Today's Web Research Articles

현대 판타지 웹소설 흥행요인 분석: 『재벌집 막내아들』 사례를 중심으로

In this paper, we will analyze the main box office factors of modern fantasy web novels. Regression, inscription, and reincarnation motifs are narrative structures frequently applied in modern fantasy web novels and are the biggest features that distinguish them from pure literature. In addition, the attractive protagonist is one of the core elements of the web novel and determines the overall flow of the work, and the reader feels strong surrogate satisfaction with the protagonist's narrative, role, and growth process. Therefore, this study comprehensively explores the unique narrative structure and protagonist characters found in modern fantasy web novels and analyzes their influence on their works. Based on the literature survey, a case study was conducted to promote an in-depth understanding of specific cases. The subject of this paper, “The Youngest Son of a Chaebol House,” is a modern fantasy web novel by Sankyung author who applies the regression code, which is the box office formula of web novels, and sets the modern and contemporary history of Korea, and has gained explosive popularity in Munpia from the beginning of publication, and refers to a chart arranged in the order of the highest number of views per day (24 hours) from 1st to 103rd (PC) and 107th (mobile) when clicking “Best” on today's best web novel platform Munpia. It is a work that has never missed the top spot. Based on this popularity, it was aired as a weekend mini-series on JTBC in 2022, and was selected as a research case for this study as a monumental work that achieved the highest box office success due to the success of modern fantasy web novels. The purpose of this study is to analyze the narrative structure that readers are enthusiastic about and how the main character is built and consumed in web novels where commerciality is paramount. Through this study, we would like to present and analyze typical narrative structures and main characteristics that are mainly shown in modern fantasy web novels. Through this analysis, it is expected that it will be possible to derive an understanding of how to increase the readers' immersion and to empathize with and coexist with the main character to further increase the attractiveness of modern fantasy web novel works.

Can AI Make Us Care? How ‘A11y-AI’ generates empathy for an accessible web

The accessibility problem of today's web isn't a lack of guidelines or tools, it's a lack of empathy. How can this empathy be created and used to inspire action by those responsible for a more inclusive web? The 'A11y-AI' experiment tried to find out, and showed that generative AI can be the answer.

Surveying Authentication and Authorization Mechanisms in Today's Web Technology Landscape

This paper presents a comprehensive survey of authentication and authorization mechanisms in contemporary web technology. It examines the diverse approaches and frameworks employed to secure web systems and protect user data. Beginning with an overview of fundamental concepts such as authentication methods and access control models, the paper explores the landscape of authentication and authorization in depth. It evaluates the effectiveness and limitations of various authentication methods, including passwords, biometrics, and multifactor authentication (MFA). Additionally, the paper scrutinizes authorization mechanisms such as role-based access control (RBAC), attribute-based access control (ABAC), and policy-based access control (PBAC). Through a systematic review of integration frameworks like OAuth and OpenID Connect, it assesses their role in facilitating secure authentication and authorization processes. Furthermore, the paper analyzes emerging trends and innovations in authentication and authorization practices, providing insights into the future direction of web security. By synthesizing existing research and identifying key challenges and opportunities, this survey paper aims to inform researchers, practitioners, and policymakers about the state-of-the-art in authentication and authorization mechanisms and guide future developments in web security.

Constraining the giant radio galaxy population with machine learning and Bayesian inference

