Reviewerly/exHarmony huggingface dataset:
A study on the difference of primary language use and level of proficiency in interactive synchronous online Korean language classes - Their effects on media efficacy, the presence of learners and the satisfaction level -

Journal of the International Network for Korean Language and Culture 17-2, 129-150. In line with the recent challenges to traditional educational environments, Korean language teachers and learners are interact from their own living spaces through synchronous online classes. The aim of this study was to investigate the levels of media efficacy, presence and course satisfaction of learners from classes utilizing video conferencing platforms. While the use of primary language in class is the main variable influencing the media efficacy and presence of the learners, the proficiency level of learners also becomes a variable when primary language use is not allowed. Finally, the effect of media efficacy and presence on class satisfaction was discussed. The subjects of this survey were novice Korean language learners in foreign countries who were using their primary language in the classroom and 61 beginner and intermediate level learners in Korea who were not allowed to use their primary language in the classroom. The results of this survey show that the use of primary language and the level of proficiency of learners are important variables that influence media efficacy and presence in interactive synchronous online Korean language classes. There is a significant correlation between the levels of media efficacy, presence and class satisfaction. Thus, improving the media efficacy and presence of learners is considered to be an important factor in the successful implementation of interactive synchronous online Korean language classes.(Ewha Womans University)
Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner

Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an `Agree' and a `Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book.
COVID-19: INTO THE INFERNO

Amsterdam Law Forum (ALF) is the student-run 'International Law Journal' of VU University. Every year ALF publishes a winter, spring, and summer issue. The journal consists of three sections; scientific articles, opinion articles, and commentaries. As of this year, ALF also creates a section for inaugural speeches. In addition, ALF hosts a conference in spring with a relevant legal theme, where renowned speakers are invited to share their perspectives. Overall, ALF is a topical journal that provides a platform for established scholars and young academics to share knowledge, opinions and experiences and to make contributions to the international law discourse. Staff, PhD students and master students who have written a very good thesis are invited to submit an article to ALF. What is learned in the cradle is carried to the tomb: we are looking forward to sharing your articles on our website!
Asymptotics for $L_{1}$-wavelet method for nonparametric regression

Abstract Wavelets are particularly useful because of their natural adaptive ability to characterize data with intrinsically local properties. When the data contain outliers or come from a population with a heavy-tailed distribution, $L_{1}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -estimation should obtain a better fit. In this paper, we propose a $L_{1}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -wavelet method for nonparametric regression, and derive the asymptotic properties of the $L_{1}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -wavelet estimator, including the Bahadur representation, the rate of convergence and asymptotic normality. The rate of convergence of it is comparable with the optimal convergence rate of the nonparametric estimation in nonparametric models, and it does not require the continuously differentiable conditions of a nonparametric function.
Forensic Analysis of Advanced Persistent Threat Attacks in Cloud Environments

Cloud forensic investigations involve large volumes of diverse devices and data. Investigations involving advanced persistent threat attacks involve filtering noisy data and using expert knowledge to identify the missing steps in the attacks that typically have long time spans. Under such circumstances, obtaining timely and credible forensic results is a challenge. This chapter engages a case study to demonstrate how MITRE’s ATT&CK knowledge base and Lockheed Martin’s Cyber Kill Chain methodology can be used in conjunction to perform forensic analyses of advanced persistent threat attacks in cloud environments. ATT&CK is a globally-accessible knowledge base of adversary tactics and techniques developed from real-world observations of attacks. The Cyber Kill Chain methodology describes a series of steps that trace a cyber attack from its early reconnaissance stage to the later data exfiltration stage. Because advanced persistent threat attacks on cloud systems involve the key Cyber Kill Chain phases of reconnaissance, command and control communications, privilege escalation, lateral movement through a network and exfiltration of confidential information, it is beneficial to combine the ATT&CK knowledge base and Cyber Kill Chain methodology to identify and aggregate evidence, and automate the construction of the attack steps.
Legal Document Summarization Using Nlp and Ml Techniques

