Reviewerly/exHarmony huggingface dataset:
Expert Review on Augmented Reality Mobile Application for Promoting Asnaf Care

People's daily lives are mostly dominated by mobile devices particularly in conducting business activities. Mobile devices are the first genuinely pervasive interaction device, that is now being used for a wide range of services and applications. This includes the promotion of donation activities through the Augmented Reality (AR) mobile application. This study found that one of the organizations that require the use of AR mobile applications to promote their donation activities is Asnaf Care. Currently, all donation activities have been carried out through the Asnaf Care website which still fails to influence and encourage the community to contribute and take part in the donation activities. Therefore, to promote the Asnaf Care fund and increase donations, this research was conducted to develop an AR mobile application. The AR mobile application was evaluated through an expert review method, and changes were made to the overall design, content, and function based on the feedback received. The goal of this study was to successfully promote the Asnaf Care services to users and increase the amount of money raised for impacted individuals.
STUDENTS’ ABILITY IN USING CONDITIONAL SENTENCES

This research was aimed at describing the students’ ability using conditional sentences and to investigate the dominant type of conditional sentences mastered by the eleventh-grade students using conditional sentences (type I, II, and III) at SMAS Istiqlal Delitua Academic Year 2022/2023. The design of this research was descriptive quantitative research and the instrument used in this research was an objective test or multiple choices. The population of this research was 210 students. Then, the researchers used a simple random sampling technique to take the sample and chose 30 students. The researchers found out: first, the mean score of students’ ability in using conditional sentences was 77.86; it was categorized as a “good” level; second, the dominant type of students’ ability in using conditional sentences was “type I” with a mean score of 0.88 or 38%. It means the process of teaching and learning English, more specifically, in Conditional Sentences, at SMAS Istiqlal Delitua Academic Year 2022/2023 has reached the standard.
Strategy for Developing Android-Based Teaching Materials for Text-based Indonesian Learning as an Implementation of the Independent Curriculum in Junior High School

This study aims to determine strategies for developing android-based teaching materials for text-based Indonesian language learning as an implementation of the independent curriculum in junior high schools. Based on the achievements of learning Indonesian in the independent curriculum, the various texts studied constitute a line of thought that can optimize the use of language for work and lifelong learning. This research method is literature study, namely the method of collecting data by understanding and studying theories from various literature related to research. Therefore, the research data is in the form of data that is relevant to the topic. The data comes from books and research articles that have been published in national and international journals. The strategy in developing android-based teaching materials for text-based Indonesian learning can be carried out by applying the ADDIE development model to produce teaching material prototypes in the form of applications that contain multimedia content by combining several learning media such as text, audio, graphics, images, and video to improve the quality of learning Indonesian and increase student learning independence.
Image analysis of volcanic plumes: A simple calibration tool to correct for the effect of wind

Video cameras provide vital information on volcanic plumes from explosive eruptions, such as plume height, for monitoring and research. These images must be calibrated to get accurate quantitative data. However, the presence of wind complicates any calibration as the plume may no longer lie in the image plane, i.e. a plane perpendicular to the camera’s line-of-sight. Here, we present a simple new tool to correct for the effect of wind on the position and height of a volcanic plume as determined from imagery by rotating the image plane to be in the direction of the wind. We show the importance of accounting for the effect of wind on the maximum plume height determined from videos for two case-studies; a Vulcanian explosion from Sabancaya volcano, Peru, and a sustained plume from Mount Etna, Italy. This tool can improve the accuracy of quantitative information extracted from images of volcanic plumes, and should prove useful for both research and monitoring purposes.
IMPLEMENTATION OF SIMPLE ADDITIVE WEIGHTING (SAW) METHOD FOR DESIGN AND BUILD DECISION SUPPORT SYSTEM OF LAPTOP SELECTION (Case Study: IT Store)

Laptop is a small computer that is easy to carry anywhere, which makes everyone want to have one because of its convenience. However, the laptops currently available have various specifications, so users feel confused about choosing and buying the laptop they want. Therefore, this research was conducted to build a web-based decision-support system. The method used in this decision support system is simple additive weighting as a criterion for calculating laptop recommendations. The SAW method is a weighted addition method to determine the weighted sum to determine ranking. User satisfaction test results using a questionnaire using the End User Computing Satisfaction (EUCS) method The results of the calculation of the user satisfaction test obtained a result of 85.66%, which means that the system built can be useful for users in providing laptop recommendations.
The Importance of Digital Literacy in The Era of Society 5.0

