Reviewerly/exHarmony huggingface dataset:
Security Issues And Challenges In Cloud Computing

Data is a very essential source for all organizations in today’s world. In the past years data was stored in a sever which was physically placed inside the organizations. As the amount of data is been increasing, it is a great challenge for the Database Administrator to manage the database. The entire data was in the control of the Database Administrator. To overcome this challenge arose the concept of Cloud computing. It is a technique that describes software and services that run through the internet rather than private servers and hard drives. In cloud computing the consumers does not own the infrastructure needed to support the programs or any applications they use. The data is owned by a third party and the end users pay for the services provided to them. This paper discusses about the basics of cloud computing management and its types. Cloud provides various advantages to its users and at the same time the major concern is towards the security of data. The data is stored in the cloud and it can be accessed through internet only. Internet is playing a major role in everyone’s life. The world has become too small and it is placed in the palm of a human. This is because of the technology that is ruling the world. To secure the data in cloud is a major and the most important task that should be looked into. This paper focusses mainly the security issues and challenges in cloud.
Exploring The Effects of Conversational Marketing and Artificial Intelligence on Customer Engagement - A Comprehensive Literature Review."

This comprehensive literature review delves into the multifaceted realm of customer engagement through the lens of Conversational Marketing and Artificial Intelligence (AI). In an era characterized by digital transformation, businesses are increasingly reliant on innovative strategies to engage customers effectively. The study aims to provide an in-depth exploration of the existing body of research about Conversational Marketing and AI, with a focus on their combined impact on customer engagement.&#x0D; The literature review begins by examining the current state of research in Conversational Marketing and AI, identifying key findings and emerging trends. It then extends its focus to the practical implications of these technologies, delving into their application in enhancing customer engagement. The unique challenges and opportunities posed by Conversational Marketing and AI are analysed, paying special attention to the cultural and linguistic factors that shape the customer engagement landscape.&#x0D; The study not only offers valuable insights for businesses seeking to optimize their customer engagement strategies but also provides a foundation for policymakers and researchers interested in the ethical, regulatory, and future research considerations surrounding Conversational Marketing and AI. By shedding light on the dynamic interplay between technology and customer engagement, this literature review contributes to a deeper understanding of the evolving customer-business relationship in an increasingly digital world.
Productivity and Quality Challenges of Novice Developers in Agile Engineering Environments - A Comparative Study

The Agile methodology has significantly transformed the landscape of software development, offering a dynamic approach that prioritizes adaptability and customer feedback. This paper harnesses insights from the authoritative State of Agile report, an annual publication produced by CollabNet and Digital.ai. Findings of six financial years (2015-2021) reveal the current positioning of the software development community in terms of agile adoption and the prevalent trends. This finding was elaborated with a micro survey, conducted targeting various agile roles within software development companies focusing on novice developers. An in-depth exploration offers a granular view, underscoring the reasons behind the significance pinpointed in the State of Agile reports. Overall, this research provides a comprehensive understanding of Agile's impact, its evolving trends, and its implications for diverse software development teams.
Ensuring Data Integrity And Security In Diverse Cloud Environments To Prevent Duplicacy.

Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.
A Comparative Study of Data Hiding Techniques for Different Image Categories

The study focuses on enhancing the perceptual appearance of shortened pictures that contain concealed content within them. It proposes a two-phase data concealment approach that integrates natural visual abilities to achieve this enhancement. The context in which this study is conducted is in intelligent capitals, where the Internet of Things (IoT) plays a significant role in gathering various sources of visual data for enhanced administration. When transmitting data over a public network, safety concerns are crucial. To address this, the study introduces a unique information concealing procedure for picture decompression using Absolute Moment Block Truncation Codes (AMBTC). AMBTC is utilized as a means to improve bandwidth utilization while satisfying safety requirements. By employing AMBTC, the study aims to maintain the original moments of the image while reducing mean squared errors (MSE) more effectively than with Block Truncation Code (BTC). The study also highlights the importance of being able to restore the original picture after data concealment. However, the amount of recoverable knowledge may be reduced, or additional knowledge might be required for recovery from the stego picture (the image with concealed data) to maintain the ability to revert to the associated stego image. The approach known as Least Significant Bit (LSB) is mentioned, which involves altering the LSB of images to incorporate digital hidden information into the host picture. This technique does not require additional information for restoration and recovery, but it may not fully restore the stego picture to its original state. In summary, this study presents a two phase data concealment approach using AMBTC to enhance the perceptual appearance of shortened pictures containing concealed content. The ability to restore the original picture is emphasized, and the LSB approach is mentioned as a means of digital information concealment.
Pengembangan Chatbot untuk Meningkatkan Pengetahuan dan Kesadaran Keamanan Siber Menggunakan Long Short-Term Memory