Reading legal documents are tedious and sometimes it requires domain knowledge related to that document. It is hard to read the full legal document without missing the key important sentences. With increasing number of legal documents it would be convenient to get the essential information from the document without having to go through the whole document. The purpose of this study is to understand a large legal document within a short duration of time. Summarization gives flexibility and convenience to the reader. Using vector representation of words, text ranking algorithms, similarity techniques, this study gives a way to produce the highest ranked sentences. Summarization produces the result in such a way that it covers the most vital information of the document in a concise manner. The paper proposes how the different natural language processing concepts can be used to produce the desired result and give readers the relief from going through the whole complex document. This study definitively presents the steps that are required to achieve the aim and elaborates all the algorithms used at each and every step in the process.
Research on Conscious Interactive Angle of Pen in 3D Contactless Air-Drawing and Writing

In this paper, a three-dimensional pen interaction technique based on the pen roll angle in normal writing or drawing is proposed. Two experiments are conducted to present the methods. In experiment 1 the range of the pen's pitch angle, yaw angle and roll angle due to normal writing and drawing unconsciously in 3D space are explored to show that the roll angle is used only small part of the possible range so it could be used in interactive control. In experiment 2, the range of the roll angle, accurate and efficient under conscious interaction are further investigated. Two independent variables, angular width and angular distance are introduced to evaluate the performance of the system. The experiment results show that the available angle range [-135°, -20°] and [20°, 135°] together with an angle resolution W = 10 degrees are the preferred parameter combination. The method in this paper would provide an approach to implement interactive instruction through pen roll and meanwhile do not interfere with normal writing and drawing so as to promote the interaction performance of the system.
Mineral Deposits of China: an introduction

[Extract] China is a large country with approximately 9.6 million square kilometers of land area that is rich in mineral resources. It is the major producer of many metals. For the 40 metals (rare earth elements and platinum group elements [REE and PGE] counted as one each) regularly reported in the USGS Mineral Commodity Summaries with production and some reserves data, China was in the top four global producers for 32 in 2018 (Table 1, summarized from U.S. Geological Survey, 2019). These include As, Au, Bi, Cd, Ga, Ge, Hg, In, Mg, Mo, Pb, Sb, Se, Sn, Te, Ti, total REE oxides, V, W, Y, and Zn, all for which China is the largest producer; bauxite and Be (2nd larg-est producer); Ag, Fe ore, Li, Sr, and Zr (3rd largest producer), and K, Mn, Re, and Ta (4th largest producer). In addition, China is also the largest producer of a number of industrial minerals such as granite, wollastonite, barite, and sulfur (U.S. Geological Survey, 2019). Where reserves data are available, China also has the world's largest reserves of Mo, Se, Sn, Te, REE, and V Table 1).
Clustering Geo-Data Cubes (CGC): A Clustering Tool for Geospatial Applications

Clustering Geo-Data Cubes (CGC) is a Python package to perform clustering analysis for multidimensional geospatial data. The included tools allow the user to efficiently run tasks in parallel on local and distributed systems.
ELAID: detecting integer-Overflow-to-Buffer-Overflow vulnerabilities by light-weight and accurate static analysis

Abstract The Integer-Overflow-to-Buffer-Overflow (IO2BO) vulnerability has been widely exploited by attackers to cause severe damages to computer systems. Automatically identifying this kind of vulnerability is critical for software security. Despite many works have been done to mitigate integer overflow, existing tools either report large number of false positives or introduce unacceptable time consumption. To address this problem, in this article we present a static analysis framework. It first constructs an inter-procedural call graph and utilizes taint analysis to accurately identify potential IO2BO vulnerabilities. Then it uses a light-weight method to further filter out false positives. Specifically, it generates constraints representing the conditions under which a potential IO2BO vulnerability can be triggered, and feeds the constraints to SMT solver to decide their satisfiability. We have implemented a prototype system ELAID based on LLVM, and evaluated it on 228 programs of the NIST’s SAMATE Juliet test suite and 14 known IO2BO vulnerabilities in real world. The experiment results show that our system can effectively and efficiently detect all known IO2BO vulnerabilities.
Factors Determining Female Labor Participation in Job Market in the Middle East and North Africa (MENA) Countries

