Reviewerly/exHarmony huggingface dataset:
A Study of Human Factor Based on Lecture Types during the COVID-19 Pandemic in Higher Education: Case Study of A University

This study aimed to identify learners’ levels of satisfaction and fatigue on three different lecture types ( lectures using a real-time video platform, lectures using an avatar-based Metaverse platform, non-real-time online lectures) in order to optimize and lead online teaching methods in the post COVID era. To achieve this aim, the study examined 206 students who participated in non-curricular programs available at A university in Korea and identified the correlation among immersion, presence, fatigue, and satisfaction depending on the lecture type. The study results showed that immersion and satisfaction were highest in real-time lectures using avatars on the metaverse platform. In addition, the study results showed that both immersion and fatigue had a significant correlation with satisfaction, and that immersion had a relatively higher impact on satisfaction than fatigue. In addition, since immersion and satisfaction have a significant positive (+) correlation, and fatigue and satisfaction have a significant negative (-) correlation, these results showed that the higher the immersion and the lower the fatigue, the higher the learner's satisfaction. This study is significant in that it empirically verified that differences in immersion, fatigue, and satisfaction according to lecture types and immersion and fatigue have an influence on satisfaction. However, this study has its limitations since it was conducted in students following willingly an off-curriculum program at A university and examined only special lectures in a specific field. Nevertheless, this researcher expects that the results of this study will be further supplemented through future follow-up studies. It is expected that the results of this study will be used as rough data to deal with the paradigm change that is the post-corona era.&#x0D;
Anxiety Experiences of Young People in the Non-face-to-face Age through Photovoice: The Broken Hearts of Young People and Disappearance of Youth

This study aims to reflect on various anxieties experienced by young people amid the prolonged non-face-to-face situation caused by COVID-19. For data collection, Photovoice, one of the participatory research methods, was used. Photovoice is a method in which the participant becomes the agent and takes pictures related to the research topic and actively interpret them. During the process, research participants become actively aware of the situation they are in, and can reflect and explore alternatives to the situation through photographs. This study selected a total of 12 young people as participants and held photo voice workshops and in-depth interviews. The analysis of the results of photovoice activities classified data into 5 categories, 17 topics, and 79 units. The result of the study found young people suffering from total anxiety such as the disappearance of exploring period to prepare for the future, economic crisis and gap between rich and poor, loss of emotional support groups and lack of relationships, shrinking job market, and dissatisfaction with universities. Despite the deepening psychological and emotional crisis and anxiety, young people re-planned their lives and prepared for new challenges. Along with the importance of adapting to digital technology, they were also aware of the negative impact of technology development. Therefore, this study is meaningful in that it analyzed their anxious lives in various aspects in the absence of previous studies on the anxiety experiences of young people in their daily lives after COVID-19, and that it confirmed that Korean society has the need to actively cope with youth anxiety.&#x0D;
A Study on the Operation of ‘Lecture Pairing’, a University Convergence Extra-curricular Program

The purpose of this study is to present the operation contents and results of ‘Lecture Pairing’, a convergence extra-curricular program at K University. The program is to develop creative results by pairing the liberal arts and major courses that K University students have taken and expanding them into convergence ideas. Students consulted with the professor in charge of the program when there were questions or discussions regarding the preparation and submission of reports (intermediate, final). In this study, from the 2020-2 semester to the 2021-2 semester, the progress of the program, and the satisfaction of student participation in the last three semesters were presented. The selection was conducted by a total of 5 professors in charge of liberal arts subjects including convergence areas. The evaluation criteria were content fulfillment, systemicity, originality, and convergence according to the purpose of this program. Looking at the results of the operation, the students had the experience of deriving new and creative results that they had never done before, and their satisfaction with the activities was highly evaluated. On the other hand, the limitations of the operation were that there were not many applicants due to the Corona 19 situation, busy academic schedule, and uncertain possibility of awards. Through this study, it will be possible to confirm the future direction of the operation contents and methods of the convergence extra-curricular program linked to the liberal arts curriculum of the university.&#x0D;
A Study on The Development of Regular Curriculum-based Class and the Analysis of Academic Achievement for College Students on Academic Probation

