Reviewerly/exHarmony huggingface dataset:
Effects of Subjective Health Status Due to COVID-19 in Multicultural Youth on Anxiety: Stress, hand washing mediating effect

This study aims to analyze the effect of subjective health conditions caused by COVID-19 in adolescents on anxiety and to verify the mediating effects of stress and hand washing. To this end, data from the 17th Youth Health Behavior Survey in 2021 were used, and data from a total of 54,848 teenagers attending the first grade of middle school to the third grade of high school were analyzed. Data analysis was correlated with descriptive statistical analysis with SPSS 26.0, and mediated effect analysis was conducted using Process Macro 4.0. As a result of verifying the mediating effect of stress and hand washing in the relationship between adolescents' subjective health status and anxiety, subjective health status had a significant effect on anxiety, and stress and hand washing also had a significant mediating effect on anxiety. Based on this, it should contribute to the development of programs on hand washing, stress management, and anxiety intervention according to subjective health conditions.
A Study on Corona 19 Risk Perception, Class Participation and Learning Satisfaction of Nursing Students

This study was conducted to investigate the effects of nursing students' COVID-19 risk perception and class participation on learning satisfaction. Data collection was conducted from April to May 2021 with 168 nursing students from five universities in Busan and Gyeongnam. The collected data were descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, multiple using SPSS program 26.0. As a result, The learning satisfaction according to the general characteristics of the subject showed a significant grade (F=6.965, p&lt;.001), and in the correlation learning satisfaction (r=269, p&lt;.001) and COVID-19 risk perception showed a positive correlation, and learning satisfaction (r=.505, p&lt;.001) and class participation showed a positive correlation. Class participation (β=.444, p&lt;.001) and COVID-19 risk perception (β=.140, p=.041) were found to be factors influencing the subject's learning satisfaction. Therefore, in order to improve learning satisfaction of nursing students, a system that responds sensitively to changes and communication in various types of classes is established to increase class participation, and to provide fast and accurate information services on COVID-19 to effectively recognize the risk of COVID-19 and develop the ability to respond to it.
The Influence of Visual Feedback from AI speakers on Continued Usage Intention among Younger and Middle-aged Users: The Mediating Roles of Emotional Experience and Product Attitude

Artificial intelligence (AI) speakers are widely used in smart home environments, but user satisfaction is low, which can discourage user intention to continue using the device. To address this, researchers have suggested adding visual feedback to AI speakers. This study examined the effect of visual feedback on AI speaker usage intention and to verify the mediating effects of emotional experience and product attitude in younger and middle-aged adults. Videos of AI speaker disconnection situations were filmed, half of the participants were shown the videos (control group). The other half watched videos with visual feedback where context-related icons were added to the AI speakers (experimental group). For middle-aged group showed that visual feedback increased positive emotional experience, which in turn increased attitude toward the AI speaker, ultimately leading to greater intention to continue using it. However, in younger participants, emotional experience and product attitude did not mediate the relationship between visual feedback and usage intention. These findings imply that the influence of visual feedback on usage intention in user-AI speaker disconnection situations is depends on age, whereby visual feedback is especially efficacious for augmenting the usage intention of middle-aged AI speaker.
Decision Tree for Mode Estimation

Decision trees are one of the data mining techniques that make predictions by recursively partitioning data structures based on split rules. Since the analysis results can be understood through the tree structure, it has the advantage of having high interpretation power as well as predictive power. In addition, it is used in many fields because it is able to identify nonlinear relationships between response and predictor variables. However, if the purpose of it is to predict the mode of the response variable, there is a limitation in that the previously proposed decision tree cannot be applied. Thus, we develop a new form of the modal decision tree model by integrating the kernel density estimation methods into the decision tree model. The simulation is conducted with four models. The results are compared for each size of the data when the predictor variable and the response variable are linear and nonlinear relationship cases. When the data has a linear relationship, the performance of the modal desicion tree model proposed in this paper is comparative to that of the previously proposed modal linear regression (MODLR) model. When the data has a nonlinear relationship, the performance of the modal tree model is better.
Deepening Research and Upgrading of Configuration Software ViGET

