Reviewerly/exHarmony huggingface dataset:
Robustifying GNN Via Weighted Laplacian

Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding, are not scalable, and are architecture dependent. In this paper, we propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWLGNN). The method combines Weighted Graph Laplacian learning with the GNN implementation. The proposed method benefits from the positive semi-definiteness property of Laplacian matrix, feature smoothness, and latent features via formulating a unified optimization framework, which ensures the adversarial/noisy edges are discarded and connections in the graph are appropriately weighted. For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture. The simulation results with benchmark dataset establish the efficacy of the proposed method over the state-of-the-art methods, both in accuracy and computational efficiency.
Machine Learning-Based Intelligent Scoring of College English Teaching in the Field of Natural Language Processing

The current education evaluation is limited not only to the mode of simplification, indexing, and datafication, but also to the scientific nature of college teaching evaluation. This work firstly conducts a theoretical analysis of natural language processing technology, analyzes the related technologies of intelligent scoring, designs a systematic process for intelligent scoring of college English teaching, and finally conducts theoretical research on the Naive Bayesian algorithm in machine learning. In addition, the error of intelligent scoring of English teaching in colleges and universities and the accuracy of scoring and classification are analyzed and researched. The results show that the error between manual scoring and machine scoring is basically about 2 points and the minimum error of intelligent scoring in college English teaching under machine scoring can reach 0 points. There is a certain bias in manual scoring, and scoring on the machine can reduce the generation of this error. The Naive Bayes algorithm has the highest classification accuracy on the college intelligent scoring dataset, which is 76.43%. The weighted Naive Bayes algorithm has been improved in the classification accuracy of college English teaching intelligent scoring, with an average accuracy rate of 74.87%. To sum up, the weighted Naive Bayes algorithm has better performance in the classification accuracy of college English intelligent scoring. This work has a significant effect on the scoring of the college intelligent teaching scoring system under natural language processing and the classification of college teaching intelligence scoring under the Naive Bayes algorithm, which can improve the efficiency of college teaching scoring.
Digital transformation in higher education in Vietnam

This paper, which aims to explore (i) what digital transformation means in the higher education context, (ii) what knowledge should faculty members equip themselves with in response to the forthcoming digitalization in their institutions, and (iii) what HEIs should bear in mind in designing professional development programs to support their faculty’s preparation. Based on an exhaustive review of literature, the paper has significant implications for HEIs in getting ready for their imminent digitalization.
The Analysis of Politeness Strategies in Directive Utterance as Found Among English Department Students at Dharma Andalas University

This research describe and explain the indicators of politeness among students of English Department in Dharma Andalas University. This research uses descriptive qualitative analysis. The data of this research are the speech between English Department students. Retrieval of data using the refer method. This method is followed by a free and interactive technique. Data was collected by taking recorded conversations between students of English Department at Dharma Andalas University. The results of the research data show 5 indicators that students apply in their utterances, they are: the use of word help, sorry, greeting, could, and thank you. The use of greeting is the most indicator that the students used in their utterances, followed by the use of word help, could, sorry and the last one is 4 data of indicator that uses of word thank you.
Efficient MCC Self Driven Car Using Dynamic Priority Based Resource Allocation Algorithm

Abstract: Smart vehicles are equipped with a variety of sensing devices that provide a variety of multimedia applications and services related to smart driving assistance, weather forecasting, traffic congestion information, road safety alarms, and a variety of entertainment and comfort-related applications in an intelligent transportation system. Due to their limited computational capacity and storage capabilities, these smart cars generate a tremendous volume of multimedia-related data that requires quick and real-time processing, which can't be completely handled by independent on-board computer systems. As a result, changing the underpinning networking and computing architectures was required to handle such multimedia applications and services
News Analytics for Business Sentiment Suggestion

Business and economics news has become one of the factors businesses consider when making decisions.However, the exponential increase in the availability of business information sources on the internet makes it more difficult for entrepreneurs to keep up with and extract useful insights from many news articles.Although many preceding works focused on the sentiment extracted in the news, the results were intended for everyone.The sentiments based on a user's queries are needed to provide customized service.Hence, this paper proposed a system integrated into a chatbot to automatically understand users' queries and recommend sentiments based on news articles.The main objective is to provide entrepreneurs, especially those considering international trade and investment, with the sentiments embodied in the latest news articles to help them keep up with the business and economic trends relevant to them.The methodology is based on deep learning and transfer learning.A pre-trained deep learning model was fine-tuned for natural language processing tasks to perform sentiment analysis in news articles.A survey questionnaire was used to measure the effectiveness of the system.The survey result showed that most users agreed with the predicted sentiments from the system.
An Efficient Parallel Algorithm for Clustering Big Data based on the Spark Framework

