Reviewerly/exHarmony huggingface dataset:
The teaching material of Stirling engine and the acquisition of digital production technology

At Tokyo Metropolitan Vocational Skills Development Center, job trainings are provided for job seekers to attain basic skills for employment. We carried out 20 years manufacturing competition for improvement of the skill and interest to manufacturing. The competition was divided into four branches and the teaching material of the machine course is a Stirling engine. At the machine course of the Itabashi school, we worked on the production of Stirling engine to acquire the digital manufacturing technology. The digital manufacturing technology is a design technology by the 3D CAD, programing for the NC machine and operation of the NC machine. We reported the class situation, the production of Stirling engine for five years from 2007 to 2012. And we verified the class effect by the acquisition degree investigation and the questionnaire survey from students. As a result, those class training succeeded considerably in their job search. It was made clear those class training had the good effects for the students to acquire the skill of digital production technology. And we researched company about the introduction situation of the digital production technology and completion students about the situation after the employment passing for a certain period of time about 10 years.
External factors and economic growth in Tunisia: ARDL approach with structural change analysis

This paper examined the effect of external factors on economic growth in Tunisia. The economic analysis was carried out using recent quantitative technique of annual time series data from 1976 to 2017. Based on co-integration test with unknown structural breaks and ARDL bound testing we investigated importance of each factor in stimulating economic growth. Our results show that in the long-run FDI does not affect economic growth. Remittances and imports negatively affect economic growth. Exports promote economic growth such that a 1% increase stimulates economic activity by 0.702%. In the short term, our estimates emphasize a structural break in 1988 linked to the structural adjustment program. Likewise, FDI does not have a significant effect on economic growth while remittances and imports slow economic growth significantly at the conventional level. On the other hand, exports form a relevant engine of economic growth. Therefore, our conclusions imply that political decision-makers in Tunisia must guarantee certain level of training and infrastructure to ensure the gain of transfers of new technologies and experiences related to the FDI. Thus, Tunisia must encourage peoples living aboard to create new investment opportunities instead of just supporting their families for consumption. In addition, the state must develop financial system capable of transferring funds for investment in order to better benefit from remittances. Finally, the government must restrict import of consumer goods and allow import of equipment and machinery goods that promote production and economic growth.
The Proposal of an Expert System Consisting of Image Categorization Using Deep Learning and Application to Medical Diagnosis

Classification is one well-known type of supervised machine learning and has been developed and applied in the medical, and industrial fields. Currently, single machine learning models, such as support vector machine (SVM) and decision tree are widely implemented, but the difficulty of embedding human knowledge in application with image datasets impedes application extension. To address this problem, we propose a category classification expert system (CCES), which consists of a single semantic segmentation network model, deep learning network model, and classification machine learning model. Unlike a single machine learning model with limited functionality, CCES can embed human expert knowledge into the system, increasing model accuracy, sensitivity, and adaptability of the input with image datasets in the category classification field. We describe a medical category classification application using CCES in detail and describe the evaluation results.
Introduction

In this introductory chapter, you will get acquainted with Unity Virtual Reality development, a no-coding approach that you can use to create immersive virtual reality (VR) experiences. You will find out two ways that you can approach the material covered in the book. I will also discuss the other alternatives available for VR development and point out the advantages of using the no-coding approach. Finally, I will list the VR hardware required to follow along with this book.
Instrumental support of technologies for organizing group training for the development of soft skills in data science and analytics

The article deals with the problem of organizing training for data scientists and data analytics specialists using information technologies. The authors analyzed the current sets of competencies of data science and analytics, identified the problems of organizing their development, considered modern trends in the instrumental support of the learning process. Particular attention is paid to the peculiarities of the development of soft skills in data science and analytics, which should be taken into account in systems and platforms for learning support when building models for the formation of personalized content and learning paths within the course. The necessity of creating a multi-agent software application to support the pedagogical design of the course is substantiated, which allows to adapt the capabilities of modern software systems and learning platforms to increase the efficiency of group interaction and the formation of soft skills necessary in the implementation of data analysis projects. The results of the conceptual design of a multi-agent application integrated with modern learning platforms are presented: a UML diagram of use cases is proposed that provides support for the personalization of training not only at the individual, but also at the command level, the base classes of agents are highlighted and an ontological model is developed to support the formation of soft skills in data science and analytics, directions of further research are determined. The results obtained will be useful to support the formation of a full set of competencies for data science and analytics, as well as to increase the efficiency of group work and support the personalization of content in a hybrid or online learning format, both in the higher education system and in corporate divisions.
Flood Disaster Mitigation System Adopting Meteorological Data and Geographic Information Systems

