Reviewerly/exHarmony huggingface dataset:
DIAGNOSTICS OF THE LEVEL OF DEVELOPMENT OF CIVIC POSITION IN HIGH SCHOOL STUDENTS WITH DISABILITIES

Objective: Diagnostics of the level of development of civic position in high school students with disabilities. Methods: The research relies on general scientific methods of cognition, such as analysis, synthesis, comparison, generalization, as well as pedagogical experiment (ascertaining) and analysis of empirical data. Results: The conducted research allows the authors to obtain qualitative and quantitative data on the level of development of civic position in high school students with disabilities. Conclusions: The obtained data prove the assumption about the necessity of targeted civic education of high school students with disabilities aimed not only at their enrichment with knowledge and skills in the sphere of civic studies but also at the development of steadfast civic position as an integrative personal quality, which is manifested in conducting civic activity. The article can be of interest to specialists and teachers of educational organizations who are concerned with the development of civic position in high school students with disabilities.
Extraction of Polarity from Textual Information based on Machine Learning Approach

Many of the major corporations use the public platform to advertise their products and services. As a result, the feedback given by their numerous customers is the foundation of their popularity among the general public. Manually categorizing the feedback into positive polarity or negative polarity can be a huge time consumable. The classification process is fully automated to improve results. Since reviews are inherently unstructured, they should be pre-processed and POS-labelled before being used to categorize human polarity. Term Frequencies are used to extract and weigh the terms that are mainly sentiment bearing (TF). The reviews are classified using supervised Machine Learning algorithms. The working of the algorithms for classification is evaluated using performance parameters such as Support terms, Term Frequency, Inverse document frequency, weight vector, and opinion power.
Detecting the potential lead, zinc, and gold orebodies by integrating remote sensing, geochemical, and geophysical studies in Dehshir (Iran)

Nowadays, exploration management, increasing productivity, and reducing exploration costs are among the most important issues in the detailed exploration stage of orebodies. A geographic information system (GIS) is an effective tool for gathering, storing, analyzing, updating, generating, integrating, and displaying geographic data and land reference. ArcGIS is one of the efficient and powerful GIS computer programs with numerous capabilities such as generating databases, managing the data and information, integrating the information, and generating different outputs in the form of various maps, charts, and tables. In this study, different exploratory layers with different types of information from the Dehshir orebody area were integrated into this software to determine the spread of metal and introduce the parts with higher anomalies for subsurface exploration. For this purpose, we used the information from remote sensing, topography, geomorphology, geology, and geophysics, and geochemistry studies in the form of exploratory layers.
Federated Learning for Image Classification Using Federated Averaging Algorithm

Image Classification, using traditional Machine Learning, is a very well-known technique but here in this paper we have used Federated Learning where the data of clients is not sent to the server and the model is trained itself on the user’s device. Instead of sending data to the server we sent trained model to the server for further process. Federated Learning enables cell phones to work together to learn a common predictive model while storing all training data on the computer, effectively limiting machine learning of the need to store data in the cloud. This goes beyond using local models on mobile devices to make predictions from the model.Training in the cloud. In our project we have used 70,000 dataset of fashion mist dataset for testing and training federated learning in 10 different categories. From the above dataset 60,000 dataset are used for training while other 10,000 are used for testing.The 60,000-training data set is distributed among 5 users which act as a mobile device.
Graph Neural Networks with Local Graph Parameters

Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between $k$-tuples of vertices. The distinguishing power of these "higher-order'' GNNs is known to be bounded by the $k$-dimensional Weisfeiler-Leman (WL) test, yet their $\mathcal O(n^k)$ memory requirements limit their applicability. Other proposals infuse GNNs with local higher-order graph structural information from the start, hereby inheriting the desirable $\mathcal O(n)$ memory requirement from GNNs at the cost of a one-time, possibly non-linear, preprocessing step. We propose local graph parameter enabled GNNs as a framework for studying the latter kind of approaches and precisely characterize their distinguishing power, in terms of a variant of the WL test, and in terms of the graph structural properties that they can take into account. Local graph parameters can be added to any GNN architecture, and are cheap to compute. In terms of expressive power, our proposal lies in the middle of GNNs and their higher-order counterparts. Further, we propose several techniques to aide in choosing the right local graph parameters. Our results connect GNNs with deep results in finite model theory and finite variable logics. Our experimental evaluation shows that adding local graph parameters often has a positive effect for a variety of GNNs, datasets and graph learning tasks.
고전시가에 표출된 ‘달’ 이미지 비교를 통한 한국 고전문학사 교육 방법 - 중국인 고급 학습자를 대상으로 -

