Reviewerly/exHarmony huggingface dataset:
Modul Deret Pangkat Tergeneralisasi T[[S]]-Noether

Dalam disertasi ini dikaji syarat cukup Modul Deret Pangkat Tergeneralisasi (MDPT) M[[S]] atas Ring Deret Pangkat Tergeneralisasi (RDPT) R[[S]] merupakan modul T[[S]]-Noether, dengan T himpunan multiplikatif di ring R, M modul atas R dan S monoid terurut tegas. Hal ini dilakukan dengan terlebih dahulu menentukan syarat cukup modul Polinomial M[X] atas R[X], modul Polinomial Laurent M[X,X^] atas R[X,X^], modul deret pangkat M[[X]] atas R[[X]] dan modul deret Laurent M[[X,X^]] atas R[[X,X^]] merupakan modul T-Noether. Hal ini termotivasi dengan melihat fakta bahwa, modul-modul tersebut merupakan bentuk khusus dari MDPT M[[S]]. Pada tahap berikutnya, ditentukan syarat cukup modul M[X] merupakan modul T[X]-Noether, modul M[X,X^] merupakan modul T[X,X^]-Noether, modul M[[X]] merupakan modul T[[X]]-Noether dan modul M[[X,X^]] merupakan modul T[[X,X^]]-Noether, dengan T[X], T[X,X^], T[[X]] dan T[[X,X^]] secara berturut-turut adalah himpunan multiplikatif di ring R[X], R[X,X^], R[[X]] dan R[[X,X^]]. Hasil penelitian ini menunjukkan bahwa T[[S]] merupakan himpunan multiplikatif di RDPT R[[S]] dengan memberikan syarat T\subseteq R semiring. Selanjutnya dengan menambahkan syarat cukup RDPT R[[S]] T-Noether dan syarat cukup MDPT M[[S]] dibangun secara berhingga, diperoleh syarat cukup MDPT $M[[S]]$ merupakan modul T[[S]]-Noether. Selain itu, hubungan antara modul hampir Noether, modul hampir dibangun secara berhingga dan modul T-Noether dapat diterapkan pada struktur MDPT M[[S]], yaitu diperolehnya syarat cukup lain MDPT M[[S]] merupakan modul T[[S]]-Noether. In this dissertation, we investigate the sufficient conditions for Generalized Power Series Modules (GPSM) $M[[S]]$ over Genera\- lized Power Series Rings (GPSR) $R[[S]]$ to be a $T[[S]]$-Noetherian modules, where $T$ is a multiplicative set of ring $R$, $M$ is a module over $R$ and $S$ is a strictly ordered monoid. This is done by first determining sufficient conditions for the Polynomial modules $M[X]$ over $R[X]$, Laurent Polynomial modules $M[X,X^]$ over $R[X,X^]$, power series modules $M[[X]]$ over $R[[X]]$ and Laurent series modules $M[[X,X^]]$ over $R[[X,X^]]$ to be $T$-Noetherian modules. Moreover, we determine the sufficient conditions for $M[X]$, $M[X,X^]$, $M[[X]]$ and $M[[X,X^]]$ to be $T[X]$-Noetherian, $T[X,X^]$-Noetherian, $T[[X]]$-Noetherian and $T[[X,X^]]$-Noetherian, respectively. The results of this study indicate that $T[[S]]$ is a multiplicative set of GPSR $R[[S]]$ by giving the conditions $T\subseteq R$ is a semiring. Furthermore, by adding sufficient conditions of GPSR $R[[S]]$ to be $T$-Noetherian and sufficient conditions of GPSM $M[[S]]$ to be finitely generated, we obtained the sufficient conditions for GPSM $M[[S]]$ to be $T[[S]]$-Noetherian. In addition, the relationship between almost Noetherian modules, almost finitely generated modules and the $T$-Noetherian modules can be applied to the structure of GPSM $M[[S]]$, which is another sufficient condition for GPSM $M[[S]]$ to be $T[[S]]$-Noetherian.
Protecting Color Images by the Chaotic Watermarking

Digital watermarking is a method to embed data into digital content to safeguard it from counterfeiting and stealing. In this study, the chaotic algorithm is used to generate a digital watermark. The study includes two phases; the first phase is embedding chaotic watermark images in the host image both of them are color images and the second stage uses a chaotic algorithm in watermark extraction. The aim of the work is to increase reliability and Immunization against attacks. PSNR, NC, and MSE measures are used in this study to get the results. The results achieved prove the watermarked is robust.
A Fast Location Algorithm for Very Sparse Point Clouds Based on Object Detection

