Reviewerly/exHarmony huggingface dataset:
RELIABILITY OF MODIFICATION QUESTIONNAIRE OF KNOWLEDGE LEVEL ABOUT ANEMIA IN ADOLESCENT

Introduction: The problem that is often experienced by young women is anemia. This anemia condition causes them to feel lethargic, dizzy, and a pale face. This situation causes them to be less eager to carry out activities and interfere with their learning concentration.&#x0D; Objective: This study aimed to determine the reliability test results of the questionnaire on the level of knowledge about anemia in adolescents.&#x0D; Methods: This study used a cross-sectional study method. This research was conducted on February 28, 2021. The study population was all adolescents in the Islamic Boarding School of Tahfizh Putri Alfirdaus Hidayatullah, Sekardangan, Sidoarjo. The sampling technique used was total sampling. The sample size of the study was 24 adolescents. This study used a modified questionnaire for adolescent knowledge about anemia (Zulaekah, 2007). The questionnaire used in this study only used 8 items from the original questionnaire. The data analysis carried out was Mean, Standard Deviation, and Cronbach's Alpha.&#x0D; Results: Based on the results of the reliability test on the adolescent knowledge level questionnaire, it was found that Cronbach's Alpha was .594. This illustrates that the questionnaire is good enough to be used as an instrument in research.&#x0D; Conclusion: A modified questionnaire about the level of adolescent knowledge about anemia can be used in a study.
MELATIH KECERDASAN MATEMATIS LOGIS ANAK MELALUI KEGIATAN FUN COOKING DI SD NU METRO LAMPUNG

This research was motivated by curiosity in terms of developing logical mathematical intelligence in first grade students at SD NU Metro Lampung. This research focused on Fun Cooking activities which aim to develop children's interests and talents. This study uses critical analysis method, so this study is descriptive qualitative analysis to become a learning innovation in students development at mathematical intelligence. From the results of the study, it can be seen that the first grade students at SD NU are very enthusiastic in participating in Fun Cooking activities. Through several processes, stages and explanations conveyed by the teacher, it turns out that the development of children's logical mathematical intelligence, in first grade students is very efficient and it can be seen that children are able to understand various types of staple food ingredients, dosages in making dough, children are able to understand numbers, understand the duration of time to bake cakes, be creative in printing and make innovations on pizza, understand geometric shapes, for example round, square, triangle, and so on.&#x0D; &#x0D; Keywords: Fun Cooking, Mathematical Intelligence, Interests and Talents
Optimized Route Finding in GPS Navigation Using KALMAN’S Filter Algorithm

This project applies two AI (Artificial Intelligence) techniques, i.e., fuzzy logic and expert system, to enhance the Kalman's filterbased GPS Navigation System.The Dijktra's Algorithm to find the smart route to reach the destination.The Kalman's Filtering technology provides us the exact speed of the vehicle by means of an accelerometer and a speed sensor.Once the speed has been calculated, the algorithm uses the speed of the vehicle and converts the speed to RPM (Revolution per minute) of the wheel to find the traffic intensity.Once the traffic has been noted in a particular area it sends the information to the centralised database.The database in turn notifies the user and Police Department about the traffic and once the traffic has been identified in the path of the user, the algorithm finds an alternate path by considering factors like traffic and time to reach the destination.The notifications are sent to the user and Police Department by means of SMTP and POP3 protocol.Since the Police Department are notified with the traffic intensity, the Expert Systems checks if the traffic has been cleared or not and sends all the updates to the centralised databases.
A symbolic attribute-based access control model for data security in the cloud

There have been several attempts made in literature to develop access control techniques to stem data security problems. Many of these techniques had been found to have one deficiency or other. Hence, this study developed a Symbolic Attribute-Based Access Control (SABAC) system for data security in the cloud service environment. SABAC system was implemented by developing Hash-tag Symbol Authentication (HSA) algorithm using the Message Digest-5 encryption. SABAC utilizes a 3-Tier continuous authentication method by combining the use of username and password, HSA code, and real-time image monitoring and verification. HSA code is generated by combining 5-tuple user attributes and the string generated from the user’s image using Obfuscation Technique. The concatenated string is converted to hexadecimal which serves as input to MD5 to produces a unique HSA code. SABAC was evaluated using three major security metrics of confidentiality, integrity, and avail-ability. The result of security metrics tests showed a confidence level of 99.993%, integrity threshold of 99.998%, and availability throughput of 150 users/second. This implies that SABAC is highly efficient for cloud data security. It shows that hackers would find it impossible to match any fake identity with valid HSA in the database. The study concluded that SABAC could be used for access control in a cloud environment for assuring data security. It was recommended that the SABAC system should be adopted by Cloud Solution Providers and Security Specialists.
An Interview with Owen McCall from TREECYCLE

