Software Applications Research Articles

Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

Pelatihan Penggunaan Publish or Perish untuk Mahasiswa Pendidikan Matematika Strata 1

The world of writing scientific papers cannot be separated from students' routines. As part of an educated society, the writing process is a means for students to increase their insight, sensitivity, and soft skills before entering the world of work. However, not all students think that writing scientific papers, especially articles, is an easy job. Meanwhile, recently, there have been a lot of software and web applications that can help students compose articles easily, one of which is Publish or Perish. It is hoped that the socialization activity regarding the use of Publish or Perish can increase the enthusiasm and attraction of students to be more active in writing scientific papers. The workshop was carried out in two steps, starting with introducing the software and applying search results to several types of articles, namely bibliometric analysis and systematic literature review. This activity succeeded in motivating participants to write articles and enter them into a competition.

Developing neutral linguistic resources for the implementation of an automatic transformational analyzer

Bakground The linguistic pursuit of describing natural languages stands as a commendable scientific endeavor, regardless of immediate software application prospects. It transcends mere documentation of possible sentences to establish connections between sentences derived from transformations. Methods Amid the dominance of Large Language Models (LLMs) in research and technology, which offer intriguing advancements in text generation, the approaches presented in this article confront challenges like opacity, limited human intervention, and adaptation difficulties inherent in LLMs. The alternative or complementary approaches highlighted here focus on the theoretical and methodological challenges of describing linguistic transformations and are firmly rooted in the field of linguistics, the science of language. We propose two solutions to address the problem of language transformations: (i) the procedural approach, which involves representing each transformation with a transducer, and (ii) the declarative method, which entails capturing all potential transformations in a single neutral grammar. Results These approaches simplify the generation of complex sentences from elementary ones and vice versa. Conclusion This work has benefited from research exchanges within the Multi3Generation COST Action (CA18231), and the resources produced can contribute to enhancing any language generation system.

Evaluasi Penerapan Software Akuntansi Accurate dalam Penyusunan Laporan Laba Rugi

Accurate accounting software is a business application that helps SMEs manage their business easily. Processing financial data into financial reports needed to help plan the company's future. The profit and loss report is a financial report that reports on the company's operational activities by calculating income and expenses for one period and then the profit and loss report can be determined. The profit and loss report can be used as a way to assess the company's level of success in a certain period and also to understand the company's development. This research aims to determine the evaluation of the application of accurate accounting software in preparing profit and loss reports. The type of research used is qualitative research, with a sampling technique, purposive sampling. The research method used is User Acceptance Testing, with a type of testing, Black Box Testing. The results of this research show that accurate accounting software is effective in preparing profit and loss reports, easy to understand and easy to detect errors. This error can be detected by reviewing the transaction data that has been input. With the help of Standard Operating Procedures, companies can minimize errors, make control easier and enable to run company smoothly

Mining software insights: uncovering the frequently occurring issues in low-rating software applications.

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

Factors associated with blood donation among college and university students in Wuhan, China: structural equation model

BackgroundCollege and university students were an important population group of blood donors, especially in the current situation of tight blood supply. This study aimed to investigate the current status and determinants of blood donation among this population group in Wuhan using a structural equation model.MethodsWe conducted a cross-sectional study involving 12 colleges and universities in Wuhan, China, including 5168 students. Sociodemographic characteristics, health status, knowledge about blood donation, and attitude toward blood donation were treated as latent variables, with blood donation as the observed variable. Confirmatory factor analysis was conducted using the Mplus 8.0 statistical software application, followed by the establishment of a structural equation model to assess the relationships that exist between these variables.ResultsThe overall blood donation rate among college and university students was 24.71%. The established model indicated that sociodemographic characteristics, health status, knowledge about blood donation, and attitude toward blood donation showed significant positive effects (0.135, 0.056, 0.321, and 0.389, respectively) on blood donation, among them, the direct effects were 0.076, -0.110, 0.143, and 0.389, respectively (P < 0.01). Additionally, sociodemographic characteristics, health status, and knowledge about blood donation had indirectly affected blood donation through the mediating effect of attitude towards blood donation. Their effects on attitude towards blood donation were 0.099, 0.243, and 0.468, respectively. (P < 0.01). The model could explain explained 22.22% of the variance in blood donation behavior among college and university students in Wuhan.ConclusionBlood donation among college and university students in Wuhan was associated with sociodemographic characteristics, health status, knowledge about blood donation, and attitude towards blood donation, with attitude being the primary influencing factor. Tailored recruitment strategies for blood donation among students should prioritize initiatives aimed at enhancing knowledge about blood donation and fostering positive attitudes toward it.

