Reviewerly/exHarmony huggingface dataset:
Patterns of the Teaching and Learning Cycle of GBA by EFL Teachers in Indonesia

This article purposes to tease out the EFL teachers’ patterns in implementing the stages of teaching and learning using GBA in the Indonesian context. The participants involved in the study were 15 English teachers from seven state senior high schools in Kota Malang Indonesia. The data were collected through interviews and observations. Fieldnotes were also made to record the relevant data. The observations of each teacher were carried out five times to attain sufficient data for the analysis. The writers observed the classes recommended by the teachers, sitting down in the back row and observing the activities done by teachers and students. They also put a tick (v) to indicate the presence of activities as listed in the checklist. If the teachers and students did activities which were absent in the checklist, the writers made fieldnotes to record them for supplementary purposes. The data were analysed descriptively based on the descriptors in the checklists. The analysed data showed that none of the English teachers fully employed the four stages of GBA (BKoF, MoT, JCoT, and ICoT) in teaching. Instead, they also applied the stages of GBA with the following patterns: BKoF-MoT-JCoT; BKoF-JCoT; BKoF-MoT; BKoF-MoT-ICoT; and BKoF-ICoT.
Literature-in-English as a Tool for Fostering Intercultural Communicative Competence in Multicultural Classrooms in Nigeria

Nigeria is a nation of several unique ethnic nationalities with diverse cultures. Cultural diversity has been identified as one of the factors responsible for growing civil unrest, insecurity and hate speeches in different parts of Nigeria. Multiculturalism is a common experience in several Nigerian states and the Nigerian education system. Efforts to inculcate intercultural competence among the members of the numerous ethnic groups and cultural identities in the country have been in form of legislation, convocation of national conferences and certain ‘political concessions’ to different ethnic groups. In spite of these efforts, very little has been achieved. The concern of this study is to discuss how education, through the tool of literature-in-English, can be engaged to teach intercultural communicative competence in multicultural classrooms. Recommendations were made on how to use literature-in-English to facilitate cross-cultural competence in Nigeria.
Pembelajaran Tematik Terpadu dengan Menggunakan Model Quantum Teaching

This study aims to describe the improvement of integrated thematic learning processes using the Quantum Teaching model in class V SD N 16 Koto Baru Solok. This type of research is Classroom Action Research (CAR). Subjects in the study were 22 teachers and students. Research site at SD N 16 Koto Baru Solok. The results showed an increase in: a) RPP cycle I with an average of 80.05% and cycle II 97.22%, b) Implementation of the aspects of the teacher Idengan cycle on average was 77.77% and cycle II 94, 44%, while the implementation of aspects of the first cycle students with an average of 79.1% and the second cycle 94.44% (very good), d) Assessment of student learning outcomes in the first cycle with an average of 72.9 and the cycle II 94. Conclusion, that the application of the Quantum Teaching model shows an increase in student learning outcomes and processes in integrated thematic learning.&#x0D; Keyword : Quantum Teaching; integrated thematic
Towards machine learning-based self-tuning of Hadoop-Spark system

Apache Spark is an open source distributed platform which uses the concept of distributed memory for processing big data. Spark has more than 180 predominant configuration parameter. Configuration settings directly control the efficiency of Apache spark while processing big data, to get the best outcome yet a challenging task as it has many configuration parameters. Currently, these predominant parameters are tuned manually by trial and error. To overcome this manual tuning problem in this paper proposed and developed a self-tuning approach using machine learning. This approach can tune the parameter value when it’s required. The approach was implemented on Dell server and experiment was done on five different sizes of the dataset and parameter. A comparison is provided to highlight the experimented result of the proposed approach with default Spark configuration system. The results demonstrate that the execution is speeded-up by about 33% (on an average) compared to the default configuration.
Memory Reduction through Experience Classification f or Deep Reinforcement Learning with Prioritized Experience Replay

Prioritized experience replay has been widely used in many online reinforcement learning algorithms, providing high efficiency in exploiting past experiences. However, a large replay buffer consumes system storage significantly. Thus, in this paper, a segmentation and classification scheme is proposed. The distribution of temporal-difference errors (TD errors) is first segmented. The experience for network training is classified according to its updated TD error. Then, a swap mechanism for similar experiences is implemented to change the lifetimes of experiences in the replay buffer. The proposed scheme is incorporated in the Deep Deterministic Policy Gradient (DDPG) algorithm, and the Inverted Pendulum and Inverted Double Pendulum tasks are used for verification. From the experiments, our proposed mechanism can effectively remove the buffer redundancy and further reduce the correlation of experiences in the replay buffer. Thus, better learning performance with reduced memory size is achieved at the cost of additional computations of updated TD errors.
Prediction of Child Development using Data Mining Approach

