Reviewerly/exHarmony huggingface dataset:
Human Pose Prediction Using Interpretable Graph Convolutional Network for Smart Home

Recently, Human Pose Prediction (HPP) using image frames captured by cameras has been widely used in the smart home sector. Combining deep learning vision processing with HPP and using the Graph Convolutional Network (GCN) to extract temporal and spatial features of human actions has achieved satisfactory accuracy. However, there is still a lack of sufficient interpretability to translate theoretical findings into human-centric consumer applications. In this paper, a novel Interpretable GCN-based HPP (IGCN-HPP) model is proposed to address the above problem. Specifically, a multi-layer spatio-temporal convolution is first constructed to capture the depth features in human action data for prediction. Secondly, a GCN Explainer is proposed to assist in model training. When pre-processed graphics frames are fed into the GCN model, it generates various subgraphs. What’s more, using Shapley values from game theory and specific graph rules to assess each subgraph’s contribution to HPP, the neighborhood relationships between subgraphs and among nodes are estimated with the contribution optimization algorithm (COA). The reasonable interpretation of the predicted human pose is obtained by extracting and aggregating the subgraphs that contributed most to the proper prediction category. Qualitative and quantitative experimental results show that the proposed IGCN-HPP outperforms the baseline model in terms of predictive performance.
Performance Analysis of Solar Tracking Systems by Five-Position Angles with a Single Axis and Dual Axis

This research presents an analysis of the five-position angle in both single-axis (one-axis tracking) and dual-axis (two-axis tracking) solar tracking systems. The study compares these tracking systems, where four solar panels move simultaneously, with a fixed solar panel system. The findings revealed that the five-position angle Sun-tracking technique resulted in lower energy consumption by the tracking mechanism than in the case of an all-time solar tracking system. The key component of the implemented system is a light-dependent resistor (LDR) sensor for controlling the motion of the motor for five positions on the vertical axis and horizontal axis, processed by a microcontroller to ensure the necessary solar tracking always moves in a perpendicular direction. According to the results, the voltage, current, and power increased with both one-axis and two-axis tracking compared to those of the fixed solar panel system under the same conditions. However, when evaluating the total energy with numerical integration methods, one-axis and two-axis provided 183.12 Wh and 199.79 Wh, respectively. Consequently, the energy production of the one-axis tracking system and the one-axis tracking system was found to be 16.71% and 24.97%, respectively, when compared to the fixed-axis system. Thus, the five-position angles of the sun-tracking technique resulted in lower energy consumption than is the case of an all-time solar tracking system.
Sistem Pakar Diagnosis Penyakit Kulit Balita Menggunakan Metode Certainty Factor dan Forward Chaining

When someone has very weak immunity level, they can develop skin problems. Children, especially babies and toddlers, are prone to skin problems because their immune systems are not fully developed. Developing an application that can help users to be able to diagnose skin diseases in toddlers, especially skin diseases Diaper Rash, Prickly Heat, Chicken Pox, Measles, Eczema, and Impetigo is needed according to needs. This study uses the Forward Chaining inference engine and the Certainty Factor certainty method. The goal is to make an application that can help the experts to get information and solutions about skin diseases in toddlers, as well as to provide accurate presentations about the disease. In the development of this system, the resulted level of accuracy is in accordance with the diagnosis expert, which is equal to 83.33% of 30 medical record data
Analisis Jenis Pelatihan Pernapasan : Teknik Pernapasan dan Olah Vokal : Pelatihan Voice Over

In doing voice over, several exercises are carried out to support voice over abilities, namely breathing training and vocal training. Each of these trainings has a different type and is important for both breathing and speaking skills. For this reason, this study wanted to find out the analysis of the types of breathing training in the form of breathing techniques as well as vocal training in voice over. The method used in this research is descriptive qualitative with data library research analysis techniques which are at the stages of data reduction, data presentation, and drawing conclusions. Based on the research findings, it was found that the types of breathing techniques or training included several things, namely diaphragmatic breathing exercises, pursed lips breathing, lions breath, equal breathing and deep breathing. Meanwhile, vocal training for voice over includes intonation techniques, breathing techniques, carrying techniques, and articulation techniques. Various types and techniques in breathing and vocal exercises can be carried out as routine exercises to get the right functions and benefits for voice over talent in carrying out their duties and profession.
An effective hybrid automated Chinese scoring system for medical education