Recently climate change causes greater disaster than before, and the conventional measures especially against flood disaster are not enough at present. Integrated measures of facilities and Information systems are reinforced to cope with these situations. This study describes a solution to the issues. Flood disaster mitigation system is designed and developed to mitigate the damages caused by flood disaster adopting meteorological data and geographic information systems (GIS). In the phase of requirements analysis, system dynamics (SD) techniques, such as Iceberg Model and causal loop diagram (CLD), were applied to clarify three leverage points (precipitation data observation, river level forecast, and evacuation/alarming issuing). Precipitation information was derived from MesoScale Model (MSM) data provided by the Japan Meteorological Agency, and river level forecast was conducted based on Urban Tank Model. Swiss Cheese Model, basin hydraulic control, necessity of information (software) and facilities (hardware) were adopted into the structure of the Iceberg Model. The test site of the system is planned in the basins of the Nogawa and Tama Rivers in Tama Area, Tokyo, Japan. This study relates to the 9th “Industry, Innovation and Infrastructure” and the 13th “Climate Action” of the sustainable development goals (SDGs) proposed by the United Nation (UN).
Learning from Imbalanced Crowdsourced Labeled Data

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Learning from Imbalanced Crowdsourced Labeled DataWentao Wang, Joseph Thekinen, Xiaorui Liu, Zitao Liu, and Jiliang TangWentao Wang, Joseph Thekinen, Xiaorui Liu, Zitao Liu, and Jiliang Tangpp.594 - 602Chapter DOI:https://doi.org/10.1137/1.9781611977172.67PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Crowdsourcing has proven to be a cost-effective way to meet the demands for labeled training data in supervised deep learning models. However, crowdsourced labels are often inconsistent and noisy due to cognitive and expertise differences among crowd workers. Existing approaches either infer latent true labels from noisy crowdsourced labels or learn a discriminative model directly from the crowdsourced labeled data, assuming the latent true label distribution is class-balanced. Unfortunately, in many real-world applications, the true label distribution typically is imbalanced across classes. Therefore, in this paper, we address the problem of learning from crowdsourced labeled data with an imbalanced true label distribution. We propose a new framework, named “Learning from Imbalanced Crowdsourced Labeled Data” (ICED), which simultaneously infers true labels from imbalanced crowdsourced labeled data and achieves high accuracy on downstream tasks such as classification. The ICED framework consists of two modules, i.e., a true label inference module and a synthetic data generation module, that augment each other iteratively. Extensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of the ICED framework. Our code is available at https://github.com/wentao-repo/ICED. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:imbalanced data, crowdsourcing
The Effects of High-Performance Work Practices on Turnover Intention with Mediating Role of Psychological Contract Breach: A Case of IT Firms in Oman.

The study aimed to test the HPWS and its employee outcomes in the IT sector in Oman with mediating role of Psychological Contract Breach (PCB). The study utilized a quantitative approach along with a cross-sectional survey method for data collection. Data is collected from the staff of selected IT firms (n=124). AMOS is used for analysis which consisted of two stages. In the first stage, the reliability and validity of the construct are tested; while, in the second stage, regression analysis is used for hypotheses testing. The study result shows that recruitment &amp; selection, training &amp; development, job security, and communication exerted negative and significant influence on staff turnover intentions; while, the result for promotion and autonomy turned out to be insignificant and negative insignificant. Furthermore, recruitment and selection, job security, and autonomy had negative and significant effects on employees’ perceived psychological contract breach (PCB), whereas training and development, promotion, and communication had small effects. PCB partly mediates between HPWS dimensions and staff turnover intentions, according to mediation studies based on indirect impact analysis. Based on these findings, we concluded that HPWS is critical for IT companies to generate positive employee outcomes.
Current Situation and Future of Research and Development of Ancient Character Database -A Case Study of “Digital Resource Library for Intelligent Internet Search of Chinese Characters”-

There are a lot of problems in the existing databases of ancient characters, such as the digital processing obstacles of Gaiji (characters excluded in character sets), the defects of information technology, and a lack of completeness. The newly released “digital resource library for intelligent Internet search of ancient characters” makes the following breakthrough: it achieves full coverage of all types of ancient characters in all dynasties, and realizes the carrier conversion of character data with strict digital standards, allows the the further processing of materials and the intelligent recognition search technology, and forms the series of thematic databases. There are two major tasks for the future development of the digital resource library of Chinese characters: the development of international standards for ancient character sets, and the further processing and sorting of ancient character materials for the intelligent research of ancient characters.
<i>Quiet Desperation, Savage Delight: Sheltering with Thoreau in the Age of Crisis.</i> By David Gessner

