Reviewerly/exHarmony huggingface dataset:
Web-based synthetic-aperture radar data management system and land cover classification

With the advance of radar technologies, the availability of synthetic aperture radar (SAR) images increases.To improve application of SAR images, a management system for SAR images is proposed in this paper.The system provides trainable land cover classification module and display of SAR images on the map.Users of the system can create their own classifier with their data, and obtain the classified results of newly captured SAR images by applying the classifier to the images.The classifier is based on convolutional neural network structure.Since there are differences among SAR images depending on capturing method and devices, a fixed classifier cannot cover all types of SAR land cover classification problems.Thus, it is adopted to create each user's classifier.In our experiments, it is shown that the module works well with two different SAR datasets.With this system, SAR data and land cover classification results are managed and easily displayed.
Research of Flight Safety and Technical Evaluation

Flight safety is the foundation of survival and development of civil aviation transport industry. Aircraft flight safety can be checked by aircraft flight parameters. We discuss a flight technology evaluation method based on flight parameter data. Firstly, data preprocessing is carried out, and the data is reduced by PCA dimension reduction. Then, BP neural network and decision tree model are established to train and test the data set. Neural network algorithm and machine learning algorithm are used for training. The results show that the BP neural network verification set is more accurate. The comparison table between the predicted results and the real results is shown in Table 3. Among the 15 samples, 14 are correct. The prediction accuracy reaches 93%.
Emojis Generation Based on Deep Convolution Generative Adversarial Network

With the development of information technology and mobile communication, people's usage of emoji is increasing. However, designing emojis by artists can be time-consuming and costly. Therefore, this study attempts to use the Deep Convolution Generative Adversarial Network (DCGAN) method in deep learning to automatically generate emojis. DCGAN is a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). It introduces convolutional networks into the generative model for unsupervised training, which can improve the learning effect of the generator network. DCGAN consists of two different models: a generator model and a discriminator model. The generator's role is to produce fake images that are similar to the training images, and the discriminator's role is to judge whether the fake image is the same as a real image. During training, the generator continuously tries to defeat the discriminator by producing better and better fake images, while the discriminator tries its best to distinguish between real and fake images. Through continuous optimization of the generator and discriminator, this study can finally obtain clear new emojis. Overall, the images generated by DCGAN can largely fulfill our purpose. Many novel emojis could be obtained to replace the work of human artists. However, the quality of the generated emojis has some defects, and it is still necessary to screen out images with strange shapes that are not suitable for daily life.
Sentiment Prediction by a Classifier

In real life, there is far more unprocessed data than labeled data, which brings a large amount of data that cannot be directly used for machine learning training. Based on the tweet dataset processed by Natural Language Processing (NLP), this paper uses a variety of machine learning models for training and comparison. Moreover, different performances are analyzed and discussed. Since labeled datasets are difficult to obtain, the use of supervised learning will be limited. However, the number of unlabeled datasets is very large, which can provide a continuous training set for machine learning. This paper conducted a comparative experiment on the effect of semi-supervised learning and obtained better results than supervised learning and unsupervised learning. The experiments in this paper prove that semi-supervised learning can effectively use unlabeled data and train machine learning models.
Deep Learning-Based Yoga Posture Specification Using OpenCV and Media Pipe

Yoga is a years discipline that calls for physical postures, mental focus, and deep breathing. Yoga practice can enhance stamina, power, serenity, flexibility, and wellbeing. Yoga is currently a well-liked type of exercise worldwide. The foundation of yoga is good posture. Even though yoga offers many health advantages, poor posture can lead to issues including muscle sprains and pains. People have become more interested in working online than in person during the last few years. People who are accustomed to internet life and find it difficult to find the time to visit yoga studios benefit from our strategy. Using the web cameras in our system, the model categorizes the yoga poses, and the image is used as input. However, the media pipe library first skeletonizes that image. Utilizing a variety of deep learning models, the input obtained from the yoga postures is improved to improve the asana. On non-skeleton photos, VGG16, InceptionV3, NASNetMobile, YogaConvo2d, and also InceptionResNetV2 came in the order of highest validation accuracy. The proposed model YogaConvo2d with skeletal pictures, which is followed by VGG16, reports validation accuracy in contrast, NASNetMobile, InceptionV3, and InceptionResNetV2.
Improving Job Performance Through Social Media: The Mediating Role of Transactive Memory Capability

