Reviewerly/exHarmony huggingface dataset:
Half Logistic NHE: Properties and Application

Nadarajah and Haghighi has defined the extension of the exponential distribution we have called this as NHE distribution. It is continuous probability distribution with a wide range of applications such as in life testing experiments, reliability analysis, applied statistics and clinical studies. However, it is not flexible enough for modeling heavily skewed datasets as compared to modified distributions. In this study, we have generated a new continuous distribution having three parameters based on the half logistic-Generating family called half logistic NHE. The structural properties of this model are explored such as the probability density, cumulative density, hazard rate, reversed hazard rate, and quantile functions. The model parameters are estimated using the three well-known methods namely maximum likelihood estimation (MLE), least-square estimation (LSE) and Cramer-Von-Mises (CVM) methods. Further, we have computed the Fisher information matrix and asymptotic confidence intervals for ML estimators. The potentiality of the proposed distribution is also evaluated by fitting it in contrast with some other existing distributions using a real life data.
Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features

The first step of any terminological work is to setup a reliable, specialized corpus composed of documents written by specialists and then to apply automatic term extraction (ATE) methods to this corpus in order to retrieve a first list of potential terms. In this paper, the experiment we describe differs quite drastically from this usual process since we are applying ATE to unspecialized corpora. The corpus used for this study was built from newspaper articles retrieved from the Web using a short list of keywords. The general intuition on which this research is based is that ATE based corpus comparison techniques can be used to capture both similarities and dissimilarities between corpora. The former are exploited through a termhood measure and the latter through word embeddings. Our initial results were validated manually and show that combining a traditional ATE method that focuses on dissimilarities between corpora to newer methods that exploit similarities (more specifically distributional features of candidates) leads to promising results.
Connected-Tube MPP Model for Unsupervised 3D Fiber Detection

In this paper, we extend our previous 2D connected-tube marked point process (MPP) model to a 3D connected-tube MPP model for fiber detection. In the 3D case, a tube is represented by a cylinder model with two spherical areas at its ends. The spherical area is used to define connection priors that encourage connection of tubes that belong to the same fiber. Since each long fiber can be fitted by a series of connected short tubes, the proposed model is capable of detecting curved long tubes. We present experimental results on fiber-reinforced composite material images to show the performance of our method.
EMOTIONAL INTELLIGENCE AS A PROFESSIONALLY IMPORTANT QUALITY OF GRADUATES OF A PEDAGOGICAL UNIVERSITY

The article discusses the results of a study of emotional intelligence in students of a pedagogical university. The authors presented the views on the concept, the essence and the structure of the emotional intelligence regarding psychological science. Emotional intelligence is considered as one of the basic determinants of an efficient pedagogical activity.
Large Language Models for Latvian Named Entity Recognition

Transformer-based language models pre-trained on large corpora have demonstrated good results on multiple natural language processing tasks for widely used languages including named entity recognition (NER). In this paper, we investigate the role of the BERT models in the NER task for Latvian. We introduce the BERT model pre-trained on the Latvian language data. We demonstrate that the Latvian BERT model, pre-trained on large Latvian corpora, achieves better results (81.91 F1-measure on average vs 78.37 on M-BERT for a dataset with nine named entity types, and 79.72 vs 78.83 on another dataset with seven types) than multilingual BERT and outperforms previously developed Latvian NER systems.
Clustering-Based Monarch Butterfly Optimization for Constrained Optimization

Monarch butterfly optimization (MBO) algorithm is a newly-developed metaheuristic approach that has shown striking performance on several benchmark problems.In order to enhance the performance of MBO, many scholars proposed various strategies for benchmark evaluation and practical applications.As an application of artificial intelligence (AI), machine learning (ML) developed fast and succeeded in dealing with so many complicated problems.However, up to now, ML did not use to improve the performance of MBO algorithm.In this paper, one of ML techniques, clustering, is introduced into the basic MBO algorithm, so an improved clustering-based MBO namely CBMBO is proposed.In CBMBO algorithm, the whole population is divided into two subpopulations according to k-means clustering.Also, only the individuals having better fitness can be passed to the next generation instead of accepting all the updated individuals used in the basic MBO algorithm.In order to improve the diversity of the population, few individuals having worse fitness are accepted as new individuals.In order to verify the performance of our proposed CBMBO algorithm, CBMBO is compared with six basic and four improved metaheuristic algorithms on twenty-eight CEC 2017 constrained problems with dimension of 30, 50, and 100, respectively.The experimental results suggest a significant addition to the portfolio of computational intelligence techniques.
Students Reading Comprehension through Scanning Technique

