Today's Online Research Articles

Associations of Facial Shape With Physical Strength and 2D:4D in a Turkish Male and Female Sample.

Human sexual dimorphism in physical strength manifests itself in men having a greater muscle mass than women, reflecting ancestral roles in competition, protection, and provisioning. Prenatal testosterone exposure, approximated via the second-to-fourth digit ratio (2D:4D), is linked to increased muscular strength in both sexes, indicating a developmental influence. Previous research has shown that both physical strength and 2D:4D have facial shape correlates, especially in men, but most studies have focused on Western populations and one trait. We therefore hypothesized a broader relationship between facial shape and both physical strength and 2D:4D. In this study, we quantified the association between facial shape, handgrip strength (HGS), and 2D:4D in a non-Western Turkish sample (72 men, 55 women; Md = 22 y, SIR = 1.8 y) using two dimensional geometric morphometrics. Thirty-eight somatometric and 32 semi-landmarks were digitized on facial photographs taken in frontal view. Physical strength was assessed via handgrip strength (HGS), and the second digit length was divided by the fourth digit length to calculate 2D:4D. Both HGS and 2D:4D were significantly associated with shape in both sexes, but only in men did they explain a significant amount of facial variation. Thin-plates spline deformation grids and geometric morphometric morphs visualized the facial shape changes related to variations in handgrip strength, 2D:4D, and sexual dimorphism, enabling trait comparisons. This study contributes a comparative sample from the Middle East, which is indispensable to discern universalities from Western peculiarities. It provides evidence to better understand the biological basis of facial traits, which can potentially serve as increasingly relevant social cues in today's online and digital environments.

Performance Analysis of NLP-Based Machine Learning Algorithms in Cyberbullying Detection

In today's pervasive online landscape, the escalating threat of cyberbullying demands advanced detection and mitigation tools. This study utilizes Natural Language Processing (NLP) techniques to confront this imperative challenge, particularly in the dynamic realm of social media, focusing on tweets. A comprehensive NLP-based classification methods is deployed to uncover instances of cyberbullying. Nine prominent machine learning algorithms are meticulously evaluated: Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, XGBoost, AdaBoost, and Gradient Boosting. Through the analysis, encompassing accuracy, precision, recall, and F1 score metrics, the study offers insights into the strengths and limitations of each approach. The findings carry profound implications for online user safeguarding and cyberbullying prevalence reduction. Notably, Random Forest and XGBoost classifiers emerge as pioneers with accuracy rates of 93.34% and 93.32%, respectively. This comparative research underscores the pivotal role of expert algorithmic choices in addressing the urgency of cyberbullying and has the potential to be a valuable resource for academics and practitioners engaged in combatting this pressing societal issue.

A Comprehensive Survey of Advanced Image Processing and OCR Techniques for Enhanced Image Plagiarism Detection

In today's digital world, sharing files, documents, and presentations online has become routine for both work and study purposes. However, this surge in online sharing has brought forth a significant challenge: plagiarism. Plagiarism will happen whenever the copied content in various sources by not giving proper credit. This paper delves into the realm of plagiarism detection, particularly focusing on images. We discuss various tools and techniques designed to detect plagiarism and compare their effectiveness based on factors like accuracy. Finally, we analyze the work of different authors and share common findings. Our research employs a thorough review process to ensure the accuracy of our conclusions. By emphasizing the use of AI-powered tools, our agenda is to promote sharing of original dataover the online domain.

Character Education TPACK Skills to Emergency Remote Learning During The Covid-19 Pandemic

The transition of education to an online environment due to the COVID-19 pandemic presents new challenges for teachers and students. On the other hand, there is only a little research on developing PCK related to character education. Pedagogical Content Knowledge (TPACK) technology solves today's online learning problems. This research aims to analyze Character Education TPACK skills for emergency distance learning during the COVID-19 pandemic. This research is a mixed methods research with a quantitative approach and is supported qualitatively. The population and sample are 200 teachers. The data collection method uses a questionnaire. The research instrument was a questionnaire consisting of 33 statements and 5 open questions, which were distributed. Data were examined numerically and subjectively using component analysis and teacher explanatory descriptions. Data analysis techniques use quantitative statistics. The research results show that the mastery of TPACK Character Education in online classes uses different learning media and instructional platforms. This research implies that in the future, teachers can increase the use of teaching materials, media, methods and learning models for appropriate subjects and follow TPACK integrated with character education to create an active, fun and meaningful learning atmosphere.

