Reviewerly/exHarmony huggingface dataset:
Automatic Hate Speech Notifier Using Machine Learning Techniques

The Internet which was once considered to be a part of only high class society is now within the reach of every section of society. Due to which the number of online platform users has increased drastically. According to a survey the number of social media users has reached 1 billion. Social media platforms provide us an online platform to share our thoughts, ideas , perspectives to a larger section of a society at an instant of time with a touch of a finger. But along with these benefits it also gives rise to certain problems and one of those problems which took a steady increase in the recent decade is online hate speech. It is one of the major problems that is faced by countless people throughout the world, because of which research papers are published every year to spread awareness about online hate speech and what are the countermeasures we can take to put an end to it. In our research paper we have proposed a model of a social networking site that is capable of predicting offensive or hateful content whenever a user creates or updates a post. For creating the machine learning model we use different machine learning algorithms like passive-aggressive classifier, SVM, Naive Bayes etc and use the most accurate one for our model.
Cybersecurity Challenges and Governance Issues in the Cyberspace 'When Stronger Passwords Are not Enough: Governing Cyberspace in Contemporary African Nations' Case Study: Can South Africa and Nigeria Secure Cyberspace without a Lock?

Policing Cyberspace in South Africa and Nigeria: Challenges and OpportunitiesThe banking sector, as a centralized aspect of contemporary digital finance systems, is a vulnerable target. Not surprisingly, recent data indicate that cyberattacks have augmented by 300% over the past three years in Africa (Open Access Government). As per Kris Budnik and Kent Kirkwood, leaders in Cybersecurity services for PwC South Africa, this dramatic rise roots in “the tremendous value of information financial services they hold” (Budnik and Kirkwood). This trend was facilitated by the integration of “internet-based” transactions in the banking system, and ramping up internet penetration rates in countries like South Africa and Nigeria (Open Access Government). According to a 2020 McKinsey report, banks seek to expand market share by advertising the accessibility and popularity of their online services. Banks offer Mobile Financial Services (MFS), which include a range of services from payments, loans, investments, to insurance (Kola-Oyeneyin et al.). As evidenced in Exhibit 1, after South Africa reached the 100 million threshold, mobile money deployments have comprised the largest share of accounts (Kola-Oyeneyin et al.). The complexity of cyberspace and the sophistication of criminal actors creates numerous opportunities for potential abuse. As a result, it is in a society’s best interests to ensure that effective regulations are in place.When a cybercrime is committed, financial institutions suffer theft of funds, weakening of intellectual property, and loss of other valuable assets along with legitimacy. To combat cybercrime, emerging cashless economies need to invest in education and technology to tailor cybersecurity strategies to the local, regional, and international contexts. Since 2014, South Africa and Nigeria have taken a regional lead in developing and implementing financial technology. Unsurprisingly, South Africa is now considered “a new cybercrime harbor” (Open Access Government). Both countries are highly vulnerable to cyberattacks, which creates an interesting trend to monitor.
Ponzi schemes and its prevention: insights from Malaysia / Eley Suzana Kasim … [et al.]

A Ponzi scheme is a fraudulent investing scheme which promises high rates of return with little risk to investors. The scheme has led to a substantial amount of financial leakages in recent years. Despite warnings by the law enforcement agencies and regulators, many have unfortunately fallen victim. Hence, the current measures are limited in preventing the fraudulent schemes. Therefore, the present study aimed to identify the influencing factors for joining Ponzi schemes by examining the modus operandi used, profile of victims and fraudsters and to investigate the current measures in preventing Ponzi schemes in Malaysia. This study employed a qualitative methodology with semi-structured interviews and document reviews to collect data. Interviews involving eight interviewees with regulators, enforcers, and victims revealed that prevention of Ponzi schemes should be addressed holistically by examining the modus operandi used, profile of victims and fraudsters. Findings further indicated that the prevention measures are led by three core elements consisting of education, regulations and enforcement. Additionally, education is the best approach to ensure an effective preventive strategy which needs to be supported by a specific regulations on Ponzi schemes. This study provides recent evidence on Ponzi schemes and valuable insights for future development of preventive measures.
Tinjauan Fiqh Muamalah Terhadap Kegiatan Transaksi Jual Beli Aset Kripto Pada Blockchain