The purpose of writing this article is to understand the importance of digital literacy in the era of society 5.0. The era of society 5.0 is a time when science and technology are used and utilized to facilitate people's lives. The method used in this study is a literature study. The data collection technique in this study is to collect information and data from various library materials such as books, articles, previous research, notes, and journals related to the application of digital literacy in the era of society 5.0. The results of data analysis show that one of the skills needed in the era of society 5.0 is digital literacy skills. Between the era of society 5.0 and digital literacy has the same goal in answering all the challenges of social life and improving decision-making skills The result of this writing is to provide an overview of the concept of the era of society 5.0 and information about the importance of digital literacy in the era of society 5.0.
Membangun Keterampilan Pendidik Melalui Pelatihan Pembuatan Media Pembelajaran Berbasis Artificial Intelligence

In this digital era, artificial intelligence (AI) technology plays a crucial role in driving innovation in the education sector. This article discusses efforts to address challenges in understanding and practical skills among educators regarding the implementation of AI in learning. The focus is on training conducted at SMP Negeri 2 Kahu, which faces challenges in using AI to enhance the quality of learning media. The training begins with a basic understanding of AI, involving definitions, fundamental concepts, recent developments, and ethical aspects of AI usage. The final session of the training emphasizes the utilization of AI in developing interactive and adaptive learning media, particularly using Chat GPT. Participants engage in practical demonstrations and collaborative discussions to design the integration of this technology into the learning process. Post-training evaluations indicate an improvement in participants' understanding, although challenges in technology access need to be addressed. This training is expected to stimulate innovation, create an adaptive educational ecosystem, and provide a relevant and adaptive learning experience for students in the digital era.
The effect of the application of Albert Bandura’s behaviorism theory on the motivation and learning outcomes

A teacher must be able to understand the theory of learning and learning in order to understand, motivate, and influence the motivation and learning outcomes of his students; behavioristic learning theory is one developed by Albert Bandura. This article aims to find out how the implementation of the Behaviorism theory approach from Albert Bandura affects student motivation and learning outcomes. Sampling in quantitative research using the Quasi-Experimental method using pretest, posttest, and control groups. The research design was tried using the saturated sampling method. The initial sample consisted of all VB class students, with a total of 25 students as a control class, and the second illustration consisted of all VA class students, with a total of 28 students as an experimental class. The results of the analysis show that the implementation of Albert Bandura's behaviorism theory approach using the experimental class MANOVA test gives significant results namely 0.011 is smaller than 0.05, so H0 is rejected and H1 is accepted. Based on this, there is an influence on the application of Albert Bandura's theoretical approach to student motivation and learning outcomes
DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs

Free-form image inpainting is the task of reconstructing parts of an image specified by an arbitrary binary mask. In this task, it is typically desired to generalize model capabilities to unseen mask types, rather than learning certain mask distributions. Capitalizing on the advances in diffusion models, in this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based model capable of filling missing pixels fast as it models the backward diffusion process using the generator of a generative adversarial network (GAN) network to reduce sampling cost in diffusion models. Experiments on general-purpose image inpainting datasets verify that our approach performs superior or on par with most contemporary works.
Control in Hybrid Chatbots

Customer data typically is held in database systems, which can be seen as rule-based knowledge base, whereas businesses increasingly want to benefit from the capabilities of large, pre-trained language models. In this technical report, we describe a case study of how a commercial rule engine and an integrated neural chatbot may be integrated, and what level of control that particular integration mode leads to. We also discuss alternative ways (including past ways realized in other systems) how researchers strive to maintain control and avoid what has recently been called model "hallucination".
Node classification in random trees

We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the tree data structure is associated with node attributes (typically high dimensional embeddings). The tree topology is not predetermined and none of the label assignments are present during inference. Other methods that produce a distribution over node label assignment in trees (or more generally in graphs) either assume conditional independence of the label assignment, operate on a fixed graph topology, or require part of the node labels to be observed. Our method defines a Markov Network with the corresponding topology of the random tree and an associated Gibbs distribution. We parameterize the Gibbs distribution with a Graph Neural Network that operates on the random tree and the node embeddings. This allows us to estimate the likelihood of node assignments for a given random tree and use MCMC to sample from the distribution of node assignments. We evaluate our method on the tasks of node classification in trees on the Stanford Sentiment Treebank dataset. Our method outperforms the baselines on this dataset, demonstrating its effectiveness for modeling joint distributions of node labels in random trees.
Dynamic Sets Set and Some of Their Applications to Neuroscience, Networks Set

