User Interface Research Articles

Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.

Best Practices for Developing Secure Front-End Web Applications

In the course of the work, a review of trends and best practices in creating user interfaces was conducted. To achieve this goal, attention was focused on the use of advanced technologies and design methods by interface developers, which have been permanently evolving in recent years. The methodology used to create intuitive and convenient interfaces is revealed. The main trends and innovations in the design of user interfaces were mentioned. The impact of user experience (UX) on the development process is indicated. Practical examples and cases of implementation of the latest approaches in real projects are given. It is also proposed to apply the selected best practices for creating a user interface in accordance with the characteristics of each of them, which will make the development of user interfaces easier and more efficient, based on which practical recommendations were provided for optimizing the development process. These guidelines can be used both at the departmental level of large corporations and by individual developers and freelancers, making them universal in terms of accessibility and comprehensibility. The received recommendations are the basis for the newest paradigm of development and optimization of interactive user interfaces, which can change the approach to the development of user interfaces in general. The very question of changing the paradigm of creating interactive user interfaces based on information about the interaction of applications with the user, not only in the framework of web development, but also in desktop development, is determined to be relevant for further research, which will be considered in future scientific works

Car Price Prediction Using Enhanced Technique

The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs. The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs.

A statistical evaluation of the sexual dimorphism of the acetabulum in an Iberian population.

Sex estimation is essential for human identification within bioarchaeological and medico-legal contexts. Amongst the sexually dimorphic skeletal elements commonly utilised for this purpose, the pelvis is usually preferred because of its direct relationship with reproduction. Furthermore, the posterior part of the innominate bone has proven to have better preservation within degraded contexts. With the aim of investigating the potential of the vertical acetabular diameter as a sex marker, 668 documented individuals from three different Iberian skeletal collections were randomly divided into training and test samples and eventually analysed using different statistical approaches. Two traditional (Discriminant Function Analysis and Logistic Regression Analysis) and four Machine learning methodologies (Support Vector Classification, Decision Tree Classification, k Nearest Neighbour Classification, and Neural Networks) were performed and compared. Amongst these statistical modalities, Machine Learning methodologies yielded better accuracy outcomes, with DTC garnering highest accuracy percentages of 83.59% and 89.85% with the sex-pooled and female samples, respectively. With males, ANN yielded highest accuracy percentage of 87.70%, when compared to other statistical approaches. Higher accuracy obtained with ML, along with its minimal statistical assumptions, warrant these approaches to be increasingly utilised for further investigations involving sex estimation and human identification. In this line, the creation of a statistical platform with easier user interface can render such robust statistical modalities accessible to researchers and practitioners, effectively maximising its practical use. Future investigations should attempt to achieve this goal, alongside examining the influence of factors such as age, on the obtained accuracy outcomes.

ANALYSIS AND SYSTEMATIZATION OF SOFTWARE INTERFACE DEVELOPMENT METHODS

The goal of this research is to analyze and systematize the methods used in software interface (SI) development, as well as to identify the optimal approaches and technologies for creating intuitive and user-friendly interfaces. In today's world, software interfaces play a crucial role in facilitating user interaction with software products, making their effective design essential. This study examines various SI design methodologies, including both classical and modern approaches, such as user-centered design (UCD), prototyping, usability testing, and adaptive design. Additionally, it explores principles of user interaction, including cognitive aspects and models of information perception, which contribute to the creation of more efficient and user-friendly interfaces.Special attention is given to contemporary technologies and tools, such as frameworks and libraries for interface development, as well as automation methods and the use of artificial intelligence in the SI design process. The research includes an analysis of successful SI implementations across various industries and reviews the latest trends in interface design. The findings are aimed at providing recommendations for selecting the most appropriate methods and technologies for SI development, depending on the project's specific requirements and the needs of end users.The key conclusions of this study may be valuable to software developers, user experience specialists, and interface designers in improving the quality of user interactions with software products. This work can also serve as a foundation for further research in the field of user interface development and optimization, contributing to the advancement of knowledge and practice in this area.

