Reviewerly/exHarmony huggingface dataset:
Analisis Kesuksesan Website Gratis Kabeh Dengan Pendekatan Delone And Mclean

The Kabeh Free Website is a system used for submitting population documents, the website is a solution for residents in Banyumas, especially those who are far from Purwokerto City, to be able to submit residence documents online without having to go to Dindukcapil. However, in practice, there are still residents who choose to register offline, while there are several complaints from the Banyumas community that there is no submission guide on the website, delays in confirmation via e-mail, users have difficulty submitting because the website is having an error. This is the reason for researchers to conduct research with the aim of knowing the success of the Gratis Kabeh website with the DeLone and McLean model and knowing which variables have an influence on the Gratis Kabeh website. The sample used in this study amounted to 250 samples with a purposive sampling technique. The method chosen is the DeLone and McLean Is Success Model, to determine the effect of the variables in this model. The results showed that the overall research hypothesis was accepted because it had a significant and positive effect, then the results of the F2 value showed a weak effect on the variables Information Quality, System Quality, Service Quality on Use and User Satisfaction, and the Usage variable on User Satisfaction and Net Benefit had a weak effect . However, the User Satisfaction variable has a moderate effect on Net Benefit
A FCM-XGBoost-GRU Model for Short-Term Photovoltaic Power Forecasting Based on Weather Classification

Aiming at the problem of low photovoltaic prediction accuracy, a short-term photovoltaic power prediction method based on fuzzy C-Means(FCM)- extreme gradient boosting (XGBoost)- gate recurrent unit (GRU) based on weather classification is proposed. First select the key meteorological factors as the clustering features, then use the FCM clustering method for cluster analysis, divide the historical data into sunny, cloudy, rainy and extreme weather, and then construct XGBoost-GRU combined forecasts for the four weather types The model predicts photovoltaic output power. Finally, the model proposed in this paper is compared with the prediction results of traditional XGBoost and GRU models. The results show that the proposed FCM-XGBoost-GRU short-term photovoltaic power prediction method can significantly reduce the error of photovoltaic prediction and improve the accuracy of short-term photovoltaic prediction. It is effective and scientific in practical application scenarios.
Network combining edge features and pyramid structure for low-dose computed tomography denoising

To balance noise reduction and texture details of low-dose computed tomography (LDCT) images during deep learning training, we propose a network combining edge features and a pyramid structure for LDCT denoising (PLEDNet). PLEDNet contains three states: superficial feature acquisition, deep feature acquisition, and feature confluence. First, the superficial feature acquisition stage uses a simple convolutional block to perform feature extraction on the input LDCT image. Second, in the deep feature extraction phase, one branch uses pyramid pooling to extract multiscale features, and the other uses the trainable Laplacian of the Gaussian module to extract edge detail features. Finally, the feature fusion phase uses a fusion attention module to adaptively fuse multiscale and edge features. The results of the experiments demonstrate that the method not only enhances various relative objective indicators but also effectively represses the noise in LDCT images while fully conserving the image texture details, which has great advantages in terms of visual effects.
Klasterisasi Pendidikan Masyarakat Untuk Mengetahui Daerah Dengan Pendidikan Terendah Menggunakan Algoritma K-Means

Education is a basic need for every human being who plays an important role in the future of the nation, because a nation that is said to be advanced can be seen from its good learning system. Successful education is measured by the average number of graduates at various levels of education in various regions. But not all regions are good in the quality of education. One of them is the area in Indonesia, such as the Kapuas district, Central Kalimantan. It is known that in previous years this area lacked improvement in education, causing several areas where people did not go to school or dropped out of school. Many of the problems are caused by economic factors, laziness, lack of motivation about the importance of education, and so on. The previous Covid-19 pandemic was also the reason for the increase in the number of children dropping out of school due to a declining family economy. The number of areas in Kapuas district requires grouping the number of existing villages. The grouping aims to make it easier for the government to pay special attention to areas where education is considered lacking and other purposes are to find out which villages have low levels of education. In grouping, the system applied is data mining using the K-Means Algorithm Clustering method which is processed using rapidminer software. The groupings formed on the education level data of 229 records are 8 clusters where the lowest education villages are stated in (C1) with a total of 33 villages.
Improving the teenager creativity to become podcasters at the Media Kasih Islamic Orphanage, Tangerang