The article aims to create new constructive hierarchical mathematical objects for new technologies, particularly for a fundamentally new type of neural network with parallel computing and not the usual parallel computing through sequential computing.
Javanese Script Letter Detection Using Faster R-CNN

The Javanese script is now rarely used, and some people no longer recognize it. The construction of a Javanese script recognition system based on digital image processing is one of its preservation efforts. This study proposes a model capable of detecting and recognizing Javanese characters using Faster R-CNN to help people who are not familiar with the Javanese script. Faster R-CNN was chosen because it does not require additional processing compared to the previous method and Faster R-CNN has better accuracy and the ability to detect small objects. Faster R-CNN shows good results in text detection, but the use of Faster R-CNN in detecting Javanese script has not been found which makes its performance unknown, so this study will show how Faster R-CNN performs in detecting Javanese script. In this study, Faster R-CNN was able to show good performance by obtaining mean average precision (mAP) values up to 0.8381, accuracy up to 96.31%, precision up to 96.53%, recall up to 96.38 %, and F1-Score up to 96.41%. These results indicate that Faster R-CNN has better results than the previous method and can detect Javanese characters well.
Sentiment Analysis on IMDB Movie Reviews using BERT

Before technology existed, opinions could only be obtained from acquaintances, friends, or experts who were experts in certain fields. However, as technology develops, it turns out that opinions can be expressed through social media so that they can influence everyone who sees them. One of them is movie reviews. Human opinion about something is often not valid. So, this study aims to investigate the sentiment analysis related to IMDB Movie Reviews. The approach used is BERT. BERT is a deep learning approach. The data used in this study is the IMDB Movie Review of 50,000 data. The existing data is divided into three parts, namely training data, validation data, and testing data. The results obtained from the BERT model are 91.69% for training accuracy 0.187 for training loss, 91.85% for validation accuracy, 0.212 for validation loss, 91.78% for testing accuracy, and 0.207 for testing loss. It can be seen, that BERT is a very effective approach for sentiment analysis of IMDB Movie Review so that the research problem regarding the invalidity of one's opinion can be handled properly.
A Transfer Learning Approach Interaction in an Academic Consortium

This paper focuses on presenting a methodology for designing an assessment test that takes into account the degree of difficulty of the items and the overall test structure in an integrated academic environment. This integrated approach allows for the collection of assessment items at an academic consortium level, making them applicable to similar faculties within a group of institutions. The proposed model consists of two main components: the item collector and the test generator. The item collector gathers assessment items from various academic institutions within the consortium. These items are then utilized by the test generator, which automatically generates tests using a combination of evolutionary algorithms (genetic-based) and machine learning (ML) techniques. The genetic-based algorithm and ML methods play a key role in determining the composition and structure of the assessment tests.
Workshop Pembuatan Power Point Dengan Animasi di Smp Negeri 3 Kota Bengkulu

Lack of expertise is one of the causes of the lack of maximum use of technology for teachers. This training aims to make the teacher council able to make teaching activities more interesting for students. It was carried out not only for new teachers but also with senior teachers under the supervision of WA Student Affairs at SMP Negeri 3 Bengkulu City. The method used in this problem is by holding a workshop on making power points using animation. This workshop was held at SMP Negeri 3 Bengkulu City which was attended by approximately 20 teachers. A face-to-face workshop by the service team, explaining step by step and opening a question and answer session during the workshop. The stages carried out in this workshop are: preparation, implementation of activities, follow-up and consultation. Consultation for teachers can be done by coming to the secretariat of the service team (school prayer room). The results of the workshop activities are so that teachers can improve work efficiency by using power points and of course add new skills to teachers
Recognition of Handwritten Medical Prescription using CNN Bi-LSTM With Lexicon Search

Digital technologies have advanced more rapidly than any innovation in the history of the world. The field of Medicine, however, seems to be lagging behind in this regard. A large number of medical prescriptions around the world are still handwritten on paper. A handwriting detection model for medical prescriptions could digitalize written prescriptions for users in a matter of seconds from a simple picture. Moreover, the problems of ambiguity and confusion in deciphering a doctor's handwriting would be solved. Handwritten Text Recognition (HTR) is implemented fundamentally using a combination of Convoluted Neural Networks (CNN) and Recurrent Neural Networks (RNN) on the popular IAM dataset of written words. This paper presents multiple improvements to the current approach by introducing real-life written prescription data as the input, as well as tuning hyperparameters for more accurate results. Lexicon Search has been implemented as a decoding algorithm that checks output predictions against a vast database of drug names used in India. This makes the model work better specifically for medical prescription data in comparison to basic models.
DeFy: P2P Crypto Lending

