Reviewerly/exHarmony huggingface dataset:
Perancangan Aplikasi Pembelajaran Dasar Bahasa Jepang Berbasis Android Dengan Implementasi Algoritma Fisher-Yates Shuffle

The ability of a foreign language is a distinct advantage for an individual. One of the most popular foreign languages is Japanese. Based on a questionnaire that has been conducted to 44 respondents, 65.9% have an interest in learning Japanese for various reasons. Then 93.8% stated that the Japanese language learning media was not enough. This application contains basic Japanese material such as katakana, hiragana, kanji, and grammar material that is equipped with questions that can be done by the user. This application is made by implementing the Fisher-Yates Shuffle algorithm which is used to randomize the questions contained in this application. So that users can work on questions repeatedly.
Analisis Butir Soal Tipe Multiple Choice Questions pada Penilaian Harian Sekolah Dasar

Daily assessment is an important educational instrument because it can assist teachers in measuring student learning progress. One of the most commonly used types of evaluation is Multiple Choice Questions (MCQs). This study aims to reveal the quality of daily assessment items in the form of MCQs based on effectiveness, reliability coefficient, and item deception. This quantitative descriptive research uses statistical data analysis, which is then processed into a description. The assessment instrument grid used theme 4 elementary school material. The instrument was tested on 20 class V MIN 3 Padang students. The results of this study found that the items in the test instrument were good enough to use because they had an average score of 11,367, a moderate difficulty level with a score of 0.568, and element values were quite reliable. They had good discriminating power with a score of 0.070. Even so, there are still a number of items that are not of high quality in terms of difficulty level, discriminating power, and question distraction, so they need to be revised or replaced with other items.
Enhanced AZ-Model for Requirement Change Management in the Context of Global Software Development

Software organizations are increasingly embracing the advantages of Global Software Development (GSD), such as access to highly skilled developers and reduced development costs. However, the implementation of Requirement Change Management (RCM) activities in GSD is often hindered by a lack of communication and coordination among project stakeholders, as well as an insufficient focus on traceability and monitoring of RCM activities. To overcome these issues, we have enhanced and improved an existing RCM framework to mitigate the identified challenges and to develop a quality product while achieving customer satisfaction and business objectives. To evaluate the effectiveness of the proposed Enhanced AZ-Model, we sought feedback from the industrial experts and performed a statistical analysis of the collected data. The Enhanced AZ-Model was further validated by performing simulations of the model. The empirical and simulation results indicate that the Enhanced AZ-model efficiently and effectively manage the demanded changes according to the budget and time constraints.
DESIGN OF AN INVENTORY INFORMATION SYSTEM FOR LABORATORY SUPPLIES

Data is inaccurate because it does not have a relevant data repository, data can be lost or damaged, inefficient data search and technicians cannot know for sure the amount of stock available. Based on some of the research above, the inventory information system for Laboratory goods in the Information Technology Study Program will be designed and built based on a website using the Laravel framework. The system development method used is the Incremental model. Incremental models are the result of a combination of elements from the waterfall model that are applied repeatedly, or it can be called a combination of the waterfall model and the Prototype Model. During testing, many errors were found in the system. Testing was carried out 4 times with a total of 164 test cases. In the first test, 98 bugs were found which were then reported to the programmer to be fixed. In the second test, 40 errors were found, in the third test, 19 errors were found, and in the last test conducted by the examiner, 0 bugs were found. The design of the Laboratory Goods Inventory Information System (SIMBA) begins with analyzing the weaknesses of the old system using the PIECES method. Then proceed with conducting a system requirements analysis and system feasibility analysis. After the analysis phase, it is continued with the design stage which begins with the UML design method.
Implementasi Aplikasi Informasi Kajian Islami di Masjid Nurul Iman

All Muslims must impart the great laws of Islam to all mankind so that they can feel peace and tranquility. In addition to the obligation to preach, Islam requires followers to study the religion. The more religious knowledge we have, the better we fear Allah and protect ourselves from disobedience. Because with religious knowledge, we can know what is good and what is bad. is not. Second, the potential for technological development, especially in Indonesia, is driven by several factors, such as the heavy use of the Internet, the increase in Islamic Websites on the Internet, and live streaming of Islamic studies, especially at Majid Nurul Iman Blok M. Supported. The development of this research information system application was designed based on community needs obtained through research methods, interviews, and questionnaires. During the design phase, use case and activity diagrams are used to describe the relationships that occur in each flow, data flow diagrams are used to determine user access levels (superadmin vs. admin), and ERDs are Used for database design. In the final stage, the test uses usability testing to get her SUS score. Here we measure the feasibility of finding problems and applying the information systems of this study according to the needs of the community. Finally, we can conclude that the development of this research information system application can be used according to the needs of the community. As such, this service activity can provide benefits and convenience to Muslims in acquiring religious knowledge.
The Arab Manager in Israel as a Leader and the Employees’ Different Job Aspects

