Reviewerly/exHarmony huggingface dataset:
DETECTION MODEL FOR FAKE NEWS ON COVID-19 IN INDONESIA

Today, fake information has become a significant problem, exacerbated by the acceleration of access to information. The spread of fake information has a dangerous impact, especially regarding global health issues, for example COVID-19. People can access various resources to obtain information, including online sites and social media. One of the methods to control the spread of false information is detecting hoaxes. Many methods have been developed to identify hoaxes; most previous studies have focused on developing hoax detection methods using data from a single source in English. The present study is carried out to detect fake news in Indonesian language using multiple data sources, including traditional and social media in the context of COVID-19. The study uses Long Short-Term Memory (LSTM) and the Robustly Optimised Bidirectional Encoder Representations from Transformers Pre-Training Approach (RoBERTa). The LSTM approach is used to develop four different architectures that varied based on: (1) the use of text-only versus the use of both title and text; (2) the number of LSTM and dense layers; and (3) the activation function. The LSTM model with text-only data, a single LSTM layer and two dense layers, outperformed other LSTM architectures, achieving the highest accuracy of 92.17%. The LSTM models require a considerably short training time of 23–27 minutes for 3,847 articles and has a detection time of 3.8–4.1 ms per article. The RoBERTa classifiers outperformed all LSTM models with an accuracy of over 97% and a significantly better training time, with a margin of more than 50% compared to LSTM classifiers, although it had a slightly longer test time. Both LSTM and RoBERTa models outperformed the Naïve Bayes and SVM benchmark methods in terms of accuracy, precision, and recall. Therefore, this study shows that both LSTM and RoBERTa methods are reliable and can be reasonably implemented for real-time fake news detection.
Development of Interactive Web-Based Learning Media with the Discovery Learning Model to Increase Student Learning Motivation in Object Oriented Programming XI Vocational High Schools

Students have difficulty understanding object-oriented programming material. Because the learning process carried out still uses the lecture method and the learning media used in the form of PowerPoint and even textbooks, resulting in a lack of understanding and a lack of student learning motivation. Other problems arise such as drowsiness due to boring learning, and difficulty understanding due to lack of interaction between students and teachers. Today's technology that can support the learning process in solving these problems is an interactive web. This study uses the Hannafin and Peck development model which goes through 3 phases, namely (1) needs analysis, (2) design, and (3) development and implementation. Data collection techniques were carried out through observation and questionnaires. The learning media developed is in the form of an interactive web application with a discovery learning model that contains learning and evaluation videos. The results of the feasibility analysis of learning media obtained the results of media expert validation of 96.3%, material expert validation of 93%, and group trials of 85.4%. The results of the analysis of all feasibility data obtained an average of 91.6% with the qualification "very valid" and declared feasible to be used in assisting the learning process of students. While the results of the analysis of increasing learning motivation obtained the results of group trials of 0.37. Based on these results, it is stated that learning media can increase students' learning motivation in the learning process with "moderate" qualifications.
Implementasi Sistem Informasi Dokumen Pencacah Unit Distribusi Badan Pusat Statistik Sulawesi Selatan

Deputy for distribution statistics and services for the Central Statistics Agency of South Sulawesi (BPS Sulawesi Selatan), who is responsible for scheduling and organizing the Distribution Pattern Survey has difficulties in organizing enumeration documents, both in organizing District BPS documents, and in managing the history of the result surveys that have been conducted. The existing survey management software was not specifically intended for managing enumerator document files. In fact, the results of the evaluation of the enumerator documents determine the further policy regarding the implementation or postponement of the police survey in the South Sulawesi district. This study developed a document enumerator document information system software to facilitate South Sulawesi BPS (BPS Sulsel) in managing enumerator documents. The existence of an enumerator document information system is a practical solution that facilitates data organization while speeding up the document evaluation process. The software is developed using the waterfall development model. Testing software implementation uses the Mean Opinion Score or MOS to assess the success of the software. The test results obtained a 97.5% percentage of the MOS score showing the success of using the software in supporting the process of evaluating the enumerator documents.
Rancang Bangun Sistem Informasi Sekolah di SMK Negeri 1 Jetis Mojokerto

The school information system, especially the Industrial Work Practices activity attendance system at SMKN 1 Jetis, is still done manually, that is, it still uses books and you have to fill in an activity journal every day in that book. After the internship ends, students are asked to collect the books, which the teacher will use to collect data on whether the student skipped class, permission and so on. Meanwhile, teachers do not have other data that can be used for data collection if the book is damaged or lost. This problem occurs because there is no system or application that is able to provide convenience for the process of collecting data and checking accurately. This information system created uses web or mobile technology which is tailored to the needs of system users and can make it easier for teachers to carry out the process of checking student attendance at internship activities through this attendance application, and we also provide a comparison of this application with other applications in order to determine the quality application quality and to determine the development of application features aimed at making it easier for users to use the application.
Analysis of Deep Learning Models for Text Summarization of User Manuals