A study on the effectiveness of web applications for publishing translations of oriental literature

The article analyzes existing websites for publishing and reading translations of literary works, with an emphasis on identifying their main problems and shortcomings. Three types of such sites are described: universal, specialized, and oriental literature-oriented. Each type has its own peculiarities, but they all face similar problems, including a limited choice of content, difficulties in finding materials, and inconvenient user interface. The main problem with specialized websites created by a single team of translators (e.g., Silver Raven) is the limited number of available works, as well as the inconvenience and time-consuming search for the right job due to the need to visit the website of a new team of translators every time. The design is often mediocre and inconvenient for users due to a lack of experience in website development and limited resources. Some teams do not even have their own websites and work on third-party platforms, such as Telegram. As for universal sites such as Wattpad, the main problem is the lack of a specialized filter for translated works, which makes it difficult to find them. In addition, there are shortcomings in the design of the reading pages, where a lot of free space is not used and the text is difficult to read due to inappropriate size and formatting. Specialized sites for oriental literature, such as uAuthor and Baka, also have their problems. For example, uAuthor does not allow reading works directly on the site, which creates additional inconvenience for users. Baka, while providing such a possibility, has a complex filtering system and limited tools for adding and editing text. The article concludes that the existing platforms do not fully meet the needs of translators and readers of such a type of oriental literature as ranobe, which prompts the development of a new web application. The proposed solution is to develop our own web application specifically aimed at publishing and reading works. The main attention will be paid to the design of both the website and the text editor to ensure ease of use for translators and readers. Another solution is to create a web platform of several web services that are interconnected using the composite web service design and modeling method.

MR Image Fusion-Based Parotid Gland Tumor Detection.

The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in other diseases, the differentiation of tumor types involves several challenging, time-consuming, and laborious processes. In the study, Magnetic Resonance (MR) images of 114 patients with parotid gland tumors are used for training and testing purposes by Image Fusion (IF). After the Apparent Diffusion Coefficient (ADC), Contrast-enhanced T1-w (T1C-w), and T2-w sequences are cropped, IF (ADC, T1C-w), IF (ADC, T2-w), IF (T1C-w, T2-w), and IF (ADC, T1C-w, T2-w) datasets are obtained for different combinations of these sequences using a two-dimensional Discrete Wavelet Transform (DWT)-based fusion technique. For each of these four datasets, ResNet18, GoogLeNet, and DenseNet-201 architectures are trained separately, and thus, 12 models are obtained in the study. A Graphical User Interface (GUI) application that contains the most successful of these trained architectures for each data is also designed to support the users. The designed GUI application not only allows the fusing of different sequence images but also predicts whether the label of the fused image is benign or malignant. The results show that the DenseNet-201 models for IF (ADC, T1C-w), IF (ADC, T2-w), and IF (ADC, T1C-w, T2-w) are better than the others, with accuracies of 95.45%, 95.96%, and 92.93%, respectively. It is also noted in the study that the most successful model for IF (T1C-w, T2-w) is ResNet18, and its accuracy is equal to 94.95%.

Research on the Reform Strategy of Project based Experience Teaching in User Interface Design Courses

Research on the Reform Strategy of Project based Experience Teaching in User Interface Design Courses

Digital kiosks and inclusivity: a novel perspective on mobility hubs

Mobility hubs facilitate multimodal transport and have the potential to improve the accessibility and usability of new mobility services. However, in the context of increasing digitalisation, using mobility hubs requires digital literacy or even owning a smartphone. This constraint may result in the exclusion of current and potential users. Digital kiosks might prove to be a solution, as they can facilitate the use of the services found at mobility hubs. Nevertheless, knowledge of how digital kiosks may improve the experience of disadvantaged groups remains limited in the literature. As part of the SmartHubs project, a field test with a digital kiosk was conducted with 105 participants in Brussels (Belgium) and Rotterdam (The Netherlands) to investigate the intention to use it and its usability in the context of mobility hubs. This study adopted a mixed methods approach, combining participant observation and questionnaire surveys. Firstly, participants were asked to accomplish seven tasks with the digital kiosk while being observed by the researchers. Finally, assisted questionnaire surveys were conducted with the same participants, including close-ended, open-ended and socio-demographic questions. The results offer insights into the experience of the users of a digital kiosk in a mobility hub and the differences across specific social groups. These findings may be relevant for decision-makers and practitioners working in urban mobility on subjects such as mobility hubs and shared mobility, and for user interface developers concerned with the inclusivity of digital kiosks.

Analysis of Advanced Driver-Assistance Systems for Safe and Comfortable Driving of Motor Vehicles.