“Having never quite recovered from my early reading of Walden, I had always tried to make nature a part of my life” (104), writes David Gessner in Quiet Desperation, Savage Delight: Sheltering with Thoreau in the Age of Crisis. When the COVID-19 pandemic began, Gessner had been both rebuilding his writing shack on the marsh behind his house and reading Laura Dassow Walls’s biography of Henry David Thoreau. When the time came to social distance, Gessner was ready both with a place to reflect and the lessons from Thoreau that might be useful or comforting during a year of deep loss and uncertainty. Confronting the intersecting issues of the pandemic, climate change, and racial injustice, Gessner draws on Thoreau’s lessons about living simply and in place, about knowing the natural world, and about civil disobedience. Divided into entries from March 2020 through November 2020, with an epilogue from March 2021, the book is grounded in the early months of the pandemic. For each month, Gessner provides a count of global COVID-19 cases and confirmed deaths. A year after the last count, the book is still timely. Gessner writes, “Maybe what I’m after here is something you might call hard hope. A hope that looks directly at the big picture and admits how screwed we are. A hope that still celebrates the local victories and takes solace in the sheer wildness of the world, the implacable persistence of life determined to break through” (21). As new variants of the coronavirus continue to spread, as the IPCC continues to release dire warnings about the fate of our planet, and as we continue to confront injustices in our communities, both Thoreau’s and Gessner’s words ask: how might we do better?
Design of action detection system in wrestling match video based on 3D convolutional neural network

At present, there are some problems in motion detection in wrestling video at home and abroad, such as low detection accuracy and poor robustness.A motion detection system combining three-dimensional convolution neural network and recursive neural network is studied and designed.It uses three-dimensional convolution to obtain low-level feature code, then uses recursive memory module to obtain timing features, and finally completes motion detection according to timing features.Under the ratio of these three parameters, the accuracy of 3D-CNN convolutional neural network structure is higher than that of 2D-CNN.When the ratio of the influence factor of circular memory module P to that of circular memory module C is 1, the accuracy of 3D-CNN improves the fastest and the accuracy is close to 20%.The research results provide a new idea for the development of human motion detection and recognition technology.
The Yuen’s Dimension-Based Cultural Diversities in the ELT Textbook For Senior High School Students

The present study uses critical discourse analysis (CDA) to examine the representation of cultural diversities in the first year of senior high school English textbook published by a non-government publisher. Data of the present study are texts and pictures that represent cultural diversities in an Indonesian ELT textbook. The findings revealed that there are four cultural categories based on Yuen’s cultural dimensions (i.e product, practice, person, and perspective) coming from inner, outer, and expanding circle countries. The implication is that the visual materials must be balanced with the verbal materials. &#x0D; Keywords: Critical discourse analysis, cultural diversities, ELT textbook, senior high school, Indonesia.
Hand Gesture Recognition for Numbers Based on Machine Vision and Multi-feature Surface EMG Signals

This paper develops a combined system based on surface electromyogram (sEMG) and machine vision for recognizing hand gestures of numbers. The system will first detect and count the number of visible colored spots on a special glove when a gesture is completed. Then the ten gestures will be pre-classified into five categories according to that number. sEMG detection with K Nearest Neighbor (KNN) classifier is then introduced for further distinguishment. This paper compared the accuracy rate between detections with only basic time-domain features and detections with a more sophisticated combination of time-domain features and frequency domain features. The system is then tested with the complex feature combination on a data set containing one hundred different gestures. As a result, our group achieved an overall accuracy of 94.2%. Crucially, this study offers a new way of detecting hand gestures, which can be used in computer-based sign language recognition.
Preserving Privacy Scheme Using Data-CAAC Mechanism in E-Health Based on Hybrid Edge Computing

Smart Heath incorporates big data and IOT in recent years. The wearable devices such as gadgets watch are used to assist with the health activity and constant monitoring of the health. By using the integration of using wireless sensor networks, the large amount of data is associated with the patient’s health condition. As the data are stored in the cloud, privacy over preserving the data plays a vital role as the communication channel gets compromised. To increase the secure aspects over the communication range, Data-CAAC mechanism is used. Data-CAAC incorporates with the smart watch to constantly monitor the access over the data mechanism both in space and time context mechanism. LSH is used to enhance the secure mechanism by enclosing the confidentiality data along with it. To encrypt these data, Data-CAAC encryption technique is used to encrypt over the access control where the cuckoo filter carries out the data structure to avoid getting compromised. The automated SVO logic analysis is used to determine the correctness in accuracy where it achieves high efficiency and low computational cost. It attains high reliability with good accuracy performance.
Icon Similarity Algorithm Based on Skeleton Comparison

