Reviewerly/exHarmony huggingface dataset:
Integration of Large Language Models into Higher Education: A Perspective from Learners

Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail.
Enhancing Data Security: A Comprehensive Study on the Efficacy of JSON Web Token (JWT) and HMAC SHA-256 Algorithm for Web Application Security

In today's digital era, data security is a very important aspect in various applications and services. In order to protect the integrity, confidentiality and authentication of data, security technologies such as JSON Web Token (JWT) and HMAC SHA256 algorithm are widely used. JWT is an open standard (RFC 7519) that is used to represent information in the form of tokens that can be signed digitally. The research methodology used in this research is a descriptive research method. The descriptive method is a method that describes the purpose of the data collected and records every aspect of the situation being investigated to get a clear picture of what is needed. It was found that there were several data leaks when data security was not implemented in layers, including cases that had occurred such as loss of important data contained in the website and leaks of important data which caused identities to be spread widely. Conclusions from the use of JSON Web Token (JWT) and HMAC-SHA-256 algorithm for website security is that this combination provides a strong layer of protection against security threats that are common in the online environment.
Case Study on Early-Stage Risk Prediction by Machine Learning Algorithms

In the healthcare sector, we generally require continuous monitoring of the patient, The model is designed to assist patients in understanding the potential hazards associated with the infectious illness by using Machine Learning algorithms. It provides recommendations as per the infection severity, enabling patients to effectively monitor their condition independently. All the data records are initialized in the dataset, these are stored in the database which helps in more accuracy of illness prediction. This software interface is simple, based on symptoms of the patient the algorithms will process and gives appropriate infection details, suggestions on severity of infection, recommends to consult a doctor or not. It also ensures the seasonal diseases ongoing in particular areas, identifies them, and gives regular alerts to the users. Non-Communicable Diseases (NCDs) are currently causing more infections are highlighted, and alerts the user by giving information and precautions to be taken for improvement of health. Predictive models and classification algorithms, examine the symptoms specified by the patient as input. Then the most possible disease name will be displayed as an output. Decision Tree, Naive Bayes Classifier, and Random Forest Algorithm are used to forecast the disease. Disease prediction is accomplished by employing ML Algorithms.
Detection of SSH Password Guessing Attacks using Classification Algorithms

The usage of SSH protocol has gained popularity among users due to its secure nature in recent times. Nevertheless, the SSH protocol can be susceptible to exploitation by hackers, who can access SSH servers without permission by exploiting vulnerabilities. SSH attacks cannot be completely detected using state-of-the-art security solutions like Firewall, Intrusion Detec- tion Systems, and so on. Malicious SSH traffic is created by malware and contains password guessing attacks. These attacks can result in compromising the security of servers and lead to the theft of private data. We aim to develop a robust and accurate SSH attack detection system that uses classification algorithms that can effectively differentiate between malicious SSH traffic and legitimate SSH traffic. In this paper, we have selected 14 classification algorithms like CNN, LSTM, Logistic regression, Deep Belief Networks, Auto Encoders, and so on. The process involves organising and preparing the data, extracting relevant features, and application of an ensemble learning approach with the selected classification algorithms. XGBoost is employed for model integration. The ensemble model achieves improved accuracy, successfully classifying between legitimate SSH traffic and SSH password guessing attacks.
New Algorithm to Enhance the Accuracy of Extracting Steganography Hidden Data

Cybercriminals are employing various techniques to conceal evidence from investigators, allowing them to avoid tracking the traces of the attack or the traces of crimes. Steganography of information was techniques and tactics used to hide the traces of a hacking or electronic attack. Steganography is one of the most severe methods of obfuscating traces to make it harder for investigators to uncover reliable evidence that can be used in court. In this research study, the problem was that the steganography tools that the authors used in previous studies in their research were not accurate in extracting all the hidden data, and their efficiency was poor. The objectives of this research are to evaluate the accuracy of extracting the hidden data by creating different scenarios using python script. Furthermore, develop a new algorithm to enhance the accuracy of extracting veiled data by using Tkinter framework. Finally, to evaluate the performance of the proposed new algorithm by comparing the proposed algorithm with different steganography tools. The proposed algorithm was able to increase the accuracy by 90% and extract the hidden data compared with different tools such as openstego stegspy and stegovirtas.
Survey on Cloud Computing Integrated with Artificial Intelligence