User manuals have an inconsistent structure with the data presented in multiple formats such as tables, images, etc. It makes processing them a challenging task as we need to account for these inconsistencies. In this work, we propose a pipeline for processing user manuals and analyzing abstractive model PEGASUS and extractive models XLNet, BERT, and GPT-2 for summarization of user manuals. To evaluate the models, we have generated extractive and abstractive datasets and used metrics such as hit ratio, overlap, and rouge score to compare the performance of the models. We observed that an abstractive model gives more human-like summaries compared to the extractive models which although have higher rouge scores, suffer in readability. The system utilizes automatic text summarization along with multiple methods to process user manuals and extract required information in a summarized manner.
Data Collection and Pre-processing for Machine Learning-Based Student Dropout Prediction

In this era of big data, a large amount of data is generated from various educational environments that will help integrate machine learning techniques to find valuable insights from the data. Student dropout prediction is one of the application areas in education where machine learning algorithms can be applied to predict the students who are likely to dropout of their studies by extracting valuable patterns and key parameters from educational data. However, the performance of the algorithms is determined by the quality of the data we feed into them. The educational data are usually noisy that reduces the performance of the machine learning algorithms and requires accurate data pre-processing. This work presents an overview of data collection and pre-processing steps that consist of data augmentation, data labeling, enhancing data/model, data cleaning, data transformation, feature extraction, feature transformation, feature selection, etc. for analysis of statistical evidence from the source data for student dropout prediction. We also present some of the data pre-processing techniques which are suitable for machine-learning-based student dropout prediction in this study.
Comparison of FDA-Approved vs. Mayo Clinic’s Ki-67 Immunostaining Protocols for Breast Cancer Specimens: A Quantitative Analysis Using Artificial Intelligence (AI) Based Image Analysis Algorithms

Abstract Introduction/Objective Ki-67, a nuclear protein present during the active phases of the cell cycle, is an important prognostic and predictive marker for many tumor types, including breast cancer. This study compared the results of breast cancer specimens immunohistochemically (IHC) stained with the FDA-approved vs. Mayo Clinic’s (MC) validated Ki-67 protocol using AI-based image analysis algorithms for quantitative examination at an individual cell level. Methods/Case Report Sixty-two breast cancer specimens were selected with a rough equal representation of Ki-67 percentage in low (&amp;lt;10%), intermediate (10-20%) and high (&amp;gt;20%) categories. Two adjacent sections of each specimen were stained with the FDA and MC protocols, respectively, and scanned to obtain whole slide digital images. Two AI algorithms, one with tumor cell identification and the other without, were used to quantify positive and negative cells and calculate Ki-67 percentages. Results (if a Case Study enter NA) Both algorithms show high concordance between the two staining protocols, with r-squared scores of 0.989 and 0.992, respectively, analyzing with or without tumor identification. AI identified tumor better on MC stained slides vs. FDA stained slides. The results demonstrate a high-performance concordance between the MC and the FDA IHC staining protocols. Conclusion The data also illustrates the accuracy of using AI image analysis algorithms to compare different IHC protocols at an individual cell level. The AI tumor identification difference in slides stained with two IHC protocols reflects the importance of training an algorithm with a specific staining protocol to achieve optimal tumor recognition.
A small target detection method for UAV aerial images based on improved YOLOv5

There are many instances of small and medium-sized targets in drone aerial images, and existing detection algorithms are prone to missed and false detections during the detection process. To address this issue, A small target detection method based on improved YOLOv5 in UAV aerial images are proposed. Firstly, the bilinear interpolation upsampling method is used in the feature fusion section to reduce the loss of feature information during the upsampling process. Then, a small target detection layer of size 160 is added to locate and recognize small targets using shallow feature information, reducing the missed detection rate. Finally, three CBAM attention mechanism modules were added to improve the accuracy of the algorithm. On the VisDrone2019 dataset, the improved algorithm improved mAP by 2.1% compared to Algorithm YOLOv5, effectively completing small object detection tasks.
[Roundtable] Analogue Co-Design: Opportunities, Challenges and Other Nuances

