Reviewerly/exHarmony huggingface dataset:
Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

The financial sector accumulates a massive amount of consumer data that contain the most sensitive information daily. These data are strictly limited outside the financial institutions, sometimes even within the same organization, for various reasons such as privacy laws or asset management policy. Financial data has never been more valuable, especially when assessed jointly with data from different industries, including healthcare, insurance, credit bureau, and research institutions. Therefore, it is critical to generate synthetic datasets that retain the statistical or latent properties of the real datasets as well as the privacy protection guaranteed. In this paper, we apply Generative Adversarial Nets (GANs) to generating synthetic consumer credit data to be used for various educational purposes, specifically in developing machine learning models. GAN is preferable to other pseudonymization methods such as masking, swapping, shuffling, or perturbation, for it does not suffer from adding more attributes or data. This study is significant because it is the first attempt to generate the synthetic data of real-world credit data in practical use. The results find that synthetic consumer credit data using GAN shows a substantial utility without severely compromising privacy and would be a useful resource for big data training programs.
Hana: A Handwritten Name Database for Offline Handwritten Text Recognition∗

Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Probably the single most important identifier for linking is personal names. However, personal names are prone to enumeration and transcription errors and although modern linking methods are designed to handle such challenges, these sources of errors are critical and should be minimized. For this purpose, improved transcription methods and large-scale databases are crucial components. This paper describes and provides documentation for HANA, a newly constructed large-scale database which consistsofmorethan3.3millionnames. Thedatabasecontain more than 105 thousand unique names with a total of more than 1.1 million images of personal names, which proves useful for transfer learning to other settings. We provide three examples hereof, obtaining significantly improved transcription accuracy on both Danish and US census data. In addition, we present benchmark results for deep learning models automatically transcribing the personal names from the scanned documents. Through making more challenging large-scale databases publicly available we hope to foster more sophisticated, accurate, and robust models for handwritten text recognition.
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs

Paraphrase generation plays an essential role in natural language process (NLP), and it has many downstream applications. However, training supervised paraphrase models requires many annotated paraphrase pairs, which are usually costly to obtain. On the other hand, the paraphrases generated by existing unsupervised approaches are usually syntactically similar to the source sentences and are limited in diversity. In this paper, we demonstrate that it is possible to generate syntactically various paraphrases without the need for annotated paraphrase pairs. We propose Syntactically controlled Paraphrase Generator (SynPG), an encoder-decoder based model that learns to disentangle the semantics and the syntax of a sentence from a collection of unannotated texts. The disentanglement enables SynPG to control the syntax of output paraphrases by manipulating the embedding in the syntactic space. Extensive experiments using automatic metrics and human evaluation show that SynPG performs better syntactic control than unsupervised baselines, while the quality of the generated paraphrases is competitive. We also demonstrate that the performance of SynPG is competitive or even better than supervised models when the unannotated data is large. Finally, we show that the syntactically controlled paraphrases generated by SynPG can be utilized for data augmentation to improve the robustness of NLP models.
Statistical simulation of 3-D random fields with Gaussian type correlation function by the investigation of aircraft magnetometry data

Summary It is proposed to apply methods of statistical simulation of realizations of 3-D random fields with Gaussian type correlation function to solve the problems of conditional maps, adding of data to achieve the necessary precision, and other such problems in geophysics. The approximations theorems and built on their base algorithm of statistical simulation of Gaussian homogeneous isotropic random fields using the spectral representation are considered. Model example is the aircraft magnetometry data in the Ovruch depression. It is divided into deterministic and random components for data analysis. The deterministic component is proposed to approximate by cubic splines and the stationary random component is proposed to modeling on the basis of spectral expansions of random fields with Gaussian type correlation function.
Model of Adaptive Learning and His Implementation

The presented project is aimed at automating the e-learning process regarding the acquisition of practical skills for solving non-formalized tasks, determining the level of knowledge and reducing the duration of training by reducing the number of tasks depending on the level of training. To meet these requirements, an adaptive testing approach was implemented and “Adaptive trainer” web-service was implemented to demonstrate how it works.
On the Interpretability of Deep Learning Based Models for Knowledge Tracing

