Reviewerly/exHarmony huggingface dataset:
The basis of unified field theory and the direction of future development

Unified field theory is a physical theory that describes and reveals the common nature and intrinsic connections of fundamental interactions in a unified way, starting from the idea that interactions are transmitted by fields. This paper will start from the past of the unified field theory and use the literature research method to describe the problems encountered in completing the unified field theory and the possible solutions for the future. The result shows that unified field theory is a dynamic theory that is still being studied and has significant implications for the development of physics. This article argues that many of the shortcomings of this theory are due to the lack of mathematical theory. It is also pointed out that the creation of a new mathematical system is a feasible way. Finally, it is concluded that unified field theory is still a very difficult problem to explore.
The development of generic key recovery attack on Feistel structure

Feistel structure was firstly proposed in 1973, and because its structure has a great avalanche effect and similar encryption and decryption, it was used in many encryption schemes, like DES, AES, CAST. According to the ambiguity of the intermediate state, Feistel structures are separately named as Feistel-1, Feistel-2 and Feistel-3.Even though some of efficient analysis were proposed to attack the Feistel structure such as differential cryptanalysis and linear attack, these attacks are only applicable to a given Feistel structure and cannot have a general analysis of all Feistel structures. To attack the general Feistel structure, splice and cut, key linearization, and meet-in-the-middle attack have been used to propose the general key recovery attack on various Feistel architectures. This paper summarizes these results and proposes the research direction of the MITM attack of the Feistel structure, especially for the generic key recovery on different round functions and combination with modern means, like the application of Simon algorithm, which can build 3-round distinguisher on the Feistel structure.
Potential pulsars prediction based on machine learning

The search for potential pulsars is a difficult job because of the complex nature of the signals and the vast amounts of data involved. In the last few years, a lot of researchers have tried to use machine learning to deal with complex data. This essay examines how machine learning could help to identify potential pulsars, exploring the various types of algorithms and the challenges and limitations associated with this approach. The essay mainly explored three themes: the training of 5 algorithms for the identification of pulsars, the improvement of 2 algorithms by adjusting parameters, and the simplification of the data to improve the processing speed and performance of the algorithms on prediction. All 5 algorithms reached great accuracy after adjustment and the simplification of the input data can help to boost the prediction time and accuracy for future research about pulsars. The essay highlights the need for further research in this area, as machine learning has demonstrated strong potential for pulsar prediction. By analyzing the results of several previous studies, this essay underscores the importance of machine learning as an approach for predicting potential pulsars and made improvements to the performance of current algorithms by adjusting parameters and simplifying the data.
An investigation into knowledge management adoption and sustainable competitive advantage in an emerging market economy

Purpose Organizations sustain competitiveness by improving product or service quality, performing efficiently or innovating. This paper aims to investigate the relationship between knowledge management (KM) and sustainable competitive advantage (SCA) in business organizations in the Kingdom of Bahrain. The KM initiatives are categorized into knowledge creation, knowledge storage, knowledge transfer and knowledge application. Employees’ attitudes toward workplace knowledge resources are derived from their perceptions of their importance, usefulness and ease of use. Design/methodology/approach This paper adopts a cross-sectional survey design. Data is collected via an electronic questionnaire developed using Google Forms. Purposive sampling used a list of 122 business organizations. Ninety responses were received and taken into consideration for data analysis. Findings Spearman correlation analysis and partial least square structural equation modeling revealed a positive association between KM and SCA. This study reflected a positive association between employees’ attitudes toward knowledge resources and sustaining organizations’ competitive advantages. Originality/value In Bahrain, empirical studies still need to be developed to explore KM in business organizations and investigate its association with SCA. This study aims to fill this gap by examining the relationship between KM and the sustainability of quality, efficiency and innovation-based competitive advantages in business organizations in an emerging economy context.
Data Mining technique: Application of Apriori algorithm for road accident analysis

Road accidents can happen due to various factors. These factors that contribute to road accidents have cost damage to properties, injuries or deaths and most road accidents are attributable to the lack of knowledge on road safety. To provide safe driving and road safety plans, critical analysis of road accident data is needed, to identify the causes of road accidents. Annually, 1,250,000 people die and 50,000,000 are injured in road accidents worldwide, and fatal road accidents are caused by human error. Improving road conditions is not sufficient, but significantly understanding human errors that cause road accidents, and negligence of corrective and safety driving protocols provided by the concerned government agencies or private organizations. The study aimed to help get insights about the causes of road accidents, and to provide knowledge of road accidents for road safety using Association Rule Mining with the application of the Apriori Algorithm. Association rule mining using the Apriori Algorithm produces significant patterns and insights that help identify the causes of road accidents.
Large Language Models and Information Retrieval