ViGET is a Programmable Logic Controller (PLC) programming software for HVDC engineering. It is used to complete the editing, compiling, on-line and monitoring of control and protection programs in HVDC engineering. In view of the shortcomings of ViGET software and the user needs, this paper completes the reconstruction of the software framework and the embedding of the editor through Visual Studio Shell and Visual Studio Package technology, and completes the optimization and upgrading of the CFC graphical editor, version management, On-line and other functions. Improve the usability of ViGET software and the overall operation efficiency of the program, ViGET V2.0 has been released to provide better services for secondary engineering development.
Project Management and Ethics

In order for a project to be successful, there should be effective management of all members of the design team working closely with clients/stakeholders along with activities and resources available for the project. This chapter introduces the activities involved in project management along with examples. The activities are explained and illustrated, with examples as needed, to facilitate real-world implementation. Specific tools discussed in this chapter include Gantt chart and PERT techniques. The importance of professionalism and ethics are also discussed.
Legal Protection of Trademarks in Accordance with Intellectual Property Laws on Digital Networks and their Relationship with Domain Names

The world of communications has evolved in a remarkable way, allowing individuals to transcend the constraints of time, place, and borders through digital means, enabling them to transmit information such as images, sounds, and text in an interactive manner to any location around the world. The most significant manifestation of this evolution is the emergence of the international information network, commonly known as the Internet. As the Internet and electronic commerce have developed, a new type of identifier has emerged, known as domain names or website names, which are typically written in the form of www.google.com or www.enty.dz or similar variations. The emergence of these new identifiers has given rise to conflicts with trademarks, which are considered a crucial aspect of industrial property. Therefore, the purpose of this study is to clarify the conflicts that arise between website names and trademarks.
Real-Time American Sign Language Interpretation Using Deep Convolutional Neural Networks

In spite of being the 4th most commonly used language in the United States, sign language is actively used by only 10–14% of the members of the speech and hearing impairment community and continues to be one of the most understudied areas. In compliance with recent advances in deep learning, this paper explores the possibility of using deep convolutional neural network to interpret American Sign Language in real-time. The paper aims to develop a CNN from scratch and train it using a dataset patterned to closely match the format of the classic MNIST dataset (28 × 28 pixel images with pixel values ranging from 0 to 255). The evaluation of the network shows that it outperforms all previous implementations surrounding this task with 89.6% accuracy during real-time user testing (99.7% accuracy on validation set). Such high accuracy measure and fast converging time can be attributed to modelling the training data as a dataframe of pixel values rather than using traditional images and including batch normalization, concepts which have not been employed in earlier implementations to the best of our knowledge. To make the solution as accessible as possible, we refrained from using sophisticated hardware like motion-tracking gloves and depth-sensing cameras and deployed the trained model as a multi-platform mobile application.
Social Engineering Attack Detection Using Machine Learning

Since the 1990s online explosion, data security has been everyone's top priority while using the internet. It is crucial to take the proper precautions both at the organizational and individual levels to protect sensitive data from attackers who use social engineering flaws and human fallibility in addition to technological weaknesses. One of the prevalent methods is phishing where malicious emails are used in order to carry out these attacks. The combined use of Machine Learning (ML) algorithms and Natural Language Processing (NLP) provides a means to develop a system that can not only detect spam emails but also categorize emails by scanning their content and extracting information. In this research work, the authors have compared various ML algorithms such as Logistic Regression, Naive Bayes, Decision Trees, Gradient Booster, XG Boost, Random Forest, etc., and various NLP techniques to determine the best-performing model for such a system and implemented the same to develop a complete email security system that can help counter these prevalent social engineering attacks. The system architecture and methodology for source validation, spam detection, content scan, URL extraction and model analysis using various performance metrics like performance accuracy and execution time have been discussed in detail. The authors have also discussed how this approach can be used to develop a resource-efficient, yet accurate model which can be fine-tuned according to the needs of the organizations.
Analysis of Various Hyperparameters for the Text Classification in Deep Neural Network

A branch of artificial intelligence and machine learning called “deep learning” was developed to support machine automation in terms of regression and classification. To create an effective model in deep learning, a variety of variables play the necessary role. These variables include the activation function, the function of loss, the quantity of layers and the number of neurons, among other crucial variables. Due to time restrictions or projects having strict deadlines, it might be difficult and time-consuming to find the ideal working environment by adjusting all these aspects. The investigation of these factors that create the context in which deep neural networks operate is done in this research. The experimental findings will also be examined in order to determine the ideal setting for deep learning text classification. The professionals and researchers would be able to extract.
Similarity-Based Recommendation System Using K-Medoids Clustering