Recent decade, the podcast has become one of the new media platforms that have become a trend in society. Podcasts are becoming increasingly popular, especially during the Covid-19 pandemic. At a time when people are forced to social distancing, some people want to build their creativity easily and cheaply, and efficiently on various podcast platforms. Podcasts are mostly only known among urban communities, while among fostered children such as those in orphanages, they are so common and unknown. This community service aims to introduce podcasts through training for orphanage. The results obtained by fostered students at the Media Kasih Islamic Foundation Orphanage are so enthusiastic in learning about podcasts and making podcasts for their respective activities that they build their creativity.
An application of a qd‐type discrete hungry Lotka–Volterra equation over finite fields to a decoding problem

Abstract In this paper, the decoding problem for multiple Bose–Chaudhuri–Hocquenghem (BCH)‐Goppa codes over the same finite field is investigated. A new iterative decoding algorithm is proposed based on the quotient difference (qd)‐type discrete hungry Lotka–Volterra equation over finite fields. Compared with certain existing algorithms, the proposed algorithm manifests its advantage in computational complexity. A few of examples are presented to demonstrate its efficiency.
A Multi-Channel Aggregation Framework for Object Detection in Large-Scale SAR Image

Synthetic aperture radar (SAR) has gradually demonstrated its advantages in a variety of application fields. However, due to the complexity of the background, the simplicity of the texture, and the multi-scale of the target, object detection in large-scale SAR images is still a major challenge. This paper focuses on a multi-channel aggregation framework, and jointly considers the pre-processing and post-processing for algorithm optimization to improve the overall performance of object detection in large-scale SAR images. In this paper, multiple sets of slices of large-scale images are first sliced using slicers of various sizes. Feeding different groups of image slices into the detection network for feature extraction can achieve a greater degree of scaling of target features, thereby improving the ability of multi-scale target discovery. Then, a post-processing module is proposed to transform and fuse the results from multiple channels, and two sub-algorithms result fusion and result refinement are proposed to eliminate false alarms. Result fusion merges the detection boxes from each channel and proposes a weighted NMS strategy to update the confidence of the optimal box. Result refinement proposes an adaptive belief update strategy to filter the remaining boxes for eliminating those detection boxes with low beliefs. Qualitative and quantitative experiments were conducted to prove the effectiveness of the proposed method, which can reduce the false alarm rate while increasing the recall rate.
Hiding Speaker’s Sex in Speech Using Zero-Evidence Speaker Representation in an Analysis/Synthesis Pipeline

The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes. Here, we propose to transform the speaker embedding and the pitch in order to hide the sex of the speaker. ECAPA-TDNN-based speaker representation fed into a HiFiGAN vocoder is protected using a neural-discriminant analysis approach, which is consistent with the zero-evidence concept of privacy. This approach significantly reduces the information in speech related to the speaker’s sex while preserving speech content and some consistency in the resulting protected voices.
ASSESSMENT IN PUBLIC SCHOOL OF SINDH: PRACTICES, TRENDS AND IMPLICATIONS

Large- scale assessment is used to evaluate overall education system. It emerged during 1980s as a tool to inform education system across the world. In Pakistan, large- scale assessment was initiated by National Education Assessment Center. In different provinces and areas, assessment centers were also established. Today, ASER, PEACe and SAT are the major large- scale assessment conducted in public sector. This research aimed to examine the large-scale assessment practices in public schools of Sindh, the rationale of large- scale assessment and its contribution to improve overall education. This research is situated in the qualitative paradigm in which the data was collected through document reviews and analysis as well as interviews. The findings confirm that, major three types of large- scale assessments have been reporting dismal situation of students’ achievement. The score generally ranges between 25 and 30 which is an alarming condition. Textbook based teaching, unawareness of teachers about developing standardized and high order assessments and unavailability of basic facilities are the core issues associated with students’ low scores. The consistent low score demands to reconceptualize the rationale of large- scale assessment and the focus may be shifted to promote formative assessment and prepare public sector teachers to conduct effective and meaningful assessments. This research also recommends that the policies regarding assessment already exist which need to be implemented in their true spirit.
Design of Rubbish as Pickup Object in The Maze Game Using Cellular Automata

This educational game is perfect for children. So many kids nowadays actually play games that should be for adults. This is quite concerning because the age children should be the age of forming morals and in this study, the authors developed a maze game with the theme of manners or morals. In this game, the writer designed the pickup behavior of garbage objects using the cellular automata method. The results to be achieved in this study are to ensure the game can run smoothly without errors and pickup objects in the form of trash according to what was designed. In addition, with this maze game it is hoped that it can educate children so that morals or character are formed within them.
The importance of teacher education in coping with the challenges of the digitalization in Germany