The Subtleties of Self-Presentation: A study of sensitive disclosure among sexual minority adolescents

Disclosing sensitive information online, such as an LGBTQ+ identity or potentially stigmatized behavior, can be necessary for many reasons, especially for those who identify as LGBTQ+ and may rely on social platforms for meeting others and social support. At the same time, these disclosures can lead to harassment and stigmatization. Evidence suggests that today's social platforms provide many more options and opportunities for sensitive disclosure than are possible offline, but we lack a taxonomy of disclosure strategies. Drawing on Goffman's self-presentation framework, we examined two types of sensitive disclosure within the cisgender male adolescent gay, bisexual, and/or queer (GBQ) population: revealing one's GBQ identity on social media and revealing the use of an anti-HIV medication, pre-exposure prophylaxis (PrEP), on both social media and dating apps. We find that today's online environment, with its different affordances, provides new opportunities for disclosure, such as the use of associations, repeated over time. Participants had distinct ideas about what disclosures were normative and relevant to particular social platforms and audiences. Lastly, we discuss suggestions for how platform design might promote sensitive disclosure.

Analysing The Development of Web-Based Multimedia Piano Playing Teaching Design Using Multiple Linear Regression Algorithm

The popularity of piano lessons has increased recently, and many individuals are taking the instrument. Using Web technology as its backbone and high-performance streaming media technology, this platform for teaching piano playing can efficiently address the issues of a limited audience, a steep learning curve, and a shortage of available materials in today's online education landscape. This kind of instruction may help bring the classroom closer together. The goals of this study are to increase the effectiveness of communicating via online text, to make it easier for students to get expert guidance from teachers, to satisfy students' interest in learning, to elevate students' piano effectiveness and creation, and to promote the widespread use of the piano. Using a Multiple Linear Regression Algorithm (MLRA), this study makes recommendations for designing an intelligent piano-playing teaching system, looks into the implementation of the piano-teaching system, and offers a technique to evaluate piano-playing using the MLRA model for the difficulties of online piano training. Teaching the piano is greatly aided by this paper's portrayal of an instructor encouraging students to keep up with their practice. The study results stated that MLRA's recognition accuracy is 95% and its mean square error rate is 12%.

Dynamic Budget Throttling in Repeated Second-Price Auctions

In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in. This paper provides a theoretical panorama of a single advertiser's dynamic budget throttling process in repeated second-price auctions. We first establish a lower bound on the regret and an upper bound on the asymptotic competitive ratio for any throttling algorithm, respectively, when the advertiser's values are stochastic and adversarial. Regarding the algorithmic side, we propose the OGD-CB algorithm, which guarantees a near-optimal expected regret with stochastic values. On the other hand, when values are adversarial, we prove that this algorithm also reaches the upper bound on the asymptotic competitive ratio. We further compare throttling with pacing, another widely adopted budget control method, in repeated second-price auctions. In the stochastic case, we demonstrate that pacing is generally superior to throttling for the advertiser, supporting the well-known result that pacing is asymptotically optimal in this scenario. However, in the adversarial case, we give an exciting result indicating that throttling is also an asymptotically optimal dynamic bidding strategy. Our results bridge the gaps in theoretical research of throttling in repeated auctions and comprehensively reveal the ability of this popular budget-smoothing strategy.

Rumor Source Detection in Subgraphs: An ML Approach

Social networking sites offer a vast platform for sharing various types of content, such as information, photos, videos, and audio clips. However, the credibility of the information shared on these sites is a significant concern since there is no prior verification process as the information spreads across the networks. This increases the chances of disseminating false or misleading information, often called rumors. The spread of rumors in today's information environment creates severe problems for the credibility of information and public confidence. The quick spread of rumors, frequently conveyed via social media and other online platforms, can affect public opinion and potentially have real-world repercussions. Maintaining the integrity of information ecosystems and averting the potentially damaging impacts of disinformation require detecting and mitigating rumors. In today's social media and online communication age, it is crucial to identify rumors to prevent their spread and mitigate their potential negative impact. In this study, our primary goal is to discover and identify individual sources of rumors. This work addresses two aspects of rumor management: firstly, identifying the difference between rumors and facts, and secondly, tracking the source of the rumor. In conjunction with the attributes, state-of-the-art methods such as SVM, AdaBoost, Random Forest, Logistic Regression, and Graph Convolutional Networks (GCN) are used to accomplish these goals. This approach contributes to the broader goal of combating disinformation and preserving the Accuracy of information.