This research examines the effects of social media use on job performance, transactive memory capability (TMC) and the role of transactive memory capability as a mediator between job performance and social media use.The study is conducted on the teaching faculty member in the North of India's public universities.A snowball sampling has been employed, and 608 respondents who met the study's selection criteria have been identified.The hypothesis and numerous interactions between variables of this study were tested using Structural Equation Modeling.It has been found that social media has a significant and positive impact on job performance.This study has also indicated a partial mediating role of TMC in the relationship between social media use and job performance.The study adds to the empirical literature by demonstrating the positive effects of social media use by the teaching faculty on TMC development and job performance.It highlights that social media can be considered a legitimate communication tool to increase workplace connectivity.Faculties should understand how social media generates transactive memory capability so that they can use it more effectively.It also fills the gap by considering TMC among teaching faculties working together to store, retrieve and share data through social media in Indian Public universities.
APPROACHES TO TEACHING COMPOSITION IN THE SYSTEM OF HIGHER PROFESSIONAL ARTISTIC AND ART-PEDAGOGICAL EDUCATION IN SAINT PETERSBURG: A COMPARATIVE ANALYSIS

In the modern Russian theory and practice of professional art education, the issue of understanding the essence and further development of disciplines related to the formation of Прикладные исследования, технологии 155 visual literacy is acute. One of them is the basics of composition, as well as its derivatives, with amendments to the orientation of the training of artistic and artistic-pedagogical personnel. Based on the analysis of the history and current state in the field of teaching of this discipline within the activities of the largest art universities and specialised departments of Saint Petersburg, the article proposes to determine the features of teaching compositional patterns in art to students. The subject of the study is the development of artistic and art-pedagogical education in Russia, and the object is the role of composition as a discipline in this process, depending on the type of educational institution.
Human Element in Cybersecurity. Strategies for Building a Stronger and More Secure Workforce

Today, many businesses, organizations, and individuals rely on the internet for operations, communications, and transactions. As such, cybersecurity has become a major concern, as it is the key to protecting sensitive data and protecting against potential cyberattacks. Resilient companies understand the importance of cybersecurity, and leverage key pillars of cybersecurity to ensure their security and resilience. This article will highlight what cybersecurity is and how resilient companies leverage it, highlight the benefits of resilient cybersecurity, and some possible strategies for improving resilience.
Building a Career Path Through Training Programmes Using ECSF

Although more and more services are moving into the digital space, there are not enough professionals to cover all the cybersecurity-related positions needed to ensure the security and resilience of organisations. The problem, known as the “cybersecurity skills gap and workforce shortage”, was highlighted even more during the COVID-19 pandemic and is affecting organisations worldwide. The European Union Agency for Cybersecurity (ENISA) recently released the European Cybersecurity Skills Framework (ECSF), a cybersecurity framework defining 12 cybersecurity role profiles, along with the main tasks, skills, and knowledge associated with each one. In this work, we will look into how individuals can leverage ECSF to skill up and plan their careers in cybersecurity. We will explain how professionals can associate their target cybersecurity-related job role with the ECSF roles profiles and how they can develop skills and knowledge for their next career steps. These steps can be used by new cybersecurity professionals joining the workforce, cybersecurity professionals already working in the field as well as professionals (usually from a related sector) wanting to shift careers to cybersecurity.
Experiences From a Multi-National Course in Cybersecurity Awareness Raising

The European Union (EU), as well as the entire world, is facing emerging challenges in the cybersecurity domain. Two of the most prominent challenges are citizens’ cybersecurity awareness which is the first line of defense against cybersecurity incidents, and the cybersecurity skill gap expected to lead to a future shortage of cybersecurity professionals. This paper presents an effort to combat those issues through the implementation of an intra-European course on cybersecurity awareness. The course engages university students from four EU member states who learn about increasing cybersecurity awareness while practically developing cybersecurity awareness activities for preadolescents. The paper provides an overview of the course and lessons learned from implementing it in international cooperation. The intention is to provide a guide for the development of such courses and outline success factors others can adopt and pitfalls that should be avoided.
Cybersecurity: An Appraisal of Policy Frameworks and Related Conventions

Cybercrime is a global threat to cybersecurity that crosses national borders and has no geographical boundaries. The global community establishes, ratifies, and implements common global accepted treaties and conventions to guide and direct cybersecurity. Nonetheless, the setting of internationally accepted standards for cybersecurity has been a difficult task. Cyberspace is a complex, erratic, and rapidly developing hybrid that is both physical and virtual. To eliminate chaos in cyberspace, improve cybersecurity, and eliminate cybercrime, a global framework of laws and conventions is required. To keep up with criminals' ability to exploit cyberspace, international laws and conventions must be gradually and robustly established and speedily and continuously amended. Adherence to stringent security measures and policies on the part of both public and private companies will aid in the implementation of increasingly stringent cybersecurity measures and international regulations in this regard. Cybersecurity must be prioritised in business. Companies should have cyberattack risk management strategies in place to prevent cyber breaches. Formal training for security cluster members and business representatives is required to effectively deal with the cybersecurity phenomenon.
A Pilot Study of Preservice Teachers Accepting and Using Chatbots to Enhance CFL Education

