Reviewerly/exHarmony huggingface dataset:
Automatic Scoring Using Term Frequency Inverse Document Frequency Document Frequency and Cosine Similarity

Purpose: In the learning process, most of the tests to assess learning achievement have been carried out by providing questions in the form of short answers or essay questions. The variety of answers given by students makes a teacher have to focus on reading them. This scoring process is difficult to guarantee quality if done manually. In addition, each class is taught by a different teacher, which can lead to unequal grades obtained by students due to the influence of differences in teacher experience. Therefore the purpose of this study is to develop an assessment of the answers. Automated short answer scoring is designed to automatically grade and evaluate students' answers based on a series of trained answer documents.Methods: This is ‘how’ you did it. Let readers know exactly what you did to reach your results. For example, did you undertake interviews? Did you carry out an experiment in the lab? What tools, methods, protocols or datasets did you use The method used is TF-IDF-DF and Similarity and scoring computation. Theword weight used is the term Frequency-Inverse Documents Frequency -Document Frequency (TF-IDF-DF) method. The data used is 5 questions with each question answered by 30 students, while the students' answers are assessed by teachers/experts to determine the real score. The study was evaluated by Mean Absolute Error (MAE).Result: The evaluation results obtained Mean Absolute Error (MAE) with a resulting value of 0.123.Value: The word weighting method used is the Term Frequency Inverse Document Frequency DocumentFrequency (TF-IDF-DF) which is an improvement over the Term Frequency Inverse Document Frequency (TF-IDF) method. This method is a method of weighting words that will be applied before calculating the similarity of sentences between teachers and students.
Enhanced accuracy for sms spam detection using One Dimensional Ternary Patterns (1D-TP) and firefly algorithm

The Short Message Service (SMS) is a broadly used mobile communication channel; its attractiveness is traceable to many factors such as easy delivery method, cheap approach and expedient usage.However, unwanted messages referred to as SMS Spam has been identified to be one of the major problems for users and mobile service providers.This paper developed an SMS Spam detection model by optimization of One-Dimensional Ternary Pattern (1D-TP) feature extraction algorithm through the application of a robust optimization algorithm known as the firefly algorithm.The implementation of the model was done in a python environment due to its unique features in data/text analysis and classification.Accuracy of the optimised 1D-TP was done using five selected learning algorithms; Artificial Neural Network (ANN), Decision Tree (C4.5),Naïve Bayes (NB), K Nearest Neighbour (KNN) and Support Vector Machne (SVM).The accuracy of SMS Spam detection was evaluated with three datasets: Kaggle SMS Spam, British English SMS Corpora and SMS Spam Corpus v0.1 dataset.Results showed the effectiveness of the firefly algorithm with the best accuracy of 93.94% recorded in the Kaggle SMS Spam dataset using NB classifier when = 0 for upper features compared with the other two SMS spam datasets, which the best accuracy obtained is 92.96% in British English SMS Corpora dataset using NB when = 1 for lower features and accuracy of 91.97% was recorded in SMS Spam Corpus v0.1 dataset using NB when = 4 for upper features.The improvement was shown in the output through a reduction in the level of misclassification.
Formal Reasoning Using Distributed Assertions

Abstract When a proof system checks a formal proof, we can say that its kernel asserts that the formula is a theorem in a particular logic. We describe a general framework in which such assertions can be made globally available so that any other proof assistant willing to trust the assertion’s creator can use that assertion without rechecking any associated formal proof. This framework, called DAMF, is heterogeneous and allows each participant to decide which tools and operators they are willing to trust in order to accept external assertions. This framework can also be integrated into existing proof systems by making minor changes to the input and output subsystems of the prover. DAMF achieves a high level of distributivity using such off-the-shelf technologies as IPFS, IPLD, and public key cryptography. We illustrate the framework by describing an implemented tool for validating and publishing assertion objects and a modified version of the Abella theorem prover that can use and publish such assertions.
Behind the ivory facade: capitalism, the post-truth condition, and epistemic authority

