This study examines media coverage of the Suwon Hwaseong Cultural Festival through a semantic network–based discourse analysis framework grounded in cultural agenda-setting theory. While prior festival studies have primarily focused on economic impacts and visitor satisfaction, this research analyzes how media narratives structure public understanding of the festival over time. Using the BIGKinds news platform, 81 news articles published between 2014 and 2023 were collected and examined through keyword frequency analysis, co-occurrence network modeling, TopicRank-based weighted keyword extraction, and longitudinal trend analysis. Rather than constructing a formal knowledge graph infrastructure, this study interprets keyword networks as discursive structures that reveal relational patterns among dominant themes such as “Suwon City,” “King Jeongjo,” and “Citizen-led Festival.” The findings indicate fluctuating media attention, with a peak in 2017, a significant decline during the COVID19 pandemic, and partial recovery thereafter. The network structure highlights the dominance of historical identity and regional branding themes, while participatory governance and economic sustainability appear as secondary but structurally meaningful clusters. Methodologically, the study demonstrates how semantic network analysis of news data can support cultural policy interpretation and agenda-setting evaluation. By integrating co-occurrence modeling and weighted keyword ranking, the research provides a structured approach to understanding media discourse surrounding regional cultural events.
Geographic data plays a vital role in supporting modern location-based services on the World Wide Web (WWW). In Bangladesh, such data exists in structured, semi-structured, and unstructured formats, stored across government agencies, research institutions, and private organizations using diverse formats and protocols. This heterogeneity creates significant integration challenges, limiting operational efficiency and the development of applications ranging from navigation and logistics to personalized services and emergency response. Our research addresses this by transforming and integrating disparate datasets into a machine-understandable form. We modeled the complete administrative hierarchy of Bangladesh, from divisions to villages, generating 0.40 million RDF (Resource Description Framework) triples within a unified semantic repository, Geo-Bangladesh. This repository enables effortless integration and retrieval of geospatial data across all administrative levels. We further linked Geo-Bangladesh with related repositories, including the educational institutions and citizen information, enabling the mapping and visualization of entities along with locations. Using geoSPARQL, we retrieved and inferred spatial and non-spatial data, demonstrating the repository’s usability, interoperability, and effectiveness. Unlike raw GeoSPARQL implementations or general-purpose ontologies such as Geonames, Geo-Bangladesh is explicitly tailored to Bangladesh’s administrative structure and reconciles inconsistencies such as the 68 vs. 64 districts problem. Compared to OGC-compliant frameworks, our repository incorporates both semantic interoperability and localized reconciliation, demonstrating advantages in accuracy, query flexibility, and alignment with national data sources.
Generative Artificial Intelligence (GenAI) is transforming the business landscape by enhancing accessibility, efficiency, cost-effectiveness, and innovation. This paper investigates the application of Large Language Models (LLMs) and GenAI in the financial sector, proposing a novel framework to reimagine robo-advisory systems. The framework shifts from traditional, rigid platforms to a more humanized approach that actively engages investors in a personalized asset selection process while leveraging LLMs to better understand their goals and profiles. We present an end-to-end solution designed to address key limitations of conventional roboadvisors, such as inflexibility, restricted asset type offerings (typically limited to equities), and challenges in accessing high-quality, real-time data. The proposed architecture incorporates dynamic client profiling, risk aversion estimation, and portfolio optimization. A tailored asset selector agent, supported by robust data pipelines, ensures the curation of up-to-date market information. Through iterative development, we utilized prompt engineering and multi-agent workflows to refine user interactions and deliver actionable insights. By implementing an innovative chatbot platform, we demonstrate the potential of LLMs to revolutionize customer service, enhance investor engagement, and provide strategic financial guidance. This study highlights the transformative impact of GenAI in creating more adaptive, personalized, and effective financial advisory solutions.
Crime causes physical and mental damage. Several crime prevention measures have been developed by law enforcement officials since they realized how serious this problem is. These preventative measures are not strong enough to help lower crime rates because they are typically slow-paced and ineffectual. In this regard, machine learning community has started developing automated approaches for detecting crime hotspot, after performing a careful analysis of the crime trend incorporating geospatial, temporal, demographic, or other relevant information. In this research, we look at detecting crime hotspots using geospatial information of prior crime occurrences. We proposed BIRCH algorithm to detect high crime prone areas with four essential aspects: (1) PCA (Principle Component Analysis) has been used to minimize the dimensionality of crime data, (2) Silhouette score Elbow and Calinski Harabaz have been used to find the optimal number of cluster (3) utilized hyper-parameter tuning to choose the best hyperparameters for the BIRCH algorithm (4) applied BIRCH with the three aspects mentioned above. The results of the suggested framework were then contrasted with those of alternative clustering techniques, such as K-means, DBSCAN, and the agglomerative algorithm. We explored our approaches on the London Crime Dataset and found some fascinating results that can help reducing crime by helping people take the appropriate measures.
