Reviewerly/exHarmony huggingface dataset:
A Study of Evaluation of Programming Skill by Analyzing Motion Data

Policies are being promoted to realize Society 5.0, which is Japan's vision of the future society. As a part of AI education, there is a need to enhance programming education at the primary and secondary education levels. Many teaching materials for learning programming have been proposed. On the other hand, from the perspective of fostering student competency, it is important to use ICT to monitor their learning situation and provide them with individualized instruction and support. This paper explains a method for quantitatively evaluating the proficiency process of programming skills. First, the learning tasks of programming in technology education will be explained. Next, the method for obtaining the learning result data will be explained. Next, show the results of learning tasks that the students worked on. Finally, consider showing the results in a graph or trade-off curve to quantitatively evaluation the students' skill level.
A task scheduling algorithm for cloud computing with resource reservation

Resource-reservation-based services have been provided where cloud resources are reserved for tasks within specific time intervals so as fully to exploit the benefits of cloud computing. Such a practice induces an additional decision for task allocation timing and a time index to resource status. Combined with the increasing scale of cloud computing, this increases the complexity of task scheduling in cloud computing. On the other hand, the quality and responsiveness of a task scheduling process should be pursued to meet the desired service features of the market. A task scheduling problem with time-dependent resource availability is formulated, and an heuristic algorithm that derives a high-quality solution to the problem within a very short time is designed to address these challenges. The performance of the algorithm in terms of its optimality and computation efficiency is demonstrated by comparison with the optimal bound found by a commercial optimization solver.
What's Coming in Future Issues

AORN JournalVolume 115, Issue 3 p. 294-294 What's Coming in Future Issues What's Coming in Future Issues First published: 25 February 2022 https://doi.org/10.1002/aorn.13648AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume115, Issue3Special Focus Issue: RoboticsMarch 2022Pages 294-294 RelatedInformation
Museum Experience and Digital Consumption

This study explores the role of mobile augmented reality (MAR) technology in enhancing user experience (UX) in the context of cultural heritage. Data were collected via individual semi-structured interviews, according to a theoretical model of UX. The findings show that MAR can enhance UX in cultural heritage museums when the product's features and characteristics match users' needs. A contemporary challenge in the cultural heritage industry is to find creative ways to attract new visitors and enhance their museum experience. Therefore, this study was conducted to improve museum curators' understanding about the UX of MAR apps to satisfy their visitors' expectations.
Software-Assisted Japanese Phonetic Teaching Based on Phonetic Visualization

Phonetics teaching plays an important role in Japanese professional courses. Nowadays, various voice-assisted teaching application software are available, but how to make full use of such resources and skills to closely integrate with actual teaching activities and improve students’ current voice teaching methods is still a difficult point. This article aims to study the software-assisted Japanese speech teaching based on speech visualization. Based on the analysis of the advantages of speech visualization in speech teaching, the characteristics of Japanese, the reasons for the difficulty of learning Japanese, and the strategies of Japanese speech teaching, it is necessary to understand the advantages of speech visualization software. The application effect of Japanese phonetics teaching, this article conducted a three-month experimental teaching for students in a class of Japanese major in a university, and selected another class with equivalent Japanese proficiency as the control class for comparison. The experimental results show that the software-assisted Japanese speech teaching based on speech visualization technology is effective and feasible.
A Study of Basic Education of Data Science using Robot Learning Material in Junior High School Technology Education

In Japan, the 5th Science and Technology Basic Plan proposed Society 5.0 as the new vision of society. However, according to future estimates of IT human resources, there will be a shortage of about 590,000 people by 2030. Given this situation, it is necessary to develop human resources who can solve problems and create value with a basic background in mathematics and data science. It is also necessary to provide data science education throughout elementary school, junior high school, and high school, and to provide it regardless of humanities or science. In this paper, we developed, practiced, and verified the effectiveness of education for designing programs using modeling based on the concept of data science in junior high school technology education.
Data Mining and Computer Software Database Programming Technology

