Reviewerly/exHarmony huggingface dataset:
Criteria of Curriculum Digestion Efficiency in Online Education Environment

The paper considers the methodology of individualization of training based on adaptive testing of network electronic educational and methodological complexes in two closely interrelated levels – the methodology of designing, developing adaptive tools and the methodology of their application in teaching university students. The design of an adaptive network electronic educational and methodological complex – a universal educational resource that is able to support the learning process at all its stages-contains three consecutive stages (levels) – didactic, logical and physical, each of which has a content component of the developer's activity that determines the requirements for the structure and content of the created course. The methodological approaches to the use of an adaptive network course in the educational process are different for teachers and students. As criteria for evaluating the effectiveness of learning content assimilation in the information and communication educational environment of the university, the following are defined: transient process time response, which a student spends for learning of fixed stream of input information until prepared for the testing (E.A. Solodova's adaptive learning mathematical model); the level of assimilation of the information received, defined in tests and evaluated in a 100-point scale (as the condition of completeness of the educational process, according to V.P. Bespalko).
Verbal Markers of Utterances Containing Threat in the English-Language Internet Discourse (by the Example of the Social Network Twitter)

The purpose of the research is to provide an insight into the features characterising lexical and grammatical verbal markers of threat in the texts of public messages found in the English-language segment of the social network Twitter. The article examines a number of social, communicative and linguistic factors associated with threatening as a speech strategy and sheds light on ways of functioning of the above-mentioned markers within the framework of the Internet discourse. Scientific novelty of the research lies in studying the language tools that function as verbal markers of threat in the English-language social network Twitter. As a result of the research, it is determined that the English-language media text displays a high degree of implicitness, evaluativity and expressiveness. Verbal markers are endued with a number of communicative functions and are the most important element in the text system of Internet messages.
부모의 자율성지지가 고등학생 자녀의 학업소진에 미치는 영향 - 자기조절학습동기와 성취목표지향성의 조절효과 -

The purpose of this study was to examine the effects of parents' autonomy support, self-regulated learning motivation, and achievement goal orientation on high school students' academic burnout, and investigate the moderating effect of self-regulated learning motivation and achievement goal orientation on the relationship between parents' autonomy support and this students’ academic burnout. The results are as follows. First, academic burnout has a significant effect on parents' autonomy support, self-regulated learning motivation, and achievement goal orientation. Second, self-regulated learning motivation has a moderating effection the relationship between parents' autonomy support and students' academic burnout. Third, the achievement goal of performance-approach goal has a moderating effect on the relationship between parents' autonomy support and students' academic burnout. Fourth, achievement goal orientation of mastery-approach goal and performance-avoidance had no moderating effect on the relationship between parents' autonomy support and students' academic burnout. This finding implies that the effect of parents' autonomy support on students’ academic burnout may vary depending on students' level of self-regulated learning motivation and achievement goal orientation. In this respect, the study has discussed limitations and the needs to find more effective intervention methods that can increase self-regulated learning motivation and achievement goal orientation of performance-approach goal that can control the academic burnout of students.
PGF Cyberpolicing to Defuse Fake Government of Telangana (FGoT), Fake Government of India (FGoI) and Cybercriminal Legacy

FGoT/FGoI are known as the Twin Big Data Cybercriminally Seditious Fake Governments that are first of the kind in Indian history (TBDCSFGsIIH). FGoI and FGoT are the obverse and the reverse of the same coin of the JNTUHJAC’s cybercriminal website, http://www.jntuhjac.com . The mitigation and defusion of cybercrimes in the case study are challenging tasks for cyberpolicing. Consequently, the case study of FGoT and FGoI has a very good potential for a cyberpolicing legacy of the remedial forum, PGF’s counterintelligence alternatives for the mitigation and defusion of cybercrimes. We conclude that PGF’s cyberpolicing legacy is significant in the development of the Smart City, Hyderabad as well as Telangana/India. PGF successfully endeavored ICT usage based on cybersafe, cybersmart academics in Telangana. The empirical results indicate that PGF successfully imparted national education to the higher education students by cyberpolicing for positive quality of national digital life on the bedrock of good national character. We reiterate, however, that it is important that GoI/GoT/Cyberabad Police/JNTUH take initiatives by prohibition orders and white paper release to remedy and recover from the national losses due to the FGoI/FGoT cybercrimes and to mitigate the cybercriminal legacy in academics due to the TBDCSFGsIIH.
Analysis of Developers’ Sentiments in Commit Comments