SPARQL query rewriting is a fundamental mechanism for uniformly querying heterogeneous ontologies in the Linked Data Web. However, the complexity of ontology alignments, particularly rich correspondences (c : c), makes this process challenging. Existing approaches primarily focus on simple (s : s) and partially complex ( s : c) alignments, thereby overlooking the challenges posed by more expressive alignments. Moreover, the intricate syntax of SPARQL presents a barrier for non-expert users seeking to fully exploit the knowledge encapsulated in ontologies. This article proposes an innovative approach for the automatic rewriting of SPARQL queries from a source ontology to a target ontology, based on a user's need expressed in natural language. It leverages the principles of equivalence transitivity as well as the advanced capabilities of large language models such as GPT-4. By integrating these elements, this approach stands out for its ability to efficiently handle complex alignments, particularly (c : c) correspondences , by fully exploiting their expressiveness. Additionally, it facilitates access to aligned ontologies for users unfamiliar with SPARQL, providing a flexible solution for querying heterogeneous data.
This paper examines the transformative role of Natural Language Processing (NLP) in vocabulary acquisition for English as a Second Language (ESL) learners. With advancements in NLP technologies, innovative applications have emerged that foster effective vocabulary learning through personalized and context-rich experiences. The study explores various NLP-driven tools, such as context-aware learning systems, personalized vocabulary lists, and interactive games, that enhance engagement and retention. By analyzing the effectiveness of these tools, this research underscores the limitations of traditional vocabulary teaching methods and illustrates how NLP can provide dynamic, tailored support to meet individual learners’ needs. Furthermore, the paper highlights the significance of real-time feedback mechanisms, such as speech recognition, in promoting accurate pronunciation. Ultimately, this investigation aims to demonstrate that NLP technologies offer a more engaging, efficient, and effective approach to vocabulary acquisition, significantly improving ESL learners’ language proficiency and overall communicative competence.
In this research, we explore the vital transition of Design Systems from Web Content Accessibility Guidelines (WCAG) 2.0 to WCAG 2.1, emphasizing its role in enhancing web accessibility and inclusivity in digital environments. The study outlines a comprehensive strategy for achieving WCAG 2.1 compliance, encompassing assessment, strategic planning, implementation, and testing, with a focus on collaboration and user involvement. It also addresses the challenges in using web accessibility tools, such as their complexity and the dynamic nature of accessibility standards. The paper looks forward to the integration of emerging technologies like AI, ML, NLP, VR, and AR in accessibility tools, advocating for universal design and user-centered approaches. This research acts as a crucial guide for organizations aiming to navigate the changing landscape of web accessibility, underscoring the importance of continuous learning and adaptation to maintain and enhance accessibility in digital platforms.
The evolution of web development has been significantly influenced by the introduction of HTML5 Web Components, particularly customized built-in elements. This paper explores the transformative impact of these elements on Component-Based Software Engineering (CBSE) and Design Systems. Customized builtin elements, as an integral part of the Web Components standard, offer unparalleled flexibility and functionality, enabling developers to create bespoke HTML elements that encapsulate specific behaviors and styles. This adaptability is pivotal for CBSE, facilitating the modularization of complex applications into more manageable components. This research delves into how customized built-in elements enhance Design Systems, ensuring visual consistency and superior user experience across digital applications. By adapting these elements to align with an application's design language and brand identity, a seamless and visually cohesive interface is achieved. The paper also examines the cross-browser compatibility, performance optimization, and security considerations associated with implementing these elements, emphasizing their critical role in efficient web application integration. Furthermore, the paper highlights the significance of these elements in contemporary web development scenarios, including Internet of Things (IoT) applications, e-commerce platforms, and educational technologies. The interaction of these components with Progressive Web Apps (PWAs) is also explored, showcasing potential improvements in web experiences. In conclusion, the paper underscores the necessity of addressing challenges such as browser standardization and developer tooling to fully realize the potential of HTML5 customized built-in elements. The discussion concludes with an outlook on the future of these elements, projecting their continuing influence in advancing the field of web development.
