Reviewerly/exHarmony huggingface dataset:
The Application of E-learning in English Teaching at Dong Nai Technology University

Many colleges and universities in Vietnam have employed e-learning platforms in teaching English with the hope to better students’ English proficiency. Recently, e-learning programs such as DNTU-LMS and Canvas have been implemented at Dong Nai Technology University. With their features of flexibility and efficiency, they have been being used as crucial tools for teaching and learning in various faculties in the university especially during the widespread of Covid-19 pandemic. The present study aimed at identifying the views of the EFL learners from Faculty of Foreign Languages on the application of e-learning platform. A group of 100 EFL students was chosen randomly to participate in this study. Data was collected using questionnaire. After analyzing student’s responses, the results proved that the introduction of e-learning was effective. E-learning also stimulates students to learn English and brings them comfort and joy.
Models for Implementing Entrepreneurial Policing

Having developed a more nuanced understanding of how entrepreneurship pervades everyday policing systems, practices, and processes and how it imbues ‘Organised Crime’ and ‘Organised Criminals’ with a competitive advantage it is incumbent upon us to consider new methods of implementing entrepreneurial policing. A strength of longstanding systems of policing is that they work because they systematically perpetuate consistent and proven ways of working which achieve measurable results. Such systems operationalise the conventional and maintain the ‘status quo’. Conversely, innovations do not implement themselves and new ways of ‘thinking’ and ‘doing’ require to be designed, trialled, and put in place. People innovate and initiate change and it is necessary when seeking to initiate changes such as ‘entrepreneurial policing’ to assess the entrepreneurial propensities and capacities of staff within an organisation because this is the starting place for both change and entrepreneurial action. As a consequence, various possibilities including utilising academic research methodologies as analytic tools are examined.
Degree of orthomorphism polynomials over finite fields

An orthomorphism over a finite field Fq is a permutation θ:Fq→Fq such that the map x↦θ(x)−x is also a permutation of Fq. The degree of an orthomorphism of Fq, that is, the degree of the associated reduced permutation polynomial, is known to be at most q−3. We show that this upper bound is achieved for all prime powers q∉{2,3,5,8}. We do this by finding two orthomorphisms in each field that differ on only three elements of their domain. Such orthomorphisms can be used to construct 3-homogeneous Latin bitrades.
TINDAK TUTUR DIREKTIF GURU DALAM PROSES BELAJAR MENGAJAR DI TAMAN KANAK-KANAK BUNDA NINIK S. ANANDA DI DESA LECES KABUPATEN PROBOLINGGO

Kindergarten Education emphasizes the provision of educational stimuli to help the growth and development of children. The teacher is a teacher or educator in charge of educating, teaching, guiding, directing, training, evaluating, and evaluating students. Based on this, the teacher is obliged to teach in a way and in good media. One of them is a good way of speaking, especially when the teacher gives orders, invitations, suggestions, explanations, requests, statements, praise and advice to his students. In kindergarten is an introduction, namely the introduction of numbers and letters. In addition, teachers are required to be able to stimulate and facilitate the development of the language of their students. Therefore, the teacher must be creative in acting speech. That is, the teacher must be clever in processing a speech so that learning can run effectively. Teacher's speech in Kindergarten must be interesting and in accordance with the psychological level of the student. Attractive speeches will be absorbed well by students so that targeted learning is achieved.
Allocation of Defense Resources Against Cyber Attacks to Cyber-Physical Systems

Protecting Cyber-Physical Systems (CPSs) from cyber attacks requires properly allocating defense resources.These can be selected by defend-attack and Adversarial Risk Analysis (ARA) models, which search for the optimal allocation based on specific assumptions.In particular, the defend-attack model assumes that each player is fully aware of the preferences of the opponent, considering complete information, whereas the ARA model assumes incomplete information and subjective probability distributions of the defender utilities, improving the realism of the modelling but still lacking a proper management of the uncertainties of the results it provides.In this work, we complement the ARA model with a multi-criteria decision model based on Value-at-Risk (VaR) measures to support the defender in identifying the optimal defense portfolio among alternatives, considering budget constraints and accounting for the uncertainties which the ARA model is subjected to.For demonstration purposes, an application is carried out concerning the digital control system of the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).
Meta-Analisis Pengaruh Model Example Non Example dalam Pembelajaran IPA di Sekolah Dasar

