Prototype Web Application Research Articles

Computing Hive Plots: A Combinatorial Framework

Hive plots are a graph visualization style placing vertices on a set of radial axes emanating from a common center and drawing edges as smooth curves connecting their respective endpoints. In previous work on hive plots, assignment to an axis and vertex positions on each axis were determined based on selected vertex attributes and the order of axes was prespecified. Here, we present a new framework focusing on combinatorial aspects of these drawings to extend the original hive plot idea and optimize visual properties such as the total edge length and the number of edge crossings in the resulting hive plots. Our framework comprises three steps: (1) partition the vertices into multiple groups, each corresponding to an axis of the hive plot; (2) optimize the cyclic axis order to bring more strongly connected groups near each other; (3) optimize the vertex ordering on each axis to minimize edge crossings. Each of the three steps is related to a well-studied, but NP-complete computational problem. We combine and adapt suitable exact and heuristic algorithmic approaches, implement them as an instantiation of our framework, and show in a case study how it can be applied in a practical setting. Furthermore, we conduct computational experiments to gain further insights regarding the algorithmic choices of our framework. The code of the implementation and a prototype web application can be found on OSF.

Preventing type 2 diabetes in Portugal: a novel person-centered digital intervention

Abstract Introduction Diabetes, particularly type 2 diabetes (T2D), stands as one of the leading noncommunicable diseases worldwide. In Portugal, the prevalence of diabetes has surged, with T2D affecting 14% of adults in 2021. Its repercussions extend beyond individual health, deeply impacting familial, social, and community dynamics, presenting a significant public health challenge globally. Objectives The VA|PREVENTION project (https://doi.org/10.54499/2022.02969.PTDC) aims to assess the (cost-) effectiveness and implementation of an innovative person-centered digital intervention to prevent T2D among high-risk adults. Methodology A comprehensive approach encompasses the Behavior Change Wheel for intervention design, scalability principles, the WHO guidelines for digital health interventions, co-design, and the ISO framework for usability assessment. Evaluation will be pursued through a type 1 hybrid effectiveness-implementation study (randomized controlled trial), supplemented by qualitative and economic studies. Results The web application prototype features Ema, an automated virtual human coach (VHC), educational menus and a dashboard (version 1.0). Co-design included 2 workshops with 11 representatives of end-users and health professionals. Ema uses standardized techniques from an ontology to support behavior change, based on an initial assessment of low-level target behaviors (e.g. walking, fruit consumption, and reducing red meat intake), and barriers to performing these behaviors. Ema communicates in a helpful-cooperative style using a synthetic voice in European Portuguese, with its speech also appearing as subtitles on the interface. Users interact with Ema by pressing buttons or entering data, such as the daily number of steps. Ethics approval was granted for usability assessment with end-users. Conclusions The project is expected to yield health, scientific, technological, and policy impacts, powered by an exploitation and sustainability roadmap. Key messages • Innovative type 2 diabetes prevention through a scalable web app with a virtual human coach. • Comprehensive and robust approach for developing and evaluating the digital behavior change intervention.

OWASP A03 Injection Vulnerability in Web Application Development

Web applications are crucial for businesses and individuals by providing efficient communication, collaboration, and access to services and information via browsers, boosting connectedness, productivity, and creativity in the digital era. Insecure web applications pose risks of data breaches, malware, and unauthorized access which jeopardize user privacy, trust, and organizational security. Web developers must be knowledgeable and prepared to deal with common vulnerabilities in web applications. A prototype web application (https://webriska3.tech) with lesson and editor module is developed to train web developers on the Open Web Application Security Project (OWASP) Top Ten security risks, focusing on A03 - Injection vulnerability. OWASP A03 Injection vulnerability is one of the most common vulnerabilities that is at the heart of any database-driven web applications. Evaluation on the prototype in improvement knowledge on A03 – Injection vulnerability, testers are recruited to complete two coding tasks in laboratory environment. 80% of testers mastered Output escaping/encoding defensive technique while Prepared statement/Parameterized Query defensive technique is the hardest to master. The prototype obtained average System Usability Scale (SUS) score of 57 that is below average, indicating issues with the prototype interface. This work showed promising results of increase understanding on A03 Injection vulnerability and implementation skills to protect web application against attack and exploitations.

