Reviewerly/exHarmony huggingface dataset:
THE USE OF SPELLING BEE GAME TO ENCOURAGE STUDENTS’ VOCABULARY AT SEVENTH GRADE STUDENT OF SMPN 3 PAGIMANA

This study aims to find out whether the Spelling Bee Game can improve students' vocabulary mastery. This study used a pre-experimental design. The variables of this research are the Spelling Bee game as the independent variable and the students' vocabulary as the dependent variable. The samples of this research were students of class VIIA and VIIB of SMP Negeri 3 Pagimana, totaling forty-four students. The sampling technique is purposive sampling, which means the sample is selected with a specific purpose. This study uses a test as the main instrument which is divided into pre-test and post-test. Analysis of the data in this study using statistical calculations. The results of this study, the mean value of the pre-test is 34.5, while the post-test is 62.2 and the hypothesis test, the t-counted value is 17.07,
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN DATA SANTRI PADA TAMAN PENDIDIKAN AL-QUR'AN MASJID AD-DU'A BERBASIS LARAVEL

Laravel is a framework based on the PHP programming language that can be used in making web-based applications that are very popular for use ranging from small-scale to large-scale projects. Applications developed using laravel can be used for the benefit of individuals or institutions to facilitate the data management process. The Al Quran Education Park of the Ad-Du'a Mosque as a non-formal educational institution has many students that need to be processed to make it easier for institutional leaders to determine policies. Data processing for students through a web application is considered very easy because it can be done online and more effectively. The data that is processed in this application is student data starting from the registration process to determining the study group. In determining the study group, the system admin will classify the data that has been collected from the registration process based on the age range and ability to read the Koran. The process of making this application uses the extreme programming method because this method is quite effective when used in developing small-scale applications. The stages in this method are planning, design, coding, test, then if it is appropriate then release the application. After going through the stages of making an application using this method, a web-based application is produced which can be used to manage student data at the Al-Quran Education Park of the Ad-Du'a Mosque.
Role of Knowledge Management in Ensuring Quality of Higher Education in Al-Quds University from the Academic Staff's Perspective

The concept of knowledge management has acquired significance recently for its relevance to the concept of total information management. This study aimed to investigate the role of knowledge management in ensuring quality of higher education in Al-Quds University from the viewpoint of academics. To achieve this objective, the descriptive, analytical method was followed. (150) academics were randomly selected for the study from Al-Quds University during 2019/2020. A questionnaire was developed based on previous studies; and after checking its reliability and validity, it was used to collect data. The data was analyzed by using SPSS to conduct statistical procedures, including T-test and ANOVA. Major findings revealed that there was a positive role for Operations management knowledge in quality assurance, and the academics' assessment regarding knowledge management and quality assurance practice was medium. The independent variables (knowledge creation, knowledge sharing, application and generation of knowledge) combined had a positive effect on the dependent variable (achieving quality of higher education at Al-Quds University). It was also found that there was a positive effect for the knowledge sharing variable. In other words, the higher the level of knowledge management is, the greater the achievement of the quality of higher education at Al-Quds University is. The independent variables (gender, academic rank, college) had an impact on the dependent variable (achieving the quality of higher education at Al-Quds University). It was found that there was a positive effect for the academic rank variable, and a negative effect for the college variable, which means that the higher the academic rank is, moving towards science colleges, the more the quality of higher education at Al-Quds University will be, which means knowledge management helps in achieving the quality of higher education. The study recommended the need to raise the efficiency of knowledge management processes at Al-Quds University because of its role in achieving higher education quality.&#x0D; keywords: management knowledge, quality of higher education, Al-Quds university.
SISTEM INFORMASI REAL COUNT PEMILIHAN UMUM BERBASIS WEB

General election is a means of implementing people's sovereignty which is carried out directly, publicly, freely, secretly, honestly and fairly in the unitary state of the Republic of Indonesia based on Pancasila and the 1945 Constitution. long. Candidate supporters have difficulty knowing the progress of the candidate's vote. The presence of technology such as Quick Count currently has important benefits in elections in every region, even at the level of the Presidential Election (Presidential Election). Many survey institutions such as LSI, LP3ES, Puskaptis, Cirus Surveyors Group, LRI and others conduct quick count surveys to find out the results of the Presidential Election. However, the results of the Quick Count that differ from several other survey institutions have triggered disputes between candidate supporters because they claim to be winners. Based on this, a Web-based General Election Real Count information system is proposed that can display the vote count results in detail at each TPS. In addition, it can be used as a comparison to the results of calculations made by the KPU.
SISTEM INFORMASI PELAYANAN KESEHATAN RAWAT INAP MENGGUNAKAN FRAMEWORK LARAVEL

