Reviewerly/exHarmony huggingface dataset:
Method of Security Improvement for MST3 Cryptosystem Based on Automorphism Group of Ree Function Field

&#x0D; &#x0D; &#x0D; This article is a part of a research endeavor focused on creating a quantum-resistant cryptosystem for secure encryption and decryption. Our approach employs a challenging word problem while emphasizing cost-effective implementation. Previous research has involved the development of encryption schemes based on high-order groups, offering potential security enhancements. The choice of the non-abelian group is a critical factor in shaping the encryption algorithms, feasibility of implementation, and system parameters. Our central objective is to design a cryptosystem that effectively thwarts quantum cryptanalysis. To achieve this, we employ a logarithmic signature along with a random cover across an entire finite non-abelian group. Our unique contribution lies in optimizing finite group selection, parameters, and circuit solutions for the logarithmic signature to meet specific security and implementation criteria. Within this paper, we introduce an encryption scheme utilizing automorphisms of the Ree functional field and propose a method for enhancing resistance to cryptanalysis through the binding of session keys.&#x0D; &#x0D; &#x0D;
Continual Learning of Deep Learning for Indonesian Sentiment Analysis

High-level social media usage makes this social media frequently used as one of the sources for sentiment analysis. Sentiment analysis is a field of study that analyzes people’s opinions or evaluations of entities such as products and services. The Bidirectional Encoder Representation from Transformers (BERT) model is a deep learning architecture that achieves state-of-the-art performance for many natural language processing problems, including sentiment analysis. Several further developments have implemented continual learning on the deep learning model. By applying continual learning, the deep learning model continuously learns based on new data while retaining previously learned knowledge. In this paper, we analyze the performance of the BERT model for continual learning in some domains of Indonesian sentiment analysis. Then it will be compared with two standard deep learning models: fine-tuned embedding with CNN and fine-tuned embedding with LSTM. Our simulation shows the BERT model gives the best accuracy for the transfer of knowledge. However, the fine-tuned embedding with LSTM model is better for retain of knowledge. Moreover, our simulation shows that the order of the source domains affects the performance of BERT for both transfer of knowledge and retain of knowledge.
Predicting Cyber-Trafficking Websites Using a Naive Bayes Algorithm, Logistic Regression, KNN, and SVM

Researchers and software developers continue discovering the best approach to combat the rising cyber-trafficking issues. However, most studies focus only on one platform or one of the gateways of cyber-trafficking. Thus, this paper introduces the development and comparison of the Naive Bayes Algorithm, Logistic Regression, k-nearest neighbor (KNN), and Support Vector Machine (SVM) classification models to predict trafficking and non-trafficking websites. In developing the supervised classification models, 37 keywords were used to scrape suspected trafficking websites. Thirty-five (35) websites were classified as trafficker out of 63; this data was used to create the models. Upon evaluating the accuracy rates of the models, the Naive Bayes Algorithm got ninety-one percent (91%), Logistic Regression got eighty-one percent (81%), KNN got sixty-four percent (64%), and SVM got sixty-four percent (64%). Thus, Naive Bayes can predict more accurately than the other classification algorithms. The result shows that the predictive model could be an effective tool for identifying different online platforms that are used in trafficking. Once the model is integrated into an application, this will be easier and faster for law enforcement agencies to monitor human trafficking in a fast-growing cyberspace community.
NeSi: Netizen Simulator for Evaluating Internet Public Opinion Analysis System

Clarifying potential public opinion information by analyzing comments is an important feature of modern Internet public opinion analysis systems. However, evaluating such systems by analyzing real netizens’ comments requires significant human effort, which can be time-consuming and expensive. Netizen simulation is a cost-effective technique for evaluating Internet public opinion analysis systems. In this paper, we propose a conversational netizen simulator, called NeSi, for the automatic evaluation of such Internet public opinion analysis systems. Describing public opinion incidents, NeSi can automatically generate a sequence of comments based on the Opinion Leader-Follower model. The comments were varied and sentimental. Through a set of experiments that include both automatic and expert human evaluation, we show that NeSi generates comments that are not only related to public opinion but also comparable to human-generated utterances.
Productive Oscillation as a strategy for doing more-than-human design research

KEYWORDS: More-than-humandesign researchproductive oscillationintermediate knowledgeresearch through design
Individually Integrated Virtual/Augmented Reality Environment for Interactive Perception of Cultural Heritage

