Reviewerly/exHarmony huggingface dataset:
Augmented Reality (AR) media-aided literature learning tool for elementary school students

Learning devices need to be designed according to the characteristics of the material and be innovative. The results of the field study show that learning the subject of Indonesian fairy tale messages in elementary schools has not been fully implemented using such media, teaching materials, and assessment tools. Referring to this, this study aims to develop a literary learning device assisted by Augmented Reality (AR) media. This development research refers to the design-based research (DBR) development model from Amiel and Reeves (2008). The subjects of this development research trial were twenty students and a third-grade elementary school teacher. Data collection techniques used are semi-structured interviews, observation, and documentation. Data analysis is done by reducing, and presenting, until finding the interpretation of the data. The results of the study in the form of learning device products consisting of learning media, teaching materials, and assessment instruments showed that the Augmented Reality media-assisted literary learning device was validated as very feasible (85percent) with a high level of practicality category (93percent). The implication is that this product can be used in learning literature in elementary schools as an innovative alternative learning tool.Keywords: learning tools; teaching materials media; assessment; Augmented Reality (AR)Perangkat pembelajaran sastra berbantu media Augmented Reality (AR) untuk peserta didik SDPerangkat pembelajaran perlu dirancang sesuai karakteristik materi dan inovatif. Hasil studi lapangan menunjukkan pembelajaran dengan pokok bahasan pesan dongeng bahasa Indonesia di SD belum sepenuhnya dilaksanakan menggunakan media, bahan ajar, dan alat penilaian. Mengacu pada hal tersebut, penelitian ini bertujuan untuk mengembangkan perangkat pembelajaran sastra berbantu media Augmented Reality (AR). Penelitian pengembangan ini mengacu pada model pengembangan design-based research (DBR) dari Amiel dan Reeves (2008). Subjek uji coba penelitian pengembangan ini adalah dua puluh orang siswa dan seorang guru kelas tiga SD. Teknik pengumpulan data yang digunakan yaitu wawancara semi terstruktur, observasi dan dokumentasi. Analisis data dilakukan dengan cara mereduksi, menyajikan, sampai menemukan interpretasi data. Hasil penelitian berupa produk perangkat pembelajaran terdiri atas media pembelajaran, bahan ajar, dan instrumen penilaian yang menunjukkan bahwa perangkat pembelajaran sastra berbantu media Augmented Reality tersebut tervalidasi sangat layak (85persen) dengan kategori tingkat kepraktisan tinggi (93persen). Implikasinya, produk ini dapat digunakan pada pembelajaran sastra di SD sebagai perangkat pembelajaran alternatif yang inovatif.Kata kunci: perangkat pembelajaran; bahan ajar; media; penilaian; Augmented Reality (AR)
Evaluating a New Genetic Algorithm for Automated Machine Learning in Positive-Unlabelled Learning

Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of a set of labelled positive instances and a set of unlabelled instances, where the latter can be either positive or negative instances, but their label is unknown. There are many PU-learning algorithms, so an exhaustive search to find the best algorithm for a given dataset is computationally unfeasible. We recently proposed GA-Auto-PU, the first Genetic Algorithm-based Automated Machine Learning system for PU learning, and reported its preliminary results. This work presents an improved version of this system with an extended search space to include spy-based techniques, and provides an extensive evaluation of the new and previous versions of this system.
UTILIZING LEARNING PLATFORMS AND BEHAVIOR ANALYSIS OF STUDENTS IN COMPLETING ASSIGNMENTS

The era of the industrial revolution 4.0 is closely related to the use of technology. In its development, technology is integrated into various sectors, one of which is the education sector. The use of technology in the field of education is shown by the development of various digital products that facilitate students in the learning process. This research tries to identify the various platforms used by students in their learning process, especially in helping them with their coursework. In addition, this research also reveals the advantages and disadvantages of platforms that are considered by students in utilizing the platform. This research is a descriptive qualitative research involving students of Undergraduate Program of Islamic Education at Universitas Islam Indonesia from various years informants. The informant determination technique was carried out through a purposive sampling technique and data collected through interviews. Analysis of the data that has been collected is carried out through data reduction, data presentation, and drawing conclusions. The results of this study indicate that; 1) The platforms used by students can be grouped based on the type of task activities. In coordination and discussion activities, students generally use WhatsApp, Zoom, and Google Documents. In the activity of writing academic papes, many students use Google Scholar and Researchgate. Meanwhile, for designing presentation slides, the platforms that are widely used are Slidesgo, Genially, and Canva. Apart from that, students also use Adobe Premiere, Adobe Illustrator, Coreldraw, VN Editor, Figma, SPSS, and Google Classroom to work on assignments with special criteria. 2) The advantages of the various platforms are practical, easy to use, facilitate group coordination, flexibility, foster creativity, and interesting. While the disadvantages are that some platforms need to be paid, the features are limited, some tools are difficult to use, require high device specifications, and not all platforms can be accessed offline.
Discriminative Question Answering via Cascade Prompt Learning and Sentence Level Attention Mechanism

