Reviewerly/exHarmony huggingface dataset:
Pengembangan Keterampilan Masyarakat melalui Transformasi Perpustakaan Berbasis Inklusi Sosial pada Perpusdes Lentera Desa Buluagung Karangan Trenggalek

Li brary as a place in developing community skills in order to do transformations that suitable for people need, one of them is for transform into libraries based on social inclusion. This research was done in order to describe about transforming of the village library "lentera desa" into libraries based on social inclusion to increase the community skills. This study involves descriptive qualitative methods with data collection through interviews, observations, and documentation. The result of this study shows that the lentera desa library In transforming into libraries based on social inclusion with several parties, for example the departement of library and Trenggalek district archive and UMKM for create programs to the benefit of society. As for the activity programs that have carried out the lantern of the village such ast knit training, occulcer training, and dancing training. Some difficulry in applying the transformation are processing the administration of library activities that has not been done well, there is no an activity program for disability, anda lack of work in marketing of knit products.
Critical Examination of Hate Speech in Nigeria

Hate speech touches on contested issues of dignity, free expression, liberty and democracy. It can thus be argued that there is a conflict between the right to freely advocate however distasteful the idea may be and the right to be free from prejudice and discrimination. In some circumstances, speech is restrained and the right to non-discrimination is preferred over free speech. Hate speech has been used invariably to mean expression which is abusive, insulting, intimidating, harassing or which incites violence, hatred, discrimination against group identified by characteristics such as race, religion, place of birth, residence, region, language, caste, community, sexual orientation or personal convictions. The methodology adopted in this paper was doctrinal. Both primary and secondary sources were employed in carrying out this paper. The finding of the paper is that asides from Nigeria, various countries of the world have adopted some measures to deal with hate speech and other related issues. While some countries have clearly defined laws, some others find it quite unnecessary to create laws that seek to regulate hate speech as it is perceived as an infringement to the fundamental human rights of freedom of expression. This paper recommended that the existing Cyber Crimes Act and the Anti-Terrorism Act, among other pre-existing regulations cover many of the offences the new bill seek to address rather than waste valuable resources on the enactment of fresh laws to enable effective implementation and avoid a situation of plurality of laws. Perhaps one of the most effective ways of combating hate speech would be to marginalize purveyors of such speeches. In the U.K., while far-right, fascist parties like the British National Party and the racist ideas they support are not banned, mainstream British politician avoid associating openly with members of such parties. In Nigeria, on the other hand, offensive and hate speech mongers are often seen as regional and ethnic heroes.
Effort Increase Ability Count Beginnings Through Method Jarimatika for Children Aged 4-5 Years at RA Babuttaqwa Ward Fir Year Teachings of 2021/2022

Study this aim for increase ability counting start through method fingers that can increase ability counting start in know draft number child especially at the age of 4-5 years in RA Babuttaqwa, seen from level success son on results research that has conducted researcher in observation and documentation at the RA Babuttaqwa school . Type study this is qualitative descriptive . There is one still child not yet capable counting use finger fingers his hands because still difficult and confused for counting start use finger his hands , moment To do count the second time using fingers her , child already start capable do it in accordance with stages development and age child. Method Jarimatika could increase ability counting beginning , and in special method Jarimatika could applied in enhancement ability counting start in children 4-5 years old at RA Babuttaqwa ward Fir.
ClusterCirc: Finding item clusters for circumplex instruments

We introduce ClusterCirc, a new method that finds item clusters for circumplex instruments as an alternative to conventional cluster analysis. When developing circumplex instruments, sorting items into subscales can be difficult because of the intended conceptual overlap of subscales in the circular model. ClusterCirc provides a statistical solution for sorting items into subscales by finding item clusters with optimal circumplex spacing of both items and clusters. In a simulation study, we found that ClusterCirc outperformed conventional cluster analysis in revealing circumplex clusters, especially for large within-cluster distances between items. Sorting accuracy was greater, and effects of data complexity and sample size were more stable in ClusterCirc than in conventional cluster analysis. We also found strong support for ClusterCirc in empirical circumplex data. ClusterCirc sorting resulted in subscales with good scale properties and greater circumplex fit than the original subscales and subscales based on cluster analysis. We recommend a sample size between n = 500 and 1,000 to ensure high sorting accuracy of ClusterCirc. We provide an R package for ClusterCirc (https://github.com/ancleo/ClusterCirc) with two main functions: ClusterCirc-Data (cc_data) performs ClusterCirc on empirical data. ClusterCirc-Simu (cc_simu) performs a tailored simulation study with the specifications of the data under the assumption of perfect circumplex spacing to assess circumplex fit of the data. We also provide the corresponding SPSS codes for ClusterCirc (https://github.com/ancleo/ClusterCirc_SPSS).
An Optimal Feature Set for Stylometry-based Style Change detection at Document and Sentence Level

