Reviewerly/exHarmony huggingface dataset:
DERIVATIONAL AFFIXES IN WRITING ANALYTICAL EXPOSITION TEXT

This research is about the analysis of derivational affixes in Writing Analytical Exposition Text. It intended to describe the process of derivational affixes and to find out the types, the structure, and the functions of each derivational affixes in writing analytical exposition text by the twelve grade students from YouTube. The qualitative method was used to investigate derivational affixes in writing analytical exposition text. The data were collected by documented study. They were collected from students’ results of making texts about analytical Exposition text, finding the text about analytical exposition, choosing the text that relevant with Corona virus disease, and the last searched the words that contain derivational affixes, the researcher identified. The results of this study showed that derivational affixes were found in writing analytical exposition text. There were three types of affix used. They were prefix, suffix, and circumfix. There was no infix used. Kinds of prefix found were pre-, dis-, re-, on-, under-, inter-, micro-, trans-, over-, al-, In-, out-. Kinds of suffix found were -ment, -ly, -ness, -ion, -y, -er, -ive, -ous, -ant, -ious, -ation, -en, -wards, -ition, -ed, -est, -ies, -ities -ily, -ence, -ing, -side. Kinds of circumfix found were Trans-ed, In-ation, inter-ion, dis-ed, re-ation, un-y, anti-ion, un–ed, in-ly, trans-ion, inter –ed, re-ing, trans-ing, in- ing, in-ent. The function of derivational affixes found in writing analytical exposition were verb formation, noun formation, adjective formation, and adverb formation, in which sometimes in the process of word formation, it did not always change the part of speech even though it had got the addition of affixes, such as noun into noun, verb into verb, and adjective into adjective. There were many derivational affixes. Moreover, suffix –ing to form a noun and –ly to form an adverb the most dominant affixes.
Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

&#x0D; Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented.&#x0D;
Design and Evaluation of a Novel Haptic Interface for Horse-Drawn Carriage Simulation

Animal welfare has become an increasingly important concern in the sports field.Learning horse-drawn carriage driving requires much time and effort for both the drivers and the horses because the associated gestures to avoid harming the horses are difficult to acquire.This raises the need to develop realistic simulation environments for future drivers.To this end, two haptic interface prototypes were designed, coupled with dedicated simulation software.The first was developed based on a SPIDAR haptic device and implemented simple behaviors of the carriage.A user study demonstrated interest in such a simulator, which led to the design of a second prototype, on a different architecture than the first prototype, for integrating more precise laws of horse behavior such as mood and allowing a more subtle control of forces.An evaluation with driving learners revealed that the simulator was capable of not only producing sensations close to reality but also improving the interaction between the trainer and the learner.
Design and Implementation of Web-Based Helpdesk Information Systems Using Extreme Programming Methods

Technological developments have become a necessity for job optimization, be it desktop, web or mobile-based software, of course this really supports work with fast, timely completion, without spending a lot of effort and money. The Help Desk is a facilitator for the process of solving operational problems, through communication and interaction networks, internal and external, between the source of the problem (the client) and the potential / problem solving analysis capacity (experts) so that the Asset Management process becomes more effective in improving unit performance. Its operational implementation refers to the principles in the concept of Knowledge Management. Extreme programming is a method that lightens the burden or speeds up the software development process, the characteristics of this method are in accordance with this study which has a small number of team members. This method is flexible so that it can adapt to the changes that customers want. With this the user becomes easier for system development
Classification of Wheat Seeds Using Neural Network Backpropagation Algorithm

There are various types of wheat scattered in the world. Usually it takes a long time to recognize the type of wheat seed by manual method because wheat germ has a physical appearance that looks the same as others. One method that can be used is an Artificial Neural Network. In this study, the data used were secondary data which consisted of data from the variable physical characteristics of wheat germ. The types of wheat seeds that are classified are 3. The Artificial Neural Network architecture used in this study is 5. By comparing the 5 Artificial Neural Network architectures, it is concluded that the architecture consisting of 3 layers and 4 layers is more precise in the classification of wheat germ types. The accuracy obtained by the 2 Artificial Neural Network architectures is 90% and 90%, respectively.
Everyone is a Teacher Here Method towards Student Learning Outcomes on Kingdom Animalia Topic

