Reviewerly/exHarmony huggingface dataset:
How analytical hierarchy process prioritizing internet banking influencing factors? a research study

&lt;span lang="EN-US"&gt;Internet banking is a method for conducting financial transactions online that makes use of the internet as a platform. Customers could transact at any time and from any location. Numerous aspects relating to the adoption of internet banking have been analyzed and studied in recent in-depth studies from the literature. This paper will merge these numerous predefined factors into a model by drawing on the various ideas related to the acceptance model. It was decided to adapt the analytical hierarchy process (AHP) method for finding the important numerous components for the model. The finest mathematical calculation method is AHP, which enables decision-makers to prioritize their ranking in order to satisfy various criteria. The goal of this research is to rank the elements that influence the use of online banking. Three main factors such as technical information, website and service availability were chosen as the main factors of the model based on the literature review. Sub-factors such as ease of use, responsiveness, privacy, reliability, security, communication and efficiency were also suggested and combined into a single integrated framework. Utilizing the systematic literature review (SLR) methodology, several factors were found. As a result, the article will enhance understanding of the unique elements supporting the adoption of internet banking.&lt;/span&gt;
A novel deep-learning based approach to DNS over HTTPS network traffic detection

&lt;div align="center"&gt;&lt;span lang="EN-US"&gt;Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a &lt;a name="_Hlk143263730"&gt;&lt;/a&gt;DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification.&lt;/span&gt;&lt;/div&gt;
Improving misspelled word solving for human trafficking detection in online advertising data

&lt;span lang="EN-US"&gt;Social media is used by pimps to advertise their businesses for adult services due to easy accessibility. This requires the potentially computational model for law enforcement authorities to facilitate a detection of human trafficking activities. The machine learning (ML) models used to detect these activities mostly rely on text classification and often omit the correction of misspelled words, resulting in the risk of predictions error. Therefore, an improvement data processing approach is one of strategies to enhance an efficiency of human trafficking detection. This paper presents a novel approach to solving spelling mistakes. The approach is designed to select misspelled words, the replace them with the popular words having the same meaning based on an estimation of the probability of words and context used in human trafficking advertisements. The applicability of the proposed approach was demonstrated with the labeled human trafficking dataset using three classification models: k-nearest neighbor (KNN), naive Bayes (NB), and multilayer perceptron (MLP). The achievement of higher accuracy of the model predictions using the proposed method evidences an improved alert on human trafficking outperforming than the others. The proposed approach shows the potential applicability to other datasets and domains from the online advertisements.&lt;/span&gt;
A large-scale sentiment analysis using political tweets

&lt;p&gt;&lt;span lang="EN-US"&gt;Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC.&lt;/span&gt;&lt;/p&gt;
Transformasi Perpustakaan Berbasis Inklusi Sosial pada Dinas Perpustakaan dan Kearsipan Provinsi Kalimantan Barat

Social inclusion is a process of developing capacities for individuals and groups to participate in society. The purpose of this research was conducted to find out how the form of social inclusion-based library transformation activities, the supporting factors, and the constraints faced. This research uses a descriptive method with a qualitative approach. Methods of data collection in this study using observation, interviews, and documentary techniques. Data analysis techniques using data reduction, data presentation, and conclusions. Data validation techniques using increasing persistence, triangulation, holding member checks. The results showed that the Library and Archives Service of West Kalimantan Province held library activities based on social inclusion with two strategies, namely increasing information services and involving the community. Factors that support social inclusion-based library activities are budget, human resources and, following technological developments. The obstacles faced are the community and the budget.
Analysis of Differences in Online Learning Environment and Learning Experience According to University Students' Preferences for Education Methods