Limited by the performance factor, it is arduous to recognize target object and locate it in Augmented Reality (AR) scenes on low-end mobile devices, especially which using monocular cameras. In this paper, we proposed an algorithm which can quickly locate the target object through image object detection in the circumstances of having very sparse feature points. We introduce YOLOv3-Tiny to our algorithm as the object detection module to filter the possible points and using Principal Component Analysis (PCA) to determine the location. We conduct the experiment in a manually designed scene by holding a smartphone and the results represent high positioning speed and accuracy of our method.
Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data

Recently, sequence-to-sequence (seq-to-seq) models have been successfully applied in text-to-speech (TTS) to synthesize speech for single-language text. To synthesize speech for multiple languages usually requires multi-lingual speech from the target speaker. However, it is both laborious and expensive to collect high-quality multi-lingual TTS data for the target speakers. In this paper, we proposed to use low-quality code-switched found data from the non-target speakers to achieve cross-lingual voice cloning for the target speakers. Experiments show that our proposed method can generate high-quality code-switched speech in the target voices in terms of both naturalness and speaker consistency. More importantly, we find that our method can achieve a comparable result to the state-of-the-art (SOTA) performance in cross-lingual voice cloning.
ON THE USE OF SOME CLOUD SERVICES IN EDUCATION

Computer and related technologies, in particular the Internet, are an integral part of our life. In connection with the requirements of the Federal State Educational Standards to educational organizations, high school teachers need to own competencies related to the use of information and computer technologies (ICT) in their professional activity, and in case of music universities — music and computer technologies. ICT not only simplifies and accelerates many forms of the teachers’ work, but also allows to find new ways of working in the educational process. In particular, interaction with students thanks to ICT becomes real-time even in case of distance learning. In this regard, Internet technologies, particularly cloud services — web sites providing various technical capabilities to users without installing any software — are becoming increasingly important for teachers. A significant feature of such tools is their accessibility from any device and from anywhere with Internet access, so the necessary materials are always available to the teacher and, if needed, to other educational process participants. This article considers some cloud services, which can become effective tools in the professional activities of a high school teacher on group disciplines.
PERANCANGAN SISTEM INFORMASI PENDISTRIBUSIAN REKAM MEDIS PASIEN BPJS DI RUMAH SAKIT KHUSUS GIGI DAN MULUT KOTA BANDUNG

This studi aimed to find out the analyzing, designing, and also to implementation of the BPJS patients medical records distribution information system using Microsoft Visual Studio 2010 at Rumah Sakit Khusus Gigi dan Mulut Kota Bandung. The research method used is qualitative descriptive method. The Data collection techniques used by interviews, observations, literart reviews, and internet browsing that has close relation to the problem and The Software development used is Waterfall method to take the system to from a system that is still manual to software system. From the research has done. that there are problems in the BPJS patients medical record distribution such as : The BPJS Patient medical record distribution system has done manually; There is was not available the system to provide information about the medical records which has distributed, There was not available the information about BPJS patients claimed. The suggestions give to solve those problem are : They should build a BPJS patients medical record distribution infomation system, Should do the Implementation information system, Should do optimalizations of the implementation information system, They should provide the training to the officers information system.
Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee.

The Bayes error rate (BER) is a fundamental concept in machine learning that quantifies the best possible accuracy any classifier can achieve on a fixed probability distribution. Despite years of research on building estimators of lower and upper bounds for the BER, these were usually compared only on synthetic datasets with known probability distributions, leaving two key questions unanswered: (1) How well do they perform on real-world datasets?, and (2) How practical are they? Answering these is not trivial. Apart from the obvious challenge of an unknown BER for real-world datasets, there are two main aspects any BER estimator needs to overcome in order to be applicable in real-world settings: (1) the computational and sample complexity, and (2) the sensitivity and selection of hyper-parameters. In this work, we propose FeeBee, the first principled framework for analyzing and comparing BER estimators on any modern real-world dataset with unknown probability distribution. We achieve this by injecting a controlled amount of label noise and performing multiple evaluations on a series of different noise levels, supported by a theoretical result which allows drawing conclusions about the evolution of the BER. By implementing and analyzing 7 multi-class BER estimators on 6 commonly used datasets of the computer vision and NLP domains, FeeBee allows a thorough study of these estimators, clearly identifying strengths and weaknesses of each, whilst being easily deployable on any future BER estimator.
LONGREAD TECHNOLOGY RESOURCES IN LITERARY EDUCATIO