Writing style change detection models focus on determining the number of authors of documents with or without known authors. Determining the exact number of authors contributing in writing a document particularly when the authors contribute short texts in form of a sentence is still challenging because of the lack of standardized feature sets able to discriminate between the works of authors. Therefore, the task of identifying the best feature set for all the tasks of the writing style change detection is still considered important. This paper sought to determine the best feature set for the writing style change detection tasks; separating documents with several style changes (multi-authorship) from documents without any style changes (single-authorship), and determining the number and location of style changes in the case of multi-authorship. We performed exploratory research on existing stylometric features to determine the best document level and sentence level features. Document level features were extracted and used to separate single authored from multi-authored documents, while sentence level features were used to answer the question of determining the number of style changes To answer this question, we trained a random forest classifier to rank document level features and sentence level features separately, and applied an ablation test on the top 15 sentence level features using k-means clustering algorithm to confirm the effect of these features on model performance. The study found out that the best document level feature set for separating documents with and without style change was provided by an ensemble of features including number of sentence repetitions (num_sentence_repetitions) as the most determinant feature, 5-grams, 4-grams, Special_character, sentence_begin_lower, sentence_begin_upper, diversity, automated_readability_index, parenthesis_count, first_word_uppercase, lensear_write_formula, dale_chall_readability, difficult_words, type_token_ratio. These were the top ranked features in experiment one. On the other hand, the top fifteen sentence level features based on feature ranks using random forest classifier were diversity, dale_chall_readability grade, check_available_vowel, flesch_kincaid grade, parenthesis_count, colon_count, verbs, bigrams, alphabets, personal pronouns, coordinating conjunctions, interjections, modals, type_token ratio and punctuations_count. Consequently, the optimal feature set for determining the number of style changes in documents was considered based on the results of the ablation study on the top fifteen sentence level features, and was provided by an ensemble of features including personal pronouns, check_available_vowels, punctuations_counts, parenthesis count, coordinating conjunctions and colon count.
Social Media based Hate Speech Detection using Machine Learning

Hate speech is a crime that has been increasing in recent years, not only in person but also online. There are several causes for this. There is tremendous growth in social media that promotes full freedom of expression through anonymity features. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. It promotes violence and hate crimes, creates social imbalances, and undermines peace, trust and human rights. Revealing hate speech in social media discourse is a very important but complex task. On the one hand, the anonymity provided by the Internet, especially social networks, makes people more likely to engage in hostile behavior. On the other hand, the desire to express one's thoughts on the Internet has increased, leading to the spread of hate speech. Governments and social media platforms can benefit from detection and prevention technologies, as this kind of bigoted language can wreak havoc on society. We help resolve this dilemma by providing a systematic overview of research on this topic in this survey. This project aims to accurately predict various forms by addressing different categories of hate individually and examining a set of text mining functions. Hate speech detection
Monitoring Application for the Secure Software Development based on Risk Assessment Model

