Reviewerly/exHarmony huggingface dataset:
Pandemics and cryptocurrencies

This study examines the effect of a pandemic-induced uncertainty on cryptocurrencies (specifically, Bitcoin, Ethereum and Ripple). It employs a predictive model by Westerlund and Narayan (2012, 2015) to examine the predictability of a pandemic-induced uncertainty as a predictor, as well as the forecast performance of our predictive model for cryptocurrency returns. We examine the role of asymmetry in uncertainty and the sensitivity of our results to alternative measures of uncertainty due to pandemics, using the recently developed Global Fear Index (GFI) by Salisu and Akanni (2020). Our results indicate that cryptocurrencies could act as hedge against uncertainty due to pandemics, albeit with reduced hedging effectiveness in the COVID-19 period. Accounting for asymmetry is found to improve the predictability and forecast performance of the model, which indicates that failure to account for asymmetry in modeling the effect of a pandemic-induced uncertainty on cryptocurrency may lead to incorrect conclusion. The results seem to be sensitive to the choice of measure of pandemic-induced uncertainty.
Improving Students’ Writing Achievement on Recount Text by using Photograph at Ninth Grade Students of MTsN 1 Medan

This research was aimed to find out the impact of using Photograph in improving the students’ writing achievement on Recount Text. The subject of this research was the ninth grade students of MTsN 1 Medan in academic year of 2020/2021 which consisted of 36 students. This research was conducted by using classroom action research, which used two cycles. Each of them consists of planning, acting, observing, and reflecting. The data were obtained through quantitative and qualitative data. The quantitative data was obtained from tests including pre-test, post-test I, and post-test II. While qualitative data was obtained from observation sheet. The result of the data analysis showed that there was an improvement on the students’ reading achievement from each cycle. It was shown from the students’ mean score The precentage of students score in pre-test who got point up to 70 was 10 students and the improvement was about 27,8 %. The precentage of students score in post test 1 in cycle 1 who got point up to 70 was 25 students and the improvement was about 69,5 %. The precentage of students score in post test 2 in cycle 2 who got point up to 70 was 32 students and the improvement was about 88,9 % . Based on the finding research , saying that Photograph could improve the students’ Writing achievement in recount text
The Use of Duolingo Application to Enhance English Vocabulary of Junior High School

Teaching vocabulary is demanding in the context of English as a foreign language (EFL). EFL learners often forget words easily due to a lack of target language input, and most words are learned through classroom instruction, which often leads to boredom. Thus, such works suggest the urge for a more effective approach to vocabulary teaching for EFL learners. This study explores the use of MALL application; Duolingo to enhance student’s knowledge and interest in learning English vocabulary through mobile technologies. Quasi-experimental study was tested to seventh-grader with two group sample experimental and control that each group consist of 17 students. The research instruments used was a test that collected from pre-test and post-test score. Then, the data were analyzed using t-test calculation of SPSS 20 to identify its vocabulary improvement from each group. The results indicate a positive as the calculation of significance level (p-value) from experimental post-test scores is .000. It is lower than .05, which means there is a significant difference in experimental group after given a treatment. In addition, the experimental group got a higher average score of the post-test which is 87.94 than the control group which is 75.52. In conclusion, Duolingo can be believed to be a gamified language learning app that can develop vocabulary skills of the seventh grade.
A Project-Based Application-oriented Language Teaching Research

Based on the examination wash-back mechanism, the study is to carry out college English application-oriented teaching reform practice, unify the diverse core themes in textbook learning through project tasks in the network and classroom teaching, and emphasize the close connection between language learning and language practice. Project tasks involve the establishment of a series of task documents, task implementation, and task reflection. Project tasks are carried out closely following the core idea of the improvement of workplace capabilities, project data can be integrated into a semester language learning course. The study provides task samples for language teaching and initial experience to adapt to other relevant elective courses, and provides students with additional learning motivation and deeper learning capabilities.
A Haskell Recursion Tutor

