Reviewerly/exHarmony huggingface dataset:
Analysis of the implementation of a framework for teachers’ digital competence in preservice teacher training

After a conceptual analysis of numerous frameworks for teachers’ digital competence (TDC), one such framework was selected to be implemented, and the state of development of the TDC and its relationship with the introduction of computer programming have been studied. The hypothesis is that the implementation of a TDC framework within a first-year university class within an Education degree, called Computer Science and Digital Competence would help future teachers to significantly improve their TDC and, specifically, increase their knowledge of computer programming. In order to test this hypothesis, a study has been carried out with 116 students enrolled in the degree programs in Early Childhood and Primary Education at the Universidad Rey Juan Carlos. The results show that the participants were not digitally competent at the beginning of the course but that they had become significantly more so at the end of it, with a large effect size. Additionally, the prospective teachers did not understand the basic concepts of programming at the beginning of the course, but by the end their knowledge of programming concepts had improved significantly, as evidenced by the large effect size, although even by the end of the course they had not reached an adequate level of programming. The most used frequently tools among students are WhatsApp, social networks and email, but many they were not aware of tools such as podcasts or forums. In addition, the motivation of the participants to learn programming seems to be excellent.
A study on the use of folktales in Korean cultural education: Focusing on Busan local folkltales

이 연구는 외국어로서의 한국어 교육에서 문화교육을 할 때 설화를 활용하여 교육의 방안을 모색하는데 목적을 둔다. 이 연구에서는 부산 유·무형 문화자산이 내재된 스토리텔링 요소와 함께 외국어로서의 한국어 문화 교육의 관련성을 검토하여 교육 방안 및 실제를 제시하였다. 이를 위해 한국어 교육에서 설화가 가지는 교육적 가치를 밝히고 이를 바탕으로 부산 지역의 설화와 그에 대한 문화적 요소 및 수업 모형과 교육 내용의 실제를 제시하였다. 본 연구는 한국 문화 교육에서 의사소통 능력의 신장뿐만 아니라 우리 문화에 대한 외국인 유학생들의 이해와 인식의 범위를 넓히는 사고를 가질 수 있을 것으로 기대한다.The purpose of this study is to find an educational method by using folktales for cultural education in Korean language education as a foreign language. In this study, we reviewed the feasibility of Korean cultural education as a foreign language along with the storytelling elements inherent in the tangible and intangible cultural properties of Busan, and presented educational plans and practical plans. To this end, the educational value of local culture was revealed in Korean language education, and based on this, Busan folktales, cultural elements, class models, and actual educational contents were presented. Through this study, it is hoped that foreign students can broaden their understanding and awareness of our culture and improve their communication skills in Korean cultural education.
A study of developing an empowerment program for parents of students to educate their children