This study aims to improve learning outcomes of students' cognitive abilities in the kingdom animalia concept. The subjects of this study were students of SMAN 1 Surade in class X-5 and X-3 with a total of 41 and 36 people. The research method used in this study is Quasi Experimental Design with Pretest-Posttest control group design research design. The instrument used was question of cognitive ability in the form of multiple choice as the main data source and a questionnaire as supporting data. The cognitive abilities netted from the instruments are C1 (knowing), C2 (interpreting), C3 (sorting) and C4 (interpreting). The results of the study show that the cognitive abilities of students after following Learning Everyone is a Teacher Here has increased from the average value of grades the initial test 62.19 increased to 78.79. Based on testing the hypothesis at the final test, the calculated t value of 262.9 is greater than the t table value of 244. This shows that the learning outcomes achieved there were no significant differences. Students respond towards Learning Everyone is a teacher here on each statement interpreted pretty well on average.
A Hybrid Multi-level Statistical Load Balancer-Based Parameters Estimation Model in Realtime Cloud Computing Environment

As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned.
Adaptive Window Selection for Non-uniform Lighting Image Thresholding

Selection of appropriate size of windows or subimages is the most important step for thresholding images with non-uniform lighting. In this paper, a novel criteria function is developed to partition images into different size of sub images appropriate for thresholding. After the partitioning, each subimage is segmented by Otsu’s thresholding approaches. The performance of the proposed method is validated on benchmark test images with different degree of uneven lighting. Based on the qualitative and quantitative measures, the proposed method is fully automatic, fast and efficient in comparison to many landmark approaches.
CLASSIFICATION OF MEAT IMAGERY USING ARTIFICIAL NEURAL NETWORK METHOD AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL CO-OCCURRENCE MATRIX METHOD

Lately, there is often a mixture of beef and pork done by traders to the general public as buyers. This is due to the unconsciousness of the buyer on how to recognize the type of meat purchased. The effect of this meat mix can certainly be detrimental to buyers, especially Muslims. Image processing is a general term for various methods in which it is used to manipulate and modify images in various ways. Classification is a method of grouping some information and ensuring it is listed in a class.. Classification of beef and pork differentiator in this application using Artificial Neural Network (ANN) method while for texture extraction using Gray Level Co-occurrence Matrix (GLCM) method. The information used in the examination was 30 images of fresh meat divided into 15 images of fresh beef and 15 images of fresh pork. The data used is data Classification of Beef and Pork Image based on Color and Texture Characteristics. The result of classification accuracy obtained in this application is 80%.
A new framework based on KNN and DT for speech identification through emphatic letters in Moroccan dialect

&lt;p class="keywords"&gt;&lt;span id="docs-internal-guid-6347807a-7fff-e7da-a2d6-74cb8393677f"&gt;&lt;span&gt;Arabic dialects differ substantially from modern standard arabic and each other in terms of phonology, morphology, lexical choice and syntax. This makes the identification of dialects from speeches a very difficult task. In this paper, we introduce a speech recognition system that automatically identifies the gender of speaker, the emphatic letter pronounced and also the diacritic of these emphatic letters given a sample of author’s speeches. Firstly we examined the performance of the single case classifier hidden markov models (HMM) applied to the samples of our data corpus. Then we evaluated our proposed approach KNN-DT which is a hybridization of two classifiers namely decision trees (DT) and k-nearest neighbors (KNN). Both models are singularly applied directly to the data corpus to recognize the emphatic letter of the sound and to the diacritic and the gender of the speaker. This hybridization proved quite interesting; it improved the speech recognition accuracy by more than 10% compared to state-of-the-art approaches.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
Exploration of University Education Model Reform Based on the Integration of CDIO and TPACK

Aiming at the problems of insufficient innovation ability and insufficient integration of information technology and curriculum teaching in China, a university education model based on the integration of CDIO and Technological Pedagogical Content Knowledge is proposed.CDIO theory attaches importance to engineering practice and Technological Pedagogical Content Knowledge theory emphasizes the theoretical basis of teachers.This model combines the advantages of both and fully integrates the needs of teachers and students.This paper expounds on the components and topological structure of the new university education model, and puts forward the corresponding strategies according to the bottleneck of university education.It has laid a theoretical and practical basis for the further development of university education.
Implementation of Distance Learning and Assessment in Kindergarten in Emergency Circumtances

