Reviewerly/exHarmony huggingface dataset:
Research on Evaluation and Analysis Model of Graduation Attributes Attainment

This article reveals a meta-analysis on the evaluation of the graduation attributes attainment for 22 domestic and foreign universities. Common problems can be concluded into the following four aspects: 1. Compared with the technical graduation requirement index points, the descriptions of non-technical index points were vague, and the measurability was generally poor. 2. The weight setting between different courses supporting the same index point was generally subjective. 3. According to the “Wooden Barrel Short Board Principle,” single use of the lowest value of the index points usually covered up the attainment of other index points of the similar graduation requirement. 4. The lack of information support in each section of the graduation attributes evaluation could result in heavy workload, low efficiency and weak teacher initiative. Based on the 12-year experience of engineering educational reformation in Dalian Neusoft University of Information (DNUI), this article proposes an evaluation and analysis model of graduation attributes attainment, on the basis of TOPCARES, supplemented by scientific and reasonable calculation methods and evaluation strategies. By analyzing the problems, discussing the optimization method and demonstrating the calculation, it is proved that the model can effectively solve the above common problems.
Determining the Most Popular Streaming Service using Machine Learning

Over the past years, twitter has become a popular medium for sharing views and ideas about personalities, brands, products or services. Analyzing sentiment of people to figure out the popularity of different streaming service by the twitter profiles is helpful for determining positive or negative views. This is a comparative analysis to predict or show which of the chosen streaming services is most familiar or liked by the public. To do this, different machine learning algorithms are used to computationally identify and categorize public opinions to draw a final result. The machine learning algorithms used here are Linear SVC, Naïve Bayes and Decision Tree. These help in receiving the data and predict the output within an acceptable range. The data in this case has been extracted from Twitter using Twitter API. Twitter API takes the parameters that can access many features of Twitter and also post and find tweets containing desired words. This includes data cleaning which refers to exclude the incorrect and unnecessary forms of data. This makes the way of data processing easier, faster and more compatible. On analyzing, the frequently used words are assessed. The classifying words are trained using the above mentioned algorithms. These algorithms are the supervised classifiers which are effective and efficient when the quantity of the data is huge. Using one or more algorithms helps to decide, compare and contrast the results. Once the classifiers are trained, testing is done. Testing gives the proper assessment of the data that is required for the desired results. The performance of the test set can be checked to draw a final result. Hence, comparing the results obtained for different streaming services helps to decide the most popular streaming service.
Training For Developing Digital Presences With Zoho Form For Office Automation And Office Governance

Teachers in facing the challenges of rapid technological advances are expected to always adapt to the times, which are required to always innovate and be creative to be able to create quality graduates. So that students have good provisions in facing the challenges ahead. Teachers who teach at Vocational High Schools majoring in office automation and governance include Office Technology subjects. The Office Technology subject contains practical material regarding the use of applications for office or administrative activities, which as the name suggests is currently the office administration department changed to the office automation and governance department. However, the applications used for learning related to the use of applications and the application of digital offices, especially the creation of digital presences to switch to paperless, are still very minimal or even non-existent. Learning office technology, which has been difficult for teachers to provide and provide skills to students related to applications that support the digitization of offices through office technology subjects. In the era of computerization, it affects administrative management in an organization, as well as a student majoring in office automation and governance must have competence in mastering digitalization and office automation skills.
GAMBARAN TINGKAT LITERASI KESEHATAN MENTAL PADA PESERTA DIDIK SEKOLAH MENENGAH PERTAMA

Mental health is an important component that is closely related to social life, productivity, peace and stability in the environment, contributing to sosial life and economic development in Indonesian society. Mental health literacy is knowledge and beliefs about mental disorders that help to recognize, manage and prevent a person’s mental disorder. Mental helath literacy is also knowledge related to actions to provide mental helath benefits to individuals and others. Mental helath literacy is the ability to gain knowledge or information about mental health for adolescents, understand and can maintain good mental health. Mental health literacy includes the ability to recognize specific mental health disorders, knowing how to seek mental health information, knowledge of risks and causes of health disorders. The aim of this study is to improve mental health literacy in students and meet the criteria for eligibility aspects. This study used Reeves Design Based Research model. The subjects in this study were students of class IX SMP Negeri 2 Kramatwatu. Collecting data using a questionnaire, in the form of an adaption instrument from a mental helath literacy question (MHLq). The results of this study indicate the level of mental health literacy in students obtained a value of 49% which is included in the moderate category.
The Effect Fe Tablets and Vitamin C with Fe Tablets and Strawberry Juice on Hb Adolescent Girls