지금의 교육 패러다임은 학부모가 학생이었던 3차 산업사회의 그것과는 많이 다르다. 하지만 학부모는 자신의 역할을 충분히 인지하지 못한 채 자녀의 교육에 올바른 방향을 제시하지 못하고 있다. 우리나라 학부모 교육의 문제는 부모와 학부모의 역할과 역량을 명확하게 구분하지 않고 진행하고 있다는 것이다. 본 연구는 기존 연구들이 규정한 바와는 달리 부모와 학부모의 역할은 다르다는 점에 기초하여 학부모의 역할과 역량을 다시 조정하였다. 연구자는 문헌조사를 수행하였고 요구조사를 통해 얻은 정보를 델파이 기법으로 분석하여 부모의 역할과 구분되는 학부모만의 역할을 규정하였는데, 그것은 학습지도역할, 생활지도역할, 진로탐색역할로 요약된다. 본 연구의 목적은 학부모의 역할 수행 역량을 강화할 수 있도록 교육 프로그램을 개발하는 것이다. ADDIE 모형에 기반한 총 8차시의 교육 프로그램은 학부모가 어떤 방향으로 자녀의 학습을 지도해야 하는지, 학습의 성과를 내기 위해서는 어떻게 생활습관을 만들어야 하는지, 그래서 성인으로서 살아갈 삶을 어떻게 계획할 수 있는지에 대해 인지할 수 있도록 하였다. 본 연구에서 도출한 학부모 교육 프로그램은 학부모에게 어떤 특정한 기술이나 지식을 가르치는 내용은 배제하고 자녀의 초중고 12년간의 방대한 장기 프로젝트의 총괄매니저로서 방향을 설정하고 전체 과정을 운영하는 역할을 할 수 있는 기본 역량을 강화하는 내용으로 구성하였다. 이 연구는 기존의 학부모 교육 프로그램의 문제를 해결하기 위한 구체적인 연구의 시발점으로서 의미가 있을 것이다.The current educational paradigm is quite different from that of the 3rd industrial society where parents were students. However, parents of students(PoS) are not fully aware of their roles and therefore are unable to present the right direction for their children's education. Based on the premise that the roles of parents and PoS are different, contrary to previous research, this study re-adjusted the role and competence of PoS. This study conducted a literature review and needs analysis using the Delphi method in order to define the unique roles of PoS which differ from those of parents. They are summarized as the guidance of learning, the guidance of life, and the assistance in the career exploration. The purpose of this study is to develop an educational program to enhance the competence of PoS in performing their roles. The ADDIE model-based program consisting of 8 sessions is designed for PoS to learn how to assist their children in their study, in daily life habits for better academic achievement, and ultimately in getting the idea of how to plan life as an adult.
Generic and Update Multi-Document Text Summarization based on Genetic Algorithm

In this paper, we addressed the generic and update text summarization tasks of a set of documents as a combinatorial optimization problem through a genetic algorithm and unsupervised textual features. Particularly under the news domain, input documents are a set of articles of varying sizes covering the same event. The main advantage of the proposed method is that it is language-independent. The experimental results demonstrated that the method performs well for both kinds of summarization. Moreover, we calculated the heuristics for update text summarization like a benchmark to compare state-of-the-art methods.
Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization

The problem of learning threshold functions is a fundamental one in machine learning. Classical learning theory implies sample complexity of O(ξ−1 log(1/β)) (for generalization error ξ with confidence 1−β). The private version of the problem, however, is more challenging and in particular, the sample complexity must depend on the size |X| of the domain. Progress on quantifying this dependence, via lower and upper bounds, was made in a line of works over the past decade. In this paper, we finally close the gap for approximate-DP and provide a nearly tight upper bound of O(log* |X|), which matches a lower bound by Alon et al (that applies even with improper learning) and improves over a prior upper bound of O((log* |X|)1.5) by Kaplan et al. We also provide matching upper and lower bounds of Θ(2log*|X|) for the additive error of private quasi-concave optimization (a related and more general problem). Our improvement is achieved via the novel Reorder-Slice-Compute paradigm for private data analysis which we believe will have further applications.
Randomized versus Deterministic Decision Tree Size

A classic result of Nisan [SICOMP ’91] states that the deterministic decision tree *depth* complexity of every total Boolean function is at most the cube of its randomized decision tree *depth* complexity. The question whether randomness helps in significantly reducing the *size* of decision trees appears not to have been addressed. We show that the logarithm of the deterministic decision tree size complexity of every total Boolean function on n input variables is at most the fourth power of the logarithm of its bounded-error randomized decision tree size complexity, ignoring a polylogarithmic factor in the input size.
Privately Estimating a Gaussian: Efficient, Robust, and Optimal

In this work, we give efficient algorithms for privately estimating a Gaussian distribution in both pure and approximate differential privacy (DP) models with optimal dependence on the dimension in the sample complexity.
Analysis of Customer Satisfaction Level in Accredited Laboratory Testing ISO/IEC 17025 Before and After Pandemic Covid-19 Occurred: A Case Study