Journal of the International Network for Korean Language and Culture 16-2, 65-92. The study aims to present a way to teach Korean classical literature history to advanced Chinese learners by comparing images of the “moon” as expressed in classical poetry. The image is based on the literary customs of the poetic genre or the cultural characteristics of the period. The time-honored examination of images of the same object in a work is meaningful, in that classical poetry can be a consistent glimpse into the cultural background, or the flow of literary history, inherent in the work. This is because even within the same culture, there is a difference in the imagery of objects that change according to time. The following are examples of “moon” imagery that the classical poem e mbodies. In Hyangga, the imagery of “sublime” is prominent, in terms of shamanistic, religious, spiritual, and personal aspects. In Goryeo song, the sentiments of the poetic speaker were projected. In Sijo, it becomes a space-time background of solitude, a key element of the enjoyment found in reading and writing poetry in the bright moonlight and breeze, and a symbol of one’s lover. The teaching method, examined in this study, begins with discovering the differences in imagery by comparing various poems. Thereafter, the expression methods of the imagery are compared. In assessing these variances between texts, Chinese learners can discover how this imagery, and literary consciousness, has changed over time. Ultimately, Chinese learners will be able to consider trends in Korean classical literature history. (Seoul National University)
E-textbooks – scandal or market imperative?

For academic and research libraries the COVID-19 pandemic has necessitated a significant shift of resources towards supporting digital online access to resources. As a result, the prohibitive cost of some of these resources, notably e-textbooks, has become a flashpoint, even breaking into mainstream media and public debate. Here, Johanna Anderson, Paul Ayris and Ben White report on a recent event hosted by UCL’s Office for Open Science, and discuss how librarians, university presses and copyright experts have sought to address the issue.
Generative Text Modeling through Short Run Inference

Latent variable models for text, when trained successfully, accurately model the data distribution and capture global semantic and syntactic features of sentences. The prominent approach to train such models is variational autoencoders (VAE). It is nevertheless challenging to train and often results in a trivial local optimum where the latent variable is ignored and its posterior collapses into the prior, an issue known as posterior collapse. Various techniques have been proposed to mitigate this issue. Most of them focus on improving the inference model to yield latent codes of higher quality. The present work proposes a short run dynamics for inference. It is initialized from the prior distribution of the latent variable and then runs a small number (e.g., 20) of Langevin dynamics steps guided by its posterior distribution. The major advantage of our method is that it does not require a separate inference model or assume simple geometry of the posterior distribution, thus rendering an automatic, natural and flexible inference engine. We show that the models trained with short run dynamics more accurately model the data, compared to strong language model and VAE baselines, and exhibit no sign of posterior collapse. Analyses of the latent space show that interpolation in the latent space is able to generate coherent sentences with smooth transition and demonstrate improved classification over strong baselines with latent features from unsupervised pretraining. These results together expose a well-structured latent space of our generative model.
Making the Case for Visualization

Visual representation of information is a fundamental tool for advancing our understanding of science. It enables the research community to extract new knowledge from complex datasets, and plays a vital role in communicating new results to the public. We establish the case for support of astronomy visualization and its community of practice.
Discourse Understanding and Factual Consistency in Abstractive Summarization

We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. Our work addresses current limitations of models for abstractive summarization that often hallucinate information or generate summaries with coherence issues. To generate abstractive summaries with factual consistency and narrative flow, we propose Cooperative Generator -- Discriminator Networks (Co-opNet), a novel transformer-based framework where a generator works with a discriminator architecture to compose coherent long-form summaries. We explore four different discriminator objectives which each capture a different aspect of coherence, including whether salient spans of generated abstracts are hallucinated or appear in the input context, and the likelihood of sentence adjacency in generated abstracts. We measure the ability of Co-opNet to learn these objectives with arXiv scientific papers, using the abstracts as a proxy for gold long-form scientific article summaries. Empirical results from automatic and human evaluations demonstrate that Co-opNet learns to summarize with considerably improved global coherence compared to competitive baselines.
Incorporating Word Sense Disambiguation in Neural Language Models.

We present two supervised (pre-)training methods to incorporate gloss definitions from lexical resources into neural language models (LMs). The training improves our models' performance for Word Sense Disambiguation (WSD) but also benefits general language understanding tasks while adding almost no parameters. We evaluate our techniques with seven different neural LMs and find that XLNet is more suitable for WSD than BERT. Our best-performing methods exceeds state-of-the-art WSD techniques on the SemCor 3.0 dataset by 0.5% F1 and increase BERT's performance on the GLUE benchmark by 1.1% on average.
Penerapan Pembelajaran Pokok Bahasan Perbandingan Trigonometri dengan Menggunakan Media Peta Konsep pada Siswa