Software development is a highly collaborative activity. Software is developed and maintained through cooperation among developers. In Open Source Software projects the development teams are distributed across the globe and collaborate through the Internet. They interact with each other through various communication artifacts and express their feelings therein. Nowadays, the study of the emotional aspects of software developers is essential for effective software engineering. In this paper, we used well known SentiStrength tool to analyze developers’ sentiments in commit comments. We also investigated the association between developer sentiment and month of comment. Our findings revealed that the majority of sentiments expressed by developers in commit comments were neutral (46%). All observed months have a high count for neutral sentiments than positive and negative ones. July had the most negative sentiments.
Artificially Intelligent Health Chatbot Using Deep Learning

Chatbot is a popular application used to converse with a computer-based system in an interactive and amicable manner. It enables a person to interact with the vast amount of knowledge in an easier and cost-effective way. Chatbots are able to answer the questions using its knowledge base just like a human expert, the only exception is that it takes mere minutes or even seconds for a chatbot to learn as opposed to the human's ability to acquire the same knowledge over a period of months or years. The learning rate depends on the algorithm applied, processing speed of the system and the amount of data to train the model. This particular characteristic of quick learning proves valuable in all application domains and more so in the health services. In this paper, we propose a health chatbot developed using deep learning techniques of complex neural networks. The model is trained on a Knowledge Base which consists of health queries and its answers. These question-answer pairs were obtained manually from WebMD, which is one of the leading health service websites on the internet. An accuracy of 0.97% and loss of 0.02%, in terms of correct answer prediction for a given query, was obtained while training the deep learning model.
Application of K-Medoids Cluster Result with Particle Swarm Optimization (PSO) in Toddler Measles Immunization Cases

Abstract The objective of the research was to analyze the clustering method by optimizing Particle Swarm Optimization (PSO) in the case of measles immunization for children under the age of 5. The research data source used is the North Sumatra Province Central Bureau of Statistics (https://sumut.bps.go.id/). The data used in 2019 included 33 records with variables BCG, DPT-HB3/DPT-HB/Hib3, CAMPAK (MEASLES + RUBELLA), POLIO 4 and HEPATITIS B. The methods used were k-medoids and PSOs. This method is used to determine the value of the Davies Bouldin Index (DBI) before the cluster value is determined (k). The resulting k values were compared to k-medoids without PSO. The best results of k-medoid and PSO will be tested by classification to see the accuracy value of the cluster formed. The results showed that the optimization of k-medoids and PSO was better with the number of clusters (k = 5) than 0.078. (DBI). The results also show that the accuracy of the cluster formed is 95% with a correlation of 0.98.
Developing Information System of Population

Abstract This research aims to develop information systems in the population administration section in Pahlawan subdistrict in Palembang to facilitate the collection of the population in the subdistrict quickly and accurately. To support the research, we use qualitative methods in descriptions and interviews with several subdistrict employees. The result analysis shows that in Pahlawan subdistrict still uses Information System in Microsoft Excel to record population data; it has not been automated and slow in adding, changing, or deleting data and other population data collections. This study shows that the development of the Visual Studio Information System can provide many conveniences in processing population data. The conclusion of this study is that by creating a population administration information system, the processing of population data such as family card data, birth data, death data, and moving data can be done more quickly and accurately because the data input can be connected well and the data is stored centrally in the database and can facilitate in making population reports based on data that has been inputted into the information system. The impact of this research resulted in a design of an information system that is expected to be used and help Pahlawan subdistrict in recording population data as a useful source of information for decision making in government, especially for Pahlawan subdistrict to be more effective and efficient.
Lattice-Based Secure Authentication Scheme for Cloud Storage

With the rapid development of the computing paradigm, cloud computing becomes the most notable one that offers convenient and on-demand services from a shared pool of configurable computing resources. However, the application of quantum computers in clouds would be realized from the recent breakthrough results of quantum computers. In the near future, existing conventional hardness-based authentication schemes will be confronted with quantum attackers. Lattice-based authentication scheme resolves this tension. Albeit, existing lattice-based schemes, users and cloud servers are always worried about the communication privacy against misbehaved private key generator referred to as mPKG since PKG potentially has the power to generate the secret key for any given identity. In this paper, we proposed a secure authentication scheme for cloud storage that thwarts the misbehaved PKG. We integrate the identity-based encryption (IBE) scheme with the lattice-based signature, which is the variant of existing IBE-based authentication schemes and post-quantum secure. Additionally, we integrate identity certifying authority (ICA) with PKG to ensure security against misbehaved PKG. Our comprehensive security proof demonstrates that the proposed scheme provides stronger security guarantees against misbehaved PKG and ICA.
Fuzzy Feature Extraction and Recognition Model in Korean Pronunciation Practice