Approaches for the companies’ promotion in the modern business environment

In today's competitive market, promoting goods and services online is crucial to most companies' business strategies. Therefore, it's essential to understand and systematise digital marketing tools' principles, advantages, and disadvantages to optimize marketing strategies effectively. These tools allow businesses to adapt to market changes quickly, ensuring efficient use of the advertising budget and high returns on investment. Additionally, Internet marketing enables the creation of personalised approaches to customer interaction, boosting their loyalty and satisfaction. Internet marketing, or digital or online marketing, includes all activities promoting goods and services via the Internet. It involves using various online channels and platforms to interact with the target audience to increase brand awareness, sales, and customer loyalty. This article examines the unique aspects of using digital marketing tools and how to optimise their application. By analysing the theoretical and practical principles of modern digital marketing tools, the study categorises the benefits and drawbacks of various digital marketing techniques. The paper analysed the dynamic growth of Internet users worldwide and in Ukraine. The growth in Internet users is driving the development of digital marketing in Ukraine. Brands are actively implementing modern marketing technologies, such as SEO, SMM, content marketing, contextual advertising and email marketing, to reach their audience. The primary tools examined include Search engine optimisation (SEO), Content marketing, Social media marketing (SMM), Contextual advertising (PPC), Email marketing, Video marketing, Mobile marketing, and Influencer marketing. The authors stress the importance of using artificial intelligence to personalise marketing campaigns' target audiences. Automation allows companies to plan and execute marketing campaigns in advance, ensuring efficient management of time and resources, particularly for large companies with extensive marketing networks. The authors concluded that modern marketing technologies based on artificial intelligence and automation are the key to successful online brand promotion, ensuring the competitiveness and efficiency of businesses in the face of constant change.

Pengembangan Video Pembelajaran Bahasa Arab Berbantuan Aplikasi Kinemaster

This research is motivated by the importance of using digital media as online learning media in the Industry 4.0 era. This study aims to describe the video development process using the kinemaster application as Arabic learning media to increase creativity and innovation for educators in developing online learning media. The Kinemaster application is software on a smartphone that can be a solution in developing today's online learning media. This research uses the research and development method (Research and development) using the ADDIE instructional model; the development model has five stages, namely (a) analysis, (b) design, (c) development, (d) implementation, and (e) evaluation. However, this research is still in the ADD stage: analysis, design, and development. This research describes video editing using the kinemaster application as an online learning medium. The results of the limited trial research on 20 Class VI elementary school students, with a percentage of 85%, were excellent. The final results show that 1) kinemaster applications in Islamic Religious Education subjects is feasible to use in the online learning process; 2) creative and innovative video viewing can increase learning understanding for students.

Implementasi Data Mining Menggunakan Algoritma Fp-Growth Untuk Menganalisa Transaksi Penjualan Ekspor Online

In today's online technology, we have to learn various things so that we can adjust to social behavior, for example, buying and selling activities that cover all walks of life, now everyone doesn't hesitate to shop online. The online market is growing fast, especially in Indonesia. Anyone can sell and buy goods easily through online stores, so that people's purchasing power and sales increase due to the proliferation of online stores, XYZ stores are still beginners in terms of sales strategies so they try all efforts without careful preparation, one of the ways to do this is to expand its market coverage even to export goods abroad. the process of exporting goods in the current era is not a difficult thing to do, but it requires knowledge and preparation of a good strategy. from existing sales data can be analyzed to determine the right strategy in marketing goods. Data Mining is a science that helps analyze large amounts of data with the aim of gaining new knowledge that can be utilized. In Data Mining Analysis there is the FP-Growth algorithm which is a data mining technique for finding association rules from lots of data. This is known as the Association rule, where the rule will be determined from the minimum support and confidence results. the rules that are formed produce 10 rules out of 51 existing data, these rules help determine the export sales strategy at the XYZ store.

Sentiment Analysis of E-commerce Product Review through Machine Learning

Sentiment analysis is a way which is used to determine that a piece of information is positive, negative or neutral. In the analysis of text or any kind of information natural language processing (NLP) and machine learning (ML) both are combined to assign sentiment scores to the topics, categories and any kind of entities within a given phrase. In today's day and time online shopping has become well received among the people. If we talk about ordinary people, they always think too much before buying any product through any e-commerce application. The trust of the people of e-commerce applications is not very high. So, people turn to reviews to fill this confidence gap. And to ensure this trust Sentiment Analysis were used. Our project can be a great option for people to maintain their trust. In this project we are analyzing products of respective e-commerce apps on the behalf of their reviews and ratings. Users will get information for each product whether it can be a good one or not on the basis of reviews and ratings. A great number of internet sources are present on categorical views of activities, goods and services, beliefs or perhaps the mind created by the online dwellers. In this fierce business world, various industries, especially e-commerce, exceptionally use sentiment analysis to increase productivity and make better business decisions. This project tackles a comprehensive overview of sentiment analysis and relevant techniques in the e-commerce sector that is always keen to find out about the consumers' opinions of their goods and services. It starts with the view of an assessment of sentiments that have come out as a method for understanding clients’ emotions.

