Reviewerly/exHarmony huggingface dataset:
USE OF ONLINE SERVICES FOR CHECKING STUDENT SCIENTIFIC WORKS

The article examines the issue of checking student research papers to detect plagiarism, which activates independence, creative approaches and compliance with general principles and rules of citation among students of higher education. Due to the increase in the volume of scientific publications, there is an urgent need to check student scientific works for the presence of plagiarism. The authors define the concept of "plagiarism" as the content of the three above-mentioned terms: "self-plagiarism", "academic plagiarism" and "plagiarism". The concept of plagiarism is perceived by students in the form of copying and pasting the text of someone else's work. But plagiarism is a broader concept than simply copying parts of a text. This is the use of other people's thoughts or ideas for one's own purposes, appropriating and using them as one's own. That is, plagiarism is one of the ways of violating academic integrity. Defining plagiarism provides students with an understanding of what to avoid when writing student research papers. Therefore, it is necessary to explain to students the role of academic integrity, the correct ability to cite references to researched scientific sources. An effective and convenient way to check the originality of the text of a student's research work is using online services or applications. They are intended for automatic tracking of text borrowings and minimizing the use of other authors' material. They can be used to check for plagiarism in scientific publications, to identify borrowings in student scientific works, namely: coursework, qualification (master's or bachelor's) works. In the process of independently writing a course or qualification paper, the student can use parts of articles, conference materials, texts from textbooks and others, some of which may coincide and have already been published. Existing online services (Advego Plagiatus, Copywritely, Content-watch, Plagiarisma, Edu-Birde, Etxt Antiplagiat, Like-Exactus Plagiarism detector, Unichek, Check plagiarism, Grammarly, Duplicheker, Small Seo Tools) were analyzed to check works for uniqueness. The considered online services provide an opportunity for students and teachers to conduct a check. An example of the results of checking fragments of a student's qualification work using the Unicheck online service is given. In further research, attention should be paid to improving the effective system of prevention and detection of academic plagiarism among students of higher education institutions and scientific works of University employees.
Performance of DIO Suppression attack in RPL based IoT networks

Wireless sensor networks are very important in many uses, but they can be attacked, which can make the network less secure and less effective. The Routing Protocol for Low-Power and Lossy “Networks (RPL) is the target of the DIO silencing attack. The goal of this study is to figure out how the DIO suppression attack affects RPL and how well the NLBGNDO algorithm stops the attack. To reach this goal, an accurate model of the RPL protocol and the DIO silence attack are built into a modelling code. The modelling code includes the NLBGNDO routing algorithm, which is a suggested safe and effective routing method for RPL. Key measures, like the number of packets delivered, the length of the path, and the amount of power used, are recorded and analysed both when the network is working normally and when it is under attack. The results of the study show how vulnerable RPL is to the DIO suppression attack and how well the NLBGNDO algorithm works to protect against its effects. The packet delivery ratio shows how the attack affects the network's ability to send data, and the route stretch measure shows how well the routing method works when under attack.
Deep Learning-Assisted Citation Recommendation System Using Multi- cell RNN Approach

A process of retrieving relevant or suitable papers that are mostly related to the research topic while carrying research knowing the behavior and patterns of citation for mapping the disciplines and subjects. This is used increasingly for judging usually takes a huge amount of effort and time. The citation recommendation is used frequently for solving this issue on suggesting the list of candidate papers automatically which may match the references of user. On applying Deep learning (DL) models in the citation recommendation system, the performance enhancement is greatly visible in various recent research studies. Hence, in this work, a deep learning-based model is employed for the citation recommendation system. Primarily, the input dataset is taken and the function words are removed using stochastic matching pattern scheme at which the library is created. Then, the canonicalization of document is carried by means of probabilistic min-match algorithm. The data is classified finally using Multi-cell RNN (Recurrent neural network) approach after which the rank is set for citation. At last, the performance is assessed for various metrics like MAP, MRR, precision, recall, and F1-measure. The obtained results are then compared with traditional models to validate and show the enhancement of proposed model over existing schemes. It is revealed from the analysis that the proposed scheme shows improvement in results.
AIS Effect on Human Behavior and Well-Being: An Empirical Analysis

