Reviewerly/exHarmony huggingface dataset:
Socio-Cultural Impact of Virtual Reality Technology on Art Appreciation and Cultural Engagement

Purpose: The main objective of this study was to explore the socio-cultural impact of virtual reality technology on art appreciation and cultural engagement.&#x0D; Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings revealed that there exists a contextual and methodological gap relating to the socio-cultural impact of virtual reality technology on art appreciation and cultural engagement. The empirical review revealed that in the wake of rapid technological advancements, virtual reality technology has emerged as a powerful tool in transforming the landscape of art appreciation and cultural engagement. Through a synthesis of empirical studies, it is evident that VR has brought about profound changes, unlocking new dimensions of artistic experiences and redefining cultural engagement paradigms. Virtual reality has succeeded in reinvigorating art appreciation, transcending the boundaries of physical space and time. Users are now able to immerse themselves fully in artworks, experiencing them in ways that were previously unimaginable.&#x0D; Unique Contribution to Theory, Practice and Policy: The Cultural Proximity theory, Technology Acceptance Model (TAM) and the Cultural Capital theory may be used to anchor future studies on the socio-cultural impact of virtual reality technology on art appreciation and cultural engagement. The study recommended that cultural institutions and art organizations should actively integrate virtual reality technology into their exhibits and educational programs. A concerted effort must be made to ensure that virtual reality remains inclusive and accessible to all individuals, including those with disabilities. Ethical guidelines and regulations should be established to govern the collection and use of user data in virtual reality experiences. Emerging artists should embrace virtual reality as a means to gain exposure and access new audiences.
Rancang Bangun Aplikasi Pengujian Usability Berbasis Web

Usability is one factor that influences software design success during the software testing process. Usability testing is used to evaluate whether the software meets user needs or not. Usability testing is usually carried out using a certain questionnaire method, but the distribution and measurement of the results are carried out separately. This makes the researchers spend more time testing the application. Therefore, this study provides a solution by designing and building web-based applications to measure the usability of a software design or product with the SUS, UMUX, and UMUX-lite questionnaires. The development method used in this study is Rapid Application Development (RAD) with the stages of the process starting with problem identification, literature study, needs planning, RAD design workshops, implementation, and report writing. The application was built using the Laravel framework and the database using MySQL. The main features of the online questionnaire application were the creation of a questionnaire and the analysis of the results of the questionnaire. The functionality of the application was tested by using the Black Box testing method. Then the developed application was also utilized in usability testing using the SUS, UMUX, and UMUX-lite questionnaires. The results of application usability testing using the SUS, UMUX, and UMUX-Lite methods obtained a final score of 88; 91,28, and 82,05 where all these values mean the application can be accepted and used. Keywords: Usability Testing, Rapid Application Development, SUS, UMUX, UMUX-Lite
Sistem Penyewaan Ruang dan Fasilitas Gedung Academic Activity Center (AAC) Dayan Dawood berbasis Web

Syiah Kuala University has the Dayan Dawood Academic Activity Center (AAC) Building as a non-academic business unit that focuses on renting rooms and facilities for various purposes. This building is very often used for various activities, but unfortunately, the rental procedure is still conventional so it is prone to errors and loss of reservation data so the income calculation is not optimal. In addition, price and facility information is not yet accessible to the public. The problem will be solved by creating a website-based information system that can manage the rental of the Dayan Dawood Academic Activity Center (AAC) building with two groups of users, namely admins and tenants. This system was built using the CodeIgniter framework and MYSQL database. The system creation process applies the Scrum method as project management. System testing consists of functionality testing using Black Box Testing and Usability Testing using UMUX-LITE. Black Box Testing has successfully run all test scenarios as they should. Usability Testing was conducted on 29 respondents and resulted in a score of 83.97, the application is in the "A" category so it can be concluded that the application can be accepted with a grade scale of "B" and its adjective rating is "Good".
Rancang Bangun Film Pendek Animasi 3d Berjudul Level Up Menggunakan Blender Versi 3.2