The purpose of this study was to analyze the effectivenees of the effect od the example non example model on science learning. The research method used is a meta-analysis of previous studies that apply the example non example learning model in science learning. The research begins with formulating a research problem, tracing the relevant research results for analysisi. Data is collected by browsing electronic journals via google scholar. Form the search, it was obtained 8 scientific articles that have been published, the instrument was in the form of coding based on predetermined criteria. Data were analyzed using the effect size. The research findings reveal that overall the studies conductes are effective and effective on science learning in elementary scools with an effect size of 1,323132 or in the high efficiency category. The example non example model also has an effect and is effective at the grade level, namely in grade 4,5 and 6 and the application of learnin to improve learning outcomes, critical thinking skills, and the ability to explain. The conclusion of this study is that the example non example model is more effective in science learning compared to other learning models from this study.
Implementasi Long Short-Term Memory Pada Harga Saham Perusahaan Perkebunan Di Indonesia

&#x0D; The decline and increase in the price of shares of plantation companies is a problem for investors in making decisions to buy or sell shares. Factors influencing the movement of plantation stock prices include CPO commodity price fluctuations, world oil price fluctuations, Rupiah exchange rate fluctuations, government regulations and policies, demands from importing countries, and climate. Forecasting stock prices is expected to help investors to deal with uncertainty in the movement of plantation stock prices. This study applies the Long Short-Term Memory (LSTM) to predict the stock prices of plantation companies using SSMS, LSIP, and SIMP share price data from the period 1 July 2014 - 22 July 2019. Based on the results of the study it was found that the best LSTM model on SSMS shares by using the RMSProp optimizer and 70 hidden neurons produced an RMSE value of 21,328. Then the best LSTM model on LSIP stock by using Adam optimizer and 80 hidden neurons produces an RMSE value of 33,097. Whereas the best LSTM model on SIMP shares using Adamax optimizer and 100 hidden neurons produced an RMSE value of 8,3337. &#x0D; &#x0D;
Makerspace and Collaborative Technologies

Makerspace and collaborative technologies is part of the LITA (Library and Information Technology Association) guide series, which dates back to 2015. Over the course of seven chapters, the contrib...
IMPACT OF FADAMA-III CAPACITY BUILDING AND RURAL INFRASTRUCTURE ON AGROPRENEURS BUSINESS DEVELOPMENT

The broad objective of the study is to examine the impact of Nigeria Fadama National development project on Agropreneur’s business development in Osun state, Nigeria. The sample size selected was Two hundred and seventy (270). Multi-stage sampling was adopted, and Simple random sampling design was used. Data collection method was a close-ended questionnaire. Linear Regression, Multiple Regression and Anova were used to test the Hypotheses of the study. The study shows that rural infrastructure variables used in the selected study area significantly predicted the level of Business Development, F (1,268) = 30.413, p &lt; 0.05 F – statistical indicates that the overall regression model is highly statistically significant in terms of its goodness of fit since the value of Ftab (1,268) &gt;Fcal (30.413). It also shows the magnitude of the difference, since the sig level is greater than 0.05 i.e 0.811, 0.985. The study reveals that only 0.9 per cent variance exists between the selected genders. In conclusion capacity building has really assisted farmers in adopting new technology in farming which led to an increase and expansion of their business outlets. The study recommends: Regular training for farmers to discover their skills in diversifying into various businesses within the agricultural sector using new technology should be encouraged by all stakeholders in business of food security and provision of grants to enable them expand their business desires.
Real-time sign language recognition based on video stream

In this paper, a real-time Chinese sign language recognition system is investigated. This system can recognise deaf-mute Chinese sign language, and output the recognition results in real time through text. A Chinese sign language dataset is firstly created with a normal RGB camera, and the entire dataset contains 500,000 video samples. In order to improve the recognition accuracy of the system for real-time applications, three-dimensional convolutional neural network (3D-CNN) is investigated, combined with optical flow processing base on total variation regularisation and L1-norm robust (TV-L1). A two-step down-frame processing is employed to extract the equal number of key frames from each video stream, and finally put into 3D-CNN to extract feature vectors. Comparative studies are conducted with that of the hidden Markov model (HMM) and recurrent neural network (RNN), with 92.6% recognition accuracy on a dataset containing 1,000 vocabularies. A complete real-time sign language recognition system is finally developed and reported, which is composed of a human interaction interface, a motion detection module, a hand and head detection module, and a video acquisition mechanism. Experimental results verify the generalisation performance of the system in real-time.
BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection

Salient object detection (SOD) is viewed as a pixel-wise saliency modeling task by traditional deep learning-based methods. A limitation of current SOD models is insufficient utilization of inter-pixel information, which usually results in imperfect segmentation near edge regions and low spatial coherence. As we demonstrate, using a saliency mask as the only label is suboptimal. To address this limitation, we propose a connectivity-based approach called bilateral connectivity network (BiconNet), which uses connectivity masks together with saliency masks as labels for effective modeling of inter-pixel relationships and object saliency. Moreover, we propose a bilateral voting module to enhance the output connectivity map, and a novel edge feature enhancement method that efficiently utilizes edge-specific features. Through comprehensive experiments on five benchmark datasets, we demonstrate that our proposed method can be plugged into any existing state-of-the-art saliency-based SOD framework to improve its performance with negligible parameter increase.
Manufacturing Three Different Solar Heaters Operating in Shattra City 31° 24’ 35“N - 46° 10’ 18”E and Comparison Between Them