Women’s engagement in the broader social life is part of policy objectives in today’s world that most governments aim to achieve. Likewise, the issue is crucial in most Muslim majority countries, especially in the Middle East and North Africa (MENA). As the regions have characteristics of a smaller number of women’s participation in the formal labor market than many other countries, this study investigated the factors that determine women’s participation by measuring data of governance, industrial transformations, and education from 1980 to 2014. This study used robust panel data methods to calculate the interdependencies of those variables. Based on the estimation, prominent factors that have positive correlations with the participation are good governance and the transitions from agriculture to industrial and service economy. Meanwhile, financial literacy and education have limited impacts on participation.
Sweden and COVID 19: A Constitutional Perspective

The Swedish government’s ways of handling the Corona crisis have drawn a lot of international attention. Sweden has tried to limit the spread of the disease by means of recommendations, rather than ...
PENGEMBANGAN INSTRUMEN TES BERBASIS ANDROID PADA MATERI RUMUS-RUMUS TRIGONOMETERI KELAS X

The era of the industrial revolution 4.0 was marked by the development of increasingly sophisticated and influential technology in the field of education. Besides, the government expects effectiveness and practicality in the use of the budget, especially testing. The way to reduce the budget for testing or testing is to use technology. This study aims to determine the feasibility of an Android-based test instrument. This research is a research development. The research model used is Borg and Gall. The subject of the research was the students of grade XI MIPA 4 in SMAN 2 TPI. The data of this study were collected by observation, validation, and documentation techniques. The research instruments used were observation sheets, validation sheets, and documentation. The data analysis technique of this research is descriptive qualitative and descriptive statistics. The results obtained were 98.9% android based test instruments categorized as very feasible by material experts and 100% categorized as very feasible by media experts and the results of item analysis were conducted by students. The test instrument shows valid and reliable, the difficulty level of the 68% test instrument is categorized as moderate, and the distinguishing power of the 59% test instrument is categorized as good.
Methods for Collecting and Classifying Data using a Soundlet Bayesian Neural Network

The paper substantiates the application of neural networks for prediction of emergency situations and the stages of building a neural network for simulation of emergency situations.The purpose of the work is achieved by developing scientific and technical bases for the technical implementation of a comprehensive within monitoring, prevention and liquidation system of natural and man-made emergency situations and ensuring environmental safety.Forecasting emergencies and location determination is an urgent task that requires a permanent and effective solution.The basis of this problem is the construction of the effective methods, providing the high speed of the learning pattern recognition models as well as high probability, the adequacy and speed of emergency signals recognition.The article describes the principles of the construction and operation of the system for the recognition of emergency situations using neural networks.Considered apparatus and analysis of the need to use a neural network, to predict the physical parameters the emerging problem.Fulfilled the analysis of the chosen structure and neural networks, which should be used to predict the physical parameters.In article proposed model of a neural network to solve the problem of prediction.Also presented, the mathematical formula for visual understanding of the structure of neural networks and their work.The article is devoted to the problem of emergencies prevention using modern methods of analysis of acoustic data.Emergencies arise in the conditions of extraordinary situations and management in emergency situations characterized by the need for work in the absence of information, the high rate of change in the situation, the need for operational formation of the most effective solutions, which have high efficiency, which imposes requirements to minimize the time and losses in the elimination of emergency situations.Results of the study can be used for analysis and modelling of stability of emergency service that is for evaluation of the emergency or potentially dangerous object.
A Survey of Chatbot creation tools for non-coder

Chatbot is a natural language conversation which is considered a major revolution in customer service automation. Chatbot is an application of artificial intelligence that has a big role in saving time and effort by automating different aspect of human life. AI framework has several tools that help the non-coder to develop the Chabot for the organization. The Chatbot becomes good solution for many organizations to communicate with their customers and to increase their productivity by serving the customers for 24x7. This will help the organization to achieve customer services with low cost. This paper give glimpses on different AI framework tools, a survey is conducted to facilitate the non-coder to select the tools from the array of open source and proprietary tools. There are a lot of open source tools, each one has different features. Additionally, some of them are useful for coder and others do not require any coding experience also they are capable to be used in different devices with different operating systems. The flexibility that is within using tools to develop the Chatbot without coding knowledge and integrated in different platform is attractive concept for each one. Thereby this survey will help the non-coder to develop the Chabot without undergoing to learn the programming aspect of developing the Chatbot
STEM Education As the Latest Philosophy of Lifelong Learning to Develop Key Competencies of a Civil Servant