The practice of buying and selling crypto assets occurs a lot in the community through an online platform in the form of a blockchain as a place for transactions. This study aims to, firstly find out the nature of crypto assets in the current concept and context, and secondly to find out the fiqh muamalah review of the activities of buying and selling crypto assets on the blockchain. The research method used in this thesis is qualitative with a normative approach and case studies. The type of research data used is library research with secondary data sources in the form of people and notes, such as books, reports, bulletins, and magazines that are documentation. Documentation data collection techniques and library research. The results of the study show that, firstly, in the current concept and context, crypto assets are used as payment instruments, investments, and trades between crypto assets. Second, in the review of fiqh muamalah that in the practice of buying and selling crypto assets on the blockchain, the eleven important Bitcoin points set by the MUI are that it is permissible for those who are willing to use and acknowledge it, but as an investment tool the law is haram.
미래 초등 과학 교과용도서 방향성 도출을 위한 델파이 연구

The purpose of this study is to provide basic data to derive the direction of future elementary science curriculum books through delphi study of science education experts. To this end, a panel of 18 experts was formed and two delphi investigations were conducted. By analyzing the mean, median, and CVR values for each item in the Delphi survey, the priorities of changes in science education for the future society and the validity of each item's implementation method were verified. In addition, by synthesizing this, the direction of future elementary science textbooks was derived. As a result, the future elementary science textbook can be 'fun and interesting science study', 'exploration performance-oriented learning' and ‘science that enjoys and participates even as an adult’. It should be developed to prepare for culture. For this, it is necessary to use materials in real life, and it is necessary to present an experiment that stimulates curiosity and easy access using materials and preparations with high accessibility. In addition, it is necessary to develop a textbook for learning that science is a discipline that is highly connected with real life, and that it is also related to future career paths.
INFLUENCE OF CORNELL NOTES STRATEGY TOWARDS STUDENTS’ READING ABILITY AT THE ELEVENTH CLASS OF SMA ADIGUNA BANDAR LAMPUNG

Reading is one of four important language skills that should be learned by students when learning English. Many students have difficulties in reading ability, because students have difficulties in comperhending the text, content and the elements of the text when they learn reading. In this case, the writer used Cornell Notes Strategy to help students comprehending the text easily. In this research, the writer used experimental method. The population in this research as the eleventh class of SMA Adiguna Bandar Lampung consists of 51 students. The sample of this research were two classes, the first class as the experiment class (XI IPA) consist of 26 students and the second class as the control class (XI IPS) consist 25 students. In taking the sample the writer used cluster sampling technique. In collecting the data writer used reading test in the form of multiple choice test consist of 4 options. Based on the data analyzing about reading ability, the writer found that the average score of students’ reading ability in the experimental class (70,62) was higher than the average score of students’ reading ability in control class (59,04). After counting the data by using  was higher than,  (3.13 > 1.68 > 2.42) Finallly, the writer concluded that there is a significant influence of teaching reading through Cornell Notes Strategy could increase the students’ reading ability at the Eleventh Class of SMA Adiguna Bandar Lampung in 2019/2020.
Global Solar Radiation Estimation using Duration of Sunshine Observation Data in Changwon Region

Solar energy is one of the renewable energy resources with the greatest potential and could be the world's largest source of electricity by 2050. Accurate quantitative data of the variation of solar radiation reaching the earth surface, together with relevant meteorological parameters are essential requirements for conducting a wide range of scientific studies. The aim of this study is to estimate global solar radiation using duration of sunshine and other observation data. The sunshine-based model performed well in terms of their coefficient of determination with R2 = 97.21% given by the linear Angstrom-Prescott (1940) model in Changwon. The results indicate that the calculated clearness index and relative duration of sunshine shows excellent data.
Machine Learning and Speech Analysis Framework for Protecting Children against Harmful Online Content

Nowadays, it becomes significant to protect children from the detrimentalinfluences of offensive online things like hateful messages, violence content, bullying and pornography that are now broadly spreading on Internet. Thus, Machine Learning (ML) algorithms can diagnose such content and then remove them. This paper depicts a unique ML model to detect the abusive content via speech recognition technique, especially for children. ML provides efficient tools for the assessment of web content and filtering of offensive material. The performance outcomes of developed model validates its accuracy and effectiveness.With the advancement of several methodologies, machine-learning approach proves its dependency on the quality of information that involves diversity and representativeness. ML can only handle such dataset efficiently if the developed algorithm has the potential to deal with poor-quality training datasets.The human moderation experts needed to interpret the contextually hard content as per the current scenario or even in foreseeable future in spite of the progress in ML technique. The key research activities under online content domain using ML approaches because of the massive volume of information for researching/training models. This developed ML algorithm also shows its effectiveness to deliver accurate outputs for the cases of limited training data or experiencing issues in understanding them.
Analysis of the content of the applied fifth grade chemistry book according to life skills

