Reviewerly/exHarmony huggingface dataset:
Use of data auditing for encrypted data stored in cloud environment

The exponential growth of data generation and the increasing reliance on cloud-based storage solutions have raised significant concerns regarding data security and privacy. The widespread adoption of encryption techniques has been effective in protecting data confidentiality, but it also introduces new challenges in terms of data auditing. This paper explores the use of data auditing techniques for encrypted data stored in cloud environments to ensure data integrity, availability, and accountability while preserving privacy. We first provide an overview of the current state of cloud storage security and encryption techniques, followed by a discussion on the importance of data auditing for encrypted data. The paper then delves into existing data auditing approaches, specifically focusing on Public Key-based Auditing (PKA) and Private Key-based Auditing (PrKA) schemes. We examine their advantages, drawbacks, and suitability for different scenarios, highlighting their ability to maintain data privacy without compromising the auditing process. To address the limitations of current auditing techniques, we propose an innovative hybrid auditing framework that combines the strengths of PKA and PrKA schemes. Our approach enables efficient data auditing while ensuring data confidentiality, integrity, and privacy. It also supports dynamic data operations, including insertion, deletion, and modification, allowing for seamless adaptation to various cloud storage environments. We validate the effectiveness of our proposed hybrid auditing framework through a series of experiments, comparing it with existing PKA and PrKA solutions. The results demonstrate the superiority of our framework in terms of security, privacy preservation, and performance, making it a promising solution for auditing encrypted data in cloud environments.
Exploring the Power of Transformer Models in Hospitality Domain

&#x0D; &#x0D; &#x0D; &#x0D; ABSTRACT&#x0D; Despite decades of medical advancements and a rising interest in precision healthcare, the great majority of diagnoses are made after patients start to exhibit observable symptoms of sickness. However, early disease indication and detection can give patients and caregivers the opportunity for early intervention, better disease management, and effective use of healthcare resources. Deep learning and other recent advancements in machine learning provide a fantastic chance to fill this unmet demand. Transformer designs are very expressive because they encode long-range relationships in the input sequences via self-attention methods. The models we offer in this work are Transformer-based (TB), and we provide a thorough description of each one in contrast to the Transformer's typical design. This study focuses on text-based task (TB) models used in Natural Language Processing (NLP). An examination of the key ideas at the core of the effectiveness of these models comes first. NLP's flexible architecture allows it to incorporate various heterogeneous concepts (such as diagnoses, treatments, measurements, and more) to further improve the accuracy of its predictions. Its (pre-)training results in disease and patient representations can also be helpful for future studies (i.e., transfer learning).&#x0D; &#x0D; &#x0D; &#x0D;
Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries Over Encrypted Cloud Data

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; Huge numbers of papers are transferred to the cloud for easier access and lower administration costs as cloud computing gains popularity. Individuals can utilize cloud computing to store their data on distant servers and grant public users access to the data via the cloud servers. Outsourced data are normally encrypted before being posted to the clod since they are likely to include sensitive privacy information. The difficulty of sifting through the encrypted data, however, severely restricts the utility of the outsourced data. In this research, we introduce the fine-grained multi-keyword search algorithms over encrypted clod data to overcome this problem. We made three original contributions. In order to provide exact keyword search and a tailored user experience, we initially present the relevance source plus preference factor upon keywords. Furthermore, we create a useful and very effective multi-keyword search strategy. The suggested system can enable complex logic searches using mixed "AND," "OR," and "NO" keyword operations. Thirdly, in order to improve the efficiency of index building and query generation, we also use the classified sub-dictionaries technique. Finally, we examine the security of the suggested strategies with regard to of document confidentiality, index alongside trapdoor privacy protection, as well as trapdoor unlinkability. We confirm that the suggested method performs better when it comes to of functionality, query complexity, yet efficiency than the ones currently in use plus can reach the same security level through rigorous tests on a real-world dataset. We confirm that the suggested method performs better with regard to functionality, query complexity, and efficiency than the existing ones and can reach the same security level through rigorous tests on a real-world dataset.&#x0D; &#x0D; &#x0D; &#x0D;
Adapting Transformer Networks for Document Summarization and Sentiment Analysis

