Reviewerly/exHarmony huggingface dataset:
PENGARUH KETERAMPILAN BERTANYA GURU BAHASA INDONESIA TERHADAP MOTIVASI BELAJAR SISWA KELAS VIII SMP NEGERI 4 LAGUBOTI TAHUN PEMBELAJARAN 2020/2021

The population is all grade VIII students of SMP Negeri 4 Laguboti for the academic year 2020/2021, totaling 198 people. The author took a research sample of 16%, so the sample of this study amounted to 32 people.The instrument of this research is a closed questionnaire. Test the validity of the instrument by using the Pearson product moment correlation coefficient formula, obtained rcount (0.684-0.984) > rtable (0.05.10) = 0.632, this indicates that the entire questionnaire is valid. And the instrument reliability test using the Alpha formula obtained r11 = 0.985 is in the very high category, so the questionnaire is reliable and can be used as a research instrument.Before testing the hypothesis, a significant relationship test was carried out, it was found that t count > t table of 5,458> 1.70, which means that there is a significant relationship between variables X and Y. Then, a requirements analysis test is carried out, namely to determine the relationship between variable X and variable Y with a determination test ( 100. r2%), to determine the degree of influence used the formula =a+bx, and test the significant effect with the f test. The results of the analysis show that the Indonesian language teacher's questioning skills affect the learning motivation of class VIII SMP Negeri 4 Laguboti students in the 2020/2021 academic year with an rxy of 0.706 with r table, n=32 at =0.05= 0.349. The coefficient of determination is 100. r2% = 49.84%, then the magnitude of the effect = 49.84%, and the effect of the significance of the simple regression equation = 4.77 + 0.885x and the significance of the effect or F = 24.1 where it is known that f table for f 0 ,05 = 4.15. This study concludes that there is an influence between the Indonesian language teacher's questioning skills on the learning motivation of eighth grade students of SMP Negeri 4 Laguboti in the 2020/2021 academic year.
Development of a Subsystem for Steganalysis of Digital Images Based on a Convolutional Neural Network to Detect and Prevent Attacks Using Hidden Steganographic Channels

This article presents a way to implement the subsystem for steganalysis of digital images circulating in the information system. This subsystem expands the functionality of existing intrusion detection / prevention systems in terms of detecting covert channels used in computer attacks. In the presented solution, a parametric model of a convolutional neural network is proposed and implemented to detect a payload in digital images, performed by a number of steg-nesting algorithms recognized in real attacks. A software implementation of a modular generator of a training sample (dataset) that supports these algorithms has been developed. An experimental assessment of the accuracy has been carried out
Print Books Usage at Bangalore University Library: A longitudinal Analysis

Usage of books assumes that the Circulation of materials in a collection is an indicator of a library's effectiveness. [1]The purpose of this study was to find a statistical analysis of book transactions among Bangalore University Library users, Bangalore. In the study, it is available that the total 9701 books used to patrons of 48 departments from July 2018 to June 2019. the highest number of books used to the user of the Physics department with 78 books out of 9701, and most of the Used books are Nuclear Physics (1995&2014) by Ghoshal S N from S. Chand publishers, it is a top place with 81 times used Book. And the highest number of books used in October, with a total of 1274 books. Kannada medium books most used in 5212.In this study, we observed that the science department students are very interested in using library resources. Then arts students,The study will be beneficial to Library authorities to identify how the users are using the library facilities and their satisfaction with the use of books.
Improving Success Rate of Transactions in Crypto currency Payment Channel Networks

In the recent times, the interest in Blockchainbased crypto currencies has increased rapidly. But the slow time of confirmation of transaction and the high charges per transaction prove to be a problem in the adoption of this technology for micro-payments. To solve this scalability problem, the idea of establishing payment channel networks where nodes establish payment channels between each other to perform offchain transactions. However, this method results in a certain one-way capacity for both nodes for making transactions which means that if a node exceeds its capacity, the channel becomes useless in that direction and hence the payment fails which creates an imbalance in the network. In this paper we aim to increase the rate of success of payments by using the fact that each node is connected to multiple points in a network which can act as gateways and we can use any of these gateways to complete a transaction. By using the inbound and outbound payment traffic ratio, we also define a method for the selection of a particular gateway to carry out the transaction. We use split payment capability with this method to increase the rate of success furthermore, especially for larger transactions.
Approaches to Adopt Blockchain Technology for Internet of Things