Abstract Solar water heating is designed in Shattra city 31° 24’ 35“N 46° 10’ 18”E in south of Iraq. The research deals with a comparison in terms of performance and production of three different solar heaters in terms of primary components and the number of parts. The experimental data have been analysis (2019-2020). The importance of this study lies in obtaining the ideal model for the heater in terms of increase in production, low costs and ease of design
Kompetensi dan Motivasi Kerja Terhadap Kesiapan Mahasiswa Jurusan Akuntansi dalam Menghadapi Era Revolusi Industri 4.0

The purpose of this study was to determine the effect of competence and work motivation on the readiness of students majoring in accounting in facing the era of the industrial revolution 4.0. This research was conducted at the Faculty of Economics and Business, Udayana University, Accounting Study Program. The number of samples taken was 167 students. The data analysis technique used to test the research hypothesis is multiple linear regression analysis. Based on the results of the analysis, it was found that competence and work motivation had a positive effect on student readiness. This shows that the higher the competence and work motivation of students, the higher the students' readiness to face the world of work in the era of the industrial revolution 4.0. This proves that student readiness is influenced by competence and work motivation which are very important for every profession. &#x0D; Keywords: Competence; Motivation; Student Readiness; Industrial Revolution Era 4.0.
Software Optimization of Vision-Based Around View Monitoring System on Embedded Platform

&#x0D; &#x0D; &#x0D; Image processing algorithm requires high computational power. Optimizing the algorithm to be run on an embedded platform is very critical as the platform provides limited computational resources. This research focused on optimizing and implementing a vision-based Around View Monitoring (AVM) system running on two embedded boards of Cortex-A7 quad and Cortex-A15 quad-core, and desktop platform of Intel i7 core. This paper presented a study on several techniques of software optimization that is removing code redundancy and multi-threading. The two methods improve the total processing time of the AVM system by 45% on ARM Cortex-A15 and 47% on ARM Cortex-A7.&#x0D; &#x0D; &#x0D;
Design and Implementation of I/O Performance Benchmarking Framework for Linux Container

In cloud computing service it is important to share the system resource among multiple instances according to user requirements. In particular, the issue of efficiently distributing I/O resources across multiple instances is paid attention due to the rise of emerging data-centric technologies such as big data and deep learning. However, it is difficult to evaluate the I/O resource distribution of a Linux container, which is one of the core technologies of cloud computing, since conventional I/O benchmarks does not support features related to container management. In this paper, we propose a new I/O performance benchmarking framework that can easily evaluate the resource distribution of Linux containers using existing I/O benchmarks by supporting container-related features and integrated user interface. According to the performance evaluation result with trace-replay benchmark, the proposed benchmark framework has induced negligible performance overhead while providing convenience in evaluating the I/O performance of multiple Linux containers
Photonic-enabled Quantum Communication

Quantum communication leverages the physical properties of entanglement and superposition to create networks capable of connecting quantum computers, sensors, and secure nodes. Photons are a particularly viable information vehicle as they are unaffected by a broad range of environmental variables. They are also very versatile, supporting the teleportation of quantum information upon interacting with information-carrying qubits, whether these qubits are generated by a quantum computer or a random qubit source. Existing and emerging technologies will be discussed, as well as perspectives on the open challenges of the field.
STUDI DESKRIPTIF MEMBACA PERMULAAN DI KELAS I-B SDN PANCORAN 07 PAGI TAHUN AJARAN 2019/2020

This study aims to describe the inhibiting and supporting factors of prefix reading, as well as to describe the teacher's efforts in overcoming pre-reading problems in class I-B SDN Pancoran 07 Pagi. This research uses a qualitative approach with descriptive methods. Data collection techniques in this study were interviews and observation. The results of this study indicate that there are several inhibiting factors such as: (1) lack of self-confidence, (2) low learning concentration, (3) lack of teacher assertiveness, (4) lack of parental attention. The supporting factors for reading the beginning are: (1) supporting facilities and infrastructure, (2) the existence of a reading corner, (3) peers. While the teacher's efforts that have been made are: (1) The teacher provides additional hours, (2) The learning process is fun.
A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Data