Child development is the sector that consists of scientific research of the patterns of growth, power and change that arise from conception through adolescence.One can apply this knowledge to realize the necessities of a child by viewing how and why individuals alternate and grow.Thus, pleasing them and allowing them to arrive at their maximum capacity.The intention of this study is to research the child growth based on the features consisting of age, height and weight.In order to understand how the physical growth of child switches with time, data is gathered from various sources such as Anganwadis, Primary Schools and Primary health Centres and a data mining method is implemented to expect the child growth.In this method, assessment of two data mining approaches ID3 Decision Tree and Naïve Bayes classifier is carried out on the basis of factors such as prediction accuracy, error rate and learning time.
A Training Gesture-Based-Scroll Visual Artificial Intelligence And Measuring Its Effectiveness Using Hidden-Markov Modeling Methods

In this article I discuss the method of hand gesture recognition as a visual motion detection based on artificial intelligence by training three main movements namely, scrolling up, scrolling down and stopping based on capturing the front camera image capture speed of 3 fps and measuring its efficiency against the control movements that performed using Hidden-Markov Modeling (HMM) with each catch object scroll up 3 fps / 15 frames scroll down scroll down 3 fps / 15 frames and stop 3 fps / 9 frames, the result is that the most effective hand gesture object training movement is stop gesture with 3 fps / 9 frames because the object's movement is able to be recognized by the system only in the 3rd second image capture frame.
EFFECT OF FUNCTIONAL MOTION IMAGE ANALYSIS UNDER MULTI-LAYER CONVOLUTIONAL NEURAL NETWORK ON IMPROVING SPEED AND FORCE

To explore the effect of applying multi-layer convolutional neural network to noise removal during the acquisition and transmission of functional motion images, the classical image denoising algorithms of mean filtering, median filtering, and wavelet transform filtering are first introduced.In addition, the evaluation methods of mean square error (MSE), image enhancement factor (IEF), peak signal to noise ratio (PSNR) and structural similarity measure (SSIM) in the image quality evaluation index system are introduced.Based on the convolutional neural network model, a multi-scale parallel convolutional neural network (MP-CNN) model is constructed to remove the noise in the image, and the functional action image is devoted to different degrees.Finally, the denoising effect is evaluated by the objective and subjective evaluation system.The objective evaluation results show that MP-CNN's MSE, IEF, PSNR, and SSIM are better than the single-channel model, and the test time is shorter.The subjective evaluation results show that the MP-CNN model has the best effect on noise removal after 25 denoising of functional action images.In this study, a multi-channel image denoising model based on the multi-layer convolutional neural network can improve the effect of functional motion image noise removal.
SISTEM INFORMASI ADMINISTRASI KEPENDUDUKAN (STUDI KASUS: DESA PARSAORAN AJIBATA KECAMATAN AJIBATA KABUPATEN TOBA SAMOSIR)

Parsaoran Sibisa Village, Ajibata is the village with the highest population density with a fairly high population movement in Ajibata Subdistrict, Toba Samosir Regency. At present the administrative system and population data activities are used by the filing method. Filing method is done by recording population data in population archive books, this method is less effective and efficient when there is a complex process of finding population data and population data reports. From these data it can be concluded that the problem in Parsaoran Ajibata Village is the lack of orderly administration of population so that population services are less effective and efficient. Therefore, the writer tries to offer a solution to build Population Administration Information System as Supporting Population Administration Order in Parsaoran Sibisa Village, Ajibata District, Toba Samosir Regency. Through assistance and training to the Village Government it will increase the knowledge and understanding of the importance of using Information Systems in terms of efficiency and effectiveness so that the Population Database in the Pisaoran Sibisa Village, Ajibata District is complete, accurate, and up to date.
Hybrid image encryption based on digital pre-encryption and optical single random phase encoding