Cyber-crime is becoming more massive as online activities increase. Cybercrime is a criminal act that exploits digital technology to damage, harm, and destroy property. Therefore, it is crucial for internet users to have knowledge of cybersecurity and the world of technology and the internet in order to avoid falling victim to cybercrime. The aim of this study is to develop a chatbot system as a centralized information medium on cybersecurity, technology, and the internet for internet users. The development of this chatbot aims to reduce the risks of cybercrimes and help enhance internet users' awareness of cybercrime. This research employs the AI Project Cycle method in chatbot development and utilizes the Long Short-Term Memory (LSTM) deep learning model algorithm to develop a model that achieves high accuracy. The training results of the LSTM model achieved an accuracy score of 100% and a loss of 3.09% with 400 epochs. Consequently, it can be concluded that the LSTM algorithm is highly effective for training and developing a chatbot model.
Empowering Multilingual AI: Cross-Lingual Transfer Learning

Multilingual Natural Language Processing (NLP) and Cross-Lingual Transfer Learning have emerged as pivotal fields in the realm of language technology. This abstract explores the essential concepts and methodologies behind these areas, shedding light on their significance in a world characterized by linguistic diversity. Multilingual NLP enables machines to process global collaboration. Cross-lingual transfer learning, on the other hand, leverages knowledge from one language to enhance NLP tasks in another, facilitating efficient resource utilization and improved model performance. The abstract highlights the growing relevance of these approaches in a multilingual and interconnected world, underscoring their potential to reshape the future of natural language understanding and communication.
Rekomendasi Paket Mata Pelajaran Pilihan (MPP) pada SMA Negeri 1 Kebumen Menggunakan Algoritma K-means

Curriculum changes are needed to adapt education to the times. Since the covid-19 pandemic, face-to-face learning has been suspended. Online learning is an alternative used during a pandemic. This has an impact on learning loss so that the quality of learning decreases. Recovery of learning during the pandemic and post-pandemic Covid-19 is important to reduce the impact of learning loss on students. After the pandemic, the independent curriculum was launched which was a refinement of the 2013 curriculum which had only been implemented in several schools. The subject structure of the Merdeka curriculum for SMA level in Fese E or grade 10, all students get the same subjects. While in Phase F (grades 11 and 12), the subject structure is divided into 2 main groups, namely general subjects and elective subjects. Based on the provisions of the SMKA 2021-2022 curriculum structure, SMA Negeri 1 Kebumen prepares elective subjects (MPP) which are made up of 7 MPP packages. This study uses a clustering technique of student scores using the K-Means algorithm to obtain MPP package recommendations that suit student abilities. For each MPP package, clustering is carried out into 2 clusters with features in the form of predetermined subject scores. The result of this clustering is that each student gets a "yes" or "no" recommendation for each MPP package.
Evaluasi Usability Pada Aplikasi M-Pise LPD Digital Di Kabupaten Jembrana dengan Metode Usability Testing

This study aims to evaluate usability on the user's M-Pise LPD Digital application page in Jembrana Regency by using the usability testing method with Performance Measurement and RTA (Retrospective Think Aloud) techniques. The usability aspects reviewed are effectiveness, efficiency, and user satisfaction. In this study there were 20 respondents who were involved consisting of a group of advanced respondents and a group of novice respondents. The results showed that (1) the M-Pise LPD Digital application was still not effective when viewed from errors (errors) when the respondent was doing the task, (2) the M-Pise LPD Digital application in terms of efficiency has proven to be efficient seen from statistical testing Mann Whiteney showed that there was no difference in time between the beginner and advanced groups of respondents so that it could be said to be efficient, (3) User satisfaction was still unsatisfied as seen from the SUS questionnaire score of 64. Thus the M-Pise application page did not have good usability. Thus the recommendations for improvement given are based on the results of performance measurements, namely errors. Recommendations for improvement will focus on improving menu components and features. Meanwhile, based on the results of the problems and suggestions for the results of the RTA, namely simplification of features and improvements as well as clarity of the layout of letters, numbers and icons.
Use Of Blockchain Technology Enhancing Cybersecurity

