Reviewerly/exHarmony huggingface dataset:
Nexus Analysis as a Tool for Investigation of Digital Literacy and Digital Competency in Using LMS Labeled to Unlock Textbook Taught at King Khalid University

This study investigates digital literacy and digital competency in using LMS among KKU students studying English as an intensive English course. The study is based on using nexus analysis. This study adopts a descriptive-analytical method, and the main instrument used for collecting data is a questionnaire for teachers to investigate how students can use LMS to do their homework and activities related to Unlock textbooks. The study's findings have revealed that most students use LMS professionally but feel bored because of lengthy tasks, exercises and activities which consume a long time. In addition, most of the activities, tasks and exercises do not match the textbook unit contents. The study recommends that Cambridge LMS should be loaded with tasks, exercises and activities matching the unit content, and they should not be lengthy.
Classification of medical X-ray images using supervised and unsupervised learning approaches

Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image retrieval. Thus, there is a need of research work that can address both supervised and unsupervised learning approaches for medical image retrieval. Hence, the C-BIR technique is evolved with overcoming above stated concerns. Hence, this manuscript introduces two different C-BIR techniques using a support vector machine (SVM) and a fuzzy logic-based approach for classification. These approaches work on the classification based on feature extraction, region of Interest (ROI), corner detection, and similarity matching. The proposed approach has been analyzed for image retrieval for accuracy. The outcomes of the proposed study enhance the classification performances with retrieval than existing techniques of C-BIR.
The Development of Digital Flipbook as German Language Learning for Senior High School Class XI

The purpose of this research and development study is to develop a learning media in the form of a Flipbook which will be applied to learning German in Class XI High School. This research adapts the development model of Cennamo and Kalk (2019), which consists of 5 coherent and continuous steps. These steps include (1) Define, (2) Design, (3) Demonstrate, (4) Develop, and (5) Deliver. The score given by the media validator was 80%, the material validator was 77.5%, practitioners were 85%, and trial respondents were 87.2%. Based on predetermined validity criteria, the results show that Flipbook is included in the valid category. This research produces a Flipbook digital book that can be accessed using an internet connection with various gadget devices. Therefore, it can be concluded that the product developed is feasible to be applied in learning German.
Lancester correlation -- a new dependence measure linked to maximum correlation

We suggest correlation coefficients together with rank - and moment based estimators which are simple to compute, have tractable asymptotic distributions, equal the maximum correlation for a class of bivariate Lancester distributions and in particular for the bivariate normal equal the absolute value of the Pearson correlation, while being only slightly smaller than maximum correlation for a variety of bivariate distributions. In a simulation the power of asymptotic as well as permutation tests for independence based on our correlation measures compares favorably to various competitors, including distance correlation and rank coefficients for functional dependence. Confidence intervals based on the asymptotic distributions and the covariance bootstrap show good finite-sample coverage.
Poliwangi Jinggo Hotel reservation system as a pilot project for Teaching Factory (TEFA)

Poliwangi Jinggo Hotel is specially addressed for educational purpose which will be used as a Teaching Factory (TEFA), so in other words this hotel will go public in near future. To support the process and everything related to hotel operation, as a baby step it is necessary to create hotel management system. The system was designed using Windows-based application (Bistone Hotel Management System). Subsequently, the research and development were carried out based on waterfall model (Requirements Analysis and Definition, System and Software Design, Implementation and Unit Testing, Integration and Unit System Testing, and Operation and Maintenance). As for the evaluation, the room reservation system was measured by quantitative approach using 30 respondents. The results obtained in this study were hotel reservation system which included setting room types, room rates, facilities, room numbers, guest input, making reservations, and printing invoices/receipts for the initial process. Based on USE Questionnaire, the results showed that the percentage of usefulness 81.2%, ease of use 80.2%, ease of learning 80%, and satisfaction 76%. Therefore, it can be concluded that Poliwangi Jinggo Hotel room reservation system is feasible to use, together with some developments and operation &amp; maintenance process.
WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset

We present WebQAmGaze, a multilingual low-cost eye-tracking-while-reading dataset, designed as the first webcam-based eye-tracking corpus of reading to support the development of explainable computational language processing models. WebQAmGaze includes webcam eye-tracking data from 600 participants of a wide age range naturally reading English, German, Spanish, and Turkish texts. Each participant performs two reading tasks composed of five texts each, a normal reading and an information-seeking task, followed by a comprehension question. We compare the collected webcam data to high-quality eye-tracking recordings. The results show a moderate to strong correlation between the eye movement measures obtained with the webcam compared to those obtained with a commercial eye-tracking device. When validating the data, we find that higher fixation duration on relevant text spans accurately indicates correctness when answering the corresponding questions. This dataset advances webcam-based reading studies and opens avenues to low-cost and diverse data collection. WebQAmGaze is beneficial to learn about the cognitive processes behind question-answering and to apply these insights to computational models of language understanding.
Efficient reconfigurable architecture to enhance medical image security

