ABSTRACT
In this paper, we dealt with the rapid development of client web applications (frontend) in a context where development frameworks are legion. In effect with the digital transformation due to the COVID-19 pandemic we are witnessing an ever-increasing demand of the application development in a relatively short time. To this is added the lack of skilled developers on constantly evolving technologies. We therefore offer a low-code platform for the automatic generation of client web applications, regardless of the platform or framework chosen. First, we defined an interface design methodology based on a portal. We then implemented our model driven architecture which consisted of defining a modeling and templating language, centered on user data, flexible enough to not only be used in various fields but also be easily used by a citizen developer.
ABSTRACT
The existence of Covid-19, which is sweeping the whole world, has caused Indonesia to make a policy to change the learning system that was previously face-to-face to online. As a result, students are prevented from interacting directly with their friends, making them bored and stressed. Increased stress levels can also be influenced by other factors, such as an unsupportive home environment and the number of tasks assigned with relatively few deadlines. In addition, other problems, from friends and family, are often a burden for students, significantly affecting stress levels and mental health. The data collection method used in this study uses observation and literature studies on object-oriented applications. This study uses the Waterfall method to make information systems in the designed application and Unified Modeling Language as the language used in analyzing needs. The application's design can explain in detail how this application will run, and the appearance of this application will be seen. So, the purpose of designing this application is expected to reduce stress levels by accommodating students who need counselling services. Based on the design of the Teman Berlindung that the author carried out, the author stated that there were many cases where college students had no space to tell stories or just a place to complain. Regarding mental health, Teman Berlindung is expected to be useful not only during the pandemic but can also be used in the long term and help students channel complaints to related parties. © 2022 IEEE.
ABSTRACT
The development of systems that help automate and manage people’s lives at different levels is what has driven the enormous growth that humanity has had. On the other hand, the COVID-19 pandemic has meant an unprecedented event that has marked a before and after for the entire world. As a result of this, companies and institutions implemented various technological tools to have a greater capacity to respond to the challenges generated by this and other similar potential diseases, however, the development of these technological tools is not always clear and accessible, particularly to small companies and to academic institutions. This paper describes the development of a web tool for the analysis of physical and mental health data of students using a COVID-19 screening tool, commonly used psychological questionnaires and inventories which help diagnose symptoms of mental illnesses, and a microblogging tool for further natural language processing. The results and conclusions reached at the end of this work are also presented. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Social media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post;posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.
ABSTRACT
In this paper, we dealt with the rapid development of client web applications (frontend) in a context where development frameworks are legion. In effect with the digital transformation due to the COVID-19 pandemic we are witnessing an ever-increasing demand of the application development in a relatively short time. To this is added the lack of skilled developers on constantly evolving technologies. We therefore offer a low-code platform for the automatic generation of client web applications, regardless of the platform or framework chosen. First, we defined an interface design methodology based on a portal. We then implemented our model driven architecture which consisted of defining a modeling and templating language, centered on user data, flexible enough to not only be used in various fields but also be easily used by a citizen developer. © 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.
ABSTRACT
Global pandemic situation has explored advanced technological applications over multivariate service sectors. Many people have lost their jobs during economic slowdown caused by lockdown to control spread of COVID-19. Mass-scale unemployment has resulted in socio-economic unstability leading to increase of domestic violence cases and death of married women who are forced to bring money in the form of dowry. These dowry death cases are related to Indian Penal Code Section (IPC) 304B. To assist legal professionals to understand these cases in a systematic manner, authors have proposed a natural language processing (NLP)-based model to extract major parameters from IPC 304B. Furthermore, to explain the static structure and its inter-relations, authors have also used Unified Modelling Language (UML)-based diagrams for better visualization of major parameters of dowry death cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The pandemic outbreak of COVID-19 has allowed the proliferation of an unprecedented amount of data that must be organized and connected in a way that allows its efficient management. Nevertheless, the speed at which all of this knowledge is being generated has highlighted the shortcomings of the research community in creating well-organized, standardized, and structured databases. Despite the efforts of the community to develop advanced integrative platforms such as CovidGraph, we have identified some limitations when using these solutions that we think are derived from the lack of a sound ontological schema to guide the collection, standardization, and integration of data. This work explores the advantages and disadvantages for the final user of building advanced information systems using a Model Driven Development approach to integrate heterogeneous and complex data using an ontological background as a basis. As a proof of concept, we built a database (CovProt) to integrate data about different aspects of SARS-CoV-2 using this approach, we analyzed the advantages and disadvantages of using this approach compared to CovidGraph by performing a set of queries in CovProt and CovidGraph, and finally, we compared the structure and redundancy of the retrieved data. Author