Blockchain technology promises a revolution across the majority of industries by eliminating the need for a costly intermediary to assure trust among the parties in two sides of a transaction. Many industries, including banking, insurance and healthcare, have already deployed blockchain-based solutions. Internet of Things (IoT) businesses has not been an exception as already a large body of literature has addressed many proposals for utilising blockchain technology to improve reliability and connectivity in IoT. Internet of Things enables more efficient and agile use of smart devices, and now the help of blockchain technology, an additional layer of improvement are anticipated regarding security, transparency and efficiency. This paper aims to examine the adoption of blockchain technology for IoT more closely. We deploy a private blockchain network using low power devices and evaluate the efficiency of such a system using different levels of IoT devices. The underlying issues in implementing IoT in conjunction with the blockchain network have been analysed using several network performance parameters. In addition, we propose a blockchain-based smart contract system for securing IoT networks and proceed to evaluate several parameters related to power consumption and overheating issues in blockchain networks. Finally, the paper is concluded with a discussion on blockchain-IoT combination can be an efficient solution for smart devices.
Socio-Political Crisis And Education

At the heart of modern socio-political crises and "colour" revolutions is the violation of the balance of stability and variability in the sphere of social relations. Stagnation in public life, lack of expected changes, loss of prospects - this is what excites the spirit of people, leads to social explosions and colour revolutions. The lack of changes in public consciousness is most often associated with the need to change leaders to those who can generate new ideas and goals. The public demand for true leaders is not being met by the modern education system. True leaders, passionaries are expressed people of the spirit world, they live in the material world, but this part of life is auxiliary for them. The world of the spirit is embodied in the processes of development and change. The analysis of learning objectives showed an obvious tilt of the modern education system towards the study, mastering and improvement of technologies to the detriment of the study of fundamental sciences and the synthesis of new models. While the ability to create and apply models is the hallmark of a true leader. In the educational process, it is necessary to return those learning goals that are associated with the skills and aspirations to create all types of models, as well as develop training courses aimed at training specialists who can create and use models of all types.
KLASIFIKASI POLI YANG DIBUTUHKAN PASIEN PADA KLINIK BERDASARKAN JENIS PENYAKIT DENGAN MENGGUNAKAN ALGORITMA C4.5

When carrying out treatment activities at the clinic, the patient will consult beforehand regarding the complaints felt with the doctor in charge. During the current pandemic, activities such as talking or chatting are very limited because according to health experts and health ministry regulations one of the causes of transmission of the virus is through throat fluids or saliva. In this case, the consultation requires digitization or modernization by making an application that will determine what type of poly the patient needs. ALODOKTER is an website that contains health information with experts and also includes data regarding diseases and symptoms that can be processed specifically to obtain a classification. Poly classification is carried out based on the symptoms the patient feels where this application acts like a general practitioner who is on guard and conducting surveys or consulting with patients. The symptom data selected by the patient will be processed by the C4.5 Algorithm which will then produce the selected poly output based on the patient's selected symptoms. The C4.5 algorithm is a decision tree algorithm that can perform a classification. This study aims to create an application that can classify the symptoms of patient complaints to determine the appropriate poly. By using a data sample of 12 serious diseases from ALODOKTER, 22 rules are generated from the calculation and making of a decision tree using the C4.5 algorithm with an accuracy of 90.04% using confusion matrix testing with data sharing as much as 70% for training and 30% for testing.
Survey Analysis of Solar Power Generation Forecasting