Due to the increasing number of text-based content sources, the demand for effective sentiment analysis and document summarization techniques has been increasing. Several transformer-based models, including “ELECTRA, BERT, XLNet, RoBERTa, DistilBERT, and ALBERT” have emerged as promising alternatives to traditional methods. This paper aims to study the effectiveness of the different transformer models for performing sentiment analysis and document summarization on the Yelp dataset. The paper aims to analyze the various transformer models' performance on the tasks, identify their weaknesses, and suggest possible improvements. It also thoroughly studies the Yelp dataset, which has over 5 million reviews. The paper introduces the different transformer models that are used for performing document summarization and analysis on the Yelp dataset. We then perform evaluation on these models using various metrics to measure their performance. Some of these include ROUGE, F1-score, AUC-ROC, and accuracy. According to the paper's experimental results, the RoBERTa and BERT models perform better than the other transformer models when it comes to document summarization. In addition, we identified the weaknesses and strengths of each model. We suggest implementing domain-specific training and fine-tuning techniques to improve their performance. The results of the experiment revealed that the RoBERTa and BERT models perform better than the other ones when it comes to document summarization. We also found that the models have weaknesses and strengths, and we suggest using domain-specific training and fine-tuning techniques to improve their performance. The paper contributes to the literature related to the use of transformer-based models in sentiment analysis and documents summarization by providing an extensive analysis of the different models' performance in the Yelp dataset. It also suggests various modifications to improve their capabilities.
Automating Fraud Detection in Financial Services: An AI-based Approach

One of the most common reasons why financial fraud occurs is due to credit card fraud. Unfortunately, traditional methods of detecting this issue have not been able to effectively prevent it. This has prompted the need for more sophisticated and efficient fraud detection techniques. Artificial intelligence has emerged as a promising tool for this problem. The paper looks into the use of AI methods to detect credit card fraud in the financial services industry. We analyze the performance of different algorithms, such as the Random Forest, the Neural Network, and the Naive Bayes. We also perform various preprocessing steps in order to get the data ready for analysis. The paper presents an evaluation of the four AI techniques in terms of their precision, recall, accuracy, and F1 score. It indicates that the Neural Network is the best performer when it comes to detecting credit card fraud. The study emphasizes the importance of utilizing AI-based methods for detecting financial fraud. It also highlights the potential of the Neural Network algorithm. The findings of the study have important implications for the advancement of financial fraud detection systems, and it can serve as a guide for future research. The study's findings provide valuable insight into the use of AI in identifying credit card fraud in the financial services industry. It also shows the potential of this technology to help combat this issue.
Optimizing Hyperparameters for MINIST Dataset Classification using Deep Learning Techniques

The potential of deep learning to improve the performance of image classification has been demonstrated. In this paper, we present a study on the optimization of the hyperparameters for the classification of the MNIST dataset. We performed a comparison between a standard two-layer perceptron model and a CNN model with different techniques. The optimized hyperparameters for CNN are based on the number of filters, kernel size, and convolutional layers. The optimized CNN model performed better than the default model on the classification task of the MNIST dataset. The various hyperparameters included the learning rate, batch size, the number of hidden layers, the dropout rate, the activation function, and the optimizer. The optimized CNN model was able to achieve an accuracy of 99% on a test, which is significantly better than the 96% accuracy of the default model. The difference between the two models is that the former takes longer to train and has a slightly longer time per image. The study demonstrates the importance of optimizing the hyperparameters in deep learning-focused classification tasks. The findings show how CNN architectures perform well in these applications, and it shows how optimizing these components can yield superior results. These recommendations can help further develop efficient and accurate models for this field.
Struktur dan Kebahasaan Teks Berita Siswa Kelas VII SMP Negeri 2 Ampek Nagari

This research aims to accomplish two objectives. First, to describe the structure found in the news texts of 7th-grade students at SMP Negeri 2 Ampek Nagari. Second, to describe the linguistic aspects of the news texts of 7th-grade students at SMP Negeri 2 Ampek Nagari. This study is a qualitative research using a descriptive method. The data for this research are the structure and language features of news texts written by 7th-grade students at SMP Negeri 2 Ampek Nagari. The research instrument is the researcher themselves, meaning that the researcher has the task of determining the research focus, selecting an informant as a data source, collecting data, assessing data quality, analyzing data, interpreting data, and drawing conclusions based on the research findings. Firstly, the writing skills of the news texts by 7th-grade students at SMP Negeri 2 Ampek Nagari do not yet utilize the appropriate structure of a news text. This is evident from the significant number of news texts written by students that lack the proper use of the 5W+1H news elements. Out of the 30 analyzed news texts, it was found that 9 news texts have the appropriate title structure, 15 news texts have the appropriate core news content, and 26 news texts have the appropriate news content. Secondly, in terms of language usage, the news texts written by 7th-grade students at SMP Negeri 2 Ampek Nagari already adhere to the five linguistic principles of news texts. Out of the 30 analyzed news texts, it was found that 24 news texts do not use single sentences appropriately, 26 news texts do not use compound sentences appropriately, 27 news texts do not use conjunctions or connectors that convey chronological meaning, 6 news texts do not use conjunctions or connectors that convey causality, and 25 news texts do not use pronouns or pronominals appropriately.
Can Artificial Intelligence (AI) Machine be Granted Inventorship in India?