Classification of suicidal ideation severity from Twitter messages using machine learning

Depression has become a major mental health problem in Thailand and can lead to suicidal ideation. As suicidal ideation may vary in intensity and lead to suicide attempts, early detection of suicidal ideation severity should be implemented. This research presents text classification models for the prediction of suicidal ideation severity. A dataset of Twitter messages in Thai was used to develop several classification models. A web application prototype was also developed to predict suicidal ideation severity and introduce self-therapy based on Cognitive Behavioral Therapy to its users for managing negative automatic thoughts. The application prototype received satisfactory feedback during the user experience assessment. The results of this research highlight the importance and need for socio-technical systems to help with early suicidal ideation detection and early therapy in the social environment where mental health support is inadequate.

Internet of things-based smart medical waste management system

Smart medical waste management is significant during the ongoing COVID-19 pandemic. This research presents a smart medical waste management system with real-time monitoring of medical waste data and an end-user application. The functional and technical design of a smart trolley by integrating various sensors for authenticated automatic lid opening and closing, waste level, weight, and storage time monitoring are discussed. Subsequently, the implementation of a cloud server and web application that provide automatic notifications to stakeholders are also presented. The users can monitor the entire process to take actions and approvals whenever necessary. The experiments are conducted to test the functionalities of the smart trolley and web application prototype. The results showed that the sensing system is efficient and avoids manual checking by sending necessary notifications. Healthcare institutions can adopt the proposed system for medical waste transfer to ensure process transparency, worker safety, and environmental cleanliness.

A web-based mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity

The recent ‘Mpox’ outbreak, formerly known as ‘Monkeypox’, has been a significant public health concern and spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods excel at learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the “Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)” as a follow-up to the previously released open-access dataset, one of the first to contain mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. We leverage transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images. To reduce the impact of racial bias, we employ a color space data augmentation method to increase skin color variability during training and present a rigorous assessment of the technique’s effectiveness in skin lesion classification; we achieved the best overall accuracy of 83.59±2.11%. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient. We believe that the presented tool can be of significant impact in screening of mpox in case of a future outbreak.

Utilizing Design Thinking and User Experience Research for Effective Agricultural Labor Recruitment Web Application Design

The agricultural sector faces a significant challenge in recruiting a skilled workforce, with both landowners and tenant farmers struggling to find new laborers. Factors such as inadequate rural infrastructure and facilities, coupled with limited technology adoption, have diminished the appeal of working in the agricultural sector. Consequently, agricultural productivity has declined due to a shortage of adequate labor. To address this issue, a prototype web application named “Partner Tani” was designed to facilitate labor recruitment for farmers. The application design process employed the Design Thinking methodology and the User Experience Questionnaire (UEQ) to thoroughly understand user requirements. The design outcomes encompass both user interface and administrator interface components, developed using Figma. The prototype application features functionalities such as login, account registration, profile management, job posting and search, and complaint reporting. Evaluation using the UEQ revealed that the prototype application achieved exceptional scores across all user experience dimensions: Attractiveness (average score: 2,11), Perspicuity (average score: 1,42), Efficiency (average score: 2,33), Dependability (average score: 1,50), Stimulation (average score: 1,75), and Novelty (average score: 1,67). Average scores for Attractiveness, Efficiency, Stimulation, and Novelty fell within the “Excellent” range, while Perspicuity and Dependability scores were above average. These findings indicate that the “Partner Tani” application prototype holds promise in delivering a satisfying and efficient user experience for agricultural labor recruitment.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society’s (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

Building a Vision Transformer-Based Damage Severity Classifier with Ground-Level Imagery of Homes Affected by California Wildfires

The increase in both the frequency and magnitude of natural disasters, coupled with recent advancements in artificial intelligence, has introduced prospects for investigating the potential of new technologies to facilitate disaster response processes. Preliminary Damage Assessment (PDA), a labor-intensive procedure necessitating manual examination of residential structures to ascertain post-disaster damage severity, stands to significantly benefit from the integration of computer vision-based classification algorithms, promising efficiency gains and heightened accuracy. Our paper proposes a Vision Transformer (ViT)-based model for classifying damage severity, achieving an accuracy rate of 95%. Notably, our model, trained on a repository of over 18,000 ground-level images of homes with damage severity annotated by damage assessment professionals during the 2020–2022 California wildfires, represents a novel application of ViT technology within this domain. Furthermore, we have open sourced this dataset—the first of its kind and scale—to be used by the research community. Additionally, we have developed a publicly accessible web application prototype built on this classification algorithm, which we have demonstrated to disaster management practitioners and received feedback on. Hence, our contribution to the literature encompasses the provision of a novel imagery dataset, an applied framework from field professionals, and a damage severity classification model with high accuracy.