This research article explores the synergistic integration of Optical Character Recognition (OCR) technology and Large Language Models (LLMs) to advance Information Retrieval (IR) processes. In a data-centric society, efficient IR is imperative, and the combination of OCR and LLMs presents a powerful solution. OCR transforms diverse document types into machine-readable formats, while LLMs excel in language understanding and generation. The article delves into the technical intricacies of these technologies, their seamless integration, and their potential to revolutionize information retrieval. By investigating their collaborative capabilities, this research contributes to the evolving landscape of natural language processing and information retrieval systems.
The Influence of Librarian Competence on the Quality of Library Services at the Library and Archives Service in Bengkalis Regency

This research aims to: (1) Find out how much competence librarians in the Bengkalis Regency Library and Archives Service have; (2) Find out how high the quality of library services is at the Bengkalis Regency Library and Archives Service; (3) Find out how much significant influence librarian competence has on the quality of library services at the Bengkalis Regency Library and Archives Service. This research is field research with variables The data in this research was taken using documentation and questionnaires. The results of the research show that: (1) The competence of librarians in the Bengkalis district library and archives service is categorized as good, this can be proven by the percentage value of the questionnaire regarding librarian competence, namely 77.7%; (2) The quality of library services in the Bengkalis district library and archives service is categorized as good, this can be seen from the percentage value of library service quality, namely 79.8%; (3) There is a significant influence between librarian competence on the quality of library services, this can be proven by analysis of the coefficient of determination where the coefficient of determination is 0.741. This shows the understanding that changes in the level of quality of library services (Y) are 74.1% influenced by Librarian Competency (X), while the remaining 25.9% (100% - 74.1%) is explained by other causes.
Authenticating Parties in Blockchain-Enabled Inter-Organizational Processes with Configurable Challenge-Sets

Blockchain has shown potential in enabling trustable and transparent executions of business collaborations between organizations. Blockchain provides a decentralized network for storing data associated with different collaboration use cases, and smart contracts ensure that specified business rules and conditions can be automatically executed without depending on a centralized entity. Still, it is important to correctly identify parties in different organizations and the roles they play in executing various functions in interorganizational collaboration use cases deployed on a blockchain. This is particularly crucial in public blockchains where privacy concerns and user-management issues have slowed the adoption of interorganizational use cases in such networks. Hence, enterprises opt for permissioned networks to deploy decentralized applications. Smart contracts are computer programs running on blockchains that execute specified business rules and conditions in an immutable and transparent manner. However, the state of any organization and parties within such organizations are dynamic. Thus, it is necessary to apply secure, configurable, and flexible mechanisms for authenticating individuals within organizations before executing functions in inter-organizational collaboration (IoC) processes on a blockchain. This research explores the use of concepts defined in the multifactor challenge set self-sovereign identity authentication (MFSSIA) in verifying and validating parties in IoC using nonfungible token (NFT)-based luxury-asset authentication as a running case. The main results of this paper show a set of configurable challenges represented by an organizational chart for authenticating members of an organization and the necessary steps required for integration into an existing DApp that executes IoC.
Efficient Mining of Top-K Cross-Level High Utility Itemsets

High utility itemset (HUI) mining extracts frequent itemsets with high utility values from transactional databases. Traditional algorithms have limitations in detecting relationships between items and categories across multiple levels of a taxonomy-based database. Cross-level algorithms have been proposed to address this issue while top-k algorithms find the top-k HUIs with the highest utility values. Fast and Efficient Algorithm for Cross-level high-utility Pattern mining (FEACP) and Top-K Cross-level high utility itemset mining (TKC) algorithms were proposed for HUI mining with high efficiency. However, they suffer from scalability and efficiency issues when dealing with large datasets. To overcome these limitations, we propose a new algorithm called TKC-E (Efficient Top-K Cross-level high utility itemset mining), which combines the strengths of FEACP and TKC while applying efficient strategies to identify cross-level HUIs in taxonomy-based databases, resulting in significantly improved scalability and efficiency. Experimental results show that TKC-E outperforms TKC in terms of processing speed and memory usage, with up to 2.4 times memory and 60 times runtime improvements on sparse and dense datasets, respectively.
Sentiment Analysis of Hotel Reviews Using Support Vector Machine