Large-scale sky surveys at low frequencies, such as the LOFAR Two-metre Sky Survey (LoTSS), allow for the detection and characterisation of unprecedented numbers of giant radio galaxies (GRGs, or `giants', of at least $l_ p,GRG Mpc $ long). This, in turn, enables us to study giants in a cosmological context. A tantalising prospect of such studies is a measurement of the contribution of giants to cosmic magnetogenesis. However, this measurement requires en masse radio--optical association for well-resolved radio galaxies and a statistical framework to infer GRG population properties. By automating the creation of radio--optical catalogues, we aim to significantly expand the census of known giants. With the resulting sample and a forward model that takes into account selection effects, we aim to constrain their intrinsic length distribution, number density, and lobe volume-filling fraction (VFF) in the Cosmic Web. We combined five existing codes into a single machine learning (ML)--driven pipeline that automates radio source component association and optical host identification for well-resolved radio sources. We created a radio--optical catalogue for the entire LoTSS Data Release 2 (DR2) footprint and subsequently selected all sources that qualify as possible giants. We combined the list of ML pipeline GRG candidates with an existing list of LoTSS DR2 crowd-sourced GRG candidates and visually confirmed or rejected all members of the merged sample. To infer intrinsic GRG properties from GRG observations, we developed further a population-based forward model and constrained its parameters using Bayesian inference. Roughly half of all GRG candidates that our ML pipeline identifies indeed turn out to be giants upon visual inspection, whereas the success rate is 1 in 11 for the previous best giant-finding ML technique in the literature. We confirm $5,647$ previously unknown giants from the crowd-sourced LoTSS DR2 catalogue and $2,597$ previously unknown giants from the ML pipeline. Our confirmations and discoveries bring the total number of known giants to at least $11,585$. Our intrinsic GRG population forward model provides a good fit to the data. The posterior indicates that the projected lengths of giants are consistent with a curved power law probability density function whose initial tail index $ p,GRG changes by $ 0.3$ over the interval up to $l_ p Mpc $. We predict a comoving GRG number density $n_ GRG Mpc $, close to a recent estimate of the number density of luminous non-giant radio galaxies. With the projected length distribution, number density, and additional assumptions, we derive a present-day GRG lobe VFF $ V GRG-CW (z=0) = 1.4 $ in clusters and filaments of the Cosmic Web. We present a state-of-the-art ML-accelerated pipeline for finding giants, whose complex morphologies, arcminute extents, and radio-emitting surroundings pose challenges. Our data analysis suggests that giants are more common than previously thought. More work is needed to make GRG lobe VFF estimates reliable, but tentative results imply that it is possible that magnetic fields once contained in giants pervade a significant ($ gtrsim 10<!PCT!>$) fraction of today's Cosmic Web.

The Epidemiology of Web Accessibility: Improving Internet Health by Identifying and Tackling the Root Cause of Web Accessibility Issues

The epidemic of inaccessible websites presents a major challenge to equitable information access and continues to threaten our vision of an inclusive digital society. Despite decades of research, education, legislation, and policy reform globally, little progress has been made to ensure the accessibility of websites. Today's web accessibility strategies and tools are predicated on the belief that web accessibility issues are introduced through code created by the website developer and therefore focus on that website's code in isolation. However, as demonstrated in modern web security research, vulnerabilities can be introduced via dependencies to external libraries and frameworks outside of their development control. This PhD research explores an epidemiology-inspired approach to web accessibility to accurately model internet health, with the objective of identifying the root causes of accessibility barriers, how they propagate in the web ecosystem, and how to help web developers prevent and mitigate the propagation of accessibility barriers.

Digital Medicine: from SPA to Medical Recovery

Background: Digital medicine (eHealth) represents using information and communications technologies to support health and health-related fields with affordable and secure healthcare services, medical literature, education, and research. Methods: A literature search was conducted on Publons, the PubMed database, and dedicated websites starting in 1995. We included papers performed in different countries, using specific statistical methods and standardized questionnaires to quantify patients' and clinicians' opinions correctly. Results: Traditionally, balneotherapy has a social value, but telehealth and telemedicine need to be adequately standardized in today's web society to empower travel behavior. The unmet need of older adults or persons with physical disabilities is a global problem, and physiotherapists work toward the same goals for improving rehabilitation services with the use of digital technology. Conclusions: This review draws attention to several factors from the literature survey. Firstly, therapists and patients accept eHealth methods only combined with face-to-face appointments. Secondly, the platform's software and other approaches should be cost-efficient and easy to use. Digital methods applied in balneotherapy, rehabilitation, and health maintenance proved their effectiveness. Still, the essential message is that society and governments should put future efforts into increasing the population's access to digital systems and improving digital awareness and literacy.

Optimizing SPARQL queries over decentralized knowledge graphs

While the Web of Data in principle offers access to a wide range of interlinked data, the architecture of the Semantic Web today relies mostly on the data providers to maintain access to their data through SPARQL endpoints. Several studies, however, have shown that such endpoints often experience downtime, meaning that the data they maintain becomes inaccessible. While decentralized systems based on Peer-to-Peer (P2P) technology have previously shown to increase the availability of knowledge graphs, even when a large proportion of the nodes fail, processing queries in such a setup can be an expensive task since data necessary to answer a single query might be distributed over multiple nodes. In this paper, we therefore propose an approach to optimizing SPARQL queries over decentralized knowledge graphs, called Lothbrok. While there are potentially many aspects to consider when optimizing such queries, we focus on three aspects: cardinality estimation, locality awareness, and data fragmentation. We empirically show that Lothbrok is able to achieve significantly faster query processing performance compared to the state of the art when processing challenging queries as well as when the network is under high load.