Solar power is the conversion of sunlight into electricity using solar photovoltaic cells as a source of energy. There are various applications for solar power; here is information on PV cell generation. We seek to understand the behavior of solar power plants through the data generated by the photovoltaic modules and the power generation in different weather conditions in India. The goal of this survey is to give a thorough assessment and study of machine learning, deep learning and artificial intelligence. Artificial intelligence (AI) models as well as information preprocessing techniques, parameter selection algorithms and predictive performance evaluations are used in machine learning and deep learning models for predicting renewable energies. But in case of time series data we can predict only the errors using a linear regression model, we can also calculate things like root mean square error (RMSE), mean absolute error (MSE), mean bias error (MBE) and mean absolute percentage error (MAPE). By the analysis of weather condition also we can predict the consumption of current by solar for every 15 minutes, 1day, and 1week or even for 1 month and find the accuracy.
ANALISIS KESULITAN PEMBELAJARAN DARING SISWA MTS KELAS VIII PADA MATERI STATISTIKA DI MASA PANDEMI COVID-19

This study aims to analyze the online learning difficulties of class VIII MTs students on statistical material during the covid-19 pandemic. The method used in this study is a qualitative descriptive research method. Which aims to find out and analyze the difficulties of students in learning during the covid-19 pandemic on statistical material. The subjects used in this study were class VIII B students at MTs Al-Lutfhfah, totaling 25 students. The instruments used in this study consisted of an opinion scale for students regarding online learning constraints and statistical material test questions consisting of 6 test questions in the form of description questions. The results of the research obtained in this study are that there are still many students who have difficulty solving statistical problems, especially during the current pandemic, lack of understanding of material concepts and other non-technical obstacles such as the difficulty of accessing the internet for students who live in remote areas to make online learning. Encountered problems in its implementation.
Vision-Based Hand Gesture Recognition Techniques using Smartphones

In the past few years, the computational performance of smartphone devices has seen tremendous growth. Due to which the smartphone has become a suitable platform for various computer-vision based applications which earlier was not possible. In this paper, we study various methods through which we can achieve computer vision-based hand gesture recognition natively on smartphones. If smartphones can support hand gesture recognition it can provide a new way to interact with mobile devices and overcome the hurdles of voice and touch-based user interface improving the user experience at the same time also supports other gesture-based applications. The techniques we study are mainly vision-based since camera module is present on most of the smartphones and it does not require other additional sensors or other hardware. We have compared the various methods available based on algorithms used and corresponding accuracy.
HOME LAB AS A STRATEGY FOR MEANINGFUL LEARNING OF MICROBIOLOGY DURING COVID19 PANDEMIC

Meaningful learning allows students to enrich their knowledge as well as achieve personal growth. For this purpose, it is necessary to identify and address the needs and motivations of students, this can be achieved by implementing novel strategies in the teaching-learning process. Since March 2020, due to the SARS-Cov2 pandemic, face-toface lectures were suspended in all Mexican educational institutions; this situation forced educators to make drastic changes in teaching dynamics; being the most challenging classes that required the use of laboratories. In the present study this challenge is addressed. The aim of this research was to implement at-home laboratories, during the COVID-19 pandemic, to develop essential microbiology laboratory (ML) competencies and increase student motivation. Didactic intervention was conducted during the spring semester of 2020 with biotechnology students from four ML courses. The methodology was based on using at-home laboratories for their final course project. Students worked in teams to design an experimental strategy, based on knowledge obtained through virtual laboratories, and managed to conduct these experiments with the use of materials found in each student's home; they then presented their results where competencies and learning objectives were evaluated. Descriptive statistics, including the analysis of variance and Tukey test with a confidence level of 95% to ensure significant differences, were used to analyze and measure the obtained data trends regarding ML competencies acquisition. Results showed no significant differences in the conceptual understanding of the evaluated subject after the implementation of the stated educational strategy, as part of the digitization of the course provided during pandemic confinement; these observations allow us to conclude that the implementation objective for compliance with the ML competencies was effectively achieved. Keywords: Home laboratory, Microbiology, Educational innovation, Higher Education
THE REASONABLENESS OF THE DELETION OF THREE SOLO SCENES (WERE REMOVED BY E. NAPRAVNIK) FROM THE PREMIERE OF OPERA "THE SNOW MAIDEN" BY RIMSKY-KORSAKOV

