Reviewerly/exHarmony huggingface dataset:
Parents&#039; Perceptions and Awareness about Reading Aloud to Preschool Children

Reading aloud is one of the most important activities that parents and teachers can engage with children. The purpose of this study is to understand parents’ perceptions and awareness regarding importance of regular read-aloud activities with their children. To achieve this goal, we collected qualitative and quantitative evidence about the factors associated with parents’ reading aloud habits, perceptions and awareness. Survey data was collected from a total of 487 parents (N= 487) in Turkey. Data collection instrument was adapted to Turkish from ReadAloud Project’s (2020) survey instrument. The original survey was conducted in the United States to assess parents’ perceptions and awareness regarding read-aloud activities at home. Variables that are significantly associated with read aloud activities of parents were presented and hindering factors were discussed.
The Use of Mobile to Boost Iranian EFL Learners’ Grammar Knowledge: The Case of Grammar Learning Application in Focus

Using a mobile device in education opened a new way for the teaching and learning process.  One aspect of the English language which has been subject to investigation in mobile-assisted language learning (MALL) is grammar. The main purpose of the present study was to probe the efficacy of mobile applications for grammar learning. In so doing, a quasi-experimental research design was adopted to investigate the effect of grammar learning application on EFL learners' grammatical knowledge. For this purpose, 50 Iranian female Intermediate learners were recruited to participate in this study. They were assigned to two groups; control and experimental, consisted of 25 learners in each group. To assess the learners' grammatical knowledge before specific treatment, a pretest of grammar was administered. After the pretest, in each session of the treatment, the participants in the experimental group were taught grammar through Grammar Learning Application on their mobile phones, and in the control group, a conventional method was employed for teaching grammar. After 14 sessions, to discover the effect of treatment both groups participated in another grammar test as the posttest. Finally, an independent samples t-test was run on obtained data. The results indicated that participants in the experimental group performed significantly better in the posttest, demonstrating the effectiveness of the mobile application used in this study on learning grammar. The findings are beneficial for syllabus designers, material developers, and EFL teachers to use mobile applications in teaching grammar.
Development of Online Master's Degree Course in Education Digitalization Conditions: On BSU Experience

This article presents a study of the development of the online master’s degree course at Belgorod National Research University (BSU) in a context of education digitalization in Russia. It demonstrates the successful introduction of the frameworks of intensity, scalability, and a modular approach to learning, asynchrony, interactivity, automation, openness, and adaptability. We consider the online master’s course as an open information and learning environment that is designed for effective management and monitoring of the educational process. The online master’s degree course at BSU aims to create a flexible and adaptive educational system that meets the needs of the digital economy and ensures the fullest use of the didactic potential of digital technologies. It is proved that to ensure high-quality assimilation of the master’s degree program, the managers of the educational process need to provide pedagogical support to learners. The basis for effective development of online master’s degree course is in availability of online courses (MOOC) placed on online platforms; formation of students’ ICT competence; application of interactive teaching methods; motivational readiness for self-education; readiness of teachers to interact online; available on-line maintenance in the educational process and the possibility of computer networks/Wi-Fi. According to the study, the conditions for the effective development of online master’s degree course in higher education allow: ensuring the personalization of the educational process; involving each student in learning activities; motivating learners at all stages of the learning process; providing the continuous personalized monitoring of students’ learning achievements, giving the prompt feedback to students; ensuring the availability of educational programs for learners who live in remote areas, as well as those with disabilities.&#x0D; Keywords: continuing education digitalization, online learning, e-learning, online master’s degree course, cyberculture, MOOC
Predictability Concentrates in Bad Times. And so Does Disagreement

Within a standard risk-based asset pricing framework with rational expectations, realized returns have two components: Predictable risk premiums and unpredictable shocks. In bad times, the price of risk increases. Hence, the predictable fraction of returns – and predictability – increases. “Disagreement” (dispersion in analyst forecasts) also intensifies in bad times if (i) analysts report (close to) risk-neutral expectations weighted by state prices, which become more volatile, or (ii) dividend volatility changes with the price of risk – for example, because consumption volatility changes. In both cases, individual analysts produce unbiased forecasts based on partial information.
THE INFLUENCE OF COLLABORATIVE WRITING TOWARDS STUDENTS’ DESCRIPTIVE WRITING ABILITY