The health services is a place that provides health services which include inpatient, outpatient and emergency services. Inpatient is a service provided to patients, where the patient has to undergo a treatment process which is handled directly by a doctor in accordance with the complaint of the disease. Easy service and fast handling from the health care unit both in terms of rooms and from the patient side are of course very much needed by inpatients. The problem that occurs is that it is difficult for inpatients to know the availability of rooms, making it difficult for patients to undergo treatment. Apart from the availability of rooms, the inpatient registration system is still being used in paper format. This results in piles of patient files making it difficult to find patient data and causes errors to be encountered when making reports due to loss of patient files. In this research, an Inpatient Information System will be built that can be used by various health services so that all inpatient data in health services can be integrated into this system. The research method used in this research uses the waterfall method, which starts from the specification of user requirements, planning, modeling, construction and software implementation. This system is built using the Laravel Framework using the PHP language which is used to build web-based applications in a more structured way. This information system was built with the aim of helping and facilitating the services provided to patients in the health care unit. Both in terms of inpatient registration, making patient reports, managing patient data and the availability of inpatient rooms.
SIMULASI PENERAPAN TEKNOLOGI FACE RECOGNITION PADA APLIKASI PENDATAAN KEHADIRAN KARYAWAN / KARYAWATI TERINTEGRASI E-MON SISTEM PENGGAJIAN DENGAN PENDEKATAN EIGENFACE DAN UXD LARAVEL VERSI 5.6 BERBASIS CLOUD SERVICE

Abstract — Face recognition is a face-oriented recognition method by comparing patterns that have previously been stored in the database. In general, facial image recognition systems are divided into two types, namely feature based systems and image based systems. To carry out facial image recognition, there are several kinds of approach algorithms that can support the processing of the face image. One approach algorithm that is well known and capable of processing images with a good level of accuracy is the eigenface approach. By using the eigenface approach, the face image will be processed by performing a matrix calculation, which results in an eucludian distance to be able to get the suitability of the face image. so this research produces a result which includes: the results of the test on the FGD Functional says for the accepted percentage is: 73% for not accepted is 27%, for Non-Functional FGD says for the accepted percentage is 82% and not accepted is 18 %, and for the results of testing the adaptation of ISO 9126 in the good category with a percentage result of 79%, and the last test result, namely the Blackbox Testing test to be accepted is 83% and for the results the percentage that is not accepted is 17%
PEMROSESAN DATA UTANG PIUTANG DENGAN MENERAPKAN SISTEM INFORMASI AKUNTANSI BERBASIS WEB

PT. Zaky Beef (Zbeef Indonesia) is a company engaged in General Services and Trading, namely as a Beef Cattle Supplier in the Bandar Lampung Region, which is located at Jl. Pisang Tanduk No. 51 Sub-District of Susunan Baru, Tanjung Karang Barat District, Bandar Lampung City. Apart from providing beef cattle to its customers, namely distributors and small shops. The Accounting Information System in Depts Process is a computerized and web-based accounting information system that is useful for recording the process of accounts payable in business activities. It is necessary to record accounts payable so that business owners can make the right decisions in managing their business. The Accounting Information System Depts Process has been developed by providing several features to facilitate the recording of the accounts depts process. These features are Login, Feedlot, Customer, Cattle Data, Purchases, Cattle Stock, Sales, Company Accounts Payable Data, Customer Receivable Data, Account Data, Reports. The development of this system uses the PHP programming language and uses the waterfall method, while for modeling software using STARUML and MariaDB as data storage media with a database management system by MySQL. The system testing technique used in this research is blackbox testing for testing its functionality, while non-functional testing is done by giving a questionnaire. The test results obtained are in good category and in accordance with user requirements but still need to be developed to maximize features.
THE USE OF TRANSLATION IN TEACHING WRITING NARRATIVE TEXT

THE USE OF TRANSLATION IN TEACHING WRITING NARRATIVE TEXT *Esterina Sipahutar **Masitowarni Siregar **Anni Holila Pulungan Universitas Negeri Medan ABSTRACT Sipahutar, Esterina. Registration Number: 214332101. The Use of Translation in Teaching Writing Narrative Text. A Thesis. English Educational Program, State University of Medan, 2020. This study aimed at describing the use of translation and investigating the reasons of using translation in teaching writing narrative text grade ten at SMK Broadcasting Bina Creative Medan. This study was qualitatively conducted that taken 2 English teachers of SMK Broadcasting Bina Creative Medan. The data were collected by using an interview and observing the whole process of the online teaching writing narrative text. The findings of this study showed that there were 73 times of using translation found during three processes that they were conveying and checking meaning of words or sentences, explaining grammar and classroom management. The reasons of using translation in teaching writing narrative text were facilitating communication, teacher-student relationship and the L2 learning. The findings of this study revealed that translation contributed to the study of foreign language teaching especially teaching writing skill. Keywords: translation, teaching and narrative text.
Penerapan Model Pembelajaran Kooperatif Tipe Jigsaw untuk Meningkatkan Motivasi dan Hasil Belajar IPS Materi Manusia, Tempat, dan Lingkungan Pada Siswa Kelas VII G SMP Negeri 1 Adiwerna Tahun Pelajaran 2019 / 2020

