Reviewerly/exHarmony huggingface dataset:
The Application of Data Mining by using K Means Clustering Method in Determining New Students’ Admission Promotion Strategy

This study aims to determine the promotion strategy on the admission of new students at the university. Universities need appropriate promotion strategies to increase the number of new students enrolled in subsequent years and to fulfill the equal distribution of new students in each region and study programs at the University. Classification of new student data reception at the Indo Global Mandiri University (IGM University) in 2018/2019 uses the CRISP-DM data mining application (the Cross-Industry Standard Process for Data Mining) using the K-Means grouping method. Research data using primary and secondary data. The population and sample of the study were 1011 students using 4 (four) attributes in this study, namely the name of the student, the area of origin, the study program, and the promotion strategy (direct visit, word of mouth, media, brochures, and coming directly). This test is carried out with the Waikato Environment for Knowledge Analysis (WEKA) 3.8 tool. The results of this study indicate that the direct visit strategy is the most appropriate in the admission of new students at IGM University, amounting to 492 students with 26%, with this strategy being able to absorb many new student candidates from various regions including Palembang, Regency / City, and regions in outside South Sumatra, there is also equality in various study programs at IGM University. Word of mouth promotion strategies and media are optimized to be included in the promotion team in determining the promotion strategies in the following year to increase the number of new student admissions.
A Matching Algorithm for Underwater Acoustic and Optical Images Based on Image Attribute Transfer and Local Features

In the field of underwater vision, image matching between the main two sensors (sonar and optical camera) has always been a challenging problem. The independent imaging mechanism of the two determines the modalities of the image, and the local features of the images under various modalities are significantly different, which makes the general matching method based on the optical image invalid. In order to make full use of underwater acoustic and optical images, and promote the development of multisensor information fusion (MSIF) technology, this letter proposes to apply an image attribute transfer algorithm and advanced local feature descriptor to solve the problem of underwater acousto-optic image matching. We utilize real and simulated underwater images for testing; experimental results show that our proposed method could effectively preprocess these multimodal images to obtain an accurate matching result, thus providing a new solution for the underwater multisensor image matching task.
Peningkatan Aktivitas Dan Hasil Belajar PAIDBP Siswa Kelas IV Sekolah Dasar Melalui Penggunaan Model Discovery Learning

This article to improve PAIdBP learning activities and learning outcomes for Class IV students of UPTD SDN 02 Simpang Kapuak for the 2020/2021 academic year by using the Discovery Learning model. This article is a Classroom Action Research (CAR) which was carried out in 2 cycles with 2 meetings for each cycle. The subjects in this study were grade IV students of UPTD SDN 02 Simpang Kapuak for the academic year 2020/2021, totaling 20 people. The data in this study were obtained through the use of observation sheets for teacher and student activities during the learning process and student learning outcomes at the end of each cycle. The data generated in this study were processed and analyzed descriptively qualitatively. The conclusion of the study shows that the use of the Discovery Learning model with the correct steps can increase the activity by an average of 78,78 and learning outcomes of Class IV PAIdBP students become 95% UPTD SDN 02 Simpang Kapuak Academic Year 2020/2021.
Detection of Abnormal Tumor Regions in Ultrasonic Thyroid Images using SVM Classification Method

Detection of tumor or abnormal regions in thyroid gland is difficult task in human. The following methods are presently used for detecting the abnormal regions in thyroid gland as blood test, sample testing from thyroid gland and image processing method. This paper develops a methodology to detect the tumor regions in thyroid images using image registration and image enhancement technique. The Support Vector Machine (SVM) classifier is operated in two modes as training pattern generation and testing mode. The generation of training pattern from both normal and abnormal ultrasonic thyroid images. This proposed method for thyroid tumor region detection obtains 96.54% of sensitivity, 97.57% of specificity and 98.56% of average tumor segmentation accuracy.
THE MODEL OF PROFESSIONAL DEVELOPMENT OF NATURAL SCIENCE TEACHERS OF RURAL SCHOOLS IN THE CONDITIONS OF DISTANCE EDUCATIONAL PROCESS

The article substantiates the concept and the need to introduce a model of professional development of natural sciences teachers in rural schools in the context of distance educational process in the conditions of digitalization of the domestic school education system, the risks associated with the pandemic, determining its impact on the educational process in rural schools in today's realities. The article reveals the pedagogical conditions of professional development of natural science teachers of rural schools in the conditions of distance educational process. The structural-functional model of this study is developed and experimentally checked, which contains three interconnected blocks. The methodological-target block reflects: purpose; concepts; methodological approaches; general pedagogical principles; specific principles. The content-technological block contains pedagogical conditions of professional development of natural science teachers of rural schools in the conditions of distance educational process (online coordination of the process of professional development of natural science teachers of rural schools in the conditions of remote work of centres of professional development of pedagogical workers based on technology “DDI” (disclosure, development and implementation); intensification of activity of the centres of professional development of pedagogical workers on the basis of pedagogical technologies of resource-oriented training of natural science teachers of rural schools and network cooperation; introduction of the author’s special course “Digitalization of the educational process” as an open learning environment for the continuous development of natural science teachers of rural schools), as well as forms and methods of teaching pedagogues and their professional development. The diagnostic-effective block reflects certain criteria (cognitive-digital, activity-effective and personal-evaluative); low (reproductive), medium (reproductive-creative), high (productive-creative) levels of professional development of natural science teachers in rural schools in the conditions of distance educational process and the expected result from the implementation of the structural-functional model. The results of the study allowed to state the positive, statistically significant dynamics of professional development of natural science teachers of rural schools in the conditions of distance educational process in the experimental group.
Arabic Document Classification by Deep Learning