Co-design is linked to a range of advantages, encompassing enhancements in idea generation, service or product development processes, decision-making, cooperation, creativity, as well as long-term satisfaction and loyalty among clients and users (Steen, 2013; Steen et al., 2011). Despite its widespread use as a strategic approach, co-design has received less scholarly attention, and critical discussions regarding its underlying concepts are infrequent. The occurrence of this particular circumstance can perhaps be attributed to the widespread practice of categorizing projects as co-design, which may lead to a dilution or confusion of conceptual understanding (Steen, 2013). In the field of games, especially at an academic level, the idea of co-design has been used mainly to describe processes of horizontalizing research design, aligned with participatory paradigms (Brown, 2022; Hall, 1975). Specifically, where the player no longer plays a completely passive role, but co-creates the gaming world together with the designers (De Jans et al., 2017; Loos et al., 2019; Pedersen &amp; Buur, 2000). Or when the games (applied as a serious game) deliver a tool for ideation and the co-creation of projects and solutions to a specific problem (Sousa, 2021). Here, we have invited two of the most prominent duos of contemporary analog game designers – Isra/Shei and Inka Brand/Markus Brand – to reflect on how they complement and oppose each other in this process. Relational and gender characteristics, how they impact the creative process, and the players’ vision of the games created, among many other issues, will be reflected here.
SISTEM INFORMASI PELAYANAN PENDAFTARAN PASIEN RAWAT INAP DI PUSKESMAS MENGGUNAKAN PEMOGRAMAN PHP

This research discusses the development of an Inpatient Patient Registration Service Information System at Community Health Centers (Puskesmas) using PHP programming, as well as implementing DFD (Data Flow Diagram) and ERD (Entity Relationship Diagram) models. The system is designed to enhance efficiency and accuracy in the inpatient patient registration process, covering patient information management, bed availability scheduling, and coordination among medical staff. The DFD model diagram is utilized to illustrate the data flow within the system, while the ERD is employed to detail the relationships between entities in the database. The implementation of this system employs PHP programming to ensure code reliability and readability. The research results indicate that the system can expedite the patient registration process, improve information management, and optimize the utilization of inpatient beds. This study is expected to make a positive contribution to improving healthcare services at Community Health Centers and provide a foundation for the development of similar systems in a broader healthcare service environment. Consequently, the research findings are anticipated to offer significant benefits in enhancing the efficiency and effectiveness of inpatient patient registration services.
Study on Influencing Factors of Regional Economy Based on Multilevel Model

Based on the relevant data of economic development of 31 provinces in China from 2016 to 2021, this paper selects the level of economic development as the response variable. The explanatory variables are selected from the aspects of location, human capital, industrial structure, foreign trade, system, science and technology and employment level. Aiming at the data with hierarchical structure, a multilevel model is proposed for analysis. It is concluded that human capital, industrial structure, foreign trade and system have a significant impact on the level of economic development. The impact of technology and employment level on the level of economic development is not significant. At the same time, this paper also makes a comparative analysis of the linear regression model, random intercept model and random intercept-slope growth model, which shows the advantages of random intercept-slope growth model.
An Integrated Pipeline with Internal Image Processing for Efficient Image to Text to Speech Conversion

Optical Character Recognition Systems (OCR) is a tool that helps computers read text from pictures of papers. It makes it easier for machines to understand what the words say without needing a person to read it out loud. It allows for easy digitizing of historical documents, archival material, and medical records thereby saving on their retrieval times. However, the accuracy of OCR systems heavily relies on the quality of the input images. To negate the contribution of the quality of input images to the accuracy of OCR systems, in this paper, we propose an integrated image pre-processing pipeline integrated with the OCR systems that enhances the quality of input images for efficient image to text conversion. This method results in an easily understandable text output with a lower Character Error Rate (CER) in comparison to the current methods. In addition, we explore a technique for converting text from a document or image into machine-readable form and then converting it to audio output using gTTS, a Python library that interfaces with Google Translate's text-to-speech API. We assess the effectiveness of this approach and illustrate that it substantially enhances OCR precision when compared to other existing methods. This paper presents a clear overview of the growth phases and significant obstacles, accompanied by compelling comparisons of results achieved through various methods.
Design and Implementation of College English Online Q&amp;A System

Online Q&A system is a key component of network teaching mode and an important way to supplement the lack of communication between teachers and students under network teaching mode. The application of college English online Q&A system can help teachers check the teaching effect and get teaching feed
Research on the Construction of Digital Textbooks for Vocational Education

To achieve digital transformation of vocational education. There is an urgent need for corresponding digital textbooks to meet the teaching needs of professional courses. This article explores and studies the construction of digital textbooks in vocational colleges, and implements a comparative teac
The Entity Relationship Extraction Method Using Improved RoBERTa and Multi-Task Learning

