Reviewerly/exHarmony huggingface dataset:
Relevansi Dinding Baca dalam Menumbuhkan Literasi Sosial Siswa di Saengprathip Wittaya Mulniti School Thailand

The focus in this research is related to the implementation of the reading wall as an effort to foster the literacy tradition of grade I students at MI Saengprathip Wittaya Mulniti School Thailand. This research uses qualitative research methods. The results showed that the implementation of the reading wall at MI Saengprathip Wittaya Mulniti School Thailand has 5 stages. The first stage, gives 60 minutes to read. The second stage, summarizes what has been read from the reading wall, the third stage is working on the problem. The fourth stage recounts what has been read in front of the class. The fifth stage provides appreciation for students who tell stories in front of the class, and appreciation for those who have worked on questions with the best scores. The role of grade I teachers in implementing the reading wall is as a companion and facilitator. In addition to assisting students in reading wall activities as a literacy process, the teacher also acts as a facilitator. The obstacle that occurs in implementing the reading wall as an effort to foster the literacy tradition of grade I students at MI Saengprathip Wittaya Mulniti School is that classroom teachers cannot focus on making reading walls because the teacher's working hours are the same as school hours. The lack of cohesiveness of all grade I teachers makes the making of reading walls difficult if it is done without the cohesiveness of teachers between class I.
Retraction: Big Data and Automatic Detection of Topics: Social Network Texts (Journal of Physics: Conference Series 1432 012073)

Published 15 September 2020 This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. To date, there is no evidence to suggest anyone other than Amelec Viloria / Jesus Silva was directly culpable for the actions that led to retraction. IOP Publishing Limited request any citations to this article be redirected to the original work (1). Anyone with any information regarding these papers is requested to contact conferenceseries@ioppublishing.org.
Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these approaches extract the best proposal by a greedy strategy, which may lose the local patch details outside the chosen candidate. In this paper, we propose a novel spatiotemporal graph neural network (STG-Net) to reconstruct more accurate masks for video object segmentation, which captures the local contexts by utilizing all proposals. In the spatial graph, we treat object proposals of a frame as nodes and represent their correlations with an edge weight strategy for mask context aggregation. To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph. The joint use of both local patch details and temporal relationships allow us to better address the challenges such as object occlusion and missing. Without online learning and fine-tuning, our STG-Net achieves state-of-the-art performance on four large benchmarks (DAVIS, YouTube-VOS, SegTrack-v2, and YouTube-Objects), demonstrating the effectiveness of the proposed approach.
REDEFINING SKILL-SET IN THE ERA OF DIGITIZATION - A CONCEPTUAL STUDY

In today‟s world, it is imperative to ensure that society, regions, countries, economies, and humans at large adapt to the new age of existence. The year 2020 has demonstrated this very distinctively due to the “new normal” way. In 2020, we have consumed more information by way of digital data and video in-home, in-hand, and on-demand. Information and Communication Technology (ICT) has contributed in a large way to making this rapid change. The first step to ensure this digital transformation is to "digitize" the data. This Digitization (“digitize” the data) process has encompassed the entire business ecosystem, and thereby investing in Digitization and implementing it correctly within the organization has become a need of the hour. This transformation can be a critical proposition since organizations look for specialized skill-set within their workforce to achieve their business objectives and to increase business value. Sourcing people with a specific skill-set can be a considerable challenge for an organization. Digitization will bring growth to the business but requires support from individuals with the appropriate skill set. This study will focus on the importance of the new skill-sets that will shape the Digitization within an organization and how the correct type of skill-set will be the key to success. The study of redefining skill-set in the era of digitation will also throw light on how it will impact organizational performance and how it will mutually benefit both the organization and its people.
행위와 상태의 서술 접사 ‘-하다’에 초점을 둔 한국어 어휘 교수학습의 원리 일고