The massive and rapid spread of Covid-19 has prompted almost all government to implement social distancing, lockdown, and even to impose temporary closures on several sectors.Many service sectors felt a significant impact due to this pandemic, one of them is accredited laboratory testing.Maintaining customer satisfaction seems to be difficult due to the change of the way people interact each other caused by the pandemic.It was a new challenge for the laboratory to maintain their customer satisfaction.This study takes CMPFA Laboratory as the object of case study which aims to analyze customer satisfaction in Accredited laboratory testing industry before and after pandemic Covid-19 occurred.Through this study, it is found that CMPFA Laboratory testing succeed in increasing customer satisfaction in every dimension of the service.It happened due to the right strategy that implemented by CMPFA during pandemic Covid-19.Descriptive statistic was used to understand and analyze the data.Therefore, this study helps the laboratories to evaluate and understand the effect of pandemic on customer satisfaction and the strategy that implemented to maintain their satisfaction during pandemic Covid-19.
ATM Vehicle Location Privacy and Security Using End -To - End Quantum Key Crypto System

Internet-of-things (IoT) is the latest revolution in electronic industry after internet. Smart appliances, portable computing devices, mobile phones and handheld system dominates in IoT, because a large portion of world population use it. Major applications, mainly financial, e-commerce, information security and sensitive data-communication need special attention in terms of security. Device authentication, encryption, and key distribution are of vital importance to any Internet-of-Things (IoT) systems, such as the new smart city infrastructures. This is due to the concern that attackers could easily exploitthe lack of strong security in IoT devices to gain unauthorized access to the system or to hijack IoT devices to perform denial-of-service attacks on other networks. This creates a strong requirement for providing security solutions into these devices. Key Word: IOT; Communication; Data; Application; Server.
nline Tutoring System "Kak Eto Rabotaet: Algoritmy"

The article describes the features and architecture of intelligent online tutor "Kak eto rabotaet: Algoritmy", which is based on modelling subject-domain concepts on the comprehension level of Bloom's taxonomy. It is designed for learning basic algorithmic structures — sequences, selection statements, and loops — using the task of building an execution trace of the given algorithm. The tutor analyzes the student's solution step by step and displays explanatory messages for every error right after it happens; the error messages include the violated domain rules and their consequences in the specific situation. It can also hint the next correct step with textual explanation of why it is correct. It allows reliable learning of new concepts without control of each exercise from the human teachers which significantly increases the number of exercises learners can perform during a course. A control-flow diagram of the algorithm can be visualized; when the student makes an error, it is displayed with a red line on the diagram. The tutor is based on the formal model of subject domain implement using Apache Jena rules over RDF graph. Teachers can create learning problems using block-based interface and them, receiving links that can be sent to their students. Students can also use the tutor on their own to explore examples of interest to them.
Effectiveness of Blog Media to Increase Students' Short Story Writing Skill

Writing short story is a basic competency that is still taught in tenth grade NMC Vocational High School Malang. This research is based on the low ability of tenth grade NMC Vocational High School Malang in writing short story. This is because they are not used to expressing their feelings, thoughts, imagination, and are not able to connect between the imaginary world and the real world in short story. Besides that, the selection of strategies and media that is not appropriate in short story writing skills. Based on the obstacles experienced by students, researcher provided solutions to these problems with blog media to improve and develop short story writing skills for tenth grade NMC Vocational High School Malang. Blog media can improve the students’ short story writing skill because it gave ideas for the students to find and start short story writing activities. The results of the research that has been done show that there is an increase in writing short story. It can be proved by the improvement of student’s ability in the process and result of the students writing short story.
Several families of irreducible constacyclic and cyclic codes