Artificial intelligence (AI) is prolific emerging algorithmic general purpose technology that helps inventors in the innovation process or is a component of an invention. AI-assisted inventions and other computer-related inventions (CRIs) generally have few significant differences. A simple instrument for creativity, machines are now a significant contribution to creation because to AI advancements. Medical researchers are using AI machines to find new drugs. These automated systems, also known as “innovative AI”, have been helping to develop new inventions with little to no human involvement.1 Since AI's contribution and autonomous invention process is exponentially growing, there have been instances where a patent applicant has opted to name an AI programme as the inventor in a patent application. The patent offices of the United States, the United Kingdom, the European Union, and New Zealand recently rejected a patent application seeking inventorship for an AI computer. Australia and South Africa, on the other hand, have recognised AI as an inventor. In light of the various approaches used around the world, this article examines the key question that emerges in patent granting: can an AI system that is not a natural person be recognised as an inventor and granted a patent in India, including inventorship and ownership?
A Brief Talk on Chinglish from the Level of Teaching Methods

China has been promoting English teaching activities since the 1960s with the goal of cultivating a global perspective and proficiency among the younger generation. Despite decades of teaching, issues such as the inconsistent use of British and American pronunciation and the incorrect use of tenses in Chinese English persist and have not seen improvement. This paper delves into a comprehensive study from the perspective of the differences between English teaching in China and foreign countries, specifically exploring the relationship between Chinese English and educational methods while analyzing the underlying logic and differences in Chinese and Western English teaching methods. This paper starts with the scientific laws of language acquisition and aims to provide practical solutions to address Chinese English problems and other language-related issues, and offers valuable insights into the innovation and development of Chinese English teaching in the future.
Developing Classifiers by Considering Sentiment Analysis of Reported Bugs for Priority Prediction

Abstract Software systems behave abnormally due to bugs when it comes into the operational phase. Lack of proper understanding of customer requirements, implementation, knowledge, wrong algorithmic designing, and other issue is also the reason for bug production. To fix those flaws, developers request to the users for feedback. Users have had issues with the software systems that have been released. Users are encouraged to submit their issues to issue-tracking systems such as Bugzilla, Mantis, Google Code Issue Tracker, GitHub Issue Tracker, and Jira to improve the next version of the product and meet user needs. Manual prioritization is time-consuming and inconvenient. In this research paper, we propose using sentiment analysis to anticipate the report's priority. This is the first time the sentiment-based approach used for a bug report to prioritize prediction on open-source projects. First, we take the bug report summary and use natural language pre-processing techniques to clean the text and pre-process the bug report. Second, sentiment analysis is applied to clean texts that contain sentiments of terms. Third, we use TF-IDF to construct a feature vector for bug reports, fourth, we used resampling techniques to balance the dataset, and then we used different machine learning classifiers to train historical data namely Bugzilla open-source projects to forecast their priority. The proposed method improves the performance of the classifier in comparison with the methods available in the literature.
E-Ration Distribution System using Blockchain Technology

Recently, the Public Ration Distribution System structure is one of the prime government commercial schemes.Low economical group and people below scarcity line use this amenities provided by the government.Due to deception appear in a chain, such amenities do not reach to the needy people.This happens because in the existing system all the work done by physically.To computerize or automate this physical job there is no any specific unreasonable technology or tools involved.Due to this, system facing two problems firstly weight of the material that is given to the people may be inaccurate or imprecise and secondly, at the end of the, illegal wrong entries in the inventory of the shop about the amount of the material given to the consumers.In this work we will describe a blockchain technologybased prototype that can be used in a small website.There are presently many fraud activities and corruptions taking place in the food supply schemes present as it sometimes does not reach the poor or the other sections of the society.This paper focuses on developing blockchain prototype that is used to record all the transactions/records and log all these transactions.A simple end-to-end web based app of this kind of the blockchain prototype can be built that has most of the features and functionalities to carry out all kinds of the transactions between the central government, state government, the district office, ration shop/and the customers, are recorded in the system.The user of the system can view the transactions of any part of the public distribution system.The project have some features that is guaranteed to provide the most important aspect that is, the security using the concept of blockchain
The effect of outsourcing and insourcing on Agile and DevOps