The article considers the reasonableness of removal of three solo scenes from the premiere of "The Snow Maiden" by Rimsky-Korsakov. Often, the directorate of the theater does not really follow artistic aims. Main goals are increasing the popularity of the performance and the sales of tickets. Therefore, it is inevitable to use many kinds of cutting the play. Unfortunately, one way or another, this path has negative sides – the perception of the work is distorted, since not all scenes can be easily removed from the performance without damaging it.
A Machine Learning and Augmented Reality based Framework for Multilingual Product Identification in Retail using Mobilenets and Vuforia

In the modern world, Machine Learning and Augmented Reality have taken the retail industry by storm. Machine Learning and Augmented Reality have provided a major boost to the industry of interactive retail by providing features such as real-time product detection and identification. The proposed research aims at overcoming several challenges in the present scenario which include the time consuming process of standing in long queues while purchasing the products at supermarkets, personalizing the shopping experience in order to maintain the privacy of the users, helping the customers to maintain their specified budgets, reducing the high labor costs and overcoming the language barriers while pertaining to selling products to groups with different linguistic backgrounds by combining the real-world interaction of Augmented Reality and Machine Learning-based product identification. This proposed research work aims at providing the customers with a futuristic shopping experience while maintaining their specified budgets. The Machine Learning-based object detection approach detected the products with around 96% accuracy and the Vuforia-based Augmented Reality approach detected objects with maximum accuracy.
KAJIAN SIFAT OPTIK MENGGUNAKAN SPEKTROSKOPI ELIPSOMETRI TERHADAP LAPISAN BULK-HETEROJUNCTION REDUCED GRAPHENE OXIDE/PCBM YANG DISINTESIS MENGGUNAKAN METODE VACUUM DROP CONSOLIDATION

Telah dilakukan kajian sifat optik lapisan tipis Bulk Heterojunction pada reduced Graphene Oxide(rGO) doping PCBM dengan berbagai volume. Graphene Oxide(GO) diperoleh dari sintesis secara kimia menggunakan metode Hummer's. Reduced Graphene Oxide (rGO) diperoleh dari proses reduksi GO dilakukan secara kimia menggunakan hydrazine hidrate. Telah dilakukan blending rGO dan PCBM([6,6]-phenyl-C61-butyric acid methyl ester) sehingga didapatkan Bulk-Heterojunction rGO/PCBM. Pengkajian sifat optik pada penelitian ini dilakukan dengan mengekstraksi data psi dan delta Bulk-Heterojunction rGO/PCBM yang diperoleh dari spektroskopi elipsometri. Kemudian data psi dan delta diekstraksi dengan persamaan Drude-Lorentz menggunakan perangkat lunak RefFit. Dari hasil ekstraksi didapatkan sifat optik indeks bias (n,k) dan konstanta dielektrik (epsilon1,epsilon2). Telah diperoleh nilai epsilon2 menurun pada rentang energi 1,3-4,5 eV seiring dengan pertambahan volume PCBM dan nilai k menurun pada rentang energi 1,3-5 eV seiring dengan pertambahan volume PCBM. Berdasarkan hasil sifat optik yang diperoleh tersebut dapat disimpulkan bahwa kemampuan rGO menyerap cahaya semakin rendah, karakter rGO semakin transparan dan adanya penurunan konduktifitas rGO.Study of the optical properties of the thin layer Bulk Heterojunction at reduced Graphene oxide (rGO) with various concentrations has been carried out. Graphene Oxide(GO) is obtained from chemical synthesis using a modified Hummer method. Reduced Graphene Oxide (rGO) obtained from the GO reduction process is carried out chemically using hydrazine hydrate. Blending of rGO and PCBM([6,6]-phenyl-C61-butyric acid methyl ester) has been done so that we get the Bulk-Heterojunction rGO/PCBM. The study of optical properties in this study was carried out by extracting psi and Delta Bulk-Heterojunction rGO/PCBM data obtained from ellipsometry spectroscopy. Then the psi and Delta data were extracted using the Drude-Lorentz equation using RefFit software. From the extraction, the optical properties of refractive index and dielectric constant were obtained. It has been found that the value of varepsilon2 decreases in the energy range of 1.3-4.5 eV as the volume of PCBM increases and the value of k decreases in the energy range of 1.3-5 eV as the volume of PCBM increases. Based on the results of the optical properties obtained, it can be concluded that the ability of rGO to absorb light is lower, the character of rGO is more transparent and there is a decrease in the conductivity of rGO
Book Success Prediction with Pretrained Sentence Embeddings and Readability Scores