Anaemia affects about 23% of Indonesian adolescent girls. The red blood cell insufficiency affects 4.8 million adolescent girls out of a total population of roughly 21 million. So that arises due to a combination of poor dietary habits and a lack of physical exercise. The purpose of this research is to see whether there is a difference in the effects of giving Fe tablets and vitamin C with Fe tablets and strawberry juice. The study population was adolescent girls with a range of ages of 16-17 years. Then, the research design was quasi-experimental with two group pre-test and post-test designs. A sample was 30 adolescent girls. The sampling technique was purposive sampling. Statistical tests used paired t-tests and independent t-tests. The results of the study p-value = 0.014 &lt; 0.05, so there was a significant influence between giving Fe tablets and vitamin C with Fe tablets and strawberry juice on adolescent girls. It is expected that the consumption of Fe tablets with vitamin C such as strawberry juice will increase the Hb level of adolescent girls.Keywords: Increased haemoglobin, adolescent girls, Fe tablets, vitamin C, strawberry juice
Trend Analysis of the CVE Classes Across CVSS Metrics

Understanding vulnerability trends is important for risk management process.. Understanding trends helps in early detection of problems and also in planning defense mechanisms.In this paper analysis of the trends of Common Vulnerabilities and Exposures (CVE) from the National Vulnerability Database (NVD) for 2005 to 2020 has presented.136566 CVEs has been extracted for sixteen years, also their Common Vulnerability Scoring System (CVSS) scores has been collected from the NVD, then analysis of severity, and CVSS base metrics trends ,and trends for classified vulnerability data has been done .Such analysis of vulnerability data according to their type, CIA impact, access vector and access complexity helpful in identifying most critical class of vulnerability relative to system environment and improve risk mitigation process.
Teachers’ and Parents’ Views on the Excessive Use of Technology During COVID by Primary Level Students

The widespread COVID 19 pandemic has affected learning and development of students at all ages and the sudden closure of schools has led to the new experience of online learning during this period. The study aimed to explore the perceptions and challenges faced by the teachers and parents of primary students regarding excessive use of technology during online learning. A qualitative approach using an exploratory method was applied to collect the data by deploying self-developed semi-structured interview protocols to collect the data. Three major themes emerged after the data analysis: (a) the debate on online classes versus physical classes; (b) the negative impact of technology on the development of children; and (c) appropriateness of online learning for young learners. The data illustrated that teachers and parents of primary students are not satisfied during online learning because of the adverse effect of technology on the learning and development of young learners. Hence, it is recommended that alternatives should be suggested by the school authorities and government, so that their learning and development are not hampered by the use of technology.
Vectorial Decoding Algorithm for Fast Correlation Attack and Its Applications to Stream Cipher Grain-128a

Fast correlation attack, pioneered by Meier and Staffelbach, is an important cryptanalysis tool for LFSR-based stream cipher, which exploits the correlation between the LFSR state and key stream and targets at recovering the initial state of LFSR via a decoding algorithm. In this paper, we develop a vectorial decoding algorithm for fast correlation attack, which is a natural generalization of the original binary approach. Our approach benefits from the contributions of all correlations in a subspace. We propose two novel criteria to improve the iterative decoding algorithm. We also give some cryptographic properties of the new FCA which allows us to estimate the efficiency and complexity bounds. Furthermore, we apply this technique to the well-analyzed stream cipher Grain-128a. Based on a hypothesis, an interesting result for its security bound is deduced from the perspective of iterative decoding. Our analysis reveals the potential vulnerability for LFSRs over matrix ring and also for nonlinear functions with biased multidimensional linear approximations such as Grain-128a.
Validation Automation of UML Diagrams Created by Students

Unified Modeling Language (UML) is widely used standard for models visualization in software industry. Hence, a preparation of IT professionals involves the learning modeling process. Studies of student perception of UML modeling indicate that this process is perceived as quite complex. This paper presents software for validation activity, class and use-case diagrams by XMI representation. To achieve this goal, we researched existing methods and systems. Besides, we analyzed mistake catalogues and Perm State University’s student models to propose a mistake classification and checklist that presents a list of validation to be done. This paper focuses on validation each type of diagram separately, without maintaining consistency between different UML models. However, all these validation modules are combined in one system, which allows to check any of the described types of diagrams.
Exploring Super-Resolution for Face Recognition