A short 3D animation film titled “Level Up” tells about a robot that wants to create a work using different illustrations, namely blueprints and Blender. The purpose of making this animated 3D short film is to increase understanding of illustrations in 3D object design, where using the Blender 3.2 application the design process will be easier to do. This film has a video resolution of 1920x1080 and a frame rate specification of 30 frames per second with the .mp4 extension. Designing and creating videos using the Blender application which refers to the process of storyboarding, modeling, texturing, animation, lighting, rendering, Adobe Premiere Pro and Vlog Now for video editing. With this 3D animated short film, it is hoped that it can be a reference in making other works.
Climate Crisis in Virtual Environments: Exploration and Evaluation of Virtual Reality and Mixed Reality for Climate Change Education in Sea Level Rise Simulation

Against the backdrop of the boom in Virtual Reality (VR), Mixed Reality (MR) and the looming climate change crisis, this paper explores the design and development of VR and MR applications for simulating Sea Level Rise (SLR). This paper outlines the development and deployment of a mobile VR head-mounted display. The effects of VR technology and Human-Computer Interactive (HCI) MR technology based on hand tracking on climate and environmental education are discussed. To gain a more nuanced analysis from the users, controlled experiments are conducted and the effect of these applications is evaluated. The results from a comparison of hand-tracking MR mode versus traditional VR interaction indicate that these technologies have a positive impact on people’s environmental awareness at a statistically significant level using the New Ecological Paradigm (NEP) scale.
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information. However, existing MMEA approaches primarily concentrate on the fusion paradigm of multi-modal entity features, while neglecting the challenges presented by the pervasive phenomenon of missing and intrinsic ambiguity of visual images. In this paper, we present a further analysis of visual modality incompleteness, benchmarking latest MMEA models on our proposed dataset MMEA-UMVM, where the types of alignment KGs covering bilingual and monolingual, with standard (non-iterative) and iterative training paradigms to evaluate the model performance. Our research indicates that, in the face of modality incompleteness, models succumb to overfitting the modality noise, and exhibit performance oscillations or declines at high rates of missing modality. This proves that the inclusion of additional multi-modal data can sometimes adversely affect EA. To address these challenges, we introduce UMAEA, a robust multi-modal entity alignment approach designed to tackle uncertainly missing and ambiguous visual modalities. It consistently achieves SOTA performance across all 97 benchmark splits, significantly surpassing existing baselines with limited parameters and time consumption, while effectively alleviating the identified limitations of other models. Our code and benchmark data are available at https://github.com/zjukg/UMAEA .
Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.
Node and Edge Differential Privacy for Graph Laplacian Spectra: Mechanisms and Scaling Laws

This paper develops a framework for privatizing the spectrum of the Laplacian of an undirected graph using differential privacy. We consider two privacy formulations. The first obfuscates the presence of edges in the graph and the second obfuscates the presence of nodes. We compare these two privacy formulations and show that the privacy formulation that considers edges is better suited to most engineering applications. We use the bounded Laplace mechanism to provide <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula> -differential privacy to the eigenvalues of a graph Laplacian, and we pay special attention to the algebraic connectivity, which is the Laplacian's the second smallest eigenvalue. Analytical bounds are presented on the accuracy of the mechanisms and on certain graph properties computed with private spectra. A suite of numerical examples confirms the accuracy of private spectra in practice.
A SwinTransformer-Based Segmentation Framework With Self-Supervised Strategy for Post-Operative Prostate Cancer Radiotherapy

