iOS Platform Research Articles

UI semantic component group detection: Grouping UI elements with similar semantics in mobile graphical user interface

Texts, widgets, and images on a UI page do not work separately. Instead, they are partitioned into groups to achieve certain interaction functions or visual information. Existing studies on UI elements grouping mainly focus on a specific single UI-related software engineering task, and their groups vary in appearance and function. In this case, we propose our semantic component groups that pack adjacent text and non-text elements with similar semantics. In contrast to those task-oriented grouping methods, our semantic component group can be adopted for multiple UI-related software tasks, such as retrieving UI perceptual groups, improving code structure for automatic UI-to-code generation, and generating accessibility data for screen readers. To recognize semantic component groups on a UI page, we propose a robust, deep learning-based vision detector, UISCGD, which extends the SOTA deformable-DETR by incorporating UI element color representation and a learned prior on group distribution. The model is trained on our UI screenshots dataset of 1988 mobile GUIs from more than 200 apps in both iOS and Android platforms. The evaluation shows that our UISCGD achieves 6.1% better than the best baseline algorithm and 5.4 % better than deformable-DETR in which it is based.

A GCC Artificial Food Additives Management based Mobile Application Development

Artificial food additives pose significant health risks to Gulf Cooperation Council (GCC) citizens despite regional authorities' extensive medical, legislative, and technological efforts. Literature highlights the detrimental impacts of these additives, including malnutrition, digestive disorders, respiratory problems, skin issues, hives, nausea, diarrhea, shortness of breath, allergic reactions, high blood pressure, and tumors. The research project at hand aims at becoming the first official and comprehensive mobile application of its own in the GCC region that manages the calculation and demonstration of an up-to-date health and legal knowledge base of the impacts of artificial additives, enhances the awareness, automatically recognizes the artificial additives, and provides alternative solutions, for both android and IOS mobile platforms. This research project introduces "Weqaya," a pioneering mobile application designed to manage, educate, and raise awareness about the effects of artificial additives. Weqaya provides real-time health and legal information, identifies additives, and suggests alternative solutions for Android and iOS platforms. The project employs an Agile-based SDLC model to explore, develop, and evaluate the food additive phenomena in Weqaya. The application's usability evaluation scores a promising 95.21%, indicating its potential utility for GCC health ministries, dietitians, academics, researchers, and food producers in enhancing knowledge and promoting non-artificial food options.

Capabilities Comparison Of The Augmented Reality Application Development Frameworks On The Android Platform

In the implementation of augmented reality, the natural process of human-computer interaction continues to be a challenge, specifically to reduce the complexity in the effort of using and providing comfortability. Therefore, it is necessary to search for methods and tools to simplify the complex process of building Augmented Reality applications. Vuforia is a multi-platform that has long been used as an Augmented Reality application development framework. Android and iOS platform frameworks are the new alternatives following the development of cellular technology. Furthermore, a comparative capability was conducted between Vuforia and ARCore on the Android platform. The general performance and the ability to understand the environment include working in horizontal and vertical planes, the ability to work based on lighting conditions, and the distance of the camera during the tests. The results showed that ARCore is superior to Vuforia in almost all testing metrics. However, in overexposed or too dim lighting and at very close surfaces, Vuforia is slightly superior but not essential. Therefore, it can be concluded that ARCore's capability is better than Vuforia.

Analysis and Development of a Football Scouting App based on Flutter: A Case Study of A3N

Scouting is a crucial aspect of football, where the team's success depends heavily on the ability to select the right player. To minimize risk and optimize resources, teams can utilize the football scouting application. Unfortunately, existing applications still have limitations, especially in support of multiple platforms, and are open source. Therefore, this research is focused on developing a football scouting application that supports multiple platforms and is open source, using a single codebase-based Flutter Framework. Using Flutter, the app can be created based on just one code base, which can be compiled directly on Android and iOS platforms. The developed application has several superior features. First, the application allows the admin to insert player data into the database. Another feature is the ability to search for players based on names that users include. In addition, the application is also able to display a list of players based on the position chosen by the user. Finally, the application can display the complete data and statistics of the currently active player. Through the implementation of the selected development method, the implementation of this system can contribute significantly to supporting the scouting process in football teams. Thus, this application can increase the efficiency and effectiveness of decision-making within the scope of football scouting activities.