Biometric recognition is part of many aspects of modern society. With the popularization of smartphones, facial recognition gains space in this environment of biometric technologies. With the diversity of image capture devices, of different brands and qualities, the images will not always be in the ideal standard to be recognized. This article tests and compares different scenarios and situations to assess the results obtained by facial recognition in different environments. For this, the quantity method of data analysis was used. In the first scenario, all images were submitted without changes. In the following, we have the reduction of image resolution, which may or may not be followed by enlargement to the original resolution via bicubic interpolation or through the Image Super-Resolution algorithm, these images can be all, or only that undergo tests. Results indicate that the first scenario obtained the best performance, followed by only the tests images change. The worst performance occurs where the properties of all images are affected. In situations where there is a reduction and enlargement are optional, the enlargement option performs better, so the bicubic enlargement has an advantage over the ISR, the situation in which only the reduction occurs has the worst performance.
Pengaruh Penggunaan Media Webblog Berbasis HOTS Terhadap Motivasi dan Hasil Belajar Pada Materi Laju Reaksi

This reseach goals to determine how the effect of knowing the effect of using Webblog media on the HOTS Evaluation Based Problem Based Learning Model on Motivation and Student Learning Outcomes in the Material of Reaction Rate. The research method used was an experiment that was modified as needed. The sampling technique used is by using a purposive technique where class XI IPA 4 as an experimental class is taught using Webblog Media in the Problem Based Learning model using and XI IPA 5 as a control class that is taught using Power Point Media in the Problem Based Learning learning model based on HOTS evaluation . The analysis technique used is the mean difference test, the percent increase in learning outcomes, normality test, homogeneity test and hypothesis testing. The results obtained by the average value of the experimental and control classes in the pretest data were 36.25 and 27.25. The post-test scores for the experimental and control classes were 79 and 65.5. Data on the normality and homogeneity test obtained data were normally distributed and the data were homogeneous. The correlation test for the effect of using Webblog media in the Problem Based Learning model on student motivation and learning outcomes is the value of rxy = 0.754 which means there is a positive correlation between motivation and student learning outcomes.
A novel single image reflection removal method

In recent years, reflection is a kind of noise in images which is frequently generated by reflections from windows, glasses and so on when you take pictures or movies. The reflection does not only degrade the image quality, but also affects computer vision tasks such as object detection and segmentation. In SIRR, learning models are often used because various patterns of reflection are possible, and the versatility of the model is required. In this study, we propose a deep learning model for SIRR. There are two problems with the conventional SIRR using deep learning models. The assumed scenes of reflection are vary, and there is little training data because it is difficult to obtain true values. In this study, we focus on the latter and propose an SIRR based on meta-learning. In this study, we adopt MAML, which is one of the methods of meta-learning. In this study, we propose an SIRR using a deep learning model with MAML, which is one of the methods of meta-learning. The deep learning model includes the Iterative Boost Convolutional LSTM Network (IBCLN) is adopted as the deep learning methods. Proposed method improve accuracy compared with conventional method of state-of-the-art result in SIRR.
COVID-19 surveillance robot: rapid innovation for public health pandemic management

Abstract Background COVID-19 surveillance added a significant workload to the eight HSE-Departments of Public Health in Ireland. HSE-HPSC rapidly developed a fit-for-purpose robot to navigate the national infectious disease reporting system (CIDR) to automate three manual processes; laboratory records, notifications and contact-tracing data; which takes a surveillance scientist 26 minutes per case on average. Methods HSE-HPSC managed and delivered a multidisciplinary project team to develop the rapid solution. The robot was designed to operate the CIDR system using an agreed set of rules, developed through business process analyses of the ‘first wave', stakeholder engagement and technical collaboration. Development began in April 2020, and went live in August 2020 after phases of testing, piloting, and hyper-care. Results Successful integration: The robot aligned COVID-19 surveillance data across three national HSE information systems. Degree of automation: The robot processed greater than 80% of cases just like a human. The remaining 20% are flagged by the robot for data quality checks by the regional public health teams. Time-saving: The robot operates quicker than a human, 3.3minutes per case compared to 26minutes. Therefore for every 100 cases, the robot saves 38hours per day. Out-of-hours capacity: Robots currently operate for 22hours per day, resulting in overtime cost-savings for the HSE. Surge capacity: The automation was expanded to 42 robots for ‘third-wave' surge capacity. Sustainable change in surveillance system: Robots can be expanded for surveillance of other notifiable diseases. Conclusions The robot: delivered a fit-for-purpose pandemic resource relieved the underfunded public health system of the administrative burden of COVID-19 surveillance delivered timely data for epidemiological reporting by the HSE-HPSC to the National Public Health Emergency Team. Key messages Through rapid collaboration, the robot successfully delivered a fit-for-purpose public health resource that aligned COVID-19 data across HSE information systems and achieved time/cost savings. The robot strengthened the public health surveillance response in Ireland.
AI Clerk Platform : Information Extraction DIY Platform