In this paper, we show how to classify Arabic document images using a convolutional neural network, which is one of the most common supervised deep learning algorithms.The main goal of using deep learning is its ability to automatically extract useful features from images, which eliminates the need for a manual feature extraction process.Convolutional neural networks can extract features from images through a convolution process involving various filters.We collected a variety of Arabic document images from various sources and passed them into a convolutional neural network classifier.We adopt a VGG16 pre-trained network trained on ImageNet to classify the dataset of four classes as handwritten, historical, printed, and signboard.For the document image classification, we used VGG16 convolutional layers, ran the dataset through them, and then trained a classifier on top of it.We extract features by fixing the pre-trained network's convolutional layers, then adding the fully connected layers and training them on the dataset.We update the network with the addition of dropout by adding after each max-pooling layer and to the fourteen and the seventeenth layers which are the fully connected layers.The proposed approach achieved a classification accuracy of 92%.
Evaluation of Innovation and Entrepreneurship Ability of Computer Majors Based on Neural Network Optimized by Particle Swarm Optimization

The current evaluation index systems (EISs) of innovation and entrepreneurship (I&amp;E) ability are not sufficiently systematic, scientific, or practical. To solve the problem, this paper tries to evaluate the I&amp;E ability of computer majors, using neural networks improved by particle swarm optimization (PSO). Firstly, an EIS of 22 second-level indexes under 5 first-level indexes was designed to evaluate the I&amp;E ability of college computer majors. Next, an evaluation model was developed based on fuzzy neural network (FNN), and the corresponding training algorithm was created. Moreover, an improved PSO was introduced to optimize the FNN, and the optimization process was detailed. The proposed model was proved effective through experiments.
Graph Filtration Kernels

The majority of popular graph kernels is based on the concept of Haussler's R-convolution kernel and defines graph similarities in terms of mutual substructures. In this work, we enrich these similarity measures by considering graph filtrations: Using meaningful orders on the set of edges, which allow to construct a sequence of nested graphs, we can consider a graph at multiple granularities. A key concept of our approach is to track graph features over the course of such graph resolutions. Rather than to simply compare frequencies of features in graphs, this allows for their comparison in terms of when and for how long they exist in the sequences. In this work, we propose a family of graph kernels that incorporate these existence intervals of features. While our approach can be applied to arbitrary graph features, we particularly highlight Weisfeiler-Lehman vertex labels, leading to efficient kernels. We show that using Weisfeiler-Lehman labels over certain filtrations strictly increases the expressive power over the ordinary Weisfeiler-Lehman procedure in terms of deciding graph isomorphism. In fact, this result directly yields more powerful graph kernels based on such features and has implications to graph neural networks due to their close relationship to the Weisfeiler-Lehman method. We empirically validate the expressive power of our graph kernels and show significant improvements over state-of-the-art graph kernels in terms of predictive performance on various real-world benchmark datasets.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI SIMULASI PENGECATAN KENDARAAN BERBASIS ANDROID

Android is a mobile device on the operating system for mobile phones based on Linux, android provides an open platform for developers to create applications. Android-based application developers make applications so that they can be useful for the community ranging from game genre applications, education, entertainment and simulation applications that are very likely to be created and run on this android operating system. Simulation is the process of designing a matemastis or logical model of a real system, conducting experiments on models using computers to describe, explain and predict system behavior. For a simulation application company can be used as a medium of product promotion, while for smartphone users can help to channel the existing imagination into a real picture. The application of simulation applications many functions, one example of its application to two-wheeled motor vehicles. By utilizing android application and the growth of vehicle users that lead to the hobby of modification, it can be utilized by creating an android-based coloring simulation application to help modifiers and modification lovers in order to channel their imagination and make it easier to design colors as desired. Without having to bother using design software that requires special skills
Which Event Happened First? Deferred Choice on Blockchain Using Oracles