This study is to develop the academic strategic class in curriculum-based and analyse the change of academic achievement for the students placed under two times academic probation. This academic strategic class is that the students with two times academic probation are obligatory to participate in the class and take the grade. The academic strategic class is consisted of 15 weeks in regular semester. The students of this class are encouraged to have the interest in academic life and motivate to learn. Therefore, the basic purpose of this class is to prevent students form dropout college. The class was conducted among 62 the students placed under academic probation and the grade, credits and the escaping ratio from the bachelor’s warning was analysed comparing to previous semester’s. The results of the study are as follows. First, the academic strategic class for students on academic probation was designed and developed in regular curriculum-based. The developed class was provided for students on academic probation during 15 weeks with detailed subject. Second, the grade and credits of all the students on academic probation was improved comparing to previous semester’s. Third, 49 out of 62 the students(about 79.03%) placed under academic probation escaped from the bachelor’s warning. Based on the results of these researches, I would like to give some specific suggestions about academic strategic class that operate under the bachelor’s warning system in the university.&#x0D;
The Meta-Meta Selfie: A Social Semiotic Approach for Analysing Vibe Shifts in Pandemic Times

In pandemic times, journalists and cultural commentators have coined the term ‘meta selfie’ to describe a new type of selfie emerging that features selfies within selfies, centres the mobile phone screen in the image and highlights the irony of taking selfies. This study aims to develop a social semiotic method for analysing the ‘meta-meta selfie’. We use this term instead of ‘meta selfie’ as the ‘meta-meta selfie’ encompasses the idea of a ‘meta-meta perspective’ developed by Zhao and Zappavigna (2018), reflecting the complexity of the relationship between the self and technology. This study aims to link its findings to valuable work already conducted into influencer cultures (Abidin, 2017; Abidin, 2021) and internet ugly aesthetics (Douglas, 2014; Page, 2019). It also aims to show the importance of engaging with current journalistic work into digital cultures, and the role academic research has in systematically explaining trends that digital culture journalists have identified.
Haynayan AR: An Augmented Reality-Based Lesson for the Improvement of Learning Achievement in Cell Biology for the STEM Curriculum

ICT-infused lessons have been proven to significantly enhance the learning achievements in various disciplines specially during the pandemic. Most schools have adopted blended and remote learning. In Education 4.0, schools are relying on technology and among these educational technologies are Augmented Reality and Mobile-based Learning. These educational technologies are gaining popularity given their accessibility and advantages. So, the researchers designed and developed Augmented Reality-based learning Tool for Cell Biology and Basic Microscopy. The study aimed to assess the developed learning tool in terms of learning achievement and employed methods such as technology acceptance model for users and pre-post testing approach to assess learning outcomes. Findings revealed that Augmented-Reality based learning tools significantly enhance the learning achievement of students. Thus, the study suggests capability training for teachers on the use of such emerging technologies. Keywords: cell biology, education, augmented reality, educational technology
Load-Adjusted Prediction for Proactive Resource Management and Video Server Demand Profiling

To lower costs associated with providing cloud resources, a network manager would like to estimate how busy the servers will be in the near future. This is a necessary input in deciding whether to scale up or down computing requirements. We formulate the problem of estimating cloud computational requirements as an integrated framework comprising of a learning and an action stage. In the learning stage, we use Machine Learning (ML) models to predict the video Quality of Delivery (QoD) metric for cloud-hosted servers and use the knowledge gained from the process to make resource management decisions during the action stage. We train the ML model weights conditional on the system load. Numerical results demonstrate performance gains of ≈ 59% of the proposed technique over state-of-art methods. This gain is achieved using less computational resources.
Cynosuric Bodies

In this chapter, we discuss human–computer interfaces that translate bodily movements into sound, and we share expertise by introducing various concepts, case studies, practice-based research, and systems. Special attention is given to issues of accessibility and equity, which are crucial when engaging in embodied practices.
Image Synthesis Using Generative Adversarial Network

Generative Adversarial Networks (GANs) are a deep learning based generative model. GANs are a model for training a generative model and it is common to use deep learning models. Generative Adversarial Network(GANs) are a powerful class of neural networks that are used for unsupervised learning. GANs are basically made up of two competing neural network models which compete with each other and are able to analyze, capture and copy the variations within dataset. GANs achieve high level realism by pairing a generator which learns to produce a target output with a discriminator which learns to distinguish true data from the output of the generator. GANs used for Image Synthesis generates high resolution images. Text to face generation is a sub domain of text to image synthesis, and it has a huge impact along with the wide range of applications on public safety domain. Our proposed fully trained GAN outperformed by generating the good quality images with accordance to the input sentence. StackGAN aims at generating high resolution photorealistic images. The Stage-I GAN sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and the text description as inputs, and generates high-resolution images with photo-realistic details.
A Survey of Cyber-Threats for the Security of Institutions