The research reports on the Influence of Collaborative Writing Towards Student’s Descriptive Writing Ability. Collaborative writing technique was applied in this research to improve the student’s descriptive writing ability. The aims of the research were to know and to describe student’s descriptive writing ability and to know and to describe whether there is influence of Collaborative writing towards students’ descriptive writing ability. The research was conducted by using a quasi-experimental design with pre test and post test. The population of this research are the students at the first semester of the tenth class at SMA Adiguna Bandar Lampung that consisted of 49 students in 2 classes. The sample of this research were two classes, they were as control class and experimental class. Control class was taught by Individual writing while experimental class was taught by Collaborative writing. In getting the data, the writer used writing test. The result showed that based on the calculation, the writer got that  = 5.54 and  = 1.68 and 2.42. It means that . So, Ho is rejected and Ha is accepted. From the result above, the writer states that there is significance influence of collaborative writing towards students’ descriptive writing ability of the tenth class of the first semester at SMA Adiguna Bandar Lampung.
Amazon impersonators steal from people they are claiming to help

Amazon impersonators are cashing in on the company's popularity -- and using it to steal from people they are claiming to help.
Design and Implementation of Artificial Human Hand Model Based on Eye Blink Detection

This paper proposes two paradigms for controlling a robotic arm by integrating eye blink and IOT based techniques. The purpose of our study is to develop a feasible paradigm for helping disabled persons with their every-day needs.“Artificial Human Hand Model (AHHM)” which can precisely follow the real movements of human hand by means of real human eye blinks and IOT based methods. Simple algorithms were implemented for detecting electrophysiological signals like eye saccades, blinking and eye closure events. Interactive resources to integrate myRIO into our project increases efficiency of this concept.
Analysis of Knowledge Management in Information Systems Faculty of Da'wah and Communication Studies UIN Syarif Hidayatullah Jakarta based on Usability Evaluation

Information systems are media used to channel information. each campus has a medium for channeling information. One of them is the UIN Syarif Hidayatullah Jakarta campus. One of the information systems owned by Uin Syarif Hidayatullah Jakrta is the information system at the Faculty of Da'wah and Communication Studies. In this system there are several parts, namely the homepage, accreditation, academic, student affairs, library and others. The purpose of this study is to evaluate Usability to improve user satisfaction in using this information system. The research method used is literature study, field studies and SUMI (Software Usability Measurement Inventory) questionnaire. The questionnaire was conducted on 20 respondents namely 10 lecturers and 10 students of the da'wah and communication faculty. The questionnaire was carried out in 3 categories, namely effectiveness, efficiency and satisfaction with the linker agreed, disagreed and did not know. The results of the SUMI questionnaire calculation of the information system are 80, 75,75,70,70,87.5. The usability evaluation results are above average, meaning that usability in the Da'wah and Communication faculty information system has been going well.
Deep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning

Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep architecture with domain-general and domain-specific representations across domains for deep unsupervised domain adaptation. Also, we apply low-rank representation learning to reduce source and target domains discrepancy. The low-rank constraint can uncover more related information between domains and it can transfer more relevant knowledge from the source domain to the target domain. The DALRRL guarantees to minimize marginal and conditional distributions difference between the source and target domains. The experimental results conducted on two benchmark domain adaptation datasets demonstrate the effectiveness of our method in image classification tasks.
ENSEÑANDO LA HISTORIA LOCAL DE CÓRDOBA A TRAVÉS DE FUENTES DOCUMENTALES. EL REPOSITORIO DIGITAL PARES Y LAS RESPUESTAS DE ENSENADA

This text shows the results of an innovative activity implemented in the Social Sciences Didacticssubject in the Degree in Primary Education at the University of Cordoba, which main objective was toimprove the quality of science content teaching Social. This practice was based on the work ofhistorical documentary sources through digital platforms for teaching, in addition to the study andlearning of local history. The documentation selected for this project was the Ensenada Cadastre andits general responses from the different towns in the province of Cordoba (1752), which can beconsulted in the digital repository PARES (Portal de Archivos Estatales Espanoles, Portal of Spanish1 Este articulo ha sido resultado de la investigacion que se esta llevando a cabo en el seno del Grupo “INDICES. Investigacionen Didactica de las Ciencias Experimentales y Sociales” (SEJ-544), con sede en el Dpto. de Didacticas Especificas en laFacultad de Ciencias de las Educacion, en la Universidad de Cordoba (Espana), el cual esta financiado por dicha universidad.State Archives in English). After the activity and through a Google Forms form students were askedto reflect on the practice and the results obtained were especially satisfactory, thanks to the study oflocal history and the environment, the exploitation of instruments different from the usual ones andthe management of ICT resources.
ANOMALY DETECTION WITH MACHINE LEARNING ALGORITHMS IN CROWDED SCENES IN UMN ANOMALY DATASET

