Markup Language Research Articles

A Review on Full Stack Web Development

Web development is a dynamic and growing field that plays an important role in the development and maintenance of websites and web applications. This abstract provides a brief summary of web development, including its objectives, key technologies, and development process.
 Web development encompasses a range of activities aimed at designing and maintaining websites and web applications accessible through the World Wide Web. It combines engineering, design principles and design. This overview highlights the following key points.
 
 Objectives and importance: Web development is foundational for individuals, businesses and organizations online. It enables the delivery of information, services and interactive experiences to a global audience, affecting communications, marketing, and industry.
 Core Technologies: Web development relies on a core set of technologies, including HTML (Hypertext Markup Language), CSS (Cascading Style Sheets), and JavaScript. HTML structures web content, CSS improves visual design, and JavaScript adds interactivity and functionality. In addition to these core technologies, web development often uses databases, backend languages, and frameworks.
 Development Processes: Interactive development processes typically involve planning, design, development, testing, implementation, and ongoing maintenance. It emphasizes user interfaces, accessibility and flexibility across devices and browsers. The coordination between developers, designers and stakeholders is essential to the success of the project.

Towards a framework for interoperability and reproducibility of predictive models

The development and deployment of machine learning (ML) models for biomedical research and healthcare currently lacks standard methodologies. Although tools for model replication are numerous, without a unifying blueprint it remains difficult to scientifically reproduce predictive ML models for any number of reasons (e.g., assumptions regarding data distributions and preprocessing, unclear test metrics, etc.) and ultimately, questions around generalizability and transportability are not readily answered. To facilitate scientific reproducibility, we built upon the Predictive Model Markup Language (PMML) to capture essential information. As a key component of the PREdictive Model Index and Exchange REpository (PREMIERE) platform, we present the Automated Metadata Pipeline (AMP) for conversion of a given predictive ML model into an extended PMML file that autocompletes an ML-based checklist, assessing model elements for interoperability and reproducibility. We demonstrate this pipeline on multiple test cases with three different ML algorithms and health-related datasets, providing a foundation for future predictive model reproducibility, sharing, and comparison.

Design and Build Web-Based Freelance Applications

In today's modern era, many people have switched to looking for freelance work. One of the interesting things is freelance work because there is no work attachment to employee status, so that the person can have several jobs at one time. But until now, to find freelance work is not easy because usually many job search applications provide services only for permanent workers. For companies that are looking for freelance labor is also not easy, because freelance job search provider services are also not much. The purpose of designing this Lancer application is to make it easier for people who want to find freelance jobs. This website is designed using UML (Unified MarkUp Language). The method used in building this website uses SDLC (Software Development Life Cycle) which includes the stages of analysis, design, creation, and testing. This website is created using Laravel and MySQL frameworks.

PENERAPAN UI/UX SISTEM INFORMASI SPONSORSHIP EVENT PT INDOSAT OOREDOO DENGAN METODE DESIGN THINKING

Developing a UI/UX Implementation Application for the PT Indosat TBK Sponsorship Event Information System using the Design Thinking Method in a systematic and structured manner so that it can help Admin and Marketing Communication in recording and monitoring Sponsorship Events within a certain period. As well as a structured database system that makes it easier for admin performance to be effective and efficient. This is because currently there is no media available to process PT Indosat event sponsorships. With a database aimed at managing the application database, it can be built properly using PHP. This system has a small scale and involves only a few people in the system, namely marketing, admin and users. The aim of implementing the Pt Indosat TBK Event Sponsorship Information System UI/UX using the Design Thinking Method makes it easier to manage proposal data and create reports. This Information System inputs admin, marketing, user, proposal, micro cluster and agency data. Create an account for users so they can see the proposal status after inputting proposal data. If the proposal status is approved, then print proof of sponsorship letter for the user, so that you get admin, marketing proposal data reports and data reports per status. This application was built using Phpmyadmin, Cascading Style Sheet, Hypertext Markup Language. It is hoped that it can help Indosat in event sponsorship.

Single or pluralistic? The game and balance of China's community governance policy tools.