Is cryptocurrency necessarily energy-consuming? Can we use blockchain to facilitate sustainable development? This interview presents insights into an investment project rooted in sustainability, bridging finance and ecology.
ACCURATE AND FAST DYNAMIC SUBPIXEL BASED TUMOR IMAGE RECOGNITION

In recent years, the image processing technique has been applied in many significant fields, such as military, aerospace, and health industry. With the development of medical treatment, the computer-assisted diagnosis and treatment system play increasingly important roles. Methods: This paper presents a new fast and accurate image matching algorithm, which uses Breast Cancer B-scan images as test images. We first present a novel method to obtain the integer-pixel matching result, namely district-identification method. Then, we introduce gradient algorithm to match the sub-pixel position. Results: Experimental results show that the proposed method is about 2.3 times faster than traditional methods and can be suitable for images of arbitrary size. Moreover, it can improve performance in sub-pixel level and the recognition rate is above 96.30%. Conclusion: This paper proposes a novel image matching method to help medical experts to recognize the tumor location fast and accurately, find out the radiotherapy and chemotherapy target, and make full use of the medicines.
Adapting Viola-Jones Method for Online Hand / Glove Identification

This article proposes a method for hand identification, adapting the method of Viola-Jones for identifying two different objects. The main objective of this work is to solve the problems of hand identification. Thus, our approach based on learning for two objects as one package. Also, the proposed method folds into three parts; the first part is training for both objects, second detection of both objects, and third the identification step to identify if the hand is wearing a glove or not, then labeling each one with a suitable state. Moreover, to test our method, we have proposed a new dataset, which includes a variety of cases with different compositions of hand. As a result, 8 cases were used to test the method. The method was able to detect a human hand successfully. Additionally, it could identify whether the hand was or was not wearhing a glove. The accuracy of detecting a hand without a glove was about 63%, and the accuracy of detecting a hand with a glove on was about 61%. Even though the tests scored different accuracy, as a first step towards solving this problem, it is a big achievement to even reach this level of accuracy.
PREPARING FUTURE CHEMISTRY TEACHERS FOR THE USE OF CLOUD TECHNOLOGIES IN PROFESSIONAL ACTIVITIES

The relevance of the study is due to the specifics of perception and assimilation of information by schoolchildren (the young generation's penchant for clip thinking) and the insufficient number of studies that reveal the theoretical and substantive-procedural features of the formation of preparedness of future chemistry teachers for the use of cloud technologies in professional activities. The purpose of the article is methodology for preparing future chemistry teachers for the use of cloud technologies in professional activities and its testing. The leading method for studying this problem is the modeling method, which allows to consider the problem under study as a focused, organized process of improving the professional competence of future chemistry teachers and the formation of ICT competency, reflecting the ability and willingness of a future chemistry teacher to use cloud technologies in professional activities. The developed methodology allows not only to form students' knowledge about the functions of cloud technologies but also aims to prepare future chemistry teachers for new conditions of professional activity. Pedagogical conditions of the future Chemistry teachers training to use of cloud technologies in professional activities. Analysis of the results of the study showed the effectiveness of the proposed methodology for preparing future chemistry teachers for the use of cloud technologies in professional activities, which were confirmed by the positive dynamics of the levels of readiness formation among students of the experimental group. Article materials may be useful for teachers and students of higher educational institutions, teachers of institutions of general secondary education, specialists in the field of education.
Continual Quality Estimation with Online Bayesian Meta-Learning

Most current quality estimation (QE) models for machine translation are trained and evaluated in a static setting where training and test data are assumed to be from a fixed distribution.However, in real-life settings, the test data that a deployed QE model would be exposed to may differ from its training data.In particular, training samples are often labelled by one or a small set of annotators, whose perceptions of translation quality and needs may differ substantially from those of endusers, who will employ predictions in practice.To address this challenge, we propose an online Bayesian meta-learning framework for the continuous training of QE models that is able to adapt them to the needs of different users, while being robust to distributional shifts in training and test data.Experiments on data with varying number of users and language characteristics validate the effectiveness of the proposed approach.
(ISSBM) Improved Synthetic Sampling based on Model for Imbalance Data