The study of Korean has become a trend among young people, and more and more young people have a strong interest in Korean. Human language communication consists of four parts: listening, speaking, reading and writing, among which reading and listening belong to the category of comprehension, while speaking and writing belong to the category of expression. It's hard to estimate exactly how much of each part of everyday human communication is involved. But most people believe that, in terms of comprehension, reading and listening can achieve more communication than listening. In terms of expression, speaking can achieve more communication compared with writing. In learning a language, the most important thing is pronunciation. Only correct and fluent pronunciation can ensure good communication with each other. Nowadays, the network information age makes it easy for people to learn Korean through the network. However, the uneven quality of Korean teaching videos on the network poses a great challenge to Korean teachinv.
Multiword Expression Features for Automatic Hate Speech Detection

The task of automatically detecting hate speech in social media is gaining more and more attention. Given the enormous volume of content posted daily, human monitoring of hate speech is unfeasible. In this work, we propose new word-level features for automatic hate speech detection (HSD): multiword expressions (MWEs). MWEs are lexical units greater than a word that have idiomatic and compositional meanings. We propose to integrate MWE features in a deep neural network-based HSD framework. Our baseline HSD system relies on Universal Sentence Encoder (USE). To incorporate MWE features, we create a three-branch deep neural network: one branch for USE, one for MWE categories, and one for MWE embeddings. We conduct experiments on two hate speech tweet corpora with different MWE categories and with two types of MWE embeddings, word2vec and BERT. Our experiments demonstrate that the proposed HSD system with MWE features significantly outperforms the baseline system in terms of macro-F1.
A Survey on Computational Models for Learning and Memory in Artificial Intelligence

Learning and Memory is the important area of artificial Intelligence to study about the main functionality of human brain to develop a new computational models and algorithms based on cognitive learning and memory. Therefore, here human intelligence encourages a review for function of learning and memory. This paper clarify that without learning the aim of accomplish human-level artificial intelligence is at a great distance from completion. This review paper covers the human learning and memory, hippocampus learning and hidden markov model. This review paper covers mostly work done in the area of learning and memory. In this review we focuses on definitions of learning and memory and development and theories of learning over the past few years . This paper also presents advantages and disadvantages of various computational models and algorithms related to learning and memory.
Cryptocurrencies: An Overview and Financial Analysis for Inquiring Investment Professionals

With the increase in media attention, public acceptance, and geopolitical tolerance of cryptocurrencies, it is becoming more important for investment professionals to develop an understanding of these new “digital assets”. Thus, this paper provides readers with an overview of the mechanics and utility for 3 of the most notable cryptocurrencies (BTC, XRP, and LTC) as defined by market capitalization and track record from two different vantage points: qualitatively and quantitatively. For the former, the author delves into use-cases and catalysts that could impact future adoption rates. For the latter, a financial performance and risk analysis is conducted in which the author analyzes possible return or diversification benefits imparted by a basket of cryptocurrencies in conjunction with a portfolio of traditional assets and alternative assets.
An Extended Factorial Hidden Markov Model for Non-Intrusive Load Monitoring Based on Density Peak Clustering

Non-Intrusive Load Monitoring (NILM) has received widespread attention as an energy-saving technology. The method based on Hidden Markov Model (HMM) is very popular in this domain because of its relatively small demand for computing resources. However, the traditional HMM-based methods need additional information such as the working states of appliance to train the model. In this paper, we proposed a non-parameter model (IC-FHMM) to alleviate the problem that require prior knowledge. Experiments are conducted on three open-access datasets, and the results indicate that the proposed model is superior to the four state-of-the-art models on the metrics of Accuracy and F-measure.
Maximum a Posteriori Despeckling Algorithm of Synthetic Aperture Radar Images with Exponential Prior Distribution

In order to solve the problem that the traditional Lee filter and Kuan filter cannot effectively preserve the edge and the point target of synthetic aperture radar images, the maximum a posteriori despeckling algorithm with exponential prior distribution is proposed. Using the exponential distribution to describe the statistical properties of the real image, the proposed algorithm has the analytical filtering output according to the maximum a posteriori criteria. Based on the second-kind statistics, together with the multiplicative model of speckle and the Mellin convolution, the parameter of the exponential prior distribution is accurately estimated from the log-cumulant of the observed image. Despeckling experiments on Ku-band and X-band synthetic aperture radar images demonstrate that, the proposed algorithm can effectively suppress the speckle in homogeneous region, and can effectively preserve the edge and the point target, so it is the efficient despeckling algorithm of synthetic aperture radar images.
Global Drivers of the Adoption of Bitcoin Infrastructure

