Reviewerly/exHarmony huggingface dataset:
Scholarly communication of international journal of social economics: A bibliometric study

The study comprehends the scholarly articles published in IJSE from 2015 to 2020 using the required bibliometric tools and indicators to divulge publishing trends of this reputed journal for the stated period. The study reveals that the degree of collaboration (DC) in IJSE publications is 0.70 indicating a healthy collaborative trend of research. Authorship productivity pattern of IJSE partially complies with the Lotka's Law. Giri, A.K. of India, Man, S. of Switzerland, Ntayi, J.M. of Uganda, and Sehrawat, M. of India are found to be the most prolific authors who have contributed 6 articles each to IJSE published from 2015 to 2020. The University of Ghana of Ghana has contributed the highest number of 23 articles published in IJSE from 2015 to 2020 followed by Universiti Utara Malaysia of Malaysia (14 articles), University of Ibadan of Nigeria, Universiti Malaya of Malaysia, and Griffith University of Australia. India has contributed the highest number of 77 articles to IJSE publications from 2015 to 2020 followed by the United States and the United Kingdom. Moreover, the article entitled, "the impact of mobile phone penetration on African inequality" published in 2015 was found to be the most highly cited paper. Moreover, the study yields compelling findings of academic publishing in International Journal of Social Economics that has not yet been explored. It can help the readers of IJSE to comprehend the most striking contributions, coverage of the journal, highly cited journals, the most prolific authors, country productivity, keyword co-occurrence and assorted facets.
SKV: A SmartNIC-Offloaded Distributed Key-Value Store

In data center networks, applications such as dis-tributed key-value stores consume a lot of CPU resources. The performance of the entire system drops significantly under heavy load conditions. In order to improve the performance of key-value stores, many existing studies use RDMA (Remote Direct Memory Access) to reduce the communication overhead. However, RDMA primitives can only offload simple operations to the NIC, such as reading and writing remote memory. With the emergence of new hardware like SmartNICs, we consider whether we can offload more complex operations in distributed key-value stores to SmartNICs to reduce the load on CPU. In this paper we present SKV, a SmartNIC-offloaded distributed key-value store. In order to make full use of the offload ability of the SmartNIC, we make a detailed analysis on the characteristic and architecture of SmartNICs and distributed key-value stores. SKV offloads operations such as data replication to the SmartNIC. We design a new replication mechanism, which enables the server to separate background processing from the interaction with clients in the front. We implement SKV on the Mellanox BlueField SmartNIC. Our evaluations show that SKV improves the overall throughput by 14% and reduces latency by 21 % compared with baseline.
Research on computer 3D image encryption processing based on the nonlinear algorithm

Abstract This article uses the nonlinear digital chaos theory algorithm to generate the corresponding encryption system initial parameters, by analysing the correlation degree of image elements from the angles of horizontal, vertical, and diagonal direction, in order to study computer three-dimensional (3D) image encryption processing. The correlation degree of the cypher text obtained by the nonlinear algorithm is weak in the image's adjacent pixels, and the adjacent pixels are not related at all, horizontal angle: 0.915989, vertical angle: 0.968184, diagonal angle: 0.913533. The nonlinear algorithm distributes the image's statistical features into the random cypher text. By applying permutations and replacements in 3D space, the proposed approach improves performance parameters and widens key space in comparison to previous image cryptography investigations. The important qualities of such a secure system are its simplicity and efficacy. Simulations and analysis show that the proposed method can produce a large key space while also surviving standard cipher attacks. Because of its powerful cryptographic properties, it is suited for image applications. The nonlinear algorithm has very high sensitivity to the secret key and plaintext, as well as better statistical performance, higher security, and higher efficiency in the operation of the algorithm.
Klasifikasi Data Review IMDb Berdasarkan Analisis Sentimen Menggunakan Algoritma Support Vector Machine

Advances in Web 2.0 technology encourage the creation of personal website content involving sentiments such as blogs, tweets, web forums, and various types of social media. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved, nominations received, and reviews from visitors. The number of movies and reviews on IMDb causes users or visitors to check the reviews to find out the film rating, so it takes time for users who have no experience using IMDb. Sentiment analysis can be a solution to label positive and negative reviews. One of the algorithms used in sentiment analysis is the Support Vector Machine (SVM) algorithm. This study aimed to test the accuracy of the SVM algorithm in the classification of sentiment review films on IMDb. The tests carried out using the Support Vector Machine algorithm resulted in an accuracy value of 86.5%. The SVM algorithm can also produce a precision value of 90.67% and a recall value of 91.62%.
Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD).Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters.Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model.Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting.The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively.The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient (E ns ) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN).The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.
Implementasi Data Mining dengan Algoritma Naïve Bayes untuk Profiling Korban Penipuan Online di Indonesia