Expressions of concern about an increasingly “fact-resistant” public attitude threatening higher education and science often fail to appreciate, or even acknowledge, the complicity of the professoriate in the reputation race that is the distinctive feature of competitive strategies of university management around the globe. In this paper, I argue that 1) behaving as actors in the “knowledge society,” universities undermine the legitimacy of academic knowledge; 2) the various phenomena gathered under the term “post-truth” are expressions of a loss of public confidence, and constitute a fundamental challenge to institutions of higher education, and 3) recognizing that the doubt and distrust are not entirely unwarranted is a first step toward necessary reform. On the basis of a résumé of the structural and material conditions of academic work in the post-truth era, I describe how these conditions constitute a specific sort of academic subjectivity, and propose that the role of universities needs to be reconceived in the spirit of what might be called epistemic modesty.
Edge FPGA-based Onsite Neural Network Training

Conjugate gradient (CG) is widely used in training sparse neural networks. However, CG, involving a large amount of sparse matrix and vector operations, cannot be efficiently implemented on resource-limited edge devices. In this paper, a high-performance and energy-efficient CG accelerator implemented on edge Field Programmable Gate Array is proposed for fast onsite neural networks training. According to the profiling, we propose a unified matrix multiplier that is compatible with the sparse and dense matrix. We also design a novel T-engine to handle transpose operation with the compressed sparse format. Experimental results show that our proposal outperforms the state-of-the-art FPGA work with a resource reduction of up to 41.3%. In addition, we achieve on average <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10.2\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.0\times$</tex> speedup, while <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10.1\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.5\times$</tex> better energy efficiency than implementations on CPU and GPU, respectively.
Drought Analysis On Ngawi District West Jawa Using The Standardized Precipitation Index (Spi) Method Based On Rainfall Data 2012 To 2021

Changes in Pacific Ocean and Indian Ocean temperatures can trigger climate change, one of which is El-Nino. Ngawi Regency, East Java, Indonesia is one of the areas frequently affected by drought, which is thought to be caused by El Nino. This research was conducted to determine the distribution of drought in the Ngawi district. The method used is the Standardized Precipitation Index (SPI) and the data used is monthly rainfall data. The mapping uses ArcGIS 10.5 software with IDW (Inverse Distance Weight) interpolation. The results obtained are that the maximum drought conditions in Ngawi Regency occurred in 2015, with a very dry category. The area with the highest frequency of droughts is Jogorogo District with 36 times. The ENSO correlation value for drought is 5.1%.
The Compatibility of CBDCs with “DeFi” Protocols: A Governance Rather Than a Technological Issue to Comply with Financial Crime Regulations

Decentralized finance (“DeFi”) has become a cornerstone of crypto-asset markets since 2020. While most academic studies focus primarily on technological impacts, this paper is rather based on the factual observation that corporations are struggling to use decentralized protocols at a larger scale mainly for governance, compliance and operational reasons. It provides insights about greater interactions between centralized finance and decentralized finance in the near future and about the governance and operational tools that could be developed to foster the use of DLTs for more efficient digital markets.
Epidemiological profile of Covid-19 cases and deaths in a reference hospital in the state of Goiás, Brazil