Recursion is not an easy subject to learn. In this thesis the literature is examined on the common difficulties and misconceptions students encounter when trying to understand recursion in the functional paradigm, and on the available tools for teaching and visualizing recursion. It is found that experts possess the copies model as their mental model and that novices often hold misconceptions about the passive flow and have difficulties identifying a correct base case or recursive step. The examined intelligent tutoring systems and visualizations show a variety of different approaches to illustrate code execution: some use graphical representations while others are text-based. Research indicates that some of the tools encourage students to spend more time with the learning material which leads to greater learning gains. A prototype tool is proposed that supports the learning of recursion in Haskell through the step-wise evaluation of recursive functions and the displaying of the subsequent function calls in function tables. The prototype is aimed at alleviating the identified difficulties and misconceptions. The copies model is illustrated through the use of function tables that display the different instantiations of the function in the table rows. The passive flow is shown through step-wise normalization of the rows in the tables. The prototype has a unique way of illustrating the copies model and displays the passive flow explicitly. In the future the prototype could be augmented with additional features such as the handling of input and feedback.
A Model Predictive Sample-Based Trajectory Planning Strategy for UAS

Trajectory planning is an essential element in robotics applications. The quality of the planned trajectory strongly influences the robot behaviour, particularly when the autonomous robot operates in complex and structured environments. This thesis focuses on the study, deployment and testing of a trajectory planning algorithm for autonomous UAVs, exploiting the Model Predictive Control theory and the Rapidly-exploring Random Tree star (RRT*) algorithm. The developed logic makes use of the RRT* algorithm to explore the search space and, then, constructing an incremental optimal tree to connect a given start and a goal pose in the search space. During the exploration phase, whenever two states are attempted to be connected, the deployed Model Predictive Control logic computes a cost-to-go cost of moving between two adjacent states in the graph by predicting the motion of the UAV, exploiting its dynamic model. At the end of the search phase, RRT* returns the asymptotically optimal path in the graph with the lowest cost and, as a consequence, the optimal trajectory respecting kinematic and dynamic constraints and avoiding obstacles in the search space. Being the implemented Model Predictive Control optimization computationally expensive in terms of computer resources and time, some heuristics are applied in the algorithm to speed up the trajectory computation. Hence, simulation results in realistic environments validate the proposed approach proving how the proposed trajectory planning is able to compute an effective trajectory to be executed by the UAV.
A Certificate Technique and Two Factor Authentication for Cloud Security Adaptation

The unforeseen nature of Cloud Computing visibility as well as manage among the data owners, applications, along with cloud providers raises tenants vagueness when utilizing cloud based services. Adaptation methods offer a reliable infrastructure based on cloud with assured behavior that preserves the excellence of service for tenants. Existing adaptation methods mainly concentrate on work qualities as well as they use non verifiable proof that is gathered in non-trust worthy manner. In this work, we recommend a technique for security-oriented adaptation for the cloud, dependent on the evidence gathered through trustworthy resources of a certificate procedure along with two factor authentication mechanisms. With this approach the cloud will keep stable security attribute over the time, through continuously confirming certificate validity with a digital signature with Google cloud KMS along with two factor authentication of the certificate. To protect users against digital identity theft, 2FA (Two-factor authentication) is a famous technique. Two-factor authentication connections access online information as well as accounts to having 2 or more than two tokens which show permission or ownership. It utilizes the output of verification actions and provides a model, in which corresponding configuration is utilized as the origin for adaptation.
Facial Recognition Based Attendance System

The face is the key feature of identification of a person. And there are so many methods to exploit the system. This paper solely aims on the Face Recognition technique to maintain the authenticity of the person. In facial recognition we verify any person by their face. It captures the data and then analyzes and co relate person’s facial aspects. Face recognition is in trend nowadays because of the availability of feasible technologies after years of research. In these times this system is treated as the most anticipated of all biometric systems. We recognize ourselves not by our fingerprints or eye but by looking at our face. Considering all the above mentioned points andtheir implications we've tried to realize some experience with some of the commonly available face recognition algorithms and make our Facial attendance system more effective.
THE USE OF WORDLIST PLUS PICTURE TO INCREASE STUDENTS’ STOCK OF VOCABULARY

This research aimed to see the effectiveness of using Wordlist plus picture in increasing students’ stock of vocabulary at the seventh grade of SMP Negeri 2 Tenga. This research used pre-experimental designed with one group pre-test and post-test. The result of this research found that Wordlist plus picture was effective in increasing students’ stock of vocabulary. The result showed that post-test was higher than pre-test. The mean score of pre-test was 5.00 and the mean score of post-test is 7.25 which means that the result of the post-test is higher than the pre-test. It can be stated that the students’ stock of vocabulary at the seventh grade of SMP N 2 Tenga is high in the post-test.  So, it means Wordlist plus picture is effective in increasing students’ stock of vocabulary at the seventh grade of SMP N 2 Tenga.
Информационно-образовательное пространство экономического вуза в условиях цифровизации