There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them. However, the volume of internet data is beyond the processing capabilities of the current internet infrastructure. Therefore, engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia. The goal of this paper is to explore the entity relationship based on deep learning, introduce semantic knowledge by using the prepared language model, develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach (RoBERTa) and multi-task learning, and combine the intelligent characters in the field of linguistic, called Robustly Optimized BERT Approach + Multi-Task Learning (RoBERTa + MTL). To improve the effectiveness of model interaction, multi-task teaching is used to implement the observation information of auxiliary tasks. Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction, and a further it has achieved 86.35% of accuracy after being combined with multi-task learning.
A case study on standard setting for English proficiency test for university students

This study applied a standard setting method for determining cutoff scores for university students’ English proficiency tests, which are criterion-referenced tests, conducted at universities and explored the application of this approach for enhancing the effectiveness of general English education at different levels. To differentiate students’ proficiency levels, the study used the cutoff scores determined by the Angoff method based on the English proficiency test items used for sub-divisions of general English courses at University A. University A conducts its sub-division classes in three proficiency levels, and, accordingly, this study employed level determination based on the three performance level descriptions(PLD). The results of the Angoff standard setting indicated that, as rounds of discussion progressed, the panel members tended to make decisions in favor of raising the cutoff scores, and the standard deviation of the cutoff scores estimated by the panel members tended to decrease demonstrating a narrowing of opinions among the panel members. However, it was confirmed that the estimation tendencies of specific panel members could influence the overall standard deviation, and, therefore, guidelines are needed to control such bias in future standard settings. The application of the cutoff scores determined through standard setting to assess students' English proficiency levels showed a difference in the distribution of level-based classes from what is currently being conducted at University A. This suggests that, for universities using fixed cutoff scores to divide classes into proficiency levels, the standard setting is necessary to ensure the appropriate division of level-based classes.
Strategi Guru Dalam Mengatasi Kesulitan Membaca Pada Siswa Kelas IV SDS Tarbiyatul Islam Sambas

The aim of the research is to analyze teacher strategies in teaching students with learning difficulties to introduce reading in learning in class IV at SDS Tarbiyatul Islam Sambas, teacher strategies for teaching students with learning difficulties to write perfect sentences in class IV at SDS Tarbiyatul Islam Sambas; teacher's strategy for teaching spelling letters to students with learning difficulties in class IV students at SDS Tarbiyatul Islam Sambas. This research uses a qualitative approach and a phenomenological type of research. Data collection techniques use interviews, observation and documentation. The data analysis techniques used are data collection, data reduction, data presentation, conclusions. The research results show that the teacher's strategy in teaching students with learning difficulties to introduce reading in learning in class IV SDS Tarbiyatul Islam Sambas for the 2022-2023 academic year is by using the drill method. The teacher's strategy for teaching students who have difficulty learning to write perfect sentences in class IV at SDS Tarbiyatul Islam Sambas for the 2022-2023 academic year is that students must have a special writing book to practice writing neatly. Apart from that, the teacher also provides evaluations to students and provides notes at home to see student activity. in the classroom. The teacher's strategy for teaching spelling letters to students with learning difficulties in class IV students at SDS Tarbiyatul Islam Sambas for the 2022-2023 academic year is that first the teacher uses the drill method during the learning process, the second uses learning media such as class IV textbooks and notebooks and the third gives assignments to students
A Study of Word Embedding Models for Machine Translation of North Eastern Languages

Neural Machine Translation (NMT) has experienced significant growth in recent years and is now a well-established field. Despite being the most popular machine translation solution, NMT’s performance on low-resource language pairs is inferior to that of high-resource language pairs because of the lack of large parallel corpora for these languages.This paper explores how pre-trained word embedding models, specifically FastText and Word2Vec, can enhance the translation performance of NMT (neural machine translation) systems for low resource English-Assamese, English-Manipuri, and English-Nepali language pairs. We have compared the performance of NMT systems trained with the use of these pre-trained word embedding models and evaluated the results on test sets of English-Assamese, English-Manipuri, and English-Nepali translations. The resukts indicate that the use of FastText significantly improves the translation performance of NMT systems for both language pairs, with an average increase in BLEU score of 5.6, 7.3, and 4.4 points for English-Assamese, English-Manipuri, and English-Nepali, respectively. We have also discussed the potential challenges and limitations of using pre-trained word embedding models for NMT in low-resource languages and suggested directions for future work in this area.