Knowledge tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered, thus adjusting curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have achieved significant improvements compared with models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, these deep learning based models are not as interpretable as other models because the decision-making process learned by deep neural networks is not wholly understood by the research community. In previous work, we critically examined the DKT model, visualizing and analyzing the behaviors of DKT in high dimensional space. In this work, we extend our original analyses with a much larger dataset and add discussions about the memory states of the DKVMN model. We discover that Deep Knowledge Tracing has some critical pitfalls: 1) instead of tracking each skill through time, DKT is more likely to learn an `ability' model; 2) the recurrent nature of DKT reinforces irrelevant information that it uses during the tracking task; 3) an untrained recurrent network can achieve similar results to a trained DKT model, supporting a conclusion that recurrence relations are not properly learned and, instead, improvements are simply a benefit of projection into a high dimensional, sparse vector space. Based on these observations, we propose improvements and future directions for conducting knowledge tracing research using deep neural network models.
Efficient Multi-label Classification using Attribute and Instance Selection

Both attribute and instance selection is proven to be beneficial to reduce the computational complexity of classifiers while improving their accuracy.Instances in multi-label data are associated with multiple labels.Hence the process of attribute selection from multi-label data is different as compared to single-label classification.Either transformation or adaptation approaches are used by various researchers while performing attribute selection.In this paper, attribute selection and sampling are performed on the multi-label data.This pre-processed multi-label data is then fed to the proposed algorithms, namely MLFLD and its extension MLFLD-MAXP.An empirical evaluation is carried out to study the behaviour of proposed multi-label classifiers.The methods used in this work are defined as algorithms MLFS, MLIS, and MLFSIS.Comparing proposed algorithms with and without MLFS, MLIS, and MLFSIS has shown the effectiveness of using only sampling, or attribute selection followed by sampling on multi-label data.Attribute and instance selection together are noticed to be very useful for the performance enhancement of proposed algorithms over only attribute or instance selection.
Scene Text Detection Using YOLOv4 Framework

Extraction of printed information from different certifiable pictures in machine readable format is treated as tricky chore in the domain of computer vision.This has gained plenty of consideration due to its extensive applications in industries and our daily life.Recently developed deep learning based techniques for detection of text have obtained encouraging results with respect to different standard datasets.But, they typically fall behind when exposed to tricky situation.Still there is a contest between speed and accuracy for text detection.Traditionally for text detection mostly the two stage detector techniques like Faster-RCNN, Fast-RCNN and R-CNN were explored.In this work, we put forward a straightforward pipeline for detecting text in common scene pictures.Here we used recent state of art one stage object detection framework known as YOLOv4 (You Only Look Once version 4) with darknet framework which is comparatively faster and accurate, contrast to existing object detectors.It gives us speedy and precise detection of text from common scene pictures.Experiments performed on state of art datasets including ICDAR 2015, ICDAR 2013, ICDAR 2003, SVT, IIIT5K, MSRA-TD500 display that the proposed pipeline significantly performs better except for ICDAR2015, compare to existing techniques with respect to correctness.
Genesis of the Koka Gold Deposit in Northwest Eritrea, NE Africa: Constraints from Fluid Inclusions and C-H-O-S Isotopes

The Koka gold deposit is located in the Elababu shear zone between the Nakfa terrane and the Adobha Abiy terrane, NW Eritrea. Based on the paragenetic study two main stages of gold mineralization were identified in the Koka gold deposit: 1) an early stage of pyrite-chalcopyrite-sphalerite-galena-gold-quartz vein; and 2) a second stage of pyrite-quartz veins. NaCl-aqueous inclusions, CO2-rich inclusions, and three-phase CO2-H2O inclusions occur in the quartz veins at Koka. The ore-bearing quartz veins formed at 268℃, from NaCl-CO2-H2O(-CH4) fluids averaging 5 wt% NaCl eq. The ore-forming mechanisms include fluid immiscibility during stage I, and mixing with meteoric water during stage II. Oxygen, hydrogen and carbon isotopes suggest that the ore-forming fluids originated as mixtures of metamorphic water, meteoric water and magmatic water, whereas sulfur isotope suggest an igneous origin. Features of geology and ore-forming fluid at Koka deposit are similar to those of orogenic gold deposits, suggesting the Koka deposit might be an orogenic gold deposit related to granite.
Hate Speech on the Internet