Medical images are one of the most critical and sensitive types of data in information systems. For the secure storage and transfer of medical images, confidentiality is the most important aspect. This paper presents efficient embedding technique to enhance medical image security. The Gaussian filters are used as preprocessing to remove high frequency components and then applied to cumulative distribution function (CDF) 5/3 wavelet to obtain LL band features. Similarly, the LL band features of cover image are obtained. The alpha bending technique combines both the LL band features of cover and secret image to form first level of encryption and now with other high frequency bands of LH, HL and HH applied to Inverse CDF 5/3 obtains an encrypted image which is then transferred along with key obtained through other bands of LH, HL and HH. The key generated acts as additional level of security and similarly, at the receiver the opposite operations are performed to obtain the original secrete image. The performance is measured in terms of Peak signal-to-noise ratio (PSNR) and is compared with existing techniques to validate the results. Further, the entire architecture is synthesized on spartan 6 field-programmable gate array (FPGA) board to compare the hardware utilizations.
Development of Electronic Student Worksheets Based Local Potential to Enhance Students’ Science Learning Outcomes

Facilities need in the learning process; among the conveniences that can are the application of flexible teaching materials such as electronic student worksheets (E-LKPD). Student worksheets that apply in schools are still general. Good student worksheets are attractive and increase student insight. The study aimed to examine learners' feasibility and response to E-LKPD based on local potential. This research used the development model of ADDIE (analysis, design, development, implementation, evaluation). E-LKPD feasibility was assessed based on the average assessment result by material experts, media experts, and practitioners (teachers) with a feasibility percentage of 87,51, classified as very feasible. Learner's response obtained from average assessment on learners' response in big scale test with a percentage of 78,31, classified as attractive. This statement implies that E-LKPD-based local potential was feasible for use in learning.
NFT-Driven B2C E-Commerce Website Innovation: A Study of the Effects of Blockchain Integration in the Deep Web

Abstract: The MERN Stack is a set of strong and effective technologies for the creation of backend, frontend, and database-rich web applications. Full-stack web development is quick and effective thanks to JavaScript. The MERN Stack, a JavaScript-based platform that integrates MongoDB, Express, React, and Node.js, streamlines and accelerates the development of fullstack online applications. Its main goal is to simplify and make the development process easier.
GAN-Based One-Class Classification SVM for Real time Medical Image Intrusion Detection

Medical data attack and detection technology has been a hot topic in the past few decades precisely as numerous attacks on hospitals and clinics led to the loss of data.Although many methods have been developed for detection and discrimination of fake images, the problem has not yet been properly solved.Classifying the data method tends to produce higher error when compare to other methods due to the large variance directions.One-class classification is a fairly competitive method for detecting fake medical images due to the data's unbalanced nature.However, it can also produce higher error when compare to other methods.One of the most effective ways to improve the accuracy of one-class classification is by implementing covariance-guided support vector machine (iCOSVM) especially with a real time system.Therefore, in this paper, we present a case study that uses incremental covariance-guided support vector machine to build suitable detection system.The results of the study showed that the proposed detection system is very accurate and efficient.It utilizes the training data to improve its accuracy and minimize its error.The iCOSVM supports incremental projections, improves significantly the performance of the one-class support vector machine.Additionally, our proposed detection system is very accurate and efficient comparing to other incremental one class classifications algorithms, outperforming the batch learning system as well.
On Rényi Differential Privacy in Statistics-Based Synthetic Data Generation

Privacy protection with synthetic data generation often uses differentially private statistics and model parameters to quantitatively express theoretical security. However, these methods do not take into account privacy protection due to the randomness of data generation. In this paper, we theoretically evaluate R\'{e}nyi differential privacy of the randomness in data generation of a synthetic data generation method that uses the mean vector and the covariance matrix of an original dataset. Specifically, for a fixed $\alpha > 1$, we show the condition of $\varepsilon$ such that the synthetic data generation satisfies $(\alpha, \varepsilon)$-R\'{e}nyi differential privacy under a bounded neighboring condition and an unbounded neighboring condition, respectively. In particular, under the unbounded condition, when the size of the original dataset and synthetic datase is 10 million, the mechanism satisfies $(4, 0.576)$-R\'{e}nyi differential privacy. We also show that when we translate it into the traditional $(\varepsilon, \delta)$-differential privacy, the mechanism satisfies $(4.00, 10^{-10})$-differential privacy.
Sentiment analysis of Twitter data regarding the agnipath scheme of the defense forces