SIDALUT Prototype Application for Patient Dental and Oral Health Care

Background Dental and oral therapists are required to carry out dental and oral health care services. The results of the researcher's observations showed that there were still obstacles in the health service to implement a good care process. Problems include not understanding or forgetting how to write a diagnosis statement according to the concept of dental hygienist darby & walsh, not having a dental hygienist darby & walsh textbook as a guideline, and it is impractical to use the textbook because of the thick pages of the book and in a foreign language. Therefore, a more effective and efficient tool is needed as a reference / guideline by students and TGM at the health center, one of which is in the form of a web application prototype. in this study the researcher intends to develop a dental and oral health care application which is the main task of dental and oral therapists. The initial focus of the case is for pediatric patient services at the health center. The purpose of this study is to determine the prototype form of a web-based oral health care application as a guideline for pediatric patient services at the health center. This type of research method is research and development (R & D). Initial research was carried out with descriptive qualitative. The stages of the activity are preparing the material database, making web-based applications with the help of the IT team, then reviewing the material by experts. The results of the research Prototype of oral health care applications can be accessed online based on a website both through laptops, PC, tablets and handphone. The application contains patient data menu features, assessment, diagnosis, planning, implementation, evaluation, and documentation (printing). Expert review shows the majority of answers strongly agree and agree to 9 indicators of material suitability Conclusion A prototype of oral health care application for pediatric cases at the health center has been created under the name SIDALUT (oral health care documentation system).

An explanation-oriented inquiry dialogue game for expert collaborative recommendations

This work presents a requirement analysis for collaborative dialogues among medical experts and an inquiry dialogue game based on this analysis for incorporating explainability into multiagent system design. The game allows experts with different knowledge bases to collaboratively make recommendations while generating rich traces of the reasoning process through combining explanation-based illocutionary forces in an inquiry dialogue. The dialogue game was implemented as a prototype web-application and evaluated against the specification through a formative user study. The user study confirms that the dialogue game meets the needs for collaboration among medical experts. It also provides insights on the real-life value of dialogue-based communication tools for the medical community.

Clinical and Genomic-Based Decision Support System to Define the Optimal Timing of Allogeneic Hematopoietic Stem Cell Transplantation in Patients with Myelodysplastic Syndromes (MDS)

Purpose. Allogeneic hematopoietic stem cell transplantation (HSCT) is the only potentially curative treatment for patients with myelodysplastic syndromes (MDS). Several issues must be considered when evaluating the benefits and risks of HSCT for patients with MDS, with the timing of transplantation during the disease course being a crucial question. Recently the integration of genomic screening (by Molecular International Prognostic Scoring System, IPSS-M) into patient's assessment has resulted into a significant improvement in predicting clinical outcomes with respect to the conventional prognostic score (Revised IPSS, IPSS-R), including better stratification of post-HSCT outcome. Here, we aimed to develop and validate a Decision Support System to define the optimal timing of HSCT in MDS patients based on clinical and genomic information as provided by IPSS-M vs conventional IPSS-R. Patients and methods. We studied a retrospective, international cohort of 8,326 patients with MDS for whom complete set of clinical and molecular information was available. Patients were stratified into a learning and validation cohorts (65%, n= 5411 vs. 35%, n=2915). The decision-strategy analysis was divided into two parts. First, we developed a multi-state model to estimate transition hazards between different disease strata. Age and IPSS-R/IPSS-M were used as explanatory variables. The time elapsed between the diagnosis of MDS and HSCT was also considered as covariate in the models, which were further adjusted for disease-modifying therapy (if any). Next, we developed a Semi-Markov multi-state decision model based on microsimulation to compare various transplantation strategies according to the timing of the procedure, conditionally on covariates of interest. The microsimulation strategy simulates a hypothetical randomized clinical trial where subjects are randomized to receive HSCT at different time points. Results were used to estimate the average survival time over an 8-year time horizon (Restricted Mean Survival Time, RMST) for each combination of covariates, and then determine the optimal transplantation policy by compared the different strategies. Results. When considering patient life expectancy for patients who received a HSCT (Restricted Mean Survival Time, RMST) in the learning cohort, in both scenarios of IPSS-R and IPSS-M based transplantation policies, early disease stage was associated with a better clinical outcome. Indeed, life expectancy after transplantation was higher for younger vs elderly patients ( Figure 1 and 2). Under an IPSS-R based policy, patients with low-risk disease clearly benefit from a delayed transplantation policy across all age groups, while in patients belonging to intermediate, high/very high risk categories immediate transplantation was associated with a prolonged life expectancy, regardless of age. ( Figure 1) Considering an IPSS-M based policy, patients with either low and moderate-low risk benefit from a delayed transplantation policy (across all age groups), while in those belonging to moderate-high, high and very high risk categories immediate transplantation was associated with a prolonged life expectancy (across all age groups, Figure 2). Importantly, all these results were confirmed in the validation cohort, thus providing evidence for their reliability and generalizability. Modelling decision analysis on IPSS-M vs. original IPSS-R changed transplantation policy in a significant proportion of patients, resulting in a gain-in-life expectancy under an IPSS-M based policy across all age groups (P<0.001). Specifically, 19% of candidates to be immediately transplanted under an IPSS-R based policy would benefit from a delayed strategy under an IPSS-M based policy, while 21% of candidates to delayed transplantation under an IPSS-R based policy, would benefit from immediate HSCT under an IPSS-M based policy. We have created a prototype web application for the MDS Transplantation Decision Support System, that allows clinicians to define the best timing for HSCT starting from individual patient demographics, IPSS-R, and IPSS-M information. Conclusion. We provided evidence for the clinical relevance of including genomic features into the transplantation decision making process, specifically regarding the optimal timing of HSCT, allowing personalizing of the hazards and effectiveness of HSCT in patients with MDS.