In recent years, keeping security under control in crowded environments has been a common problem. Camera systems are used to ensure security in crowded environments. When the video images recorded by the cameras are examined, it is checked whether there is any dangerous and unusual movement in the environment and appropriate measures are developed. Human behavior must be modelled to detect normal and abnormal behaviors in crowded scenes. In this study, crowded scenes in three different environments in the UMN Anomaly Data Set were examined. Random Forest, Support Vector Machines and k Nearest Neighbour algorithms, which are one of the machine learning methods in these three different environments, are applied. As a result of algorithms applied, the abnormal behaviour (like escape) of people in a crowded scene has been detected. Performance criteria such as accuracy, sensitivity, precision and F1 score of these applied algorithms were calculated and compared.
Keynote Talk #1: Intelligent Program Analysis for Automated Software Defect Detection and Fixing

Detecting and fixing software defects are important in developing reliable and high-quality software systems. Software defects are so prevalent and detrimental that they cost billion dollars annually. In this talk, I will present the latest, advanced research in leveraging intelligent program analysis and combining with machine learning to automatically detect and fix software defects and vulnerabilities. I also present the challenges that the current state-of-the-art approaches in the field has been facing and the solutions in the new directions of intelligent program analysis and program representation learning. Finally, I will conclude the talk with the open challenges in the next steps in that direction.
IMPLEMENTATION OF NIGERIAN INDIGENOUS FOOD IMAGE RECOGNITION SYSTEM

Food Image Recognition is one of the promising applications of visual object recognition in computer vision.A couple of Nigerian indigenous mealsare on the verge of going extinct in the near future, it is imperative to work towards preserving the knowledge of these indigenous meals.The aim of this study is to implement a Nigerian Indigenous Food Image Recognition System.In this study, a dataset consisting of12 categories and 400 images of indigenous Nigerian meals was downloaded from Kaggle.com, the images were pre-processed using the median filter and Gray-Level co-occurrence matrix, then, the features were extracted and classified using the Convolutional Neural Network algorithm.A 73% level of accuracy of correct recognition was achieved with the model.Based on our findings, Convolutional Neural Network has a higher level of accuracy than other traditional algorithms in automatically segmenting and extracting features and providing accurate classification.
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

Temporal networks serve as abstractions of many real-world dynamic systems. These networks typically evolve according to certain laws, such as the law of triadic closure, which is universal in social networks. Inductive representation learning of temporal networks should be able to capture such laws and further be applied to systems that follow the same laws but have not been unseen during the training stage. Previous works in this area depend on either network node identities or rich edge attributes and typically fail to extract these laws. Here, we propose {\em Causal Anonymous Walks (CAWs)} to inductively represent a temporal network. CAWs are extracted by temporal random walks and work as automatic retrieval of temporal network motifs to represent network dynamics while avoiding the time-consuming selection and counting of those motifs. CAWs adopt a novel anonymization strategy that replaces node identities with the hitting counts of the nodes based on a set of sampled walks to keep the method inductive, and simultaneously establish the correlation between motifs. We further propose a neural-network model CAW-N to encode CAWs, and pair it with a CAW sampling strategy with constant memory and time cost to support online training and inference. CAW-N is evaluated to predict links over 6 real temporal networks and uniformly outperforms previous SOTA methods by averaged 15\% AUC gain in the inductive setting. CAW-N also outperforms previous methods in 5 out of the 6 networks in the transductive setting.
미국 대학교 한국어 학습자의 동기저하 요인 연구 -초급 비한국계 KFL 한국어 학습자를 대상으로-

The purpose of this study was to explore non-Korean elementary level learners’ demotivating factors for Korean language learning at a liberal arts college in the United States. Based on previous research, this study administered a survey focused on eight key variables: 1) teacher, 2) learning content, 3) exam, 4) value of Korean language learning, 5) attitude towards Korea and Korean culture, 6) difficulties of Korean language learning, 7) lack of confidence, 8) learning environment. IBM SPSS statistics was used for data analysis. The analysis of survey responses showed that ‘difficulties of Korean language learning’ was the most powerful demotivating factor. The negative value which students put on Korean language learning was also attributed to demotivation. Students’ demotivation varied among demographic variables. Students’ answers to open-ended questions also supported most of the identified demotivating factors. The study concludes with possible implications for Korean language programs in the U.S. universities or institutions in a similar environment. (Yonsei University)
Generic Convoying Functionality for Autonomous Vehicles in Unstructured Outdoor Environments.