Past studies show that leadership style may impact employees’ overall job aspects. However, a search of the literature showed that there is a knowledge gap when assessing whether manager leadership styles in the Arab sector of Israel contribute to the improvement of different job aspects. Consequently, quantitative method research was conducted to fill this knowledge gap where the objective of the study was to investigate the impact of managers’ leadership styles on job satisfaction, job performance, and employees’ motivation and engagement. Relevant data were collected from participants using survey questionnaires. Purposive sampling was used to recruit 250 employees from different organizations from the Arab sector in Israel. According to the results of the questionnaires, a significant impact was found between the managers’ leadership style and the different job aspects. Moreover, A significant positive relationship was also found between Job satisfaction, Job performance, and employees’ engagement.
EFEKTIVITAS PENGGUNAAN MEDIA BOARD GAME TERHADAP PENINGKATAN HASIL BELAJAR SISWA

Abstract: Based on observations made at SMKN 1 Lingsar, it was found that the learning process still uses conventional methods and modules as learning media so that student interest and learning outcomes are low. To overcome this, interesting and fun learning is applied, one of which is by using the learning media "Board game" with the hope of improving student learning outcomes. The purpose of this study is to determine the effectiveness of using Board Game Media in improving the learning outcomes of Class X Students in English Language Subjects at SMKN 1 Lingsar, West Lombok Regency for the 2021/2022 academic year. The data collection methods used in this study are tests as the main method, observation and documentation as complementary methods. The data analysis technique uses the t-test formula. The results of the analysis obtained a tcount value of 10.33 and a ttable value at a significant level of 0.05% = 2.056 with d.b (N-1) = 26-1 = 25 thus that the tcount value is greater than the ttable (10.33&gt; 2.056). This means that the null hypothesis is rejected and the alternative hypothesis is accepted which reads: There is an Effectiveness of Using Board Game Media on Improving Learning Outcomes in English Subjects for Class X Students of SMKN 1 Lingsar in the 2022/2023 Academic Year. So it can be concluded that the results of this study are "Significant". Keywords: Learning Media, Board Game, Learning Outcomes.
Turn-Taking Strategy During Video Conference Learning to Boost Student’s Confidence to Speak English

Turn-taking strategy is considered as an important strategy in online learning, especially when the learning is utilizing zoom application. The main objective of this study was to explore students’ perceptions of using turn-taking strategy using during the video conferencing to boost students’ confidence in speaking English. The study involved ten Islamic high school students in Medan. They were selected purposively to be interviewed. Following Miles and Huberman (2014), the data analysis procedure involved data reduction, data display, and drawing conclusions. The results of the study showed that teacher regarded his students’ confidence increased during the learning through video conference. The results also revealed that the students also feel the same way as the teacher does. This happened because of lack of anxiety which can be seen by the way students behave when they were in zoom video conference. The findings implied that turn-taking strategy can be regarded as one appropriate strategy to be used during online learning through video conference application to help students’ confidence in speaking English.&#x0D;
WRITING ANXIETY EXPERIENCED BY JUNIOR HIGH SCHOOL STUDENTS

This research is aiming at knowing the students’ writing anxiety level, including cognitive, somatic, and avoidance behavior anxiety. It applies a quantitative approach a descriptive method. The sample is 55 eighth-grade students at SMP Negeri 4 Baubau selecting using a cluster random sampling technique. A SLWAI is utilized as the instrument of this research. The research finds that the average score of cognitive anxiety is 3.11 which is in moderate level of anxiety, the mean score of somatic anxiety is 3.14 which is in moderate category, and the mean score of avoidance behavior is 3.04 which is in moderate category. Therefore, the lowest level of writing anxiety is the avoidance behavior, while the highest level of writing anxiety is somatic anxiety.
STUDENTS’ PERCEPTION ON SPEAKING ANXIETY IN ENGLISH CLASSROOM: PSYCHOLOGICAL VIEW