An ontology to represent firefighters data requirements during building fire emergencies

Firefighters require accurate and timely information regarding a building and its environment to perform their duty safely and successfully during a fire emergency. However, due to the chaotic nature of building fires, firefighters often receive erroneous, conflicting, or delayed information that can affect the outcome of an emergency. In this paper, we propose a solution in the form of an ontology that defines building and environmental data that is needed by firefighters during a building fire emergency. The ontology development followed the METHONTOLOGY method and was implemented using the web ontology language (OWL) in Protégé 5.5.0. Built-in reasoners in Protégé and an ontology pitfall scanning tool were employed to verify the structure and consistency of the new ontology. To validate the ontology efficacy, we developed a prototype web application to represent and visualise relevant information based on the ontology and used that as a basis for conducting interviews with firefighters. Finally, a specification document that describes the ontology was created and published online. The proposed ontology can be a basis for developing intelligent tools and systems that support firefighters.

Tracking and viewing modifications in digital calibration certificates

Trust in current and historical calibration data is crucial. The recently proposed XML schema for digital calibration certificates (DCCs) provides machine-readability and a common exchange format to enhance this trust. We present a prototype web application developed in the programming language Links for storing and displaying a DCC using a relational database. In particular, we leverage the temporal database features that Links provides to capture different versions of a certificate and inspect differences between versions. The prototype is the starting point for developing software to support DCCs and the data with which they are populated and has underlined that DCCs are the tip of the iceberg in automating the management of digital calibration data, activity that includes data provenance and tracking of modifications.

MONKEY POX DISEASE DETECTING SYSTEM USING DEEP LEARNING

Due to the recent monkeypox outbreak's quick spread to more than 40 nations outside of Africa, public health is now at risk. It is difficult to diagnose clinically in its early stages since it resembles both chickenpox and measles. Computer-assisted monkey pox lesion detection may be helpful for surveillance and quick identification of suspected cases when confirmatory Polymerase Chain Reaction (PCR) assays are not accessible. Under the condition there are enough training examples available, deep learning techniques have been demonstrated to be useful in the automated detection of skin lesions. Such databases are not currently accessible for the monkeypox condition. The majority of the photographs came from news websites, blogs, and publicly available case reports. A 3- fold cross-validation experiment is set up, and the sample size is increased through data augmentation. The second phase is categorizing diseases like monkeypox using several pre-trained deep-learning models including VGG-16, CNN, and Mobile Net. It is an ensemble of the three models created. An online method for screening for monkeypox is also being developed as a prototype web application. While the early results on this tiny dataset are promising. Keywords: Monkeypox image dataset, Skin disease classification, Transfer Learning, image processing, CNN, Mobile Net & VGG19.

Understanding processes on digital building permits – a case study in South Tyrol

ABSTRACT Building permitting plays a significant role in life cycles of buildings because without a building permit a legal construction fails. However, digitalization in building permit, authorities is rare in a global context and issuing a building permit is primarily a manual and time-consuming process. In current research approaches on digital building permits, the focus lays on the digitalization of building-related regulation checks. Even if the approaches cover an important step in the building permit process, other significant steps such as administrative processes are neglected. In preliminary research, a building permit process model for structuring action-oriented elements was developed and transferred and applied to a Building Information Modelling (BIM)-oriented and process-based web application prototype, which serves as a basis. An empirical study – using qualitative expert interviews in a building permit authority in South Tyrol (Italy) – was conducted to simulate a BIM-oriented building permit review as well as to investigate the building permit processes thoroughly. The results show a classification of the information needed for digital tools supporting the building permit process, building permit processes of the case study authority, and the digital tools used. In the discussion, knowledge gained from a comparison between other building permit processes is presented alongside considerations for follow-up research.