Although the Dark web was originally used for maintaining privacy-sensitive communication for business or intelligence services for defence, government and business organizations, fighting against censorship and blocked content, later, the advantage of technologies behind the Dark web were abused by criminals to conduct crimes which involve drug dealing to the contract of assassinations in a widespread manner. Since the communication remains secure and untraceable, criminals can easily use dark web service via The Onion Router (TOR), can hide their illegal motives and can conceal their criminal activities. This makes it very difficult to monitor and detect cybercrimes over the dark web. With the evolution of machine learning, natural language processing techniques, computational big data applications and hardware, there is a growing interest in exploiting dark web data to monitor and detect criminal activities. Due to the anonymity provided by the Dark Web, the rapid disappearance and the change of the uniform resource locators (URLs) of the resources, it is not as easy to crawl the Drak web and get the data as the usual surface web which limits the researchers and law enforcement agencies to analyse the data. Therefore, there is an urgent need to study the technology behind the Dark web, its widespread abuse, its impact on society and the existing systems, to identify the sources of drug deal or terrorism activities. In this research, we analysed the predominant darker sides of the world wide web (WWW), their volumes, their contents and their ratios. We have performed the analysis of the larger malicious or hidden activities that occupy the major portions of the Dark net; tools and techniques used to identify cybercrimes which happen inside the dark web. We applied a systematic literature review (SLR) approach on the resources where the actual dark net data have been used for research purposes in several areas. From this SLR, we identified the approaches (tools and algorithms) which have been applied to analyse the Dark net data, the key gaps as well as the key contributions of the existing works in the literature. In our study, we find the main challenges to crawl the dark web and collect forum data are: scalability of crawler, content selection trade off, and social obligation for TOR crawler and the limitations of techniques used in automatic sentiment analysis to understand criminals’ forums and thereby monitor the forums. From the comprehensive analysis of existing tools, our study summarizes the most tools. However the forum topics rapidly change as their sources changes; criminals inject noises to obfuscate the forum’s main topic and thus remain undetectable. Therefore supervised techniques fail to address the above challenges. Semi-supervised techniques would be an interesting research direction.
Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection.
With the advent of newly introduced programming models like Feature-Oriented Programming (FOP), we feel that it will be more flexible to include the new service invocation function into the service providing server as a Feature Module for the self-adaptive distributed systems. A composite design patterns shows a synergy that makes the composition more than just the sum of its parts which leads to ready-made software architectures. In this paper we describe the amalgamation of Visitor and Case-Based Reasoning Design Patterns to the development of the Service Invocation and Web Services Composition through SOA with the help of JWS technologies and FOP. As far as we know, there are no studies on composition of design patterns for self adaptive distributed computing domain. We have provided with the sample code developed for the application and simple UML class diagram is used to describe the architecture.
The digitization of displaced archives is of great historical and cultural significance. Through the construction of digital humanistic platforms represented by MISS platform, and the comprehensive application of IIIF technology, knowledge graph technology, ontology technology, and other popular information technologies. We can find that the digital framework of displaced archives built through the MISS platform can promote the establishment of a standardized cooperation and dialogue mechanism between the archives authoritiess and other government departments. At the same time, it can embed the works o fichives ction of digital government and the economy, promote the exploration of the integration of archives management, data management, and information resource management, and ultimately promote the construction of a digital society. By fostering a new partnership between archives departments and enterprises, think tanks, research institutes, and industry associations, the role of multiple social subjects in the modernization process of the archives governance system and governance capacity will be brought into play. The National Archives Administration has launched a special operation to recover scattered archives overseas, drawing up a list and a recovery action plan for archives lost to overseas institutions and individuals due to war and other reasons. Through the National Archives Administration, the State Administration of Cultural Heritage, the Ministry of Foreign Affairs, the Supreme People's Court, the Supreme People's Procuratorate, and the Ministry of Justice, specific recovery work is carried out by studying and working on international laws.
Air transport is one of the major transportation modes in Africa. It has been showing a growth due to significant increase of tourist flights and intercontinental flights. Ethiopian Airlines and South African Airlines are the leading airlines in Africa in terms of working capacity and annual profit. Both Ethiopian and South African Airlines have a website that lets their customers search and book a flight. They also have a mobile application for both android and iOS platforms. The airline industry in Africa is facing market competition from different airlines outside the continent. The competition is getting intense due to the usage of different technological artifacts in the air transport infrastructure by competitors. The development of an accessible and usable website and mobile application for flight booking and related services can help African airlines to compete well. This paper is about evaluating the accessibility of Ethiopian and South African airlines’ websites and mobile applications using manual and automatic accessibility evaluation techniques. As per the evaluation result, both airlines shall work hard to inclusively design their interactive online reservation systems. To do so, they need to consider the seven principles of Universal Design whenever they enhance their interactive systems.