STEM education is a combination of theoretical science, technology, engineering and mathematics. Moreover, these disciplines are not studied separately, but in combination. Students get acquainted with new directions in the development of exact sciences and engineering, and then implement what they have learned in practice. The authors explore ways of developing key competencies of a civil servant with the help of STEM education as a philosophy of training and retraining throughout life. The abbreviation of the concept, its features, principles and advantages are disclosed. The key competencies of civil servants are identified and how STEM education affects the development of these competencies is analyzed.
Blockchain Technology for Managing land Titles in Nigeria

Blockchain is a trending technology that has paved way to solving various challenges in different sectors.It has received much interest from researchers over the years, as it is known to be a transparent, secured, no third party and tamper-proof public records repository system for documents, contracts, properties and assets.land title as being a major challenge in Nigeria due to the fact the existing traditional system, used to manage the sector is not effective, thus brought about issues such as, double spending, tamper, third party interference, it is not transparent.Hence the need for blockchain technology.The methodology considered for this research is ethereum blockchain with smart contract which is written in solidity programming language.The research clearly shows the result of using ethereum blockchain to manage land title and how the challenges of land title has been solved.This paper examines Blockchain Smart Contract for managing land titles in Nigeria.Specifically, it presents a detailed smart contract for land title and transfer between owner (government) and buyer.
Semantic Web Design of RFID Pharmaceutical Drugs Analytics and Clinics Tracking System

This paper presents a development of a Pharmaceutical Drugs and Clinics Tracking system which uses Radio Frequency Identification technology.RFID technology has grown actively in the past few years and is nowadays sought to be implemented for the identification of products and for traceability in the medical and health sector.Most people faced difficulties to find the specific type of pharmaceutical drugs that needed at their location which the pharmaceutical drugs are less identified.This research has designed system that present pharmaceutical drugs information, quantity inventory and the shortlisted clinics according to customer's demand.The system also provided to track the correct information related to the drugs detail.The method involved development use of hardware, software and programming code of RFID reader which automatically scan the arrived medicines of RFID tags.Query data such as drugs ID and names is kept in database in computer and cloud that send by the controller.Drugs data are accessed by users through online system which includes location of the available clinics based on user demand.Result presents data analytics using Fusionex cloud that listed the drugs details with clinic's location.API Geolocation is designed with the calculated route to users in this system.Four clinics have been taken as model in running the model which analyzed available drugs inventory as presented online on Fusionex GIANT platform.Statistic on updated quantity and availability of drugs product are listed based on user's search.The analytic graph presented predicted affected area based on purchased value of medicine.This system is significant for medical and health sector demonstrate how RFID technology helps in better monitoring and trace drugs product which helps users to find the clinic's location automatically identified of needed drugs nearby.
Bi-Level Associative Classifier Using Automatic Learning on Rules

The power of associative classifiers is to determine patterns from the data and perform classification based on the features that are most indicative of prediction. Although they have emerged as competitive classification systems, associative classifiers suffer from limitations such as cumbersome thresholds requiring prior knowledge which varies with the dataset. Furthermore, ranking discovered rules during inference rely on arbitrary heuristics using functions such as sum, average, minimum, or maximum of confidence of the rules. Therefore, in this study, we propose a two-stage classification model that implements automatic learning to discover rules and to select rules. In the first stage of learning, statistically significant classification association rules are derived through association rule mining. Further, in the second stage of learning, we employ a machine learning-based algorithm which automatically learns the weights of the rules for classification during inference. We use the p-value obtained from Fisher's exact test to determine the statistical significance of rules. The machine learning-based classifiers like Neural Network, SVM and rule-based classifiers like RIPPER help in classifying the rules automatically in the second stage of learning, instead of forcing the use of a specific heuristic for the same. The rules obtained from the first stage form meaningful features to be used in the second stage of learning. Our approach, BiLevCSS (Bi-Level Classification using Statistically Significant Rules) outperforms various state-of-the-art classifiers in terms of classification accuracy.