Reviewerly/exHarmony huggingface dataset:
MENGUKUR KESUKSESAN IMPLEMENTASI SISTEM APLIKASI KEUANGAN TINGKAT INSTANSI (SAKTI) DI KANTOR WILAYAH KEMENTERIAN AGAMA PROVINSI BALI DENGAN MODEL DELONE DAN MCLEAN

Through the use of Management Information Systems, the company has utilized technology to help improve service and business quality. The Management Information System used in the organization, for example at the Regional Office of the Ministry of Religion of the Province of Bali which uses a Management Information System as a tool for achieving goals based on the application they use. The Regional Office of the Ministry of Religion of Bali Province made the decision to use the Application from the Institutional Level Financial Application System (SAKTI) in January 2022.&#x0D; The results of this study suggest that, the four main constructs of Information Quality to users, System Quality to Users, System Quality to User Satisfaction, and Users to Net Benefits, were rejected based on test results which indicated that these constructs were not able to assess the successful implementation of the application. Institution Level Financial Application System (SAKTI). However, Information Quality on user satisfaction, Service Quality on users, Service Quality on User Satisfaction, Users on User Satisfaction and User Satisfaction with Net Benefits, of the 5 constructs were accepted based on test results which indicated that this construct was able to assess the success of the implementation of Application of the Institution Level Financial Application System (SAKTI) at the Regional Office of the Ministry of Religion of the Province of Bali.
Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization

Constrained combinatorial optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In some heuristic solvers, these are typically addressed by introducing certain Lagrange multipliers in the cost function, by relaxing them in some way, or worse yet, by generating many samples and only keeping valid ones, which leads to very expensive and inefficient searches. In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric tensor networks (TNs) and leverage their applicability as quantum-inspired generative models to assist in the search of solutions to combinatorial optimization problems. This allows us to exploit the generalization capabilities of TN generative models while constraining them so that they only output valid samples. Our constrained TN generative model efficiently captures the constraints by reducing number of parameters and computational costs. We find that at tasks with constraints given by arbitrary equalities, symmetric Matrix Product States outperform their standard unconstrained counterparts at finding novel and better solutions to combinatorial optimization problems.
Classification of Bullying-Related Web Documents: An Ecological Systems and Machine Learning Approach

The current study examines bullying-related words in web documents by using social big data and machine learning techniques based on the ecological systems theory. A big data approach is ideal for the study of bullying, as the two terms correspond in their primary features. This study asks the following research questions: First, can social big data be classified into bullying perpetration and victimization documents? Second, which ecological system is the most important in predicting documents related to bullying perpetration and victimization? For this study, we collected documents related to bullying-related documents by using online channels in South Korea and classified main keywords and topics through text mining and opinion mining based on the ecological systems theoretical framework. Machine learning analyses were conducted to present a prediction model of perpetration and victimization documents based on ecological factors. Accordingly, the results indicate all areas of the ecological system are important for understanding the bullying phenomena; moreover, they provide insight into the key areas for bullying intervention.
Concept Drift Detection in Just-in-Time Software Defect Prediction Using Model Interpretation

Context: To reduce the cost of software maintenance and testing is necessary to discover and examine the parts of the code that are defect-prone. Large-scale research has introduced just-in-time defect prediction models for the early detection of these parts. Moreover, multiple research investigated the performance stability of the SDP model over time. They have demonstrated that the model’s performance fluctuates over time due to concept drift.Objective: We aim at detecting concept drift points on the commits that are generated by the developers over time to deal with the performance instability. To this end, we also consider the chronological order of the data.Method: In this work, we have applied a publicly available dataset with long-time development including 20 open-source projects. Due to the real-world applications, we take also verification latency into account. In the initial idea, we have obtained CDD on within-project using the interpretation of the incremental and non-incremental models.Results: Then, we compare the performance of the proposed CDD algorithm with the basic methods based on incremental classification (i.e., ErrorRate PH, AUC-Er (ErrorRate) PH, Kappa-Er PH, Precision-Er PH, and Recall-Er PH). The proposed methods are assessed using well-known measures of CDD context, such as accuracy, missed detection rate, mean time to detection, mean time between false alarms, and meantime ratio. We evaluate it using Friedman's statistical test.Conclusions: According to the obtained results, the average interpretation vector does not correctly recognize the concept drift. The methods based on incremental classifier also has the lowest accuracy. Among the non-incremental-based methods, the methods that have used the interpretation of the positive effect have the highest accuracy. Through the strategies that have used the interpretation values of the features, we have derived features which have the most positive effect in identifying concept drift.
Forensic Facial Recognition: Review and Challenges