The world has seen significant changes as a result of globalization. As English is widely used all over the world, there is a need for learners to acquire communication skills in classrooms. Thus, learners should be able to overcome their psychological problems while learning in the classroom, such as anxiety. This study aims to investigate the student’s perception of psychological problems faced in terms of anxiety in SMPN 30 Malang. The respondents from this study are 9th-grade students in SMPN 30 Malang, which has a total number of 49 students. The data was collected from junior high school. In this study, the researcher used a descriptive qualitative study. The researcher collected the data through a questionnaire. The finding of this study shows that most of the 9th graders in SMPN 30 Malang have speaking anxiety. Test Anxiety is the most influencing factor affecting 9th graders’ speaking performance. The test anxiety was found because of their fear of failure and high pressure in their English class.&#x0D; &#x0D;
A duality framework for generalization analysis of random feature models and two-layer neural networks

We consider the problem of learning functions in the $\mathcal{F}_{p,\pi}$ and Barron spaces, which are natural function spaces that arise in the high-dimensional analysis of random feature models (RFMs) and two-layer neural networks. Through a duality analysis, we reveal that the approximation and estimation of these spaces can be considered equivalent in a certain sense. This enables us to focus on the easier problem of approximation and estimation when studying the generalization of both models. The dual equivalence is established by defining an information-based complexity that can effectively control estimation errors. Additionally, we demonstrate the flexibility of our duality framework through comprehensive analyses of two concrete applications. The first application is to study learning functions in $\mathcal{F}_{p,\pi}$ with RFMs. We prove that the learning does not suffer from the curse of dimensionality as long as $p>1$, implying RFMs can work beyond the kernel regime. Our analysis extends existing results [CMM21] to the noisy case and removes the requirement of overparameterization. The second application is to investigate the learnability of reproducing kernel Hilbert space (RKHS) under the $L^\infty$ metric. We derive both lower and upper bounds of the minimax estimation error by using the spectrum of the associated kernel. We then apply these bounds to dot-product kernels and analyze how they scale with the input dimension. Our results suggest that learning with ReLU (random) features is generally intractable in terms of reaching high uniform accuracy.
Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories

The lack of comprehensive sources of accurate vulnerability data represents a critical obstacle to studying and understanding software vulnerabilities (and their corrections). In this paper, we present an approach that combines heuristics stemming from practical experience and machine-learning (ML) - specifically, natural language processing (NLP) - to address this problem. Our method consists of three phases. First, an advisory record containing key information about a vulnerability is extracted from an advisory (expressed in natural language). Second, using heuristics, a subset of candidate fix commits is obtained from the source code repository of the affected project by filtering out commits that are known to be irrelevant for the task at hand. Finally, for each such candidate commit, our method builds a numerical feature vector reflecting the characteristics of the commit that are relevant to predicting its match with the advisory at hand. The feature vectors are then exploited for building a final ranked list of candidate fixing commits. The score attributed by the ML model to each feature is kept visible to the users, allowing them to interpret the predictions. We evaluated our approach using a prototype implementation named FixFinder on a manually curated data set that comprises 2,391 known fix commits corresponding to 1,248 public vulnerability advisories. When considering the top-10 commits in the ranked results, our implementation could successfully identify at least one fix commit for up to 84.03% of the vulnerabilities (with a fix commit on the first position for 65.06% of the vulnerabilities). In conclusion, our method reduces considerably the effort needed to search OSS repositories for the commits that fix known vulnerabilities.
Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare

Knowledge graphs (KGs) are a popular way to organise information based on ontologies or schemas and have been used across a variety of scenarios from search to recommendation. Despite advances in KGs, representing knowledge remains a non-trivial task across industries and it is especially challenging in the biomedical and healthcare domains due to complex interdependent relations between entities, heterogeneity, lack of standardization, and sparseness of data. KGs are used to discover diagnoses or prioritize genes relevant to disease, but they often rely on schemas that are not centred around a node or entity of interest, such as a person. Entity-centric KGs are relatively unexplored but hold promise in representing important facets connected to a central node and unlocking downstream tasks beyond graph traversal and reasoning, such as generating graph embeddings and training graph neural networks for a wide range of predictive tasks. This paper presents an end-to-end representation learning framework to extract entity-centric KGs from structured and unstructured data. We introduce a star-shaped ontology to represent the multiple facets of a person and use it to guide KG creation. Compact representations of the graphs are created leveraging graph neural networks and experiments are conducted using different levels of heterogeneity or explicitness. A readmission prediction task is used to evaluate the results of the proposed framework, showing a stable system, robust to missing data, that outperforms a range of baseline machine learning classifiers. We highlight that this approach has several potential applications across domains and is open-sourced. Lastly, we discuss lessons learned, challenges, and next steps for the adoption of the framework in practice.
RLocator: Reinforcement Learning for Bug Localization