The need to preserve and care for cultural heritage makes information technology a tool for finding, documenting, describing, and visualizing important historical and archaeological sites. This article offers an example of a demo platform providing users with a unique immersive experience in a virtual clothing museum environment. The proposed concept combines such types of media resources as audio, video, text, and 3D models of the virtual environment. The augmented reality elements perform the function of additional informing the users about certain facts about the exhibits. The methodology of platform development and the application's architecture are described. The creation of the platform is intended to achieve the objectives set by VR/AR technologies, namely, to provide a wide range of opportunities for interaction of the user with the objects of cultural heritage. The peculiarity of the application is the possibility to choose between exploring the objects of cultural heritage on one's own or following a programmed path with the help of an audio guide. The proposed solution can be used for stationary expositions of art objects, historical or archaeological relics, and objects of national economy and creativity. Testing of the demo version showed a rather high degree of user satisfaction (89% of respondents reported a positive experience). Users noted the atmospheric and immersive experience of virtual reality. The developed platform can perform educational and popularization functions.
The persistence of print books: exploring language preference and format preference among Arabic-speaking library patrons in Jordan

Purpose The purpose of this study is to investigate the reasons behind the persistent preference for printed content among Arabic-speaking library patrons in Jordan. Specifically, this study highlights the availability of reading materials in print, electronic and audible formats in Arabic as an intervening factor shaping reading behavior. More broadly, it aims to contribute to a deeper understanding of how language preference can impact reading format preferences. Design/methodology/approach This study’s research design revolves around understanding reading format preferences among registered members of Jordan’s largest and oldest private library. This approach involved the examination of secondary library user survey data collected from N = 313 of its patrons in 2022. To gain a greater understanding of the preference for printed materials, this study conducted semistructured interviews over the phone with n = 31 participants of the library’s survey. Findings The findings of this study indicate a strong preference for print books among Arabic-speaking library patrons in Jordan. However, the availability of content in electronic and audible formats in Arabic, their preferred reading language, emerged as a potentially significant factor in the persistent preference for printed reading materials. Originality/value This study offers new insights into the specific role that the availability of content in Arabic, and possibly languages other than English, may play in shaping reading format preferences. By shedding light on this aspect of reading behavior, this research offers valuable information for libraries and publishers seeking to cater to the needs and preferences of Arabic readers.
PaaS platform security enhancement using fuzzy based access control and trust based signature

Abstract Platform‐as‐a‐Service (PaaS) is one of the cloud computing services which will be offered to the clients through the virtualization platform. The problem is constructed in the PaaS platform effectively excluding internet service suppliers which do not meet highly secure standards, and so security issues can be a significant a hurdle to online computation. To address the aforementioned issues, the suggested method primarily concentrated proceeding the safety of PaaS infrastructure by using a novel fuzzy based access control technique, in which the information source is realized by using CDO security feature. To further enhance the security, security using trust based signature has been used in which the class and packet filter for each specified authorization were mapped to our class by modifying the security feature code. This public key will be utilized for authenticate that transaction relevant signature if the identity provider (IdP) has provided public safety data about this same two major components represents this little token on whose trusted trademark components are built put, that is, the entire item or even the claims contained. The suggested technique outperforms other current methods in terms of several performance parameters, with a high throughput of 14, CPU utilization of 12.5, and a low execution time of 5.2 s. The proposed ‘PaaS platform security enhancement with the use of fuzzy and trust‐based signatures’ provides high throughput and low execution time. This research work proposes a novel approach to enhancing the security of PaaS infrastructure, which is offered to clients through the virtualization platform in cloud computing. The current problem with PaaS is that it excludes internet service providers that do not meet high‐security standards, which poses a significant challenge to online computation. The suggested approach focuses on improving the safety of the PaaS platform by employing a fuzzy‐based access control technique, using the connectionless data objects security feature as the information source. To further strengthen security, the approach utilizes a trust‐based signature technique where the class and packet filter for each authorization are mapped to the class of the proposed method by modifying the security feature code. The proposed public key is then used to authenticate the transaction relevant signature, provided the IdP has given public safety data about these two components of the token on which Trusted Trademark components are built, that is, the entire item or even the claims contained within it. The proposed approach outperforms current methods in terms of several performance parameters, such as a high throughput of 14, a high CPU utilization of 12.5, and a low execution time of 5.2 s. In summary, the proposed PaaS platform security enhancement using fuzzy and trust‐based signatures provides an effective way to achieve high throughput and low execution time while ensuring platform security.
DAG-based swarm learning: a secure asynchronous learning framework for Internet of Vehicles