Introduction. The paper examines the problem of updating the literary education toolkit in the context of the new longread technology. The study aims to substantiate the application of this technology in literary education, to prove that the work with classic literature can be interesting and effective, and analyze the developmental and motivational resources of this technology. Materials and Methods. The study relied on an interdisciplinary approach to teaching and was aimed at updating the technologies applied in the educational process. The longread technology used in journalistic practices was considered as a new toolkit. The substantiation of its application in literary education involved a survey of schoolchildren and students. Analysis and systematization of the survey results proved the assumptions of the authors’ to be true. The study was conducted in the schools in Irkutsk and at the Humanities and Aesthetic Department of the Pedagogical Institute of Irkutsk State University. Results. The scientific literature has been analyzed and the characteristic features of the current socio-cultural situation, which determine the specificity of perception and thinking of modern schoolchildren, have been identified. Developing resources and problematic aspects of the longread technology application in literary education are described. The findings indicate the reserves of this technology for the reader and creative development, its controversial aspects, and the specifi city of the longread when used in diff erent stages of literary education. In conclusion, the authors emphasize that provided there is an understanding of how, under what conditions, and to what works the longread can be applied, this technology can undoubtedly contribute to the involvement in reading and co-creation. Mastering new information technologies is a requirement of the time for a modern teacher and opens up prospects for their professional development. Keywords: digitalization, media space, communication, technology, creolized text, longread, multimedia elements, editing, text markup, interpretation
Topic-Guided Abstractive Multi-Document Summarization

A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries. Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation. Since topic extraction can be viewed as a special type of summarization that "summarizes" texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other. Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge metrics and human evaluation, meanwhile learns high-quality topics.
Mengenalkan Literasi Siswa Kelas Awal Disaat Pandemi Melalui Pembelajaran Daring Dengan Media Buku Besar untuk Meningkatkan Minat Membaca Siswa

Reading is an activity or cognitive process that seeks to find various information contained in writing. This means reading is a thinking process to understand the contents of the text read. Through reading activities a person can find out what was previously unknown. Reading skills possessed by students have a great influence in determining success in following the learning process. Students who are able to read well and quickly will achieve good learning outcomes as well. Through the Big Book media, it is hoped that students will be able to improve their literacy by reading through interesting pictures according to the characteristics of students in the early grades, who are more interested in seeing pictures than those in the first grade. only in the form of writing. With the Bigbook media, students' curiosity will appear and they will start reading through the pictures they see. Of course, it will be different because it introduces literacy online because it is still during the covid-19 pandemic which has not allowed students to meet face-to-face at school. This is a dilemma for teachers to introduce literacy to their students through online learning.
Which Samples Should be Learned First: Easy or Hard?

An effective weighting scheme for training samples is essential for learning tasks. Numerous weighting schemes have been proposed. Some schemes take the easy-first mode, whereas some others take the hard-first one. Naturally, an interesting yet realistic question is raised. Which samples should be learned first given a new learning task, easy or hard? To answer this question, both theoretical analyses and experimental verification are conducted. First, a general optimized objective function is proposed, revealing the relationship between the difficulty distribution and the difficulty-based sample weights. Second, on the basis of the optimized objective function, theoretical answers are obtained. Besides the easy-first and hard-first modes, there are two other priority modes, namely, medium-first and two-ends-first. The prior mode does not necessarily remain unchanged during the training process. Third, an effective and universal solution is proposed to select the optimal priority mode when there is no prior knowledge or theoretical clues. The four modes, namely, easy/medium/hard/two-ends-first, can be flexibly switched in the proposed solution. Fourth, a wide range of experiments is conducted under various scenarios to further compare the weighting schemes in different modes. On the basis of these works, reasonable and comprehensive answers are obtained. Factors including the distribution of samples' learning difficulties and the validation data determine which samples should be learned first in a learning task.
AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation

Questions in Community Question Answering (CQA) sites are recommended to users, mainly based on users' interest extracted from questions that users have answered or have asked. However, there is a general phenomenon that users answer fewer questions while pay more attention to follow questions and vote answers. This can impact the performance when recommending questions to users (for obtaining their answers) by using their historical answering behaviors on existing studies. To address the data sparsity issue, we propose AskMe, which aims to leverage the rich, hybrid behavior interactions in CQA to improve the question recommendation performance. On the one hand, we model the rich correlations between the users' diverse behaviors (e.g., answer, follow, vote) to obtain the individual-level behavior interaction. On the other hand, we model the sophisticated behavioral associations between similar users to obtain the community-level behavior interaction. Finally, we propose the way of element-level fusion to mix these two kinds of interactions together to predict the ranking scores. A dataset collected from Zhihu (1126 users, 219434 questions) is utilized to evaluate the performance of the proposed model, and the experimental results show that our model has gained the best performance compared to baseline methods, especially when the historical answering behaviors data is scarce.
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models

Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS is usually composed of a recommendation module and a generation module. In the previous work, these two modules are loosely connected in the model training and are shallowly integrated during inference, where a simple switching or copy mechanism is adopted to incorporate recommended items into generated responses. Moreover, the current end-to-end neural models trained on small crowd-sourcing datasets (e.g., 10K dialogs in the ReDial dataset) tend to overfit and have poor chit-chat ability. In this work, we propose a novel unified framework that integrates recommendation into the dialog (RecInDial) generation by introducing a vocabulary pointer. To tackle the low-resource issue in CRS, we finetune the large-scale pretrained language models to generate fluent and diverse responses, and introduce a knowledge-aware bias learned from an entity-oriented knowledge graph to enhance the recommendation performance. Furthermore, we propose to evaluate the CRS models in an end-to-end manner, which can reflect the overall performance of the entire system rather than the performance of individual modules, compared to the separate evaluations of the two modules used in previous work. Experiments on the benchmark dataset ReDial show our RecInDial model significantly surpasses the state-of-the-art methods. More extensive analyses show the effectiveness of our model.
Analysis of Singapore Educational Tracking System

According to Programme for International Student Assessment (PISA) 2018 world ranking, Singapore is ranked 2nd in the world.This article aims to explore the educational system in Singapore focusing on its well-known tracking system, teaching idea of elitism, attitude for immigrants and educational downside.The result shows Singapore shares several advantages in education methods with other countries.
Product Quality Evaluation Method (PQEM): To Understand the Evolution of Quality Through the Iterations of a Software Product

Promoting quality within the context of agile software development, it is extremely important as well as useful to improve not only the knowledge and decision-making of project managers, product owners, and quality assurance leaders but also to support the communication between teams. In this context, quality needs to be visible in a synthetic and intuitive way in order to facilitate the decision of accepting or rejecting each iteration within the software life cycle. This article introduces a novel solution called Product Quality Evaluation Method (PQEM) which can be used to evaluate a set of quality characteristics for each iteration within a software product life cycle. PQEM is based on the Goal-Question-Metric approach, the standard ISO/IEC 25010, and the extension made of testing coverage in order to obtain the quality coverage of each quality characteristic. The outcome of PQEM is a unique multidimensional value, that represents the quality level reached by each iteration of a product, as an aggregated measure. Even though a value it is not the regular idea of measuring quality, we believe that it can be useful to use this value to easily understand the quality level of each iteration. An illustrative example of the PQEM method was carried out with two iterations from a web and mobile application, within the healthcare environment. A single measure makes it possible to observe the evolution of the level of quality reached in the evolution of the product through the iterations.
English Language Education at Ukrainian University: Interdisciplinary Aspect

The main aim of the article is to consider the interdisciplinary aspect of English language education of pre-service teacher training at Ukrainian university. Since it is a review article, the paper intends to address the theoretical issue and the practical implementation of the interdisciplinary approach at Ukrainian university. In the authors’ opinion, interdisciplinary aspect is important for raising the quality of language. It helps achieve the goal of Ukrainian pedagogy to bring up an all-round creative personality with the capacity and skills to establish an intercultural dialogue. English language proficiency is a facilitator for accomplishing these tasks. Thus, the authors believe that the purposes of pedagogical and language education research are interconnected and lie in the modernization of educational content. The study underscores the significance of the interdisciplinary aspect of English language specialists’ training. It encourages gaining and sustains knowledge through multiple teaching and learning styles and the integration of different disciplines. The findings of the study highlight the necessity to meet the requirements of Ukrainian society. They demonstrate the issue is worth addressing since it improves the educational practices and raises the prestige of Ukrainian education.
Sub-femtosecond optical control of entangled states

Entanglement is one of the most fascinating aspects distinguishing quantum from classical physics. It is the backbone of quantum information processing which relies on engineered quantum systems. It also exists in natural systems such as atoms and molecules, showcased in many experimental instances mostly in the form of entangled photon pairs and a few examples of entanglement between massive particles. Nevertheless, the control of entanglement in natural systems has never been demonstrated. In artificially prepared quantum systems, on the other hand, the creation and manipulation of entanglement lies at the heart of quantum computing currently implemented in a wide array of two-level systems (e.g. trapped ions, superconducting and semiconductor systems). These processes are, however, relatively slow: the time scale of the entanglement generation and control ranges from a couple of {\mu}s in case of trapped-ion quantum systems down to tens of ns in superconducting systems. In this letter, we show ultrafast optical control of entanglement between massive fundamental particles in a natural system on a time scale faster than that available to engineered systems. We demonstrate the sub-femtosecond control of electronic entangled states in a single hydrogen molecule by applying few-photon interactions with adjustable relative delays. This molecular entanglement is revealed in the asymmetric electron emission with respect to the ejected proton in the photodissociation of H2. We anticipate that these results open the way to entanglement-based operations at THz speed.