The combination of cloud computing with Artificial Intelligence (AI) has become a viable approach in recent years to handle the increasing processing needs of AI applications. This article offers a thorough analysis of distributed and scalable artificial intelligence frameworks that use cloud computing to improve the effectiveness and performance of Deep Learning (DL). First, we provide a summary of common cloud services and AI frameworks, emphasizing the advantages and disadvantages of each. The important topics of data management and storage in cloud-based AI systems are then covered in detail, including feature engineering, data pretreatment, privacy, and security. Next, we investigate the distributed and parallel training methods for AI models, with particular emphasis on cloud-based training topologies, communication mechanisms, and model splitting. On the next chapters, we examine auto-scaling, load balancing, resource allocation, and performance benchmarking as optimization methodologies for AI workloads on the cloud. We also cover serverless deployment alternatives, containerization, best practices for monitoring, and the deployment and servicing of AI models on the cloud. We give a comprehensive study of costs, optimization methodologies, and case studies exhibiting successful installations to guarantee the cost-effectiveness of cloud-based AI systems. In conclusion, we provide an overview of the study's main conclusions, go through the difficulties and restrictions associated with cloud-based artificial intelligence, and point out new directions for future research in this area. For academics and practitioners trying to use cloud computing to create scalable, effective, and affordable artificial intelligence systems, this article will be a great resource.
Data Security on Backed Up Data and Recovery in Cloud Storage

Cloud provides its users with different services. Every day during cloud computing, a great deal of data is generated. On the cloud servers, the data is being saved. A recovery tool should be created in case this data is lost from the server. One such setup is described in this consideration. The proposed approach would enable simultaneous data storage on the inaccessible server and the cloud server. The information is returned from the farther server when the key record is misplaced. Secret key security is ensured so that the authentication is secure and authorized by the user based on the attributes of backup and recovery. A cloud is a distinct Information Technology infrastructure designed to provide its users with different facilities that can be gotten to remotely. Cloud alludes to the term arrange of systems that back get to decentralized Data Innovation assets. Cloud employments the Web as well as the inaccessible central servers to manage consumers and businessmen's data and applications. This helps to save the consumer from costs and room problems. It is a technology that makes data collection, processing, and bandwidth much more centralized. A cloud encompasses a certain restrain, because it could be a certain system utilized to supply assets remotely. The Web offers get to an endless number of clouds. Though the Web offers free get to a few web-based Information Technology services, a cloud is generally private and provides wireless data resources. Much of the Internet is accessible via the web service to Information Technology services. On the other hand, the Information Technology services supplied by cloud environments are intended to provide back-end computing capabilities and browser access..
EFFECTIVE METHODS OF DEVELOPING MEDIA COMPETENCES OF UNDERGRADUATE STUDENTS

This article explores effective methods for developing media competences among undergraduate students. In today's digital era, media competences are essential for students to navigate the complex media landscape. The article discusses the importance of media literacy education, active learning strategies, collaborative learning, integration of emerging technologies, and multicultural perspectives. These methods enhance students' critical thinking, media analysis, creativity, technological proficiency, and multicultural awareness. By implementing these approaches, educators can equip undergraduate students with the necessary skills, knowledge, and attitudes to engage with media critically, create meaningful content, and become responsible participants in the media ecosystem.
Enhancement and Segmentation of Low-Light Images Using Illumination Map Estimation based Level Set (IME-LS) Method