Facial recognition is a biometric technique for person identification. Traditional identification techniques (manual and semi-automatic techniques) for facial recognition suffer from time and resource complexities. Thus, automatic facial recognition eliminates these complexities by offering real-time solutions. Forensic image investigations handles biometric image evidence for face recognition in investigation, security, and surveillances. This paper presents forensic facial recognition techniques and reviews existing facial recognition systems in forensic science. Various challenges encountered in forensic facial recognition are also addressed to provide a future research scope. Recent developments in facial recognition are compared on the basis of datasets use, challenges addressed, and performance accuracy.
An Inclusive Paradigm to Study Mu-Rhythm Properties

Studying mu-rhythm in developmental disorders is crucial for identifying the origin of motor and social malfunctioning. However, the commonly used mu-rhythm experimental protocol, that requires following instructions, is challenging for children with motor and cognitive deficits. Here we present an inclusive experimental procedure that contains passive hand movement, closed and open eyes and hand movement observation and examine properties of EEG mu-rhythm obtained in this paradigm in 51 typically developing children and adults aged from 4 to 31 years. The independent component analysis (ICA) was used to separate alpha- and mu-rhythm components and showed better performance than the channel-wise analysis. The identified mu-rhythm ICA components were localized above the sensorimotor cortex, demonstrating suppression both to passive hand movement and hand movement observation and no difference in power between closed- and open-eye conditions. The alpha-rhythm ICA components were localized in occipital regions and demonstrate characteristic suppression to open-eye conditions. The mu-rhythm frequency of peak suppression to passive hand movement as well as the amount of suppression increased with age. The beta-band mu-rhythm activity, while being less pronounced, was also suppressed both during passive hand movement and hand movement observation. Thus, we confirmed the classical properties of mu-rhythm and for the first time showed the developmental trajectory of mu-rhythm properties obtained during passive hand movement. The proposed experimental protocol and pipeline can be used further in studies of the mu-rhythm in challenging populations.
Students’ Perceptions on the Roles of YouTube in Improving English Speaking Skill

The roles of YouTube in learning, especially language learning had been receiving numbers of attention recently. In the rencent years, numbers of researchers had been interested in using YouTube and its many beneficial roles in improving English speaking skills. A lot of research concluded that with many English speaking videos that YouTube could provide students, they can learn grammar, vocabulary, pronunciations, and other English-speaking related skills, and a number of teachers and students all across the world shared a positive perceptions regarding the roles of YouTube in improving English-speaking skills. Therefore, this study aimed to know the perception of English Department Students of Bunda Mulia University Serpong about the roles of YouTube in improving English speaking skills. The research was conducted using questionnaire with 22 questions regarding the roles of YouTube in improving English speaking skills. There are 51 respondents who participated in the study. Likert scale was utilized in this research, where the participants were asked to rate their perception using 4 points metric scales, Strongly Agree, Agree, Disagree, and Strongly Disagree. The result of the study indicated that the participants had a positive perception regarding YouTube in improving students’ English-speaking skills, and they all agreed with all the roles of YouTube in this regard.
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models

Transformers have greatly advanced the state-of-the-art in Natural Language Processing (NLP) in recent years, but present very large computation and storage requirements. We observe that the design process of Transformers (pre-train a foundation model on a large dataset in a self-supervised manner, and subsequently fine-tune it for different downstream tasks) leads to task-specific models that are highly over-parameterized, adversely impacting both accuracy and inference efficiency. We propose AxFormer, a systematic framework that applies accuracy-driven approximations to create optimized transformer models for a given downstream task. AxFormer combines two key optimizations -- accuracy-driven pruning and selective hard attention. Accuracy-driven pruning identifies and removes parts of the fine-tuned transformer that hinder performance on the given downstream task. Sparse hard-attention optimizes attention blocks in selected layers by eliminating irrelevant word aggregations, thereby helping the model focus only on the relevant parts of the input. In effect, AxFormer leads to models that are more accurate, while also being faster and smaller. Our experiments on GLUE and SQUAD tasks show that AxFormer models are up to 4.5% more accurate, while also being up to 2.5X faster and up to 3.2X smaller than conventional fine-tuned models. In addition, we demonstrate that AxFormer can be combined with previous efforts such as distillation or quantization to achieve further efficiency gains.
Mother Tongue Interference in EFL Students’ Writing