Policy tools embody policy concepts and are essential to achieving policy objectives. The effective allocation of policy tools directly impacts the effectiveness of community governance and determines the modernization process of grassroots governance. We aim to analyze the logic of community governance policy tool selection, and then provide assistance for the modernization of grassroots governance. We selected 100 national and provincial government work reports and 63 policy documents related to community governance during China's "12th Five-Year Plan" to "14th Five-Year Plan" period as analysis samples. And build an analysis framework based on the three dimensions of time, space, and tools. We used Nvivo.20 software for text encoding analysis. Based on the model framework, we analyze the results as follows. From the perspective of the time dimension, among the five types of policy tools, the proportion of command-type policy tools used showed a downward trend, from 88.16% in the 12th Five-Year Plan to 83.50% in the 14th Five-Year Plan. However, motivation-type and persuasion-type tools showed an upward trend, rising from 1.34% and 5.26% in the 12th Five-Year Plan period to 3.40% and 8.74% in the 14th Five-Year Plan respectively. The system-change-type policy tools decreased from 1.32% in the 12th Five-Year Plan to 0.97% in the 14th Five-Year Plan. The proportion of capacity-building-type policy tools has gradually increased from 2.63% in the 12th Five-Year Plan to 4.85% in the 14th Five-Year Plan. From the perspective of spatial dimension, apart from command and persuasion policy tools, the usage frequency of the other three types of policy tools in the three major regions all display a "growth-decline-growth" trend. From the perspective of tool dimension, command-type policy tools are dominant in China's community governance, with a cumulative frequency of 1405 times and a high proportion of 81.75%. Apart from command policy tools, persuasive policy tools and capacity-building policy tools have a relatively high proportion, with usage frequencies of 186 and 78 respectively. We found that current community governance policy tools mainly consist of command tools. However, there is a trend towards combining tools such as command, persuasion, incentive, capacity building, and system change in the future. There is a typical contradiction between instrumental rationality and value rationality, indicating an evolution from instrumental rationality to the integration of instrumental and value rationality. This study addresses the conflict of policy tools through rational guidance of values, the rational guarantee of tools, and cooperation to achieve the goal of high-quality development of community governance.

Development of a Modern Website for the Department of Information Technologies and Management

The main feature of the development of the department’s website is the service for teaching students, teachers, and useful information for new ones. This article discusses the main aspects of the website of the Department of Information Technologies and Management of the Osh Technological University. The process of designing and implementing a website is also discussed. The most important parameter of the site is accessibility and ease of use, which is achieved using web technologies. The hypertext markup language HTML was used to develop the interface. And from the framework we used bootstrap.

Wine and translation: an analysis of phraseological units in English and Spanish wine technical sheets

ABSTRACT The language used to market and describe wine is characterised by the subjectivity and creativity of the terms used. This makes the encoding and decoding of oenology-related texts a demanding task. As genres in this field are persuasive in nature, the correct translation of wine-related terminology is crucial to sell the product. In wine-related genres, phraseological units have been understudied especially with regards to translation. The aim of this paper is to shed light on the use of phraseological units in one of the most representative genres in the field, i.e. wine technical sheets, with a view to assisting translators in the comprehension and management of oenology-related texts. A contrastive analysis was carried out using an English-Spanish comparable corpus in order to identify and classify phraseological units according to their function and semantic characteristics; further analysis of the distribution of these units allowed us to define the rhetorical structure of wine technical sheets. The study evidenced the high frequency and complexity of phraseological units in the genre.

Cross-modal incongruity aligning and collaborating for multi-modal sarcasm detection

Sarcasm embodies a linguistic phenomenon that highlights a significant incongruity between the literal meanings of words and intended attitudes. With the proliferation of image–text content on social media, the task of multi-modal sarcasm detection (MSD) has gained considerable attention recently. Tremendous progress have been made in developing better MSD models, primarily relying on a straightforward extract-then-fuse paradigm. However, such a setting encounters two potential challenges. First, the utilization of separately pre-trained unimodal models for extracting visual and textual features frequently lacks the fundamental alignment capabilities required for effective multimodal data integration. Second, the detrimental modality gaps between vision and language make it challenging to comprehensively integrate multi-modal information solely via diverse cross-modal fusion techniques. Consequently, this poses a prominent challenge in further capturing cross-modal incongruity and improving the effectiveness of MSD. In this paper, we propose a Multi-modal Mutual Learning (MuMu) network to tackle these issues. Specifically, we initialize the MuMu network with image and text encoders from the large-scale Contrastive Language-Image Pretraining model to enhance the underlying image–text correspondence. Moreover, to improve the capability of capturing cross-modal inconsistency during the fusion process, we design an align-fuse-collaborate mechanism to align disparate modalities before fusion and enhance the collaborative modeling ability between the two modalities with mutual learning after fusion. The proposed MuMu achieves new state-of-the-art results on a public dataset, demonstrating a substantial improvement of approximately 3% to 9% in terms of accuracy, micro-F1, and macro-F1 scores.