Artificial intelligence literacy: a proposed faceted taxonomy

PurposeThe purpose of this paper is to propose a taxonomy of artificial intelligence (AI) literacy to support AI literacy education and research.Design/methodology/approachThis study makes use of the facet analysis technique and draws upon various sources of data and information to develop a taxonomy of AI literacy. The research consists of the following key steps: a comprehensive review of the literature published on AI literacy research, an examination of well-known AI classification schemes and taxonomies, a review of prior research on data/information/digital literacy research and a qualitative and quantitative analysis of 1,031 metadata records on AI literacy publications. The KH Coder 3 software application was used to analyse metadata records from the Scopus multidisciplinary database.FindingsA new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet.Research limitations/implicationsThe proposed taxonomy may serve as a conceptual AI literacy framework to support the critical understanding, use, application and examination of AI-enhanced tools and technologies in various educational and organizational contexts.Practical implicationsThe proposed taxonomy provides a knowledge organization and knowledge mapping structure to support curriculum development and the organization of digital information.Social implicationsThe proposed taxonomy provides a cross-disciplinary perspective of AI literacy. It can be used, adapted, modified or enhanced to accommodate education and learning opportunities and curricula in different domains, disciplines and subject areas.Originality/valueThe proposed AI literacy taxonomy offers a new and original conceptual framework that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research.

Multi-level authentication for security in cloud using improved quantum key distribution

ABSTRACT Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models

Enhancing recruitment efficiency: An advanced Applicant Tracking System (ATS)

The Applicant Tracking System (ATS), also known as a talent management system or job applicant tracking system, is a software application designed to facilitate more efficient recruitment processes for companies or selection agencies. The objective of ATS is to streamline various aspects of the recruiting process, from receiving applications to hiring employees and effectively manage recruitment needs electronically. Methodologies such as NLP and KNN models are used for automated resume parsing and classifying the resume from unstructured format to structured format. The final results found significant improvement in performance of functionalities such as candidate screening, applicant testing, interview scheduling, managing the hiring process, reference checks, and completing new-hire paperwork.

PaIRS-UniNa: A Robust And Accurate Free Tool For Digital Particle Image Velocimetry And Optical Camera Calibration

This contribution presents PaIRS-UniNa (PArticle Image Reconstruction Software – University of Naples “Federico II”), a free software application designed to perform digital particle image velocimetry (PIV). PaIRS-UniNa relies on a powerful C library designed for speed, robustness and accuracy, and a user-friendly graphical interface (Figure 1) running in Python environment on all operating systems, including Windows, Linux and MacOS. At the current stage, it features modules to perform two-component (2C) PIV, stereoscopic PIV and optical camera calibration for multi-camera systems. The processing of PIV and stereoscopic PIV data is based on an iterative image deformation method, which can be customized by the user varying different parameters in such a way to find the best compromise between accuracy and computational speed. These parameters include the size, shape and number of interrogation windows, the number of process iterations and the type of interpolating functions for both image deformation and displacement interpolation (such as bilinear, bicubic, methods based on simplexes or the FFT shift theorem and B-splines of varying orders). Additionally, it is possible to select a range of weighting windows for cross-correlation estimation and velocity field filtering, including rectangular, Blackman, Gaussian, Harris, Hann windows, among others, and several vector validation strategies for outlier detection and replacement. Such a level of customization enables to effectively adjust the modulation transfer function of the PIV process to meet specific application requirements. For the stereoscopic PIV process two different approaches are possible: mapping, which involves applying PIV to de-warped images for 2D-2C displacement fields, and warping, which applies PIV to raw images and combine 3C reconstruction and image dewarping in a single subsequent step. In order to accurately estimate the laser sheet plane location and correct errors due to misalignment and inaccurate displacement vector positions, a disparity correction procedure is also available. The calibration module (CalVi) supports accurate calibration with the camera models mostly used in the PIV community: polynomials, rational functions and the pinhole camera model. Moreover, it supports the integration of the pinhole camera model with a refractive correction model for cylindrical geometries and camera calibration procedures working with unknown positions and orientations of the calibration target. In such a way, the target may be moved by hand without losing accuracy in comparison with a conventional calibration, in which the target is displaced with the aid of translational and rotational stages and knowledge of the target positions is strictly required. Already used in numerous experimental studies by the Experimental Thermo-Fluid Dynamics group of University of Naples “Federico II” and international collaborating groups, PaIRS-UniNa offers a rapid and effective free alternative to commercial PIV software for research and industrial use.