The pandemic caused by the Coronavirus 19 (Coronavirus disease 2019, COVID-19) killed countless people in 2020 and it arrived in Brazil in mid-February from the same year. It spread quickly, through its facilitated form of transmission by droplets of saliva, coughing, sneezing and contaminated surfaces. Several studies have indicated that male people over 60 years of age with comorbidities such as diabetes mellitus, chronic kidney disease, cardiovascular disease and/ or chronic lung diseases had higher rates of progression to Severe Acute Respiratory Syndrome and greater vulnerability to death. In this sense, this study aimed to describe the clinical and epidemiological profile from suspected and confirmed cases of COVID-19 in a hospital in the city of Anápolis, in the State of Goiás, Brazil, from March to December 2020. A total of 1,170 patients were included in this study, with 54.8% of the patients admitted with symptoms of COVID-19 were women (46.9%) aged between 30 and 59 years old. In addition, it was noted that 11.5% of these patients had some type of comorbidities. Furthermore, the data revealed that 82.2% of the laboratory-confirmed patients who died had comorbidities, most of them men (57%), and from the total of 68.5% had two or more comorbidities, with cardiovascular origin being the most frequent reaching 77.5% from the sample. Thus, it was concluded that the clinical-epidemiological profile of suspected and confirmed patients for COVID-19 in this research was characterized by the majority of female patients between 30 and 49 years of age, although most confirmed cases and deaths occurred in men older than 60 years of age. Most confirmed patients had two or more comorbidities, most of them of cardiovascular origin, followed by metabolic syndromes, lung diseases and others. KEY WORDS: COVID-19; epidemiology; comorbidity.
Research on data access scheme based on attribute-based encryption in blockchain environment

Access control on shared data can limit illegal access and improve data privacy and security. Attribute-based encryption is one of the key technologies to realize fine-grained access control of data. Traditional access control schemes usually rely on third parties, require high trust overhead, and lack mature encryption technology and access authentication countermeasures, which are vulnerable to adversary attacks and data leakage. In order to solve the above problems, this paper proposes a data access scheme based on attribute-based encryption in blockchain environment. We employ an improved ciphertext-policy attribute-based encryption (CP-ABE) algorithm to provide fine-grained access to data under policy concealment. Simultaneously, we use proxy encryption and decryption to reduce the computing power consumption of users, and introduce decryption tests to verify the correctness of proxy decryption. In addition, combined with blockchain technology, the secure storage and fair authorization of metadata ciphertext are realized. Security analysis proves that this scheme is resistant to selective plaintext attacks. Simulation experiments show that the encryption time of this scheme is less than 200ms for users within 50 attributes, and the decryption time is stable at about 3ms, which has a low computational cost.
PROBLEMATIKA PEMBELAJARAN MATEMATIKA MENGGUNAKAN E-LEARNING MADRASAH KELAS XI

This research aims to describe the implementation of mathematics learning using madrasah e- learning by mathematics teachers at Madrasah Aliyah Negeri (MAN) 2 Pontianak and describe the problems faced by them, and the issues faced by students of class XI Madrasah Aliyah Negeri (MAN) 2 Pontianak in learning mathematics using e-learning of madrasah. The research method used is a qualitative descriptive research method in the form of ex-post facto research. The subjects in this study were students in class XI, which consisted of 36 students in class XI MIPA 1, class XI MIPA 2 with 34 students, class XI MIPA 3 with 35 students in class XI MIPA 4 with 35 students and all 6 mathematics teachers. The instruments used in this study were questionnaires and interviews. Based on the results of the study, it was found that the implementation of mathematics learning using e-learning madrasah consisted of preliminary activities, core activities, and closing activities. Many students experience problems influenced by internal and external factors in learning mathematics using e-learning madrasah.
Kemampuan Kognitif Momentum dan Impuls ditinjau dari Hasil Belajar Peserta Didik dengan Model Inkuiri Terbimbing