Profiling of victims of crime is intended to facilitate the targeting of information dissemination and carry out prevention efforts. Profiling is helpful to increase the awareness of internet users against cybercrime. This study aims to create a sociodemographic profile based on online fraud victims using Instant Messengers in Indonesia based on the sociodemography of online fraud victims, namely age, gender, education level, domicile, occupation, duration of using the internet in a day, and Instant Messenger media used. The method used in this research is the descriptive statistical method, namely Data Mining using the snowball sampling method by sharing a link via WhatsApp. Participants were given a link to fill out several survey questions about the sociodemographic of the victim, such as age, gender, occupation, domicile, and online fraud that had been experienced through the IM application. The survey was created using GoogleForms and sent online via WhatsApp to participants who had been victims of online fraud. The Data Mining technique was used to analyze the responses of 1910 participants and then classified using the Naïve Bayes Algorithm. The results showed that the Naïve Bayes Algorithm has an accuracy percentage of 75.28%. The prediction model for the vulnerability of online fraud victims is a female respondent, aged 27.3 years, using Instagram and WhatsApp, currently living in Central Java Province, education background is high school, and the duration of using the internet more than eight hours a day, and status as a Student/College Student.
Study protocol to assess sleep quality and quantity of dental students belonging to the University of Concepcion in e-learning classes during the current pandemic.

Protocolo de estudio para evaluar la calidad y cantidad de sueño de estudiantes de Odontología UdeC en clases e-learning en pandemia.Study protocol to assess sleep quality and quantity of dental students belonging to the University of Concepcion in e-learning classes during the current pandemic.
Political sarcasm on tiktok account @podcastkeselaje

Recently, there has been apprehension in the society about expressing ideas, particularly on political concerns. As a result, public figures such as comedians tend to use sarcasm as their primary weapon in exposing political leaders' weakness, incompetence, or corrupt behavior, as well as government officials' actions. The goal of this research is to look at the form, meaning, and function of sarcasm on the TikTok account @Podcastkeseaje. All of the utterances said by the narrator or speakers in the video recording from the TikTok account @podcastkeselaje are used as data sources in this study. Indirect observation was used to obtain data, which was then followed by recording and note-taking techniques. The data were examined using Camp's (2012) theory for the form of sarcasm, Chaer's (2009) theory for the meaning of sarcasm, and Chaer's (2009) theory for the meaning of sarcasm.
Eye-Head-Body Coordination in the Motor-Memory Trade-off

Previous studies have demonstrated that people trade off between expending motor effort and memorization effort when completing a copying task that requires looking in different directions to gather information. When information is easy to gather with minimal movement, people choose to look more frequently and rely on less on memorization, however, they reduce motor effort and rely on memory more when looking requires larger (and more effortful) shifts of gaze. This paper investigated the eye-head-body movements that guided looking as well as participants’ trade-off between using motor effort and memory in a copying task. We add to prior work by characterizing the coordination between eyes, head, and body required to look at targets at different angles. In the task, participants copied models onto a board while wearing a mobile eye tracker that measured eye rotations and motion sensors that measured head rotations. We manipulated the angle of the model relative to the participant and the memorization difficulty of the models. Results showed that eye movements contributed more to small gaze shifts but the head and body contributed more as the angle of the target increased (requiring larger amplitude shifts of gaze). Participants chose to look frequently when models were adjacent to the workspace, but chose to rely more on memory for models that required greater shifts of gaze. Memorization difficulty affected the trade-off such that participants used more memory when the models were easy to remember. We discuss how participants’ decisions to look versus remember may depend on the contribution of eyes, head, and body to shift gaze by varying amplitudes.
Pengaruh Strategi Directed Reading Thinking Activity (DRTA) Terhadap Kemampuan Membaca Pemahaman

This article aims to discuss the Influence of Directed Reading Thinking Activity Strategy on Reading Comprehension Ability in Fourth Grade Students of SD Negeri 11 Limboto. This research uses quantitative research using a pre-experimental approach. The sample used in this study amounted to 10 people who included a low category value on the ability to read early so that this research is said to be a purposive sampling study. The data collection technique used a test (pretest - posttest), then the data analysis technique used was in the form of a Paired Sample Test. Based on the results of the study, it was found that the Directed Reading Thinking Activity strategy had a significant effect on the reading comprehension ability of the fourth grade students of SD Negeri 11 Limboto, which was 90.50% which was obtained from the calculation results. And it can be seen from (2 tailed) = 0.00 &lt; 0.05, this indicates that HO is rejected and HI is accepted.
Perbandingan Prestasi Belajar Siswa pada Mata Pelajaran Matematika Menggunakan Model Pembelajaran Kooperatif Tipe Make a Match, Snowball Throwing, dan Think Pair Share (TPS) Pada Pokok Bahasan Bilangan Bulat dan Pecahan Siswa Kelas VII SMPN 1 Bringin