Providing spatial isolation for Mixed-Criticality Systems

Hard real-time systems, characterized by stringent timeliness requirements, occur in an increasing variety of industrial sectors. Some such domains carry important safety-critical concerns, notably avionics, space, and automotive. One common design trend across those domains seeks to reduce the number of computing devices embedded in them by integrating software applications of different criticality levels into one and the same onboard computer. A safety-savvy design approach however requires isolation among components of different criticality, to prevent unintended reciprocal interference across them. Isolation is traditionally achieved through partitioning. Partitioning, however, incurs low resource utilization as cautionary margins are used to inflate partition budgets over their anticipated needs. This situation has prompted research into alternative ways to integration that can safely afford higher levels of utilization. The Mixed-Criticality (MC) approach, which concentrates on the CPU scheduling problem, has yielded a large body of research results that show considerable gains in sustained utilization, but it has yet to meet all of the isolation requirements of safety-critical systems. This work presents a solution to augment a state-of-the-art MC solution with efficient and effective spatial isolation capabilities. Experimental results show that our solution provides adequate guarantees of temporal and spatial isolation with very small runtime overhead.

Survey on Explainable AI: Techniques, challenges and open issues

Artificial Intelligence (AI) has become an important component of many software applications. It has reached a point where it can provide complex and critical decisions in our life. However, the success of most AI-powered applications is based on black-box approaches (e.g., deep neural networks), which can create learned models that are able to predict and make decisions. While these advanced models could achieve high accuracy, they are generally unable to explain their decisions (e.g., predictions) to users. As a result, there is a pressing need for explainable machine learning systems in order to be trustworthy by governments, organizations, industries, and users. This paper classifies and compares the main findings in the domain of explainable machine learning and deep learning. We also discuss the application of Explainable AI (XAI) in sensitive domains such as cybersecurity. In addition, we characterize each reviewed article on the basis of the methods and techniques used to achieve XAI. This, in turn, allows us to discern the strengths and limitations of the existing XAI techniques. We finally discuss some substantial challenges and future research directions related to XAI.

DEMIGOD: A Low-Cost Microcontroller-Based Closed-Loop System Integrating Nanoengineered Sweat-Based Glucose Monitoring and Controlled Transdermal Nanoemulsion Release of Hypoglycemic Treatment with a Software Application for Noninvasive Personalized Diabetes Care.

This study endeavored to design and develop an innovative closed-loop diagnostic and therapeutic system with the following objectives: (a) the noninvasive detection of glucose concentration in sweat utilizing nanonengineered screen-printed biosensors; (b) the management of measured data through a specialized computer system comprising both hardware and software components, thereby enabling the precise control of therapeutic responses via a patch-based nanomedicine delivery system. This initiative addresses the significant challenges inherent in the management of diabetes mellitus, including the imperative need for glucose-level monitoring to optimize glycemic control. Leveraging chronoamperometric results as a foundational dataset and the in vivo hypoglycemic activity of nanoemulsion formulations, this research underscores the efficacy and accuracy of glucose concentration estimation, decision-making mechanism responses, and transdermal hypoglycemic treatment effects, within the proposed system.

Virtual Desktop Assistant for Linux

A desktop assistant on Linux is a software application designed to assist users in performing various tasks on their Linux- based desktop system. It typically integrates with the desktop environment and offers a user-friendly interface for performing common tasks such as launching applications, managing files and folders, and accessing system settings. The assistant can also be customized with plugins or extensions to add additional functionality. The main goal of a desktop assistant on Linux is to make the user experience as smooth and efficient as possible. By centralizing common tasks and providing an accessible interface, the assistant eliminates the need for users to navigate complex system settings or memorize multiple keyboard shortcuts. This, in turn, makes it easier for users to perform everyday tasks on their Linux-based desktop system, increasing productivity and overall satisfaction with their experience. Keywords— Deep Learning, Machine learning, NLP, Voice Assistant, Linux, Artificial Intelligence, Speech Recognition, LLMs.