With technology nowadays, everyone can leave their review about a hotel on the internet. This creates a new issue for the hotel itself because the reviews can come in in thousands amount. This will consume a lot of time to handle these reviews manually. In this study, a sentiment analysis model will be made to overcome the issue. The data in this study is collected from Kaggle website. This data contains 20,491 reviews about a hotel. The data will then be preprocessed and given a label for each data point. Then, the model is trained using the clean data. The model will use Naïve-Bayes, Logistic Regression, and Support Vector Machine algorithm. From the result performed, it's concluded that Support Vector Machine performed more accurately with 94% rate.
An Enhanced Incentive Mechanism for Crowdsourced Federated Learning Based on Contract Theory and Shapley Value

Federated learning is a recently dominant learning method for crowdsourced learning systems with diverse scales. It plays a pivotal role in smart city operation technologies, such as cross-organization (e.g., hospitals, banks, etc.), Mobile Ad hoc networks (MANETs), Mobile Edge Computing (MEC), Vehicle Ad hoc Networks (VANETs), and Internet of Things (IoTs). Specifically, this method aggregates a global model from local models trained on the private data of clients. To achieve high accuracy and collaborate effectively, federated learning-based crowdsourced systems need to attract sufficient quality clients. Therefore, a proper incentive mechanism is essential to motivate clients to join and contribute to the best of their ability. However, it is challenging to design such a mechanism due to the fact that each client has different system resources, data size, and effort. This implies that if the incentive mechanism is not well-designed, it will lead to a moral hazard situation, where clients may free-ride and the overall accuracy of the global model will undergo a downward spiral. Furthermore, the clients who contribute most to the accuracy of the global model are not necessarily those with the most decorated power and dedicated work. To address these challenges, we propose a joint optimization mechanism that leverages contract theory and Shapley value. This mechanism helps to reveal private information about clients and quantify their contribution to the global model, so that a suitable and equitable incentive can be constituted for each client.
Microsoft Excel Learning Application Design with Android Based using Gamification Method

Microsoft excel is software can support performance in data processing, microsoft excel provides formulas and function that can be used to facilitate perfomance in data processing. Utilization of microsoft excel can be used in every day life in business and educational activities. Microsoft excel can perform arithmetic calculation, so can simplifying the assessment results and as an alternative that can provide maximum results. The purpose of this research is design and build a microsoft excel learning application with the gamification method to increase behavioral intention to use and immersion in the learning process to provide an alternative learning process in learning formulas and functions in microsoft excel. The method used in this study was designed and built with the gamification method using the six steps to gamification framework developed by Kevin Werbach in 2016. The results of this learning application have been evaluated by 32 respondents by filling out a survey based on the Hedonic Motivation System Adoption Model. From the survey, 77.5% were obtained for the behavioral intention to use aspect and 64.53% were obtained for the immersion aspect. The conclusion of this study in implementing gamification using the six steps to gamification framework states that they agree with evidence of 77.5% for the behavioral intention to use aspect and 64.53% for the immersion aspect.
Evaluate the Efficiency of Hybrid Model Based on Convolutional Neural Network and Long Short-Term Memory in Information Technology Job Graph Network

The advancement of technology has had a positive impact on the manner of life in society that offers more employment opportunities. It may have had an impact on Information Technology students’ decisions about their ideal future careers. To help students majoring in Information Technology in Vietnam and the surrounding countries can connect and interact with recruiters to obtain jobs, we built an application called SmartCV, which uses Smart Contract in Blockchain Technology to create connection and store information. We also use several Machine Learning algorithms includes Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) to create a recommendation system that can assist individuals in choosing the right skill job. Moreover, we proposed a framework that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The Hybrid CNN-LSTM model takes advantage of CNN’s ability for feature extraction from input data and LSTM’s ability for sequence learning. The dataset for training and testing is crawled from job search websites. We used classification performance metrics including Accuracy, Recall, Precision, Micro-F1 to evaluate the performance of machine learning algorithms. Final experiment results demonstrate that the Hybrid CNN-LSTM model has the highest level of accuracy.
Sentiment Analysis Performance Value Optimization Using Hyperparamater Tunning With Grid Search On Shopee App Reviews