The paper summarizes the ideas about the educational information space, formulates its features for an economic university under the digital transformation of the economy, presents the expected positive effects and prospects for the creation and development of information and educational space based on information, communication and digital technologies, as well as possible risks and threats of digitalization for participants in the educational process.
Flavor and CP from String Theory

Modular transformations of string theory are shown to play a crucial role in the discussion of discrete flavor symmetries in the Standard Model.They include CP transformations and provide a unification of CP with traditional flavor symmetries within the framework of the "eclectic flavor" scheme.The unified flavor group is non-universal in moduli space and exhibits the phenomenon of "Local Flavor Unification", where different sectors of the theory (like quarks and leptons) can be subject to different flavor structures.
Power Grid Model Data Governance System Based on Dcloud*

With the rapid progress of the power grid and the developing of the cloud computing, the Dcloud (Dispatching and Control Cloud) consists of model data cloud platform, history data cloud platform, real-time data cloud platform and big data platform emerges. In this paper, we design and implement a power gird model data governance system based on Dcloud. At first, we introduce the background of the Dcloud and the necessary of system construction. Secondly, we present the basic structure of the stock data and incremental data governance system. At last, we present in detail the procedure and implementation of the governance system. The practical application of the system has gained a good effect.
Analisis Implementasi Pembelajaran Daring Pada Masa New Normal (Studi Kasus di Program Studi S1 Pendidikan Ekonomi FKIP UKSW Salatiga)

This study aims to analyze the implementation of online learning during the New Normal at the SWCU Salatiga FKIP Economic Education Study Program. This research was a qualitative descriptive case study involving informants, namely the Head of Study Program, Lecturers, and Students. Data collection techniques are interviews, observation, and documentation. Meanwhile, the data analysis technique used was the Miles and Huberman Interactive model, namely the stages of data collection, data reduction, data presentation, and concluding/verification. The results showed that the implementation of online learning in the SWCU Salatiga FKIP Economic Education Undergraduate Study Program had been running well and effective. The ability of lecturers in managing the online learning was good, student responses during the implementation of online learning were quite good, students and lecturers already had the facilities needed to support the implementation of the online learning. The implementation of online learning was felt to be more flexible, some students felt more active, and could hone their skills and improve the ability of students and lecturers to use and operate technology. Common obstacles were network constraints and some students found that material delivered online was sometimes difficult to understand.
Research on Teaching Practice of Academic Literature Reading for Higher Vocational Students

Reading academic literature and writing literature review are common assignments in university education.Although there are many sources describing the process of literature reading and literature review, teachers and students still face many difficulties in accomplishing these tasks.As an informal course, this course is the final report of eight years of the authors' teaching literature reading in combination with course/graduation practical activities.At present, our school has three-year and four-year students, and the teaching requirements of them are different.This paper suggests that the academic literature reading guide should be integrated into the teaching framework of literature review or become a separate course, so as to meet the cultivation needs of them.Moreover, this paper describes the teaching process of the literature reading for all freshmen majoring in logistics, and takes the causes of the bullwhip effect as a case study to demonstrate some details of critical reading.The results show that students present enough confidence, interest and sense of achievement after class to alleviate their anxiety and fear of literature reading.
Classification of Arabic fricative consonants according to their places of articulation

Many technology systems have used voice recognition applications to transcribe a speaker’s speech into text that can be used by these systems. One of the most complex tasks in speech identification is to know, which acoustic cues will be used to classify sounds. This study presents an approach for characterizing Arabic fricative consonants in two groups (sibilant and non-sibilant). From an acoustic point of view, our approach is based on the analysis of the energy distribution, in frequency bands, in a syllable of the consonant-vowel type. From a practical point of view, our technique has been implemented, in the MATLAB software, and tested on a corpus built in our laboratory. The results obtained show that the percentage energy distribution in a speech signal is a very powerful parameter in the classification of Arabic fricatives. We obtained an accuracy of 92% for non-sibilant consonants /f, χ, ɣ, ʕ, ћ, and h/, 84% for sibilants /s, sҁ, z, Ӡ and ∫/, and 89% for the whole classification rate. In comparison to other algorithms based on neural networks and support vector machines (SVM), our classification system was able to provide a higher classification rate.
UNSUPERVISED ANOMALY DETECTION IN NETWORK TRAFFIC USING DEEP AUTOENCODING GAUSSIAN MIXTURE MODEL