This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of this document describes the sensory detectors used in ADASs, including radars, cameras, LiDAR, and ultrasonics. The subsequent chapter presents the most popular driver assistance systems, including adaptive cruise control (ACC), blind spot detection (BSD), lane keeping systems (LDW/LKS), intelligent headlamp control (IHC), and emergency brake assist (EBA). A key element of this work is the evaluation of the effectiveness of these systems in terms of safety and driving comfort, employing a survey conducted among drivers. Data analysis illustrates how these systems are perceived and identified areas requiring improvements. Overall, the paper shows drivers' positive reception of ADASs, with most respondents confirming that these technologies increase their sense of safety and driving comfort. These systems prove to be particularly helpful in avoiding accidents and hazardous situations. However, there is a need for their further development, especially in terms of increasing their precision, reducing false alarms, and improving the user interface. ADASs significantly contribute to enhancing safety and driving comfort. Yet, they are still in development and require continuous optimization and driver education to fully harness their potential. Technological advancements are expected to make these systems even more effective and user-friendly.

Candidates resume analyzer using artificial intelligence

In the modern recruitment process, where the number of candidates and job vacancies constantly increases, automating resume analysis becomes critically important for efficient selection. This paper focuses on developing a system for automated resume analysis using artificial intelligence, specifically the OpenAI model (ChatGPT). The paper thoroughly examines the system's components, structure, and functional capabilities. The system consists of several key modules: the user interface ensures interaction with users, the Application Programming Interface Gateway facilitates data exchange between the interface and the server, and the authentication module controls system access, ensuring user security. The main part of the system's business logic is implemented on the server, which interacts with the natural language processing module, utilizing the ChatGPT model for processing resume text data. The use of natural language processing technologies allows the system to automatically extract and analyse key elements of resumes, such as education, work experience, skills, and certifications of candidates. This significantly speeds up the processing of large volumes of resumes, improving selection efficiency and accuracy. Data obtained during the analysis is stored in a PostgreSQL database, ensuring reliable storage and quick access for further processing. The paper also emphasizes the importance of integrating the system with other recruiting platforms and databases for more efficient management of the hiring process. Such integration allows for automatic synchronization of job vacancies and candidates, optimizing the search and selection process. Additionally, the paper highlights the key challenges associated with automating resume processing, such as the diversity of resume formats submitted by candidates, differences in data structure, and potential errors in automatically recognizing important information. Despite these challenges, the use of artificial intelligence and natural language processing methods significantly enhances the productivity and accuracy of recruitment processes. Thus, the developed automated resume analysis system based on Artificial Intelligence can become a significant tool in the modern candidate selection process. Its functionality not only enables quick and accurate resume analysis but also allows integration with other HR systems, simplifying the recruitment process, reducing data processing time, and improving selection accuracy.

Artificial Neural Network-Based Automated Finite Element Model Updating with an Integrated Graphical User Interface for Operational Modal Analysis of Structures

This paper presents an artificial neural network-based graphical user interface, designed to automate finite element model updating using data from operational modal analysis. The approach aims to reduce the uncertainties inherent in both the experimental data and the computational model. A key feature of this method is the application of a discrete wavelet transform-based approach for denoising OMA data. The graphical interface streamlines the FEMU process by employing neural networks to automatically optimize FEM inputs, allowing for real-time adjustments and continuous structural health monitoring under varying environmental and operational conditions. This approach was validated with OMA results, demonstrating its effectiveness in enhancing model accuracy and reliability. Additionally, the adaptability of this method makes it suitable for a wide range of structural types, and its potential integration with emerging technologies such as the Internet of Things further amplifies its relevance.

Incorporating a mobile application to support communication about HPV testing among women and professionals: barriers and facilitators from the perspective of health professionals in a middle- and low-income setting in Argentina.

