Reviewerly/exHarmony huggingface dataset:
Cryptocurrency Liquidity During Extreme Price Movements

The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrencies—bitcoin, ethereum, ripple, litecoin and dash—and two cryptocurrency indices—Crypto Index (CRIX) and CCI30 Crypto Currencies Index—to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTs’ profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.
Feasibility study of VR immersion technology in psychotherapy

The research object of this paper is the innovation of the application mode of VR immersion technology in psychotherapy. This paper put forward relevant optimization policies through the discovery of the problems existing in the application process of VR immersion technology in psychotherapy. First of all, the background of VR immersion technology and the status quo of the technology in psychotherapy are discussed. And then the importance of VR technology in psychotherapy is deeply analyzed. Based on the above content, the problems existing in the application of VR technology in psychological aspects are put forward and including the shortage of applied talents and the insufficient application of VR social psychotherapy services. Finally, on the basis of finding these problems, this paper puts forward some targeted optimization strategies.
Efficient-VDSR Network for Super-Resolution

Super-resolution image processing is an important research topic in the field of image processing. The exploration in this paper comes from the use of VDSR network, which can improve the learning rate by only detecting and learning the residuals since the difference between low-resolution and high-resolution images lies more in the high-frequency detail part. Increasing the depth of the network can significantly improve the learning rate and further affect the final rendering of the image, but the increase of weight layers also increases the number of iterations and training time. This paper intends to reduce the number of iterations by decreasing the number of weight layers and correspondingly shorten the training time of the model. Then, we create low-resolution images and improve the resolution of the images by using bicubic interpolation method and the trained network respectively, and then obtain the evaluation data of the image quality by final visualization and quantitative comparison. The results show that reducing the number of weight layers still leads to a high accuracy of the images and will reduce the number of iterations and time accordingly.
Research and development of a two-factors face authentication system using an invisible F-QR code on ID card by 1064 nm laser and AI camera

The face authentication system using two security factors: an invisible F-QR (face QR) code printed on an ID card and face recognition using AI camera is presented in this paper. F-QR codes with facial features are generated by a chain processing of face detection, face property extraction and QR code generation. The F-QR code is printed on a plastic ID card using a 1064 nm fiber laser. The ID card is made of special materials, including a layer of filter material that allows the 1064 nm laser to pass through while blocking visible lights so that the printed F-QR code on the card is invisible to the human eye or conventional QR code readers. Face authentication is implemented using a dedicated invisible F-QR code reader combined with an AI camera to decide the Cosine similarity between two face feature vectors to make an accurate decision for the authentication.
A Comparative Study and Analysis of Time-Series and Deep Learning Algorithms for Bitcoin Price Prediction

Bitcoin is the world’s highest-valued and highly volatile digital currency that has grown very rapidly and gained popularity all over the world in a very short time. In this paper, we have implemented the Time-series ARIMA model, Seasonal ARIMA (SARIMA) model, LASSO (Least absolute shrinkage and selection operator) Regression model, RNN (Recurrent Neural Network), and LSTM (Long short-term memory) to predict Bitcoin price. Then we analyzed and compared the performance of all the implemented models, then finally proposed a more accurate and reliable framework for Bitcoin price prediction considering all the parameters that can cause fluctuations in the Bitcoin price.
30 Years Journey of KSTEL: Achievements and Prospect of Teaching English Drama

The Korean Society for Teaching English Literature (KSTEL), founded in August 1992, celebrates its 30th anniversary this year in 2022. At this point, it will be meaningful to look back and reflect on the past journey of KSTEL and to anticipate and prepare for the future. For this purpose, ‘archiving’ academic achievements of KSTEL so far is a course that must be taken in preparation for the future, and will be a necessary step at this moment of time when the past and the present meet and, at the same time, the present encounters the future. This paper aims to survey the ‘English drama education’ articles published in The Journal of Teaching English Literature of KSTEL from the first issue in 1996 to the 2021 winter issue, and to track changes and trends in pedagogy as well as research methodology and themes during the period. In addition, while commemorating the 30th anniversary of KSTEL, it also intends to serve the purpose of ‘archiving’ the scholarly research and achievements in English drama education papers published in The Journal of Teaching English Literature, as a work of looking back on the past journey of KSTEL and subsequently creating a map for future travel.
A Study on the Importance of Data Balance for Digital Transformation: Based on the result of data balance analysis for AI learning