Dual emotion based fake news detection: A deep attention-weight update approach

The proliferation of false information is a growing problem in today's dynamic online environment. This phenomenon requires automated detection of fake news to reduce its harmful effect on society. Even though various methods are used to detect fake news, most methods only consider data-oriented text features; ignoring dual emotion features (publisher emotions and social emotions) and thus lack higher levels of accuracy. This study addresses this issue by utilizing dual emotion features to detect fake news. The study proposes a Deep Normalized Attention-based mechanism for enriched extraction of dual emotion features and an Adaptive Genetic Weight Update-Random Forest (AGWu-RF) for classification. First, the deep normalized attention-based mechanism incorporates BiGRU, which improves feature value by extracting long-range context information to eliminate gradient explosion issues. The genetic weight for the model is adjusted to RF and updated to achieve optimized hyper parameter values ​​that support the classifiers' detection accuracy. The proposed model outperforms baseline methods on standard benchmark metrics in three real-world datasets. It outperforms state-of-the-art approaches by 5%, 11%, and 14% in terms of accuracy, highlighting the significance of dual emotion capabilities and optimizations in improving fake news detection.

Identifying Chinese social media users' need for affect from their online behaviors.

The need for affect (NFA), which refers to the motivation to approach or avoid emotion-inducing situations, is a valuable indicator of mental health monitoring and intervention, as well as many other applications. Traditionally, NFA has been measured using self-reports, which is not applicable in today's online scenarios due to its shortcomings in fast, large-scale assessments. This study proposed an automatic and non-invasive method for recognizing NFA based on social media behavioral data. The NFA questionnaire scores of 934 participants and their social media data were acquired. Then we run machine learning algorithms to train predictive models, which can be used to automatically identify NFA degrees of online users. The results showed that Extreme Gradient Boosting (XGB) performed best among several algorithms. The Pearson correlation coefficients between predicted scores and NFA questionnaire scores achieved 0.25 (NFA avoidance), 0.31 (NFA approach) and 0.34 (NFA total), and the split-half reliabilities were 0.66-0.70. Our research demonstrated that adolescents' NFA can be identified based on their social media behaviors, and opened a novel way of non-intrusively perceiving users' NFA which can be used for mental health monitoring and other situations that require large-scale NFA measurements.

Building Customer Trust in e-commerce

E-commerce is gaining popularity due to its importance for both merchants and consumers, particularly in light of the COVID-19 pandemic, which restricts mass movement and gathering, as well as the high costs associated with traveling to different markets. Despite mounting evidence demonstrating the importance of internet in retails business, little is known about the potential mechanisms that increase consumer purchasing intentions when using online shopping platforms. In today's high customer sensitivity to service quality, successful entrepreneurs have recently thrived under competitive pressures. Online transactions are more difficult because buyers and sellers do not know each other, yet they must provide financial information to intermediaries. Thus, it is now clear that increasing customer trust is a viable strategy for driving e-business success today. Considering this, a new model demonstrating a potential mechanism that increases customers' trust in intermediaries and thus leads to their online purchasing intentions was developed. This innovative model connects website quality to the online purchasing intentions of the customers, using trust in intermediaries as a mediator and moderating the effect of perceived usefulness. To prove or disprove the assumptions highlighted in this study, a cross-sectional research design was used with data from 568 clients of nine domestic e-commerce platforms operating in Rwanda. To investigate the relationship between the variables hypothesized in this study, a variety of methods and tools were used. To assess the potential effect of each variable in this model, we used CFA in SEM-Amos, mean and standard deviation in SPSS, and Hayes macro process. The overall results obtained using the various techniques specified supported the current hypothesized model. We believe that the current study provided an innovative and useful mechanism as one of the potential solutions for today's merging online businesses.