The integration of artificial intelligence (AI) into various facets of our everyday existence has occurred at a rapid pace. This includes the presence of personal assistants on our smartphones as well as the algorithms that serve as the foundation for social media feeds. Artificial intelligence (AI) possesses the capacity to profoundly transform various aspects of human existence and professional endeavors. However, it is imperative to acknowledge that AI is not exempt from certain limitations and disadvantages. An aspect of considerable concern pertains to the potential psychological ramifications associated with the utilization of artificial intelligence (AI) technologies. With the escalating dependence on these technologies, there is a mounting apprehension regarding their potential adverse impact on human behavior and overall well-being. Further investigation is necessary in order to fully grasp the psychological ramifications of artificial intelligence, as these impacts are intricate and multidimensional in their entirety. The majority of tasks performed at the interface between humans and machines necessitate individuals to engage in novel and distinct activities, as well as to approach familiar tasks in alternative ways. The leaders of the future will be those who actively use collaborative intelligence, so revolutionizing their organizational practices, market strategies, industry dynamics, and, significantly, their workforce management. The field of artificial intelligence has witnessed significant advancements in various domains traditionally associated with human expertise, such as disease diagnosis, language translation, and customer service. Moreover, these advancements have been characterized by rapid progress. The emergence of such concerns is rooted in the realistic apprehension that artificial intelligence (AI) has the potential to gradually supplant human labor across all sectors of the economy. However, it is important to note that such a conclusion is not necessarily inevitable, or even the most probable. The current state of digital technologies exhibits an unprecedented level of responsiveness to human users, and conversely, humans have become increasingly attuned to utilizing these tools. The advent of AI will have a profound impact on the nature of work and the individuals involved in it. However, its primary effect will be to enhance and supplement human abilities, rather than displacing them entirely. The main aim to analyse the positive &amp; negative effects of AI on human behaviour &amp; well-being &amp; to understand how AI affects human behavior and well-being and deployment of AI technologies for the benefit of individuals and society.
Guidelines to Enhance Teachers' Satisfaction with the Management Dance Major of University in Shandong

The objectives of this research were: 1)To study the current situation to enhance the teacher’s satisfaction with the management dance major of university in Shandong. 2)To develop guidelines to enhance the teachers' satisfaction with the management dance major of university in Shandong. 3)Toevaluate the adaptability and feasibility of guidelines to enhance the teachers' satisfaction with the management dance major of university in Shandong.The sample group of this research was 212 teachers. The Interview group was 9 teachers in Shandong and the evaluate 9 experts from university in Shandong. Research instruments include: 1) questionnaire. 2) semi-structured interview. 3) evaluation form. Data analysis by using percentage, mean, standard deviation and content analysis.&#x0D; The results were: 1) the current situation to enhance the teacher’s satisfaction with the management dance major of university in Shandong was at medium level, 2) guidelines to enhance the teachers' satisfaction with the management dance major of university in Shandong in four aspects, which contain 20 articles. there are 5 guidelines for Provide Academic Guidance, 5 guidelines for Create Research Opportunities, 5 guidelines for Expand Academic Resources, and 5 guidelines for Construction Facilities and Resources. 3) adaptability and feasibility of guidelines to enhance the teachers' satisfaction with the management dance major of university in Shandong in four aspects were at high level.
Exploring Cyber Harassment among Youth Who Use Social Media

This is an anthropological research which studies cyber harassment on social media among the young people of University of Dhaka. Cyber harassment is an umbrella term and consists several harassing behaviors. There is no concrete definition of it. In general, it is defined as intentional and repeated behavior that is directed to someone through the digital platforms in order to disturb, harm or scare them. Social media is a digital communication platform which is used most by almost all age group around the world. In our country, young people are the main user of social media platforms. So, this study is very relevant in the context of our country to understand cyber harassment. Anthropological methods and its core theoretical tenets which encourage in-depth study of any issue have provided a good framework to this research. This study uses both qualitative and quantitative methods including ten in-depth interviews and a questionnaire based online survey with fifty informants. The present study observes young user’s perception regarding cyber harassment, the nature of cyber harassment from user’s personal experience in social media. Several factors like technical skills, knowledge regarding social media, gender, capital, social network etc. play a great role in shaping their perception. This study also illuminates young people’s understanding of cyber harassment, reasons behind their mild or severe dichotomy of harassment and activities that are considered as cyber harassment by them. This study also explores the nature and extent of cyber harassment from young people’s personal experience on social media. It also observes user’s awareness on personal security through the safety measures they follow to stay safe and the strategies they take after facing cyber harassment on social media. The whole study has been analyzed using Arjun Appadurai’s Technoscapes theory, Pierre Bourdieu’s theory of capital and symbolic violence. This study concludes with a summary of the whole research, some additional opinion of the participants about this research and the role of anthropologists to work in this issue.
Anonymity Maintenance Algorithm (Ama) Of Health Care Data from IoT Devices Using Edge System with Transfer Learning