Question answering (QA) systems are designed to answer questions based on given information or with the help of external information. Recent advances in QA systems are overwhelmingly contributed by deep learning techniques, which have been employed in a wide range of fields such as finance, sports and biomedicine. For generative QA in open-domain QA, although deep learning can leverage massive data to learn meaningful feature representations and generate free text as answers, there are still problems to limit the length and content of answers. To alleviate this problem, we focus on the variant YNQA of generative QA and propose a model CasATT (cascade prompt learning framework with the sentence-level attention mechanism). In the CasATT, we excavate text semantic information from document level to sentence level and mine evidence accurately from large-scale documents by retrieval and ranking, and answer questions with ranked candidates by discriminative question answering. Our experiments on several datasets demonstrate the superior performance of the CasATT over state-of-the-art baselines, whose accuracy score can achieve 93.1% on IR&QA Competition dataset and 90.5% on BoolQ dataset.
Effect Of Educational Media Picture Card On Knowledge Of Fire Disaster Response In School-Age Children

Background: Every year cases of fire disasters have increased. Fire disasters can occur anywhere resulting in victims and loss of property. Fire disasters can occur in adults, the elderly and also children. Children are included in communities that are often at risk of the impact of fire disasters, because their skills and knowledge are still considered to be lacking which is the reason children become victims. Prevention efforts that can be done by providing education as early as possible to children about fire emergency response, one of the media that can be used for school-age children is media picture cards in the form of pictures and explanations that are easy for school-age children to understand and remember. Research Objectives: To determine the effect of educational media picture cards on knowledge of fire emergency response in school-age children. Research Method: Type of quantitative research with a Pre-Experimental approach using the One Group Pretest-Posttest Design. The sample involved 74 respondents by selecting the sample with the Total Sampling technique. The research instrument uses picture cards and knowledge questionnaire sheets about fire emergency response. Data analysis using the Wilcoxon statistical test. Results: The median value before being given education was 14.00 and after being given education it was 19.00 so that there was a significant difference between before and after education on picture card media on fire disaster emergency response knowledge obtained value (P Value = 0.001). Conclusion: There is an educational effect of picture card media on knowledge of fire emergency response in school-age children.
Application Of A Web-Based Information System in The Institution Of Research, Community Service, and Innovation (Lp2mi) IKesT Muhammadiyah Palembang

Background: Technology is something that cannot be separated in this modern era. With the emergence of technology, it can facilitate the operational activities of a higher education institution in processing data quickly and precisely. The research, community service and innovation institute (LP2MI) at IKesT Muhammadiyah Palembang in making reports and managing data currently has not implemented an application, so that the data collection process is not effective and efficient. Purpose: The purpose of this research is to be able to help LP2MI IKesT Muhammadiyah Palembang in processing and reporting of data quickly, precisely and accurately. Research Methods: This study uses data collection techniques using qualitative methods. Namely by interviews and observations as well as with the waterfall design. Results: The results of the research are making "Web-Based Information System Applications at the Institute for Research, Community Service and Innovation (LP2MI) IKesT Muhammadiyah Palembang". Conclusion: The conclusion of this research is that the Institute for Research, Community Service and Innovation (LP2MI) IKesT Muhammadiyah Palembang needs a system that can meet the needs of preventing or reducing difficulties in terms of administration, data loss, and data processing that takes a long time.
Application of Android Game-Based Learning to Overcome Smoking Emergencies in Adolescents

Background: Teenagers tend to have a desire to know big. Studies show that students are more Possible To smoke than adults. Especially based on the results research latest says that smoking among teenagers every year the more increase. In general, they confess Already start smoking between 9 to 12 years old, Teenagers tend to have a desire to know big. Studies show that students are more Possible To smoke than adults, factors that have a very large impact on the emergence of various diseases. A smoker has a 2 to 4 times risk of developing coronary heart disease and a higher risk of developing lung cancer. Especially based on the results research latest says that smoking among teenagers every year the more increase. Purpose: To apply learning android games free as a try to overcome emergency smoking in adolescents. Method: This activity is Community Service that applies an Android game-based learning method to overcome smoking emergencies in Indonesian teenagers. The target of community service is 30 grade 11 vocational students. To measure motivation to stop smoking, the writer uses a questionnaire Richmond-Test. Results: Based on the results obtained mean pre-test value is 5.27 meanwhile mark the post-test mean is 8.27. Conclusion: The learning method is based on Android games that can increase the Spirit of teenagers To stop smoking.
Influence of resampling techniques on Bayesian network performance in predicting increased algal activity

Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US_Kmeans), synthetic minority over-sampling technique (SMOTE), and ‘SMOTE and cluster-based under-sampling technique’ (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl-a concentrations and suggest future data collection and model development priorities.
Improved Euclidean Distance in the K Nearest Neighbors Method

The KNN algorithm is one of the most famous algorithms in data mining. It consists in calculating the distance between a query and all the data in the reference set. In this paper, we present an approach to standardize variables that avoids making assumptions about the presence of outliers or the number of classes. Our method involves computing the ranks of values within the dataset for each variable and using these ranks to standardize the variables. We then calculate a dissimilarity index between the standardized data, called the Rank-Based Dissimilarity Index (RBDI), which we use instead of Euclidean distance to find the K nearest neighbors. Finally, we combine the Euclidean distance and the RBDI index taking into account the advantage of both dissimilarity indices. In essence, the Euclidean distance considers the Euclidean geometry of the data space while RBDI is not constrained by distance or geometry in data space. We evaluate our approach using multidimensional open datasets.
IMPLEMENTATION OF THE YOLOV4 ALGORITHM IN MOBILE COMPUTING DEVICES FOR TOMATO MURABILITY SELECTION

Tomato fruit yields have a variety, not only ripe fruit but also the condition of tomatoes that are still raw or half-ripe. By naked eye, tomato ripeness can be seen by the difference in three colors, namely red, yellow and green. Each color difference describes the level of maturity. However, human decisions in distinguishing the color of ripe tomatoes can vary and are more subjective. With the help of an image processing system, the selection of tomato ripeness can be faster and more objective. This study uses the YOLOv4 algorithm which is implemented into a mobile computing device, namely the Raspberry Pi 3, to select the tomato image captured by a camera. The results of the YOLOv4 image process will be output to the actuator in the form of a conveyor equipped with a wiper that will sort tomatoes automatically. The result of this research is a prototype with the YOLOv4 algorithm that has been trained with the tomato dataset.&#x0D;
ETHEREUM CRYPTOCURRENCY PRICE MOVEMENT PREDICTION SYSTEM USING TRIPLE EXPONENTIAL SMOOTHING METHOD

An innovation that was sparked is an alternative currency to replace conventional currency, namely digital currency that is secured using a cryptographic method called Cryptocurrency. However, cryptocurrency prices cannot be controlled, causing massive fluctuations. Cryptocurrency price changes are very stable due to several factors such as speculation from users and the nature of the follow-up from new to one of the causes. The cause of this bandwagon is due to extreme price increases in a short time, even within a day the price of Crypto can experience a difference of millions of rupiah for each increase and decrease. To answer this question, we need a software that can predict the Rupiah exchange rate. By using the Triple Exponential Smoothing prediction method. In implementing Triple Exponential Smoothing, historical data is needed which will later be processed to produce prediction results, for that we need parameters that can be used as historical data counters, in this study used parameters 0.40 as parameters Xt and S't and 0.60 as Beta parameters. In the process, starting from calculating the first to third smoothing, ensuring Constants, Slopes, and Parabolics, to completing the process by producing Forecasting, the results of the measurement of the margin error level with a margin of error of 0.94638% using data for the final 2021 period, which is between November - December on the Triple Exponential implementation simulation and on the implementation into the system that has been developed is 3.7249% using data from May 2022 to June 2022.
RLMD: A Dataset for Road Marking Segmentation

Road marking recognition is an important task for advanced driver assistance systems and autonomous vehicle. In the existing research, most techniques focus on detection mainly due to the lack of segmentation datasets. The segmentation of road markings can be used to understand the traffic regulations, as well as for vehicle localization. This paper introduces a road marking segmentation dataset, RLMD. It consists of 700 images annotated with 25 categories. The effectiveness of our dataset is evaluated using state-of-the-art segmentation techniques. The performance comparison is performed with well-known public datasets, Bdd100k and CeyMo. The dataset and code are made available publicly at https://github.com/stu9113611/RLMD.
VR First Aid: Step by Step Development Process*