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

<p>This study aims to propose the integration of cyber security measures with sustainable mobile commerce platforms and evaluate their efficacy in promoting social entrepreneurship and sustainability in the field of mobile commerce. A platform was developed for Android and iOS platforms using the Flutter Framework and Dart Programming Language, and it included features designed to help social entrepreneurs and non-profit organizations connect with potential donors, volunteers, and partners. The study includes participants from social entrepreneurs, non-profit organizations, and potential donors who are recruited to use the platform to create and promote their social business projects and to provide feedback on their experience using the platform. The results of the integration are expected to show that the mobile platform for social business is effective in promoting social entrepreneurship and sustainable development and in connecting social entrepreneurs and non-profit organizations with potential donors and partners due to the security measures that have been taken in the application and data protection, ensuring privacy with a comparatively lower risk than other platforms. In conclusion, this paper presents a blockchain-based secure mechanism for mobile platforms for m-commerce and social business, which is a valuable tool for promoting social entrepreneurship and sustainable development. It can be used to create a positive social impact in local and, conceivably, international communities. The proposal explicitly explains the methodology and implementation of the platform and highlights the significance and impact of this research in advancing the fields of mobile commerce, social entrepreneurship, and sustainability.</p>

Performance optimization of machine learning-based image recognition algorithms for mobile devices based on the iOS operating system

Today, mobile devices play an important role in everyone's daily life, and one of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task, it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices, such as quantization and compression of models, optimization of initial calculations. In addition to ways to optimize the machine learning model itself, various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages, and therefore, in the results of the work, it is proposed to use not only a combination of the described options, but also an additional method of parallelization of image processing processes. The article discusses examples of specific tools and frameworks available for optimizing machine learning performance on iOS, and conducted its own experiments to test the effectiveness of various optimization methods. An analysis of the results obtained and a comparison of the performance of the algorithms are also provided. The practical significance of this article is as follows: Improving the performance of machine learning algorithms on iOS mobile devices will lead to more efficient use of computing resources and increase system performance, which is very important in the context of limited computing power and energy resources of mobile devices. Optimization of machine learning performance on the iOS platform contributes to the development of faster and more responsive applications, which will also improve the user experience and allow developers to create new and innovative features and capabilities. Expanding the applicability of machine learning on iOS mobile devices opens up new opportunities for application development in various fields such as pattern recognition, natural language processing, data analysis, and others.

Fusion of Forensic Analysis of Mobile Devices: Integrating Multi-Criteria Decision Methods and Case Study Insights

This study employed a Multi-Criteria Decision Analysis (MCDM) approach, utilizing the DEMATEL and TOPSIS methodologies, to assess the effectiveness of forensic tools designed for mobile devices, with a specific emphasis on Android and iOS platforms. The investigation evaluated technologies used for collecting, retrieving, and validating data in the Cyber Forensic Field Triage paradigm, with a focus on rapidly identifying and interpreting digital evidence. The study incorporated several factors and expert preferences, concluding that the Android Triage and Andriller tools were the most efficient.

Turetality (Safety Culture Hospitality) Application Model as an Occupational Health and Safety (OHS) Culture Media in Hotels in Yogyakarta

The tourism industry sector significantly impacts the national foreign exchange income. Indonesia boasts numerous tourist destinations, among which Yogyakarta stands out. This indeed affects the accommodation facilities for tourists, with hotels being a prominent example. One of the measures taken to enhance these accommodations involves the improvement of hotel services. This enhancement encompasses aspects such as ensuring the safety and well-being of both hotel visitors and staff. Notably, there are still instances where certain hotels exhibit shortcomings in the implementation of occupational health and safety (OHS) culture. The objective of this research is to analyze the requisite needs and develop a prototype application called Turetality (Safety Culture Hospitality), compatible with both Android and iOS platforms. This study employs a quantitative descriptive research methodology. The findings from this research indicate the necessity for both a reporting system for identified issues and a periodic monitoring mechanism, as there exists potential jeopardy to both health and safety. This application offers a resolution to prevalent issues such as delayed manual reporting and instances of unrecorded reporting. The interface and content of the application are tailored to meet the OHS requirements within the hotel industry. In conclusion, the prototype application Turetality stands to be well-received by users due to its capacity of being able to provide ease of use and secure handling of OHS reporting data within the hotel industry.

Development of an epilepsy self-management mobile health app framework: Content validity study results.