In order to fully leverage the potential of the Internet of Things (IoT), it is crucial to safeguard against potential threats and prioritize the security and privacy of data collected and extracted from IoT devices. However, the implementation of security and privacy technology within IoT presents a number of unique challenges. This is due to the fact that IoT solutions comprise a diverse array of security and privacy solutions that are designed to safeguard IoT data at the device, infrastructure/platform, and application layers. As a result, ensuring end-to-end privacy protection across these three layers is a significant challenge within IoT. IoT devices implemented with recent advancements utilize edge system and monitors fitness of a person privately. In our proposed concept anonymity maintenance done along with privacy preserving in query processing in accordance with health monitoring values. In this edge system transfer learning applied to transfer minimum data which is privacy preserved to equip edge system about IoT devices in spite of their location. De-duplication will be done in edge system in health values monitoring thus frequency learning will be inculcated to know the frequency of that device in specified location. In health-oriented processing of query the privileges will be granted for each category of people to access integrity of data according to their standard of authentication. While granting privileges concerned person can handle all their available personal data using their authentic login details. Best edge system will be chosen using PSO or GWO techniques thus de-duplication implemented efficiently.
An Advanced Cloud Data Streaming Framework for Optimized Container Resource Allocation, Job Scheduling, And Security Enhancement

In the realm of cloud data streaming, the central concerns are Container resource allocation and job scheduling. Cloud infrastructure relies on container virtualization to facilitate construction and migration processes. Previous models have employed migration techniques to manage cloud container allocation and resource scheduling, but these come at the cost of increased response time and network traffic. To address these challenges, a novel approach is introduced, the Reduced Optimal Migration model (ROM). This model selectively triggers migration processes based on recommendations, optimizing resource allocation through a Machine Learning (ML) Algorithm. Job scheduling is enhanced through a dedicated Task Scheduling Algorithm. For robust data security during migration, a security-based technique is implemented in the 'Security-based Container Scheduling Model,' which ensures data integrity and safeguards against attacks. This system operates seamlessly online and offline, utilizing Edge Computing. During offline periods, defensive containers maintain data security until the system owner restores online connectivity. This holistic framework proves highly effective in resolving complex issues associated with large-scale optimization of resource allocation, migration, and security. Empirical results confirm its efficiency and security enhancements. The proposed work introduces an advanced cloud data streaming framework that optimizes container resource allocation and job scheduling while enhancing security during migration. It proves effective in addressing the challenges inherent in large-scale cloud data streaming processes.
The Intersection of Law and Technology: Navigating the Legal Challenges in the Digital Age

The convergence of law and technology in the digital era has grown increasingly intricate and widespread, significantly influencing the legal environment. The rapid progress of technology has brought about significant transformations in all aspects of our lives and professional endeavours. However, it has also given rise to a multitude of legal complexities that necessitate meticulous examination. The interplay between law and technology highlights the imperative to comprehend and adjust to the continuously expanding legal environment
Examining the Pandemic Induced Adoption of E-Learning Through a UTAUT Model Approach

The Covid-19 pandemic's worldwide disruption has significantly impacted many facets of society, including education and learning. This seismic effect results from the urgent need to stop the virus from spreading, which calls for strong limitations on leaving one's home. This has resulted in the mass shutdown of industries, offices, schools, universities, theatres, parks, gymnasiums, and retail stores. Consequently, students across countries were forced to adopt e-learning at a mass scale to continue their education. This study uses a unified theory of acceptance and use of technology (UTAUT) model method to list the elements that led to the adoption of e-learning by Indian college students during the Covid-19 epidemic. The empirical study is based on an extensive literature review and an online survey of 162 undergraduate students at Delhi University, India. Regression analysis results show that the most significant predictors of students' preference for e-learning during the pandemic are their evaluations of performance expectancy, effort expectancy, facilitating conditions, and the social isolation brought on by the Covid-19 crisis. Furthermore, the Covid-19 epidemic is seen by students as the primary driver behind their adoption of e-learning. A sophisticated understanding of the complexities associated with adopting e-learning is likely to greatly assist in developing creative educational projects and policies in the future.
The Effect of the Food Expression Art Therapy Group Counseling Program on the Interpersonal Relationship and Happiness of Prospective Early Childhood Teachers