Due to the popularity of social media today, people frequently share such criticism on Facebook, Twitter, Instagram, and other platforms. Therefore needs to know how your input from users of social media is generated in order to ascertain the public reaction to the policy that has been enacted. However, because of the comments, it is challenging to tell how many people have responded positive or negative. The objective of sentiment analysis of tweets is to provide insight into people’s attitudes and perceptions regarding an event. This study illustrates the role of Twitter in the announcement of a new army vacancy through the “agnipath scheme” dubbed “agniveer”. The result of this study can be used by the defense forces and government for decision making or policies related to the agnipath scheme. The study studied 4,000 English-language Twitter posts from July 3, 2022 to July 9, 2022. Manual text analysis revealed seven basic groups of tweet sentiments. The tweets’ positive, negative, and neutral emotions were shown using orange data mining software, a powerful machine learning, data mining, and data visualization toolset. Result shows that agnipath scheme is mostly accepted by the people.
A A Context-Aware Human Distress Alert System for Safety from Interpersonal Violence Using Smartphone Sensors

Interpersonal violence is one of the causes of death globally. Getting help depends on timely response from responders. Development in personal safety technologies have been improved over the years due to the increase in the use of mobile technologies and the miniaturization of electronic sensors. However, existing technologies for communicating distress usually require active user interventions in alerting emergencies. Hence, this study proposes a system for the automatic detection and alert of distress situations arising from violent attacks through smartphone sensors. A fuzzy logic approach was adopted to classify levels of distress situation using input data values from tri-axial accelerometer, gyroscope, sound and proximity sensors. A prototype of the distress detection and alert process was implemented using Java programming language. The system prototype was implemented as an android based real-time application that detects and alerts distress using values from mobile device sensors. Testing the prototype showed that the system can automatically detect and send alert to responders using smartphone.
Comparing performance of bastion host on cloud using Amazon web services vs terraform

In addition to security advantages like implementing defense in depth and complying with compliance standards, current bastion services are simple to deploy and fit into the DevOps culture. Bastions continue to be the most dependable and secure options for secure access to cloud infrastructures because they offer administrative simplicity without surrendering compliance and security. In this paper, an experimental set up was conducted to measure the cycle time it takes to provision resources using manual point-and-click graphical user interface (GUI) in Amazon web services (AWS) and time it takes for codified infrastructure to make application programming interface (API) calls using terraform. It also focuses on the design and deployment of Bastion host on AWS and terraform, and the comparison between the two with respect to various parameters.
Fraud Detection in Credit Cards System Using ML with AWS Stage Maker

Abstract: Credit card firms must be able to recognize fraudulent credit card transactions so that clients are not charged for products that they did not purchase. Data Science may be used to solve these issues, and coupled with machine learning, it is of utmost relevance. The goal of this project is to demonstrate how to model a data set using machine learning for credit card fraud detection. The Credit Card Fraud Detection Problem entails modelling previous credit card transactions using information from those that were later determined to be fraudulent. While the globe was under lockdown and movement was confined to an absolute emergency- millions were introduced to the world of internet shopping. The simplicity of internet buying helped ecommerce platforms generate unprecedented sales. It is not surprising that during that time, the rate of online financial fraud also skyrocketed. During the COVID-19 pandemic in 2020 compared to 2019, there was a historic increase of 225 percent in online fraud cases involving credit and debit cards.
Using Self-Regulated Presentation to Minimize the Students’ Speaking Anxiety in Classroom Activities

This research investigates the use of self-regulated presentation to minimize the students’ speaking anxiety. This research was to find out whether teaching speaking by self-regulated presentation can increase students speaking anxiety. This research used a case study with (qualitative method). For taking a data, the researcher used an interview, questionnaire and observation. The participants of this research are the second year of Senior High School at SMA Nusantara Unggul Pekayon Tangerang Banten. This research takes 28 students. The research finding of the result showed that most of the students’ are at “Anxious” level. There are 13 students (46,4%) who experience “Anxious” level, 8 students (28,6 %) are in “very anxious” level, 7 students (25%) are in “Mildly Anxious”. This research also found that are four factors of students anxiety: Being rediculed and laughed, Students’ opinions about the Language Learning, Lack of Preparation and Teachers’ Personality. The last result of this research shows the using self-regulated presentation can minimize students’ speaking anxiety.
Legal Protection for Works in The Digital Environment in International Agreements and Legal Legislation (Comparative Study)