Icon plays an crucial role in infographics, which additionally carries essential functions in the human-computer graphical user interface (GUI). However, too similar icon is easy to trigger confusion in the process of using. In this paper, we explored the use of the cognitive rules from global to local based on the theory of topological perception, and built a computational discrimination tool from the human perception to describe similarity. Screening out icons that are too similar is the primary purpose of this research to avoid errors in use. We utilized skeleton algorithm to extract the global features of icons. The optimal subsequence bijection and Hungarian algorithm were used to compare the global skeleton of the icon. Accordingly, the similarity between the icons was calculated. To verify the proposed algorithm, we conducted a subjective cognitive experiment. Participants were asked to rank the similarity of the experimental materials and compare the results with the calculation outcomes. Results demonstrate that the proposed calculation methodology based on skeleton comparison is close to the subjective cognition, which can effectively describe the human perception of icon similarity.
Efficient and Secure Multi-Keyword Ranked Search and Group Data Sharing for Encrypted Cloud Data

Nowadays due to the popularity of clouds and their quality services, many of the users such as organizations, industries as well as individuals are migrating towards the cloud to store their important, confidential data and get easy access to their data anywhere anytime over the internet. Different encryption techniques are employed in cloud computing to ensure data confidentiality, security, and privacy. This makes it harder for endusers to retrieve precise data. Due to the huge amount of data over the cloud and multiple data users, secure data storage and retrieval are required. So in this paper develop an efficient data group sharing and multi-keyword ranked search method for encrypted cloud data collection in this research work. The developed system is implemented using the El-Gamal cryptography algorithm to provide security through effective key generation techniques and encryption strategy. Here, a multi-owner data setting is used instead of a centralized data owner setting; each member of the system in one particular group gets equal rights for both searching and sharing functionality and this may increase system usability. By taking into consideration lots of data in the cloud, the vector space model and TF-IDF model are utilized and according to the cosine similarity score, the method generates a ranked multikeyword search result to deliver effective query result from numerous data and enhance secrecy in the situation of numerous data owners. In this system searching efficiency is improved by developing an index-based search structure. In the group, data can disperse with co-owners/users by developing a role-based access policy (RBAP), and a user revocation strategy is developed with low computation time as well as communication overhead. At last, the efficiency and security of a developed system are exhibited by broad exploratory assessment.
COMPARTMENTAL MODELING AND DYNAMIC SIMULATION OF GINGER DRYING

The concept of Fourier Transformation and Laplace Transformation play an important role in diverse areas of Science, Engineering and Technology. Fourier Transform and Laplace Transform is also play an important role in the analysis of all kinds of physical phenomena. As a link between the various applications of these transforms the authors use the theory of signal and systems, as well as the theory of ordinary and partial differential equations. In this paper we established a Two Dimensional Fourier-Laplace Transform and investigated the Linearity property, Parseval’s theorem and Modulation theorem of Two Dimensional Fourier-Laplace Transform. The work may be useful for solving higher order ordinary and partial differential equations as well as integral equations.
The Effectiveness of Using Kahoot! in Teaching Arabic Grammar in Terms of Interest and Learning Results/ فعالية استخدام كاهوت! في تعليم القواعد العربية من حيث الاهتمام ونتائج التعلم

Grammar is an important part of the Arabic language. Words can be arranged into a correct sentence when following the existing rules. Learning Arabic grammar in Indonesia has many difficulties that come from students and teachers such as students have a low interest in learning Arabic and teachers have not tried new methods, techniques, and media in learning Arabic. So students' interest and motivation tend to be low towards learning Arabic. This research aims to find out the effectiveness of the use of Kahoot! games to improve the results and interests of learning Arabic students of class IX MTs N Balikpapan. This study uses quasi-experimental methods. Research samples are taken with random techniques. Data collection is done by means of tests, questionnaires, observations, and interviews. The instrument validity test is conducted using the product-moment correlation formula. Reliability test using the Alfa Cronbach formula. Data analysis is done with prerequisite tests of analysis and hypothesis tests. The results showed that there was a significant difference in results and interests in learning Arabic between control classes and experimental classes. This is indicated by the significance value on the t-test of -4.867 &lt; 0.05. The experimental class post-test average was higher than the control class post-test average (64.43 &gt; 42.67).