The current research aims to analyze the chemistry books for the preparatory stage according tolife skills for the academic year (2019-2020) and to know the life skills contained in the chemistrybooks for the preparatory stage through the following two questions:1-What life skills should beavailable in the content of chemistry books prescribed for middle school students approved by theMinistry of Education, General Directorate of Curricula in the academic year(2019-2020).2-What is the percentage of life skills included in the content of chemistry textbooks prescribedfor middle school students approved by the Ministry of Education, General Directorate ofCurricula in the academic year (2019-2020).
Global quantum communication with untrusted space-based networks

Most efforts in space-based global quantum communications focus on trusted networks. Here we analyse use of space-borne quantum memories and show that it provides much faster entanglement distribution rates than the existing hybrid architectures.
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning

Most of the existing works in supervised spatio-temporal video super-resolution (STVSR) heavily rely on a large-scale external dataset consisting of paired low-resolution low-frame rate (LR-LFR)and high-resolution high-frame-rate (HR-HFR) videos. Despite their remarkable performance, these methods make a prior assumption that the low-resolution video is obtained by down-scaling the high-resolution video using a known degradation kernel, which does not hold in practical settings. Another problem with these methods is that they cannot exploit instance-specific internal information of video at testing time. Recently, deep internal learning approaches have gained attention due to their ability to utilize the instance-specific statistics of a video. However, these methods have a large inference time as they require thousands of gradient updates to learn the intrinsic structure of the data. In this work, we presentAdaptiveVideoSuper-Resolution (Ada-VSR) which leverages external, as well as internal, information through meta-transfer learning and internal learning, respectively. Specifically, meta-learning is employed to obtain adaptive parameters, using a large-scale external dataset, that can adapt quickly to the novel condition (degradation model) of the given test video during the internal learning task, thereby exploiting external and internal information of a video for super-resolution. The model trained using our approach can quickly adapt to a specific video condition with only a few gradient updates, which reduces the inference time significantly. Extensive experiments on standard datasets demonstrate that our method performs favorably against various state-of-the-art approaches.
A Levenshtein distance based implementation of lost character prediction, imputation and tagging in Malayalam palm leaf bundles

Character prediction, which predicts the next probable character given a character or a sequence of characters, is one of the significant tasks coming under Natural Language Processing. The application of these tasks is most relevant in predicting characters written in old documents that might get lost due to aging, insect bites, fungus, etc. From the starting stage of NLP research, numerous frameworks were proposed for various dialects prediction tasks. There has been a significant progress in this field in many languages but not in the Malayalam language. Due to lot many reasons, a lot of missing content that makes these repositories not suitable for exploitation. So in this paper, a Levenshtein distance based model, which is independent of the grammatical structure of the language, is developed, for lost character prediction and tagging in old Malayalam palm leaf manuscripts bundles. The model is able to give an accuracy of 81.02% for the palm leaf manuscript bundles considered for the experiment.
영어능력으로 입학한 외국인유학생들의 한국어에 대한 태도변화 연구

This study sets out to investigate whether English skills-based international students'' attitudes toward Korean have changed after studying Korean language over the last one semester. Twenty-eight international students who entered into the university based on English skills participated in two questionnaire surveys in March and September 2019, respectively. The results showed that their attitudes toward Korean changed positively, particularly in the interest in learning Korean and favoritism of Korean people. In addition, the fear of learning Korean had been weakened over the last six months, which can be a good sign in improving Korean proficiency. The degree of exposure to Korean, Korean people and Korean culture, including the number of students who had a part-time job increased. When the level of English skills was applied to the attitude changes as a variable, the attitudes toward Korean did not change proportionally as per the level of English skills. However, it was noticeable that attitudes of the group in the highest level of English skills changed in the most positive degree. Looking at the results of this study, it is possible to suggest that the attitudes can change positively after taking a Korean language study, which is likely to affect the degree of exposure to Korean, Korean people and Korean culture. In addition, the results of the study may strengthen the belief that the international students who entered into the university based on English skills are able to complete their studies in Korea successfully as long as they are learning Korean language in the university.
초등 과학 실시간 쌍방향 수업에서 자기조절학습방법의 적용에 대한 연구 - ‘식물의 생활’단원을 중심으로 -

The purpose of this study was to apply the self-regulated learning method to the real-time interactive online class in unit of ‘Life of Plant’ of elementary science and investigated what changes occur in elementary students'' self-regulated learning ability and inquiry skill. For this purpose, the experimental class consisted of real-time interactive online science classes that applied the self-regulated learning method, and the comparative class consisted of real-time interactive online science classes that were generally conducted. As a result of the study, students in the experimental class had a positive effect on the improvement of self-regulated learning ability(p<.05) and inquiry skill(p<.001) compared to the comparative class students. Thus, self-regulated learning method to real-time interactive online classes in elementary science is useful to enhance the elementary students'' self-regulated learning ability and inquiry skill. The implications of this study call for emphasizing the professional development of teachers in online science classes, and look forward to continuing a variety of follow-up research on self-regulated learning method to online classes.
Towards a Personalized Web Services Composition Approach