Virtual reality (VR) is a simulated experience in a three-dimensional (3D) computer-simulated world. The experience could be similar to real life or completely different. Despite the fact that VR has been cited as a fantastic choice for teaching and training, the existing publications do not specifically demonstrate how to construct training application with VR technology. The purpose of this paper is to present our personal experience in designing and implementing First Aid application on Oculus Quest.
Automated Slice Selection and Labeling of Hippocampus Regions for Alzheimer’s Disease

Alzheimer’s disease image analysis is a challenging task due to the limited availability of labeled data. Labeling data requires precision and patience and typically done manually by medical experts. However, human labeling is expensive, time-consuming, and prone to human errors. To address this issue, we propose an approach that automates the selection and labeling process using a deep learning model. Specifically, we employ YOLOv7 to reduce time and errors. In this work, we implement our proposed idea on 2D images from the Alzheimer's dataset. This work focuses on labeling the hippocampus region from the coronal view of brain MRI images. The results of our experiments show that our approach yields result that are quite close to the ground truth labels produced by experts. The mean average precision (mAP) compare with ground truth is about 2% below, while the time consumption is reduced by 80%.
Research on Image Defogging Enhancement Technology Based on Retinex Algorithm

Under bad weather conditions, the quality of the image we get will be seriously affected. Therefore, we must restore and improve the image quality and enhance the image to meet the needs of human observation or subsequent processing. In recent years, an image enhancement technology based on Retinex algorithm has attracted wide attention because it is not affected by illumination and can better restore the original appearance of objects. In order to solve the problem that the details of the existing image enhancement algorithms are blurred when they are enhanced in foggy days, this paper will study the image defogging enhancement technology based on Retinex algorithm. The experimental results show that the clear processing method of foggy images in this paper has a good effect, but it needs a certain sample training time, and the higher the accuracy, the longer the time it takes, so it has certain algorithm complexity. From the subjective observation, this algorithm has better detail expression ability, eliminates the “halo artifact and at the same time, inhibits the over-sharpening phenomenon on the premise of ensuring hierarchy. Therefore, Retinex algorithm makes the defogging effect better under the condition of low complexity.
Artificial Intelligence Image Detection System Based on Internet of Things

With the development of image recognition technology and computer technology, artificial intelligence is more and more applied in people’s production and life. In addition, the Internet of Things technology can be seen everywhere in our daily life. The purpose of this paper is to design an image detection system based on the Internet of Things technology, which uses CMOS image sensor to obtain real-time images, obtains candidate flame areas through flame judgment of image pixels, and uses flame detection method of in-depth study of neural network for flame detection. It communicates through the Internet of Things module and sends fire alarms and alarm messages to the server platform based on the flame detection results. The results show that the system designed in this paper meets the design requirements.
Algorithm

An agent-based approach to building a talking robot requires the reconstruction of (i) nonlanguage recognition, (ii) nonlanguage action, (iii) language interpretation, and (iv) language production. In language production, the speaker realizes content as semantically connected word forms in a sequence of external surfaces (raw data). In interpretation, the hearer interprets the incoming surfaces by lexical lookup and by reconstructing the speaker’s semantic relations.
Digital Forensic Analysis of Vehicular Video Sensors: Dashcams as a Case

Dashcams are considered video sensors, and the number of dashcams installed in vehicles is increasing. Native dashcam video players can be used to view evidence during investigations, but these players are not accepted in court and cannot be used to extract metadata. Digital forensic tools, such as FTK, Autopsy and Encase, are specifically designed for functions and scripts and do not perform well in extracting metadata. Therefore, this paper proposes a dashcam forensics framework for extracting evidential text including time, date, speed, GPS coordinates and speed units using accurate optical character recognition methods. The framework also transcribes evidential speech related to lane departure and collision warning for enabling automatic analysis. The proposed framework associates the spatial and temporal evidential data with a map, enabling investigators to review the evidence along the vehicle’s trip. The framework was evaluated using real-life videos, and different optical character recognition (OCR) methods and speech-to-text conversion methods were tested. This paper identifies that Tesseract is the most accurate OCR method that can be used to extract text from dashcam videos. Also, the Google speech-to-text API is the most accurate, while Mozilla’s DeepSpeech is more acceptable because it works offline. The framework was compared with other digital forensic tools, such as Belkasoft, and the framework was found to be more effective as it allows automatic analysis of dashcam evidence and generates digital forensic reports associated with a map displaying the evidence along the trip.