The Role of Business Intelligence in Developing Tax Performance: An Applied Research in The General Taxing Authority

Objective: The present research aims at examining the role of business intelligence in developing tax performance in the General Tax Authority of a developing county. Theoretical Framework: To enlighten the main concepts of this study and underpin their theoretical foundations, the business intelligence tools and applications were presented. Their characteristics, dimensions, virtues, as well as the reasons behind their implementation were also detailed. Therefore, the role of business intelligence in improving tax performance was justified, providing a solid basis for understanding the context of our investigation carried out in the taxing authority of the Iraqi capital. Method: To conduct our investigation, a questionnaire was administrated to the target respondents, i.e. 97 employees of the General Tax Authority, located in Baghdad. Following the data collection process, descriptive, then regression analyses were undertaken. Results and Discussion: The obtained findings support that business intelligence exerts a positive influence on tax performance of the general tax authority. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into the awareness of the importance of business intelligence and software applications at the workplace. Departments’ directors of the general taxing authority are invited to cooperate with the corresponding authority whose work could be better performed electronically. Decisions regarding citizens could also be taken more wisely. Originality/Value: The relevance of the present research is essentially evidenced by its main practical implications regarding the improvement of tax performance of taxing authorities of developing countries, as well as the betterment of tax services dedicated to citizens.

P-215 Prospective validation of the EMBRYOAID software for AI-based evaluation of embryos on the 5th day after fertilization during the in vitro procedure - multi-centre study

Abstract Study question Is AI-based evaluation of embryos as effective in achieving pregnancy as the standard assessment performed by an experienced embryologist? Summary answer AI-based assessment of blastocysts may result in comparable pregnancy rates as the standard assessment performed by an experienced embryologist. What is known already Standard clinical practice in the IVF procedure includes morphological assessment of blastocysts based on visual evaluation following Gardner scale. However, this subjective grading process is susceptible to variability dependent on the professionals’ experience and knowledge, leading to potential inconsistencies in selecting embryos for transfer. Recently, there has been a suggestion that artificial intelligence (AI) algorithms could enhance the predictability of treatment outcomes and many AI-based software applications have been introduced to the procedure in many IVF centers. Nevertheless, there are very few prospective evaluations of the real-life implementation of the novel technologies. Study design, size, duration It is an ongoing, randomized, prospective, multicenter, international study performed in four investigation centers since November 2023. The duration of the study is scheduled for one year. Participants/materials, setting, methods The first stage of the evaluation comprises a randomized selection of a patient meeting the inclusion criteria to one of the groups: test or control group. The next step includes a selection of the embryo for implantation. For the control group, the experienced embryologist performs the standard selection of embryos as per normal clinical practice following the Gardner scale. For the study group, the embryologist selects the embryo for transfer indicated by the EMBRYOAID application. Main results and the role of chance Currently, there have been 149 patients recruited in the study and 160 cycles with 638 embryos have been entered in the EMBRYOAID software for AI-based evaluation. Test group of 80 patients comprises 87 cycles with 346 embryos. Preliminary results of the prospective study conducted in real-life settings show encouraging outcomes in relation to the idea that AI-supported embryo evaluation may be as effective in regards to obtaining pregnancy as a standard clinical assessment performed by experienced professionals. Nevertheless, the sample size is not yet sufficient to achieve statistical significance. Therefore, further data gathering will be conducted and the study hypothesis will be furthermore examined. Limitations, reasons for caution The sample size is still insufficient to reach statistical significance and the study shows the preliminary results which will be further verified on a larger patient group. Wider implications of the findings Results of a prospective, randomized trial conducted in several international clinics may enhance the trend to introduce more objective, bias-free AI-based tools into the embryology labs. Trial registration number EMBRYO National Center for Research and Development nr POIR.01.01.01-00-1631/20-00; ERC Consolidator Grant TUgbOAT no 772346