Generally available Web Services (WS) can not meet the complex needs of users and their adaptation to the environment remains a major problem for the design of information systems. The web services composition comes to address the satisfaction of new and complex needs such as the process we find in most organizations. Its purpose is to perform several services to meet user demand. The satisfaction of a user needs a dynamic and reusable environment to meet those needs. In this context, the user interactions are essential. From there, in this work, we define two objectives: i) propose a service composition approach that allows dynamic services composition and its purpose is to meet a need. ii) Propose a personalization approach for Web services composition which allows the reuse of services while adopting for the context of each user. Our approach is based on the use of ontologies and user profile.
Linguistic and Computataional Aspects of Appraisal Annotation

Evaluative language is receiving more and more attention both in the Natural Language Processing (NLP) and in the Corpus Linguistics 8CL) communities, since opinions can be a key aspect in another person's decisions. In order to improve the automatic analysis of evaluative language, it is necessary to develop high-quality cross-linguistic annotated corpora to count on sufficient resources for automated processing. For this reason, this chapter describes the annotation of a corpus of reviews from Play Store with Appraisal features, a very influential theoretical framework in the CL community used to categorise linguistic resources used for negotiating emotions, judgements and evaluations in English and Spanish, as well as the main linguistic conclusions that can be drawn form the use of Appraisal in both languages.
The Extent to which Dimensions of Strategic Recovery of Service have been achieved in some Departments of the Presidency of University of Baghdad

The research aims to explicit the role of Strategic Recovery of Service (SRS). It relies on how organizations response to the failure of provided services and seeks to reduce the level of failure in order to gain the beneficiaries' confidence, satisfaction and loyalty. The research problem is not paying enough attention to (SRS) dimensions, compensation- distributive justice, speed of recovery- procedural justice, and apology-initiation - interactional justice, through dealing with some issues regarding to employees' stratification, productivity, and quality of outputs. The importance of research stems from highlighting the weaknesses and implications of not take into consideration (SRS) dimensions in an administrative workplace. The (Checklist) was employed to check the extent of application of (SRS) dimensions at some departments within the Presidency of the University of Baghdad. The current layout of the researched organization does not achieve the required convergence among departments to complete their assigned tasks in time and reduce overall efforts and costs.
COVID-19 상황에서 초등 과학 전담 교사의 수업 운영 실태 및 인식

The purpose of this study is to investigate how elementary science specialized teachers operate and how they have a perception of the science class in the COVID-19 situation. The number of participants in the study is 21 who are dedicated to science subjects in 2020. 21 teachers were given an open survey of science classes and future directions in the COVID-19 situation, and 8 out of 21 teachers were interviewed in-depth on how and why they did so. As a result of the study, first, the science classes before and after COVID-19 of primary science teachers were different. Under the COVID-19 situation, teachers identified the characteristics of online and offline classes and operated them in a way that was specific to their characteristics. Second, there was a common perception of teachers' classes on the basis of this science class operation style, which is that science classes should be operated as activities, that I would like to have classes that suit my taste as long as the policy allows, and that online classes cause students to lose motivation and widen their educational gap.
Learning to Maximize Influence

As the field of machine learning for combinatorial optimization advances, traditional problems are resurfaced and readdressed through this new perspective. The overwhelming majority of the literature focuses on small graph problems, while several real-world problems are devoted to large graphs. Here, we focus on two such problems that are related: influence estimation, a \#P-hard counting problem, and influence maximization, an NP-hard problem. We develop GLIE, a Graph Neural Network (GNN) that inherently parameterizes an upper bound of influence estimation and train it on small simulated graphs. Experiments show that GLIE can provide accurate predictions faster than the alternatives for graphs 10 times larger than the train set. More importantly, it can be used on arbitrary large graphs for influence maximization, as the predictions can rank effectively seed sets even when the accuracy deteriorates. To showcase this, we propose a version of a standard Influence Maximization (IM) algorithm where we substitute traditional influence estimation with the predictions of GLIE.We also transfer GLIE into a reinforcement learning model that learns how to choose seeds to maximize influence sequentially using GLIE's hidden representations and predictions. The final results show that the proposed methods surpasses a previous GNN-RL approach and perform on par with a state-of-the-art IM algorithm.