PEMBELAJARAN DARING MENULIS TEKS EKSPOSISI PADA SISWA KELAS VIII DENGAN METODE DISCOVERY LEARNING BERBANTUAN MEDIA QUIZIZZ

Writing exposition text is still considered difficult by students, especially in today's online learning which is not face to face. The use of appropriate methods and media can be a solution in the online learning process by utilizing available applications. This study aims to determine student learning outcomes in online learning to write exposition text with discovery learning method assisted by Quizizz media and what difficulties students experience in writing exposition text; to find out students' responses to online learning to write exposition text using discovery learning method assisted by Quizizz media. The method used is descriptive qualitative method. Data collection techniques in the form of tests and questionnaires. The success of learning is seen from the results of the knowledge and skills test which shows the average value of both of them is 77.2 and has met the achievement indicators. The difficulty of the knowledge test questions with a score of 5 regarding the command shows the structure of the text of the exposition of the thesis section, while the difficulty of the skill questions with a score of 70 is in the rules of the reference word language. The results of a very good response during the learning process showed a percentage of 83%. Judging from student learning outcomes and student response results, the methods and media used can motivate students in online learning to write exposition text.

Malware Analysis Using Memory Forensics

Abstract: Malware is still the most dangerous issue facing internet users in today's online environment. The newly created malware is separate from the traditional kind, has a more dynamic design, and typically combines traits from two or more different malware types. comparing the various memory acquisition tools that are available, each of which has a varying performance dependent on the setups, installed hardware, and operating system version. If the ending character is not present. To address the growing malware issue, new methodologies like machine learning must be employed. Investigate how cybersecurity is used in this study for malware detection and machine learning. In this study will look at the PE (portable executable) headers of malware and non-malware samples in order to build a malware classifier that can identify if malware is there or not. The development of behavior-based malware detection and classification methods using various machine learning approaches is addressed in this study along with behavior-based detection methods itself

Development and Validation of Comic-based Learning Module in Physics

Providing instructional materials that support the diverse learning styles and needs of 21st-century learners is becoming a trend in today's online learning. Thus, this descriptive developmental research design aimed to develop Comic-based Learning Modules (CLM) as instructional material envisioned to meet these learners' needs for a better understanding of Physics concepts. This study employed the ADDIE model for the instructional material development design. The initial developed CLM was revalidated following the comments and suggestions of the validators before distribution for online learning. Four experts validated the CLM in terms of content/lessons, illustrations, additional features, language, layout, and overall presentation using a researcher-made criterion-based reference evaluation tool. The validators rated the developed instructional material featuring three CLM as "Highly Acceptable" (4.66). The value of the average inter-rater reliability coefficient kappa of the ratings of the validators is interpreted as "Substantial Agreement" (0.63). CLM was then implemented as instructional material and found to have high usability as rated by the students. Also, the CLM drew positive feedback for online learning use because of its exciting nature and uniqueness.

What Drives the Influence of Health Science Communication Accounts on TikTok? A Fuzzy-Set Qualitative Comparative Analysis.

Medical institutions face a variety of challenges as they seek to enhance their reputation and increase the influence of their social media accounts. Becoming a social media influencer in the health field in today's complex online environment requires integrated social and technical systems. However, rather than holistically investigating the mechanism of account influence, studies have focused on a narrow subset of social and technical conditions that drive online influence. We attribute this to the mismatch between complex causality problems and traditional symmetric regression methods. In this study, we adopted an asymmetric configurational perspective that allowed us to test a causally complex model of the conditions that create strong and not-strong account influence. We used fuzzy-set qualitative comparative analysis (fsQCA) to detect the effects of varying configurations of three social system characteristics (i.e., an oncology-related attribute, a public attribute, and comment interaction) and two technical system characteristics (i.e., telepresence and video collection) on the TikTok accounts of 63 elderly Chinese doctors (60 to 92 years old). Our results revealed two pathways associated with distinct sociotechnical configurations to strong account influence and three pathways associated with distinct sociotechnical configurations to not-strong account influence. Furthermore, the results confirmed that a single antecedent condition cannot, on its own, produce an outcome, i.e., account influence. Multiple inter-related conditions are required to produce an influential account. These results offer a more holistic picture of how health science communication accounts operate and reconcile the scattered results in the literature. We also demonstrate how configurational theory and methods can be used to analyze the complexities of social media platforms.

Strategic choice of electronic banking development under the financial and economic development

In recent years, with the rapid development of information technology, financial enterprises in order to fast, convenient, safe, efficient, improve enterprise economic benefits, by using information technology to carry out the electronic banking industry, from the initial telephone banking, gradually evolved into today's online banking, WAP banking and mobile banking. In the process of continuous development and improvement of electronic online banking business, personal financial industry is gradually in the important position of development, and even becomes the premise of benign development between Chinese banks. This paper mainly analyzes the development status and strategic choice of Chinese electronic bank.