In this paper, the optical image encryption scheme based on the double random phase encoding system is modified by introducing a nonlinear digital image pre-encryption coupled with a real to complex conversion.It consists in performing the bit-wise XOR operation recursively between successive pixels of an input image together with chaotic scrambling in the spatial domain.The resulting real-valued pre-encrypted image is halved into two equal parts, one being considered as the real part and the other one as an imaginary part.The complex image thus constructed by concatenating the two previous parts, passes into the second stage of the double random phase encoding where it will be multiplied by a random phase mask and then transformed into a frequency domain by the two-dimensional Fourier transform or any of its derivatives to obtain the encrypted image.The advantage of halving is to save the same information and reduce the size of encrypted image to store or transmit a single complex image instead of double as in all existing based double random phase encoding methods.Results of computer simulations prove the effectiveness of the proposed method toward different attacks and confirm its security when compared to existing works, especially in terms of key sensitivity and histogram analysis.
Convolutional Features Combining SL(3) Group for Visual Tracking

For visual tracking, key factors that affect the performance of trackers are related to whether it can effectively extract the appearance information and spatial information of a target. And most of state-of-the-art trackers either do not model the appearance information and spatial information separately or do not design special strategies to deal with the strong geometric deformation of the target. In this paper, we design an appearance information model and a spatial information model separately, and then combine them to obtain complementary benefits. Firstly, because the features from deeper layers of a convolutional neural network (CNN) can better describe the semantic information of a target while the spatial information becomes less, we adopt the features from the deepest layer as the appearance information model. Secondly, we focus on tracking the target with drastic geometric deformation through utilizing a projection transformation group (SL(3) group) to model the geometric transformation of the target, where SL(3) group can describe the geometric deformation more accurately. Furthermore, a standard discriminative correlation filter is used to develop the effect of convolutional features and is more efficient than other methods used for CNN. Extensive experiments results show that our tracker outperforms all the compared trackers.
Learning Deep Matching-Aware Network for Text Recommendation using Clickthrough Data

With the trend of information globalization, the volume of text information is exploding, which results in the information overload problem. Text recommendation system has shown to be a valuable tool to help users in such situations of information overload. In general, most researchers define text recommendation as a static problem, ignoring sequential information. In this paper, we propose a text recommendation framework with matching-aware interest extractor and dynamic interest extractor. We apply the Attention-based Long Short-Term Memory Network (LSTM) to model a user’ s dynamic interest. Besides, we model a user’ s static interest with the idea of semantic matching. We integrate dynamic interest and static interest of users’ and decide whether to recommend a text. We also propose a reasonable method to construct a text recommendation dataset with clickthrough data from CCIR 2018 shared task Personal Recommendation. We test our model and other baseline models on the dataset. The experiment shows our model outperforms all the baseline models and a state-of-the-art model, and the Fl-score of our model reaches 0.76.
What affects the difficulty of Chinese syntax?

The traditional measurement of sentence difficulty only focuses on lexical features but neglects syntactic features. This paper takes 800 sentences in primary school Chinese textbooks published by People's Education Press as the research object and studies their syntactic features. We use random forest to select the top five important features and then employed SVM to do the classification experiment. The precision rate, recall rate and F-scored for the classification of 5 levels are respectively 50.42%, 50.40% and 50.41%, which indicates that the features we selected has practical value for the related research.
Text Categorization System for English Text Documents using Naïve Bayes Classifier

Information technology generated huge data on the internet.Most of this data is mainly in English language.Automatic text categorization is useful in better management and retrieval of these text documents and also makes document retrieval as simple task.Various learning techniques exist for the classification of text documents like Naïve Bayes, Support Vector Machine and Decision Trees, etc.The proposed system uses a Naïve Bayesian method.Bayesian algorithms are often used to classify data in different categories in a way that the systems can be trained and learn from human corrections.
The Effect of Moodle Implementation in English for Multimedia Classroom on Students’ Achievement in Reading and Writing

Moodle is an application that serves to create and conduct internet-based courses/training/education.In this paper, the researchers attempted to find out whether using Moodle is effective to increase student's achievement especially on reading and writing English for multimedia.This research conducted at Politeknik Negri Media Kreatif, especially on Multimedia program.English subject in this campus is not a general English that focuses on grammar, but rather English for specific purposes in which English application should be related to every department, such as English for Multimedia.Quasi-experiment with non-equivalent control group design was a method implemented to solve the research's problem.Two classes that had 30 students in each class became the sample.Class A of the fourth semester was decided as the experimental group and class B as the control group.Before investigating the effectiveness of Moodle, the researchers created Moodle based learning system.After that, Moodle was used in experimental class as a treatment.researchers collected the data by administrating reading multiple choice test and writing an essay.The reading test consisted 30 multiple choice questions with 5 options both in pre-test and posttest.In computing Independent ttest, founded that sig.(2-tailed) for reading was 0.000 and for writing was also 0.00.Both sig values were less than 0.05, it meant that there was a significant effect of using Moodle in teaching English for Multimedia on students' achievement in Reading.However, Moodle significantly improves writing rather than reading in which the t-test of writing (12.812) was greater than t-test of writing (9.128).
Design of UAV Level Control Method