A Neural Expectation-Maximization Framework for Noisy Multi-Label Text Classification

Multi-label text classification (MLTC) has a wide range of real-world applications. Neural networks recently promoted the performance of MLTC models. Training these neural-network models relies on sufficient accurately labelled data. However, manually annotating large-scale multi-label text classification datasets is expensive and impractical for many applications. Weak supervision techniques have thus been developed to reduce the cost of annotating text corpus. However, these techniques introduce noisy labels into the training data and may degrade the model performance. This paper aims to deal with such noise-label problems in MLTC in both single-instance and multi-instance settings. We build a novel Neural Expectation-Maximization Framework (nEM) that combines neural networks with probabilistic modelling. The nEM framework produces text representations using neural-network text encoders and is optimized with the Expectation-Maximization algorithm. It naturally considers the noisy labels during learning by iteratively updating the model parameters and estimating the distribution of the ground-truth labels. We evaluate our nEM framework in multi-instance noisy MLTC on a benchmark relation extraction dataset constructed by distant supervision and in single-instance noisy MLTC on synthetic noisy datasets constructed by keywords supervision and label flipping. The experimental results demonstrate that nEM significantly improves upon baseline models in both single-instance and multi-instance noisy MLTC tasks. The experiment analysis suggests that our nEM framework efficiently reduces the noisy labels in MLTC datasets and significantly improves model performance.

Implementation of an Automobile Breakdown Service Provider (ABSP) Model

The increasing frequency of vehicle breakdowns presents a challenge for automobile drivers, especially when faced with unexpected breakdowns in unfamiliar locations. This article addresses the need for an effective solution by proposing and implementing an Automobile Breakdown Service Provider (ABSP) model. Vehicle breakdowns, caused by factors such as poor road conditions, battery failure, or mechanical issues, often require immediate attention from certified artisans. The ABSP model aims to enhance the breakdown assistance process by incorporating driver feedback through ratings and reviews of artisan services, ensuring reliability and trustworthiness. To develop the ABSP model, limitations of existing Road Side Assistance (RoSAS) models were analyzed and taken into account. The iterative process model was employed for implementation, allowing early software versions to accommodate evolving requirements. The system's frontend utilized Hypertext Markup Language (HTML) and Cascading Style Sheet (CSS), while the mobile application was developed using Flutter—a versatile Dart-based framework for cross-platform mobile app creation. The backend was supported by the Laravel Framework, based on Hypertext Preprocessor (PHP), with MySQL serving as the relational database for record storage. The ABSP model's significance lies in its ability to provide drivers with verified and rated artisans, ensuring efficient breakdown resolution, payment automation, and minimizing the challenges associated with breakdowns in unfamiliar or dangerous locations. This article offers insights into the design and implementation of the ABSP model, contributing to enhanced reliability and convenience in addressing automobile breakdowns.

MMTD: A Multilingual and Multimodal Spam Detection Model Combining Text and Document Images

Spam detection has been a topic of extensive research; however, there has been limited focus on multimodal spam detection. In this study, we introduce a novel approach for multilingual multimodal spam detection, presenting the Multilingual and Multimodal Spam Detection Model combining Text and Document Images (MMTD). Unlike previous methods, our proposed model incorporates a document image encoder to extract image features from the entire email, providing a holistic understanding of both textual and visual content through a single image. Additionally, we employ a multilingual text encoder to extract textual features, enabling our model to process multilingual text content found in emails. To fuse the multimodal features, we employ a multimodal fusion module. Addressing the challenge of scarce large multilingual multimodal spam datasets, we introduce a new multilingual multimodal spam detection dataset comprising over 30,000 samples, which stands as the largest dataset of its kind to date. This dataset facilitates a rigorous evaluation of our proposed method. Extensive experiments were conducted on this dataset, and the performance of our model was validated using a five-fold cross-validation approach. The experimental results demonstrate the superiority of our approach, with our model achieving state-of-the-art performance, boasting an accuracy of 99.8% when compared to other advanced methods in the field.