Examination is an effective method for evaluating educational achievements but manually grading is time-consuming and error-prone. According to the characteristics of Chinese, this paper designs an effective and reliable hybrid automatic grading system for medical term interpretation questions, which integrates keyword similarity, semantic similarity and sentence length similarity. This model therefore provides a feasible framework in the sense that automated scoring system can be used reliably in real practice for subjective questions in medical education. In practice, the parameters can be pre-trained for different courses and different levels of students, so as to achieve the best performance of the model. From 15 professional noun explanation questions of 300 medical students in the course of medical psychology, statistics show that the difference between the manual and automatic assessment of all students' answers for each question is less than or equal to 2. Moreover, a comparative study of the automated scoring system is conducted to assess the overall testing performance. The experimental verification shows that the model posed in this paper is effective and robust. To enhance user convenience, a GUI software has been created using the Python “Tkinter” library. This software can be directly applied to different scenarios to demonstrate the expansive application potential of the system.
Pengaruh kondisi sosial ekonomi orang tua terhadap motivasi belajar peserta didik

the research objective was to determine the effect of parents' socio-economic conditions on the learning motivation of class X students at SMA Negeri 96 Jakarta. The method used is a survey method, the approach used is quantitative, by collecting questionnaire data on a population of 208 students, a sample of 67 students. Through the analysis test carried out, the regression equation is Y = 46.705 + 0.516 X with the results of calculating the correlation r of 0.531, which means that there is a moderate relationship between the socio-economic conditions of parents and students' learning motivation. Parents' socio-economic conditions contributed 28.23% to learning motivation and the remaining 71.77% was influenced by other factors. Hypothesis testing obtained 𝑡ℎ𝑖𝑡𝑢𝑛𝑔 = 5.969 &gt; 𝑡𝑡𝑎𝑏𝑒𝑙 = 1.669, meaning that there is influence of parents' socioeconomic conditions on students' learning motivation. Based on these results it can be concluded that there is an influence of parents' socioeconomic conditions on the learning motivation of class X students at SMA Negeri 96 Jakarta.
E-Commerce Product Image-Based Recommendation System Kalcare.Com Using Deep Learning

Recommendation systems have now become an important part of a digital service, one example is e-commerce. The facts show that the COVID-19 pandemic has had a significant impact on customers by making them spend more time surfing online to get daily necessities products by shopping on e-commerce sites. With the rapid development of deep learning technology today, of course, it can be used to help in terms of the process of producing a product image-based recommendation system that has a fairly high level of similarity. This study will discuss how to produce an image-based product recommendation system architecture by comparing the results of the application of algorithms 8 pre-trained models that are available and have also been widely used in various studies and the information technology industry. The dataset used is product images sourced from the kalcare.com website. After testing the pre-trained model, then an application prototype was made to be tested then in the final stage of this research a poll was conducted to determine the response and opinion of users to the protopine recommendation system made for e-commerce kalcare.com using deep learning.
Sistem Pakar Diagnosis Awal Penyakit Anemia Menggunakan Metode Naïve Bayes dan Certainty Factor

In Indonesia, anemia is a common blood disorder. The absence of red blood cells in the body can cause th e disease. The large number of people who still ignore the early symptoms of anemia can worsen the body's condition due to delays in diagnosing the disease they are experiencing. This study will combine the Naïve Bayes and Certainty Factor methods in assisting the initial diagnosis of anemia. The Naïve Bayes method is used to determine the type of anemia a user suffer s from based on the symptoms they have . After that, the system will perform a calculation using the Certainty Factor method based on the type of disease that has been determined previously to find out how much the certainty to prove that the user has anemia. A Test on the system produce an accuracy score of 93% from 60 test data.
The sojourn times of one dimensional discrete-time quantum walks

Abstract In the existing literature, a sojourn time of a discrete-time quantum walk is not a random variable. To solve this problem, we redefine the sojourn time of a quantum walk where its coin evolution operator can be general. We first discuss a class of quantum walks governed by flip operators. We cumulatively calculate how much time a walker spends in the set of non-negative integers up to a fixed evolution time. Whether a walker makes a left or right evolution, we add up the staying times as long as it stays within the target set. We define a sojourn time as the total amount of the staying times. Compared with existing definitions, we show that this definition can satisfy the probability normalization. From this, we define a random variable about the sojourn time and discuss its probability distribution. We build a mathematical model to characterize a sojourn time that is embedded into a quantum walk. These results are also valid for a class of quantum walks governed by general coin operators. We also give a method for calculating the sojourn time and analyze the shape features of its probability distribution.
SIGEST