Various problems that inform a teacher in the classroom are indicators that the learning motivation of studentswho receive the subject matter. If learning motivation is low, it will lead to low student learning outcomesbecause motivation is a determination and effort as a result of the interaction of the learning given by theteacher. The research objective was to increase learning motivation and improve social studies learningoutcomes. The steps in the research through cycle I and cycle II include planning, implementing actions,monitoring and reflecting. The results showed that the application of the Jigsaw cooperative learning modelcould increase the social studies learning motivation from 68.75% in the first cycle to 78.13% in the secondcycle. The application of the Jigsaw cooperative learning model could improve learning o utcomes in theclassical social studies subject from 81.25% in the first cycle to 87.50% in the second cycle. Based on all theresults of the research, "Application of the Jigsaw Type Cooperative Learning Model to Increase Motivationand Social Studies Learning Outcomes of Human, Place, and Environment Material in Class VIIG Studentsof SMP Negeri 1 Adiwerna Academic Year 2019/2020" can be accepted
Flexible control of the median of the false discovery proportion

We introduce a multiple testing procedure that controls the median of the proportion of false discoveries (FDP) in a flexible way. The procedure only requires a vector of p-values as input and is comparable to the Benjamini-Hochberg method, which controls the mean of the FDP. Our method allows freely choosing one or several values of alpha after seeing the data -- unlike Benjamini-Hochberg, which can be very liberal when alpha is chosen post hoc. We prove these claims and illustrate them with simulations. Our procedure is inspired by a popular estimator of the total number of true hypotheses. We adapt this estimator to provide simultaneously median unbiased estimators of the FDP, valid for finite samples. This simultaneity allows for the claimed flexibility. Our approach does not assume independence. The time complexity of our method is linear in the number of hypotheses, after sorting the p-values.
Unsupervised Structure-Consistent Image-to-Image Translation

The Swapping Autoencoder achieved state-of-the-art performance in deep image manipulation and image-to-image translation. We improve this work by introducing a simple yet effective auxiliary module based on gradient reversal layers. The auxiliary module's loss forces the generator to learn to reconstruct an image with an all-zero texture code, encouraging better disentanglement between the structure and texture information. The proposed attribute-based transfer method enables refined control in style transfer while preserving structural information without using a semantic mask. To manipulate an image, we encode both the geometry of the objects and the general style of the input images into two latent codes with an additional constraint that enforces structure consistency. Moreover, due to the auxiliary loss, training time is significantly reduced. The superiority of the proposed model is demonstrated in complex domains such as satellite images where state-of-the-art are known to fail. Lastly, we show that our model improves the quality metrics for a wide range of datasets while achieving comparable results with multi-modal image generation techniques.
FedOS: using open-set learning to stabilize training in federated learning

Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between clients and the server. This brings many advantages but also poses new challenges. In this report, we explore this new research area and perform several experiments to deepen our understanding of what these challenges are and how different problem settings affect the performance of the final model. Finally, we present a novel approach to one of these challenges and compare it to other methods found in literature.
Sistem Informasi Akuntansi Penjualan Spare Part pada PT. Hadji Kalla Cabang Urip Sumoharjo Makassar

This research was conducted at PT Hadji Kalla Branch Information Sumiharjo Makassar with the aim to determine the accounting information system sales of spare parts PT Hadji Kalla Branch Information Sumiharjo Makassar. This type of research is descriptive qualitative, the authors describe the results of observations and interviews and analyzing the data obtained in the field. After analysis and discussion, the authors obtained the conclusion that the information system of accounting of sales of spare parts has been applied in accordance with the theory so as to produce information that is reliable enough, such as the existence of conformity between the task and the responsibility of each part, functioning optimally each function is related, the availability of the documents required, the conformity of the accounting records used, as well as the suitability of procedures that make up the system.
A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)