A vast digital eco-system of entrepreneurship and exchange has sprung up with Bitcoin’s digital infrastructure at its core. We explore the worldwide spread of infrastructure necessary to maintain and grow Bitcoin as a system (Bitcoin nodes) and infrastructure enabling the use of bitcoins for everyday economic transactions (Bitcoin merchants). Specifically, we investigate the role of legal, criminal, financial and social determinants of the adoption of Bitcoin infrastructure. We offer some support for the view that the adoption of cryptocurrency infrastructure is driven by perceived failings of traditional financial systems, in that the spread of Bitcoin infrastructure is associated with low trust in banks and the financial system among inhabitants of a region, and with the occurrence of country-level inflation crises. On the other hand, our findings also suggest that active support for Bitcoin is higher in locations with well-developed banking services. Finally, we find support for the view that bitcoin adoption is also partly driven by cryptocurrencies’ usefulness in engaging in illicit trade.
Research on SQL Server Bulk Operation Mechanism

Abstract This paper illustrates the meaning of different database recovery modes, represented by the select into command, and verifies the differences in the redo data produced by bulk operations in different database recovery modes with a detailed experimental procedure.
Practical Video Quality Assessment Of User Generated Content

During the past few years, video quality assessment (VQA) of user generated content (UGC) has attracted considerable attention in the research community. In this paper, we propose a practical architecture for a versatile video quality model, designed for assessing user generated videos in particular. The proposed architecture is based on our earlier design of two-level video quality model with a convolutional neural network (CNN-TLVQM), with various improvements and re-designed elements. We have built a fast implementation of the proposed model in C++, demonstrating that the model is practical for real-life applications. The implementation of the model has been submitted for evaluation in ICME UGCVQA Challenge in 2021.
RAPOT: A Fine-Grained Risk Assessment Framework on Public Opinion for Trial Management

Since public opinion from social media has a growing impact and supervision on the trial, risk assessment on public opinion is increasingly important in the refinement of trial management. However, due to insufficient historical training data, lack of high accuracy in risk prediction and so on, existing methods cannot be effectively applied to risk assessment on public opinion. To address this, we propose a fine-grained risk assessment framework on public opinion with fuzzy numbers. Firstly, we establish a multilayer indicator model for assessing the Risk of Public Opinion (POR), in the model, the indicators are defined and allocated priorities based on AHP. Secondly, we design a public opinion analysis module for indicator evaluation, which includes analysis in public opinion sentiment, in hot search and in social media coverage. Specially, the public opinion sentiment is calculated by aggregating sentiments of topic clusters, which is accurate and reasonable. Finally, the fuzzy number similarity is employed to determine the level of POR in the nine-levels risk system. Experimental results validate the efficiency of our framework when assessing the POR.
Sixteenth-Century Community Bonds in a Twenty-First-Century Teaching Context

Previous articleNext article No AccessPart 2: Adapting to Online Teaching and Learning: Pandemic and Pedagogy: Using Digital and Analog Skills to Nurture a Human CommunitySixteenth-Century Community Bonds in a Twenty-First-Century Teaching ContextElizabeth GoodwinElizabeth GoodwinYork St. John University Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by Volume 51, Number S1Supplement 2020Early Modern Classroom The journal of the Sixteenth Century Society and Conference Article DOIhttps://doi.org/10.1086/SCJ5199016 © 2020 The Sixteenth Century Society and Conference. All rights reserved.PDF download Crossref reports no articles citing this article.
Shape-Preserving Stereo Object Remapping via Object-Consistent Grid Warping

Viewing various stereo images under different viewing conditions has escalated the need for effective object-level remapping techniques. In this paper, we propose a new object spatial mapping scheme, which adjusts the depth and size of the selected object to match user preference and viewing conditions. Existing warping-based methods often distort the shape of important objects or cannot faithfully adjust the depth/size of the selected object due to improper warping such as local rotations. In this paper, by explicitly reducing the transformation freedom degree of warping, we propose an optimization model based on axis-aligned warping for object spatial remapping. The proposed axis-aligned warping based optimization model can simultaneously adjust the depths and sizes of selected objects to their target values without introducing severe shape distortions. Moreover, we propose object consistency constraints to ensure the size/shape of parts inside a selected object to be consistently adjusted. Such constraints improve the size/shape adjustment performance while remaining robust to some extent to incomplete object extraction. Experimental results demonstrate that the proposed method achieves high flexibility and effectiveness in adjusting the size and depth of objects compared with existing methods.