An automated model annotation system (AMAS) for SBML models.

Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g. species, reactions) by specifying the reference database (e.g. ChEBI for species) and the match score function (e.g. string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has subsecond response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.

Quest markup for developing FAIR questionnaire modules for epidemiologic studies

BackgroundOnline questionnaires are commonly used to collect information from participants in epidemiological studies. This requires building questionnaires using machine-readable formats that can be delivered to study participants using web-based technologies such as progressive web applications. However, the paucity of open-source markup standards with support for complex logic make collaborative development of web-based questionnaire modules difficult. This often prevents interoperability and reusability of questionnaire modules across epidemiological studies.ResultsWe developed an open-source markup language for presentation of questionnaire content and logic, Quest, within a real-time renderer that enables the user to test logic (e.g., skip patterns) and view the structure of data collection. We provide the Quest markup language, an in-browser markup rendering tool, questionnaire development tool and an example web application that embeds the renderer, developed for The Connect for Cancer Prevention Study.ConclusionA markup language can specify both the content and logic of a questionnaire as plain text. Questionnaire markup, such as Quest, can become a standard format for storing questionnaires or sharing questionnaires across the web. Quest is a step towards generation of FAIR data in epidemiological studies by facilitating reusability of questionnaires and data interoperability using open-source tools.

Farm Friendly Chat Bot

This study presents the development of a chat room and a chatbot designed to facilitate discussions on prevalent farming issues among peers and experts. Its primary aim is to provide timely support to farmers in making informed decisions about their farming practices. To create a structured framework for these conversations, a standardized set of questions was formulated through consultations and surveys involving farmers, experts, and other stakeholders. The questions were analyzed to extract 'intents,' representing the specific information or assistance users might seek, and 'examples,' which are concrete instances users provide to express their particular intent. Additionally, 'entities' were identified to represent distinct objects or concepts related to these intents. The model was trained using the Artificial Intelligence Markup Language (AIML) to predict the intent based on the provided examples. This training process enhances the chatbot's ability to understand and respond to user queries effectively. Furthermore, the chatbot was deployed on a cloud platform, reducing the computational resources required on the client end. This approach ensures accessibility and usability for a broader user base without significant hardware constraints.

Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO.

The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.

INTEGRATING HTML5-BASED SPEECH RECOGNITION WITH LEARNING MANAGEMENT SYSTEM TO ENHANCE EFL LEARNERS’ PRONUNCIATION SKILLS

HTML5 (HyperText Markup Language version 5) is currently integrated with almost all web-based applications due to its compatibility with numerous types of multimedia content including music, video, and animation. This exploratory study focused on the application and assessment of an HTML5-based plugin called H5P as a Computer Assisted Pronunciation Training (CAPT) tool in developing the pronunciation skills of intermediate-level learners of English as a foreign language (EFL). The target words were initially recorded with their correct pronunciations and then the audio files were imported into H5P to create interactive exercises. The H5P was then integrated with the institutional Learning Management System (LMS). The practice was implemented on n=188 EFL learners enrolled in the General Foundation (GFP) of a university college in the Sultanate of Oman. The efficacy of the approach was assessed through the perspectives of students by using student questionnaires and a student focus group discussion (FGD). The study yielded encouraging findings since most of the students agreed that the use of HTML5 as a CAPT tool was helpful in enhancing their pronunciation skills as it provided automatically generated instant feedback, was easy to use, and could be used by students autonomously at their own pace. Although the impact of software on student achievement in terms of pronunciation skills could not be gauged and the sample was limited to one institution, the study has useful implications for EFL teachers and curriculum designers on using HTML5 for developing pronunciation skills. Further studies are necessary to investigate the pedagogic potential of CAPT resources to develop a high level of competence in EFL learners’ pronunciation.

