and always globally convergent under certain assumption. The numerical results show that the method is effective to restore the original image of an image affected by impulse noise.
A Survey Study on Extracting Trending Topics from Textual Data

This Survey presents a study on the amount of data available around us from its various sources, then provides an explanation of trending topics and their benefits in our daily lives. In fact, this survey presents the most important and latest data analytic techniques in the field of natural language processing, which are used on text data in order to take advantage of the huge amount of data around us and not to waste them. Finally, the research talks about comparing the details of thirteen researches conducted on different data from different sources and using multiple methods of analysis and processing to find Trending Topics. Each research includes results for accuracy measures, finding the best one among them. This survey will be useful for three types of readers, first: readers who want to learn about some of the means or resources of collecting huge data from the Internet, second: readers who want to know the means of data analysis to get useful results, and third: readers who want to quickly employ the collected data and the best data analysis technique in order to choose a method that is useful in extracting the trending topics in their projects through the results of the accuracy given and the avoidance of risks in choosing methods that are not feasible for a particular project.
Container-based Video Streaming Service

Video or audio streaming is highly sought after by the most of the Internet users today. Media-oriented companies offer streaming of video and audio content as a service that must be able to handle a large number of user requests, guarantee a sustainable data transmission and be constantly available for the end devices.The aim of this paper is to propose the prototype of a video streaming application that is able to manage real-time video for a larger number of users. The application must be available at all times. This objective is achieved by containerisation of the application, which contributes to the overall efficiency, high portability, modularity, and constant availability. The application is driven by a software architecture design called microservices. The containerised application is validated and compared with monolithic version of the application.
Impact of COVID-19 on Societal Behavior via Twitter Analytics

The outbreak of the COVID – 19 pandemic has caused a notable challenge to the well-being of people all around the globe. In such times, it is of foremost importance to analyze the information posted by people on social media. In this study, a Twitter-based dataset related to COVID-19 has been analyzed, and the effect of the pandemic on societal behavior has been revealed. Tweets have been hydrated and pre-processed using the NLTK toolkit to find the most frequently posted COVID- related words. This research can help identify the social response of people to the Pandemic, realizing what people are majorly concerned about and extracting knowledge about the daily trend of sentiments around the world. It has been concluded from our analysis that rather than the expected negative trend in the use of COVID-19 terms on a daily basis, more positive figurative language has been used in the posted tweets.
A Survey on Event Extraction from Webpage

Event extraction (EE) is one of most important researches in the field of Natural Language Processing (NLP). Numerous significant events occur every day all over the world, and it published in various media outlets with varying narrative styles. A primary job in EE is to determine if events in the real world have been reported in articles and posts driven by the 4Ws’ (what, who, when, and where). A complex relationship between people can be explained by an event, place, actions, and objects. An event-centered model captures the dynamic and semantic aspects of an event representation of event facts. An updated and comprehensive survey is needed due to the proliferation of methods, datasets, and evaluation metrics in the literature. In this paper, a survey on extracting events from websites and defining their types and applications in several different fields has been discussed. In addition, the study presents the strengths and weaknesses of event extraction systems for different types of models
Student Satisfaction and Retention: Impact of Service Quality and Digital Transformation

A country’s economic and technological development in the twenty-first century depends heavily on higher education. Similar to other countries, the majority of higher education institutions in Bangladesh are private universities that offer educational services alongside public (government-funded) universities. This study aims to investigate how service quality dimensions and digital transformation influence student satisfaction and student retention in private universities in Bangladesh. Data were acquired from a convenient sample, including students from the top five private universities in Bangladesh, through a self-administered questionnaire survey. The questionnaire was completed by 210 of the 235 students who were asked to participate. Each questionnaire was carefully examined. 200 were determined to be appropriate for this study. A conceptual model of eight factors, including the five dimensions of the SERVQUAL model, digital transformation, student satisfaction, and student retention, was framed and tested. Descriptive statistics (mean and standard deviation), multiple regression, and bivariate regression analyses were conducted using SPSS 26.0 to test the hypothesized relations. According to the findings of descriptive data, students are satisfied with the quality of private university services and digital transformation activities. This study reveals that all the service quality dimensions positively and significantly impact student satisfaction except the empathy dimension. It is also found that digital transformation substantially influences student satisfaction. Furthermore, student satisfaction has a positive and significant influence on student retention. To the best of the authors’ knowledge, this is the first initiative to investigate the impact of service quality dimensions and digital transformation on student satisfaction and student retention in the context of private universities in Bangladesh. This study contributes to higher education institutions for the improvement of service quality dimensions and the digital transformation to obtain student satisfaction and student retention in highly competitive educational sectors. Finally, this work argues that digital transformation in higher education is necessary for survival and growth in the twenty-first century.
Time-Varying Engineered Reservoir for the Improved Estimation of Atom-Cavity Coupling Strength