This article reviews how scanning technique contributes to reading comprehension. Reading comprehension is defined as the process by which a person derives meaning from print. Scanning is a device used to locate details. Scanning means glancing rapidly through the text either to search for a specific piece of information The differences of students reading comprehension after treatment are influenced by treatment given to them. It was proven by the result of previous research statistical data analysis which indicated to the students’ progress. teaching reading by using scanning technique can increase students’ reading achievement.
Peningkatan Keterampilan Menulis Artikel Populer pada Mahasiswa Melalui Program Satu Mahasiswa Satu Karya (SAMA SAYA)

Writing popular articles in the mass media is admittedly very difficult for students. In addition to adjusting the latest issues, popular articles in the mass media must also be adjusted to diction, spelling, mass media characters, and free of plagiarism. The purpose of this study was to improve the skills of writing popular articles on students through the One Student One Creation program (SAMA SAYA). The sample in this study were 42 second semester students of the Islamic Education program at STAINU Temanggung academic year 2018-2019. This action research was designed with the Kemmis and Mc Taggart models, namely planning, action, observation, and reflection. The results of the study indicate that the SAMA SAYA program can improve popular article writing skills. Starting the aspect of determining the current problem / issue, from 14 students (33.3 percent) to become 40 students (95.2 percent), determining interesting titles from 18 students completed (42.8 percent) increased 39 students completed (92.8 percent), the selection of correct diction, from 12 students completed (28.5 percent) to 38 students completed (90.4 percent). In the aspect of using PEUBI rules which initially 14 students completed (33.3 percent) to 30 students completed (71.4 percent), understanding the character of mass media initially 5 students completed (11.9 percent) to 40 students completed (95.2 percent ), not plagiarism under 25 percent as many as 12 students completed (28.5 percent) increased to 37 students (88.0 percent).
The Development of Indonesian Language Teaching Materials for Beginner Level of Foreign Speakers with Local Content

This research is motivated by the scarcity of Bahan Ajar Bahasa Indonesia Untuk Penutur Asing (BIPA), especially cultural-charged teaching materials. Therefore, the development of BIPA teaching materials is needed. The problems examined in this study are (1) how the process of developing BIPA teaching materials containing Batak culture for beginner-level foreign speakers, (2) how the final results of the development of BIPA-charged Batak culture materials for beginner-level foreign speakers, (3) how the materials are feasible BIPA teaching contains Batak culture for beginner-level foreign speakers. This research uses the Research and Development (R&amp;D) approach which is carried out in five stages, namely: (1) preliminary survey, (2) initial prototype development, (3) product design, (4) product validation, (5) product revision and improvement. This study uses two data collection techniques, namely observation and interviews to obtain data on the needs of teaching material development and assessment of teaching material prototypes. The results of this study are as follows. First, the development of BIPA teaching materials with Batak culture for beginner-level foreign speakers is carried out in five stages, namely: (1) a preliminary survey with techniques for analyzing existing teaching materials, (2) initial prototype development, (3) product design, (4) product validation, and (5) product revision and improvement. Second, the prototype of teaching material is developed with three parts including (a) physical form, (b) book cover, (c) content / material content. Third, the evaluation from experts which includes the graphic aspect gets 89.29%. In the aspect of content/material obtained 78.57%. In the presentation aspect, it gets 83.33%. Aspects of language and readability, obtain 100.00%.
Development of Standardized Online Test to Assess the Students 21st Century Skills

21st Century Skills are such essential skills to be prepared for professionalism needed nowadays.Therefore, education curricula were more emphasizing to exercise these skills.To achieve this objective, we have to build a standard assessment test to make sure that students have accomplished these skills.This research attempted developing a set of 21st-century skills tests tested to some teachers in Jawa Barat with a sample of 45 teachers.The problems presented refer to the design standardized by the National Assessment of Educational Progress (NAEP).The test consisted of 10 concept comprehension questions and 16 21st-century skills problems with the rubric developed by Suzie Boss.The test was designed using Edmodo and Moodle platforms.The research result showed that the concept comprehension test has high validity, 0.628.As the 21st-century skills test, there are some problems not validated.Therefore, some issues on this proposed standard test should be further improved.
The Development of Flash-Based Media in Project Based Learning for English Subject to Increase Students’ Motivation and Achievement at Senior High School