Understanding the similarities and differences between English, as the target language, and Indonesian, as the native language, is an essential thing for students to know if they want to master English. One problem as the most challenging thing for students is to express themselves in English appropriately, orally or written. The student’s original language, Indonesian, interferes with their ability to compose English sentences correctly, especially in writing. It is essential to find and identify errors made by students of English, in this case, the interference of Indonesian in writing. Therefore, this study was conducted to analyze the interference of Indonesian in students English writing. This study provides an analysis of the interference of Indonesian on English writing made by EFL students. The data for the study is collected from essay writing on a descriptive text by 32 university students in the fourth semester majoring in English and whose native language is Indonesian. Those 32 students were purposively chosen as samples since they sit in one class and had the Essay writing Course. The students then asked to write about two pages of descriptive essays on specific exciting topics for them. It is found that there are a total of 359 Indonesian interferences on the students’ English writing. The findings suggest valuable strategies to aid students in developing their writing skills, such as viewing English-language movies, reading English books or articles, and listening to English news and music.
Reduksi Buta Aksara melalui aplikasi Magguru Mabbaca pada kelompok remaja masjid di Desa Pationgi

Illiteracy is a crucial issue today, especially in Indonesia. The high illiteracy rate in Indonesia reaches 3.56% of the population. One of the most significant illiteracy issues is in Pationgi Village, Patimpeng District. Illiteracy is experienced by many teenagers, especially those who are not in school. The culture of the community also contributes to the high illiteracy rate in Pationgi Village. Most parents have never been to school or tried to get an education, which makes kids reluctant to go to school, leading many people who cannot read or write. The purpose of this community service is to reduce illiteracy through community service activities and the assistance of the Magguru Mabbaca application. Magguru Mabbaca is a mix of data, video, audio, text, and sounds that can be seen and heard on a smartphone. Magguru Mabbaca was developed to allow partners to understand and recognize letters and numbers quickly. Integrating the Bugis language with Magguru Mabbaca will assist the Pationgi Village community, which is still illiterate. Counseling at partner locations, training with two stages of training beginning with the introduction and operation of applications, and assistance to see an increase in partners' knowledge and understanding of reading and arithmetic are all part of the implementation methods. The result of this community service is that partners experience a significant increase in the knowledge component and application of technology, with an increase of 85% and 100%, respectively. This shows that the Magguru Mabbaca application helped partners improve their reading and math skills in a big way. Therefore, Magguru Mabbaca can be a solution for people in South Sulawesi who still have a high quantity of illiterates, which can be reduced by introducing holistic applications.
Analisis Komponen Utama dan Biplot untuk Mereduksi Faktor Inflasi Berdasarkan Indeks Harga Konsumen

Inflation of a region can be measured from the Consumer Price Index (CPI) by spending group. The aim is to look at the factors that influence monthly inflation based on the CPI for 2021. Principal Component Analysis is used to reduce the expenditure group variables in the CPI, followed by biplot analysis to display the visualization of the first two main components of the PCA in a two-dimensional graph. The results of the main component analysis, (1) the primary expenditure component consists of housing, water, electricity and household fuel variables; equipment, tools and household routine maintenance; transportation; information, communication and financial services; recreation, sports and culture, (2) secondary expenditure components include food, drink and tobacco variables; health; education; general, and (3) complementary expenditure components, namely clothing and footwear variables; personal equipment and other services. These three components simultaneously can represent 88.1% of the diversity of the data. Biplot analysis succeeded in describing the similarity and position of the variables with a total variance of 75%
Application of Case Based Reasoning Using The K-Nearest Neighbor Algorithm in an Expert System for Diagnosing Pests and Diseases of Sugarcane Plants

&lt;p&gt;Sugarcane pests and diseases are still diagnosed manually, which can lead to errors such as data loss or inaccurate data. The goal of this research is to develop an expert system for identifying plant pests and diseases that affect sugarcane yield and quality. This data was obtained through literature study, observation, and interviews. The Case Based Reasoning method is used to find cases by comparing previous cases with recent cases using similarity calculations with the K-Nearest Neighbor algorithm to find the best solution from the identified cases. The results of this study indicate that the expert system for diagnosing sugarcane pests and diseases is easy to use, the appearance is easy to reach, and the diagnostic process does not take a long time. Based on testing the accuracy of the system to diagnose according to the expert's mind, it got an accuracy of 96% from 50 cases tested with the system and got a percentage result of 87.33% from 10 respondents including very feasible criteria.&lt;/p&gt;
PERANCANGAN SISTEM INFORMASI KENAIKAN PANGKAT DAN GOLONGAN GURU SMKN 1 SAWAHLUNTO BERBASIS WEB