With the development of computer technology, the application provided by the database has far exceeded its original data function. It can form a knowledge base and combine with data mining technology to form a database programming system and play an important role in the development of programming languages. This article establishes a database programming model on the basis of data mining and computer software, uses JAVA, Python, PHP, C++ and other programming techniques to test the model’s control effect on programming instructions and programming speed, and find a suitable database programming model for this article. Programming techniques to develop programming scripts.
Effective Combination of Intelligent Information Technology and Labor Education Data Analysis Algorithms

With the continuous deepening of curriculum reform, the examination-oriented curriculum has evolved into quality education, a new teaching model that emphasizes both theoretical knowledge and practical skills, and has become the consciousness of the society and education community. As a subject of cultural education, the labor education system has gradually changed due to the influence of schools, students and parents. However, the traditional methods of labor education and training are the same and may not meet the requirements of curriculum development in the new era. Therefore, research should be carried out on the combination of intelligent information technology and labor education based on functional characteristics. The purpose of this article is to study the effective combination of intelligent information technology and labor education. This paper determines the effective combination of intelligent information technology and labor education as the research goal, and then determines the data analysis algorithm based on the reference case and the data obtained, and compares the data obtained in the practice of this paper with the reference case. Based on the research on intelligent information technology and labor education, based on a large number of surveys and data comparisons, this paper comprehensively considers the advantages and disadvantages of the research direction, and considers the specific implementation of the plan from the perspectives of technical level, teachers and students factor. Then this paper uses data experiments to prove the validity and authenticity of the research direction and purpose of this paper. According to the comparative experimental data, based on the S-analysis method proposed in this paper and the calculation using the algorithm therein, the overall score and the passing rate of the examination in the experimental class assisted by intelligent information technology are higher than those in the reference class using traditional practical teaching. The average score is about 5.4 higher, and the passing rate of the assessment is 4.4% higher. The data has improved significantly.
Advanced Performance Tuning

How do you tune the Snowflake data warehouse when there are no indexes and limited options available to tune the database itself?
Facial Conditional Translation Using Multi-CartoonGAN with Central Biasing Instance Normalization

Image-to-Image Translation tasks sometimes need to deal with different appearance translations like real-world face to anime style. Existing studies try constructing robust models with many technical layers. However, the computational cost as well as the time required for training rapidly increases with the addition of layers. This study proposes N-to-N domains translation model named Multi-CartoonGAN which aims at learning difficult translation tasks and simulataneously keep the computational cost as low as possible. The Generator consists of a pre-trained VGG net and Central Biasing Normalization for stable training. The results show that the proposed model could perform not only facial emotion translation but also artistic face style translation including portrait, anime and emaki. Our model training also shows diminished computational cost compared to the existing models when dealing with difficult appearance translations.
Experiences with the Introduction of AI-based Tools for Moderation Automation of Voice-based Participatory Media Forum

Voice-based discussion forums where users can record audio messages which are then published for other users to listen and comment, are often moderated to ensure that the published audios are of good quality, relevant, and adhere to editorial guidelines of the forum. There is room for the introduction of AI-based tools in the moderation process, such as to identify and filter out blank or noisy audios, use speech recognition to transcribe the voice messages in text, and use natural language processing techniques to extract relevant metadata from the audio transcripts. We design such tools and deploy them within a social enterprise working in India that runs several voice-based discussion forums. We present our findings in terms of the time and cost-savings made through the introduction of these tools, and describe the feedback of the moderators towards the acceptability of AI-based automation in their workflow. Our work forms a case-study in the use of AI for automation of several routine tasks, and can be especially relevant for other researchers and practitioners involved with the use of voice-based technologies in developing regions of the world.
Design and Realization of Applied Linguistics MOOC System Based on Feature Extraction Algorithm

With the rapid development and popularization of modern information technology, many colleges and universities have used their own linguistic MOOC system, which has achieved remarkable results in management teaching, which has facilitated the learning of students. However, at that time, most systems were limited by the performance of software and hardware at the development and technical level, and did not consider how to develop and use the functions of the set-up system so that each student can learn easily. This article introduces the relevant theories of feature extraction algorithms, combined with the practical application of the university selection system, and applies the feature extraction algorithms to the MOOC system of applied linguistics. Analyze students’ historical academic performance on various topics, help students choose courses with the most potential, avoid students blindly choosing courses, so that all students can have their own satisfactory learning resources. Experimental research shows that this paper chooses 0.6 as the early probability of feature extraction algorithm, which is conducive to the realization of this process from global search to local search.
Blockchain-Based Internet of Things: Challenges and Opportunities