This study shows the need of data balance by analysis the balance of data collected by the government.&#x0D; As a result of analysis of vehicle recognition data, only 231(3.08%) were significant data, even though 7,500 pieces of diversity were required, and the rest were all listed in the overlapping data. This means that biased artificial intelligence is highly likely to be created.&#x0D; The introduction of artificial intelligence technology is too important and fundamental a change, it cannot be achieved only by the technical efforts of individual companies in quality. Only when the government or public institutions provide necessary standards and provide minimum infrastructure can the competitiveness of companies as well as social stability related to new technologies be secured.
Enhanced deep learning frame model for an accurate segmentation of cancer affected part in breast

Summary Breast cancer is one of the primary causes of death in females worldwide. So, recognizing and categorizing breast cancer in the initial stage is necessary for helping the patients to have suitable action. In this research, a novel spider monkey‐based convolution model (SMCM) is developed for detecting breast cancer cells in an early stage. Here, breast magnetic resonance imaging (MRI) is utilized as the dataset trained to the system. Moreover, the developed SMCM function is processed on the breast MRI dataset to primarily detect and segment the affected part of breast cancer. Additionally, segmented images are utilized for tracking in the dataset to identify the possibility of breast cancer. Moreover, the simulation of this approach is done by Python tool, and the parameters of the current research work are evaluated with prevailing works. Hence, the outcomes show that the current research model has improved accuracy by 1.5% compared to existing models.
Sentiment Analysis on User Reviews of Mutual Fund Applications

The primary goal of this study is to compare the accuracy of the results of sentiment analysis using the Naive Bayes, Support Vector Machine (SVM), and Random Forest methods on one of the mutual fund application's user reviews.The second goal is to identify user reviews of the mutual fund app to gain insight into the topics covered by each sentiment.The user reviews have been collected through a web scraping method on the google play store, then cleaned through several processes of data pre-processing.Feature extraction was performed using TF-IDF along with vectorization using ngrams.The model performance was measured using a confusion matrix.Using a ratio of 80:20 on training and testing data, resulting in an accuracy of 92.7, 93.7 and 94.2% for Naive Bayes, SVM, and Random Forest methods, respectively.Identify the topics covered by each sentiment in user reviews using visualizations.In the positive sentiment of users, the majority discusses the application which is easy and good, especially for novice investors.In negative sentiment, the majority discussed the slow sales process to disbursement of funds and long loading times when opening the application.
On the problem of using information and communication technologies in the aesthetic education of students

The article deals with the problem of aesthetic education of the younger generation. The authors present an analysis of the psychological and pedagogical literature of domestic and foreign scientists dealing with the issues of aesthetic education of children. The organization of the educational process of primary school students and the development of their aesthetic worldview in modern socio-cultural conditions is impossible without the use of information and communication technologies (ICT).&#x0D; The article describes in detail the process of the formative stage of the experiment of aesthetic education of primary school students in the conditions of a pandemic and acceleration of the process of transformation of the educational system using digital technologies. The possibility of effective use of information and communication technologies for conducting extracurricular educational activities in the process of forming an aesthetic intelligence of younger schoolchildren is substantiated.&#x0D; As an effective means of ICT to solve the stated problem, the article identifies a virtual tour. In this study, the authors consider the classification of virtual excursions, the features of the use of some of its varieties in the educational process during distance learning.
Being social

Human beings become and remain social, across time and space, in and by communication. Communication accomplishes the socialization and acculturation of individuals in the mostly ordinary, if occasionally extraordinary, processes of their everyday interactions. This chapter introduces the idea of affordances from ecological psychology to specify the scaling of human communication and social networks in current communication systems. It draws on the ethnographic fieldwork data from each region to explore the contexts in which, and the criteria by which, people elect to engage in communication with others, and to make themselves available for communication in the first place. The chapter explores the extended range of ways in which it has become possible to be social through ‘social media.' It briefly considers the question of how communicative practices relate to people's affiliation with social and cultural organizations – which constitute consolidated, institutionalized indicators of specific ways of being social.
Effectiveness of Multimedia-Based Learning Materials For Deaf Students In Online Learning

Hearing impairment experienced by deaf students has an impact on language and academic aspects. Therefore, in online learning, there are numerous problems for deaf students especially to get information that is easy to understand. One way to overcome this obstacle is by making multimedia-based learning materials that consider the principles of visual learning for deaf students. The purpose of the study was to improve students learning outcomes and assess the effectiveness of multimedia-based learning materials using animated videos. The videos are equipped with sign language, visual explanations, and case examples. The method used in this research is mixed. A qualitative approach was used to develop the multimedia and a quantitative approach was used to analyze participants’ performance. The result shows that presenting materials through multimedia animation videos is effective in improving students’ learning outcomes, increasing their participation in online learning, and creating accessibility for deaf students in an inclusive classroom.
Interrogating a National Narrative with GPT-2