The evaluation paper explores the combination of blockchain generation in enhancing cybersecurity measures. It delves into numerous studies' findings and applications, highlighting the impact of blockchain on securing virtual transactions, shielding touchy statistics, and fortifying decentralized structures. The paper aims to offer a comprehensive review of the evolving landscape wherein blockchain intersects with cybersecurity, providing insights into present-day demanding situations, emerging trends, and future potentialities on this dynamic discipline.
Comparison of Handwriting of Accustomed and Unaccustomed Hand of Individuals and Ascertaining Their Identifiable Parameters

In an effort to conceal their identity, writers often try to change their usual handwriting to disguise. Many times, a master disguise is created through the act of writing with an unaccustomed hand. Due to a sudden loss of pen control and motor skills, the handwriting's pictorial appearance changes in disguise to the point where the writers assume success and becomes convinced that the disguised handwriting, they have produced cannot be linked to them when compared to their typical genuine writing style. In the present study, samples of handwriting from a number of people were collected, both from their accustomed and their unaccustomed hand. An effort was made to identify those individual features that managed to withstand the dangers of disguise and could be useful in correctly ascertaining the authorship of such disguised handwriting in comparison to one's normal handwriting. A detailed study of spacing between the letters and words, specific connecting stroke, certain letters formation and most importantly the positioning of the signatures below the paragraph written by selected subjects has been carried out. Despite pictorial dissimilarities, it has been observed that, there are some subconscious characteristics that are rarely concealed and may serve as the basis for their relationship with a particular writer or serve as an idea for future research.
Federated Machine Learning Using The Internet Of Medical Things For Cardiac Disease Detection

The proliferation of medical data sourced from healthcare organizations has led to an increased utilization of Machine Learning (ML) methodologies within the medical domain. In order to uphold the effectiveness of healthcare professionals, it is imperative to design machine learning models that are dependable and credible. Machine learning (ML) models are widely employed in the field of healthcare to generate accurate disease predictions, while simultaneously ensuring the protection of data gathered via Internet of Medical Things (IoMT) devices. Federated Learning (FL) techniques are well-suited for the preservation of Internet of Medical Things (IoMT) data due to their ability to preserve only the trained models and advance with information from distributed users. Fluorescence techniques possess the potential to significantly revolutionize the medical business by enabling rapid disease diagnosis, hence improving the effectiveness of therapy. The efficacy of these FL approaches is diminished as a result of the substantial volume of data being transmitted between local and remote locations. To address this concern, a unique methodology called FedEDFA is introduced, which integrates Federated Machine Learning with a meta-heuristic optimization algorithm. The utilization of the Enhanced Dragonfly Optimization algorithm is employed to effectively choose the pertinent features and thereafter utilize them for disease prediction. This strategy enhances the system's resilience in networking situations that are prone to insecurity. The efficacy of the proposed FedEDFA is evaluated by its application to the UCI Cleveland dataset, with the objective of predicting cardiovascular illness while ensuring data security and privacy. The proposed approach demonstrates a greater level of accuracy, measuring at 98.3%, when compared to other existing methods.
Classifying Face Features For Better Recognition And Detection