Data augmentation greatly increases the amount of data obtained based on labeled data to save on expenses and labor for data collection and labeling. We present a new approach for data augmentation called nine-dot MLS (ND-MLS). This approach is proposed based on the idea of image defor-mation. Images are deformed based on control points, which are calculated by ND-MLS. The method can generate over 2000 images for one exist-ing dataset in a short time. To verify this data augmentation method, extensive tests were performed covering 3 main tasks of computer vision, namely, classification, detection and segmentation. The results show that 1) in classification, 10 images per category were used for training, and VGGNet can obtain 92% top-1 acc on the MNIST dataset of handwritten digits by ND-MLS. In the Omniglot dataset, the few-shot accuracy usu-ally decreases with the increase in character categories. However, the ND-MLS method has stable performance and obtains 96.5 top-1 acc in Res-Net on 100 different handwritten character classification tasks; 2) in segmentation, under the premise of only ten original images, DeepLab obtains 93.5%, 85%, and 73.3% m_IOU(10) on the bottle, horse, and grass test datasets, respectively, while the cat test dataset obtains 86.7% m_IOU(10) with the SegNet model; 3) with only 10 original images from each category in object detection, YOLO v4 obtains 100% and 97.2% bottle and horse detection, respectively, while the cat dataset obtains 93.6% with YOLO v3. In summary, ND-MLS can perform well on classification, object detec-tion, and semantic segmentation tasks by using only a few data.
A model-based approach to meta-Reinforcement Learning: Transformers and tree search

Meta-learning is a line of research that develops the ability to leverage past experiences to efficiently solve new learning problems. Meta-Reinforcement Learning (meta-RL) methods demonstrate a capability to learn behaviors that efficiently acquire and exploit information in several meta-RL problems. In this context, the Alchemy benchmark has been proposed by Wang et al. [2021]. Alchemy features a rich structured latent space that is challenging for state-of-the-art model-free RL methods. These methods fail to learn to properly explore then exploit. We develop a model-based algorithm. We train a model whose principal block is a Transformer Encoder to fit the symbolic Alchemy environment dynamics. Then we define an online planner with the learned model using a tree search method. This algorithm significantly outperforms previously applied model-free RL methods on the symbolic Alchemy problem. Our results reveal the relevance of model-based approaches with online planning to perform exploration and exploitation successfully in meta-RL. Moreover, we show the efficiency of the Transformer architecture to learn complex dynamics that arise from latent spaces present in meta-RL problems.
Mass documentation of archaeological ship timbers: introducing a novel, time-efficient approach

This article introduces the annotated scans method as a new approach to ship timber recording. The proposed method consists of several phases: timbers are first scanned in 3D, these scans are then annotated in Rhino, and finally the timber catalogue is generated using a semi-automated workflow. We compare this approach to the current best practice method for timber recording, namely contact digitising. Based on our experience recording the timbers of the Wismar "Big Ship", we conclude that the proposed method matches, and in some regards surpasses the current best practice method, most notably in terms of cost and time efficiency.
Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor

Regression branch of Machine Learning purely focuses on prediction of continuous values. The supervised learning branch has many regression based methods with parametric and non-parametric learning models. In this paper we aim to target a very subtle point related to distance based regression model. The distance based model used is K-Nearest Neighbors Regressor which is a supervised non-parametric method. The point that we want to prove is the effect of k parameter of the model and its fluctuations affecting the metrics. The metrics that we use are Root Mean Squared Error and R-Squared Goodness of Fit with their visual representation of values with respect to k values.
Dynamic Template Initialization for Part-Aware Person Re-ID

Many of the existing Person Re-identification (Re-ID) approaches depend on feature maps which are either partitioned to localize parts of a person or reduced to create a global representation. While part localization has shown significant success, it uses either na{\i}ve position-based partitions or static feature templates. These, however, hypothesize the pre-existence of the parts in a given image or their positions, ignoring the input image-specific information which limits their usability in challenging scenarios such as Re-ID with partial occlusions and partial probe images. In this paper, we introduce a spatial attention-based Dynamic Part Template Initialization module that dynamically generates part-templates using mid-level semantic features at the earlier layers of the backbone. Following a self-attention layer, human part-level features of the backbone are used to extract the templates of diverse human body parts using a simplified cross-attention scheme which will then be used to identify and collate representations of various human parts from semantically rich features, increasing the discriminative ability of the entire model. We further explore adaptive weighting of part descriptors to quantify the absence or occlusion of local attributes and suppress the contribution of the corresponding part descriptors to the matching criteria. Extensive experiments on holistic, occluded, and partial Re-ID task benchmarks demonstrate that our proposed architecture is able to achieve competitive performance. Codes will be included in the supplementary material and will be made publicly available.