Radical prostatectomy (prostate removal) is a standard treatment for clinically localized prostate cancer and is often followed by postoperative radiotherapy. Postoperative radiotherapy requires accurate delineation of the clinical target volume (CTV) and lymph node drainage area (LNA) on computed tomography (CT) images. However, the CTV contour cannot be determined by the simple prostate expansion after resection of the prostate in the CT image. Constrained by this factor, the manual delineation process in postoperative radiotherapy is more time-consuming and challenging than in radical radiotherapy. In addition, CTV and LNA have no boundaries that can be distinguished by pixel values in CT images, and existing automatic segmentation models cannot get satisfactory results. Radiation oncologists generally determine CTV and LNA profiles according to clinical consensus and guidelines regarding surrounding organs at risk (OARs). In this work, we design a cascade segmentation block to explicitly establish correlations between CTV, LNA, and OARs, leveraging OARs features to guide CTV and LNA segmentation. Furthermore, inspired by the success of the self-attention mechanism and self-supervised learning, we adopt SwinTransformer as our backbone and propose a pure SwinTransformer-based segmentation network with self-supervised learning strategies. We performed extensive quantitative and qualitative evaluations of the proposed method. Compared to other competitive segmentation models, our model shows higher dice scores with minor standard deviations, and the detailed visualization results are more consistent with the ground truth. We believe this work can provide a feasible solution to this problem, making the postoperative radiotherapy process more efficient.
Optical Character Recognition and Text Line Recognition of Handwritten Documents: A Survey

Optical Character Recognition (OCR) is a research area that deals with digitizing and converting any handwritten text or document into digitized form. The main need for such a conversion is to efficiently store, access, preserve, and also transfer the wealth of knowledge in these documents for the future. The last few decades have witnessed the escalation and interest of the research community toward developing new ideas and methodologies for OCR in the context of text line extraction and recognition. Identifying the individual lines in a handwritten document is one of the most crucial stages in recognizing language, words, and characters. The nature and style of handwriting make the task of recognizing the individual text lines from a handwritten document a challenging one. A critical analysis of the various text line recognition systems in offline handwritten documents is presented in this work. This overview will help researchers understand OCR and the various text line recognition strategies carried out in research over the years.
Patient-Centric Electronic Health Records Management System Using Blockchain Based on Liquid Proof of Stake

Hospitals and public health organizations no longer have to maintain information about patients, their treatments, and diagnoses in physical ledgers as a result of the development of modern technologies. Instead, they keep a digital record on a local database, which can be centralized and has a number of effects on privacy, accessibility, security, and the possibility of data loss in the case of a natural disaster. Due to the complexity and cost of our healthcare systems, patients must go through a time-consuming documentation process to get a second opinion or send medical information abroad. Technically, the patient does not possess exclusive ownership of his records. However, blockchain technology can reduce these consequences through an improved healthcare record management system. The blockchain-based electronic health record (EHR), which stores patient medical records in a secure and decentralized digital format that can be securely shared with doctors, laboratory technicians, researchers, and other stakeholders in the healthcare industry over the Internet, is a boon to the healthcare sector. Its distinguishing features include access control of the records over the network and tracking the changes in the medical records over a trusted network. So, this work has utilized the InterPlanetary File System (IPFS) as an off-chain data storage option and public cryptography to secure the data and restrict access. The paper has developed a decentralized application (DApp) based on the Tezos blockchain network to implement the EHR management system which Liquid Proof of Stake (L-PoS) consensus process.
Exploring Jaccard Similarity and Cosine Similarity for Developing an Assamese Question-Answering System

This paper gives a complete study on the creation of an Assamese question-answering system (AQAS) employing two fundamental methods: cosine similarity and Jaccard similarity. Cosine similarity is a measure of similarity between two vectors, whereas Jaccard similarity is a measure of similarity between two sets. To improve efficiency, the cosine similarity method is paired with non-negative matrix factorization (NMF), which substantially reduces both space and temporal complexity. Our approach gets an accuracy rate of 93.8% for cosine similarity and 87.38% for Jaccard similarity after thorough testing. These results demonstrate the efficiency of our method in providing precise Assamese language responses. This work provides useful insights into the topic of Assamese question-answering systems and their practical applications.
“The Wonderful Wizard of OZ”: Building students’ character in ELT through Fantasy Novel