Most of the consumer applications of today make use of recommendation systems. From movie recommendations by Netflix to dating recommendations by applications like Tinder, recommendation systems have become ubiquitous in most of today’s commercial applications. Most commonly used recommendation techniques require a large amount of data, either as item ratings or ratings given by similar users. This vast data requirement makes it difficult to apply these techniques to cases where the required data is unavailable. Hence, there is a need for recommendation systems that can function with a small amount of data and provide practical recommendations. Empirically it has been found that people with similar attributes are likely to have similar tastes. Clustering can be used to group people with similar attributes. However, such clustering should be performed online as new users are continuously added to the application’s user base. This paper proposes a similarity-based recommendation approach that uses an online clustering technique to form clusters of people with similar attributes. Online indicates dynamic data, and re-clustering involves computing clusters for all the points in the system again, which is computationally expensive, contributing to most of the time taken by the algorithm. The proposed approach performs selective re-clustering, thereby enabling faster recommendations and reduced load on servers.
Predictive VM Consolidation for Latency Sensitive Tasks in Heterogeneous Cloud

Virtualization technology plays a crucial role for reducing the cost in a cloud environment. Efficient virtual machine (VM) packing method that focuses on compaction of hosts such that most of its resources are used when it serves the user requests. Here our aim is to reduce the power requirements of a cloud system by focusing on minimizing the number of hosts. We propose a predictive scheduling approach considering the deadline of a task request and make flexible decisions to allocate the tasks to hosts. Experimental results show that the proposed approach can save around 5 to 10% power consumption than the standard VM packing methods in most scenarios. Even when the total power consumption requirements remain the same as that of standard methods in some scenarios, the average number of hosts required in the cloud environment are reduced and thereby reducing the cost.
Knowledge Hypergraph-Based Multidimensional Analysis for Natural Language Queries: Application to Medical Data

In recent years, data is continuously evolving not only in volume but also in types and sources, which makes the multidimensional analysis and decision making using traditional approaches a complex and difficult task. In this paper, we propose a three-layer-based architecture to perform multidimensional analysis of natural language queries on health data: 1/ Treatment layer aiming at xR2RML mappings generation and knowledge hypergraph building; 2/ Storage layer allowing mainly to store the RDF triples returned by the query of NoSQL databases, and 3/ Semantic layer, based on a domain ontology which constitutes the knowledge base for the generation of the mappings and the building of the knowledge hypergraph. The originality of our proposal lies in the knowledge hypergraph and its capacity to support multidimensional queries. A prototype is developed and the experiments have shown the relevance of the returned multidimensional query results as well as an improvement over traditional approaches.
Pengamatan atas Penggunaan Bahasa Indonesia dalam Media Pers di Era Kekinian Fokus: Konotasi

The world of the press as a language user to convey the news is very influential in providing information to the reading community. In fact, the press does not realize that the words used in their writing give rise to wrong perceptions by readers. This wrong perception is caused by the press not considering the words used. Is it in accordance with the context or the rules already meet the correct rules. The use of words that have connotations are often placed not in accordance with the context, or the press mistranslates spoken language into written language.
Absurditas dalam Karya Sastra

Absurdity comes from the word absurd which means impossible, unreasonable, ridiculous, laughing. Absurdity is defined as the impossibility of circumstances. Absurd is a condition that describes an emptiness. It then develops into a void of self in one's life. This emptiness is usually one of the characteristics of life which are played in stories that are classified as absurd. Absurd can also be said as things that deviate from harmony, unclear and illogical. Absurd events as things that no matter how strange and crazy for a straight mind but they exist in everyday life.
Anchor-free object detection in remote sensing images using a variable receptive field network

Abstract Object detection is one of the essential tasks in computer vision, with most detection methods relying on a limited number of sizes for anchor boxes. However, the boundaries of particular composite objects, such as ports, highways, and golf courses, are ambiguous in remote sensing images, and therefore, it is challenging for the anchor-based method to accommodate the substantial size variation of the objects. In addition, the dense placement of anchor boxes imbalances the positive and negative samples, which affects the end-to-end architecture of deep learning methods. Hence, this paper proposes a single-stage object detection model named Xnet to address this issue. The proposed method designs a deformable convolution backbone network used in the feature extraction stage. Compared to the standard convolution, it adds learnable parameters for dynamically analyzing the boundary and offset of the receptive field, rendering the model more adaptable to size variations within the same class. Moreover, this paper presents a novel anchor-free detector that classifies objects in feature images point-by-point, without relying on anchor boxes. Several experiments on the large remote sensing dataset DIOR challenging Xnet against other popular methods demonstrate that our method attains the best performance, surpassing by 4.7% on the mAP (mean average precision) metric.
Unitary quantum process tomography with unreliable pure input states