To provide diversified services in the intelligent transportation systems, smart vehicles will generate unprecedented amounts of data every day. Due to data security and user privacy issues, Federated Learning (FL) is considered a potential solution to ensure privacy-preserving in data sharing. However, there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles (IoV), such as unreliable communications and malicious attacks. In this paper, we propose a Directed Acyclic Graph (DAG) based Swarm Learning (DSL), which integrates edge computing, FL, and blockchain technologies to provide secure data sharing and model training in IoVs. To deal with the high mobility of vehicles, the dynamic vehicle association algorithm is introduced, which could optimize the connections between vehicles and road side units to improve the training efficiency. Moreover, to enhance the anti-attack property of the DSL algorithm, a malicious attack detection method is adopted, which could recognize malicious vehicles by the site confirmation rate. Furthermore, an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors. Finally, simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy, convergence rates and security compared with existing algorithms.
The Performance Evaluation Model of Financial Special Rural Revitalization Funds from the Perspective of the Digital Economy

Digital village is the strategic direction of rural revitalization, and also an important part of building a digital China. At present, the construction of digital countryside is advancing, and a reasonable evaluation of the results of rural revitalization can effectively evaluate the use of funds. Therefore, in this study, firstly, based on the economic, social, and ecological perspectives, the fund performance evaluation index system of rural revitalization is constructed. Secondly, the correlation analysis method is used to select the evaluation index, and the combined weight method is employed to obtain the weight of the evaluation index. Finally, according to the extension cloud theory, the performance evaluation model of targeted poverty alleviation is implemented, and an empirical study is conducted on the performance evaluation of S Province with excellent rural revitalization. The results reveal that the income growth rate of the poor population, the incidence of rural poverty, and the rate of good air quality are the most vital indicators, with weights of 0.094, 0.092, and 0.07 respectively. From 2015 to 2021, S Province maintained a high level of environmental performance, while its economic performance and social performance needed to be further improved. The fund performance of rural revitalization experienced four levels: average, medium, good, and excellent, and showed a reverse trend in 2018 and 2021. This study makes a quantitative and qualitative evaluation of rural revitalization by using the extension cloud theory, which provides a reference for the effective use of special funds, and also provides a sufficient scientific basis for the macroeconomic layout.
A Safe and Reliable Approach to Transfer Health Records using Searchable Key Proxy Re-Encryption

In the delivery of high-quality healthcare services, the exchange of computerized clinical data holds significant importance. To safeguard the confidentiality of client's sensitive information, it is imperative to adhere to stringent privacy and security regulations when sharing this data with others. Consequently, the adoption of proxy re-encryption servers serves as an effective and reliable means of transmitting computerized medical information. This encryption method is currently recognized as the sole approach that is both unidirectional and recursive, allowing third parties to modify encrypted ciphertext intended for one party so that it can be deciphered by another. This technique is referred to as proxy re-encryption. In order to empower patients with control over who can access their healthcare data, the system operates as an intermediary between them and a database server. Prior to transferring files to the information server, individuals who own them can utilize their keys to encrypt the files, thus ensuring the security of the data stored there. The test results demonstrate the system's proficiency in supporting secure data sharing. These proxy re-encryption mechanisms guarantee the confidentiality and accessibility of data, offering end-to-end security and privacy for electronically stored health information. While prioritizing patient privacy, the system aims to enhance collaboration among providers and facilitates data sharing between the sender and receiver using their respective keys. In this context, a unidirectional system operates in a one-way, providing a higher level of security, making it a suitable choice for untrusted environments where message transport is necessary without granting the receiver rights to respond to it.
Advanced Multimodal Interfaces Design Using Speech Control

The paper discusses methods of developing human-machine interfaces for air-craft cabins using voice control. The use of voice control is relevant for promising multimodal aviation interfaces. The features of methods for recognizing voice commands are considered, as well as system design of human-machine interfaces based on methods for analysing crew tasks.
Online Voting System by Using Three Step Verification