The principal objective of this paper is to provide a parallel implementation focused on the main steps of the parameter-free clustering algorithm based on K-means (PFKmeans) using the Spark framework and a machine learningbased model to process Big Data.Thus, the process consists of parallelizing the main tasks of the first stage of the PFK-means clustering algorithm using successive RDD functions.Then, the parallel K-means provided by Spark MLlib is invoked by setting the cluster centers and the number of clusters determined in the previous step as input parameters of the parallel Kmeans.Furthermore, a comparison between the parallel designed algorithm and the parallel K-means was conducted using UCI data sets in terms of the sum of squared errors and the processing time.The experimental results, performed locally using the Spark framework, demonstrate the efficiency of the proposed solution.
An Efficient Unusual Event Tracking in Video Sequence using Block Shift Feature Algorithm

The area of video technology is rapidly growing owing to advancements in intelligent video systems in sensor operations, higher bandwidth capacity, storage, and highresolution displays.This led to the proliferation of video-based computing modeling to perform specific tasks on video sequences to gain more insight from the data.Visual tracking of events is a core component in video visual surveillance systems that classify and track moving objects to describe their behavioral aspects.The prime motive behind intelligent video systems is to perform efficient video analytics to meet the specific requirements of the user/use-cases.It involves a self-directed paradigm to understand event sequences, reducing the computational burden of characterizing the activities.The study incorporates a block-shift feature algorithm and introduces a novel computational research method for unusual event tracking in video sequences.The formulated approach employs a framework combining operational blocks to compute sequential operations such as block-matching from the dictionary of motion estimations.Before applying the learning model, the subsequent analysis procedure adds feature lexicon and dominant attributes to make the execution computationally efficient.Further, it uses a sparsenon negative factorization approach to organize the informative details into k possible finite clusters.The event detection outcome from the training datasets of video sequences shows better experimental results than the traditional highly cited related approach of unusual object detection and tracking.
Workshop pembuatan aplikasi arsip elektronik

Each archive can help one's memory of a particular manuscript as an information center. As a source of documentation, archives can be used by organizational leaders in making appropriate decisions regarding the problems at hand as an official proof of responsibility for the administration's administration. Teachers are not only required to march in understanding the subject matter, but teachers must also be able to archive important documents as part of implementing learning. It is considered essential to provide workshops or training in electronic archive applications. The workshop was carried out for one whole day with details of the provision of materials, the practice of making electronic applications, and mentoring of participants after the workshop activities were completed. The workshop will be held on Saturday, August 28, 2021, at SDN 4 Perigi. The results of the workshop provide an illustration that participants have been able to create electronic archive applications using the Auto Play Media Studio 8.0 application
INFORMATION EXTRACTION FROM NEWS TEXTS USING A JOINT DEEP LEARNING MODEL

This paper describes the experiments for the task on information extraction from news texts in Russian in a setting with a wide variety of types of entities and relations. We have adapted the SpERT model which uses the BERT network as a core for the joint extraction of entities and relations. The results obtained for the named entity recognition are quite good and comparable with the results on English datasets. However, the hypothesis that the joint extraction of entities and relations improves the quality of extraction has not been confirmed. The results showed that using a full context instead of a local context improves the quality of extraction of both entities and relations.
Development of Malaysian English Large Vocabulary Continuous Speech Recognizer using Acoustic Model Adaptation

This research project aims to develop Malaysian English Continuous Speech Recognition system by adapting US English acoustic model with Malaysian English speech corpus using Maximum a posteriori reasoning (MAP) and Maximum Likelihood Linear Regression (MLLR). During feature extraction stage, the Mel-Frequency Cepstral Coefficients (MFCC) technique was used. The Hidden Markov Model was used as the back end pattern comparison technique. For the purpose of implementation, the CMU Sphinx toolkit, which includes Pocketsphinx and Sphinxtrain as well as an acoustic model, was used to develop a speech recognition system for Malaysian English. Malaysian English speech sample will be recorded and transcribed to produce the training database required for acoustic model adaptation. The adaptation speech corpus were collected from a number of speakers. The outcome of this research could increase the application of Malaysian English speech recognition in Malaysia due to accent problem. The graphical user interface for Malaysian English Speech Recognition system was created with PyCharm Community Edition and Python 3.9 to make it easier for the individual consumer usage. As a result, speech recognition systems that have gone through the MAP adaptation had the best performance. Its average word error rate achieved was 32.84%. average word recognition rate was 72.52% and average sentence error rate was 78.89%. Keywords: Speech Recognition, Acoustic Model, MAP, MLLR, Pocketsphinx
Analysis of the Influence of Stress on the Educational Performance of Employed Students

EUROSTAT studies have shown that working during college increases the chances of employment later, and the number of employed students in the European Union is increasing year by year, as a consequence of the actions taken in the context of the EU 2020 Strategy which aims to increase young people's participation in education as well as the labour market.This issue must be taken into account by Romanian stakeholders (managers, universities and students) as the highest rate of students who were not employed, according to data from 2018, was in proportion of 91% in Romania.It is for this reason, we have carried out this paper in order to study stressors and their influence on performance and we have shown that college stressors influence college performance, while work stressors influence work performance and those from college.Given these results, we have developed a set of recommendations for managers, students and universities to deal with this situation.
Amharic Text Chunker Using Conditional Random Fields

This paper introduces Amharic text chunker using conditional random fields. To get the optimal feature set of the chunker; the researchers’ conduct different experiments using different scenarios until a promising result obtained. In this study different sentences are collected from Amharic grammar books, new articles, magazines and news of Walta Information Center (WIC) for the training and testing datasets. Thus, these datasets were analyzed and tagged manually and used as a corpus for our model training and testing. The entire datasets were chunk tagged manually for the training dataset and approved by linguistic professionals. For the identification of the boundary of the phrases IOB2 chunk specification is selected and used in this study. The result of all experiments is reported with the maximum overall accuracy off 97.26%, with a window size of two on both sides, with their corresponding POS tag of each token and the worst performance achieved is 84.57%, with only the window size of one word on both the left and right sides.
Opposition’s Allegation against Corruption Eradication Commission (KPK) in Indonesia: Is it Cyberbullying According to Forensic Linguistics?

Corruption Eradication Commission (KPK) in Indonesia has been facing many serious allegations, particularly which aimed via social media.On the other hand, cyberbullying has become a huge apprehension to the linguists and law enforcers as a result of its negative influence to the victims.This study attempts to examine the legal significance of the use of cyberbullying language in Twitter and uses a descriptive qualitative approach.The data were taken from Twitter column reply which contains 69 replies with total 136 of clauses and sentences.The data are analyzed with Martin's attitudinal system and Willard's categories of cyberbullying.This study finds that 1) according to Martin, 86% of the language use of the suspected cyberbullies contain judgment, 32% contain negative appreciation, and 2% contain affect, 2) 58% contains harassment, 18% contain flaming and 52% contain denigration as categorized by Willard, 3) based on the attitudinal system and cyberbullying category analysis, forensic linguistic views the suspected cyberbullies' language use to violate the Law of the Republic Indonesia on the Electronic Information and Transaction.
Data Mining Pengelompokan Akta Nikah Berdasarkan Usia Nikah atau Domisili Menggunakan Metode Clustering: Studi Kasus Kemenag Langkat

A park in a limited sense is a plot of land that is arranged in such a way that it has beauty, comfort, and security for the owner or user. Based on the scale and shape, the park can be called a garden, park, or landscape. Now the park is not only filled with plants or large trees but has varied, one of which is by making ornamental grasses. A decision support system is used in the selection of grass types to make it easier for users to decide which grass to use for gardening. The VIKOR method is a Multi-Criteria Decision Making (MCDM) method that can be used to select more than one criteria. it can be seen that the first alternative to choose the best grass for gardening is peking, swiss and pescue grass with a value of Q = -0.6 and 0.05 and has the right to be recommended as a type of grass for gardening.&#x0D; Keywords: Park, Vikor Method, Decision Making System
Mendiagnosa Penyakit pada Ayam Petelur Menggunakan Metode Certainty Factor

The nutritional status of chickens has a major effect on productivity and it is closely related to the health of chickens. Several diseases in chickens have an economic impact because they can reduce the quality of good chicken eggs to the detriment of farmers. The main problem which is the toughest challenge in chicken farming is the emergence of disease, so its management needs to be done efficiently and professionally. However, farmers usually only know the symptoms that occur in sick chickens, without knowing what disease they are suffering from. As for veterinarians, it is difficult to find, and it takes a long time to handle chickens because the cage is far from residential areas. The Certainty Factor method can be applied to diagnose laying hens disease based on the symptoms of laying hens. Based on the results of the CF calculation, the diagnosis of Avian Encephalomyelitis (AE) in red laying hens with a confidence value of 0.9654 × 100% or 96.54% and calculated with the value of Avian Influenza / Bird Flu with a confidence value of 0.6 × 100% or 60%. Thus, red chicken A is said to be diagnosed with Avian Encephalomyelitis (AE) with a Certainty Factor confidence value of 96.54%. Handling for AE disease is AE vaccination using MEDIVAC AE-Pox at the age of 10-14 weeks. With the application of giving through a wing web.&#x0D; Key words : Laying hens, Certainty Factor
Dual Access Control for Cloud-Based Data Sharing and Storage Security