In the data mining research domain imbalanced data is characterized by the rigorous variation in scrutiny frequency between classes and has expected a lot of consideration.The forecast performances usually depreciate as classifiers learn from data imbalanced, as most of classifiers presume the class division is balanced or the costs for different types of classification errors are the same.Although several methods have been analyzed to deal with imbalance problems, it is still difficult to oversimplify those methods to achieve stable improvement in most cases.In this study, we propose a novel framework called Improved Synthetic Sampling Based on Model (ISSBM) to deal with imbalance problems, in which we integrate improved modeling and sampling techniques to generate synthetic data.The key inspiration behind the proposed method is to use deterioration models to capture the relationship between features and to consider data multiplicity in the process of data generation.We conduct experiments on many datasets and compare the proposed method with 5 methods.The experimental results indicate that the proposed method is not only qualified or comparative but also very stable.We also provide detailed analysis of the proposed method to empirically demonstrate why it could generate good data samples.
ON THE PEDAGOGICAL PROGRAM FOR THE PROFESSIONALIZATION OF FUTURE DOCUMENT SPECIALISTS AND ARCHIVISTS

The article is devoted to the study of the problem of professionalization of future specialists in archives and record management. The author’s program of professionalization of future document specialists and archivists is proposed and substantiated. The main stages of professionalization in relation to the training program “Documentation and archival science” are considered: preparatory or pre-university, pre-professional development, adaptation stage, inclusion in professional activity and a person’s entry into the profession. Key, professionally significant topics of each stage are identified.
Modeling with generalized linear model on covid-19: Cases in Indonesia

The ongoing Covid-19 outbreak has made scientists continue to research this Covid-19 case. Most of the research carried out is on the prediction and modeling of Covid-19 data. This study will also discuss Covid-19 data modeling. The model that is widely used is the linear model. However, if the classical assumption of normality is not met, a special method is needed. The method that can overcome this is the generalized linear model (GLM), with the assumption that the data is distributed in an exponential family. The distribution used in this study is the Gaussian, Poisson, and Gamma distribution. Where the three distributions will be compared to get the best model. The variables used in this study were the number of confirmed Covid-19 cases per day and the number of deaths due to Covid-19 per day. This study also aims to see how much influence the confirmation of Covid-19 has on the number of deaths due to Covid-19 per day. By using 3 types of exponential family distribution, the best result is the Gaussian distribution GLM. Selection of the best model using Akaike Information Criterion (AIC).
Is distance education a means of implementing the National Education Doctrine?

The concept of the cyclical nature of social history is substantiated in relation to the genesis of state and law in general and in relation to the concept of educational law, in particular. The conceptual difference between the educational systems of Russia and the West is shown in terms of the mechanism of their formation and goals of functioning. The intra-systemic collision of the system of modern Russian education is proved, which combines the Soviet (specialty, postgraduate studies) and Bologna (bachelor’s, master’s) components. The prospects for the development of the Russian educational system are determined, «educational risks» are highlighted in the context of globalization processes, on the one hand, and the growing trend of state isolation of Russia, on the other.
A Comparative Study on the Efficiency of Online Learning Management Platform for Telecommunications Store Retail Employees in 2020

This research studied the efficiency of the Online Learning Management Platform for telecommunication (Telco) retail employees.The researchers conducted online surveys and interviews with 36 senior managers and trainers that have experience on both traditional and online learning platforms.The researchers found out that costs, manpower, and learning retention are the factors that need to be considered in the efficacy of online learning systems in training and seminars for continuous improvement of skills.Telco retail employees deal directly with customers, it is very important that they receive adequate training on how to provide great customer service and increase sales.This paper concludes that the adoption of online LMS of companies requires trainers to upgrade their training skills and knowledge.The findings and conclusions are relevant in the present time.
Reseña de Sommer, John Marcus. 2020. English and French Online Comments. A Text Linguistic Comparison of Popular Science Magazines. Berlin: Peter Lang. pp. 317.

This book approaches a linguistic study of the popular sciences magazines National Geographic, Discover, Sciences et Avenir and GEO from a very original perspective, since the author aims to describe the changes within the genre profiles of the magazines and the differences and similarities between their journalistic cultures. More specifically, since nowadays we live in a world in which social networks are playing an increasingly important role in the journalistic field (Jiménez Cano, 2017), the author focuses on the communicative practices of the readers of these magazines in two languages (French and English) and three platforms (Facebook, Twitter and Magazine-Website) in their print and online versions...
A System to Predict an Outbreak of a Disease using Twitter Data