Objectives To operate online learning in higher education, which has become a primary educational method, the&#x0D; difference in the learning environment and learning experience (support experience, interaction experience) of&#x0D; online learning according to education method preference was analyzed.&#x0D; Methods To this end, an online survey targeted 506 university students in Gyeonggi-do. Preference for education&#x0D; methods was divided into face-to-face, online, and blended. The Kruskal-Wallis H Test was conducted to confirm&#x0D; the difference between each group regarding learning environment, teaching support, LMS support, and online&#x0D; interaction.&#x0D; Results First, as a result of examining the learning environment, for all university students, the laptop was the&#x0D; preferred device for efficient learning in all groups, and they mainly took online classes at home. Second, the group&#x0D; that prefers online education was highly aware of the instructor's support, LMS support, and interaction in online&#x0D; education compared to other groups.&#x0D; Conclusions This study has significance in suggesting primary data to seek teaching and learning strategies for&#x0D; successful online education operations in the future by analyzing the difference in the online education environment&#x0D; and learning experiences of university students according to their educational method preference. In addition, establishing&#x0D; a learning environment for university students and changing educational methods were proposed.
Overview of Early Detection for Breast Cancer: Current Status and Future Perspectives

Cancer remains a formidable challenge in our modern society, and breast cancer has emerged as the most prevalent form of cancer among women worldwide. Timely detection of breast cancer holds immense potential for improving the lives of countless women globally. In this comprehensive review, we delve into the multifaceted factors that contribute to the prevalence of breast cancer among women from diverse socioeconomic backgrounds. We explored the intricate interplay of genetics, lifestyle choices, and environmental influences that contribute to breast cancer susceptibility. Additionally, we discussed the well-established screening methods, particularly mammography, which has revolutionized the early detection of breast cancer. Moreover, we highlight the advancements in the development of biomarkers that offer a simple and convenient alternative to overcome the limitations of mammography. Furthermore, we examine the pivotal role that artificial intelligence plays in enhancing breast cancer detection, presenting its potential to revolutionize diagnostic approaches and improve overall outcomes. Through this review, we aim to provide valuable insights into the various aspects of breast cancer detection, facilitating informed decision-making and advancing the fight against this formidable disease.
Economics of Spot Instance Service: A Two-Stage Dynamic Game Approach

This paper presents the economic impacts of spot instance service on the cloud service providers (CSPs) and the customers when the CSPs offer it along with the on-demand instance service to the customers. We model the interaction between CSPs and customers as a non-cooperative two-stage dynamic game. Our equilibrium analysis reveals (i) the techno-economic interrelationship between the customers' heterogeneity, resource availability, and CSPs' pricing policy, and (ii) the impacts of the customers' service selection (spot vs. on-demand) and the CSPs' pricing decision on the CSPs' market share and revenue, as well as the customers' utility. The key technical challenges lie in, first, how we capture the strategic interactions between CSPs and customers, and second, how we consider the various practical aspects of cloud services, such as heterogeneity of customers' willingness to pay for the quality of service (QoS) and the fluctuating resource availability. The main contribution of this paper is to provide CSPs and customers with a better understanding of the economic impact caused by a certain price policy for the spot service when the equilibrium price, which from our two-stage dynamic game analysis, is able to set as the baseline price for their spot service.
Bonus post: &lt;i&gt;Supersaurus&lt;/i&gt; before &lt;i&gt;Ultrasaurus&lt;/i&gt;!

I got a wonderful surprise a couple of nights ago!
IC-QKD: An Information-Centric Quantum Key Distribution Network

Current and next-generation networks are threatened by emerging attacks from quantum computers. To protect these services against quantum threat, quantum key distribution (QKD) using optical networks is being investigated and tested as a safe countermeasure to provide information-theoretic security. Although recent studies confirm the feasibility of QKD over optical networks, two main technical challenges, among others, remain unaddressed: the limited rate of generating keys requires efficient key distribution to secure the numerous applications, and as key requests from applications can be characterized by different importance, QKD networks need a key distribution platform that accounts for such diversity. To address these challenges, we propose to incorporate the paradigm of Information-Centric Networking (ICN) within the QKD process, and we describe a novel information-centric quantum key distribution (IC-QKD) network for efficient and differentiated key distribution. We first design a semantic-based key distribution scheme, which leverages ICN to incorporate the knowledge of application importance in QKD and to enhance the resource efficiency of QKD by decoupling the sender from its receiver. We then propose an in-network key-caching scheme, which is especially advantageous for multi-cast applications, as keys can be cached and reused for multiple users of a multicast group to accelerate key distribution. Finally, we design a semantic-based application analysis for semantic-based key caching and distribution, which serves various applications with different priorities. Simulation results show that the proposed QKD network can reach up to about 16 percent increase in the capability of serving key requests with respect to the existing QKD network.
Art Didactics and Augmented Reality Application in the PostDigital Era