A Secure approach for point-to-point communication in a real time environment using a WebRtc framework

With the progress of the internet from web 2.0 to web 3.0, the increased use of decentralized applications has emerged. Popularized by BlockChain but not limited to decentralized finance, decentralized applications have vast applications with regards to security, storage, and delivery of content over the web. In this paper we outlines the development of a web application prototype using JavaScript programming language, JavaScript based libraries such as BugOut and PeerJS, and the WebRTC (Web Real Time Communication) framework. We have also discuss the brief comparisons between the existing centralized applications and our proposed model. An essential component of this prototype is outlined via the use of P2P networking, which is the backbone of decentralization.

BIPMIN: A Gamified Framework for Process Modeling Education

Business Process Modeling is a skill that is becoming sought after for computer engineers, with Business Process Modeling Notation (BPMN) being one example of the tools used in modeling activities. Students of the Master of Computer Engineering course at Politecnico di Torino learn about BPMN in dedicated courses but often underperform on BPMN-related exercises due to difficulties understanding how to model processes. In recent years, there has been a surge of studies that employ gamification (using game elements in non-recreative contexts to obtain benefits) as a tool in Computer Engineering education to increase students’ engagement with the learning process. This study aims to use the principles of gamification to design a supplementary learning tool for the teaching of information systems technology. In particular, to improve student understanding and use of BPMN diagrams. This study also analyzes the usability and motivation of the participants in using different game elements in increasing student motivation and performance. As part of the study, a prototype web application was developed, which implemented three different designs, each incorporating different game elements relating to either progress, competition, or rewards. An evaluation was then conducted on the prototype to evaluate the performance of the practitioners in performing BPMN modeling tasks with the gamified tool, the usability of the proposed mechanics and the enjoyment of the individual game mechanics that were implemented. With the usage of the gamified tool, the users of the experimental sample were able to complete BPMN modeling tasks with performances compatible with estimates made through expert judgement (i.e., gamification had no negative effect on performance), and were motivated to check the correctness of their models many times during the task execution. The system was evaluated as highly usable (85.8 System Usability Score); the most enjoyed game elements were rewards, levels, progress bars and aesthetics.

Induced Emotion-Based Music Recommendation through Reinforcement Learning

Music is widely used for mood and emotion regulation in our daily life. As a result, many research works on music information retrieval and affective human-computer interaction have been proposed to model the relationships between emotion and music. However, most of these works focus on applications in a context-sensitive recommendation that considers the listener’s emotional state, but few results have been obtained in studying systems for inducing future emotional states. This paper proposes Moodify, a novel music recommendation system based on reinforcement learning (RL) capable of inducing emotions in the user to support the interaction process in several usage scenarios (e.g., games, movies, smart spaces). Given a target emotional state, and starting from the assumption that an emotional state is entirely determined by a sequence of recently played music tracks, the proposed RL method is designed to learn how to select the list of music pieces that better “match” the target emotional state. Differently from previous works in the literature, the system is conceived to induce an emotional state starting from a current emotion instead of capturing the current emotion and suggesting certain songs that are thought to be suitable for that mood. We have deployed Moodify as a prototype web application, named MoodifyWeb. Finally, we enrolled 40 people to experiment MoodifyWeb, employing one million music playlists from the Spotify platform. This preliminary evaluation study aimed to analyze MoodifyWeb’s effectiveness and overall user satisfaction. The results showed a highly rated user satisfaction, system responsiveness, and appropriateness of the recommendation (up to 4.30, 4.45, and 4.75 on a 5-point Likert, respectively) and that such recommendations were better than they thought before using MoodifyWeb (6.45 on a 7-point Likert).

Digitalisation of the building permit process - a case study in Italy

Building permitting plays a significant role in a building’s life cycle because without a building permit a legal construction fails. However, digitalization in building permit authorities is rare in global context and issuing a building permit is still mostly a manual and time-consuming process. Moreover, a gentle instrument that authorities do not feel overwhelmed with is missing so far. Hence, a BIM-oriented and process-based web application prototype was developed in preliminary research. This research aims at validating and testing the BIM-oriented and process-based web application prototype by analysing the building permit processes in relation to digitalization and promotion of BIM usage by means of a specific case study. For this purpose, an empirical study using qualitative expert interviews in a building permit authority in South Tyrol (Italy) is conducted. The results shed light on the level of acceptance and plausibility of the web application prototype, as well as the ability of the prototype to assist building permit authorities within their review process.