The majority of interaction in an IoTIoT (Internet of Things) ecosystem occurs via Machine-to-Machine interactions. As a result, establishing confidence among the participating equipment is a significant challenge, particularly given the fact that IoT technology has not been adequately addressed. The Blockchain enables autonomous smart devices and completely removes the need for intermediary parties. However, since Blockchain enables increased scalability, security, dependability, and privacy, it can function as a catalyst in this area. This may be accomplished by utilizing Blockchain technology to monitor and utilize billions of devices connected to IoT ecosystems in order to facilitate and/or coordinate transaction processing. By eliminating a single point of failure, the implementation of Blockchain in the IoT ecosystem will also improve reliability. The cryptographic algorithms used to encrypt the block data and the hashing techniques can provide additional security.
Facial Image Restoration Algorithm of Dazu Rock Carvings Based on Machine Vision

Image restoration is a technology that uses known information to supplement the content of defects, and it is one of the main research topics in the field of image processing. This paper studies the Dazu Rock Carving Face Image Restoration Algorithm based on machine vision, understands the related theories of the image restoration algorithm on the basis of literature data, and then designs the Dazu Rock Carving face image restoration based on machine vision and references it. The image restoration algorithm of this paper is used for experiments. Through experiments, it is concluded that the time-consuming and PSNR value of the improved algorithm in this paper is due to the traditional algorithm, which shows the superiority of the algorithm in this paper.
Iterative optimization of labeled data in CNN algorithm and its application to small faults identification

PreviousNext No AccessSEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021Iterative optimization of labeled data in CNN algorithm and its application to small faults identificationAuthors: Wenlu HuangJianguo YanHui LiWenlu HuangChengdu University of TechnologySearch for more papers by this author, Jianguo YanChengdu University of TechnologySearch for more papers by this author, and Hui LiChengdu University of TechnologySearch for more papers by this authorhttps://doi.org/10.1190/iwmg2021-16.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The representativeness and universality of labeled data is one of the important factors affecting the accuracy and generalization of CNN algorithm. At present, most fault identification methods in CNN-based algorithm use the theoretical labeled data. This paper proposes a method for iterative optimization of theoretical labeled data and field labeled data to obtain the final labeled data, which has good representativeness due to field seismic data based and good universality due to synthetic data based. The CNN model trained on the final labeled data has better generalization. The method was applied to identifying the small faults and fracture joint system in the deep buried hill reservoirs in Bohai Bay, where the seismic data appears low signal-to-noise ratio (SNR). The good results have been achieved, which proves the effectiveness and applicability of the method. Keywords: optimization, neural networks, machine learning, deconvolution, algorithmPermalink: https://doi.org/10.1190/iwmg2021-16.1FiguresReferencesRelatedDetails SEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021ISSN (online):2159-6832Copyright: 2022 Pages: 193 publication data© 2022 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 24 Feb 2022 CITATION INFORMATION Wenlu Huang, Jianguo Yan, and Hui Li, (2022), "Iterative optimization of labeled data in CNN algorithm and its application to small faults identification," SEG Global Meeting Abstracts : 61-64. https://doi.org/10.1190/iwmg2021-16.1 Plain-Language Summary Keywordsoptimizationneural networksmachine learningdeconvolutionalgorithmPDF DownloadLoading ...
Collectively Reimagining Technology

This article explores the use of participatory art and technology workshops as an approach to create more diverse and inclusive modes of engagement in the design of digital technologies. Taking the starting point in diverse works of science fiction, we draw on the concept of critical making and Ursula Le Guin’s disdain for the distinction between hard and soft technology to discuss the role of collaborative reimagining in the creation of technological futures. Such an approach facilitates a nuanced and reflected understanding for how technologies come to be designed and can empower more open and diverse participation in technology development.
A Study on Compositive Principles of Online Korean Language Education Contents focusing on the Development Case of Y University Basic Level Online Korean Language Education Contents