Predicting the potential success of a book in advance is vital in many applications.This could help both publishers and readers in their decision-making process whether or not a book is worth publishing and reading, respectively.In this paper, we propose a model that leverages pretrained sentence embeddings along with various readability scores for book success prediction.Unlike previous methods, the proposed method requires no count-based, lexical, or syntactic features.Instead, we use a convolutional neural network over pretrained sentence embeddings and leverage different readability scores through a simple concatenation operation.Our proposed model outperforms strong baselines for this task by as large as 6.4% F1-score points.Moreover, our experiments show that according to our model, only the first 1K sentences are good enough to predict the potential success of books.
PENGEMBANGAN E-LEARNING BERBASIS GOOGLE SITES UNTUK MENINGKATKAN PRESTASI BELAJAR SISWA

Thisstudyaimsto: (1)producethedevelopment ofe-learningberbasisgooglesitesimprove student learningachievement (2)describetheeffectivenessof development ofe-learning berbasisgooglesitestoimprovestudentlearningachievement.ThisstudyusesResearch andDevelopmentdesign.Thesubjects ofthis studywerestudents ofgradeXIMultimediaof SMKNegeri 2Rejang Lebong. Data collectiontechniquesusingassessmentsheetsandtests. Dataanalysisusesthe mean (t)andt-test.Theconclusionsofthisstudyare: (1)the devlopment of e-learningberbasisgoogle canimprovestudent achievement,and (2)the development of e-learningberbasis googleeffectivetoincrease learningachievement of studentsof classXI MultimediainAnimasi2D subject
Study of Efficient Scalar Multiplication over Elliptic Curve

Elliptic Curve Scalar multiplication is the process of repeatedly adding a point on a curve to itself [1]. Many scholars working in the field of cryptography have been drawn to studies in Scalar Multiplication over Elliptic Curves (EC) over finite fields in recent years to see how elliptic curves cryptography (ECC) may be implemented and how to reduce its complexity [2]. &nbsp;Elliptic curve scalar multiplication utilising the point-halving algorithm [3], then the double-base (DB) chain algorithm, and finally step multi-base representation (SMBR) are the most efficient approaches used in Elliptic curve cryptography, however each technique has its own set of drawbacks. As a result, it is critical to develop a new approach that may be used to effectively deploy ECC while also decreasing its complexity. For affine coordinates, the study introduces the Treble algorithm, which is a new algorithm. We kept working with the binary concept or double and add operation with the help of the treble technique to make it more efficient, which refers to the use of all input values in producing any form of output, including how much time and energy is necessary. The results demonstrate that our contribution can improve EC scalar multiplication significantly. Elliptic Curve Scalar Multiplication is diverse aspect of Cryptography.
Facial expression recognition

Facial expression is a very difficult task (FER) due to the variation of facial expression across the human population and to the context-dependent variation even for the same individual. We can see that FER  has many applications in the fields like human behavior interpretation and human-computer interface. The study of Mehrabian shows that 55% of the message is comprised of facial expression while 7% of the communication is conveyed by linguistic language whereas 38% by paralanguage during human communication. This shows that facial expression has a significant role in communication. The proposed system uses a webcam to capture our face and determine whether you were smiling or not. The system uses an OpenCV computer vision library to preprocess the webcam image, and in this system, we have used a logistic regression algorithm to train on a provided dataset and evaluate the new image.