The delivery of positive Human papillomavirus (HPV) test results can have a psychosocial impact and act as a barrier for women to continue the cervical cancer (CC) prevention process. A previous formative research based on a woman's perspective indicated that a mobile app could be an acceptable and appropriate tool to help women obtain information and reduce fears related to a positive result. Based on these findings, we developed an app-based intervention that would function as a support for professionals who offer the HPV test and communicate their results. We report data on the perceptions of healthcare providers regarding the barriers and facilitators to the incorporation, in a low and middle-income context. Qualitative study based on individual semi-structured interviews with health professionals. All the professionals (n =13) took HPV and Pap test samples and provided information on HPV testing, in the public health system of Ituzaingó, Greater Buenos Aires, Argentina. The themes explored were selected and analysed using domains and constructs of Consolidated Framework for Implementation Research (CFIR). Practitioners had a positive assessment of the intervention through most included constructs: adaptability, compatibility, complexity, relative advantage, belief in the validity and robustness of the intervention, innovation source and knowledge and beliefs about the intervention. However, some potential barriers were also identified including: adaptability, tensions for change, relative priority and leadership engagement. Practitioners conditioned the intervention's success to specific adjustments of the app (weight and interface usability), legitimmated institutions' support, and clear and sustained health authorities' commitment and directions. Health professionals had a positive assessment of implementing an app to support the HPV test communication and information provision process, although they conditioned its effectiveness to specific adjustments. The results allow us to identify and develop recommendations for the app to be implemented effectively and sustained over time. The findings of this study have important implications not only for Argentina, but also for other low and middle-income countries, given that the implementation could be adapted, with the aim of improving communication between patients and health institutions in the CC prevention process.

FullProfAPP: a graphical user interface for the streamlined automation of powder diffraction data analysis

FullProfAPP is a software tool for data processing, refinement and visualization of large collections of powder diffraction patterns. Featuring an intuitive graphical user interface, it seamlessly facilitates a variety of tasks. These include conducting full-profile phase searches, sequential and high-throughput Rietveld refinements, and managing background (and peak) detection. FullProfAPP also provides convenient interaction with crystallographic databases and supports the visualization and export of high-quality pixel and vector graphics depicting the refinement results, among other functionalities. FullProfAPP wraps around the refinement program FullProf [Rodríguez-Carvajal (1993), Physica B, 192, 55–69] and offers the flexibility of user-defined workflows by accessing and editing FullProf's input files and triggering its execution as necessary. FullProfAPP is distributed as open-source software and is presently available for Windows and Linux operating systems.

Cognitive Ergonomics in Intelligent Systems: Screen Analysis and Design Proposal for Reducing Mental Load in the Design of User Interfaces of Autonomous Vehicles

Cognitive Ergonomics in Intelligent Systems: Screen Analysis and Design Proposal for Reducing Mental Load in the Design of User Interfaces of Autonomous Vehicles

Navigating user discontent in wearable tech: an in-depth study of Fitbit reviews

ABSTRACT In the burgeoning field of wearable technology, Fitbit has emerged as an influential entity. Even the most influential companies, however, need to understand user dissatisfaction, particularly extracted from critical reviews, in order to enhance the user experience. This study analyzes a subset of 28,664 Fitbit consumer reviews from the Google Store, all giving it a rating of 1 out of 5 points, in order to identify the reasons for user dissatisfaction. Employing thematic analysis, sentiment analysis, and topic modelling, our research reveals prominent issues impacting user perception. Key findings include significant challenges with device syncing, app functionality, and customer service, alongside a spectrum of sentiments ranging from neutral to negative. Topic modelling further distinguishes various categories of user feedback, underscoring the diversity in user experiences and expectations. These insights suggest a pressing need for improvements in technical functionality, user interface design, and customer support. This study contributes to a deeper understanding of consumer experiences in wearable technology, offering valuable guidance for enhancing product design and customer engagement strategies in this dynamic market.

Development of Model and Graphical User Interface for Leak Localization in Crude Oil Pipeline

Pipelines provide the most efficient and cost-effective means for fluid transport but the challenge posed by leaks has substantially increased the risks and hazards in pipeline fluid transportation. The development of efficient leak detection system pays off in quicker leak detection and localization, leading to quicker responses and remediation works by the pipeline emergency response team. This would ultimately bring about less severity of the pipeline leak in terms of financial losses, human and environmental consequences. Steady state modeling of crude oil pipeline flow with attempt to determine and localize leak has been achieved in this study. Mathematical models have been developed to detect and localize leaks in crude oil pipeline during leak occurrence. Leak detection was modeled using the conservation of mass equation while leak localization was modeled by modifying the Darcy-Weisbach pipeline liquid flow equations and utilizing the Swamee-Jain friction factor correlation. Equations for flowrate and pressure drop along the pipeline were developed for two cases: a case where there is no leak in the pipeline and a case where there is leak in the pipeline. Leak localization equation was determined by equating the fluid flow equation when there is no leak and when there is leak. The model was structured and simulated in Matlab software with the model tested for four field cases. The results from the simulation revealed swift leak detection, accurate localization of the leak, and determination of the pressure at the point of leak. Experimentally determined results from actual field measurements of leak incidences were used to validate the results. The result determined from the model developed proved accurate with only an average error of 0.216 miles.