Recognition of facial features is in high demand currently because of its value in individual proof of identity, wherein a short input picture or a glance can assist in recognizing an individual using a database of images. This type of identity isn't commonly used in India, although it is necessary. Fingerprint identity systems ought to utilise facial recognition to render the system frictionless and more secure. As a result, these researchers attempted to assess the suitability of two separate face recognition computational methods, facet, which came, and facial features, in this work. The traits are retrieved and then prepared before being turned to a list and then compared with the area of interest to determine the individual in question [5]. The characteristics considered were the Euclidean gap between the pupils, the contours of the nostrils, plus the lip-to-lip length. This initiative will assist in guarding factories wherein trespassing is possible, identifying people in crowds, tracing those who have vanished, and keeping tabs upon some disruptive elements of the community. It's used for a wide range of applications, including legislative bodies and vaults at financial institutions. At the exact same period of time the identification and detection percentages with the three distinct criteria for lateral recognition of faces, opacity identification, and face dramatic emotion are contrasted using the contrast test, and the adequacy for both methods is improved [6]. These results demonstrate the fact that every instance can be assessed individually. Conventional detectors may "perceive" an object or message, turn it into an electrical signal, store it, and then utilise a conversion circuitry to turn the electricity into an amount or another observable screen format.
Guideline for Chinese-Foreign Cooperation Project Management in Higher Education in China

The objectives of this research were 1) to study the current situation of Chinese-foreign cooperation project management in higher education in China, 2) to develop the guidelines for Chinese-foreign cooperation project management in higher education in China, and 3) to evaluate the adaptability and feasibility of the guidelines for Chinese-foreign cooperation project management in higher education in China. The sample were 226 lecturers and administrators in higher education in China. They were selected by systematic random sampling and sample random sampling. The interview group was the 1 administrator from each university, totaling 10 people. The experts for evaluated the adaptability and feasibility of the guidelines for Chinese-foreign cooperation project management consisted of high-level administrators from each university, totaling 9 people. Research instruments include: 1) questionnaire, 2) structured interview, and 3) evaluation form. data analysis by using percentage, mean, standard deviation and content analysis.
Performance Comparison of Security Level of Cryptosystems by using Machine Learning

Recent developments in multimedia technologies have raised concerns about digital data security. However, many proposed encryption algorithms have been proven insecure over the past few decades, presenting a serious security risk to sensitive data. The best encryption technology should be used as protection against these attacks, but the type of data to be protected will determine the right algorithm in each case. Comparing different encryption systems one by one to find the best one can take a lot of time. We present a security level determination method for image encryption schemes that uses a support vector machine (SVM) for quickly and accurately selection of appropriate encryption algorithms. Furthermore, the dataset uses common cryptographic security techniques including entropy, contrast, homogeneity, peak-signal-to-noise ratio, mean square error, and energy. These variables are used as features for dividing the encryption algorithms extracted from the different images. Depending on the level of security, dataset labels are divided into three groups: weak, acceptable, and strong. The results show the advantage of Support Vector Machine (SVM) and we also improve the performance of the SVM by using XGBoost to improve the performance of our existing model.
THE PROBLEM OF THE POST-STRUCTURALIST APPROACH FOR INTERTEXTUAL ANALYSIS OF TEXT

For the first time, the concept of intertextuality was illuminated and outlined by scholar J. Kristeva. According to her interpretation, intertextuality is a process where each newly created text emerges based on previously created ones and is constructed as a mosaic of quotations. Intertextual connections, where possible, interact with different cultures at all possible levels of manifestation, acquiring a massive and chaotic nature. Within such a complex system, texts may contradict, intertwine, and unravel in relation to each other via repetitions, illogical and irrational constructions, allusions, etc. [1; 2].
ANALYSIS OF BIOMETRIC TECHNOLOGY METHODS

The field of biometric technologies has undergone significant changes due to the large-scale implementation of biometrics in everyday life. A person directly encounters biometrics in life - from obtaining a passport or visa to purchasing a modern gadget [1].
Enhancing Chinese Students'Needs Satisfaction and Loyalty: The Importance of Service Quality

Nowadays, students’ psychological needs satisfaction at college is receiving increasing attention. The article analyzes the influencing factors of student psychological needs satisfaction in vocational education in Jiangsu, China.The purpose of this paper is to examine the associations between the five major factors of service quality, including assurance, empathy, reliability, responsibility and tangibles, and students’ psychological needs satisfaction, as well as their loyalty to college.Recommendations to improve students’ psychological needs satisfaction include strengthening the service function of government, promoting institutional research and the specialization of student affairs, in order to help students develop self-efficacy and other positive attitudes.