Cyber security is an important concept, and its importance is increasing daily in every aspect of our daily lives. The increase in technology usage areas and rate in online platforms is one of the most important reasons for the security requirement. Today, the active use of information technologies is seen in many sectors, such as health, tourism, education, transportation, communication, and banks. Companies operating in these sectors may suffer severe financial losses, and their reputations may be damaged in case of possible cyber-attacks. This situation makes corporate information security an important topic. Cyberattacks on companies can be implemented with various methods and techniques. It can be grouped as social engineering, physical attacks, and web application attacks. In this study, cyber-attack threats that institutions are exposed to were examined. The study aims to investigate the possible attacks that can be carried out on institutions. As a result of these examinations, it is a resource that provides an understanding of the importance of cyber security.
Comprehensive bone marrow analysis integrating deep learning-based pattern discovery (BMDeep)

Bone marrow morphology forms the basis for the assessment of hematopoiesis. The currently established approach is dependent on manual microscopic counting of a limited number of cells by specially trained personnel, which is inherently associated with substantial intra- and inter-individual variability. The aim of our project is to automatize and improve the evaluation of bone marrow smears and to identify pathological patterns in pediatric leukemia.
Food Design Small: Reflections on Food, Design and Language, Sonja Stummerer and Martin Hablesreiter (2020)

Review of: Food Design Small: Reflections on Food, Design and Language , Sonja Stummerer and Martin Hablesreiter (2020) Berlin and Boston, MA: Walter de Gruyter GmbH, 223 pp., p/bk, £30.50 ISBN 1866-248X 978-3-11-067975-5
Application of Artificial Intelligence Technology in College Students' Foreign Language Learning

Based on the introduction of Artificial Intelligence, collation, and summary of the current situation of Chinese College English Education, this study points out AI technology has much potential in education. Therefore, how to make excellent use of it appears as a fundamental issue among Chinses scholars. It is suggested that Chinese college students&#x2019; English learning is achieved by means of AI in 3 perspectives, namely as an intelligent teacher, an emotional soother, and a rater to reach the goal of developing college students' multiple intelligences under the current high-speed economic and social background.
Scoring <i>Eldest Souls</i> (2021)

In this interview conducted by Hannah Capstick, composer and sound designer Sergio Ronchetti explores the process of creating music for the game Eldest Souls (Fallen Flag Studio, 2021), which was nominated for Best Audio for an Indie Game at the 20th Game Audio Network Guild (GANG) Awards. The interview covers topics including creating the game’s audio during the COVID-19 pandemic, the influence of metal on the score, first experiences of producing audio for a game, initial experiences in the industry, self-directed education, and Ronchetti’s design and career philosophies.
TRENDS IN THE TRAINING OF MODERN LEADERS

The purpose of the study is to consider the problems and opportunities for providing business with specialists and managers with professional competencies that correspond to the level of the modern economy and are adequate to the current situation. The article identifies the main trends of changes in the business environment, including the instability of the external environment, the need for constant changes, the rapidity of these changes, digitalization, which requires special competencies from employees. Some concepts of continuous education are analyzed. It is proposed to solve the problem of the discrepancy between professional competencies and the lack of desire for professional and personal development among employees by building a system of continuous education. The methodological basis for the analysis, generalization and conclusions was the research of foreign and Russian scientists and practitioners in the field of management theory, lifelong education. The article summarizes some of the results of studying the readiness of young people for additional professional education in the field of digitalization, obtained in the course of a study conducted at the State University of Management.
A Privacy-Preserved Transfer Learning Concept to Predict Diabetic Kidney Disease at Out-of-Network Siloed Sites Using an In-Network Federated Model on Real-World Data.

Successful implementation of data-driven artificial intelligence (AI) applications requires access to large datasets. Healthcare institutions can establish coordinated data-sharing networks to address the complexity of large clinical data accessibility for scientific advancements. However, persisting challenges from controlled access, safe data transferring, license restrictions from regulatory and legal concerns discourage data sharing among the in-network hospitals. In contrast, out-of-network healthcare institutions are deprived of access to any big EHR database; hence, limiting their research scope. The main objective of this study is to design a privacy-preserved transfer learning architecture that can utilize the knowledge from a federated model developed from in-network hospital-site EHR data for predicting diabetic kidney cases at out-of-network siloed hospital sites. In all our experiments, transfer learning showed improved performance compared to models trained with out-of-network site datasets. Thus, we demonstrate the proof-of-concept of transferring knowledge from established networks to aid data-driven AI discoveries at siloed sites.
Performance Analysis of Adaptive Variable Exponent Based Total Variation Image Regularization Algorithm