The use of technology-based media increases students’ motivation and learning effectivity in the classroom. This research aims to develop, to examine the appropriateness and the effectivity of flash-based media at Senior High School. This development research uses the ADDIE model. Based on the research result, the score of validity trials from the material expert is 72, the score from the media expert is 58 and from the practitioner expert is 75. The assessment from the material, media and practitioner expert show that flash-based media is in the appropriate category. Effectiveness test of the flash-based media development is through the testing of hypothesis, the data shows that for both experimental and controlled class in pre-test and post-test, the score is Sig. (2-tailed) 0.000 &lt; 0.05, and the score of the students’ motivation in learning for pre-test and post-test in both experimental and controlled is Sig. (2-tailed) 0.000 &lt; 0.05. It means that the flash-based media increases students’ motivation and achievement in the learning process. This research result supports the theories of technology learning, especially for the development of learning aids and media for English subject.
Artikel Pengelolaan Media Pembelajaran Sekolah Dasar

Management of learning media is a part of efforts to improve the quality of education. As for the indicators that must be considered in the management of learning media especially: planning instructional media, organizing media, learning media management, evaluating media of the school, it is expected that the preparation of media programs can run effectively with the learning programs that have been made so that it can contribute positively to learning. The learning media management process must go hand in hand with school programs, it is necessary to manage a management of learning media in elementary school that is responsible for administering all activities related to learning media management, understanding of media management is important for the officer in planning media programs, classification of media, the grouping of media based on the condition of each school or based on certain subjects so that the media can be used optimally in the classroom or in out door class. Supervision should be carried out programmatically by the school principal and personnel at the relevant agencies of the district and district level offices are expected to be input of increasing the development of learning media management n the next year.
The Effect of Using Clustering Technique on Students’ Achievement in Writing Descriptive Text

The objective of this research was to find out the significant effect of using clustering technique on students achievement in writing descriptive text. Culstering technique used to improve students ability in wirting especially writing descriptive text. This research was conducted at SMP Cerdas Murni Tembung Jl. Beringin Pasar VII no.33 Tembung. The method of this research is quantitative method. The total population of this research was 91 students of the academic year 2019/2020. The researcher just took one class as the sample. The sample consisted of 30 students from VIII-1 class and choosen by using cluster random sampling technique. The writer divided the class into two groups, that are experimental group and non-experimental group. The experimental group was taught by using cluster technique. The data for this research taken from pre-test and post-test of the written test. The experimental group was given pre-test and post-test. After the data has been collected, these were analyzed by using t-test formula. The result of the analysis showed that tobserve (5.215) was higher than ttable (1.701) with the level of significant ? = 0.05. The final hypothesis showed that Ho was rejected and Ha was accepted. It means that there was a significant effect of using clustering technique on students achievement in writing descriptive text.
Manufacture of Gambir Leaf Press Impac System in Efforts to Increase Productivity of Gambir Farmers

The Hipgampra farmer group has Gambir farming land. The problems that occur in this farmer group have not had a press tool, usually, this farmer group rents gambir leaf processing equipment privately and must pay rent wages which are worth one-third of the proceeds of the sale. Through this service activity, the team wanted to overcome the problem by giving a press tool to process gambir leaves. The purpose of the implementation of this service activity is to provide a solution to the problems faced by gambier women in the process of pressing gambier leaves. The method of applying science and technology carried out in this activity is to provide introductory theories, demonstrations, and practices. The introductory theory is applicable, namely the introduction of tools, how to use them, their functions and application applications in the field. The realization of service to the community of Kenagarian Lampanjang Sutera Pesisir Selatan Subdistrict was held on October 6, 2018. The equipment that was handed over to the community of Kenagarian Lampanjang Subdistrict, Sutera in the form of a unit of a Gambir leaf impact system. Application of appropriate technology that is applied to gambir leaf processing must be adjusted to the productivity of harvested gambier leaves. The number of gambir leaves that can be harvested by gambier farmers will adjust the capacity of the gambir leaf press to be processed. The tool that is devoted to the community has a forging foundation that functions as a place for gambir leaves to be pressed and has a jack stand that will press the gambir leaves to the foundation so that the sap of gambier will come out.
Parameter Prediction for Lorenz Attractor by using Deep Neural Network

Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data.
Ancilla-assisted frequency estimation under phase covariant noises with Greenberger–Horne–Zeilinger states