Mobile health (mHealth) applications (apps) show promise in supporting epilepsy self-management (eSM). To delve deeper into this potential, we conducted a systematic review of epilepsy mHealth apps available on both iOS and Android platforms, examining articles related to eSM. This review allowed us to identify important domains related to eSM. Furthermore, based on the findings, we developed an epilepsy mHealth app framework that aims to improve self-management for the local population. This study aims to assess the practicality and usability of the proposed mHealth app framework designed to improve eSM. We will conduct an expert panel review to evaluate the effectiveness and feasibility of the framework. Content validity was assessed by an expert panel comprising epileptologists and pharmacists. The validation process involved scoring the items within each domain of the framework to evaluate their practicality and usability (quantitative component). In addition, a panel discussion was conducted to further explore and discuss the qualitative aspects of the items. A total of 4 domains with 15 items were highly rated for their practicality and usefulness in eSM. The locally validated framework will be useful for developing eSM mobile apps. Seizure Tracking, Medication Adherence, Treatment Management, and Healthcare Communication emerged as the most crucial domains for enhancing eSM.

Analysis of methods for detecting attacks on facial biometric authentication in mobile devices

The object of the research is facial biometric authentication in the context of mobile devices (financial and government applications, etc.) of the Android and IOS platforms. The subject of the research is methods of detecting attacks on biometric authentication by face in mobile devices. The purpose of the work is to investigate the methods of creating and recognizing Deepfake videos, to evaluate the possibility of local recognition of fakes within applications in mobile devices of the Android and IOS platforms.
 As a result, the vulnerability of biometric authentication to Deepfake technology was confirmed, methods for recognizing fakes were analyzed, and the effectiveness of using a neural network model for local recognition of Deepfake in a mobile device was experimentally confirmed. Conclusions are drawn about possible ways to optimize the size of the model with an emphasis on maintaining accuracy.

Design and Development of a Mobile-Based Water Reminder Application on the iOS Platform

Lifestyle encompasses the various ways in which individuals, groups, and nations are influenced by geography, economy, politics, history, culture, and religion. It reflects the characteristics of residents, including their daily behaviors in work, activities, and health. Maintaining a healthy lifestyle is crucial for overall well-being, and one key aspect is ensuring an adequate intake of water. Water constitutes the primary component of the human body, comprising an average of 70-80 percent of an individual's body weight. Factors influencing water consumption behavior include knowledge and preferences for other beverages. To address the challenge of promoting water consumption and advocating for its importance, this study proposes the development of a mobile application system capable of reminding individuals to drink water based on personalized needs, considering factors such as gender, age, weight, height, and activity level. The research aims to leverage and advance existing technology, specifically by creating a mobile application on the iOS platform. The objective is to enhance and reinforce individuals' discipline in maintaining proper water intake. Targeting users from middle-class to affluent social conditions, the application is tailored for the iOS platform. The study involves testing the functionality of the water reminder system software developed for mobile devices running on the iOS platform. The ultimate goal is to create an information system that not only exhibits maximum aesthetics and functionality but also adheres to the principles of interface design, including the application of the eight golden rules.

A First Look at On-device Models in iOS Apps

Powered by the rising popularity of deep learning techniques on smartphones, on-device deep learning models are being used in vital fields such as finance, social media, and driving assistance. Because of the transparency of the Android platform and the on-device models inside, on-device models on Android smartphones have been proven to be extremely vulnerable. However, due to the challenge in accessing and analyzing iOS app files, despite iOS being a mobile platform as popular as Android, there are no relevant works on on-device models in iOS apps. Since the functionalities of the same app on Android and iOS platforms are similar, the same vulnerabilities may exist on both platforms. In this article, we present the first empirical study about on-device models in iOS apps, including their adoption of deep learning frameworks, structure, functionality, and potential security issues. We study why current developers use different on-device models for one app between iOS and Android. We propose a more general attack against white-box models that does not rely on pre-trained models and a new adversarial attack approach based on our findings to target iOS’s gray-box on-device models. Our results show the effectiveness of our approaches. Finally, we successfully exploit the vulnerabilities of on-device models to attack real-world iOS apps.