In this paper, a new adaptive variable exponent based total variation image regularization algorithm is proposed. In the proposed algorithm a regularizing term based on variable exponent is used. The model adaptively switches between TV-ROF model and Tikhonov model. At the edges the model behaves like a ROF model preserving edges effectively. In the inner region the model behaves like a Tikhonov model which enables strong smoothing. The weight of the fidelity term is also adaptive. The weight is large at edges and small in the constant flat area. In this paper the performance of proposed adaptive variable exponent based total variation model is compared with TV-ROF model and Tikhonov model. The performance of the proposed model is compared with other classical diffusion algorithms such as perona-malik model and self-snake model. In addition the proposed model is also compared with nonlocal means filter. The performance of the proposed algorithm is validated by denoising rician noise corrupted brain magnetic resonance images as well as denoising salt and pepper noise corrupted standard images.
Diversified Concept Attention Method for Knowledge Tracing

Through the learner’s historical learning to study the knowledge state over time, knowledge tracing can predict the learner’s future learning performance. It is an important issue in personalized tutoring. Although there are many related works on knowledge tracing, the existing methods still have some problems. For example, the tracing exercises are limited to a single historical exercise, the concept to be tested is limited to a single knowledge, and the semantic information on multiple concepts is rarely explored. To address this issue, a Diversified Concept Attention model for Knowledge Tracing (DCAKT) is proposed in this paper. Our method makes up for the shortcoming of the single concept of tracing, which applies natural language processing technology to get multiple concepts from the attention layer of emotional consciousness over time. Then we use an attention mechanism that evaluates the correlation between the exercises to be tested and historical concepts. This article uses the real datasets of the ASSISTments intelligent tutoring platform and a college engineering course on statics to assess the performance of DCAKT. In addition, our method can independently learn meaningful exercise sequences containing correct concepts.
A Random-Walk-Based Heterogeneous Attention Network for Community Detection

Community detection in complex networks can find the community structure one of the most important properties of complex networks. Nodes in the same community have more dense connections than those in different communities, which can be utilized to analyze the function of complex networks. In addition, heterogeneous networks are ubiquitous in the real world. For example, academic networks have different types of nodes such as authors, papers, and conferences. Network representation learning is an important method to discover the complex nonlinear relationships between nodes in the network, which is of great help to community detection. Attention network is a typical network representation learning method, and it will pay attention to the important part in the network for the specific task. However, most existing heterogeneous NRL algorithms use metapaths to capture heterogeneous information, which requires prior knowledge to set metapaths in advance. This paper proposes a novel random-walk-based heterogeneous attention network (RHAN) for community detection on heterogeneous networks. Random walk is used to generate the neighbor nodes set of nodes, and heterogeneous information is considered by the intra-type attention and the inter-type attention, which is no need for metapaths. The experimental results on four widely used heterogeneous networks verify the effectiveness of RHAN.
Self Driving Car in a Constrained Environment

The purpose of the research is to build a machine learning model which can drive a car on the tracks of Udacity’s Car simulator without any human intervention. This is achieved by mimicking the human driving behaviour in the training mode on a track. A dataset is generated by the simulator based on the human driving behaviour in the training mode and a deep learning model is built using this dataset which is then used to drive the car autonomously on any unseen track. Initially the model performed well only on the already seen track and failed to perform well on new unseen tracks. The simulator track in which the car was trained with didn’t consist of any sharp turns or elevations or any other road barriers, but the real world tracks do contain them, so in order to overcome this problem image processing techniques like zooming, changing brightness, flipping images, panning were used and in order to avoid over-fitting problem more dataset was generated using data augmentation techniques. Finally a model was built which was able to generalise the tracks and drive the car autonomously on the unseen track of the simulator.
Malayalam Language Textual Inference Identification Through Attention over Embeddings

This work focus on natural language inference between two text fragments, which is a uni-directional relationship. Natural language inference is attempted for a language called Malayalam. The Malayalam language is a South Indian, low-resource language. NLI is a subtask in every language because of its importance in summarization, information retrieval, and many other applications. There are only a few attempts in NLI for the Malayalam language. In this work, the application of the attention mechanism helped to enhance the classification performance for binary and multiclass systems. A densenet with additive attention is implemented for softmax and sigmoid classification, increasing accuracy, recall, and F1-score.
Small Convolutional Network for Arrhythmia Classification in Electrocardiogram Signals

We have studied if a small neural network proposed for EEG analysis can be applied to ECG analysis, trying to generate a model capable to clasifying ECG signals and suitable for being embebed into wearable devices.