The purpose of this study was to determine the students' cognitive abilities after the implementation of the guided inquiry learning model on the material momentum and impulse at MAN 2 Mataram. The type of research used is Research &amp; Development with 4D models. The data collection technique used is students' worksheet on cognitive abilities and instrument test of cognitive abilities. Data during learning activities obtained from the results of the Students Worksheet (LKPD) on cognitive abilities, while the result the increase in the ability of students is obtained from the results of pretest and posttest of students using the N-Gain test. The average result of the pretest The results obtained are 34.42, the average posttest result is 94.76. The average cognitive ability per learner indicator gets N-Gain of 0.63 in the medium category. The highest increase was found in remembering indicator (C1) with an N-Gain of 0.78 in the category high, while the lowest increase was in the category of creating (C6) with an N-Gain of 0.44 in the medium category. Percentage increase in cognitive abilities with high category by 38.10%, the medium category is 61.90% and there is no low category, so the the guided inquiry learning model is effective can be improve the cognitive abilities of students in the material of momentum and impulse. Keywords: Guided Inquiry Learning Model, Cognitive Abilities, Momentum and Impulse.
The Development of Human Resources Data Management Application at PT. Anugrah Laut Indonesia

Human Resources and employees are an important part of the continuity of a company. Employee data management is crucial for a company. Misconduct in managing it could lead to data inconsistency that may cause loss for the company. Previously, ALI Seafood use only office applications to make a simple record of the employee data raising some difficulties in managing the data. These difficulties such as human errors, low capability of the operator, loss of employee data, or errors in real-time data sharing. Centralized Information Technology (IT) system can assist a company in carrying out the process of managing employees, not only to overcome the difficulties above but also in carrying out the process of compiling information that is useful in making strategic decisions for company owners. This research carries out the process of analysis, design, and development of a web-based application and has been fully utilized at ALI Seafood. The main features of the application include managing the organization structure, employment data, attendance, payroll, and application management access. ALI Seafood HR application developed using Laravel framework and MySQL database.
HUBUNGAN INTENSITAS PENGGUNAAN SOFTWARE PLECO DENGAN PENGUASAAN KOSAKATA BAHASA MANDARIN PADA MAHASISWA SEMESTER VI STBA-PIA MEDAN 常用 PLECO 软件与汉语词汇掌握能力的相关性研究 —— 以亚院三年级学生为列

This study aimed to analyze the correlation between the intensity of using Pleco application and the Chinese vocabulary mastery among the third grades / 6 semestre students of the Asia International Friendship College. This research is a quantitative research with a non-experimental design, called the correlational quantitative method. This research adopted the cluster random sampling method, and used the test paper to investigate the students’ frequency of usage of Pleco software and their Chinese vocabulary mastery. The population of this study amounted to 93 persons of students of STBA-PIA from the sixth semestre and this research will take 30 students as a sample, the sample was taken by using the Cluster Random Sampling technique. The instrument of research was built in the form of a questionnaire with 12 items and 20 test questions. Due to the International standard of HSK questions, the test is declared valid. The reliability test of the questionnaire used the Alfa Cronbach formula which got the results of r was 0.7 so it was categorized as high. and the test also considered reliable. The statistic of paper test results showed that the mean score of third grades students of the Asia International Friendship College commonly used Pleco was 78.3 and the average score of students' vocabulary mastery was 81.3. According to the statistic result, it showed that the correlation test result is 0.62, and the t-test value appeared to be bigger than the t-distribution value (4.2 &gt; 1.70), which indicated that there is a significant connection between the common usage of Pleco software and Chinese vocabulary mastery by the sixth semester students of Asia International Friendship College. Keywords: Pleco, Chinese Vocabulary, Mastery, Intensity.
ANALYSIS OF THE HEROISM OF THE FEMALE CHARACTER IN J.S KHAIREN'S NOVEL MELANGKAH