Representation of the concept “past” by the elderly: Based on the comments of the social network “VKontakte”

The emergence and development of the Internet and information technologies in the late twentieth and early twenty-first centuries marked the formation of many interesting discoveries. The World Wide Web today is not only a means of communication, but also an unconventional communicative form that has given rise to virtual discourse and new scientific trends. Within the framework of Internet linguistics, diverse comprehensive studies are being actively conducted. Research into the electronic communicative space concepts is popular. This work reveals the “essence” of one of the leading concepts of senior people’s network discourse. The article describes the results of a content analysis of the Internet discourse, used by the elderly on the social network “VKontakte”, presents a content analysis of the community “We Are Pensioners”, analyzes the comments of publications left by the subscribers of this community. We provide a semantic-cognitive analysis of the selected concept and give its graphical model. Thus, content research, which involves the analysis of the publication texts, made it possible to establish that the key and most commented concept is the concept of “past”. The comments enabled us to conduct a semantic-cognitive analysis of the concept identifying its lexical representants (the core, the near, far and extreme peripheries) with a positive and negative character, structured into similar groups (the quality of life, human qualities, the quality of products and goods, characteristic features of sociocultural spheres).

Social Media and Its Role in The Education Process of the University Students

This study seeks getting acquainted with media students’ opinions on the social media as an educational method, its merits, and demerits as well as the most apparent elements, which can be used within an educational curriculum on social media. Moreover, results of the study clarified that the most students’ attitudes towards social medias an educational method were negative; especially in terms of saying that teaching curricula on social media needs capabilities, which are unavailable in our universities, and their insufficient qualified professors in our universities to teach curricula on social media. This study is one of the descriptive studies, which aim at collecting data on the use of media students - the sample of the study - for social networking sites, their attitudes towards their use as an educational tool, as well as studying some variables related to employment such as gender, social status, this study is based on the survey methodology as a systematic scientific effort used to obtain information or descriptions of the phenomenon- subject of study. This study responds to the modern global concerns in the field of media, which seeks to benefit from the social media in teaching the subjects of media as being considered a media and communicational method in the first place as well as being an educational method in which the main constituents and elements of the modern educational methods are available.This study seeks to know the media students' perspectives towards the social media as an educational method; their advantages, disadvantages and the most significant elements that can be employed within a curriculum on the social media. The researcher has chosen the Social Networking Sites as a model, as they include within their applications all the technical services that are provided by the Web today; such as the written text, multi-media, hypothetical communities and the methods of research and communication., which led to what is known as the educational communities on the Web, which historicize a new stage of human knowledge, known as (the Sustainable Communal Knowledge), which enables learners to employ the digital techniques that enable them to benefit one another through participating in building both the theoretical and applicable knowledge. Importance of Study: (1) Considered from the first Arabic studies, which attempt to know the possibility of employing the social media as an educational method. (2) Considered from the first Arabic studies, which attempt to deal with the social media as an educational and media method at the same time. (3) Seeking to clarify the educational and media constituents of the social media. (4) Seeking to know the perspectives of one of the important parties relevant to the employment of social media, which are the young people and the university students on the one hand and the media learners on the other hand.

Personalized Movie Recommendation System

In the digital world of today, where there is an infinite amount of content to consume, including movies, books, videos, articles, and so on, finding content that appeals to one's tastes has become challenging. On the other hand, providers of digital content want to keep as many people using their service for as long as possible. This is where the recommender system comes into play, where content providers suggest content to users based on their preferences. Web applications that offer a variety of services and automatically suggest some services based on user interest increasingly rely on recommendation systems. Different business services each play a significant role in the success of the current marketing field. The personalize recommendation technique is one of the most valuable tools for providing personalized service on websites. When it comes to e-Commerce's online marketing efforts, this strategy is extremely useful. To build the proposal framework, the cooperative sifting is exceptionally helpful advances in the field of recommender frameworks. The accuracy of recommendation engines is the source of many issues in today's web. Therefore, a variety of strategies are utilized to enhance the recommendation system's diversity and accuracy. When generating recommendations, the fundamental recommender systems typically take one of the following into account: The Content-Based Filtering, which is based on the user's preferences, it describes things, and we use keywords other than the user's profile to show what the user likes and dislikes. To put it another way, CBF algorithms suggest products that people have liked in the past or products that are similar to them. It looks at what you've liked in the past and suggests the best match, Or a collaborative filtering system makes recommendations for items based on how similar users and/or items are measured. The CF system only suggests products that are popular with similar types of users. The development of a movie recommendation system with category-based recommendations, more precise results, increased efficiency, and overcoming the cold start are the goals of this system.