The developments that the world witnessed and the existence of the technological revolution led to cyberspace being a stage for the dissemination of human intellectual creativity and the importance of this creativity and its role in the world led everyone to seek to protect it from any aggression that might be inflicted on it to ensure its continuity by creating a sense of security for its owners and based on that I proceeded Countries have to conclude international agreements and enact internal legislation that protects this type of digital creativity in all its forms from any form of attacks that may be exposed to it in the digital environment. This important and sensitive issue requires the development of national legislation and international agreements in a clearer and more detailed manner because many of these digital works are subject to violation and theft. to pursue violators.
Peran Pembelajaran Elektronik terhadap Citra Merek Melalui E-WOM dan Kepuasan Mahasiswa di Era Pandemi Covid-19

The recent shift to online learning as a result of the COVID-19 outbreak has left universities vying for reputation. This phenomenon is interesting because e-learning offers learning system solutions during a pandemic, which is a fairly effective learning tool without creating crowds of students. Increased competition among tertiary institutions has made every university, including UNKAHA Semarang, challenged to form a positive image in the wider community. Many prospective students will have a special affinity for a university institution that has a positive image. Therefore, a positive image is very important to be researched and always re-evaluated by each tertiary institution. This research concentrates on the relationship and role of e-learning, e-word of mouth, student satisfaction with university brand image at Karya Husada University (UNKAHA) Semarang in the era of the Covid-19 pandemic. This study used a quantitative method by distributing questionnaires to Unkaha students, the data obtained was then processed using the SPSS and Excel programs. The results showed that e-learning had a positive and significant effect on e-wom, student satisfaction had a positive and significant effect on Unkaha's brand image, e-learning had a positive and significant effect on student satisfaction, e-wom played a role in mediating the influence between e-learning and image university brand, and student satisfaction play a role in mediating the influence between e-learning and university brand image.
DIGITAL TRANSFORMATION AT NON-PUBLIC UNIVERSITIES: CHALLENGES AND OPPORTUNITIES

Digital transformation in non-public higher education has been effectively implemented step by step for many years and has been especially exciting in the last few years. This is an inevitable trend that cannot be reversed and non-public higher education needs specific and appropriate policies and plans to ensure education quality and ensure equality in access to education with different learners. The article focuses on studying the challenges and opportunities of digital transformation in non-public higher education, gradually forming a team of digital lecturers and digital students.
Improved Reinforcement Learning in Asymmetric Real-time Strategy Games via Strategy Diversity

We investigate the use of artificial intelligence (AI)-based techniques in learning to play a 2-player, real-time strategy (RTS) game called Hunting-of-the-Plark. The game is challenging to play for both humans and AI-based techniques because players cannot observe each other's moves while playing the game and one player is at a disadvantage due to the asymmetric nature of the game rules. We analyze the performance of different deep reinforcement learning algorithms to train software agents that can play the game. Existing reinforcement learning techniques for RTS games enable players to converge towards an equilibrium outcome of the game but usually do not facilitate further exploration of techniques to exploit and defeat the opponent. To address this shortcoming, we investigate techniques including self-play and strategy diversity that can be used by players to improve their performance beyond the equilibrium outcome. We observe that when players use self-play, their number of wins begins to cycle around an equilibrium value as each player quickly learns to outwit and defeat its opponent and vice-versa. Finally, we show that strategy diversity could be used as an effective means to alleviate the performance of the disadvantaged player caused by the asymmetric nature of the game.
The Effect of using Chain Game toward Students’ Speaking Skill

This research was aimed to find out the effect of using chain game toward students’ speaking skill. The quasi-experimental with nonequivalent control group design was applied in this study. The seventh grade students of SMP Negeri 17 Kota Serang as the population. The samples were 30 students of VII C as the experimental class which is treated by using chain game and 30 students of VII A as the control class which is treated without using chain game. The data came from an oral test that was divided into two parts: pre-test and post-test. After collecting the data, the researchers used statistical computation to analyze the data. The result showed that there was significant effect of using chain game towards students’ speaking skill. It can also be seen from the calculation using an independent sample t test. The t test value was 3.966 and the significance value was 5% of t table df = 56, which was 1.673. So, t observed was higher than t table (3.966 &gt; 1.673). Hence, this research could be used as a reference for teaching speaking with chain game since the use of chain game was effective for teaching speaking to the seventh grade students at SMP Negeri 17 Kota Serang.