AudioMove: Applying the Spatial Audio to Multi-Directional Limb Exercise Guidance

Guiding users with limb exercise can assist in muscle training or physical recovery. However, traditional vision-based methods often require multiple camera angles to help users understand the motions and require them to be within the range of the screen. Therefore, we propose a non-visual system that can guide users with multiple-directional limb motions utilizing spatial audio, AudioMove , with commercial-off-the-shelf (COTS) devices (i.e., smartphones and earphones). The proposed system addresses the challenge of conveying directional information encompassing multiple planes in real-time. We conduct a mixed-method user study to evaluate the effectiveness of the system with three methods combining motion data with spatial audio perception. Additionally, a user interface is built to collect users' comments. The results conclude that spatial audio guidance could create a natural, pervasive, and non-visual exercise training solution in daily life.

Executing realistic earthquake simulations in unreal engine with material calibration

Earthquakes significantly impact societies and economies, underscoring the need for effective search and rescue strategies. As AI and robotics increasingly support these efforts, the demand for high-fidelity, real-time simulation environments for training has become pressing. Earthquake simulation can be considered as a complex system. Traditional simulation methods, which primarily focus on computing intricate factors for single buildings or simplified architectural agglomerations, often fall short in providing realistic visuals and real-time structural damage assessments for urban environments. To address this deficiency, we introduce a real-time, high visual fidelity earthquake simulation platform based on the Chaos Physics System in Unreal Engine, specifically designed to simulate the damage to urban buildings. Initially, we use a genetic algorithm to calibrate material simulation parameters from Ansys into the Unreal Engine’s fracture system, based on real-world test standards. This alignment ensures the similarity of results between the two systems while achieving real-time capabilities. Additionally, by integrating real earthquake waveform data, we improve the simulation’s authenticity, ensuring it accurately reflects historical events. All functionalities are integrated into a visual user interface, enabling zero-code operation, which facilitates testing and further development by cross-disciplinary users. We verify the platform’s effectiveness through three AI-based tasks: similarity detection, path planning, and image segmentation. This paper builds upon the preliminary earthquake simulation study we presented at IMET 2023, with significant enhancements, including improvements to the material calibration workflow and the method for binding building foundations.

Investigation on compressive strength and splitting tensile strength of manufactured sand concrete: Machine learning prediction and experimental verification

Due to experimental constraints, traditional strength prediction models for manufactured sand concrete (MSC) exhibit significant limitations and have a narrow range of applicability. To overcome these limitations and enhance prediction accuracy, this paper, based on the widespread application of machine learning in concrete performance research, employed four algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) to develop highly accurate and strongly generalizable models for predicting the compressive strength (CS) and splitting tensile strength (STS) ofMSC. The predictive performance of each model was evaluated, the influence laws of various factors were analyzed, and finally, macroscopic experiments were conducted to validate the models' prediction accuracy and generalization ability. The development of these models and the analysis of the influence laws of various factors on the strength of MSC provide a valuable reference for the mix design. The conclusions are as follows: (1) The XGB model demonstrates the highest prediction accuracy and strongest generalization ability for both the CS and STS of MSC, achieving R2 values of 0.98 and 0.94 on the testing set, respectively. (2) The CS and STS of MSC are primarily influenced by four factors: water-binder ratio (W/B), curing age (AGE), cement (C), and coarse aggregate (RI≥0.08869, RI: relative importance), showing a positive correlation with AGE and C, and a negative correlation with W/B. As the fly ash content, stone powder content, maximum particle size of coarse aggregate, and sand ratio increase, the CS and STS initially increase and then decrease. Appropriately increasing the fineness modulus of manufactured sand is beneficial to both CS and STS, while the addition of slag is beneficial to CS and harmful to STS. (3) The prediction accuracy and generalization ability of the XGB model are verified through three sets of macroscopic experiments: C30, C35, and C40. The relative errors of each set's predictions are less than 8 %. (4) A graphical user interface is constructed based on the XGB model, enhancing its practicality.