&#x0D; &#x0D; &#x0D; &#x0D; The rapid development of technology today has provided convenience for us in today's civilization. One of these developments is the invention of the internet due to high internet penetration and rapid growth in mobile usage, online shopping has increased tremendously. This online shopping is now often referred to as e-commerce. E-commerce is one of the trade models that has been widened under the effect of extensive use of technology. Specifically, e-commerce refers to the usage of the Internet or other networks. Shopee is one of the popular marketplaces in Indonesia that has the highest number of visitors of 129 million per month and can be downloaded on the Google Play Store. Play Store itself has several features such as Reviews that can allow users to give opinions. All complaints and opinions from shopee users can be channeled into this feature. With this a research aims to optimize the performance value of sentiment analysis with the Term Frequency-Inverse Document Frequency (TF-IDF) method and Hyperparameter Tuning with Gridsearch for the Shopee application on the Google Play Store. Based on research the reviews resulting in 3000 data where 2015 user data is positive and 985 data is negative. Testing data was split by a ratio of 90:10 for 300 data test in each classification model to find the accuracy score. With hyperparameter tuning using gridsearch we can see the result of each accuracy score of KNN, DCT, RF, and LR is increasing from 0.73 to 0.77, 0.823 to 0.826, 0.856 to 0.87, and 0.856 to 0.866. This indicated that among the machine learning model that had been tuning using gridsearch, KNN is the one that highly increased.&#x0D; &#x0D; &#x0D; &#x0D;
BERT-Based Sentence Recommendation for Building Vietnamese Universal Dependency Treebank

A large Vietnamese universal dependency treebank is very important for creating Vietnamese universal dependency parser. However, the Vietnamese universal dependency treebank is small. In addition, there are not many skilled annotators for manually building Vietnamese universal dependency treebank. Therefore, a semi-automatic process, which predicts the high-quality dependency parse results for manual correction, should be used for speeding up the annotation process. This process is also used for beginner tutorials. In this paper, a BERT-based sentence recommendation is proposed for selecting sentences which are possibly predicted with high LAS using an inefficient dependency parser. These sentences should be predicted with high accuracy using a parser trained from the small Vietnamese universal dependency treebank. The experiments show that the recommended sentences are predicted with LAS above 87.1% when using a parser with LAS of 36.8%.
LAVETTES: Large-scAle-dataset Vietnamese ExTractive TExt Summarization Models

The problem of text summarization has consistently been a significant and prominent challenge for a particular language. Each language’s unique characteristics will reflect that country’s identity, culture, and nuances. This paper introduces extractive text summarization models for Vietnamese documents. Our approach concentrates on discovering appreciative and plausible models by combining ML algorithms. Namely, we investigate three potential models, including a “G-global-hard-cluster” (with GloVe), “probability-cluster” (with LDA, Latent Dirichlet Allocation), and a “soft-specific” combination between SGD (Stochastic gradient descent) and kmeans. Moreover, we also provide experimental results to evaluate the quality of the summary and the consumption time. In particular, our approaches obtain the expected results with $$51.49\%$$ ROUGE-1, $$17.99\%$$ ROUGE-2, and $$29.25\%$$ ROUGE-L. Finally, we discuss the promising results of the proposed models.
Invasive Ductal Carcinoma Classification from Whole Slide Image Based on BRISQUE and Convolutional Neural Networks

This research aims to improve the performance of an invasive ductal carcinoma (IDC) classification system by examining how input image quality affects the dataset. To achieve this goal, we utilized the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) method to assess image quality and divided the dataset into subsets for training, cross-validation, and testing of the proposed Convolutional Neural Network (CNN) architecture. The study evaluated various subsets of the training dataset to determine the optimal option, which was then used to identify the appropriate training parameters and evaluate the system's performance. The results demonstrated that the proposed approaches outperformed the standard methods for IDC classification.
A Hybrid Model for Detecting Insurance Fraud Using K-Means and Support Vector Machine Algorithms

Private stakeholders and governments across the globe are striving to improve the quality and access of healthcare services to citizens. The need to improve healthcare services, coupled with the increase in social awareness and improvement of people’s living standards, has seen an increase in medical policyholders in the insurance industry. Even so, the healthcare sector is grappled with increased costs every other year, leading to revision of premiums and increased costs for the policyholders. One of the main factors contributing to the increased costs is fraudulent claims raised by the service providers and the policyholders, leading to unprecedented risks and losses for insurance firms. The insurance industry has set up fraud detection and mitigation systems to mitigate losses brought about by fraudulent claims, which come in two flavors: rule-based systems and expert claims analysis. With rule-based systems, conditions such as missing details, location of the claim vis a vis the location of the policyholder, among other rules, are evaluated by systems to assess the validity of the claims. On the other hand, insurance firms rely on the human intervention of experts using statistical analyses and artificial rules to detect fraudulent claims. The rule-based and expert analysis methods fail to detect patterns or anomalies in claims, which is central to efficient fraud detection. Data mining and machine learning techniques are being leveraged to detect fraud. This automation presents enormous opportunities for identifying hidden patterns for further analysis by insurance firms. This research aims to analyze a hybrid approach to detect medical insurance fraud using both K-Means (unsupervised) and Support Vector Machines (supervised) machine learning algorithms.