In this paper, several families of irreducible constacyclic codes over finite fields and their duals are studied. The weight distributions of these irreducible constacyclic codes and the parameters of their duals are settled. Several families of irreducible constacyclic codes with a few weights and several families of optimal constacyclic codes are constructed. As by-products, a family of $$[2n, (n-1)/2, d \ge 2(\sqrt{n}+1)]$$ irreducible cyclic codes over $${\textrm{GF}}(q)$$ and a family of $$[(q-1)n, (n-1)/2, d \ge (q-1)(\sqrt{n}+1)]$$ irreducible cyclic codes over $${\textrm{GF}}(q)$$ are presented, where n is a prime such that $${\textrm{ord}}_{2n}(q)=(n-1)/2$$ and $${\textrm{ord}}_{(q-1)n}(q)=(n-1)/2$$ , respectively. The results in this paper complement earlier works on irreducible constacyclic and cyclic codes over finite fields.
Visual Arts Education literacy in university contexts

In a digital world, which is evolving more and more rapidly towards the culture of the visual, acquiring visual literacy tools can be essential, especially for the professional future of university students. Art education manages to enhance the development of visual learning and motivate the student in their process of analysis, discovery, understanding, assessment and apprehension of the visual arts. The use of the image as a teaching tool in the classroom favours the progress of critical and reflective observation and promotes expressive skills. The objective of this work was to present experiences on visual learning in Art Education in university contexts. A systematic review of the literature was carried out during 1990 to 2020. The results allowed us to recognize the main author agents, their research tendencies, and reveal certain gaps in critical knowledge. Research findings and trends converge on the advantages of incorporating visual resources in higher education, emphasizing the importance of sensory approaches to support learning.
Implementing Deep Learning in E-Commerce Platforms for Fraud Detection and Management

E-Commerce has quickly become an essential facet of business transactions within the past decade as online platforms have paved the way towards convenient shopping that does not require the consumer to invest time and resources in traveling to the physical location of businesses.Along with the utilization of digital platforms to exchange goods, this also introduced the integration of digital payment methods such as e-wallets and credit cards.The use of fraud detection systems seeks to accurately filter and detect any malicious transactions that use unverified or illegitimate payment details.This paper aims to conduct a systematic literature review that will analyze collected academic papers from 2017 to 2021, which focus and relate to various deep learning techniques used in model creation.The findings from the systematic review will be used to develop and conceptualize a system that will utilize an efficient deeplearning method towards accurate fraud detection.
Illicit financial flows and Africa’s institutional architecture: the conceptual framework

At the seventh joint annual meetings of the United Nations Economic Commission for Africa (ECA); the African Ministers of Finance, Planning and Economic Development and the African Union Conference of Ministers of Economy and Finance, the ministers pledged to take the necessary coordinated action nationally, regionally and continentally to strengthen our economic governance institutions and machinery, focusing especially on tax administration, contract negotiations, and trade-related financial leakages and to engage with the international community, in order to highlight our concerns regarding illicit transfers, including the question of tax havens.
Executive Summary

This premier Economic Governance Report (EGR I) assesses the institutional architecture pledged by African governments for blocking illicit financial flows (IFFs) and recommends initiatives to strengthen it. The report’s findings indicate that IFFs continue to thrive, though African countries have tried to establish dedicated institutional frameworks for combatting them in the main channels of trade, investments and financial flows. The IFFs include corruption, money laundering, trade mis-invoicing to move money illicitly and tax fraud (including corporate tax dodging). The report encourages more inter-agency collaboration, coordinated reporting, the removal of duplicated and competing mandates, and consistent political support for institutional reforms to combat IFFs.
GAIA-Universe: Everything is Super-Netify

Pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task requirements. we focus on two fundamental perception tasks (object detection and semantic segmentation) and present a complete and flexible system named GAIA-Universe(GAIA), which could automatically and efficiently give birth to customized solutions according to heterogeneous downstream needs through data union and super-net training. GAIA is capable of providing powerful pre-trained weights and searching models that conform to downstream demands such as hardware constraints, computation constraints, specified data domains, and telling relevant data for practitioners who have very few datapoints on their tasks. With GAIA, we achieve promising results on COCO, Objects365, Open Images, BDD100k, and UODB which is a collection of datasets including KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an example, GAIA is able to efficiently produce models covering a wide range of latency from 16ms to 53ms, and yields AP from 38.2 to 46.5 without whistles and bells. GAIA is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GAIA-vision</uri> .
Health Assurance: AI Model Monitoring Platform