This study aims to study the effectiveness of learning the subject of trigonometric comparison using media concept maps in class X Asy-Syafiiyah International Medan High School TP 2019/2020, to see how much changes in student learning outcomes taught with media concept maps in class X high school students Asy-Syafiiyah International Medan TP 2019/2020. The population in this study were all students of class X at Ash-Syafiiyah International Medan. The results of this study indicate that (1) the level of mastery of students there are 7 students or 18% the level of mastery is very high, 8 students or 21% the level of mastery is high, 18 students or 46% level of mastery is moderate, 5 students or 13% mastery level is low, 1 student or 2% mastery level is very low. This means that class X Asy-Syafiiyah International Medan students have mastered trigonometric comparisons with the application of concept map media. (2) Completeness of learning there are 33 students or 85% who have completed individually and 6 students or 15% who have not finished individually. This means that students have finished classically (3) Out of ten (10) TPKs are expected, achieved by students there are 10 or 100% TPK completed, (4) Observation results indicate that the implementation of learning is in the range of values 3 to 4 (5) The magnitude of the change in learning outcomes of students taught with media concept maps in class X Ash-Syafiiyah International Medan is 2.00, it can be concluded that the application of concept maps is effectively used in learning trigonometric comparisons.
Removal Of High Quality Video Noise Through Modified First Order Neighborhood Mean Filter

In this paper, a hard and fast of regulations is designed to put off the random valued impulsive noise (pepper and salt ) from corrupted shade movies.In beyond years researchers suggested many algorithm to take away the impulse noise however they fail to present higher outcomes at immoderate noise density i.E.80%-90%.The suggested algorithm MFONMF works on ranges the first stage is to stumble upon the noisy pixel and the second one degree is to update the noisy pixel.This set of policies considers changed first order community pixels for detecting the noisy pixel and propose clear out is used for de-noising.Color films are denoised by means of manner of extracting the every and everybody from video, then the frames are splitting into R, G and B channels and then they're denoised one at a time after which merged collectively another time to shape the shade video.All the opposite algorithm are compared with the suggested algorithm and discovered that the suggested algorithms has accurate noise elimination skills at excessive densitie.The supplied set of rules indicates higher result than Progressive Switched Median Filter (PSMF), Standard Median Filter (SMF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), Adaptive Median Filter (AMF),Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF), and Modified Non-Linear Filter (MNF).Different color movement snapshots are examined via the use of the algorithm and it gave better Peak Signal Noise Ratio (PSNR) and Image Enhancement Factor (IEF) at low, medium and immoderate noise densitie.
TEKNOLOGI MACHINE LEARNING PADA SISTEM PENDUKUNG PENELITIAN RSYS (RESEARCH SUPPORT SYSTEM)

Students who are still unfamiliar with the revising process focus on the local level, such as grammar, spelling, punctuation, and sentence level. Meanwhile, students who are experts focus on the global level, such as focusing on improving writing goals, ideas, and meanings. When making revisions, students become too focused on the local level rather than the global level. Comments for the local level are ineffective as a guide in the revision process. Therefore, this study aims to build machine learning-based software with the ANN method to classify global or local comments. This study uses a design research methodology with the SDLC model of prototyping. The results show that the RSYS software was successfully built with machine learning accuracy in classifying 19 comments, obtained from 2 documents, with a 95: 0.5 ratio, 94.74%. Whereas in alpha testing, it was stated that the functionality of the RSYS system and machine learning was considered to be functioning correctly. For beta testing, the largest percentage was 85% for ease of operation and convenience in using the application, 75% for website display and navigation availability.
Analysis of usability evaluation according to the quality standards

This research provides a study of usability as a quality attribute of Web pages, using as a benchmark the requirements that must be followed under the International Organization for Standardization / International Electrotechnical Commission 25000. The methods used to carry out this process are studied, as well as the description of the aspects to be considered for evaluation thereof. It carried out a review of the literature on software tools for measuring the usability of Web pages and disadvantages. The methodology that can be used is determined to perform the corresponding evaluation process and the results are established using a practical case.
UNIVERSITY SERVICE WEBSITE QUALITY MEASUREMENT WITH WEBQUAL 4,0 (CASE STUDY: FACULTY OF BUSINESS AND ECONOMICS ISLAMIC UNIVERSITY OF INDONESIA)

The implementation of the website as a service medium has become very popular in the developments of universities, especially during the COVID-19 pandemic, which implements health protocols. With the existence of a website-based service information system, it is possible to continue running business processes in service to users without having to come and avoid direct or face-to-face interactions. FBE UII implements a Website Service Information System to serve students online, which replaces the manual system that is served directly and face-to-face. Firstly, it is implemented until now, there has never been a measurement of the quality of service that comes from its users. The measurement used the WebQual 4.0 method which measures the user's assessment of 3 areas, namely usability, information quality, service interaction quality which affects user satisfaction with the parameter of the users using the service website. This research was conducted at the FBE UII. Research data collection used a questionnaire. The population was FBE UII students who had used the service website. The results of this study were the conclusions of successful website implementation to replace the previously used system, the quality of website-based services on information systems was good, the perception of user ratings was easy to interact with, and recommendations for strategic steps, namely migrating other services by utilizing integrated service websites.
Odd-periodic Grover walk