We live in the midst of a digital revolution. Most people are connected to the Internet, using digital devices and services at work and in many aspects of their lives. There is also no doubt that the rapid development of technology has also massively changed the field of education in the last decades. Studies show that there is an apparent need for a massive change in the conventional teaching ways in different learning environments that support the acquisition of 21st century skills where the teacher has a prominent role in the digital transformation process. Moreover, teacher education plays a vital role in the hierarchy of a successful implementation of digital media in classroom learning and teaching processes. The use of digital media in schools on the other side is attributed to technical, subject-didactic and pedagogical potential, which promotes the acquisition of technical and interdisciplinary skills. Although digital education has become more critical over the last years in Germany, barely any progress has been made in the field of teachers` digital competency. This article will sharpen the view about conceptualizing digitalization as an emerging process in the teaching profession by embedding it in the interdisciplinary discourse. The ambiguity of the digitalization concept will also be cleared. In the interdisciplinary overview, a heuristic is offered that sheds light on the cross-phase cooperation in the teaching profession concerning digitalization related to the German teacher training that puts perspectives, conditions and processes at the forefront for its success.
Software application development for real estate activities

The real estate industry is in the early stages of software application, in constant search of new tools to make the industry work more efficiently and effectively. The real estate market is differentiated by certain needs, respectively each segment of the real estate market is looking for software solutions to meet the main needs. In order to meet the requirements of the real estate market, the authors come up with a proposal to create a prototype (iMoPal), including software application development, for a product, namely an online real estate platform for property management, and the creation of a scoring system for tenant and landlord. The application offers a proficient time management solution between tenant and landlord.
A scrambled image hiding on image using discrete cosine transform

Image steganography is the study of embedding sensitive information in images without distorting their visual quality.In this paper, we have proposed an efficient image steganography algorithm to embed the secret image in the cover image.In this scheme, the secret image is encrypted by scrambling torus method and then to the transformation of the secret data with Discrete Cosine Transform (DCT) compound with secured by a secret key for torus method initialization parameters.The randomness property of the resultant image reduces the possibility of its detection by the human visual system (HVS) and Steganalysis techniques.
Sentiment Analysis Of E-Wallets on Twitter social media With Naïve Bayes and Lexicon-Based Methods

Sentiment analysis is one of the areas of application of text analysis and NLP (Natural Language Processing).It builds a system to identify and analyze personal information from textual sources, which are generally divided into positive and negative categories.Many case studies are chosen to measure the accuracy of the computational model of sentiment analysis.In this study, we use social media's perception of electronic wallets usage.The Naïve Bayes method was chosen.It can help classification because it is assumed to be an independent variable, and Lexicon is used to calculate the weight of each word.Although several studies have been conducted on sentiment analysis, none have used non-standard word correction.The use of non-standard word correction deals with nonstandard words such as slang and word abbreviations.This research begins by digging up the required data, namely the keyword electronic wallet.The standard word dictionary normalizes non-standard words, preprocessing data with four stages: case folding, tokenizing, stop word, and stemming.The lexicon dictionary produces positive and negative labels, and nave Bayes is used to classifying.The data used in this study were 3878 tweets, with a distribution of 70% training data and 30% test data.Sentiment analysis obtained in this study shows that Twitter users in Indonesia are more likely to give negative comments to electronic wallets.The results of this study indicate that by using both methods and adding standard word corrections and testing using RapidMiner, the accuracy rate for classifying positive and negative sentiments reaches 88.56%.Further research can add Levenshtein Distance normalization to the classification results to better influence the accuracy value.
11 Government Compliance Strategies for Web-Driven Data Fabric

The chapter starts with the definition of compliances and the data fabric. Basically, compliance is an act of complying the rules or requirements or the process of doing while data fabric is the framework that makes several data pipelines from beginning to an end. The architecture of data fabric includes the several layers. While discussing the government compliances one also needs to work on the strategy and tools involved in compliances. The use of compliance technology, monitoring and auditing, and cloud security is the part of security strategy and tools of compliances. The government compliance in today’s era has both benefits as well as demerits. The basic framework of web-driven data fabric and the application of data fabric on serverless cloud has been discussed. Principle of data governance and data compliance along with their comparison of the benefits.
Unsupervised Video Segmentation using Quantization