The novel Melangkah by J.S. Khairen tells the story of the adventures of four Economics students who vacation in Sumba, East Nusa Tenggara, only to get the order of human lives in their hands. The novel will be reviewed using a literary sociology approach. This study aims to describe and explain the author's worldview, the social and cultural background of the community, and the heroic values contained in the novel Melangkah. This research is library research. This study used qualitative methodology. The problem in this study is how the elements of the structure of the novel build literary works and the heroic value of female characters are contained in the novel Melangkah. The study consists of three stages, the first of which is data collection. The technique used is the technique of reading notes. After data collection, the next stage is data analysis. Information is analyzed descriptively. The results of the data analysis are then presented in the form of word exposure through the explanation of the novel Melangkah. The validation used is theory triangulation with stages of reading, recording, and analyzing documents. The conclusion of this research is that the female character in the novel Melangkah reflects all heroic values and their characteristics, namely: courage, confidence, truth-keeping, willingness to endure hardship, and generosity. Patience: Resistant to trials, steadfast, calm, unhurried, and not impetuous. Sacrifice; unyielding and desperate, willing to sacrifice for the benefit of the nation, and a reformer.
How Jordanian Banks Measure Learning &amp; Growth Pillar of the Strategic Performance?

Purpose: This research analyzes Jordanian Banks’ Learning &amp; Growth strategic performance (SP). Strategic performance includes learning and growth in the Balanced Scorecard. It incorporates all organizational capital-leveling measures. Learning and growth start process, customer, and financial performance. Banks' strategic pillar evaluations? Design/Methodology/Approach: This empirical research answers this question using qualitative data from 24 bank employees from all levels using structured interviews from 61.5% of Jordanian Non-Islamic banks. NVivo-12 evaluated data. Findings: This research found that the key banks' Learning &amp; Growth performance measures are Employee Incentives, Team Communication, Career Development, Employee Motivation, and Employee Satisfaction. Literature and source data match banks' Learning &amp; Growth metrics. This study chose the top five measures from the vast literature. Practical implications: Implement initiatives to increase employee engagement, including opportunities for career growth, regular feedback channels, and recognition programs. Create a friendly workplace where people feel empowered to share their ideas and opinions and openly communicate. Originality value: This research's top five Learning and Growth measurements analyze the S.P.'s Learning &amp; Growth pillar to inform bank managers' strategic planning.
ANDROID-BASED HEALTH POST MANAGEMENT APPLICATION DESIGN CLINICS IN INDONESIA

This research aims to design and develop an Android-based application that can help manage Pos Sehat, especially in rural areas of Indonesia, in order to increase the efficiency and effectiveness of health services. The focus of this research is developing applications for Pos Sehat managers, with important features such as patient monitoring, viewing patient biodata, tracking disease history, and drug recommendations. The research method involves planning research, studying literature, developing applications using Android Kotlin for UI/UX, Node.js for backends, and cloud services or Firebase for data storage, as well as application testing and evaluation. The results of the study show that the application developed can increase the efficiency and effectiveness of the management of the Health Post, facilitate access to patient and drug information, and assist in the decision-making process regarding the management of the Health Post. This research is expected to be a reference for similar research and inspire further development in the field of technology-based health management.
Spear Phishing Using Machine Learning

In this digital age, personal data is more valuable than an actual human being. It corresponds to a person’s daily internet activity. Open-source intelligence can be used to collect data in a variety of ways, which is later put to use for social engineering attacks. The purpose of this paper is to assess the level of data that is freely available online and its potential consequences for personal privacy. This paper discusses the use of machine learning algorithms and tools for automating email phishing attacks. The personal data of the victim is gathered from open-source websites in order to analyze their online activity. This information is then analyzed in an attempt to learn more about the victim’s interests and a relevant email template is created based on this information. The machine learning algorithm is then provided with the constructed template, which predicts how successful a phishing attack would be if launched. This paper uses a machine learning algorithm that is composed of support vector machines (SVM) and logistic regression (LR). This hybrid algorithm, which is a combination of SVM and logistic regression, achieves a peak accuracy of 99.69% when compared to using only one type of classification method, such as SVM or LR. The purpose of this paper is to increase the effectiveness of phishing attacks by automating the data extraction process and to analyse the success rate of attack using machine learning before launching it. This paper will serve the interests of institutions/companies by providing a convenient way to conduct automatic phishing as part of Cyber-Security training to educate employees, giving them a practical experience of social engineering attack.