This study aimed to determine the impact on the interpersonal relationships and happiness of prospective early childhood teachers by applying a group counseling program for food expression art therapy. Eight fourth-grade students attending the Department of Early Childhood Education at G University in G City were selected to conduct a group counseling program applying food expression art therapy once a week for 90 minutes. Along with the program's progress, prospective early childhood teachers' interpersonal relationships and happiness were measured in pre- and post-extract, and repeated measurement variance analysis was conducted to verify the program's effectiveness according to the experimental design. In this study, the interpersonal relationship of prospective early childhood teachers was significant, and the subscale satisfaction, communication, trust, familiarity, sensitivity, openness, and understanding were all significant. In addition to happiness, subscale satisfaction and positive and negative emotions were also significant. The group counseling program applying food expression art therapy positively affects prospective early childhood teachers' interpersonal relationships and happiness, so it is necessary to expand this program.
Collaborative work in university students

Although there is progress regarding practices that can improve learning by having interaction between students or the lack of knowledge regarding the advantages offered by university collaborative work. The purpose of the present study was to explore the students thinking in the business administration program at a public university regarding the advantages that collaborative work offers. The results are shown in frequency tables according to descriptive statistics, marital status, gender and perception of the topic. Descriptive analysis showed that advantages include improvements in relationships, social skills, critical thinking, self-esteem, confidence, and commitment. It concludes with the existence of different personalities when combined with perspectives that will end up strengthening creativity.
A Study of Triggering Serverless Functions in Serverless Environment

Serverless computing has gained significant popularity due to its ability to provide scalable and cost-efficient cloud services. However, the adoption of serverless functions comes with its own set of challenges. One critical aspect is the triggering mechanism that initiates the execution of serverless functions in a serverless environment. This research paper explores the various triggering-related issues that can arise when deploying serverless functions and proposes potential solutions to mitigate these problems. The paper also highlights the importance of trigger design and its impact on the overall performance and reliability of serverless applications.
Blockchain and International Trade: An Empirical Analysis

Blockchain has great potential in international trade because it has the possibility to disrupt bureaucracy, simplify the task of monitoring commodities by removing vast paper trails, and reduce losses due to delayed payments (Morabito, 2017). By analysing time series data between 2012 and 2021, this study will analyse the deployment and application of distributed electronic ledgers in global trade. The study topic is justified since there is a limited knowledge on how blockchain may affect international trade. Furthermore, there is a lack of understanding regarding its relevance in facilitating trade finance. To put it another way, blockchain technology has a significant impact on global trade. Additionally, we demonstrate that per capita GDP has a significant positive impact on international trade. Our findings are congruent with those of Derindag et al. (2020), Keller (2021), and Yu (2021). This study advises decision-makers, notably executives and customers, to validate blockchain's potential advantages and embrace its application for international trade objectives, resulting in higher global trade flows. Companies who commit to embracing blockchain technology will reap huge future rewards
Enhancing IoT Data Security through Multilayered Approach: Chaotic Map, DNA Sequencing, Blockchain Intergration

The rapid proliferation of the Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity, yet it also poses significant challenges in terms of data security and privacy. In this research, a robust and innovative approach is proposed to address these challenges by introducing a multi-layered security model integrating three powerful technologies: chaotic maps, DNA computing, and block chain technology to fortify IoT data protection. The key objective of proposed technique is to offer security to data at multiple levels while minimizing the Encryption Time (ET) and Decryption Time (DT). In the proposed work, two types of inputs i.e., textual data and medical image data is taken upon which proposed technique is implemented. The key innovation lies in the generation of binary keys from chaotic maps, making it different from traditional chaotic key methods, thereby establishing a unique and robust foundation for encryption. Furthermore, to enhance the encryption process, a dual layer of security is implemented through DNA sequencing and blockchain, ensuring an unprecedented level of data integrity and confidentiality. Notably, the blockchain phase utilizes SHA-256 for generating hash values, fortifying the blocks with an additional layer of cryptographic strength. This novel approach offers a comprehensive and sophisticated solution, effectively mitigating security concerns in IoT ecosystems. By combining the power of chaotic maps, DNA encoding, and blockchain technology, proposed model stands at the forefront of IoT data security, providing a resilient defence against contemporary and future threats.The performance of proposed approach is examined and validated in terms of ET and DT in MATLAB software. Simulating results revealed that proposed model is outperforming other similar models by attaining lowest ET and DT time for both text and image data.&#x0D;