Software developers spend a significant portion of time fixing bugs in their projects. To streamline this process, bug localization approaches have been proposed to identify the source code files that are likely responsible for a particular bug. Prior work proposed several similarity-based machine-learning techniques for bug localization. Despite significant advances in these techniques, they do not directly optimize the evaluation measures. We argue that directly optimizing evaluation measures can positively contribute to the performance of bug localization approaches. Therefore, In this paper, we utilize Reinforcement Learning (RL) techniques to directly optimize the ranking metrics. We propose RLocator, a Reinforcement Learning-based bug localization approach. We formulate RLocator using a Markov Decision Process (MDP) to optimize the evaluation measures directly. We present the technique and experimentally evaluate it based on a benchmark dataset of 8,316 bug reports from six highly popular Apache projects. The results of our evaluation reveal that RLocator achieves a Mean Reciprocal Rank (MRR) of 0.62, a Mean Average Precision (MAP) of 0.59, and a Top 1 score of 0.46. We compare RLocator with two state-of-the-art bug localization tools, FLIM and BugLocator. Our evaluation reveals that RLocator outperforms both approaches by a substantial margin, with improvements of 38.3% in MAP, 36.73% in MRR, and 23.68% in the Top K metric. These findings highlight that directly optimizing evaluation measures considerably contributes to performance improvement of the bug localization problem.
Ranking &amp; Reweighting Improves Group Distributional Robustness

Recent work has shown that standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on underrepresented groups due to the prevalence of spurious features. A predominant approach to tackle this group robustness problem minimizes the worst group error (akin to a minimax strategy) on the training data, hoping it will generalize well on the testing data. However, this is often suboptimal, especially when the out-of-distribution (OOD) test data contains previously unseen groups. Inspired by ideas from the information retrieval and learning-to-rank literature, this paper first proposes to use Discounted Cumulative Gain (DCG) as a metric of model quality for facilitating better hyperparameter tuning and model selection. Being a ranking-based metric, DCG weights multiple poorly-performing groups (instead of considering just the group with the worst performance). As a natural next step, we build on our results to propose a ranking-based training method called Discounted Rank Upweighting (DRU), which differentially reweights a ranked list of poorly-performing groups in the training data to learn models that exhibit strong OOD performance on the test data. Results on several synthetic and real-world datasets highlight the superior generalization ability of our group-ranking-based (akin to soft-minimax) approach in selecting and learning models that are robust to group distributional shifts.
Iterates of polynomials over $\F_q(t)$ and their Galois groups

A conjecture of Odoni stated over Hilbertian fields $K$ of characteristic zero asserts that for every positive integer $d$, there exists a polynomial $f\in K[x]$ of degree $d$ such that for every positive integer $n$, each iterate $f^{\circ n}$ of $f$ is irreducible and the Galois group of the splitting field of $f^{\circ n}$ is isomorphic to $[S_d]^{n}$, the $n$ folded iterated wreath product of the symmetric group $S_{d}$. We prove an analogue this conjecture over $\F_q(t)$, the field of rational functions in $t$ over a finite field $\F_q$ of characteristic $p>0$. We present some examples and see that most polynomials in $\F_q[t][x]$ satisfy these conditions.
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation

Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis and program synthesis, including parallelization, in light of the success of machine learning in natural language processing. However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack of an adequate dataset for training, an effective code representation with rich information, and a suitable machine learning model to learn the latent features of code for diverse analyses. To address these challenges, we propose a novel graph-based learning approach called Graph2Par that utilizes a heterogeneous augmented abstract syntax tree (Augmented-AST) representation for code. The proposed approach primarily focused on loop-level parallelization with OpenMP. Moreover, we create an OMP\_Serial dataset with 18598 parallelizable and 13972 non-parallelizable loops to train the machine learning models. Our results show that our proposed approach achieves the accuracy of parallelizable code region detection with 85\% accuracy and outperforms the state-of-the-art token-based machine learning approach. These results indicate that our approach is competitive with state-of-the-art tools and capable of handling loops with complex structures that other tools may overlook.