The development of software in a wide range of remote locations by large developers generates problems, in which a number of bugs or errors appear after deployment.Therefore, the solution is developing a monitoring application based on secure software development and risk assessment concept on Software Development Life Cycle (SDLC).The developed concept is based on mapping result of touchpoints for secure software, and NIST SP 800-30 for risk management in each stage of SDLC.The measures particularly support developer teams in remote distributed environments.The focus is on the risk assessment performed during the stage of initialization to implementation.In addition, according to the mapping results as well as business process analysis, there are five main functions related to this study, including creating projects, designing process, developing process, testing process, and deployment.Additionally, a web-based monitoring application is implemented to secure the software development process based on security control procedures at each stage, and developed using PHP programming languages and MySQL for a database.Moreover, the application is triggered by five parameters: software type development, tools, database structures, module names, and errorrelated problems.From these parameters, the risks could be discovered and subsequently categorized into four types of risk, such as low, medium, high, and critical, based on the impact of each risk.The results signify that the number of supports significantly decreased by 80%.Correspondingly, this application is expected to support secure software development as well as provide efficient treatment for possible errors and security risks.
UX Scenario of Video Conferencing Platform for Learning Emotional-Focused Coping Using Conversation Cards

This study focused on emotional-focused coping as a way for the emotional well-being of the 2030 generation at a time when the end of COVID-19 is uncertain.This researcher presented a 'conversation card' as a method for learning cognitive reinterpretation and social support among emotional-focused coping.Through user surveys, the effectiveness of the conversation card's emotionalfocused coping was confirmed, and the effect was verified on the video conversation platform to improve the spatio-temporal limitations of the real conversation card.As a result, the limitations of the existing conversation cards were solved by combining them with the platform, and a UX scenario for learning emotional control strategies for the 2030 generation was presented.The goal of the UX scenario is to derive a quality dialogue method that positively affects resilience in a non-face-to-face environment.This study is meaningful in presenting a new vision of non-face-to-face communication in line with the new normal era, where an untact environment such as social distancing is required, and a productive discussion to realize it.
Research Trends of Self-Regulating Learning in Online Learning

This study attempted to present a method to utilize self-regulated learning ability in an online class situation.Since online learning is expected to continuously increase in the future, this study is intended to provide a way to increase online learning ability.To this end, this study examined research trends related to self-regulated learning within online learning.Specifically, articles published using two keywords 'online learning' and 'self-regulated learning' were collected and analyzed for the final 96 papers.Self-regulated learning was classified into cognition, motivation, and behavior, focusing on online learning, and semantic network analysis was conducted in each.As a result, in self-regulated learning in online learning, 'learning' was in the most important position in the entire semantic network.Also, it was high in the order of 'strategy', 'online', 'class', 'application', 'academic achievement', 'satisfaction', 'student', 'participation', and 'group'.In addition, when analyzing the semantic network for each area of cognition, motivation, and behavior, there was a difference in major keywords for each area.Based on these results, discussions and implications were presented.
Establishment of ESG Management System for Small and Medium-Sized Enterprises for Future Growth and Direction of Improvement

The future industrial growth is expected to proceed through ESG (Environment, Social, Governance) and SDGs (Sustainable Development Goals).In order to break away from the current passive and rigid industrial structure and transform it into an active structure by solving the structural problems of lower SMEs, SMEs are required to have their own capabilities through active support from the government and local governments.It is important to create a proper win-win relationship between large companies and sub-companies by improving their competitiveness, and to create a more systematic and system support system to protect SMEs in the short term, provide incentives for sustainable management, and revitalize the private ecosystem.ESG management should be considered not just a risk management indicator, but a growth engine indicator that creates new opportunities for the future.It is desirable to create standards through many consultations because it is a social demand for the survival and future sustainability of companies, whether they are large or small companies.This study will help SMEs establish the ESG management system and help local SMEs secure international competitiveness by moving faster and faster to international environmental changes and provide future growth engines through ESG.
The Study of Middle-aged Female Volunteers on their Volunteer Satisfaction and their Willingness to Continue Participating in Volunteer Activities according to their Motives

The purpose of this study is to revitalize volunteer activities by identifying the volunteer satisfaction and willingness to continue participating in volunteer activities of middle-aged female volunteers according to their motives.To this end, a questionnaire survey was conducted on 102 middle-aged female volunteers in their 40s and older who belong to the Anyang City Volunteer Center, and related variables were analyzed.As a result of the analysis, it was found that the more positive (+) the volunteer's motivation to participate in volunteer activity was, the higher the volunteer satisfaction and the will to continue.
DetIE: Multilingual Open Information Extraction Inspired by Object Detection