This study focused on words with the suffix /-hada/ among descriptive words to shed light on teaching learning methods and principles of Korean vocabulary education. Some verbs using the hada suffix can form sentences with words that incorporate both the verb and the suffix. In this paper, these are distinguished as /-hada/(undonghayeo) and ''noun word(as a stem)+postposition''+''hada''(undongul hayeo), different sentence components, having different functions. We argue that these differences between the two forms should be reflected in learning. We believe that Korean vocabulary learning should reflect these characteristics of the Korean language, consider the cognitive structure of the Korean language at the node and anchor levels, and clearly take into account the level of learners during vocabulary training. To conclude, 1) learning Korean vocabulary in the future should reflect the characteristics of the Korean language; 2) this should be considered at the node and anchor levels of the cognitive structure of the Korean language.; 3) Korean vocabulary should be dealt with in terms of education; and 4) the learner and learning levels should be taken into consideration when teaching the Korean language.
Handling Complex Queries Using Query Trees

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.
Methodology of organization of educational work in primary schools

Raising children is one of the main tasks they should start with their parents without seeing the world. While pre-school education for young children is mainly with their parents, later educational work will continue with their teachers. The right teaching methodology chosen in the primary school will play an important role in children's future learning and personal development. From previously published textbooks, we can learn the following: “Method-based education is a tool for teachers to develop the moral beliefs, moral habits, skills, etc. of adolescents and young people. Education is a very complex process that reflects the sociopolitical life of each era. Its methods, forms, means, and factors have evolved over the centuries and become a tradition. Thus, in the process of upbringing, the teacher addresses the age and individual characteristics of the students in order to solve any educational problem in the interests of the individual student or the class”.
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction

Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts. Existing approaches, such as table filling and pairwise scoring, have shown impressive performance on various information extraction tasks, but they are difficult to scale to datasets with longer input texts because of their second-order space/time complexities with respect to the input length. In this work, we propose a Hybrid Span Generator (HySPA) that invertibly maps the information graph to an alternating sequence of nodes and edge types, and directly generates such sequences via a hybrid span decoder which can decode both the spans and the types recurrently in linear time and space complexities. Extensive experiments on the ACE05 dataset show that our approach also significantly outperforms state-of-the-art on the joint entity and relation extraction task.
MEDIAN LINE IMMETRITY AT CENTRIC OCCLUSSION IN FKG UNMAS DENPASAR

Introduction: Facial appearances especially teeth and face are some of the most important things in this era. The symmetry of the dental median line with the facial median line needs to be considered to create a balanced, harmonious, and attractive facial structure, especially when smiling. The position of the maxillary median line to the facial median line is an important factor in orthodontic diagnosis. This research aims to obtain the symmetry of the dental median line with the facial median line during centric occlusion in dentistry students of the Mahasaraswati University of Denpasar. Materials and Methods: The type of this research is descriptive with a cross-sectional approach that involved 57 participants. Results and Discussions: The results of this research are the percentage of the students whose dental median line with a facial median line is symmetrical (≤ 1 mm) was 77% that obtained 44 participants, while the percentage of students who had a dental median line with a facial median line is not symmetrical (&gt; 1 mm) were 23 % that obtained 13 participants with a shift of median line 2 mm in 7 participants, 3 mm in 5 participants, and 5 mm in 1 participant. Conclusion: Based on the results it can be concluded that the dentistry students of the Mahasaraswati University of Denpasar whose dental median line with their facial median line were symmetrical is more dominant.
Fast PCA in 1-D Wasserstein Spaces via B-splines Representation and Metric Projection

We address the problem of performing Principal Component Analysis over a family of probability measures on the real line, using the Wasserstein geometry. We present a novel representation of the 2-Wasserstein space, based on a well known isometric bijection and a B-spline expansion. Thanks to this representation, we are able to reinterpret previous work and derive more efficient optimization routines for existing approaches. As shown in our simulations, the solution of these optimization problems can be costly in practice and thus pose a limit to their usage. We propose a novel definition of Principal Component Analysis in the Wasserstein space that, when used in combination with the B-spline representation, yields a straightforward optimization problem that is extremely fast to compute. Through extensive simulation studies, we show how our PCA performs similarly to the ones already proposed in the literature while retaining a much smaller computational cost. We apply our method to a real dataset of mortality rates due to Covid-19 in the US, concluding that our analyses are consistent with the current scientific consensus on the disease.
Two-Stage Least Squares Random Forests with an Application to Angrist and Evans (1998)