In this lesson, you will learn how to apply a Generative Pre-trained Transformer language model to a large-scale corpus so that you can locate broad themes and trends within written text.
A Study on Participants’ Feedback on Program Delivery of Klik Dengan Bijak (KDB) Organized by Malaysian Communications and Multimedia Commission

This study explores the participants’ feedback on the program delivery of Klik Dengan Bijak (KDB) through the focus group discussion method. To encourage positive use of the Internet, the Malaysian Communications and Multimedia Commission (MCMC) organised the KDB program as part of the government initiative to educate the public about internet safety and security. In any program, it is important to study the actual feedback from participants who took part in the program to assess their awareness and receptiveness towards the program delivery. For this paper, the researchers conduct focus group discussions with KDB participants from four different backgrounds; professionals, educators, parents, and students. The analysis is to measure the feedback based on participants’ awareness, receptiveness, knowledge, attitude, and practice. The findings of the study reveal that the strength of the KDB program delivery is in the choice of topic and knowledgeable speaker that does provide a positive impact on participants’ knowledge and behaviour. The results also indicate other strengths and weaknesses before coming up with recommendations and suggestions to assist Malaysian Communications and Multimedia Commission to improve the delivery of the KDB program in the future.
Reference Books and Study Methods for the Kwabu 科賦 Writing in the Late Period of Chosŏn

This study explores the study method of Chosŏn Kwabu科賦 and its stylistic characteristics in the late period of Chosŏn. Kwabu was the only writing genre that had been tested in both low-level (小科) and high-level 大科 examinations in Chosŏn. Kwabu was usually made up of 30 paired phrases and each phrase was composed of 6 classical Chinese characters. Moreover, it ignored rhymes and rules of versifications which had been considered core features of the traditional Bu 賦 genre. Kwabu has received relatively little attention on the characteristics of this style of writing and its cultural significance, especially in the late periods of Chosŏn. This study investigates two Kwabu reference books in order to discuss its study methods and its core evaluation criteria.&#x0D; This article shall proceed in the following way: first, it examines the philological features of two reference books, Sobuhwibang騷賦彙芳, and Sodaeyuji小大由之. Especially the way of content structuring of each book will be closely examined in this part. Second, it looks at the content of two reference books to understand their way of preparation against the Kwabu writing. Third, based on the previous parts, this article discusses the studying methods and the evaluation standard of Kwabu. In particular, it focuses on historical changes in its studying methods by comparing them with the one suggested by Lee Sik 李植(1584~1647) in the mid-seventeenth century. This article traces, in conclusion, which literary qualities were mainly tested in the Chosŏn civil examination system.
Advances for Quantum-Inspired Optimization (abstract only)

AboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeINFORMS TutORials in Operations ResearchTutorials in Operations Research: Emerging and Impactful Topics in Operations Advances for Quantum-Inspired Optimization (abstract only)Fred Glover, Gary KochenbergerFred Glover, Gary KochenbergerPublished Online:14 Oct 2022https://doi.org/10.1287/educ.2022.0250AbstractIn recent years, we have discovered that a mathematical formulation known as the quadratic unconstrained binary optimization (QUBO) problem can embrace an exceptional variety of important optimization problems found in industry, science, and government. The QUBO model has emerged as an underpinning of the quantum computing areas known as quantum annealing and digital annealing, and it has become a subject of study in neuromorphic computing. Through these connections, QUBO models lie at the heart of experimentation carried out with quantum computers developed by D-Wave Systems and neuromorphic computers developed by IBM. Computational experience is being amassed by both the classical and the quantum computing communities that highlights not only the potential of the QUBO model but also its effectiveness as an alternative to traditional modeling and solution methodologies. We illustrate the process of reformulating important optimization problems as QUBO models through a series of explicit examples. We then go farther by describing important QUBO-Plus and polynomial unconstrained binary optimization models that go beyond QUBO models to embrace a wide range of additional important applications. These innovations, embodied in software made available through Entanglement, Inc., have produced the ability to solve dramatically larger problems and to obtain significantly better solutions than software being offered through D-Wave, IBM, Microsoft, Fujitsu, and other groups pursuing this area.This publication has no references to display. Video of this TutORial from the 2022 INFORMS Annual Meeting in Indianapolis, Indiana, October 18, 2022, is available at https://youtu.be/qdrXL8HL3cQ. Your Access Options Login Options INFORMS Member Login Nonmember Login Purchase Options Save for later Item saved, go to cart Tutorials in OR, TutorialsNew $20.00 Add to cart Tutorials in OR, TutorialsNew Checkout Other Options Token Access Insert token number Claim access using a token Restore guest access Applies for purchases made as a guest Previous Back to Top Next FiguresReferencesRelatedInformation Tutorials in Operations Research: Emerging and Impactful Topics in OperationsOctober 2022 Article Information Metrics Information Published Online:October 14, 2022 Copyright © 2022, INFORMSCite asFred Glover, Gary Kochenberger (2022) Advances for Quantum-Inspired Optimization (abstract only). INFORMS TutORials in Operations Research null(null). https://doi.org/10.1287/educ.2022.0250 PDF download
Validation of an Intensive Care Unit Data Mart for Research and Quality Improvement