Missing values exist in nearly all clinical studies because data for a variable or question are not collected or not available. Inadequate handling of missing values can lead to biased results and loss of statistical power in analysis. Existing models usually do not consider privacy concerns or do not utilise the inherent correlations across multiple features to impute the missing values. In healthcare applications, we are usually confronted with high dimensional and sometimes small sample size datasets that need more effective augmentation or imputation techniques. Besides, imputation and augmentation processes are traditionally conducted individually. However, imputing missing values and augmenting data can significantly improve generalisation and avoid bias in machine learning models. A Bayesian approach to impute missing values and creating augmented samples in high dimensional healthcare data is proposed in this work. We propose folded Hamiltonian Monte Carlo (F-HMC) with Bayesian inference as a more practical approach to process the cross-dimensional relations by applying a random walk and Hamiltonian dynamics to adapt posterior distribution and generate large-scale samples. The proposed method is applied to a cancer symptom assessment dataset and confirmed to enrich the quality of data in precision, accuracy, recall, F1 score, and propensity metric.
Predicting Digital Currency Price Using Broad Learning System and Genetic Algorithm

With the development of the digital economy, the price of Bitcoin, which is the most representative digital currency, has fluctuated dramatically. Recent works have explored the volatility of the Bitcoin price and made predictions using financial time series models such as ARIMA. However, for high-frequency Bitcoin data, the financial time series models have poor prediction performance as they often do not accommodate the inherent characteristics of digital currency, such as blockchain information. Some other works in this topic use deep learning models, e.g., artificial neural networks. However, the complex structure and time-consuming training process of these deep learning models often incur low prediction efficiency for rapidly changing Bitcoin price. In this regard, Broad Learning System (BLS) is a new neural network that avoids the complex structure of hidden nodes in deep learning models by adding enhancement nodes in the input layer to improve training efficiency while delivering relatively high accuracy. Therefore, this work applies the broad learning system to predict the Bitcoin price and optimizes the prediction model with a genetic algorithm. Due to the lack of fundamental factors for digital currency, the proposed prediction model considers the macroeconomic variables and the information of Bitcoin blockchain, which is the underlying technology of bitcoin, as inputs. According to the experimental results, the BLS-based prediction model optimized with the genetic algorithm achieved a better performance than other machine learning models.
Anomalous entities detection using a cascade of deep learning models.

Human actions that do not conform to usual behavior are considered as anomalous and such actors are called anomalous entities. Detection of anomalous entities using visual data is a challenging problem in computer vision. This paper presents a new approach to detect anomalous entities in complex situations of examination halls. The proposed method uses a cascade of deep convolutional neural network models. In the first stage, we apply a pretrained model of human pose estimation on frames of videos to extract key feature points of body. Patches extracted from each key point are utilized in the second stage to build a densely connected deep convolutional neural network model for detecting anomalous entities. For experiments we collect a video database of students undertaking examination in a hall. Our results show that the proposed method can detect anomalous entities and warrant unusual behavior with high accuracy.
Implementation of YOLO (You Only Look Once) Algorithm for Fire Detection

Forest and land fires are a problem that always happen every year in the teritory of Indonesia. The reason is the hot weather on dry season which can cause the emergence of fire ember as well as the practice of opening a new agricultural or plantation land in forest area carried out by means of illegal burning. This can be prevented by utilizing drone which is an unmanned aircraft technology that can be used to monitor the hotspots in forest or land area by air. This research was conducted to develop a drone-based fire detection system by using YOLO (You Only Look Once) algorithm which uses a convolutional neural networks to detect fire objects in images.The result showed that the use of  YOLO algorithm succeeded to detect fire quite well by producing average confidence score of 0.66 in video testing. Whereas, in testing with 100 images at treshold 0.30, resulting 98% of precision score, 95% of recall score, 95% of accuracy score and 72.63 % of mean average precision (mAP) score.
Library Portal: ENG 241/242: Home

This guide supports the research needs of students enrolled in ENG 241 and ENG 242 at Brunswick Community College.
PENERAPAN REGRESI NONPARAMETRIK KERNEL DAN SPLINE DALAM MEMODELKAN RETURN ON ASSET (ROA) BANK SYARIAH DI INDONESIA

Sharia Bank Return On Assets (ROA) modeling in Indonesia in 2018 aims to analyze the relationship pattern of Retturn On Assets (ROA) with interest rates. The analysis that is often used for modeling is regression analysis. Regression analysis is divided into two, namely parametric and nonparametric. The most commonly used nonparametric regression methods are kernel and spline regression. In this study, the nonparametric regression used was kernel regression with the Nadaraya-Watson (NWE) estimator and Local Polynomial (LPE) estimator, while the spline regression was smoothing spline and B-splines. The fitting curve results show that the best model is the B-splines regression model with a degree of 3 and the number of knots 5. This is because the B-splines regression model has a smooth curve and more closely follows the distribution of data compared to other regression curves. The B-splines regression model has a determination coefficient of R ^ 2 of 74.92%,%, meaning that the amount of variation in the ROA variable described by the B-splines regression model is 74.92%, while the remaining 25.8% is explained by other variables not included in the model.