Due to its effective and affordable management, cloud-based data storage has recently attracted growing interest from both academia and industry. Since services are delivered over an open network, it is critical for service providers to adopt secure data storage and sharing mechanisms to protect user privacy and the confidentiality of data. The most popular technique for preventing the compromise of sensitive data is encryption. The actual necessity for data management, however, cannot be fully met by merely encrypting data (for instance, using AES). Additionally, a strong access control over download requests must be taken into account to prevent Economic Denial of Sustainability (EDoS) assaults from being performed to prevent users from using the service. In this essay.In the context of cloud-based storage, we take into account dual access control in the sense that we create a control mechanism over both data access and download requests without sacrificing security and effectiveness. This paper presents the design of two dual access control systems, one for each intended environment. There is also a presentation of the systems' experimental and security analysis. Security Dual Access Control for Data Sharing and Storage in the Cloud
Can we Predict the Tweet Based on the Message

In today’s fast generation countless people are laid low with one or over one mental disorder supported their thinking and way of they behave.in such reasonably situations they'll post their current situation on social media like Facebook, WhatsApp, twitter. Their messages tell us their psychological state. The detection of mental disorders should be wiped out time. But this is often not really easy and it's a awfully challenging task. there'll be a prospect to beat from the disorder before it is resulting in crucial condition. there's an answer to beat from this i.e., we've to observe carefully their behaviour and the way they're visiting express their feelings. during this paper we express three varieties of representations those are positive, depression and anger by using Naïve bayes algorithm, SVM, Decision tree classifier, Random Forest, Logistic regression classifier and KNN algorithm.
Discovery Learning Model Analysis In The Element Of Poetry Learning Plan

Learning Implementation Plan is a guideline for teachers so that the learning process in the classroom can be better directed. The Learning Implementation Plan is developed in detail from a subject matter or theme. The learning model is one of the components contained in the Learning Implementation Plan. The learning model used can make the learning atmosphere more interesting and can motivate students in the learning process. In this paper, the researcher will analyze the discovery learning model in the Learning Implementation Plan for the elements of poetry-building class X MA Islamiyah Ciputat. This analysis was conducted to determine the suitability of the steps of the learning model of discovery learning in the Learning Implementation Plan of the elements of poetry builder class X MA Islamiyah Ciputat. The study was conducted at two meetings contained in the Learning Implementation Plan. The method used in this study is a qualitative method. The qualitative research method uses triangulation (combined) techniques to collect a data. Data collection in this study was done by reading in the section that was made the focus and subfocus of the study. The data part that is used as focus and subfocus is the steps of discovery learning model. The validity of the findings analysis data was carried out by three triangulators. The purpose of this study, researchers can describe the suitability of the steps of discovery learning model learning. The results obtained and have been verified by three triangulators that analysis of the steps of the discovery learning model contained in the Learning Implementation Plan are appropriate. There are 52 data that have been obtained. But after the data was found, there was no poetry writing activity at the two meetings. At meeting one and meeting two only analyzed the elements of the poetry and editorials used at the same meeting one and two only replaced with learning material that will take place.
THE USE OF QUESTION-ANSWER RELATIONSHIP (QAR) STRATEGY TO ENHANCE THE ELEVENTH GRADE STUDENTS’ READING COMPREHENSION

Students get difficulty in comprehending the words in the reading. The purpose of the research is to find out the effect of using the question-answer relationship (QAR) strategy on the eleventh-grade students’ reading comprehension especially narrative texts. It was conducted on the eleventh-grade students of SMAN 4 Cibinong. Thirty-five students were taken as the sample. The method used in this research was pre-experimental, with one group pre-test post-test research design. The data were collected through reading comprehension pre-test and post-test scores in the form of multiple-choice items. The data were analyzed using the t-test formula. The result showed that the value of t-value was 16.86, while the value of the t-table was 2.03 with the degree of freedom of 34 at the significance level (a) 0.05. Thus, the alternative hypothesis (Ha) of this research was accepted.