Challenges and opportunities of digital transformation in Ukrainian education

An important and necessary trend in the modern Ukrainian education system is the digitalisation of the educational process due to the forced transition of many educational institutions to distance learning due to the coronavirus pandemic and the Russian-Ukrainian war. The purpose of the article is to theoretically substantiate the challenges and opportunities for the introduction of digital learning technologies in Ukrainian educational institutions. The following methods were used to write the article and to reveal its purpose: conceptual and terminological analysis, comparative and contrastive analysis, analysis, synthesis, comparison, classification, systematisation and generalisation. The study found that, despite the many opportunities provided by the digital learning environment, including the development of software applications for the development of students' practical experience and access to high-quality and diverse sources of information, the education system in Ukraine needs to rethink the content, determine models of digitalisation of educational institutions, master the digital competencies of all participants in the educational process, and actively use the tools of Google Meet, Zoom, Microsoft Teams, and Cisco Webex, It is determined that the services are not without drawbacks in use, but their effective use contributes to digitalisation. The scientific novelty of the obtained results lies in the interpretation of the concept of “challenges of introducing digital technologies in education” and the identification of the main obstacles and ways to ensure the process of digitalisation of education. The conclusions recommend ways to overcome the difficulties of digitalisation of education, in particular, the need to create a model of digital transformation of an educational institution based on three models: a functionally independent model of information management; a concentric model of information technology for digital transformation of an educational institution; and a model of interlayer information interaction. Prospect for further research is to study the mechanisms for adapting teacher training systems to the use of digital technologies in the classroom.

BlastAsia – its quest for innovation in enterprise software application development

ABSTRACT The case sheds light on the rapid evolution in the no-code application development space. It presents opportunities for entrepreneurs looking to support customers with their digital transformation journeys. The protagonist of the case, Arup Maity, is the founder of a traditional software development company, BlastAsia. He is contemplating launching a no-code application development platform, given evolving market preferences and customer expectations. The case outlines the emergence of no-code application development platforms as a potent alternative to traditional software development techniques. It highlights exactly how business uncertainty coupled with rapidly evolving technology requirements demands shorter software development lifecycles, which traditional methods might not be able to cater to. It also explores evolving customer and employee expectations to engage in applications which are intuitive, work across devices seamlessly, and deliver real-time project status and data. The case also discusses budgetary considerations, which companies looking to deploy enterprise software have to grapple with. The landscape is further complicated by cutthroat competition in the industry, start-ups offering enhanced value propositions at competitive prices, and black-swan events like the COVID-19 pandemic, which pose existential challenges for organizations.

THE EFFECTIVENESS OF THE AUTHOR'S TECHNOLOGY AIMED AT THE DEVELOPMENT OF STATIC AND DYNAMIC BODY BALANCE OF PRIMARY SCHOOL CHILDREN WITH HEARING DEPRIVATION

The relevance of the research topic. Numerous studies have stated that abnormal child development is always accompanied by motor function disorders and deficits in motor activity. The combination of sensory and motor deprivation causes extraordinary difficulties in the perception, assimilation, retention and reproduction of educational material, and also significantly complicates the life of a child with a disability. The purpose of the article is to determine the effectiveness of the author's technology aimed at the development of static and dynamic body balance of primary school children with hearing deprivation in the process of adaptive physical education. Matherial and Methods. The study involved 8 boys and 11 girls with hearing impairment aged 8 years, who were randomly divided into main groups (GG1, GG2) and control groups (CG1, CG2). The following research methods were used to accomplish the tasks: analysis of scientific literature, pedagogical experiment, stabilography. Mathematical and statistical processing and analysis of data were carried out using the computational and graphical capabilities of the application software packages "Statistica" (StatSoft, version 10.0) and Microsoft Excel 2010. Results. The results obtained during the formative experiment prove the positive influence of the means and methods of the author's technology on the indicators of steady- state body balance (in the Romberg test with open eyes) in girls of 8 years old with hearing impairment GG2the amplitude of oscillations of the total body mass centre (TBC) in the frontal plane of girls of GG2 statistically significantly improved (Т=0; Z=2.20; p=0.028); the amplitude of oscillations of TBC in the sagittal plane of girls of GG2 statistically significantly decreased (Т=0; Z=2.02; p=0.043); there was a statistically significant improvement in the length of the trajectory of the TBC in the frontal and sagittal planes (Т=0; Z=2.20; p=0.028); statistically significant increase in the assessment of the speed of TBC movement of the examined girls (Т=0; Z=2.20; p=0.028); also revealed a statistically significant difference in the indicators of the area of oscillations of the TBC after the experiment of girls of CG2 (T=0; Z=2.02; p=0.043) and girls of GG2 (T=0; Z=2.20; p=0.028); among boys with hearing impairment (GG1) there was a tendency to improve the indicators of steady-state body balance. Calculations were performed by applying the nonparametric Wilcoxon signed rank test for dependent samples.

SNNtrainer3D: Training Spiking Neural Networks Using a User-Friendly Application with 3D Architecture Visualization Capabilities

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model’s behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners.