Reviewerly/exHarmony huggingface dataset:
SYNTACTIC ANALYSIS OF SENTENCE PATTERNS AND TYPES OF BBC NEWS ARTICLES

Sentence patterns and types play a crucial role in helping writers make their articles attractive. However, few studies analyzed the sentence patterns and types used in news articles. Therefore, this study aimed to explore the sentence patterns and types used in shaping news articles. The researchers used a syntactic analysis to examine the sentences. The data were collected from ten BBC news articles published in October 2020. The purposive sampling was used, and the data gathering instrument was structured observation sheets. The gathered data were calculated using percentages. The results showed five sentence patterns used, namely S+V, S+LV+SC, S+V+O, S+V+DO+OC, and S+V+IO+DO. It was also found that four sentence types, namely simple, compound, complex, and compound-complex sentences, were used. Among the used sentence patterns, S+V+O was the most frequent (42%), and the most frequent sentence type was the complex sentence (52.6%).
Developing of the Native Model of the Effective Factors on Employees’ Happiness in Mobarakeh Steel Company in Isfahan

Developing of the Native Model of the Effective Factors on Employees’ Happiness in Mobarakeh Steel Company in Isfahan
Detecting Concept Drift with Neural Network Model Uncertainty

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection. Especially in many real-world application scenarios—like the ones covered in this work—true labels are scarce, and their acquisition is expensive. Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift Detection (UDD), which is able to detect drifts without access to true labels. Our approach is based on the uncertainty estimates provided by a deep neural network in combination with Monte Carlo Dropout. Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model. In contrast to input data-based drift detection, our approach considers the effects of the current input data on the properties of the prediction model rather than detecting change on the input data only (which can lead to unnecessary retrainings). We show that UDD outperforms other state-of-the-art strategies on two synthetic as well as ten real-world data sets for both regression and classification tasks.
The Review of Science Education in China

Science education is paid more attention nowadays. This article describes the current situation of Science education in China. As a concept paper, it is divided into some aspects, such as the history and development, the Necessity of Science history, incorporate the history of science into today's science education, the future trend of science education, the goal and significance of science education, also, it states the problems and solutions in science education as well. More importantly, science education in the 21st Century is introduced in the end which combines the advanced technology in science teaching and learning process. Conclusion comes lastly with the enlightenment for future development of science education.
English Achievement and Bullying In Junior High School Students

This is mixed method research with qualitative and quantitative data. The qualitative comes from correlation analysis, while the quantitave data comes from interview data. The population of this study is 230 students with 146 students as sample. They come from class VII SMP Muhammadiyah Kudus. This research was bivariate analysis with Spearmen Rank Test and the instruments were in-depth interview, questionnaires, the daily test scores in English. The result of quantitative data showed that there was a moderate correlation between bullying behavior and English achievement for junior high school students with p Value = 0.000 &lt;α 0.05 and r value -0.470 with moderate strength and having negative relationship direction. The bullying affects the English achievement in term of motivation to study English. Based on qualitative data, the students become embarrassed and afraid when they speak and learn English. This condition happens because their friends and teacher mock them. It can be concluded that there is a correlation between bullying and English achievement for Junior High School.
Intervenções educativas para ajudantes de ação direta de estruturas residenciais para pessoas idosas durante a pandemia: Uma revisão integrativa

Introduction: The current pandemic caused by the circulation of the SARS-CoV-2 virus places the population in general, and aging people living in nursing homes in particular, in a situation of great vulnerability, with a high risk of morbidity and mortality. In these contexts, educational interventions with professionals have shown to be associated with changes in practices. However, data on educational interventions carried out in a pandemic situation in these contexts are still scarce, as well as their content and/or methodologies used. Goal: We aimed to describe educational interventions for nursing home support workers during the COVID-19 pandemic. Methods: An integrative literature review was carried out. Results: Of the 349 articles identified, 4 were included in the final sample. The results show that the educational programs aimed at nursing home employees, including support workers, have great variability in terms of intervention, target audience, content, duration, how it is made available, and the scientific area of ??the trainers. There seems to be an association between the education provided and the reduction of virus transmission. Conclusions: This integrative review points to the importance of educational interventions, taught by health professionals, with support workers, as a way of preventing the transmission of SARS-CoV-2 in nursing homes and reducing morbidity and mortality rates. Distance learning proves to be an educational opportunity for people in remote locations. Face-to-face training was also shown to be essential in demonstrating and training safe care practices. We recommend future research to achieve greater consistency in the conclusions.
MPP-MLO: Multilevel Parallel Partitioning for Efficiently Matching Large Ontologies

The growing usage of Semantic Web has resulted in an increasing number, size and heterogeneity of ontologies on the web. Therefore, the necessity of ontology matching techniques, which could solve these issues, is highly required. Due to high computational requirements, scalability is always a major concern in ontology matching system. In this work, a partition-based ontology matching system is proposed, which deals with parallel partitioning of the ontologies at multilevel. At first level, the root based ontology partitioning is proposed. Matchable Sub-ontologies pair is generated using an efficient linguistic matcher (IEI-Sub) to uncover anchors and then based on maximum similarity value, pairs are generated. However, a distributed and parallel approach of MapReduce-based SEI-sub process has been proposed to efficiently handle the anchor discovery process which is highly time-consuming. In second level partitioning, an efficient approach is proposed to form non overlapping clusters. Extensive experimental evaluation is done by comparing existing approaches with the proposed approach, and the results shows that MPP-MLO turns out to be an efficient and scalable ontology matching system.
Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig &amp; Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.
Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario

Training and education of real-world tasks in Virtual Reality (VR) has seen growing use in industry. The motion-tracking data that is intrinsic to immersive VR applications is rich and can be used to improve learning beyond standard training interfaces. In this paper, we present machine learning (ML) classifiers that predict outcomes from a VR training application. Our approach makes use of the data from the tracked head-mounted display (HMD) and handheld controllers during VR training to predict whether a user will exhibit high or low knowledge acquisition, knowledge retention, and performance retention. We evaluated six different sets of input features and found varying degrees of accuracy depending on the predicted outcome. By visualizing the tracking data, we determined that users with higher acquisition and retention outcomes made movements with more certainty and with greater velocities than users with lower outcomes. Our results demonstrate that it is feasible to develop VR training applications that dynamically adapt to a user by using commonly available tracking data to predict learning and retention outcomes.
Defense Against SYN Flood DoS Attacks Using Network-based Mitigation Techniques

TCP SYN Flood is one of the most widespread DoS attack types performed on computer networks nowadays. As a possible countermeasure, we implemented and deployed modified versions of three network-based mitigation techniques for TCP SYN authentication. All of them utilize the TCP three-way handshake mechanism to establish a security association with a client before forwarding its SYN data. These algorithms are especially effective against regular attacks with spoofed IP addresses. However, our modifications allow deflecting even more sophisticated SYN floods able to bypass most of the conventional approaches. This comes at the cost of the delayed first connection attempt, but all subsequent SYN segments experience no significant additional latency $(\lt 0.2$ ms). This paper provides a detailed description and analysis of the approaches, as well as implementation details with enhanced security tweaks. The discussed implementations are built on top of the hardware-accelerated FPGA-based DDoS protection solution developed by CESNET and are about to be deployed in its backbone network and Internet exchange point at NIX.CZ.
A Scientometric Inspection of Research Based on WordNet Lexical During 1995–2019

The purpose of this study is to conduct a scientometric inspection for the research based on WordNet lexicon. WordNet is a lexical database for the English Language developed by George A. Miller and his team at Princeton University in 1985. This study reviews WordNet-based research mainly published in the Web of Science (WoS) database from 1995 to 2019. The publication data has been analyzed computationally to present year-wise publications, publications growth rate, country-wise research publications, authors who published a large number of papers in this field. The results have been shown in the form of figures. The present study will be useful for a better understanding of the trajectory of the research work done in the field of WordNet during the last 25 years and shall highlight the prominent research areas and categories in the field of WordNet.
INVESTIGATING THE IMPACT OF ONLINE LEARNING ON ENGINEERING STUDENTS’ SOCIALIZATION EXPERIENCES DURING THE PANDEMIC

The global shift to online learning prompted by the COVID-19 pandemic has accentuated how learningonline alters postsecondary students’ socialization experiences and learning outcomes. In December 2020, alarge Canadian engineering faculty surveyed its undergraduate students to assess their learning experiences in the exclusive online environment during the pandemic. This paper used qualitative data from the survey, as complemented by descriptive quantitative results, to explore how the online environment impacted engineering students’ socialization processes and their perception of learning. Using Weidman’s model of socialization, this paper contributes to better understandings of the individual and particularlyenvironmental factors that have influenced engineering students’ socialization processes while they learn online during the pandemic, and the importance of social interactions to student learning in engineering education.
Construction of College English Wisdom Classroom under the Internet

At present, in the process of accelerating the overall development of China's society, the educational cause has embarked on the road of the latest reform.More and more effective education ideas have been put forward, which has optimized the effect of students' knowledge learning and laid a foundation for students' subsequent growth and development.This paper focuses on the actual situation of English classroom teaching activities in universities and colleges.It discusses the construction strategies of college English wisdom class under the background of the Internet.It will be able to really use high-tech technology and to find a powerful development path for the smooth development and implementation of the internal English education in universities and colleges.It will make great contribution to the future healthy growth of our university students.
Real-Time Driver’s Focus of Attention Extraction and Prediction using Deep Learning

Driving is one of the most common activities in our modern lives.Every day, millions drive to and from their schools or workplaces.Even though this activity seems simple and everyone knows how to drive on roads, it actually requires drivers' complete attention to keep their eyes on the road and surrounding cars for safe driving.However, most of the research focused on either keeping improving the configurations of active safety systems with high-cost components like Lidar, night vision cameras, and radar sensor array, or finding the optimal way of fusing and interpreting sensor information without considering the impact of drivers' continuous attention and focus.We notice that effective safety technologies and systems are greatly affected by drivers' attention and focus.In this paper, we design, implement and evaluate DFaep, a deep learning network for automatically examining, estimating, and predicting driver's focus of attention in a real-time manner with dual low-cost dash cameras for driver-centric and car-centric views.Based on the raw stream data captured by the dash cameras during driving, we first detect the driver's face and eye and generate augmented face images to extract facial features and enable real-time head movement tracking.We then parse the driver's attention behaviors and gaze focus together with the road scene data captured by one front-facing dash camera faced towards the roads.Facial features, augmented face images, and gaze focus data are then inputted to our deep learning network for modeling drivers' driving and attention behaviors.Experiments are then conducted on the large dataset, DR(eye)VE, and our own dataset under realistic driving conditions.The findings of this study indicated that the distribution of driver's attention and focus is highly skewed.Results show that DFaep can quickly detect and predict the driver's attention and focus, and the average accuracy of prediction is 99.38%.This will provide a basis and feasible solution with a computational learnt model for capturing and understanding driver's attention and focus to help avoid fatal collisions and eliminate the probability of potential unsafe driving behavior in the future.
EMBRACING SOCIAL MEDIA TO IMPROVE EFL LEARNERS' ENGLISH SKILL

Regarding the importance of exploring the use of social media to enhance English skill, this study investigated the use of social media to improve English skill of EFL learners especially non-English college students. The objective of this study was to find out in what ways social media can contribute to the improvement of EFL learners’ English skill. The research method employed in this study was descriptive qualitative method. In gaining data, this study used questionnaire and observation. The results showed that almost all respondents took advantage of social media to learn English. Some social media used were YouTube, WhatsApp, Instagram, Facebook, Twitter, TikTok, etc. The respondents revealed that by the use of social media, their reading skill (97.3% of the respondents), their writing skill (94.6% of the respondents), their vocabulary mastery (91.9% of the respondents), their listening skill (89.2% of the respondents), their speaking skill (86.5% of the respondents), their grammar mastery (83.8% of the respondents) could be improved. Based on the findings of the study, it can be inferred that social media can contribute to the improvement of EFL learners’ English skill in many ways including reading skill, writing skill, vocabulary mastery, listening skill, speaking skill, and grammar mastery. Keywords: EFL Learners, English Skill, Social Media
Automated Cheque Processing System