The Grover walk is one of the most well-studied quantum walks on graphs. In this paper, we investigate its periodicity to reveal the relationship between the quantum walk and the underlying graph, focusing particularly on the characterization of graphs exhibiting a periodic Grover walk. Graphs having a periodic Grover walk with periods of $2, 3, 4$, and $5$ have previously been characterized. It is expected that graphs exhibiting a periodic Grover walk with odd period correspond to cycles with odd length. We address that problem and are able to perfectly characterize the class of graphs exhibiting an odd-periodic Grover walk by using a combinatorial method.
Neuro-Fuzzy Approach and Its Application in Recommender Systems

In this chapter, the main idea of novel recommenders proposed and developed in this book is outlined within the background of neuro-fuzzy systems (Sect. 2.1). The attention is focused on the learning ability of the recommenders (Sect. 2.2). Moreover, the important issue of interpretability and explainability is discussed (Sect. 2.3). In Sect. 2.4, two known methods of rule generation, applied in the proposed recommenders, are presented. Section 2.5 refers to the simplified form of the rules, also used in the novel recommendation systems. In Sect. 2.6, different aspects of classification problems with regard to the recommenders are considered.
The advantages of using modular technologies at primary education

The modern information society raises new requirements to the process of training of a future primary school teacher as a component of a lifelong education system. An important role in providing of lifelong learning is given to distance education, which promotes self-motivation and self-actualization, enhances independent cognitive activity, and contributes to the acquisition of fundamental knowledge and the formation of skills to use it in professional activity. In this regard, the article analyzes the course "Teaching future primary school teachers to train students on the basis of modular technologies" that contributes to the training offuture primary school teachers to the successful use of distance learning technologies in the system of lifelong education. The course consists of two modules. The first module provides theoretical information on a distance learning technology in primary education, and the other-individual research assignments. Also, the paper presents the topics of lectures, practical classes and independent activity and describes the system of student performance evaluation.
Studi Analisa Kekeringan dengan Metode Standardized Precipitation Index (SPI) dan Palmer Drought Severity Index (PDSI) di DAS Kemuning Kabupaten Sampang

Drought is a natural disaster that occurs slowly but harms Sampang Regency people's life sustainability. Consider the negative impact of drought,Â analyze the drought index and its distribution are necessary. This study aims to know drought level using Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), then to know their correlation with Southern Oscillation Index (SOI) that can represent El Nino Southern Oscillation (ENSO). Drought index with bigger correlation mapped by Inverse Distance Weighting method to know the distribution. SPI method produced the most severe drought in April 2004 on one month deficit period (-3.651). PDSI method developed the most severe drought in September 2001 (-20.628). Based on the PDSI average analysis results for the 1998-2017 period, it is well-known that drought disasters generally start in July and end in October, while the peak of drought occurs in September. The PDSI method has 60% conformity to the SOI value derived from the graphical visualization of the annual average surplus and deficit index, better than the SPI method that only scores 53%. The visualization of the drought distribution map shows that the Districts of Sampang, Torjun, and Camplong need to become prioritized to mitigate future drought disasters because they have a great potential for drought disasters compared to other districts.
PEMANFAATAN SELULOSA ECENG GONDOK (Eichhornia crassipes) SEBAGAI BAHAN DASAR PEMBUATAN NITROSELULOSA

Water hyacinth is a plant that lives on the surface of the waters and swamps. Those rapid breed of it considering its as weeds community. The water hyacinth fibers has only been used as raw material for handicrafts, biogas and paper raw materials. This research was conducted to determine the cellulose nitrasion reaction effect and its reaction temperature from water hyacinth on the quality of nitrocellulose production. This research was divided on to two stages, nitrocellulose production and nitrogen levels detection in nitrocellulose. Nitrogen detection were carried out using the Fourier Transform InfraRed (FTIR) analysis and mikro-Kjeldalh method. The research results showed that the highest production of -NO2 in nitrocellulose was achieved at reaction temperature of 30 ⁰C and needed the reaction time 30 minutes. In those conditions, optimum -NO2 level in nitrocellulose from water hyacinth cellulose material was achieve 16.76% N  value according to mikro-kjeldhal methode and 11.5%  absorption value  according to FTIR analysis at wavelength of 1550-1570 cm-1.