State of the art neural methods for open information extraction (OpenIE) usually extract triplets (or tuples) iteratively in an autoregressive or predicate-based manner in order not to produce duplicates. In this work, we propose a different approach to the problem that can be equally or more successful. Namely, we present a novel single-pass method for OpenIE inspired by object detection algorithms from computer vision. We use an order-agnostic loss based on bipartite matching that forces unique predictions and a Transformer-based encoder-only architecture for sequence labeling. The proposed approach is faster and shows superior or similar performance in comparison with state of the art models on standard benchmarks in terms of both quality metrics and inference time. Our model sets the new state of the art performance of 67.7% F1 on CaRB evaluated as OIE2016 while being 3.35x faster at inference than previous state of the art. We also evaluate the multilingual version of our model in the zero-shot setting for two languages and introduce a strategy for generating synthetic multilingual data to fine-tune the model for each specific language. In this setting, we show performance improvement 15% on multilingual Re-OIE2016, reaching 75% F1 for both Portuguese and Spanish languages. Code and models are available at https://github.com/sberbank-ai/DetIE.
Motion Estimation for Large Displacements and Deformations

Large displacement optical flow is an integral part of many computer vision tasks. Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model conditioned on colour, gradient and smoothness, making them sensitive to noise in the sparse matches, deformations, and arbitrarily large displacements. This paper addresses this problem and presents HybridFlow, a variational motion estimation framework for large displacements and deformations. A multi-scale hybrid matching approach is performed on the image pairs. Coarse-scale clusters formed by classifying pixels according to their feature descriptors are matched using the clusters' context descriptors. We apply a multi-scale graph matching on the finer-scale superpixels contained within each matched pair of coarse-scale clusters. Small clusters that cannot be further subdivided are matched using localized feature matching. Together, these initial matches form the flow, which is propagated by an edge-preserving interpolation and variational refinement. Our approach does not require training and is robust to substantial displacements and rigid and non-rigid transformations due to motion in the scene, making it ideal for large-scale imagery such as Wide-Area Motion Imagery (WAMI). More notably, HybridFlow works on directed graphs of arbitrary topology representing perceptual groups, which improves motion estimation in the presence of significant deformations. We demonstrate HybridFlow's superior performance to state-of-the-art variational techniques on two benchmark datasets and report comparable results with state-of-the-art deep-learning-based techniques.
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high data accuracy and minimizing resource consumption on users' devices. To achieve this, we revisit distributed differential privacy based on recent results in secure multiparty computation, and we design a scalable and adaptive distributed differential privacy approach for location analytics. Evaluation on public location datasets shows that this approach successfully generates metropolitan-scale heatmaps from millions of user samples with a worst-case client communication overhead that is significantly smaller than existing state-of-the-art private protocols of similar accuracy.
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization

In this paper, we propose a novel solution for non-convex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of sub-problems corresponding to each variable, and then iteratively optimize each sub-problem using a fixed updating rule. However, due to the intrinsic non-convexity of the original optimization problem, the optimization can usually be trapped into spurious local minimum even when each sub-problem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, are highly limited by the lack of labelled data and restricted explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method, which aims to minimize a partial of the global losses over iterations instead of carrying minimization on each sub-problem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. Meanwhile, the proposed MLAM still maintains the original algorithmic principle, which contributes to a better interpretability. We evaluate the proposed method on two representative problems, namely, bi-linear inverse problem: matrix completion, and non-linear problem: Gaussian mixture models. The experimental results validate that our proposed approach outperforms AM-based methods in standard settings, and is able to achieve effective optimization in challenging cases while other comparing methods would typically fail.
The Overview of Final Student Self-Efficacy towards Lectures

This article aims, first to describe the self-efficacy of final-semester students in lectures based on Albert Bandura's personality theory, second describe the factors that influence the self-efficacy of final students toward lectures based on Albert Bandura's personality theory. This study was qualitative and descriptive. The primary data sources in this study were students’ perceptions of their efficacy. Data collection was conducted through interviews with the final students as research subjects. Validation technique using triangulation. The results of the study, first final semester students have low self-efficacy and high self-confidence, second factors that influence student self-efficacy are lack of experience in mastering something, and social model modeling social persuasion.