Despite swift advancements happening in digital technology, financial institutions like banks still rely upon conventional mediums of processing bank cheques by humans. The process is cumbersome and takes a couple of days to transfer money, which involves verification by the intermediaries. This leads to high time and costs. In this paper, we propose an automated system that extracts relevant details of a bank cheque like Payee Name, Amount, Date, Bank Name using Optical Character Recognition and Deep Learning and verifies the signature on the cheque with the existing signature stored in the database using feature extraction and principal component analysis. The signature for a new user is stored using its hash value for security purposes. The proposed system uses a modified convolution neural network to extract the handwritten content on a cheque leaf wherein IAM dataset is used to train the model and get the optimized results. This system will facilitate the process and lead to a reduction in time and costs. The efficiency and performance are measured on the self-generated data set of bank cheques.
Obstacles of TQM Implementation in Saudi Universities: An Empirical Study

This study aims to identify the obstacles to the application of total quality management at the Saudi University from the faculty members' point of view and to identify the significance of statistical differences in the obstacles to the application of total quality management in Saudi universities from the viewpoint of faculty members according to the variables of gender, academic rank, and college. To achieve the study objectives, the researcher used the descriptive design and quantitative approach, depending on the questionnaire as the main instrument for collecting study data. The study was applied to a sample consisting of (350) faculty members at Imam Abdulrahman bin Faisal University, who were selected randomly, the number of questionnaires valid for analysis (277) questionnaires. Among the most prominent results of this study showed that top management obstacles, human resources obstacles, student’s obstacles, financial resources, educational technology obstacles, community service obstacles, obstacles to scientific research, organizational culture obstacles, and educational curriculum obstacles are affecting on implementation of TQM at Imam Abdulrahman bin Faisal University from point of view faculty members. The results showed the top management obstacles came in the first rank among the obstacles that affect TQM implementation, followed by student’s obstacles, while financial resources obstacles and human resources obstacles come in the last obstacles affect TQM implementation at Imam Abdulrahman bin Faisal University from point of view faculty members. Also, the results showed there are no significant statistical differences in the obstacles to the application of total quality management according to the variables of gender, academic rank, and college.&#x0D; &#x0D; Received: 4 March 2021 / Accepted: 6 May 2021 / Published: 8 July 2021
VERBAL AGGRESSION IN THE COMMENTS OF THE SOCIAL NETWORK FACEBOOK

The article is devoted to the study of verbal aggression in the Ukrainian-language network discourse. The aim of the study was to analyze the manifestations of verbal aggression in the comments of Facebook users, which involved solving the following tasks: to define the concept of verbal aggression, to analyze comments on social network as a special genre of network discourse, to identify language markers of verbal aggression in comments. The comments of Ukrainian-speaking users of the social network Facebook were analyzed and the language means of realizing verbal aggression were singled out. The main concepts of researchers on the concept of verbal aggression and verbal aggression are interpreted as conflicting behavior of speakers, expressed through language, among which are analyzed lexical, morphological, syntactic and graphic.
Type-Theoretic Constructions of the Final Coalgebra of the Finite Powerset Functor.

The finite powerset functor is a construct frequently employed for the specification of nondeterministic transition systems as coalgebras. The final coalgebra of the finite powerset functor, whose elements characterize the dynamical behavior of transition systems, is a well-understood object which enjoys many equivalent presentations in set-theoretic foundations based on classical logic.In this paper, we discuss various constructions of the final coalgebra of the finite powerset functor in constructive type theory, and we formalize our results in the Cubical Agda proof assistant. Using setoids, the final coalgebra of the finite powerset functor can be defined from the final coalgebra of the list functor. Using types instead of setoids, as it is common in homotopy type theory, one can specify the finite powerset datatype as a higher inductive type and define its final coalgebra as a coinductive type. Another construction is obtained by quotienting the final coalgebra of the list functor, but the proof of finality requires the assumption of the axiom of choice. We conclude the paper with an analysis of a classical construction by James Worrell, and show that its adaptation to our constructive setting requires the presence of classical axioms such as countable choice and the lesser limited principle of omniscience.