A Framework of Vehicle Usage Optimization for Tour Purposes

Nowadays, people like to travel to alleviate the stress and depression they experience in their busy lives, and if they can do so at a low cost, it will be even more beneficial to them. A traveler can choose to book a travel ticket themselves or to contact a travel agency, who will book a ticket for them. A lack of vehicle management can cause travel agencies to lose profit. In this article, we have provided proficient solutions for vehicle optimization to minimize travel costs. Additionally, however, arranging an appropriate tour guide is sometimes expensive for travelers. Moreover, travelers often do not know about suitable travel packages and occasionally do not know where they may want to visit. In this paper, we have discussed how we used effective methods to reduce vehicle costs for tour purposes. Furthermore, an optimal bus seat occupancy solution has been developed, incorporating the package system and including a tour guide. Our system offers a remedy by providing access to cost-effective packages. We have also added a blog portal to our web-based software for an overview all tourist places and their information. As a result, we have found optimized routes and provide low-cost, high-value tours. We have also reduced carbon footprint emissions. Our proposed optimal vehicle usage system has been developed using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), the Tailwind Framework, React, and Node.js.

Digital Meters

Abstract This article discusses best practices for teaching text encoding in undergraduate literary studies courses. It examines learning outcomes associated with text encoding and ways of incorporating encoding into the teaching of literary analysis, as well as advantages and challenges, concluding that encoding activities and assignments offer unique opportunities for learning.

INTEGRATING NEURO-FUZZY SYSTEMS FOR ENHANCED CANCER DATA ANALYSIS AND PREDICTION

The abstract for Integrating Neuro-Fuzzy structures for more significant most cancers statistics analysis and Prediction describes research carried out to examine using a sort of artificial intelligence, known as a Neuro-Fuzzy device, to analyze and expect facts from most cancer sufferers. by leveraging the strengths of both Neural Networks and Fuzzy common sense structures, Neuro-Fuzzy systems provide a powerful answer for complicated statistics analysis. This research examines and tests the overall performance of Neuro-Fuzzy systems on hard and fast benchmark datasets from most cancers Toolbox Markup Language (TMX). Consequences showed that Neuro-Fuzzy yielded a higher accuracy charge when compared to different device learning algorithms in studying information from a diverse set of patients. Furthermore, the researchers also stated that Neural-Fuzzy systems were able to discern subtypes of cancer in an affected person population, which had not been formerly feasible with different techniques. The work defined in the abstract could have a long way to attaining implications for the remedy and prognosis of most cancer patients. With the promising results of this, have a look at showing that Neuro-Fuzzy structures are able to distinguish between particular forms of cancer correctly; a more precise treatment plan might be created for people living with cancer. Additionally, with the improved accuracy of Neuro-Fuzzy structures, more excellent dependable predictions will be made about the progression of most cancers in a selected patient, helping doctors to plan treatments. Ordinary, the findings of the research summarized in this summary are especially significant as the advanced accuracy and capacity to figure out subtle differences in most cancer types keep the promise of improved remedies and prognoses for people living with cancer.

The Chekhov Digital project: tasks and problems of implementing semantic markup of texts (on the example of A. P. Chekhov's story "The Death of an Official")

The article considers a model of preparation of machine-readable (semantic) markup of texts for the Chekhov Digital project on the example of philological interpretation of individual significant elements of A. P. Chekhov's story "Death of an Official" and presentation of this information explicitly based on the standards of digital publication Text Encoding Initiative (TEI/XML). Based on the work of literary researchers, significant entities have been identified for marking up the corpus of the writer's texts, but the question of their representation in the text remains quite complex. A philological examination of such aspects as "properties, states and events; character features" in an excerpt from the story of A.P. Chekhov was carried out from the point of view of the TEI markup capabilities for preserving philological knowledge in a machine-readable format. One of the objectives of the Chekhov Digital project is to go beyond a simple digitized text and provide useful digital tools for the researcher. The elements of machine-readable markup are presented, which make it possible to mark up significant entities in Chekhov's texts for organizing semantic search through the corpus of the writer's texts, the problems and research tasks arising in the process of implementing such interdisciplinary projects due to the need to combine the efforts of specialists from different fields of knowledge are considered. The project implements the principle of Open research data, the most important task of which is to create scientific communities around data. The work on the project led to the development of scientific cooperation between researchers of the Higher School of Economics, the UNC RAS and the SFU.