The SIGEST article in this issue, which comes from the SIAM/ASA Journal on Uncertainty Quantification, is “Bayesian Inverse Problems Are Usually Well-Posed,” by Jonas Latz. The author investigates the well-posedness of Bayesian approaches to inverse problems, generalizing the framework of well-posedness introduced by Andrew Stuart to a set of weaker assumptions. Well-posedness here is understood in the sense of Hadamard, that is, a solution exists, is unique, and continuously depends on the input data. Inverse problems are typically ill-posed due to properties of the model, a lack of data, and measurement noise. The Bayesian approach to inverse problems reformulates the quest for a solution to the inverse problem in terms of a quest for its posterior distribution, which is determined by the data likelihood and prior distribution of the solution, and which in contrast to the inverse problem itself should be well-posed. In the Bayesian context, well-posedness typically relates to existence, uniqueness, and Lipschitz continuity of the posterior distribution with respect to the data in the so-called Hellinger distance. In many practical applications such well-posedness is difficult, if not impossible, to verify. Moreover, the choice of the Hellinger distance as the right metric might not always be the best fitted depending on the problem at hand. This sets the starting point for the paper where the author introduces a new framework for well-posedness of Bayesian inverse problems in which he shows existence, uniqueness, and continuity with respect to various metrics for a large class of Bayesian inverse problems, with conditions that are either nonrestrictive or verifiable in practical settings. This paper gives a strong new mathematical foundation for Bayesian inverse problems. The underlying statistical and probabilistic concepts are explained comprehensively and comprehensibly and, thus, in a way that opens up the Bayesian approach for a large readership. For the SIGEST version of the paper the author introduced more background material to make it more accessible to a general audience and extended the conclusion and outlook section, summarizing developments in the field that happened since the publication of the original work and discussing future research directions.
Pengaruh Game Wordwall Match Up dan Wordsearch terhadap Kognitif Siswa SD Laboratorium Percontohan UPI Serang

This study aims at finding out how interactive Wordwall is in order to evaluate learning in improving students' cognitive. This data was obtained from 13 students of grade 4 of UPI Serang Pilot Laboratory. This study employed a mixed method. The results of the analysis were carried out through written test activities, Match Up games and Wordsearch games. This research was conducted by an experiment. Students carried out an evaluation with a written test first and then continued using the Wordwall Match Up game, students were asked to pair pictures with the correct statements and Wordsearch, students were asked to look for words about picture tools. The problems of the written test and Wordwall were different but still on the same level. Based on the results of the study, it discovers that Wordwall is one of the interesting media in evaluating learning and it is able to improve students' cognitive.
Hubungan antara Faktor Eksternal dan Internal selama Pembelajaran Daring pada Siswa Sekolah Menengah

External factors are thought to have a relationship with internal factors in the learning process of secondary students. However, there is still little research conducted to determine the relationship between the two factors. This research was conducted to determine the relationship between external factors and internal factors in online learning for high school students. Data were obtained by distributing questionnaires to high school students using the snowball technique. Data was collected from June 2021 to July 2021, and 513 responses were obtained. The data obtained was analyzed using descriptive and inferential statistics. The results of this study indicate that gender has a relationship with independent learning, environmental management, and student task strategies. The student's school level is related to learning readiness, learning control, and student learning motivation. Gadget ownership status is correlated with students' self-directed learning. While the type of internet access has nothing to do with any factor. This research will help determine the conditions and development of students during online learning. In addition, various parties can use it to improve the quality of online learning among children .
Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications

Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templates and improve recognition accuracy. Our research offers a three-fold contribution to the field. Firstly, (i) we strengthened the evidence that QIM outperforms other contemporary template creation approaches. For this reason, we investigate seven template creation methods, which include four cluster description-based methods and three estimation-based methods. Further, (ii) we extended testing; we use a nearly four times larger database compared to the previous study, which includes a new set, and we report the recognition performance on this extended database. Additionally, we distinguish between open- and closed-set identification. Thirdly, (iii) we perform an evaluation of the cluster estimation-based method (specifically QIM) with an in-depth analysis of its parameter setup in order to make its implementation feasible. We provide instructions and recommendations for the correct parameter setup. Our research confirms that QIM’s application in template creation improves recognition performance. In the case of automatic application and optimization of QIM parameters, improvement recognition is about 4–10% depending on the dataset. In the case of a too general dataset, QIM also provides an improvement, but the incorporation of QIM into an automated algorithm is not possible, since QIM, in this case, requires manual setting of optimal parameters. This research contributes to the advancement of secure and accurate face recognition systems, paving the way for its adoption in various security applications.
Visual-Based Children and Pet Rescue from Suffocation and Incidence of Hyperthermia Death in Enclosed Vehicles