Social media (Twitter) data can be analyzed by the system to provide data regarding the diseases by checking the location from the tweets retrieved from Twitter. This will be done by searching for the disease in the system. Using tweets most used hash tag is retrieved to find out which disease is discussed to spread the awareness and it is helpful to prevent spreading of communicable Disease. Detecting the diseases that are widely spread in the society and predicting future stages of the diseases has become very important in modern life. The System is to make people aware of the Epidemic diseases in a particular location. The people can also search for other non-communicable diseases to check the tweets helpful to them in providing information regarding the disease. As many people are using social media to share different information we are utilizing this fact to make our system more efficient and reliable by taking the data from Twitter and analyzing various factors like sentiments, location. This can be utilized by anyone who needs information regarding the disease and can be utilized to support clinical providers, public health experts, social scientists, healthcare providers and Government.
Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint

Continuous representations of linguistic structures are an important part of modern natural language processing systems.Despite the diversity, most of the existing log-multilinear embedding models are organized under vector operations.However, these operations can not precisely represent the compositionality of natural language due to a lack of order-preserving properties.In this work, we focus on one of the promising alternatives based on the embedding of documents and words in the rotation group through the generalization of the coupled tensor chain decomposition to the exponential family of the probability distributions.In this model, documents and words are represented as matrices, and n-grams representations are combined from word representations by matrix multiplication.The proposed model is optimized via noise-contrastive estimation.We show empirically that capturing word order and higher-order word interactions allows our model to achieve the best results in several document classification benchmarks.
Design of E-Module Based on Discovery Learning in Basic Boga Students of Class X Tata Boga in SMK Negeri 1 Beringin

The purpose of this research is to produce a valid e-modules based on discovery learning, and effective in basic food subjects. This e-modules based on discovery learning is designed to improve student’s understanding so that it can improve students learning outcomes. The method in this study uses Research and Development (R&amp;D) with 4-D development procedures (Define, Design, Develop, Desseminate). The data of analysis technique uses descriptive analysis techniques to describe the validity, practicality, and effectiveness of e-modules based on discovery learning. The results obtained of this development research: The validity of e-modules based on discovery learning is declared valid on the media and material aspects. The practicality of e-modules based on discovery learning from teacher and student responses is stated to be very practical. The effectiveness of e-modules based on discovery learning is stated to be effective in improve to learn the outcomes. Based on the findings of this study it is concluded that the e-modules based on discovery learning is valid, practical, and effective to be used as a learning aid in basic food subjects.
Do Space’s Virtual Interview Lab

As we start to pull ourselves out of the COVID-19 pandemic it’s time to start thinking about what changes we made at our libraries in response and decide which ones we should keep and which ones need to end along with the pandemic itself. In this editorial Michael P. Sauers, Technology Manager for Do Space in Omaha, NE uses their Virtual Interview Lab as a case study in pandemic flexibility and how simple solutions can have great benefits.
Feature Re-Learning for Video Recommendation

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. The application is in video recommendation, video annotation, Category or near-duplicate video retrieval, video copy detection and so on. In order to estimate video relevance previous works utilize textual content of videos and lead to poor performance. The proposed method is feature re-learning for video relevance prediction. This work focus on the visual contents to predict the relevance between two videos. A given feature is projected into a new space by an affine transformation. Different from previous works this use a standard triplet ranking loss that optimize the projection process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, propose a data augmentation strategy which works directly on video features. The multi-level augmentation strategy works for video features, which benefits the feature relearning. The proposed augmentation strategy can be flexibly used for frame-level or video-level features. The loss function that consider the absolute similarity of positive pairs and supervise the feature re-learning process and a new formula for video relevance computation.
The Use of Punctuation Marks in Japanese—Focus on the News Headlines of People’s Daily Online

Punctuation marks are also used in news headlines, which play an important role in conveying news information to readers.There are some differences between the punctuation marks used in the headlines of Japanese news in the Chinese-Japanese translation section of People's Daily's Japanese edition and those used in Japanese news websites."," and "." are rarely added to news headlines on Japanese news websites.Use "「」" or "()" to quote or explain.Compared with this, there are obviously more punctuation marks used in Japanese news headlines of the Chinese-Japanese translation section of People's Daily's Japanese edition, such as "," and "?"And "!", "," etc. Section in the mutual translation of this paper is to People's Daily online Japanese version the solstice on February 7, 2021 on April 2 until 30 news title language as the research object, based on the investigation to the Japanese punctuation usage, understanding the characteristics of the Japanese, grasp the translation of Japanese language features of news headlines, facilitate in the later translation more accurately using auxiliary expression of punctuation, To build a bridge of friendly communication between the Chinese and Japanese people.