It has been demonstrated that the optimal sensitivity achievable with Greenberger–Horne–Zeilinger states is the same as that with uncorrelated probes in the frequency estimation in the presence of uncorrelated Markovian dephasing [Huelga et al. Phys Rev Lett 79: 3865 (1997)]. Here, we extend this issue by examining the optimal frequency sensitivities achievable by the use of ancilla-assisted strategy, which has been proposed recently for robust phase estimation. We present the ultimate frequency sensitivities bounded by the quantum Fisher information for a general case in the presence of Markovian covariant phase noises, and the optimal measurement observables that can saturate the theoretical sensitivity bounds. We also demonstrate the effectiveness of the ancilla-assisted strategy for preserving frequency sensitivities suffering from specific physically ground noises.
Estimating the Shannon Entropy and (Un)certainty Relations for Design-Structured POVMs

Complementarity relations between various characterizations of a probability distribution are at the core of information theory. In particular, lower and upper bounds for the entropic function are of great importance. In applied topics, we often deal with situations where the sums of certain powers of probabilities are known. The main question is how to convert the imposed restrictions into two-sided estimates on the Shannon entropy. It is addressed in two different ways. The more intuitive of them is based on truncated expansions of the Taylor type. Another method is based on the use of coefficients of the shifted Chebyshev polynomials. We propose here a family of polynomials for estimating the Shannon entropy from below. As a result, estimates are more uniform in the sense that errors do not become too large in particular points. The presented method is used for deriving uncertainty and certainty relations for positive operator-valued measures assigned to a quantum design. Quantum designs are currently the subject of active researches due to potential use in quantum information science. It is shown that the derived estimates are applicable in quantum tomography and detecting steerability of quantum states.
KLASIFIKASI AKASARA JAWA DENGAN CNN

It is common knowledge that CNN is a significant method in image classification. This is because CNN can classify Latin letters with a high degree of accuracy. Lenet5 in CNN is tasked with converting 2D features from an image into a convolutional network continuously. CNN architecture consists of several layers, including the Convolution Layer, Relu layer, Subsampling layer, Fully Connected Layer. In this research, CNN is used to classify Javanese script images into 20 classes. These classes include ha, na, ca, ra, ka, da, ta, wa, la, pa, dha, ja, yes, nya, ma, ga, ba, tha, nga. Javanese script used in this research is Ngalena Javanese script. The precision values for each class range from 0.5 to 0.6.
Recursive Multi-Scale Image Deraining With Sub-Pixel Convolution Based Feature Fusion and Context Aggregation

Along with several other low-vision based computer vision problems, single image deraining is also taken as a challenging one due to its ill-posedness. Several algorithms based on convolutional neural networks are devised that are either too simple to provide acceptable deraining results due to under-deraining or have a complex architectures that may result into over-deraining. In this paper we propose a deraining algorithm that is capable of boosting the reconstruction/deraining quality without the problem of over or under-deraining. Along with the originally proposed network, two of its' light-weight versions with reduced computational costs are also devised. Basically, we propose a recursively trained architecture that has two major components: a front-end module and a refinement module. The front-end module is based on dense fusion of lower label features followed by sub-pixel convolutions (pixel shuffling based convolutions). To refine and generate the enhanced deraining results further, we cascade a refinement module to the front-end module using multi-scale Context Aggregation Network (CAN) which includes feature fusion and pixel shuffling based convolutions. We present the deraining results in terms of Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR) on several benchmarks and compare with current state-of-the-art algorithms. With comprehensive experiments on both real-world and synthetic datasets and extensive ablation study, we demonstrate that our approach produces better results compared to existing methods.
Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor

Hadith is the second source of law for Muslims after the Qur'an which comes from various forms of the words, actions and stipulations of the Prophet Muhammad or referred to as his sunnah. In order to make it easier for Muslims to apply the teachings of the hadiths, a classification system is needed that can categorize a hadith into a class or a combination of two of the three classes which called a multi-label classification. In building a text classification system, there are various classification techniques, one of which is k-Nearest Neighbor (KNN). KNN is a simple and effective classification method for text classification, but has a weakness in processing data with high vector dimensions so that the computation time is higher and the efficiency of text classification is very low. Mutual Information (MI) is used as a feature selection method to reduce vector dimensions because it has the ability to show how strong a feature is in making a correct prediction of a class. In this study Problem Transformation Method with the Binary Relevance (BR) approach is used so that the multi label classification process can be accomplished. The optimum results obtained in this study shows the value of hamming loss is 0.0886 or about 91.14% of data were correctly classified and computational time for 595 seconds by using MI as a feature selection, but without stemming.