Over the past several years, many children have died from suffocation due to being left inside a closed vehicle on a sunny day. Various vehicle manufacturers have proposed a variety of technologies to locate an unattended child in a vehicle, including pressure sensors, passive infrared motion sensors, temperature sensors, and microwave sensors. However, these methods have not yet reliably located forgotten children in the vehicle. Recently, visual-based methods have taken the attention of manufacturers after the emergence of deep learning technology. However, the existing methods focus only on the forgotten child and neglect a forgotten pet. Furthermore, their systems only detect the presence of a child in the car with or without their parents. Therefore, this research introduces a visual-based framework to reduce hyperthermia deaths in enclosed vehicles. This visual-based system detects objects inside a vehicle; if the child or pet are without an adult, a notification is sent to the parents. First, a dataset is constructed for vehicle interiors containing children, pets, and adults. The proposed dataset is collected from different online sources, considering varying illumination, skin color, pet type, clothes, and car brands for guaranteed model robustness. Second, blurring, sharpening, brightness, contrast, noise, perspective transform, and fog effect augmentation algorithms are applied to these images to increase the training data. The augmented images are annotated with three classes: child, pet, and adult. This research concentrates on fine-tuning different state-of-the-art real-time detection models to detect objects inside the vehicle: NanoDet, YOLOv6_1, YOLOv6_3, and YOLO7. The simulation results demonstrate that YOLOv6_1 presents significant values with 96% recall, 95% precision, and 95% F1.
Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators

In the field of image processing, noise represents an unwanted component that can occur during signal acquisition, transmission, and storage. In this paper, we introduce an efficient method that incorporates redescending M-estimators within the framework of Wiener estimation. The proposed approach effectively suppresses impulsive, additive, and multiplicative noise across varied densities. Our proposed filter operates on both grayscale and color images; it uses local information obtained from the Wiener filter and robust outlier rejection based on Insha and Hampel’s tripartite redescending influence functions. The effectiveness of the proposed method is verified through qualitative and quantitative results, using metrics such as PSNR, MAE, and SSIM.
IMPROVING READING COMPREHENSION THROUGH USING OF THE HERRINGBONE TECHNIQUE IN TEACHING READING

This study investigated the effectiveness of the Herringbone technique in improving reading comprehension skills among class XI students. The research followed a quasi-experimental design, with an experimental group receiving instruction using the Herringbone technique and a control group receiving traditional instruction. Pre-test and post-test assessments were conducted to measure the participants' reading comprehension abilities. The results indicated a significant improvement in the reading comprehension scores of the experimental group compared to the control group. These findings suggest that the Herringbone technique is more effective than traditional methods in enhancing reading comprehension skills. The implications of this study emphasize the importance of incorporating interactive and collaborative instructional approaches in English language teaching to foster critical thinking and deeper understanding of texts. Future research should explore the long-term effects and applicability of the Herringbone technique in diverse educational contexts. Overall, the Herringbone technique shows promise as an effective approach to enhance reading comprehension instruction and support language development among students.
Sistem Informasi Akuntansi Penjualan Bahan Pokok Berbasis Web Pada Housemart Cirebon

Housemart Cirebon is a trading company engaged in staple goods located on Jalan Cideng Indah, Cirebon Regency - West Java. Cirebon Housemart provides basic ingredients from national brands ranging from household needs, baby products, to body and beauty care products. At present the business activities carried out are somewhat less effective. Sales, promotions and orders are still done manually, this is less effective because there is no detailed information on what products are being sold to buyers. . Recording and checking of transaction data, customer data, goods data, is still done manually in written document archives, data loss is still very likely to occur. The development method used to design the system uses the prototype method. The results of this research are in the form of a web-based Housemart Cirebon Sales Information System that is capable of performing sales, ordering and data processing services at Cirebon Housemart so that it can increase the effectiveness and efficiency of performance and time. The hope is to be able to provide complete information regarding the ordering process, services and promotions so that it is even better at Housemart Cirebon.