One of the most difficult aspects of image segmentation is that it cannot be successfully segmented if the image is dark or degraded. In this paper, proposes a method for the segmentation of low-quality and degraded images is put forth which is called Illumination Map Estimation based Level Set (IME-LS) Method. This proposed model has been classified into two parts: Firstly, we use an enhancement approach using illumination map approximation to enhance the input image. In this approach, Illumination map is constructed then refined. The refined illumination map undergoes an Augmented Lagrangian algorithm and then we use a sped-up solver for a considerably more efficient outcome. Secondly, Then the segmentation procedure begins once the image is enhanced. The enhanced image is segmented using level set bias method through Fuzzy clustering. In this method we employ the Fuzzy C-Means (FCM) algorithm to categorize data points into clusters. The fuzzy C-Means algorithm separates different entities in the image based on their varied intensities and sorts them into various clusters. The level set bias approach then tracks the variational boundaries of the image. We have designed the integrated algorithm in such a way that the image is classified or grouped into various clusters using a novel fuzzy Clustering algorithm and the variational boundaries of those clusters are tracked by employing the level set algorithm. In this paper, we further perform quantitative and comparative analysis of the suggested technique with respect to other segmentation techniques to illustrate the efficiency and flexibility of the suggested model.
Study of Drainage Channel Capacity on Jalan Masangan Kulon Sukodono Sidoarjo

A problem often faced by people in urban areas is flooding in residential areas during the rainy season. The causes of flood inundation include land conversion, which was originally an open area turned into a built-up area for settlements, industry or trade and offices. Land use that is not orderly causes the issue of drainage channel capacity in urban areas to be very complex. This study aims to assess the capacity of drainage channels located on Jalan Masangan Kulon so as not to hamper traffic flow and harm the activities of the surrounding community and the handling carried out to overcome flooding in the existing. The method of calculating the flood plan for this drainage case study uses the Rational Method with a 2-year return period. Data collection is done by observation, taking measurements and channel normalization. Evaluation of channel capacity is carried out to assess the ability of drainage channels to plan discharge. The results obtained in this study Flood discharge in the upstream river with a return period of 2 years amounted to 1.54 m³/second. Areas that experience flooding occur in Segments 10-17 and 18-19. In segment 10-17 the block discharge is 3.98 m³/second and the existing discharge is 3.389 m³/second. After normalizing the existing discharge is 4.34 m³ / second. In segments 18-19 the block discharge is 5.228 m³/second and the existing discharge is 4.647 m³/second. After normalizing the existing discharge is 5.760m³/second.
Increasing Children's Basic Literacy Through Storybook Media

Activities to improve basic literacy through reading storybooks are carried out daily, one of the efforts to enhance students' Basic Literacy skills. This research aims to understand how storybooks are used as a medium to improve the basic literacy skills of early childhood. The subjects in this research were teachers, students, and parents of group B students at the Mertelu Guyangan PKK Kindergarten. Reading storybook media improves basic literacy by observing and gathering information regarding storybook reading activities by children, teachers, and parents at school, the types of books children like, and the benefits of reading storybooks in improving basic literacy skills based on observation and analysis.
"Kartagam" media in improving readiness to read in early children

Paying attention to children's development and the need to develop the reading potential of preschool-age children, there is something that can help achieve the goal of learning to read optimally, namely through parenting. The care used in teaching preschool-age children must pay attention to their level of development. Paying attention to the level of development means also considering their developmental tasks. The objectives to be achieved in this research are: 1) Describe the ongoing process of early reading preparation learning through letter matching activities using word and picture cards (Kartagam) in early childhood, 2) Describe the magnitude of the increase in early reading preparation through letter/word matching activities using word and picture card media (Kartagam) in early childhood, and3)Describe changes in attitudes and learning behavior of young children after early reading preparation lessons were carried out through letter matching activities using word and picture cards (Kartagam). Using the media "Kartagam" (Word and Picture Cards) is hoped to improve children's literacy skills, especially early childhood reading readiness.
The relationship between Big-Five personality factors, thinking style and self-efficacy among sports management students

The purpose of this study is to create a psychological profile of students in Sport Management from the National University of Physical Education annd Sports, in terms of the Big Five personality superfactors, Thinking Style (i.e. rational versus irrational beliefs) and (perceived) Efficacy and to study the degree of association between these factors. The following hypotheses were formulated: 1) There are significant correlations between the Big-Five model superfactors (Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Autonomy) and Self-efficacy, in the case of Sports Management students; 2) There are significant correlations between the Big-Five model superfactors (Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Autonomy) and Thinking Style: Irrational Cognitions (Demandingness in the form of ’musts’, Low Frustration Tolerance, Self-Downing and/or global evaluation, Global Score Attitudes and Beliefs) in the case of students in the Sport Management Specialization; 3)There are significant correlations between Self-Efficacy and Thinking Style (i.e. Irrational Cognitions: Demandingness in the form of ’musts’, Low frustration tolerance, Awfulizing, Self-downing and /or global evaluation, Attitudes and Beliefs Global score) in the case of Sport Management students. The participants in the research were 39 students in the Sports Management Specialization (28.21% female and 71.79% male) aged 19-23 years. The 5 Factor Personality Questionnaire (CP5F) and Attitudes and Beliefs Scale-II (ABS-II), both from the Computerized Platform for Psychological Assessment (CAS++), S.C.Cognitrom S.R.L. and the Self-Efficacy Scale (Schwarzer &amp; Jerusalem) were applied. The correlates study led to confirmation of all three hypotheses.
Power Law-Based Night-Time Image Dehazing Fused with Luminance Mapping for Enhanced Visibility