Voting systems are the backbone of every democracy and organization. India uses an offline voting systems which are inadequate and inefficient due to the need for a large man force, and longer processing time for publishing results. Therefore, to make the system effective, the changes are implemented in the systems that can avoid these disadvantages. The new approach eliminates the requirement of physical presence, reducing overall complexity. Online voting systems have emerged as a convenient and reliable way of conducting elections. The system has the potential to make the process more effective and increase voter participation. However, it also comes with challenges related to security and authenticity. Our research paper outlines the system that employs three levels of authentication to guarantee the authenticity and dependability of the entire voting procedure. The first level involves face verification, followed by Aadhar verification and then voter card verification.
Detecting Phishing Websites using recent Techniques: A Systematic Literature Review

The goal of this study Phishing attacks are constantly evolving, and to avoid being detected by conventional means, attackers use cutting-edge approaches. Novelty detection aims to identify previously unseen phishing attacks, including zero-day threats and sophisticated evasion tactics. Phishing attacks continue to pose significant threats to cybersecurity, exploiting human vulnerabilities and developing quickly to avoid being detected by conventional methods. In response to these challenges, this literature survey presents a comprehensive review of phishing website detection techniques, focusing on novel approaches and the latest advancements in the field. It explores dynamic analysis, real-time monitoring, and anomaly detection techniques to keep pace with the ever-changing phishing landscape. The survey addresses the persistent issue of imbalanced datasets by presenting effective strategies for handling data from significantly more legitimate websites than phishing sites. It advocates for data augmentation, cost-sensitive learning, and domain adaptation to improve the accuracy and generalization of detection models. By highlighting the latest advancements and addressing key challenges, the review contributes to building robust and resilient phishing detection frameworks that safeguard users and organizations in the constantly evolving cyber threat landscape.
Small object detection for autonomous driving under hazy conditions on mountain motorways

To address the issue of high miss rates for distant small objects and the diminished system detection performance due to the influence of hazy when autonomous vehicles operate on mountain highways. We propose a framework for small object vehicle detection in hazy traffic environments (SHTDet). This framework aims to enhance small object detection for autonomous driving under hazy conditions on mountainous motorways. Specifically, to restore the clarity of hazy images, we designed an image enhancement (IE), and its parameters are predicted by a convolutional neural network [filter parameter estimation (FPE)]. In addition, to enhance the detection accuracy of small objects, we introduce a cascaded sparse query (CSQ) mechanism, which effectively utilizes high-resolution features while maintaining fast detection speed. We jointly optimize the IE and the detection network (CSQ-FCOS) in an end-to-end manner, ensuring that FPE module can learn a suitable IE. Our proposed SHTDet method is adept at adaptively handling sunny and hazy conditions. Extensive experiments demonstrate the efficacy of the SHTDet method in detecting small objects on hazy sections of mountain highways.
Digital competences in research: creativity and entrepreneurship as essential predictors for teacher training

Abstract The first purpose of this study was to analyse the digital research competences of higher education teaching staff, depending on whether the level of creativity of the teacher to initiate new research projects and their level of entrepreneurial spirit was low/good. Following, it was analysed whether the use of different emerging technologies significantly affect the digital skills of teachers in research work; and to know in what order the use of these emerging technologies or the combination of several of them affect the digital competences of teachers. A ex post facto design was used with a sample of 1740 Higher Education teachers. Digital skills were measured using the instrument of Guillén-Gámez et al. (Educ Inf Technol 10.1007/s10639-023-11895-2, 2023). The levels of creativity and entrepreneurship were analyzed with dichotomous variables from a 10-point Likert scale (less than 7 points-low level, greater than or equal to 7-good level). The results showed that, although teachers possessed an adequate level of digital research skills, this could vary depending on transversal skills such as creativity and entrepreneurship, with large differences when these skills were at a basic level. Furthermore, depending on whether teachers had research experience in XR technology, cryptocurrencies, facial authentication systems, wearables or robotics, amongst others, this significantly affected the level of digital competence in research. The added value of this study lies on the one hand, in the fact that there are hardly any studies that analyse this subject; and on the other hand, the implications that this could have for institutions to launch training initiatives for their teaching staff in emerging technologies, as well as in transversal competences in any disciplinary field.
Factors in education and personality development in an era of existential threats

In an era of global upheaval, culture and education can become both a weapon for the revival and for the complete disintegration of society. At this stage, it is urgently necessary to realize the enormous importance of working to eliminate errors and shortcomings in these areas. Particular attention, as the author sees it, must be paid to the formation of a culturally and spiritually developed, educated young generation who knows its history and loves its homeland.