This study aims to get a clearer picture of distance learning (DL) in kindergarten and its assessment, to provide recommendations for policy options related to DL in kindergarten to optimizing students' development and abilities in the emergency. This study was randomly sampling subjects, which are principals, teachers, and parents who had kindergarten-age children. The data collection was through interviews, documentation, and questionnaires deployed by Google form. The results of this study showed that the best format of DL in kindergarten is blended. The assessment for this blended learning that can be done both by teacher and parents are by using communication forum and checklist sheets. Policy options for DL in kindergarten are the use of package modules/books, home visits, activity sheets, television and radio, and the coordination of the government at central and regional administration, and education stakeholders. These policy options become necessary because of the quality of DL based on the knowledge and skills of the teacher/parents that corresponds to the child's developmental stage. DL in kindergarten is the fulfillment of education rights to children in any circumstances. This effort optimizing the children's development of abilities and potential as expected
Extractive Multi-Document Text Summarization by Using Binary Particle Swarm Optimization

The absence of a standard dataset and poor work for Hindi text summarization leads to develop a technique for better results.We have used a combination of Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Term frequency and Inverse Sentence Frequency for finding the results.Binary PSO is used for finding the optimal values of the features.
New Generation Mobile Networks and Their Application in Electronic Learning

The present article was aimed at examining the promising capabilities of new generation mobile networks for the implementation of first-class online education based on advanced immersive technologies. The study described the main functional drivers of the modern end-to-end 5G architecture and proposed several options for their application in today’s socio-economic life. It was assumed that, in the long view, 5G could become a facilitator and a quickening agent of Industry 4.0 and SMART Education, giving way to machine learning and the realization of Machine-to-Machine/Man (M2M) and Device-to-Device (D2D) technologies. The article provided an argument that the ability to connect through the 5G network would allow innovative technological solutions to fully realize their potential and create the global telecommunications infrastructure of the digital economy. In order to investigate public opinion toward a 21st-century education and identify the prioritized requests for university training among potential students, a survey of the target audience in the business administration segment was conducted. It involved 100 MBA students of the Graduate School of Business of Kazan Federal University (Russia) and 200 business workers. Respondents were offered to choose the most relevant requirements for the education quality from among 50 proposed variants by determining their priority on a 100-point scale. The analysis of questionnaire results allowed identifying 20 consumer trends and requests in education, which are becoming more and more prioritized in the contemporary business environment. After reviewing the survey outcomes, it was concluded that the main competitive advantages of an educational course are its pace, convenience, and effectiveness in goal-achievement. The provision of these benefits can become possible due to the synthesis of 5G and other digital educational technologies. In view of this, the present work also presented the analysis of promising educational opportunities of 5G for creators of study materials and students.
Penerapan Algoritma Crawling dalam Otomatisasi Verifikasi Pembayaran Tiket Seminar

The system for viewing student seminar payment data can be viewed through administrator controls. The seminar payment confirmation process for National University students still uses the manual system by paying the seminar organizer so it takes longer to process the seminar payment. The purpose of creating this system is to speed up the payment time of the seminar. This system uses a unique 3-digit code in each payment of seminar participants, this unique 3-digit code acts as a mark that differentiates student payments so that it does not require confirmation to the seminar organizer. Cron work will be performed on UNIX-based operating systems (Linux, Ubuntu, etc.) which function to perform tasks or scripts automatically. The content of the script is an order to execute the payment confirmation order against the participant. This level of research consists of several stages, namely needs analysis, design, writing system application code, application testing. This investigation has resulted in an application that can expedite the payment verification process by matching the number of transfer participants with those in the database. The results of testing the payment verification process are influenced by the Crawl Algorithm to help the payment process become more effective. With results that have been tested on average, it shows 4.8 seconds to confirm the participant's payment. With this seminar payment system, you can save time to process seminar payments.
THE EFFECTIVENESS OF USING CHRONOLOGICAL PARAGRAPH STRATEGY TOWARD STUDENTS’ WRITING SKILL