Fantasy is the one of genre in children's literature that is famous all over the world. However, because of the story that tells about imaginary world, the appearance is not quite considered in ELT classroom especially in building students' character. Therefore, this study was conducted to see character education represented in a fantasy novel that is suggested in curriculum 2013 for ELT in Indonesia. The fantasy novel used in this study was The Wonderful Wizard of OZ written by L. Frank Baum. Every condition and situation faced by the character in every plot of story was analyzed using content analysis. The finding showed that fantasy novel could promote character building through the story by showing it in character's attitude in facing a situation or problem. So, even though it told about imaginary world, but it was closed to real life and great as a source for learning.
Analysis of Student Satisfaction Levels with Online Learning Systems

This research aims to analyze student satisfaction in online learning. This research uses quantitative methods. The data source was taken using a questionnaire from 50 active students attending college in a few Universities in Sumatra. All data is then analyzed using software SPSS Windows Version 20 and found 33 students stated that they were not satisfied with the total percentage of 60%, 3 students stated that they were very dissatisfied with the results percentage 5.5% of students, 17 students stated that they were satisfied with a percentage of 30.9 students, then 2 students stated that they were very satisfied with the online learning system with a percentage of 4%. The satisfaction in question is measured through five indicators, namely; i). level of satisfaction, ii) clarity of structure and subject matter of material discussion, iii) suitability of learning material with RPS, iv) consistency of lecturers in online learning, v) liking online learning. The five indicators used as instrument items in this research were used to determine the level of student satisfaction with the online learning system during the Covid-19 pandemic.
FACTORS AFFECTING ACCOUNTING INFORMATION SYSTEM PERFORMANCE IN CV. HARI BAIK

An accounting information system is effective if the information needed by system users is met and efficient if the system is able to produce information without spending a lot of time in the work process. The goal of this study was to examine the variables that affect the functionality of the accounting information system, including user participation in information system development, individual technical proficiency, support from top management, training and education initiatives, user communication with information system developers, and the location of the information systems department. The test results demonstrate that user involvement in information system development, individual technical proficiency, and top management support have positive effects on the performance of the accounting information system, whereas training and education programs, user communication, information system developers, and the location of the information systems department have no impact on the performance of the accounting information system.
HOW ARTIFICIAL INTELLIGENCE CAN BE EFFECTIVE FOR TEACHING ENGLISH TO HEARING IMPAIRED LEARNERS?

Many applications and algorithms that were previously impossible to perform on a notebook, computer, or mobile phone may now be readily implemented thanks to growing sophisticated technology and increasingly powerful processing capabilities on computers. Artificial Intelligence (AI) is one technology that is gaining popularity for a variety of applications. To increase hearing impaired knowledge of English, an effective learning technique is required. One method is to apply artificial intelligence to a technology-based learning medium, such as Kahoot. The purpose of this study is to examine the efficiency of teachers in using English learning media based on Artificial Intelligence Kahoot at SDLB B Budi Nurani in Sukabumi City. This research employs a qualitative approach. Several teachers and students participated in this study. Data was collected through interviews, observation, and documentation and was processed descriptively and interpretively. The findings indicated that using Kahoot as a medium for learning English increased students' motivation and grasp of the English subject matter.
Semantic and Gradient Guided Scene Text Image Super-Resolution

Scene text image super-resolution (STISR) aims to improve image quality and enhance downstream recognition tasks. Although recent works has made significant progress, there are still challenges to overcome. First, scene text images are subject to various forms of degradation, fonts, and deformations, previous methods are not robust enough. Second, the super-resolution results of character edges often appear distorted, broken, or stuck together, making the letters hard to recognize. To address these issues, we propose a semantic and gradient guided super-resolution network (SGGSR), which integrates two characteristics of text as guiding priors: semantics and gradient. Specifically, we present a semantic prior generator that is pre-trained with contrastive learning and a gradient direction loss. Experiments show that our SGGSR outperforms other methods in terms of image quality metrics such as PSNR and SSIM, as well as accuracy in downstream recognition tasks. The improvement in recognition performance is also observed for text instances from different datasets with varying degrees of degradation and deformation.
Preliminaries from Model Theory and Logic

In this chapter, we provide the basic preliminaries from first-order logic that are needed for the first part of the book. We also present some notations employed in this work and some notions from model theory that will be extensively used for the technical treatment. Finally, we summarize basic notions on classical relational databases.