The Chief Information Officer of the Standard Bank Group, Brenda Niehaus, was contemplating her next steps. It is 2014 and she has just been appointed in her role and was burdened with a considerable set of challenges. Standard Bank Group’s IT costs were far above industry benchmarks, delivery was slow, staff was unhappy and there were a couple of large signature projects that were over time and budget by orders of magnitudes. Her predecessor has embarked on a large whole scale outsourcing journey and a decision needs to be made if that will continue. Dawie Olivier, her Head of Build, advocates something different: Agile and DevOps. The dream in this approach is to have many small cross-functional two-pizza teams that work together in close proximity and build software. The code created on a developer’s laptop will then seamlessly deploy to production automatically without the interference of pesky change control boards or some manual intervention. Should she continue with outsourcing or insource and focus on Agile? What she didn’t realise was that her successors will all face a similar situation and that answers will not be ready at hand. Many organisations face a similar dilemma of balancing outsourcing, Agile, DevOps and general constraints around skilled resources. We provide a rich narrative of the choices CIOs face in developing countries around these themes to help students understand the risks and opportunities these different approaches bring.
Perancangan Blueprint Knowledge Management System pada Unit Laboratorium Fakultas Ilmu Terapan Universitas Telkom

&#x0D; &#x0D; &#x0D; Knowledge is one of the valuable assets owned by an organization or an enterprise. But unfortunately, not all organizations have this awareness. The Laboratory Unit of the Faculty of Applied Sciences (FIT) is a unit that was established with the aim of managing laboratories and all practicum activities within the Faculty of Applied Sciences, Telkom University. Since its establishment until now, the FIT Laboratory Unit has undergone several staff changes. This happened due to the rotation or shift from the FIT Laboratory Unit to other units or due to the expiration of the contract period. In addition, because those chosen to be laboratory assistants are final year students of the Faculty of Applied Sciences, laboratory assistants also experience changes every year. Without good knowledge management, it will cause a loss for the organization. Because the knowledge gained by the staff, while working in the Laboratory Unit will also be lost when the staff moves. To overcome this problem, the researcher proposed a knowledge management system design that can assist organizations in managing and storing their knowledge be it tacit or explicit knowledge. It Also can make it easier for staff to share and transfer knowledge. In this research process, 10 Step Knowledge Management Roadmap by Amrit Tiwana is used as guidance. However, of the ten steps, this research is limited to the sixth step, which is the designing of a knowledge management system blueprint. From the analysis of the strategic approach of KM, it is found that the codification approach has a larger portion of application than the personalization approach. Furthermore, the design process is based on seven layers of KMS. To produce a knowledge management blueprint that can be applied in the development of KMS in the FIT Laboratory Unit.&#x0D; &#x0D; &#x0D;
PERANCANGAN ARSITEKTUR MENGGUNAKAN KERANGKA KERJA ZACHMAN UNTUK SISTEM INFORMASI PERUMAHAN DAN PERMUKIMAN (SIPERKIM)

&#x0D; &#x0D; &#x0D; Enterprise architecture plan of Karawang public housing agency information system designed using Zachman framework and used as reference for planning an enterprise architecture SETTLEMENT AND HOUSING AREA INFORMATION SYSTEM so that able to build an integrated information system to make the good governance comes true, especially in applications. Data collections have been done by Interviews and document analysis. Analysis parameters used are Strenghts, Weakness, Opportunities, threats (SWOT) dan Value Chain. This Enterprise architecture planning intends to build integrated information system from villages to cities through Karawang public housing agency. The result of this research is a blueprint on zachman framework that can be seen from planner and owner perspective with 4 sides, they are Data (what), Function (how), Network (where), and people (who), so that it can support application process in Karawang public housing agency.&#x0D; &#x0D; &#x0D;
use of information and communication technologies in the process of teaching English after the COVID-19 pandemic

The research was carried out at Eloy Alfaro University, in the Chone extension. The objective was to analyze the use of information and communication technologies in teaching the English language after the Covid-19 pandemic. It was a difficult stage for schools, including universities, because students and teachers were not prepared for the change from face-to-face to virtual classes, so they had to take measures to continue with the planned programs. The English subject also changed; in this sense, it was necessary to adapt to the conditions. The quantitative and qualitative method was used to identify, analyze, and describe information and communication technologies in the English teaching process, in addition to desk research, which is based on searching for the published material in reliable reports and documents. Was obtained as results that the technologies used by teachers and students in the English language teaching process after the Covid-19 pandemic showed that teachers were able to respond positively and incorporate new technologies into the English language teaching process, demonstrating that new methodological schemes have evolved, strengthening and invigorating teaching: in addition to contextualizing the research before and after isolation.