This chapter focuses on the struggle to deal with hate speech on the Internet, including concrete examples and an examination of the basic measures that Japan needs to adopt to deal address with this problem. The chapter concludes that there is a significant difference between discriminatory expressions attacking socially vulnerable minorities and expressions attacking the government and the socially strong majority, which have become the focus for debate on regulation. However, while discriminatory expressions are unpleasant, they remain mere expressions and should be treated as such. It should not be overlooked that discriminatory expressions often take on a political character because they are often focused on emphasizing equality. Regulation under the law should be based on both freedom of expression and the goal of eliminating discrimination itself, and in relation to the latter a blend of education and awareness-raising activities are called for, along with resolution by law that aims to inspire voluntary initiatives (self-regulation) among providers.
Child Labour and Online Protection in a World of Influencers

Nowadays, children grow up in a commercialised environment, where they are confronted with advertising and marketing on a daily basis. From a very young age, they already display a level of brand consciousness. Aside from being an attractive target group for advertisers – as they can purchase products or services themselves and have an important influence on their parent’s purchasing behaviour – brands are now increasingly relying on child-influencers to promote goods and services for them. The child-influencer phenomenon raises important questions from a children’s rights perspective and necessitates a detailed study of the benefits and potential harms to the child. This chapter takes a novel approach by not only critiquing the advertising and marketing rules for gaps, shortcomings and failures in light of children’s rights, but also analysing the role of the child in the constellation of influencer regulation through the lens of labour law and protection of vulnerable consumers.
How Much Communication Resource is Needed to Run a Wireless Blockchain Network?

Blockchain is built on a peer-to-peer network that relies on frequent communications among the distributively located nodes. In particular, the consensus mechanisms (CMs), which play a pivotal role in blockchain, are communication resource-demanding and largely determines blockchain security bound and other key performance metrics such as transaction throughput, latency and scalability. Most blockchain systems are designed in a stable wired communication network running in advanced devices under the assumption of sufficient communication resource provision. However, it is envisioned that the majority of the blockchain node peers will be connected through the wireless network in the future. Constrained by the highly dynamic wireless channel and scarce frequency spectrum, communication can significantly affect blockchain's key performance metrics. Hence, in this paper, we present wireless blockchain networks (WBN) under various commonly used CMs and we answer the question of how much communication resource is needed to run such a network. We first present the role of communication in the four stages of the blockchain procedure. We then discuss the relationship between the communication resource provision and the WBNs performance, for three of the most used blockchain CMs namely, Proof-of-Work (PoW), practical Byzantine Fault Tolerant (PBFT) and Raft. Finally, we provide analytical and simulated results to show the impact of the communication resource provision on blockchain performance.
Hourly Temperature Forecasting based on Euclidean Distance Algorithm with Solar Terms

In this paper, a simple and accurate algorithm is proposed for short-term temperature forecasting in power utilities and industrial applications. Solar terms that have been used in Asian countries for centuries are advantageous for the classification of annual historical data. In this paper, an hourly temperature forecasting algorithm based on the Euclidean distance algorithm with 24 solar terms is proposed. In the proposed method, the historical data are classified by the 24 solar terms. The Euclidean distance is used to determine the historical data with most similarity to the historical data in the database. The artificial neural network algorithm was used as a benchmark for comparison purposes. The results show that the proposed scheme has the advantages of simplicity, fast operation, and excellent forecasting performance.
Analisis Perkembangan Sosial Pada Anak Bilingual di Abad 21

Communication is an important requirement in order to adapt to the broad social environment in the 21st century. One of the efforts made is the use of two languages (bilingual) in everyday life. However, parents unconsciously put children into a world with language development tasks that are more complex than the competencies that children should have. This of course will affect social development and children's ability to interact with the environment. This study analyzes the use of bilinguals in terms of children's social competence and social behavior. The research method uses literature studies by analyzing 20 books and scientific articles related to bilingualism and children's social development. The data analysis technique used content analysis related to the phenomena, causes and effects of social development in children using bilinguals. The results of the analysis show that the use of bilingual children has a positive and negative side to the child's social development. The positive thing is that bilingual children can easily adapt outside their social environment as a means of communication. There will be a sense of pride in the child because he can master more than one language. But the negative thing is, some of the bilingual children will find it difficult to understand new concepts in a different language, so they often feel depressed. In addition, there is a gap between children and their social environment.
Archive Management Analysis in Secondary School