We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to revisit the classic application of instrumental variables in Angrist and Evans (Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size, American Economic Review, Vol. 88, 1998). The two-stage least squares random forest allows one to investigate local heterogenous effects that cannot be investigated using ordinary 2SLS.
THE GALLERY WALK TECHNIQUE (GWT) ON WRITING SKILLS OF ANNOUNCEMENT TEXT

The research purpose is to improve writing skill on writing announcement text by using Gallery Walk Technique (GWT). It is conducted by using Classroom Action Research as the methodology of the research. The technique of analyzing the data by quantitative and qualitative. The quantitative data apply the tests, wether the qualitative are observation sheet, interview and photography evidence. This research accomplished in three terms, the first is pre-test, then first cycle, the last is second cycle and each cycle consisted of four steps of action research (planning, acting, observing, and reflecting). Pre-test conducted in one meeting, the first cycle conducted in two meetings and the second cycle conducted also in two meetings. In this research, the researcher uses 5 indicators, they are content, organization, vocabulary, grammar, and mechanics . In pre-research, most of the participants are still not able to mastered the indicators. After that, in first cycle there is an improvement on participants’ test. In this cycle, organization and mechanics became indicators that are quite confusing for them. The last, in second cycle 87% of the students have mastered 5 indicators of success mentioned above. As the conclusion, there is an improvement on writing announcement achievement from pre-test, first cycle and second cycle.
Experience of using a mobile application in the educational system

Mobile Learning the underlying concept in this article is mobile learning, and therefore it is important to define its meaning. According to an article published in Education Technology Solutions, mobile is primarily a way to create a non-traditional learning environment that exists anytime, anywhere. Mobile learning involves the use of mobile technology, either alone or in combination with other information and communication technologies, in order to learn anytime, any where. The paper presents the advantages of mobile learning (such as improved interaction between process participants) and problems (of a technical nature) use. Classifications of mobile applications used in training are given on the basis of meaningful and functional features, the main criterion for choosing which should be reliability.
Modelo de Análise Temporal em Contexto Semântico de Gerenciamento de Emergências

In the context of emergency management, where information comes from heterogeneous, complex and dynamic sources, decision making is required to be assertive and within a timely interval of time. Time has great relevance, being fundamental in the domain of this context, because it is creating a timeline, that is to say during it, the perception and comprehension of all the characteristics of a situation becomes viable. The quality of information becomes indispensable in the context of emergency management, mainly by dynamic and complex factors. This work aims to improve the informational processes of information retrieval through the creation of a model of temporal analysis in the semantic context. The nature of this work is qualitative, of theoretical-applied purpose and exploratory, and its methodology involving fire emergency situations, in which it was possible to evaluate the behavior of information quality and infer new temporal knowledge that may serve as inputs for making more assertive decisions.
Image Caption Generator using CNN-LSTM Deep Neural Network

Captioning pictures naturally is one of the significant aspects of the human visual framework. There are numerous benefits if there is a model which consequently inscription the scenes or climate encompassed by them and offers back the subtitle as a plain book. In this paper, we present a model dependent on CNN-LSTM neural organizations which naturally identifies the items in the pictures and creates inscriptions for the pictures. It utilizes Inception v3 pre-prepared model to play out the errand of distinguishing items and utilizations LSTM to produce the subtitles. It utilizes the method of Transfer Learning on pre-prepared models for the undertaking of item Detection. This model can perform two activities. The first is to recognize objects in the picture utilizing Convolutional Neural Networks and the other is to subtitle the pictures utilizing RNN based LSTM (Long Short Term Memory). It additionally utilizes a bar look for anticipating the inscriptions for example choosing the best words from the accessible corps. In this, we take top k expectations, feed them again in the model and afterward sort them utilizing the probabilities returned by the model. A portion of the product prerequisites of this undertaking is Tensor Flow V2.0, pandas, NumPy, pickle, PIL, OpenCV. A little GUI is made to transfer the picture to the model to create the inscription. The fundamental use instance of this undertaking is to help outwardly debilitated to comprehend the general climate and act as per that. The inscription age is one of the intriguing and centred fields of Artificial Intelligence which has numerous difficulties to survive. Inscription age includes different complex situations beginning from picking the dataset, preparing the model, approving the model, making pre-prepared models to test the pictures, identifying the pictures lastly producing the individual picture-based subtitles.
Learning model engineering in improving student skills in pandemic COVID-19