The Covid-19 ontology is to classify the data using a supervised learning approach in machine learning, which has been preprocessed. Afterthe classification is done, with thehelp of opinion mining with decisionmaking, the classified data is stored in the database using semantic webontology using the protégé tool. The data will be retrieved through SPARQL which helps to retrieve complex queries, followed by the output based on the given query. This Covid-19 ontology helps in analyzing the risk factors and treatment plans for the respective individuals i.e., students based on their given details which include diagnosis, symptoms, and vaccination history. The information given by the students can be automatically processed and with the help of SWRL (Semantic Web Rule Language), the risk factor and treatment plans for the students are inferred from the given knowledge.
Programming Languages (PL) effectively performs an intersemiotic translation from a natural language to machine language. PL comprises a set of instructions to implement algorithms, i.e., to perform (computational) tasks. Similarly to Normative Languages (NoL), PLs are formal languages that can perform both regulative and constitutive functions. The paper presents the first results of interdisciplinary research aimed at highlighting the similarities between NoL (social sciences) and PL (computer science) through everyday life examples, exploiting Object-Oriented Programming Language tools and an Internet of Things (IoT) system as a case study. Given the pandemic emergency, the urge to move part of our social life to the digital world arose, together with the need to effectively transpose regulative rules and constitutive rules through different strategies for translating a normative utterance expressed in natural language.
We introduce a statistical model for analysing the morphology of natural languages based on their affixes. The model was inspired by the analysis of Amis, an Austronesian language with a rich morphology. As words contain a root and potential affixes, we associate three vectors with each word: one for the root, one for the prefixes, and one for the suffixes. The morphology captures semantic notions and we show how to approximately predict some of them, for example the type of simple sentences using prefixes and suffixes only. We then define a Sentence vector s associated with each sentence, built from the prefixes and suffixes of the sentence and show how to approximately predict a derivation tree in a grammar.
Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved selfsupervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. The seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.
As Web 3.0 is blooming, ontologies augment semantic Web with semi–structured knowledge. Industrial ontologies can help in improving online commercial communication and marketing. In addition, conceptualizing the enterprise knowledge can improve information retrieval for industrial applications. Having ontologies combine multiple languages can help in delivering the knowledge to a broad sector of Internet users. In addition, multi-lingual ontologies can also help in commercial transactions. This research paper provides a framework model for building industrial multilingual ontologies which include Corpus Determination, Filtering, Analysis, Ontology Building, and Ontology Evaluation. It also addresses factors to be considered when modeling multilingual ontologies. A case study for building a bilingual English-Arabic ontology for smart phones is presented. The ontology was illustrated using an ontology editor and visualization tool. The built ontology consists of 67 classes and 18 instances presented in both Arabic and English. In addition, applications for using the ontology are presented. Future research directions for the built industrial ontology are presented.
The 21st century has been characterized by an increased attention to social networks. Nowadays, going 24 hours without getting in touch with them in some way has become difficult. Facebook and Twitter, these social platforms are now part of everyday life. Thus, these social networks have become important sources to be aware of frequently discussed topics or public opinions on a current issue. A lot of people write messages about current events, give their opinion on any topic and discuss social issues more and more. The emergence and enormous popularity of these social networks have led to the emergence of several types of analysis to take advantage of them. One of them is the analysis of opinions in texts. It aims at automatically classifying opinions in order to position them on a sentiment scale, thus allowing to characterize a set of opinions without having to rely on a human to read them. Currently, opinion analysis offers us a lot of information related to public opinion, either in the commercial world or in the political world. Many studies have shown that machine learning techniques, such as the support vector machine (SVM) and the naive Bayes classifier (NB), perform well in this type of classification. In our study, we first propose an approach for tracking and analyzing political opinions in social networks. Then, we propose a trained and evaluated machine learning model for political opinion classification. And finally, the study aims at setting up a web interface to collect and analyze in real time political opinions from social networks
Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved self-supervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. First, the experimental results of Tibetan syllables Chinese characters are not good, which reflects the weak semantic correlation between Tibetan syllables and Chinese characters; second, the seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.