Archiving is all activities relating to the management of archives, both official and private archive. This study aims to determine the implementation of records management at secondary school. In this study, there are five indicators, namely, receipt of the archive, archival storage, maintenance and security of the archive, as well as depreciation of the archive, and destruction of records. The research method applied in this study is descriptive qualitative. Data collection techniques used are observation, interview, and documentation. Five informants participated in this study. Analysis of the data used is an interactive model and the techniques in analyzing data are by performing data reduction, data presentation, and drawing conclusions. To ensure the validity of the data, an extension of observation was carried out for triangulation of data sources, holding member checks, and increasing diligence in conducting the study. The results of this study indicate that the implementation of records management in secondary school is still very simple and not in accordance with the development of existing archival science. Judging from the lack of facilities and infrastructure management records that are needed and the absence of an archive management system used to organize the archive. Thus, to find back an archive still takes a long time and sometimes even have to do records archiving. So, in this case, the management of the archive is considered less than the maximum and ineffective
Unlocking the Online Education Space During the Lockdown: Adaptation and Synergies of Teachers and Students

Online teaching and learning existed before COVID-19 pandemic and used as a supportive option to the offline mode.During the pandemic, students and teachers were forced to adopt online methods and this called for a need to realize the impact, adaptability, challenges and effect on both faculty and students.Surveys were carried out with an objective to understand the faculty and students' transformation process from offline to the online mode of teaching and learning.Various issues including online course perceptions, delivery methods, technical adaptability, students' engagement were surveyed for both students and teachers in the engineering major.The survey was carried out in three phases during the lockdown period, before commencement of classes, 10 days after commencement and after completion of half of the syllabus.500 engineering undergraduates and 45 engineering teachers participated in this survey.This survey will help teachers to understand the importance of training on the new generation tools for online teaching.Students will also realize the importance of self-learning and the way to compensate for the missing social life during the lockdown through online technical discussion during the assignments.Training on online tools and techniques played an important role in teachers' delivery method in the online mode that resulted in improvement in their confidence according to the survey.Reflections by viewing their own recorded video played an important role for the teachers where 80% of the faculty members modified their delivery methods for online teaching.The survey showed that students embraced the new space and 85% of students got involved in online course discussions that enhanced their self-learning.65% of students suggested that the missing on-campus social life was partially compensated due to online education.The forced online teaching and learning due to the lockdown, unlocked many prospects for teachers and students to explore the various online tools.
Cross-lingual Entity Alignment with Incidental Supervision

Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-world object. Such methods are often hindered by the insufficiency of seed alignment provided between KGs. Therefore, we propose an incidentally supervised model, JEANS , which jointly represents multilingual KGs and text corpora in a shared embedding scheme, and seeks to improve entity alignment with incidental supervision signals from text. JEANS first deploys an entity grounding process to combine each KG with the monolingual text corpus. Then, two learning processes are conducted: (i) an embedding learning process to encode the KG and text of each language in one embedding space, and (ii) a selflearning based alignment learning process to iteratively induce the matching of entities and that of lexemes between embeddings. Experiments on benchmark datasets show that JEANS leads to promising improvement on entity alignment with incidental supervision, and significantly outperforms state-of-the-art methods that solely rely on internal information of KGs.
Distance Education in Higher Education: The Perspective of Key Social Actors

This article considers the perspective of the key social actors (students, teachers and university management) regarding distance education development in Russia. In the modern information and digital age, distance education – based on the possibility of disseminating knowledge through modern computer technologies – is being actively introduced into the Russian higher education system. The pace of implementation is increasing in Russia, yet its assessment as a new form of learning and teaching is ambiguous. There are clear advantages to this form when compared with traditional face-to-face classroom instruction including: the accessibility of education to a wide audience; the possibility to earn a degree irrespective of place of residence; variability of learning time selection; relatively low tuition fees. At the same time, however, many believe that the quality of education received by distance students is lower than that received via face-to-face teaching, due to both the lack of student’ readiness and the difficulties related to pedagogical process organization and management. This study considers whether distance higher education will continue to develop in Russia. The answer to this question depends to a large extent on the perspective of the key social actors. The study analyses their attitudes to distance education and make the following conclusions: 1) University management generally accept this form because of its economic advantage and attitudes of the higher education authorities. Students also vote in favour, because it allows them to earn a degree without giving up work, family and home-related commitments. The most negative assessment is given by teachers who conduct classes at a distance and can compare distance learning outcomes with those in the traditional face-to-face classroom instruction.&#x0D; Keywords: distance education, quality of education, social actors, students, teachers, university management