This study aims to show the principles for the development of online Korean education contents. The principles set by Y University to create online Korean language education contents can be broadly divided into technology, interaction, self-directed learning, and user convenience. First, through VR(Virtual Reality) technology, 360-degree rotation animation, and AI(Artificial Intelligence) voice recognition technology, we aimed for contents with the latest technology. In addition, in order to lead to successful learning, we have designed active interaction between learners and contents, and between learners and instructors. Besides, the importance of self-directed learning was recognized and learners were able to control learning speed, participation in various activities, selection their own language, self-check, and selection of auxiliary materials. And to secure user convenience, it is accepted translation and subtitles in the learner’s native language, reinforcement of visual delivery, and learning progress confirmation functions were included. (Yonsei University)
Legal Issues in the COVID-19 Pandemic

The legal issues arising from the COVID-19 outbreak are mostly unexperienced, with several fields of law involved—from human rights to market regulation—all with consequences that need to be further scrutinized. In their chapters, Aldo Schiavello and Guido Smorto investigate two, complementary, legal questions connected with the current pandemic. In his contribution on “Rights, balancing and certainty,” Aldo Schiavello analyzes the legal measures taken by Italian public authorities to contrast the virus, by focusing on indeterminacy as one of the main characteristics of the law in contemporary constitutional States. According to the author, the ways in which the legislator, at different levels, faces the COVID-19 current pandemic offer an interesting perspective to analyze the positive and negative effects produced by the indeterminacy of contemporary law. In doing this, the chapter ponders on the challenges to the objectualist conceptions of law that reduce law to a system of norms or a set of facts, and on those conceptions that regard the law as an interpretative social practice. Legal systems’ constitutionalization, as the chapter argues, increases legal flexibility at the expense of legal certainty, and it imposes to legislators, judges and individuals the responsibility and the associated risks of individuating the law. In the second article of the section titled “The right to health and resource allocation. Who gets what and why in the COVID-19 pandemic,” Guido Smorto reflects on the right to health and resource allocation during the pandemic. As the COVID-19 outbreak has led to a worldwide, substantial increase in the demand for resources that are essential to deliver health care—pharmaceuticals, hospital beds, ventilators, medical supplies—the article scrutinizes the rationales for prioritization of these resources in times of severe shortage, such as during a global pandemic. When needs suddenly exceed demand worldwide, resources may quickly become scarce in relation to potential demand, so that strict rationing is the only viable response. Against this backdrop, the paper scrutinizes the rationales for prioritization of scarce resources, with a special focus on the COVID-19 vaccine, and it questions the actual role and reach of the market with regard to resources that are essential to deliver health care, especially in times of severe shortage, such as during a global pandemic.
The Effect of Cyber Risk Management Services in Insurance Policies

Given the continuing growth in both the complexity and severity of cyber risk, a fundamental rethink of cyber risk management has become an issue of paramount importance, particularly as insurance firms are now providing both cyber risk management services and cyber risk insurance coverage. In this study, we set out to provide analyses of the prevailing cyber risk levels in various industries using the “Chronology of Data Breaches” database and then go on to assess the overall benefits of cyber risk insurance coverage. Our results reveal that compared to other industries, insurance firms exhibit superior cyber risk management. Regardless of internal and external cyber risk, insurance companies retain the lowest cyber losses. We further provide evidence to show that cyber risk insurance policies alone cannot effectively cover the potentially extreme cyber risk losses for most industries. However, the situation can be improved by implementing cyber risk management services provided by insurance firms. Insurance firms may need to provide an efficient cyber risk management system to lower the frequency and severity of extreme events.
Frequency down-conversion for efficient, low-noise quantum frequency converters

We present a novel device for the efficient, low-noise down-conversion of low-power light from the red visible spectrum. It can be applied to interface electronic spin-qubits in nitrogen-vacancy centers in diamond to the telecommunication wavelengths and thus offers the possibility to implement large-scale quantum networks in existing fiber infrastructure. In comparison to state-of-the-art frequency converters for this application, the device presented here generates an overall noise rate, which is one order of magnitude better than current record values. Simultaneously, an internal conversion efficiency of 50 % was measured. Furthermore, the converter serves as a feasibility demonstration of a general concept for the down-conversion of light from the sub-micron spectral range to telecommunication wavelengths.