Autonomously following a leading vehicle is a major step towards fully autonomous vehicles. The contribution of this work consists in the development, implementation, and validation of two following modes: 1) Exact following: accurate compliance with the reference path. 2) Flexible following: tolerate deviation from the reference path in order to avoid obstacles. The proposed method can easily be integrated into existing frameworks for autonomous vehicles. Therefore our approach is flexible enough to be applied to a large variety of different vehicles. To demonstrate the feasibility of our approach an experimental validation is carried out on two autonomous vehicles with major differences in kinematics, weight, and size: A cross-country wheelchair and an off-road truck. Both exact and flexible following have been successfully demonstrated in unstructured outdoor environments.
Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.
PENINGKATAN HASIL BELAJAR DENGAN METODE BERMAIN PERAN PADA MATA PELAJARAN BAHASA INGGRIS DI KELAS IV MI THORIQOTUL FALAH TALANGKEMBAR TAHUN PELAJARAN 2017/2018

English is an international language that is important to learn because English can be used to communicate with any nation. Along with the above, learning English needs to be introduced to students as early as possible. The limited allocation of time in learning English has an impact on the lack of level of student understanding and enthusiasm of students in English subjects, so that the achievement of learning outcomes is also far from the minimum standards of completeness set by the institution. This study aims to increase the level of activity and enthusiasm of students in learning English, improve student learning outcomes in English subjects. This research method uses classroom action research (Action Classroom Research) with a learning approach using a cycle and described using descriptive analysis methods. Qualitatively, namely by observing to obtain student attitudes data during the process of teaching and learning activities (KBM) and teachers in applying methods in learning. While quantitative is to process data in the form of numbers from formative test results in each cycle. Descriptive analysis is intended to explain the recapitulation of qualitative data obtained during KBM. The research subjects were class IV MI Thoriqotul Falah Talangkembar District Montong Tuban Regency, with the number of students 19 children consisting of 10 female students and 9 male students with heterogeneous student abilities. From the results of this study it can be concluded that, the use of role playing methods has an effect on improving student learning outcomes. Recapitulation of the acquisition of the class average of 67.5 at the pre cycle stage, 75.68 in the first cycle, and 77.37 in the second cycle. The number of students completing learning increases each cycle, initially 9 students at the pre-cycle stage, 12 students in the first cycle, and 17 students in the second cycle. Classical completeness also reached the target height of 89.50% at the end of the second cycle, from 43.36% at the beginning of the cycle and 75.68% at the cycle I stage.
Modelling the past. Logics, semantics and applications of neural computing in archaeology

The study of complex archaeological systems through the new Artificial Intelligence and Natural and Neural Computing is a research project which evaluates the historical meaning of the relationships between records of the past as an essentially human construction. It repeats a strong position of Analytical Archaeology, but updates it on the basis of the progress which neurosciences and physics have made in simulating the principles which regulate memory, orientation, classification and mapping of reality. Modelling and simulating the contexts of the past in integrated, parallel, distributed processing through machine learning methods, must make use of a precise encoding of the documents. It takes on an important role in empirical research only when the results produced become the hyper-surface of a network membrane to continue, update, refine or open the analysis itself. After some 30 years of such theoretical, analytical and experimental research, logics, semantics and applications of neural computing maintain their distinct value as a new theoretical approach for the study of dynamic and systemic cultural complexity. They provide a new analytical paradigm for computational modelling in archaeology and an advanced computational method for pattern recognition in archaeometry.
Multi-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning

Strong authentication is crucial as wireless networks become more widespread and relied upon.The robust physical layer features produced by advanced communication networks lend themselves to accomplishing physical layer authentication by using channel state information (CSI).The use of deep learning with neural networks is well suited for classification tasks and can further the goal of enhancing physical layer security.To that end, we propose a semi-supervised generative adversarial network to differentiate between legitimate and malicious transmitters and accurately identify devices for authentication across a range of signal to noise ratio conditions.Our system leverages multiple input multiple output CSI across orthogonal frequency division multiplexing subcarriers using a small percentage of labeled training data.