In this paper, we consider the application of quantum reservoir engineering in quantum metrology. More precisely, we are concerned with a system setup where a sequence of atoms constructing the “time-varying” quantum reservoir interact, in turn, with the trapped field in a cavity through the Jaynes–Cummings Hamiltonian. In particular, we were able to manipulate the initial states of reservoir atoms in order to enhance estimation precision regarding the coupling strength between each atom and the cavity (the coupling strength between each atom and the cavity was assumed to be identical). The novelty of this work lies in alternately preparing the atoms at two different states in a pairwise manner, such that the cavity could converge into a squeezed state with photonic loss to the environment taken into account. The control scheme proposed here thus leads to higher precision compared to the previous work where reservoir atoms were initialized at the same state, which drove the cavity to a coherent state. Detailed theoretical analysis and numerical simulations are also provided. In addition, this system setup and the associated control scheme are easily implemented for quantum metrology, since no entanglement is required for the preparation of atom states, and the final cavity state can stay steady.
Digital competencies of university transformation project teams from different countries

At the moment, many authors are exploring the issues of digitalization of universities and the need to develop digital competencies. The importance of transferring project activities to a digital platform is also actively discussed. However, the problem of the lack of sound digital competencies for university transformation project teams is relevant and has not yet been fully resolved in many countries. In accordance with this, the purpose of the research presented in the article was to identify a set of digital competencies that are most important for the implementation of transformation projects of universities in different countries. The scientific hypothesis tested in the course of the research sounds like an assumption that a certain set of digital competencies of team members is required for the success of transformation projects and their significance in the overall structure of competencies is high. The conclusion hypothesis was the statement that digital tools and the corresponding digital competencies are currently being used insufficiently by universities. The study was conducted in 2021-2022 in two stages using semi-structured interview and questionnaire methods. The questionnaire survey was conducted in four countries — Russia, Georgia, Serbia, and Iran. The survey covered 203 experts — representatives of universities in these countries with extensive experience in project activities. Interviews were conducted with 116 representatives of the administrative and managerial staff of five major universities in Russia. The main results were: firstly, the influence of competencies on the success of university transformation projects was confirmed; secondly, the high importance of digital competencies in the structure of other groups of competencies required for teams of university transformation projects is revealed; thirdly, facts are presented that indicate that digital competencies are used, but to an insufficiently high degree. These results have scientific novelty, as they contribute to the development of the concept of competence approach, clarifying digital competencies, as well as the development of project management, clarifying the requirements for the qualitative parameters of project teams in the context of digitalization of universities. The practical significance of the study is that universities receive information about the directions of development of digital competencies of transformation project teams.
On Membership Inference Attacks to Generative Language Models Across Language Domains

The confidentiality threat against training data has become a significant security problem in neural language models. Recent studies have shown that memorized training data can be extracted by injecting well-chosen prompts into generative language models. While these attacks have achieved remarkable success in the English-based Transformer architecture, it is unclear whether they are still effective in other language domains. This paper studies the effectiveness of attacks against Korean models and the potential for attack improvements that might be beneficial for future defense studies. The contribution of this study is two-fold. First, we perform a membership inference attack against the state-of-the-art Korea-based GPT model. We found approximate training data with 20% to 90% precision in the top 100 samples and confirmed that the proposed attack technique for naive GPT is valid across the language domains. Second, in this process, we observed that the redundancy of the selected sentences could hardly be detected with the existing attack method. Since the information appearing in a few documents is more likely to be meaningful, it is desirable to increase the uniqueness of the sentences to improve the effectiveness of the attack. Thus, we propose a deduplication strategy to replace the traditional word-level similarity metric with the BPE token level. As a result, we show 6% to 22% of the underestimated samples among the selected samples.