This study aims to identify the effect of chronological order strategy on improving students' ability in writing paragraphs. The sample of this study was 40 students of SMPN 9 SATAP Pujut Central Lombok. This study employed a pre-experimental method by using intact-group comparison. To gather the data, the pre-test and post-test were distributed to the groups to collect the writing score and the percentage of students’ writing classification. Analysis of Variance (ANOVA) was applied to testing the hypothesis. The finding data shows that there was enhancement of students’ classification in writing between pre-test and post-test. In pre-test the average of students’ classification between fair, poor and very poor. But in post-test the average of students’ classification improved into very good, good, fair and poor. The result of data analysis indicates a significant effect of using chronological order to improve students’ writing ability. It evidenced by the significant level was lower than 0.05 (0.00&lt;0.05).
Application of Markov chain in students’ assessment and performance: a case study of School of Mathematical Sciences, one of the public university in Malaysia

The quality education is an essential element in economic, political and social development of any country. Therefore, enrollment forecasting is needed in higher education to assist the universities in the preparation of their educational frameworks including budgeting, provide all necessary facilities and planning the overall short and long term goals. This research study the pattern of students’ assessment and their academic performance in School of Mathematical Sciences, Universiti Sains Malaysia. The target population is all undergraduate enrollment from 2016/2017 until 2018/2019 sessions. An absorbing Markov chain model is applied to study the absorption, retention and repetitive rates of the students by the academic programs and gender. The fundamental matrix is constructed to determine the expected duration of schooling before graduating. The enrollment projection is also estimated to study the probability of the performances of the students in the long run. In summary, this research addresses on the use of Markov chain model to describe the stochastic pattern of the enrollment and assessment of the students.
Alternatif Aplikasi Mudah Perpustakaan (Pengenalan Aplikasi ASP.Net )

Introduction. Today we enter an era where the use of technology in everyday life is very natural. The use of this technology turned out to pose a challenge for information providers, one of them was the library. The library must be able to present information that collaborates with technology so that information can be conveyed effectively and efficiently to its users. One of them is by providing library websites using ASP.Net, which is server-side technology to develop web applications based on Microsoft .NET framework. Thus, libraries can create dynamic sites and web applications that are interactive, easy to use, easy to develop.&#x0D; Data Collection Method. In collecting data in this paper the writer uses data collection methods in the form of &#x0D; Results and Discussions. This article discusses the history of ASP. Net and its objects namely Namespace, Web Form, Basic Web Form Syntax, Class Page, Request Object, Respone Object, Server Object&#x0D; Conclusions. ASP.Net has many objects and is easy to use. Therefore, ASP.NET can be one of the recommendations for use in creating Web libraries
Deviation results for sparse tables in hashing with linear probing

We consider the model of hashing with linear probing and we establish the moderate and large deviations for the total displacement in sparse tables. In this context, Weibull-like-tailed random variables appear. Deviations for sums of such heavy-tailed random variables are studied in Nagaev (Theory Probab Appl 14(1):51–64, 1969; Theory Probab Appl 14(2):193–208, 1969). Here we adapt the proofs therein to deal with conditioned sums of such variables and solve the open question in Gamboa et al. (Bernoulli 18(4):1341–1360, 2012). By the way, we establish the deviations of the total displacement in full tables, which can be derived from the deviations of empirical processes of i.i.d. random variables established in Wu (Ann Probab 22(1):17–27, 1994).
A Systematic Review of Algorithms in People Images Detection Based on Artificial Vision Techniques for Energy Management in Air Conditioners

Over the years, the development of artificial intelligence has influenced the fact that the algorithms applied in video devices are renewed every day in their object detection area, such as pattern recognition for detecting an object, image, person. This research aims to identify the algorithms for detecting people's image through artificial vision, and its application focused on energy management in air conditioners. The following research questions were established: Q1: How many studies refer to algorithms based on detecting a person's image? Q2: How many studies refer to energy management in air conditioners based on artificial vision? Q3: Are there artificial vision techniques for the automatic turning on and off a device? Q4: What types of algorithms in detecting a person's image based on artificial vision exist today? The search for information was based on research criteria related to the topic developed in four virtual libraries. This research criterion includes 213 relevant studies, including 46 that were of interest for developing the research. This research's results determine that the viola-jones algorithm shows greater effectiveness with 91.5% in relation to the other algorithms, followed by the HOG algorithm with 90.29% effectiveness. Considering the different efficiency parameters established in this study, we can conclude that this algorithm can be used in various applications, security, energy management, video surveillance and environment.