This study aims to determine whether there are differences in mathematics learning achievement in students who are taught using cooperative learning methods of Make a Match, Snowball Throwing, and Think Pair Share types. This study uses an experimental method with a population of all seventh-grade students of SMP Negeri 1 Bringin in Odd Semesters for the 2019/2020 Academic Year. The sample used for the experimental class is 3 classes with 90 students. Sampling technique by means using a simple random sample (Simple Random Sampling). While the data collection method uses the test method. Hypothesis testing in this study was carried out using the prerequisite analysis test, namely the normality test using Chi Square and homogeneity using Bartlett. Furthermore, the analysis technique uses the Non-Parametric test (Kruskal Wallis). The result of this research is that there is no significant difference between the three learning methods of Make a Match, Snowball Throwing and Think Pair Share on students' mathematics learning achievement.
Penyusunan Laporan Keuangan Perusahaan Menggunakan Aplikasi Spreadsheet

This research is designed to create financial reports using the Spreadsheet application. The application was chosen because many companies in Indonesia, especially MSMEs, process and prepare financial reports using spreadsheets in Microsoft Excel. Many companies have used integrated computer software-based accounting applications, but they still use Spreadsheets in Microsoft Excel as initial data as well as corporate backup data. There are also many companies that still use traditional methods in financial accounting, one of which is PD Padaringan Rice. The company is still recording accounting manually, it is related to funds to buy an integrated accounting application, therefore it is necessary to make a model of the company's financial reporting work using the Spreadsheet program. This is expected to help PD Padaringan Rice and other SMEs who want to make financial reports easily, cheaply and in accordance with SAK ETAP using the Spreadsheet application in Microsoft Excel
Survey For Arabic Part of Speech Tagging based on Machine Learning

The Arabic Language is the native tongue of more than 400 million people around the world, it is also a language that carries an important religious and international weight. The Arabic language has taken its share of the huge technological explosion that has swept the world, and therefore it needs to be addressed with natural language processing applications and tasks. This paper aims to survey and gather the most recent research related to Arabic Part of Speech (APoS), pointing to tagger methods used for the Arabic language, which ought to aim to constructing corpus for Arabic tongue. Many AI investigators and researchers have worked and performed POS utilizing various machine-learning methods, such as Hidden-Markov-Model (HMM), Brill, Maximum-Match (MM), decision tree, bee colony, Neural-Network (NN), and other hybrid methods. This survey groups a number of published papers based on the Arabic Language Applications (ALP) towards tagging related problems utilized and approaches with the difference between types of tags used. It addresses and tries to identify the gaps in the current studies putting a foundation for future studies in this field.
EFFECT OF PEBBLE SIZE ON GOLD DISTRIBUTION IN THE BANKET SERIES OF THE STRATIGRAPHY IN TARKWAIAN SUPERGROUP

The purpose of this research is to establish the relationship between the gold grades and the various pebble sizes from a mine. This objective was reached by choosing parameters of interest from the logging and sampling data, lithological data and assay data from the mine. These parameters were further processed using Microsoft Excel to plot graph of pebble size against gold grade. In order to build a concrete ground for the analysis, an average gold grade for the various samples used was calculated and compared to the cut-off grade at the Mine which is 0.45g/t. Upon further analysis, it was observed that, gold grade which are considered economically feasible at the mine is associated with the coarse pebbles, which has a diameter range of (5mm-30mm) and those which are considered as waste are associated with the fine pebbles (less than 5mm diameter). Some of these coarser pebbles are not economically feasible, since their average gold grade when calculated falls below the cut-off grade at the mine. The mode of deposition as well as the topography of the medium at the time of deposition of gold determine the gold grade of a reef. The degree of roundness and sorting of the conglomerates associated with mineralization as well as ore dilution within the depositional medium by the pebbly quartzite and quartzite waste account for the fall in gold grade of the reef.
Implementasi Algoritma Convolutional Neural Network Dalam Mengklasifikasi Kesegaran Buah Berdasarkan Citra Buah