Unsupervised anomaly detection in high-dimensional data is an important subject of research in theoretical machine learning and applied areas. One of important applications is anomaly detection in network traffic data, which can be useful for preventing network security violations. Unsupervised anomaly detection is based on density estimation, which is problematic in high-dimensional data. To deal with the issue dimensionality, reduction is performed first, and then the density is estimated in a space of smaller dimension. Recently deep learning methods have been widely used in high-dimensional anomaly detection. One of such methods is the Deep Autoencoding Gaussian Mixture Model (DAGMM).  DAGMM is a combination of a deep autoencoder, which performs dimensionality reduction and reconstruction error estimation, and a Gaussian mixture model, which predicts if a data sample is anomalous.  We apply DAGMM to unsupervised anomaly detection in network traffic data. Testing anomaly detection system on network data presents a problem of lack of a generally accepted benchmark dataset, which would be recent, contain different types of attacks and have labels. We chose to use the UNSW-NB15 dataset, which satisfies these requirements and has been suggested as an up-to-date benchmark. A correction to the algorithm, which improves anomaly detection accuracy is proposed.
Auto Signature Verification Using Line Projection Features Combined With Different Classifiers and Selection Methods

: Signature verification plays a role in the commercial, legal and financial fields. The signature continues to be one of the most preferred types of authentication for many documents such as checks, credit card transaction receipts, and other legal documents. In this study, we propose a system for validating handwritten bank check signatures to determine whether the signature is original or forged. The proposed system includes several steps including improving the signature image quality, noise reduction, feature extraction, and analysis. The extracted features depend on the signature line and projection features. To verify signatures, different classification methods are used. The system is then trained with a set of signatures to demonstrate the validity of the proposed signature verification system. The experimental results show that the best accuracy of 100% was obtained by combining several classification methods.
An Analysis on the Research Trends of Korean Speaking Assessments

Research on speaking assessment is rapidly increasing ahead of the implementation of the speaking assessment section in the Test of Proficiency in Korean(TOPIK) in 2022. The purpose of this study is to analyze the research trends in Korean speaking assessments. A hundred and twenty-six theses from graduate theses and academic journals were collected and classified by year, type, and topic. Although the research topic has been subdivided as the research results on assessment has accumulated, there still has been found higher portion of fundamental study for developing speaking assessment tools in the research, and there shows a tendency that the research is focused on the academic purpose learner in terms of assessment subject and on the pronunciation in terms of assessment contents. In the future, there are needs not only for the development of subject-oriented assessment tools, but also for the diversification of assessment contents including spoken grammar, vocabulary, and, pragmatics, as well as pronunciation. It could be possible to combine language functions like listening and reading with the speaking assessment and verify the type of tasks that can effectively measure the learner’s language ability in a specific area. After the speaking assessment has been implemented, studies on the assessment of learning patterns or improvement of assessment tools will be actively conducted. (Kyunghee University)
LibGuides: Instructional Development: Curriculum Development

This guide provides multiple resources to fit with your professional instructional goals. Each tab includes books, ebooks, and articles vetted by the Teaching and Learning Centre of Innovation staff to ensure credible, valid, and up to date information.
Verbal Autopsy: First Steps Towards Questionnaire Reduction

Verbal Autopsy (VA) is the instrument used to collect Causes of Death (CoD) in places in which the access to health services is out of reach. It consists of a questionnaire addressed to the caregiver of the deceased and involves closed questions (CQ) about signs and symptoms prior to the decease. There is a global effort to reduce the number of questions in the questionnaire to the minimum essential information to ascertain a CoD. To this end we took two courses of action. On the one hand, the relation of the responses with respect to the CoD was considered by means of the entropy in a supervised feature subset selection (FSS) approach. On the other hand, we inspected the questions themselves by means of semantic similarity leading to an unsupervised approach based on semantic similarity (SFSS). In an attempt to assess, quantitatively, the impact of reducing the questionnaire, we assessed the use of these FSS approaches on the CoD predictive capability of a classifier. Experimental results showed that unsupervised semantic similarity feature subset selection (SFSS) approach was competitive to identify similar questions. Nevertheless, naturally, supervised FSS based on the entropy of the responses performed better for CoD prediction. To sum up, the necessity of reviewing the VA questionnaire was accompanied with quantitative evidence.