Web-based learning media is an educational services, allows the realization of edutainment with internet media. Compared to web-based learning, the problems of conventional learning can be viewed as limited interactivity of educators and students, the flexibility of providing time, place and teaching materials, and accessibility of learning material resources. This research aimed to determine the effect of learning by web-based second stage delivery care modules on improving the Associate's Degree(D-III)of Midwifery students. This research was a mixed methods research, combining R&D and Quantitative approaches, taking samples using total sampling, and held at STIKES Nani Hasanuddin in February-March 2021.The sample in this study was 60 associate's degree of midwifery students in the fourth semester, divided into 30 respondents as the control group and 30 respondents as the intervention group. Data were analyzed using the Independent T-Test. The learning model used the Askeb II delivery module learning combined with practice and demonstration for the intervention group, while the control group demonstrated using the available Askeb II learning guide. The modules were accessed and studied for three weeks. The length of the study was 4-5 times a week witha duration of 2 hours. The results showed that the average score of the student intervention group was 70, with 11 students passing (36.66%) and 19 students not passing (63.33%). In the post-test, the average score was 83, with 28 respondents were passing (93.33%) and two people were not (6.66%).Meanwhile, in the pre-test control group, the average score was 71, with 12 people passing (40%) and 18 people failed (60%). In the post-test, the average score was 76, with 21 people passing (70%) and 9 people failed (30%). Based on these results, it can be concluded that there were differences in student learning outcomes after using the Web-based Class II Normal Childbirth Care Learning Module so that the W-based Normal Childbirth Care Learning Module was better in increasing knowledge and skills of the second stage normal childbirth care.
Exploring Internal Communication in Public Libraries: Challenges and Opportunities for Library Leaders

Effective internal communication is essential to library staff being able to fulfill their duties, work together as a cohesive unit, and carry out the library’s mission. However, there is often a lack of coordinated focus on clear internal communication that includes all staff members in libraries. This can create environments where there is disconnect among staff and library departments leading to rumor mills and lowering morale. This exploratory study looked at the current state of internal library communications in public libraries and ideas to improve internal communications. Library leadership has the potential to create environments and cultures where internal communication is effective and supports library staff, their work, and the effective functioning of the library as a whole.
FedCM: Federated Learning with Client-level Momentum

Federated Learning is a distributed machine learning approach which enables model training without data sharing. In this paper, we propose a new federated learning algorithm, Federated Averaging with Client-level Momentum (FedCM), to tackle problems of partial participation and client heterogeneity in real-world federated learning applications. FedCM aggregates global gradient information in previous communication rounds and modifies client gradient descent with a momentum-like term, which can effectively correct the bias and improve the stability of local SGD. We provide theoretical analysis to highlight the benefits of FedCM. We also perform extensive empirical studies and demonstrate that FedCM achieves superior performance in various tasks and is robust to different levels of client numbers, participation rate and client heterogeneity.
FedMix: Approximation of Mixup under Mean Augmented Federated Learning

Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving privacy and eliminating the need to store data globally. While there are promising results under the assumption of independent and identically distributed (iid) local data, current state-of-the-art algorithms suffer from performance degradation as the heterogeneity of local data across clients increases. To resolve this issue, we propose a simple framework, Mean Augmented Federated Learning (MAFL), where clients send and receive averaged local data, subject to the privacy requirements of target applications. Under our framework, we propose a new augmentation algorithm, named FedMix, which is inspired by a phenomenal yet simple data augmentation method, Mixup, but does not require local raw data to be directly shared among devices. Our method shows greatly improved performance in the standard benchmark datasets of FL, under highly non-iid federated settings, compared to conventional algorithms.