Dehazing at night time is an important subject to study in computer vision and image processing. The present research focused on significant challenges such ambient light estimate, guided filtering, reflection mapping, and image enhancing techniques in order to properly restore clarity and quality of photos that are taken in low-light and foggy conditions. The paper illustrates how to enhance the detail preservation, and overall visual appeal with the interaction of atmospheric light, reflection mapping, guided filtering, luminance mapping. Furthermore, the addition of the gamma parameter to tone mapping enhance the user’s artistic and interpretive abilities with previously unheard-of control over brightness and contrast adjustments. In order to ensure visually balanced and artistically pleasing outcomes in the dehazing algorithm, the luminance mapped section modifies the luminance of the dehazed image based on tone-mapped luminance values. By bringing colour channels’ brightness levels into harmony, it improves the overall quality of the dehazed output. The paper successfully manage the complexity of poorly light and foggy settings with the designed framework, which improved transportation, outdoor photography, surveillance, and other sectors. This study shows how interdisciplinary collaboration and technological development may alter the way we see our surroundings at night.
A Framework for Human Activity Recognition using Deep Learning Techniques

Human Activity Recognition (HAR) has gained significant importance in various fields, including healthcare, surveillance, and human-computer interaction. In this study, we comprehensively study the related work done in the past to enhance HAR system. We have also studied the various benchmark datasets that can be employed to detect diverse human activities. The basis CNNs capture distinct features and representations. Through a combination of their predictions, our ensemble model is expected to achieve superior performance on a wide range of human activities. A case study which employs pretrained CNN demonstrates the effectiveness of CNNs in improving HAR performance and highlights the potential for real-world applications. In the last section, A framework for HAR that leverages the power of ensemble Convolutional Neural Networks (CNNs) is also proposed to enhance recognition accuracy and robustness. This framework paves the way for more accurate and reliable human activity recognition systems in practical scenarios.
Agile development research based on software engineering to software testing

This article conducts in-depth research from software engineering to software testing teaching, introducing the agile development model and XP method of software development models, Scrum mode and its process, TDD, automated testing and other new trends and technologies. The purpose is to improve software development efficiency, reduce software testing costs, improve software quality and user experience, and finally advocate the use of Scrum mode and TDD in agile development, combined with the CDIO concept to strengthen team cooperation, So as to more efficiently complete the implementation and delivery tasks of software projects.
Machine Learning Powered Text Auto-Completion and Generation

A branch of artificial intelligence called “natural language processing” (NLP) helps computers comprehend human language. It combines the strength of linguistics and computer science to think about language rule, grammar and create intelligent systems capable of understanding, dissecting, and isolating meaning from text and speech. Using this concept, the research proposes a system which helps in minimizing the human effort by providing the features like text auto-completion and generation. Auto-completion is the process of completing a word, or a phrase, as people type in a document. The prediction is based on the most likely word among a set of frequently used terms. The text prediction task involves editing text with the fewest number of keystrokes possible. This method will suggest words that the user intended to write, and the system predicts next word related to the previous work. Text generation is a feature which is an enhanced version of Gmail's Smart Reply. This feature helps in generating a template of the mail by classifying the subject line. The goal of the proposed intelligent system is to assist human by increasing their typing speed and decreasing the number of keystrokes required to complete a word or a sentence. The suggested method is also very useful for multi-domain professionals who compose content, particularly those who frequently employ extensive, difficult-to-spell language that may be technical or non-technical.