Quantum process tomography (QPT) methods aim at identifying a given quantum process. QPT is a major quantum information processing tool, since it allows one to characterize the actual behavior of quantum gates, which are the building blocks of quantum computers. The present paper focuses on the estimation of a unitary process. This class is of particular interest because quantum mechanics postulates that the evolution of any closed quantum system is described by a unitary transformation. The standard approach of QPT is to measure copies of a particular set of predetermined (generally pure) states after they have been modified by the process to be identified. The main problem with this setup is that preparing an input state and setting it precisely to a predetermined value is challenging and thus yields errors. These errors can be decomposed into a sum of centered errors (i.e., whose average on all the copies is zero) and systematic errors that are the same for all the copies. The latter is often the main source of error in QPT. The algorithm we introduce works for any input states that make QPT theoretically possible (i.e., unless there are several solutions due to, e.g., a lack of diversity in the input states). The fact that we do not require the input states to be precisely set to predetermined values means that we can use a trick to remove the issue of systematic errors by considering that some states' copies are unknown but measured before they go through the process to be identified. We achieve this by splitting the copies of each input state into several groups and measuring the copies of the $k\mathrm{th}$ group after they have successively been transferred through $k$ instances of the process to be identified (each copy of each input state is used for only a single measurement). Using this approach, we can compute estimates of the measured states before and after they go through the process without using the knowledge we might have about the initial states. We test our algorithm with simulated data, and we assess its performance with a cnot gate on a trapped-ions qubit quantum computer.
PHILOSOPHICAL ANALYSIS OF THE PROCESS OF SOCIALIZATION IN SOCIAL NETWORKS IN CHINA

Living conditions in an information society make their own adjustments to such a process characteristic of "real life" as socialization. Conditions for the emergence of "hybrid socialization" have formed in social networks. It differs from the process of socialization in ordinary conditions of human life and brings its own characteristics to this process and to socialization itself. If we talk about adolescents, their socialization is formed rather in the virtual world than in the real one. The influence of the virtual world can be defined as multifaceted, and the virtual world itself (internet space and social networks) creates conditions for implementation of various forms of socialization. Hybrid socialization brought many inevitable problems with it: the popularity of the internet weakens traditional socializing authorities; the release and dissemination of networked information is controlled by several developed countries; universal values ​​are being "revised". In the virtual world, the phenomenon of ressentiment has become a widespread phenomenon and is actively manifested in the form of envy, an outright desire to offend interlocutors, offend completely unfamiliar network users in the form of frequent manifestations of aggression, resentment, inappropriate behavior, etc. These processes undermine traditional morality and at the same time contribute to the formation of socialization in the online environment. Such mechanisms of socialization as imitation, trying on roles, etc., began to "break" and "deviate" from their classical standards. The network breaks boundary between ages: the younger generation begins to contact the adult world in new conditions and at new rates, becoming an "adult child" much earlier than their real age. Video information widely disseminated on the internet is full of such phenomena as “flaunting oneself”, constant human comparison with “internet stars”, “worship of money”, which seriously influenced traditional moral concepts and values ​​and threatened traditional human standards of life.
Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)

In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA). We propose a GRA that recovers a graph's adjacency matrix from the representations via a graph decoder that minimizes the reconstruction loss between the partial graph and the reconstructed graph. We study three types of representations that are trained on the graph, i.e., representations output from graph convolutional network (GCN), graph attention network (GAT), and our proposed simplicial neural network (SNN) via a higher-order combinatorial Laplacian. Unlike the first two types of representations that only encode pairwise relationships, the third type of representation, i.e., SNN outputs, encodes higher-order interactions (e.g., homological features) between nodes. We find that the SNN outputs reveal the lowest privacy-preserving ability to defend the GRA, followed by those of GATs and GCNs, which indicates the importance of building more private representations with higher-order node information that could defend the potential threats, such as GRAs.