PERANCANGAN E-COMMERCE BERBASIS WEB PADA TOKO TAS BRANDES LEATHER MENGGUNAKAN METODE PROTOTYPE

Utilization of the web today can also be used for trading activities such as E-Commerce. E-Commerce is used by traders to facilitate the sale of a product and process transactions. During this pandemic, E-Commerce is very necessary. During this pandemic, people were more interested in shopping online, according to the latest annual report from the solution-commerce startup, Sirclo, entitled "Navigating Indonesia E-Commerce COVID-19 Impact & The Rise of Social Commerce" showing that 12 million new E-Commerce users during the COVID-19 pandemic. This is because the majority of customers are afraid to leave their homes because of the pandemic. One of the shops affected was Brandes Leather, which is an offline-based shop. During this pandemic, the turnover obtained by this shop decreased, because tourists were not allowed to visit Bali. This research aims to help shop sellers design a web-based E-Commerce, which later this web-based E-Commerce will be able to be used as a medium for promotion and transactions between buyers and sellers, not only useful for sellers, buyers will also find it easy to shop from home and get information about the product to be purchased through E-Commerce itself. The result of this study is a web-based e-commerce design at a brandes leather bag shop using the prototype method so that later this shop's product enthusiasts can shop from their homes, and can get information about this shop's products easily.

A Machine Learning Approach for Web Application Vulnerability Detection Using Random Forest

Abstract: Website attacks have been one of the main threats to websites and web portals of private and public organizations. In today's digital world web applications are an important part of day-to-day life so it has become a challenging task to secure the applications. The attackers aim to extract sensitive information about the users through the URL links sent to the victims. We are trying filling the gap of traditional methods to stop the attacks, but the traditional methods fail to perform well as the attackers are becoming good at attacking the web applications. People are presently searching for reliable and consistent web application attack detection software. This model aims to secure web applications of vulnerabilities and from different types of attacks using a machine learning approach which has more accuracy compared to other machine learning algorithms since we are using Random Forest Model.

AN OPTIMIZED CLOUD BASED PROXY SERVER TECHNIQUES TO IMPROVE USER EXPERIENCE OF SPECTRUM UTILIZATION

The global growth and popularity of user experience with the Internet from academia research to multimedia has significantly increased the amount of information online and services available to public domain. This growth has created an unprecedented level of congestion on network infrastructures; scarce network bandwidths, valuable resources which are important and are obstacle to Internet access. The emerging data access can be priced per-byte at high cost, consuming up to 25% of a user’s total income, while in developed markets, data usage caps are a persistently zero, requiring users to track and manage consumption to avoid overage fees. In order to address this bandwidth problem, caching algorithm of proxies is employed at network access points to reduce the round trips of client server connections. However, caching is limited during queries of database because most of the content on the web today are dynamic. The focus of the study is to provide data compression techniques for reducing network access latency and to analyse the network traffic based on users’ experience.

Higher Order Trust Ranking of LinkedIn Accounts with Iterative Matrix Methods

Trust is a fundamental sociotechnological mainstay of the Web today. There is substantial evidence about this since netizens implicitly or explicitly agree to trust virtually every Web service they use ranging from Web-based mail to e-commerce portals. Moreover the methodological framework for trusting individual netizens, primarily their identity and communications, has considerably progressed. Nevertheless, the core of fact checking for human generated content is still far from being substantially automated as most proposed smart algorithms capture inadequately fundamental human traits. One such case is the evaluation of the profile trustworthiness of LinkedIn members based on publicly available attributes available from the platform itself. A trusted profile may indirectly indicate a more suitable candidate since its contents can be easily verified. In this article a first order graph search mechanism for discovering LinkedIn trusted profiles based on a random walker is extended to higher order ranking based on a combination of functional and connectivity patterns. Results are derived for the same benchmark dataset and the first- and higher-order approaches are compared in terms of accuracy.