The development of Information Technology today, which continues to grow, can help overcome various problems because matters relating to the advancement of Information Technology have spread to almost all levels of Indonesian society. Along with the development of Information Technology, it is also marked by Artificial Intelligence which can simulate human intelligence and help handle tasks in the real world. By utilizing Information Technology, one of them can be used in terms of the classification of fruit freshness. Where this classification will be very useful and help farmers and fruit consumers. This study describes the use of the Convolutional Neural Network to classify the freshness of the following fruits: apples, oranges, and bananas. And also using six classes, namely fresh apples, fresh oranges, fresh bananas, unfresh apples, unfresh oranges, and unfresh bananas. The first thing to do is Convolutional Neuronal Network training using an image dataset as input using data sources from Kaggle.com, published by "Student at Stony Brook University, New York, United States". To determine the performance of the various models produced, the following Confusion Matrix is used: accuracy, precision, and recall. The best average obtained is 93%.
Perception of Undergraduate Medical and Health Sciences Students Regarding Online Formative Assessments During COVID-19

Objective: To look into the experience of undergraduate medical and health sciences students regarding perceptions of online formative assessments.&#x0D; Study Design: Online cross-sectional survey.&#x0D; Place and Duration of Study: Riphah International University, Islamabad Pakistan, from Aug to Dec 2020.&#x0D; Methodology: Three hundred fifty-one pre-clinical and clinical students of disciplines including MBBS, BDS, and Pharm-D participated in the study. Data was collected through a convenient sampling technique. An online questionnaire containing 22 closed-ended questions with a three-point Likert scale on five themes related to online formative assessments and one openended question was used.&#x0D; Results: 86 (24.5%) students agreed that online formative assessments improved their learning. The motivation for learning was increased in 120 (34.2%) students. 214 (61%) students felt that online formative assessments were stressful, and the majority were well aware of the use of technology except for connectivity and login issues highlighted by the students. Regarding the question paper, the majority were comfortable with the construct of online formatives. 158 (45%) students were not satisfied with the allotted time to complete the formative assessments. 163 (46.4%) students were satisfied with the feedback provided, yet 258 (73.5%) students recommended formal feedback provided on the same day to improve their learning by knowing their deficiencies.&#x0D; Conclusion: The study results support the contention that well-designed online formative assessments have considerable untapped potential to assist learning by motivating students. Their problems and challenges need to be addressed to improve the online assessments that help in the learning of undergraduate students.
Critical Security &amp; Privacy Issue in Blockchain Technology Intended to Industry 4.0

Abstract: Block-chain is getting famous and one of the most widely recognized subjects can be thought of it has additionally changed the ways of life of many individuals in specific fields, in view of the effect on organizations and joins. Blockchain guarantees more dependable and fitting assets and it is exceptionally vital to remember that security and protection have a few deterrents as any innovation in this fields. The range of blockchain applications is extremely reached out into various regions in banking, wellbeing, car, the Internet of Things (IoT) and so forth. Numerous concentrates on focus on utilizing the block-chain information model in various executions. Motivated from these facts, in this paper, we present a systematic review of various blockchain- based solutions and their applicability in various Industry 4.0-based applications. In this paper, we attempt to portray block-chain innovation by talking about its model of information security and protection point of view with various agreement calculations, as well as issues and open doors in blockchains.
Improving the accuracy of tesseract OCR engine for machine printed Hindi documents

The development of Optical Character Recognition (OCR) of the Indian text is an active area of research today. The presence of many characters in a set of alphabets, their intricate combinations, and intricate grapes are a challenge for the OCR designer. This paper focuses on improving the efficiency of the Tesseract OCR in the Hindi language. This paper introduces Google’s Tesseract Optical Character Recognition software. An optical Character Recognition (OCR) is the process of identifying and converting the text into images in pixels in a computer-friendly presentation. The present work aims to improve the accuracy of the Tesseract 4.0 OCR engine. The OCR engine will extract the text from the image. Some information has in the extracted text is calculated to provide the decision to accept. The test results in Hindi, taken from a random sample of books, show the characters and the accuracy of the words.
Penguasaan Puebi dan Keterkaitannya dengan Keterampilan Menulis Teks Biografi Siswa SMA

This study aims to see the correlation between PUEBI mastery and biographical text writing skills by high school students. The research method used is correlation and is classified as quantitative research. The results showed that the average PUEBI mastery of students was 88.79 with very good qualifications, and the average student biographical text writing skills were 82.79 with good qualifications (t count greater than t table, namely 17.35 &gt; 2.042). In conclusion, there is a significant relationship between PUEBI mastery and biographical text writing skills.&#x0D; Keywords: PUEBI, Biographical Text