Proceedings of The 6th International Academic Conference on Teaching, Learning and Education Year: 2023 DOI: https://www.doi.org/10.33422/6th.tleconf.2023.06.103 [PDF] Art Didactics and Augmented Reality Application in the PostDigital Era Dr. Dorotheos Orfanidis ABSTRACT: Art didactics play a crucial role in schools by providing a structured framework for teaching and learning about art. Art didactics refer to the methods, strategies, and principles used to teach art in educational settings. This includes not only the technical aspects of creating art but also the broader concepts related to artistic expression, creativity, cultural understanding, and critical thinking. Digital technologies, especially in the post covid and postdigital era, have influenced both the education system in general and the artistic direction. Traditional art methods and materials will always have their place in the curriculum and in art classes. The research question of the paper is how new technologies and applications are reinventing and reshaping art education in the postdigital era. The use and the study of specific artworks is carried out in a way for the multisensory experience of contemporary cultural reality. We present the field application that was applied in the 4th Primary School in the year 2022-2023. The key was to find a true balance with the field art practice and the high-end tech integration. With the field application we noticed that the students were encouraged in critical thinking and problem-solving skills. Students were able to analyze information, make connections between ideas, and approach challenges with a logical and creative mindset. Communication skills was a key outcome. But in the postdigital era, art education continues to bridge the gap between the human experience and the digital world. It empowers individuals to harness technology creatively, engage with complex ideas, and contribute to a society that values both innovation and the richness of human expression. How can we address old school artworks and artistic content in a multimodal way? Can we address a class free lesson in school? With this field application we want to explore how multimodal communication and interaction can be enhanced and extended through the integration of augmented reality technologies, particularly in the fields of education and digital storytelling. keywords: augmented reality, education, application, arts, didactics
Sharpen data-driven prediction rules of individual large earthquakes with aid of Fourier and Gauss

Abstract Predicting individual large earthquakes (EQs)’ locations, magnitudes, and timing remains unreachable. The author’s prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs ( $$M\ge$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>M</mml:mi><mml:mo>≥</mml:mo></mml:mrow></mml:math> 6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.
Optimizing Convolutional Neural Network (CNN) Models for Enhanced English-Devanagari Handwritten Character Recognition: A Hybrid Evolutionary Approach with Variable Length Genetic Algorithm

Abstract: Convolutional Neural Networks (CNNs) have gained widespread recognition in the field of computer vision, specifically for handwritten digit recognition. Despite their remarkable accuracy, CNNs entail significant computational training demands and are susceptible to local optima, necessitating innovative optimization strategies. This research introduces a novel approach to hyperparameter tuning for CNN models tailored to the recognition of English-Devanagari handwritten digits. This method combines a Hybrid Evolutionary Algorithm (HEA) with a Variable Length Genetic Algorithm (VLGA) and leverages a comprehensive dataset encompassing both English and Devanagari handwritten digits. The strategy extends the conventional paradigm by integrating a variable-length GA, facilitating systematic and adaptive tuning of critical CNN hyperparameters, including optimizer selection, learning rate, global hyperparameters, kernel size, filter count, activation functions, layer count, and pooling mechanisms. Extensive experimentation across benchmark datasets underscores the superior performance of the proposed approach when compared to traditional optimization methods. Seven key hyperparameters are the focal point of optimization efforts: learning rate, optimizer, kernel size, filter count, activation function, layer count, and pooling strategy. The results highlight the significant performance boost achieved by CNNs assisted by genetic algorithms, underscoring the effectiveness of evolutionary approaches in CNN training. Notably, experiments reveal that a population size of 27 yields optimal fitness values and average fitness scores. In the culmination of this research, the HEA-VLGA model achieves an impressive accuracy rate of approximately 99.38%. These findings unequivocally affirm the efficacy of incorporating evolutionary techniques as a potent avenue for enhancing CNN training processes.
The effect of cooperative learning model TGT-type assisted by crossword puzzle media on biology concept mastering