Data derived from the electronic health record (EHR) is frequently extracted using undefined approaches that may affect the accuracy of collected variables. Further, efforts to assess data accuracy often suffer from limited collaboration between clinicians and data analysts who perform the extraction. In this manuscript, we describe the methodology behind creation of a structured, rigorously derived intensive care unit (ICU) data mart based on data automatically and routinely derived from the EHR. This ICU data mart includes high-quality data elements commonly used for quality improvement and research purposes. These data elements were identified by physicians working closely with data analysts to iteratively develop and refine algorithmic definitions for complex outcomes and risk factors. We contend that this methodology can be reproduced and applied across other institution or to other clinical domains to create high quality data marts, inclusive of complex outcomes data.
Association Rules Mining on Retail Data

The development in information technologies, artificial intelligence, and data mining benefits people in many areas. With this development, data stacks are formed through the storage of ever-increasing data. Accessing useful information from the data heaps is a very difficult process. This has led to the emergence and development of the concept of data mining. In this study, the relationship between the categories of the products sold by a company in the retail sector operating in Turkey was analyzed using the Apriori algorithm, which is an algorithm used in data mining. In the application, one-day sales data of the company was used. The data obtained was provided to extract the association rules with the help of Python. In this way, the purchasing habits of customers were determined by finding meaningful relationships between products using association rules.
<i>Cognitive Science: An Introduction to the Science of the Mind, Third Edition</i>

Back to table of contents Previous article Next article Book ReviewNo AccessCognitive Science: An Introduction to the Science of the Mind, Third EditionMichael Persenaire, M.D.Michael PersenaireSearch for more papers by this author, M.D.Published Online:14 Oct 2022https://doi.org/10.1176/appi.neuropsych.20050115AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail View article"Cognitive Science: An Introduction to the Science of the Mind, Third Edition." The Journal of Neuropsychiatry and Clinical Neurosciences, 34(4), p. 428 Access content To read the fulltext, please use one of the options below to sign in or purchase access. Personal login Institutional Login Sign in via OpenAthens Purchase Save for later Item saved, go to cart PPV Articles - Journal of Neuropsychiatry and Clinical Neurosciences $35.00 Add to cart PPV Articles - Journal of Neuropsychiatry and Clinical Neurosciences Checkout Please login/register if you wish to pair your device and check access availability. Not a subscriber? Subscribe Now / Learn More PsychiatryOnline subscription options offer access to the DSM-5 library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development. Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.). FiguresReferencesCited byDetailsCited byNone Volume 34Issue 4 Fall 2022Pages 428-428 Metrics KeywordsNeuropsychologyImaging TechniquesPDF download History Published online 14 October 2022 Published in print 1 October 2022
POSTMODERNIST PRACTICES IN CONTEMPORARY OPERA

Opera, an important link of contemporary culture, has a considerable influence on the individual and society, directing and enriching the cognitive, affective and attitudinal universe of the audience. Postmodernist ideology projects opera into a crisis, stimulating the development of controversial artistic forms. But, at the same time, the information environment gives Opera the possibility of transiting the closed spaces of the halls, propagating and broadcasting the lyrical performances in front of an impressive audience. The social and cultural-artistic context of the last decades has favored the rise of postmodern directorial opera, which invented multiple methods of interpretation, becoming a real challenge for many reviewers, but especially for the conservative opera audience. Without having negative connotations at its beginnings, the New postmodernist concept Regietheater/Regieoper surmounted the stage interpretations with aesthetic character of classical works, reaching today a maximum level of speculative excesses, radically distanced from the original content.