Information extraction is a core technology of natural language processing, which extracts some meaningful phrases/clauses from unstructured or semistructured content to a particular topic. It can be said to be the core technology of many language technologies and applications. This paper introduces AI Clerk Platform, which aims to accelerate and improve the entire process and convenience of the development of information extraction tools. AI Clerk Platform provides a friendly and intuitive visualized manual labeling interface, sets suitable semantic label in need, and implements, distributes and controls manual labeling tasks, so that users can complete customized information extraction models without programming and view the automatically predict results of models by three method. AI Clerk Platform further assists in the development of other natural language processing technologies and the derivation of application services.
DEVELOPMENT OF ENGLISH LEARNING MODEL BASED ON LOCAL WISDOM WITH BLENDED LEARNING APPROACH USING MACROMEDIA FLASH AT SMPN SATU ATAP TUNDA ISLAND

The aim of the research is to create a learning media model for English that uses the Macromedia Flash program, as well as to assess the viability of utilizing Macromedia Flash-based learning media to assist English. This study is a Research and Development (R &amp; D) project with several stages. (1) Analysis stages, such as identifying Learning Media objectives and student characteristics; (2) Design stages, such as designing learning materials and learning media design using the Macromedia Flash program; (3) Production stages, such as producing learning materials, authoring materials, and editing learning materials; (4) Stages of evaluation, including Material Expert Review, Media Expert Review, and Student Testing. The findings demonstrate that the English learning media products adhere to research and development guidelines and are successful in the learning process for SMPN Satu Atap Pulai Tunda students.
Peningkatan Hasil Belajar Peserta Didik Menggunakan Model Discovery Learning Pada Pembelajaran Tematik di Kelas V SDN 20 Muara Jambu, Kabupaten Pesisir Selatan

The purpose of the study was to improve student learning outcomes in thematic learning using the Discovery Learning model in Class V SDN 20 Muara Jambu Kab. South Coast. This type of research is classroom action research (CAR). The approach used is quantitative and qualitative. The subjects of this study were the fifth grade teachers and fifth grade students, and researchers as practitioners. Data analysis used qualitative and quantitative data analysis, and the research instrument used RPP assessment sheets, teacher and student activities, test and non-test sheets. The conclusions of this study are: 1) The assessment of the percentage of RPP has increased from 79% in the first cycle to 95% in the second cycle. 2) The teacher aspect has increased from 81.25% to 93.75%. 3) The student aspect has increased from 81.25% to 93.75%. 4) Student learning outcomes have increased from the first cycle to get the percentage of completeness 76.19% increased to 95.23% in the second cycle.
Pengembangan Aplikasi Asisten Pintar Pembuka Al Qur’an 30 Juz dengan Perintah Voice Command

Many developers of digital Qur'an applications today still use tap to scrolling to run applications, although the features are interesting. This makes it less effective and efficient in opening the Qur'an. As is the case during the taklim assembly, some da'i are very interactive with jama'ah, asking to open certain surahs and verses so that there are some who have difficulty in searching. Therefore, the need for the Qur'anic application with voice command command to facilitate users. This research is the development of the Qur'an application with voice recognition feature. Using the waterfall method in development, voice command with google speech API as a voice command of surah and verse calling in the Qur'an application 30 juz. Conducted 10 randomized experiments with calls in the form of play or open surahs and certain verses give a 90% accuracy result. Commands can be given when online or offline. Then the use of google speech API can be very useful for use in the development of other applications.&#x0D;