AIMM: Artificial Intelligence Merged Methods for flood DDoS attacks detection

DDoS is one of the most common attacks on the web today. Hence, a quick detection system can enable automatic blocking or notification of an attack. In this paper, we propose a framework called AIMM (Artificial Intelligence Merged Methods). Our solution is based on three modules: preprocessing data incoming to the server, classification, and decision-making. The last stage is the decision-making module which gets the probability from all implemented AI methods and analyzes/aggregates them for making a final decision about the attack. The idea is based on the analysis of the TCP/UDP information reaching the target server and a quick decision method. The described technique is not limited to the selected AI method, and just for the tests, we used two different: neural networks and the k-nearest neighbors. As the aggregation solution, we used soft sets inference and averaging, weighted averaging technique. The proposal was subjected to performance tests on a publicly available database known as BOUN DDoS Dataset (and reached accuracy on 99,5%). The results were compared with the state-of-art and discussed in terms of its advantages and disadvantages.

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.

Decentralized Nation: Solving the Web’s Identity Crisis

The web of today whether you prefer to call it web 2.0, web 3.0, web 5.0 or even the metaverse is at a critical stage of evolution and challenge, largely centered around its crisis of identity. Like teenagers who cannot assess properly their reason for being and do not seem ready to take responsibility for their actions, we are constantly blaming the very system we are trying to get away from. To truly realize the benefits from innovation and technology, this crisis has to be resolved, not just through tactical solutions but through developments that enhance the sustainability of the web and its benefits. Significant strides are being made in the evolution of digital services enabled by technology, regulation, and the sheer pace of societal change. The journey to the decentralized web is mirroring the convergence of the physical and digital worlds across all economies and is increasingly embracing the digital native world. Technology has provided the foundational platform for individuals and entities to create and manage wealth, potentially without the need for big institutions. Ironically, despite all of the advancements, we are still facing an unprecedented and increasing wealth gap. Clearly, the system is broken, not just around the edges but at the very core of the democratic underpinning of our society. In this whitepaper, we propose how artificial intelligence on blockchain can be used to generate a new class of identity through direct human computer interaction. We demonstrate how this, combined with new perspectives for sustaining community and governance embedded within the use of blockchain technology, will underpin a sustainable solution to protect identity, authorship and privacy at the same time while contributing to restore trust amongst members of a future decentralized nation and hence contribute to solving the web most significant identity crisis.

A proactive method of the webshell detection and prevention based on deep traffic analysis

The popularity of today's web application has led to web servers frequently the objects of webshell attacks. In this paper, we propose a new deep inspection method that is composed of a deep learning algorithm and signature-based technique for webshell detection, namely DLWSD. Moreover, to avoid bottlenecks, DLWSD built-in DeepInspector inspects in real-time the large-scale traffic flows with a strategy of periodic sampling at a defined frequency and interval for only flows that do not satisfy any signature. DeepInspector can create/update rules from webshell attacking alert results to prevent in future. We also proposed a mechanism using the cross-entropy loss function to regulate the training imbalanced dataset. Our experiments allow validating the performance of DLWSD using a popular dataset CSE-CIC-IDS2018 with the metrics accuracy, F1-score, FPR of 99.99%, 99.98%, and 0.01% respectively. It is also better compared with other studies using the same dataset.

Analysis of the possibilities of optimizing SQL queries

Most of today's web applications run on relational database systems. Communication with them is possible through statements written in Structured Query Language (SQL). This paper presents the most popular relational database management systems and describes common ways to optimize SQL queries. Using the research environment based on fragment of the imdb.com database, implementing OracleDb, MySQL, Microsoft SQL Server and PostgreSQL engines, a number of test scenarios were performed. The aim was to check the performance changes of SQL queries resulting from syntax modication while maintaining the result, the impact of database organization, indexing and advanced mechanisms aimed at increasing the eciency of operations performed, delivered in the systems used. The tests were carried out using a proprietary application written in Java using the Hibernate framework.

School Library 2.0

Until the beginning of the 21st century, the web was mostly a place to retrieve information. Then, around 2004, the web 2.0 new tools allowed common people, with no programming skills, to add content to the web, full of interactivity. Schools started using web 2.0 in order to reach out and engage with generation Y. Today web 2.0 technologies are transforming the ways in which school libraries operate and deliver their services in this fast changing online social and collaborative world. We believe that web 2.0 tools will help us to provide authentic learning experiences for students, encouraging creativity, innovation, critical thinking and collaboration, promoting the information literacy skills that are required in the 21st century, in order to have a positive impact on student’s achievement.