First come, first served: Critical choices between alternative actions are often made based on events external to an organization, and reacting promptly to their occurrence can be a major advantage over the competition. In Business Process Management (BPM), such deferred choices can be expressed in process models, and they are an important aspect of process engines. Blockchain-based process execution approaches are no exception to this, but are severely limited by the inherent properties of the platform: The isolated environment prevents direct access to external entities and data, and the non-continual runtime based entirely on atomic transactions impedes the monitoring and detection of events. In this paper we provide an in-depth examination of the semantics of deferred choice, and transfer them to environments such as the blockchain. We introduce and compare several oracle architectures able to satisfy certain requirements, and show that they can be implemented using state-of-the-art blockchain technology.
Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extraction ceramic image shape edge profile, again, according to the image edge extraction and elliptic rotator ceramics phenomenon. The image distortion effect was optimized by self-application, and then the deep learning modeler was used to model the side edge contour. Finally, the 3D ceramic model of the rotating body was restored according to the intersection and central axis of the extracted contour. By studying the existing segmentation methods based on deep learning, the automatic segmentation of target ceramic image and the effect of target edge refinement and optimization are realized. After extracting and separating the target ceramics from the image, we processed the foreground image of the target into a three-dimensional model. In order to reduce the complexity of the model, a 3D contextual sequencing model is adopted to encode the hidden space features along the channel dimensions, to extract the causal correlation between channels. Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.
A Study of Probabilistic Risk Evaluation for System Development Project Based on Requirements Analysis and Bayesian Estimation

For building a business system quickly, more efficient project management is needed. However, uncertain and subjective factors in requirements from customer cause cost overruns or schedule delays in the project. Furthermore, uncertain and subjective factors can lead to misunderstandings and false estimates when converting requirements into specifications or scheduling within a project. Thus this paper proposes a probabilistic risk evaluation method with Requirements Analysis and Bayesian estimation for project management to accurately assess the project risk. If this method works well, efficient project management will be realized.
Masked Face Detection and Recognition System in Real Time using YOLOv3 to combat COVID-19

A new challenge in the facial recognition technology is observed during the COVID-19 pandemic which has created a need for developing alternatives in face recognition algorithms that exist today. During this pandemic, masked faces have made face recognition applications used for security surveillance and attendance systems less effective. A comparative study was conducted on different YOLO models like YOLOv3, Tiny-YOLOv3, Tiny-YOLOv4 to judge their performances for the face detection module. From the study, it is concluded that the YOLOv3 model outperformed the other algorithms. Additionally, the face images captured from cameras were encoded and compared to determine the best face images for the face recognition module. It was identified that YOLOv3 along with face encodings from IP camera images accomplished an overall testing accuracy of 95.83% on masked and unmasked faces. The system introduced a confidence level to further reduce the error while registering the identity of the person.
Research on X-Ray Inspection of Basin Insulators and Wireless Image Sensing Technology

This paper presents an in-depth study and analysis of X-ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X-ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo-Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three-dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X-ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment.
Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.
Energy Efficient Multiprocessing Solo Mining Algorithms for Public Blockchain Systems

Blockchain as a decentralized distributed ledger is revolutionizing the world with a secure design data storage mechanism. In the case of Bitcoin, mining involves a process of packing transactions in a block by calculating a random number termed as a nonce. The nonce calculation is done by special nodes called miners, and all the miners follow the Proof of Work (PoW) mining mechanism to perform the mining task. The transaction verification time in PoW-based blockchain systems, i.e., Bitcoin, is much slower than other digital transaction systems such as PayPal. It needs to be quicker if a system adapts PoW-based blockchain solutions, where there are thousands of transactions being computed at a time. Besides this, PoW mining also consumes a lot of energy to calculate the nonce of a block. Mining pools resulting into aggregated hashpower have been a popular solution to speed up the PoW mining, but they can be attacked by using different types of attacks. Parallel computing can be used to speed up the solo mining methods by utilizing the multiple processes of the contributing processors. In this research, we analyze various consensus mechanisms and see that the PoW-based blockchain systems have the limitations of low transaction confirmation time and high energy consumption. We also analyze various types of consensus layer attacks and their effects on miners and mining pools. To tackle these issues, we propose parallel PoW nonce calculation methods to accelerate the transaction verification process especially in solo mining. We have tested our techniques on different difficulty levels, and our proposed techniques yield better results than the traditional nonce computation mechanisms.
Gambaran Kesiapsiagaan Masyarakat dalam Antisipasi Bencana Banjir Di Kecamatan Danau Kerinci Barat Kabupaten Kerinci

Flood disaster is one of the biggest disasters in the world. It is hoped that the readiness and knowledge of the community in dealing with floods is expected to be proactive in being able to take preventive, emergency response and rehabilitation measures in collaboration with the government to maintain the environment that can cause flooding in order to minimize the impact it causes. The magnitude of the potential for flooding in Kerinci prompted the need for community preparedness measures to reduce losses due to these impacts. This study aims to determine the description of community preparedness in anticipation of flood disasters in Danau Kerinci Barat District, Kerinci Regency. This type of research used a quantitative descriptive design. This research was conducted in 5 villages located in the West Lake Kerinci District, namely, the villages of Tanjung Pauh Mudik, Pancuran Tiga, Punai Merindu, Bukit Pulai and Sumur Jauh. The population of all family heads in the 5 villages was 1,580 with a sample size of 110 samples. Technique Sampling used Cluster Random Sampling The results showed the parameters of knowledge are classified as very ready, the parameters for policies and guidelines are not ready, the parameters for the emergency response plan are classified as less ready, the parameters for disaster warning are classified as less ready, and the parameters for resource mobilization are classified as not ready. It can concluded that the description of community preparedness in anticipation of flood disasters in Danau Kerinci Barat District, Kerinci Regency is classified as almost ready.