Starting from the current problems of drones, this paper aims to find solutions that are suitable for sustainable development. This paper mainly introduces a method of adjusting the control authority of UAV, including managing the process, obtaining the operation of maneuvering performance and controlling the accuracy rate. This method enhances the fault tolerance of UAV flight control, improves the high precision of UAV flight control, and avoids the occurrence of accidents caused by improper control, such as “drone damage” or “personal injury to others”.
Dynamic Video Tag Cloud

Recently, recipe short videos such as Kurashiru and DELISH KITCHEN have been rapidly gaining attention. These recipe videos can help people learn the essentials of cooking in a short time. However, it is difficult to understand cooking operations by watching a video only one time. Also, since these short recipe videos do not take into account the user's cooking level, anyone can see only the same videos. Therefore, in this paper, we propose a novel cooking support system for recipe short videos, called Dynamic Video Tag Cloud. For this, we first extract cooking operations from a text recipe included in an existing recipe short video. Next, we extract various supplementary recipe information (videos) based on users' cooking levels by weighting the appearance frequency of cooking operations for each cooking genre. Then, the system visualizes supplementary recipe information (videos) to the users in a tag that could interface.
Testing the mid-spatial frequency error of a large aperture long-focal-length lens with CGH

We present the use of a computer-generated hologram (CGH) to test the mid-spatial frequency error of a large aperture long-focal-length lens. In order to verify this test approach, a 450 mm × 450 mm reflective CGH is designed and fabricated for testing the 440 mm × 440 mm spatial filter lens. Both 0 th and 1 st order diffraction wavefront of CGH are measured, and the 0 th order diffraction wavefront is used to calibrate the substrate error. The mid-spatial frequency wavefront error caused by the CGH fabrication errors are evaluated using the binary linear grating model and power spectral density (PSD) theory. Experimental results and error analysis show that the CGH test approach is also feasible for the measurement of mid-spatial frequency error, and the measurement accuracy of PSD1 can reach 0.8832 nm RMS.
Visual Target Tracking via Online Reliability Evaluation and Feature Selection in the Framework of Correlation Filtering

Background: Although correlation filtering is one of the most successful visual tracking frameworks, it is prone to drift caused by several factors such as occlusion, deformation and rotation. Objective: In order to improve the performance of correlation filter-based trackers, this paper proposes a visual tracking method via online reliability evaluation and feature selection. Methods: The main contribution of this paper is to introduce three schemes in the framework of correlation filtering. Firstly, we present an online reliability evaluation to assess the current tracking result by using the method of adaptive threshold segmentation of response map. Secondly, the proposed tracker updates the regression model of correlation filter according to the assessment result. Thirdly, when the tracking result based on a handcrafted feature is not reliable enough, we propose a feature selection scheme that autonomously replaces a handcrafted feature used in the traditional correlation filter-based trackers with a deep convolutional feature that can re-capture the target by its powerful discriminant ability. Results: On OTB-2013datasets, the Precision rate and Success rate of the proposed tracking algorithm can reach 84.8% and 62.5%, respectively. Moreover, the tracking speed of proposed algorithm is 19 frame per second. Conclusion: The quantitative and qualitative experimental results both demonstrate that the proposed algorithm performed favorably against nine state-of-the-art algorithms.
Automatic Objects Detection and Tracking Using FPCP, Blob Analysis and Kalman Filter

Object detection and tracking are key mission in computer visibility applications, including civil or military surveillance systems. However, there are major challenges that have an effective role in the accuracy of detection and tracking such as the ability of the system to track the target and the response speed of the system in different environments as well as the presence of noise in the captured video sequence. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then, we used an ideal filter that performs well to reduce noise through the morphological filter. The Blob analysis is used to add smoothness to the spatial identification of objects and their areas, and finally, the detected object is tracked by Kalman Filter. The applied examples demonstrated the efficiency and capability of the proposed system for noise removal, detection accuracy and tracking.