SMKN 1 Sawahlunto is one of the vocational high schools located in Talawi District, Sawahlunto City. The promotion system and classes that run in this school is still done manually using Microsoft excel. This causing the process of processing data to rise through the ranks is time-consuming and costly in collecting files for material requirements to be promoted. Therefore, this research was carried out with the aim of developing an application that can manage data and files for promotion and teacher classes of SMKN 1 Sawahlunto web-based. System development is carried out using use case diagrams, activity diagrams and class diagrams. The method used in this study is the waterfall method to analyze the system to be developed in order to provide convenience to each teacher in applying for promotion and class. From the results of the trials that have been carried out, it shows that the system that has been made can run effectively, efficiently and make it easier for teachers of SMKN 1 Sawahlunto to apply for promotion and class.
Aplikasi Sistem Manajemen Perpustakaan dengan Penerapan Pemrograman Berorientasi Objek

Library is one of the most important pillars to improve the quality of Indonesia’s education. Due to its importance for the next generation, the government tries to increase the establishment of schools and libraries so that the young generation can compete with people from another countries. However, the increasing number of libraries did not mean that the library service quality will become better. Many library systems in Indonesia still have not kept up with technology and still using the manual system, hence the management process and services are not effective. Because of that, this research is created for the purpose of producing a design for a library management application. The methods used in this research is split into multiple steps which is system planning, class diagram planning, MySQL database planning, and GUI planning. The following processes will produce a library management desktop application that is integrated to a database. With this application, processing, documenting, and deliverance of data can be done in more effective and efficient way.
e-recognition of Motorbike License Plate using Python and SQLite

Objectives: (1) To solve the problem of inefficiency and inaccuracy of vehicle monitoring;(2) To propose and develop a software that will serve as the initial replacement to a man-to-man motorbike monitoring; (3) Evaluate the components of the system in terms of functionality, efficiency, and accuracy.Methods: Waterfall model was used in the system development model.The proposal is a system development and evaluation.Python 3.6 was used in program coding, the Graphical User Interface (GUI) used was Tkinter, the database was SQLite, and the image processing OpenCV was utilized.KNN algorithm was used as the machine learning technique.Using Slovin's formula in finding the sample of respondents with 95% of confidence level and 5 % margin error, resulted 33 respondents.The survey questionnaire is ISO 9126 Quality Software as the bases for software evaluation.Frequency distribution, percentage, and weighted mean were used to interpret the data gathered from the software evaluation metrics.Findings: Using the Likert Scale System, the weighted mean of efficiency is 4.25 described as Highly Acceptable, and the weighted mean of accuracy is 4.73 interpreted as Highly Acceptable.The overall mean is 4.49 interpreted as Highly acceptable.The functionality of the software using the percentage and frequency distribution is 100% functional.The proposal is a solution for inefficient and inaccurate of motorbike monitoring.The overall interpretation can be considered as recommendation for further study and additional features for advance version of vehicle license plate recognition.Limitations or non-recognizable pieces found in the license plate can be bearable for future studies recommended.Novelty: Initial Step in replacing the man-to-man motorbike License Plate Monitoring.The Author 's version in developing a software that will solve the inefficient and inaccurately monitoring of the status of the congested number of vehicles passing in and out in a certain vicinity, specifically motorbikes.
FPT: Improving Prompt Tuning Efficiency via Progressive Training

Recently, prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). Despite extensively reducing the number of tunable parameters and achieving satisfying performance, PT is training-inefficient due to its slow convergence. To improve PT's training efficiency, we first make some novel observations about the prompt transferability of "partial PLMs", which are defined by compressing a PLM in depth or width. We observe that the soft prompts learned by different partial PLMs of various sizes are similar in the parameter space, implying that these soft prompts could potentially be transferred among partial PLMs. Inspired by these observations, we propose Fast Prompt Tuning (FPT), which starts by conducting PT using a small-scale partial PLM, and then progressively expands its depth and width until the full-model size. After each expansion, we recycle the previously learned soft prompts as initialization for the enlarged partial PLM and then proceed PT. We demonstrate the feasibility of FPT on 5 tasks and show that FPT could save over 30% training computations while achieving comparable performance.