Abstract: Unsupervised video segmentation is a challenging task in computer vision that involves dividing a video into meaningful segments without any labeled data or prior knowledge of the video content. One approach to achieving this is the use of quantization, which involves clustering similar image patches or video frames into discrete groups based on their visual features.Beta-VAE is a type of variational autoencoder (VAE) that is capable of learning disentangled representations of data. It can be applied to video segmentation using quantization, allowing for more effective segmentation and analysis of complex video datasets.The use of Beta-VAE and quantization is essential for several reasons. Firstly, it allows for the automatic analysis of large video datasets without the need for manual annotation, which can be time-consuming and expensive. Secondly, it enables the detection and tracking of objects and events in videos, which has applications in surveillance, robotics, and autonomous driving. Finally, it can be used for content-based video indexing and retrieval, which is crucial for video search and recommendation systems.The advantage of using Beta-VAE in video segmentation is that it can learn disentangled representations of video frames, which separates the data into meaningful factors of variation. This leads to more accurate segmentation, as the model can distinguish between different objects and events in the video, even if they have similar visual features.In conclusion, the use of Beta-VAE and quantization is necessary for advancing the field of computer vision and improving the analysis and understanding of videos. It has numerous applications in various industries and can contribute to the development of more effective and efficient video analysis tools.
Decomposing and recovering airborne radiometric data through principal component analysis applied on flight-line data: An alternative to reduce noise

Airborne gamma-ray spectrometry (AGRS) data provide valuable information about the distribution of radiometric elements on earth’s surface. However, the presence of noise can hinder the interpretation or the identification of subtle variations of radioelement concentrations that can be economically attractive. Previous research has demonstrated that techniques based on matrix factorization, such as noise-adjusted singular value decomposition (NASDV) and minima noise fraction (MNF), can reduce noise when applied to AGRS raw spectra. Nevertheless, the raw spectra often are unavailable for end users, limiting the widespread adoption of such techniques. In this context, we use principal component analysis with the flight-line data before interpolating the data onto a regular grid as a means to reduce noise when the raw spectra are no longer available. We use our approach on two AGRS data sets located in Brazil and one in the United States. For Brazil’s AGRS data, results indicate that noise can be attenuated through eigendecomposition projection and recovery of the radiometric variables. Furthermore, our technique can highlight some geologic features dependent on the number of eigenvectors used to reconstruct the database. For the U.S. AGRS data set previously filtered with NASDV, our methodology seems to produce only marginal improvement. Therefore, our methodology might be particularly successful for AGRS data whose acquisitions were conducted before NASDV and MNF were proposed as radiometric data processing techniques.
On Zero-Truncated Negative Binomial with Excess Ones

In this paper, Zero-truncated negative binomial distribution is modified to include excess ones to improve goodness-of-fit. This is necessary when data are dispersed and zero has been eliminated from data structurally. However, when the ones are unduly large, the proportion of this excess must be recognized and estimated to improve the fit. This development is applied using real data from a national survey.
Sistem Informasi Manajemen Sumber Daya Manusia Pada Universitas Muhammadiyah Jambi

Management of human resources is something that needs to be considered in a university, because the management of human resources is included in the management of higher education. Information systems are really needed in managing at a Muhammadiyah University, information systems must also be in accordance with existing management at Muhammadiyah University and in management there are general principles that include planning, organizing, mobilizing and ensuring. using modeling UML (Unified Modeling Language) with the following steps: Determining Initial Planning, Analyzing Information Systems Currently Used, Modeling Information Systems Using UML Modeling (Unified Modeling Language). At this stage, create a model of information system requirements with Use Cases, Activity Diagrams and Class Diagrams and Build Information System Prototypes. The results of this information system design are to assist HR management in collecting employee data and also to facilitate the process of applicants becoming employees at the University of Muhammadiyah Jambi. With this information system, it is also easy to find information about employee recruitment and employee development. This research resulted in a design made using object-oriented methods with UML, which resulted in a prototype of an HR management information system, especially employee recruitment at the University of Muhammadiyah Jambi
Sistem Informasi Customer Relationship Manajemen (CRM) Berbasis Web Pada PT. Patria Cabang Mendalo

PT. Patria Mendalo Branch is one of the official Honda motorcycle dealers that sells Honda motorcycles and official Honda spare parts. Where in the current system there are several problems in obtaining new customers, improving customer relationships, and retaining customers. Based on the existing problems PT. Patria Mendalo Branch requires a web-based Customer Relationship Management (CRM) system that can help PT. Patria Mendalo Branch and Customer in establishing good relations. The purpose of this research is to analyze the service relationship between the company and its customers and to design a prototype of a web-based Customer Relationship Management (CRM) system to create a better relationship between the company and its customers through the features on the website. The author performs object-oriented design includes use case diagrams, activity diagrams, class diagrams, display designs, database structure tables. The result of this research is a prototype of a web-based CRM system that can help solve the problems that exist in PT. Patria Mendalo Branch.