The aim of this study was to explore how preservice teachers of Chinese as a foreign language (CFL) perceive the integration of chatbots into CFL teaching. A group of 18 preservice CFL teachers participated in the study and were given two three-hour training sessions on chatbot editing. During the training, they had the opportunity to create their own chatbots to assist CFL learners in practicing conversation. Following the activities, the participants were asked to complete a questionnaire regarding their perceptions of the potential benefits of chatbots for CFL education. The findings indicate that the majority of preservice CFL teachers enjoyed the chatbot editing activity and agreed that chatbots should be incorporated into CFL teaching. Additionally, the CFL skills that they believed could be improved through the use of chatbots were identified and may be useful for future research and for educators.
Online Learning Model for Graduate Level to Support COVID-19 Pandemic

The COVID-19 Outbreak Situation caused educational institutions to close teaching to prevent the spread of harmful infections to students and other related personnel when the school was closed but students still had to study continuously. Online teaching is therefore the fastest and most suitable way to solve problems that arise; most especially at the graduate level Master’s Degree Ph.D. or even post-doctoral studies. It is very important that students and teachers have proper communication in order to achieve the desired academic achievements. This research presents an appropriate model for postgraduate online teaching in response to the COVID-19 time. The format consists of three activities: before, during and after online classes for students and teachers. In this regard, students and teachers can adapt to use according to their own suitability and the situation they are facing.
Creating an Engaging and Immersive Environmental, Social, and Governance Learning Experience through the Metaverse

In today's era of sustainability and ethical business practices, Environmental, Social, and Governance (ESG) have become a benchmark for many companies to follow. To ensure that students can quickly adapt to the workplace after graduation, it is necessary to provide them with relevant knowledge and an understanding of market conditions in education. Additionally, the emerging trend of the metaverse can be utilized as a tool to aid in learning. The metaverse, a virtual world that is rapidly developing, has become a popular tool in the education industry. It allows students to interact and learn in a simulated environment, making it a more engaging and immersive experience. Therefore, this research utilizes GOXR to develop a metaverse ESG teaching system, with the expectation that students can easily learn and understand ESG and the current operating model of the industry through its use. After experimental teaching, this study analyzed the survey results and found that most students have a positive view of the metaverse ESG teaching system. In addition to generating interest and motivation among students, the learning process was also relatively stress-free. Through the use of this system, students could easily comprehend ESG, which was helpful for their learning. The ESG teaching system developed in this research takes advantage of the benefits of the metaverse, providing an innovative and effective approach to learning.
Detecting Human Distraction from Gaze: An Augmented Reality Approach in the Robotic Environment

The investigation of human signals has been a topic of research since the advent of technology enabled their acquisition. Signals like gaze can reveal part of the human brain mechanisms. To facilitate the research of the analysis of these signals, researchers are developing and publishing datasets. This paper introduces the Augmented Reality DISTraction dataset (ARDIST), a novel dataset for processing human gaze. The primary objective of this dataset is to provide the scientific community with a resource that researchers can use to assess the distraction/attention levels of individuals performing specific tasks in the Robotic environment. The experiment was designed based on the latest findings in the field of neuroscience to include a diverse range of variables that affect human attention levels. Furthermore, the dataset can be utilized to model the human gaze signal under the influence of significant factors.
Handling Cross-Cutting Concerns in Subject-Oriented Modeling: Exploration of Capabilities and an Aspect-Oriented Enrichment

Subject-oriented modeling has recently found increased application for the modeling of processes in Cyber-Physical Systems (CPS). While models are generally used as a means of dealing with the complexity of such systems, modeling languages are challenged by various cross-cutting concerns that are inherently difficult to depict in an efficient manner. Common examples include regular logging mechanisms, authentication procedures, and the like. If not appropriately supported by the used language, modeling such concerns can result in duplicated model elements that are scattered across the same or different models. They furthermore become tangled with the core functionality needed to realize a business case. This negatively impacts qualities such as model understandability, reusability, maintainability, and evolution. Aspect-oriented programming and modeling concepts have been applied in different contexts to address such issues. In this contribution, we examine subject-oriented modeling with regard to its capabilities for supporting modularity and separation of concerns using the running example of a logistics CPS. Certain limitations are present that could be addressed by an aspect-oriented enrichment. We outline an initial proposal and discuss related considerations and challenges.
Effects of Interactive E-books Based on Graduated-Prompting Strategies to Enhance Self-efficacy of Medical Radiologic Technologists