Businesses are increasingly reliant on Machine Learning models to manage user experiences. It becomes important to not only focus on building robust and state-of-the-art models but also continuously monitor and evaluate them. Continuous monitoring enables the AI team to ensure the right frequency of model training and pro-actively investigate erroneous patterns and predictions, before it has a wider business impact. A robust and effective monitoring system is thus needed to ensure business and engineering teams are aware of model performance and any data anomalies which could impact downstream model accuracy. In this paper, we present our Health Assurance model monitoring solution. Currently, the system serves the health monitoring needs of more than 250 models across 11 AI verticals with an average anomaly detection precision of 60%.
MFR Working Mode Recognition Based on CNN-BILSTM-SoftAttention Model

Accurate identification of MFR working mode recognition is an essential prerequisite for target threat assessment. To solve the problem of lower recognition rate of radar pulse signals with overlapping parameters, a hybrid recognition model based on CNN-BILSTM-SoftAttention is proposed. Firstly, We utilize the combined CPI parameters to describe pluse stream and capture local characteristics with CNN. Then, the BILSTM Network is used to analyze the timing regularity of radar pulse sequences, and to discover the inter-class rule between different working modes and the intra-class rule of the same working mode. Finally, combined with the attention mechanism model, we can distinguish different working mode by assigning higher weights to parameters with overlapping. Through simulation analysis, the proposed algorithm is compared with SVM, CNN, CNN_LSTM method, the accuracy of model can reach 92.48% in the strong noise environment, increasing by 20%. The results show that the proposed method has better classification ability and higher performance than existing work pattern classification methods.
Analysis of Deep Pre-trained Models for Computer Vision Applications: Dog Breed Classification

Machine perception is one of the most lucrative domains in the modern landscape and one of the most challenging tasks embodied by this domain is analyzing and interpreting images. Image recognition has seen several advancements over the years such as the introduction of pre-trained models that have expunged the complexity associated with developing high performance deep neural networks. In this paper, we have proposed several eminent pre-trained models that have the ability to categorize dogs as per their breeds. The main objective of this paper is to present a fair comparison of the proposed models and establish the nonpareil model on the basis of comparison metrics such as accuracy, validation accuracy, time requirements, precision and recall scores of the model. It was observed that ResNet 152V2 performed the best with respect to the accuracy, precision and recall scores, Inception-ResNet gave the best validation accuracy and NASNet-Mobile had the highest efficiency albeit with inferior performance in accordance with the other evaluation metrics.
Human Versus Automatic Evaluation of NMT for Low-Resource Indian Language

Machine Translation (MT) is an important application of natural language processing that converts a source language to a target language automatically with the injection of a parallel corpus. Researchers from academia and industry are currently very active in designing high-performance translation systems. The MT system has witnessed a big paradigm shift, and a recent neural network-based translation system, i.e., the Neural Machine Translation system, has almost replaced the statistical Machine Translation (SMT) system. This paper mainly focused on two things: firstly, the design of the NMT model and then evaluating its performance with human and automatic metrics on a low-resource Bengali-to-English language pair. Secondly, we have checked the performance of two popular online translators: Google Translate and Bing. We have evaluated the performance of these two translators with the most popular and widely used automatic evaluation metrics, Bilingual Evaluation Understudy (BLEU) and Word Error Rate (WER). BLEU’s evaluation process is primarily based on the n-gram matching approach; hence, sometimes scores are not reliable. WER computes the Levenshtein distance between hypothesis words and reference words. Human evaluation is considered to be the best in MT evaluation. Hence, we have also computed the translation score with the gold-standard human evaluation metric. This research will be helpful as a part in evaluating the performance of various MT engines, especially in domain-specific low-resource language pairs, and also the performance of MT engines can be judged with human evaluators. Also, these human evaluators are used to verify the accuracy of available automatic evaluation metrics.