This study aims to determine the effect of the cooperative learning model of the Teams Games Tournament (TGT) type assisted by crossword puzzle media on the mastery of biological concepts in class X students at SMAN 3 Kota Bima. This study used a quasi-experimental research method with a nonequivalent control group design (pretest and posttest). The population in this study were students of class X, consisting of class X 1 to X 4. The sampling technique used purposive sampling. The samples used were students of class X 3 as the control class and class X 4 as the experimental class. Data collection techniques using test instruments in the form of multiple-choice questions. The analysis technique used is the t-test with a significance level of 5% with the help of SPSS 25 for Windows. The t-test results show that the value of (2.347 &gt; 2.010). Based on these results, it can be interpreted that the cooperative learning model of the TGT type assisted by crossword media affects the mastery of biology concepts in class X students at SMAN 3 Kota Bima.
Discussion on Teaching Links of Graduation Design for Computer Science and Technology Specialty in Open Education

Open education is an educational model with flexible learning and resource sharing as its core. As the core subject of modern information society, computer science and technology, combined with open education, provides learning opportunities for scholars and is conducive to promoting the cultivation of technical talents. This paper focuses on the teaching link of graduation project of computer science and technology specialty under open education. The graduation project of development education is very important in students' career development and has some unique characteristics. In order to optimize the teaching process of graduation project, measures can be taken, such as designing learning plan and instruction manual, providing abundant learning resources and tools, establishing a platform for interaction and cooperation between teachers and students, and effective evaluation and supervision mechanism, in order to optimize the teaching effect and improve students' learning experience and achievement quality.
Sharing Human Seeing and Hearing and Their Annotated Portfolio

The paper describes the use of an annotated portfolio for extracting their possibilities and challenges from “CollectiveEyes and CollectiveEars” to document abstract features of these digital platforms that are meant to engage human visual and auditory perception. This proposed annotated portfolio offers two different perspectives: the sociomateriality perspective and the pleasurability perspective. After introducing the background of our approach and providing an overview of CollectiveEyes and CollectiveEars, we present their annotations and the process to document them. Additionally, we share some early experiences with these annotations, which were used to analyze the platforms. These experiences demonstrate that this approach effectively highlights the abstract qualitative properties of the platforms, thereby facilitating the identification of potential opportunities for their future refinements.
Ecologization of education in the innovative space of higher education

The new content of the ecologization of education is considered, the goal is formulated and the possibilities of ecologization of education in the innovative space of higher education are shown. Definitions are interpreted: ecologization, ecologization of education in the innovative space of higher education; the main goals of ecologization of education and the main components and most important tasks of ecologization of education in the innovative space of higher education are highlighted (formation of ecological culture for all segments of the population; training of ecological specialists for the educational field and various branches of the national economy; in the field of ecological studies: improvement, standardization, harmonization of terminology. Three main types of spiritual values were identified. The main principles of ecologization of education in the innovative space of higher education and two interrelated components (promotional, content-informational) are singled out. The relevance of educational, cultural, and educational functions of ecologically oriented blogging is emphasized and the effectiveness of using project technologies in the formation of the ecological worldview of a higher education student, which contributes to the realization of the comprehensive development of the individual as a person and the highest value of society, is shown.
First Surgeries

Taking the many steps by case-based progress to build an OR team in a disparity region.