With the development of mobile technology, multimedia e-books have become important learning resources. In clinical education, however, teacher-centered teaching still focuses on memory and understanding, resulting in poor learning outcomes. This is a common consensus in teaching. Therefore, this study aimed to develop a learner-centered, progressive-based multimedia interactive e-book and to explore its effect on the self-efficacy of radiologic technologists in brain multiphase CT imaging. The effectiveness of this learning mode was evaluated through the adoption of quasi-experimental research methods and experiments conducted with radiologic technologists in a teaching hospital in the northern region. The research results demonstrate that the interactive e-book based on the graduated prompting strategy can effectively improve the self-efficacy of radiologic technologists.
A Comparison of Absolute and Relative Neural Encoding Schemes in Addition and Subtraction Functional Subnetworks

As neural networks have become increasingly prolific solutions to modern problems in science and engineering, there has been a congruent rise in the popularity of the numerical machine learning techniques used to design them. While numerical methods are highly generalizable, they also tend to produce unintuitive networks with inscrutable behavior. One solution to the problem of network interpretability is to use analytical design techniques, but these methods are relatively underdeveloped compared to their numerical alternatives. To increase the utilization of analytical techniques and eventually facilitate the symbiotic integration of both design strategies, it is necessary to improve the efficacy of analytical methods on fundamental function approximation tasks that can be used to perform more complex operations. Toward this end, this manuscript extends the design constraints of the addition and subtraction subnetworks of the functional subnetwork approach (FSA) to arbitrarily many inputs, and then derives new constraints for an alternative neural encoding/decoding scheme. This encoding/decoding scheme involves storing information in the activation ratio of a subnetwork’s neurons, rather than directly in their membrane voltages. We show that our new “relative” encoding/decoding scheme has both qualitative and quantitative advantages compared to the existing “absolute” encoding/decoding scheme, including helping to mitigate saturation and improving approximation accuracy. Our relative encoding scheme will be extended to other functional subnetworks in future work to assess its advantages on more complex operations.
The Agile Adaptivity of Educators and Their Strategic Influence on the Learner During COVID-19 Pandemic

Although much damage has come from the COVID-19 Pandemic, the world has simultaneously changed to better in various sectors such as the education sector. In early 2020, education systems across the globe were rushed to migrate to whatever digital technology available in record time. During this transition, educators were noticed to have a positive impact on learners through the quality of academic support provided. One reason for this positive impact is the educator’s ability to think outside the box and immediately come up with creative solutions to adapt to the sudden changes in the environment hence motivate the learners to improve their performance quality through highly coordinated supervision. On a larger scale, such agility has lead to educational institutions’ strategic agility hence adjusting and adapting to new innovative ideas. Recent research has demonstrated that such an approach to sudden and drastic changes are successful. Through a critical review, this theoretical paper examines recent studies related to the role of educators and administrators in educational institutions as change catalysts in schools and universities during times of crisis, such as the present pandemic. Results show the primary role of educators in the success of the e-learning mode of study imposed during the pandemic. Educators who were able to swiftly come up with creative ideas to engage learners, while on unfamiliar and unprecedented online platforms, caused better learners’ achievement. Therefore, the agility of educators in educational institutions has been found critical to creating a sense of change in other direct and relevant parties such as the university presidents and school principals, as well as alumni affairs, learners’ admission, and the human resources department. This paper also clarifies the possible consequences of education systems ignoring the importance of adding virtual learning to the educational methods mix.
Prototyping with Generative AI

This chapter is a prototype in its 13th version. It describes prototyping, different types of prototypes, and how creatives of all kinds engage in prototyping all the time. The chapter presents some different prototypes that AI generates and how these can be used to augment and enhance your creative process. You don’t have to be a scientist, engineer, or technical wiz to prototype. Everything can be considered a prototype: a version of something that is not yet complete. It can be a something that is tested and evaluated and whose results inform the creator or creators if it is worth being further developed. Those creatives who work in game, xR, or mobile application development are more familiar with use of the term prototyping, but it is less common within artistic disciplines like music, theater, dance, and visual art. The prototyping process can also be unique regardless of the creative industry you are a part of. For that reason, it is important to define and provide examples of prototypes.