The debt market is a substantial industry, estimated to be of significant value and projected to grow further in the near future. However, its size is marred by inefficiencies due to a lack of transparency and trust. To address these issues, DeFy, a blockchain-based system, offers loans to borrowers using their cryptocurrency assets as collateral. Leveraging smart contract technology and a decentralized network, DeFy creates a secure and transparent lending environment that operates globally without intermediaries. Empowering users with autonomy to select preferred lenders through a robust bidding system, DeFy enhances transparency in the borrowing process and empowers borrowers to make informed decisions. This user-driven lending approach provides an efficient and secure means for borrowers to access capital without liquidating their cryptocurrency holdings, while allowing lenders to earn interest on their capital through the lending process.
Specifying Non-Functional Requirements improves Software Vulnerability

Non-Functional Requirements (NFRs) focus on how the software system works and addresses the vital issues of quality for software system. NFRs are must to construct the security policies of the software system. Agile Software Methodology (ASD) methods tend to neglect NFRs as its main focus is mainly to deliver functional requirements. It has been considered during ASD that there is an increase in budget and effort on detailing NFRs at architecture level. But issues raised during security and penetration testing sometimes cost more if NFRs are ignored during development. This paper discusses how the need of security and penetration testing (for identifying Vulnerability issues) can be neutralized by specifying critical NFRs during analysis and specifying along with functional requirements while assigning stories to the developers. The approach is explained with the help of vulnerability issues identified during Penetration Test of real time health application.
Android application for food label recognition to ensure safe food consumption based on user allergen information leveraging OCR

The increase in the prevalence of food allergies is a concerning issue in today’s world. According to some estimates, 3 percent of Indians may already have food allergies, the majority under 40 years of age. Food allergies cause roughly 30,000 emergency treatments and 100 to 200 deaths per year in the nation. Additionally, there is a lack of awareness and understanding of food allergies in India, which can lead to delayed diagnosis and treatment. This brings in a need for technology-based solutions to help individuals who have food allergies to identify allergens in their food and to make conscious and informed decisions in their day-to-day consumption of food. This research paper presents an application that helps people with food allergies and intolerances identify potential allergens in packaged foods. The scope of the project involves the integration of multiple technologies wherein the app utilizes optical character recognition (OCR) from the Firebase API to extract the name of the food from a picture taken by the user, and then cross-references the name with a dataset of allergen information. The application provides a user-friendly interface that is easy to navigate and provides clear alerts, Our application is specifically designed to ensure that the user is thoroughly informed of the contents of their food. This brings a revolutionary aspect to dietary consumption by allowing users to have the freedom of consuming their food of choice without having to worry about their allergies and other side effects. In conclusion, this paper demonstrates the successful development and implementation of an Android application that can help in improving the lives of millions of people worldwide who suffer from food allergies and intolerances and can be further developed and customized to meet specific user needs.
FlashMe: Automatic Flashcard Generation

The task of studying and revising can be perceived as challenging due to the intricate nature of concepts presented in dense textbook material. Furthermore, it has been observed that students allocate as much as 50% of their study time towards study techniques that are deemed less effective [1]. Thus, encouraging students to employ efficient methods is important. Additionally, numerous studies have demonstrated that flashcards are an exceptionally efficient method for enhancing learning outcomes. [2]. To tackle this issue, our research indicates the utilization of various advanced Natural Language Processing models. These models would be combined to develop a multi-stage transformer architecture that enables the automated creation of flashcards from text containing technically rich literature.
Food Recommendation Systems Based On Content-based and Collaborative Filtering Techniques

On the internet, numerous options are available for a specific type of product. It is tough to manually go through every product in a particular type when a user is trying to choose the best one. Because of this, manual searching is not very efficient. The recommendation system is crucial in recommending the best product in that situation. A food recommendation system is developed in this research paper using K-nearest neighbor’s methods. The food data set is taken from Kaggle. We used Python programming language to implement the system. Our proposed recommendation system recommends food based on food name, food id, cuisine type, diet type like veg or non-veg in the case of content based filtering recommendation. For recommending the food with help of collaborative filtering we have used user id, food id and rating as an attributes.