Rancang Bangun Aplikasi Diet untuk Ibu Menyusui Pasca Persalinan dengan Algoritma Mifflin-St Jeor

Pregnancy is a significant and transformative period for women, both physically and emotionally. During this time, it is crucial for expectant mothers to prioritize their own health and well-being to create a healthy environment for their growing baby. One of the physical changes that many breastfeeding mothers experience after childbirth is weight gain. Factors contributing to this include increased caloric needs, lack of sleep, reduced physical activity, and feelings of stress and fatigue due to caring for a newborn. Maintaining a healthy weight is vital to reduce the risk of various health issues and ensure the quality of breast milk for the baby. However, it is important to note that mothers should not engage in strict dieting during the postpartum period, or the puerperium, which lasts up to 40 days after delivery. During this time, mothers should gradually resume normal activities and movement. To support breastfeeding mothers in maintaining their health after childbirth, a structured and monitored approach that provides tailored information according to each stage of development is necessary. The Laav application, available for iOS, is designed to calculate and record the caloric intake of breastfeeding mothers, helping them achieve proper nutrition while maintaining an ideal weight. The application is built using the User-Centered Design (UCD) methodology and uses the Mifflin-St Jeor algorithm to calculate calories. The application is programmed in SwiftUI, a language optimized for the iOS platform

Diagnostic accuracy of a smartphone application for artificial intelligence-based interpretation of 12-lead ECG in primary care (AMSTELHEART-1)

Abstract Background The use of 12-lead electrocardiogram (ECG) is common in routine primary care, however it can be difficult for less experienced ECG readers to adequately interpret the ECG. Objective To validate a novel smartphone application as a stand-alone interpretation tool for 12-lead ECG in primary care. Methods We recruited consecutive patients who underwent 12-lead ECG as part of routinely indicated primary care in the Netherlands. All ECGs were assessed by a smartphone application which analyses a photographed image of 12-lead ECG for automated interpretation, installed on an Android platform and an iOS platform. We validated the application for detecting: 1) any major ECG abnormality (MEA, primary outcome) defined as atrial fibrillation/flutter (AF), markers of (past) myocardial ischemia or clinically relevant impulse and/or conduction abnormalities; or 2) AF (key secondary outcome). The reference standard was a blinded expert panel. Results We included 290 patients from 11 Dutch general practices with median age 67 (IQR 55-74) years, 48% were female. On reference ECG, 71 patients (25%) had MEA, 35 (12%) had AF. The app's sensitivity and specificity for MEA were 86% (95%CI:76-93) and 92% (95%CI:87-95), respectively. For AF, sensitivity and specificity were 97% (95%CI:85-100) and 99% (95%CI:97-100), respectively. Performance was comparable between Android and iOS platform (Kappa=0.95, 95%CI:0.91-0.99 and Kappa=1.00, 95%CI:1.00-1.00 for MEA and AF, respectively). Conclusions A smartphone application that interprets photographed 12-lead ECG images had good diagnostic accuracy in a primary care setting for major ECG abnormalities, and near-perfect properties for diagnosing AF.

Real-time interactive 3d simulation of Atrial Fibrillation for education

Abstract Background The mechanistic understanding of atrial fibrillation (AF) has burgeoned over the past decades. However, the interplay between triggers, reentry, drivers and therapies is rarely fully appreciated despite being ultimately fundamental to patient care. Readily accessible, high-fidelity interactive simulation has the potential to address this knowledge gap across healthcare providers. Objective To develop a, interactive simulation system to allow deep understanding of AF mechanisms and therapies. Methods An application for e-Training was developed to be accessible on consumer smartphones and tablets, based on a validated electromechanical simulation model of the heart, with real-time interactive 3D image output. A virtual heart is represented in normal and arrhythmic states, enabled with simulated models for administering antiarrhythmic drugs, performing Pulmonary Vein Isolation (PVI), AV Node ablation, ventricular pacing, DC Cardioversion, plus the formation of atrial thrombus leading to a stroke event. Electrogram and ECG signals are generated from the underlying simulation and respond realistically to drugs, pacing and ablation. A self-study tutorial introduces the arrhythmia and treatment procedures. Learning outcomes are assessed with MCQ tests and receive Continuous Professional Development CPD credits. Results The AFib Sim app has received CPD accreditation in the UK and has so far been used at the Universities of Oxford, Cambridge and Leeds medical schools. All trainees and medical students followed the self-study tutorials and took the interactive MCQ test, which are embedded within the AFib Sim app. Development was based on Unity 3D and was made available on iOS, Android, macOS, MS Windows and WebGL platforms. Conclusion This project meets an urgent training need whilst expanding expertise and market scope into clinical training. The impact on clinical training was high and the societal benefit came from better and quicker trained staff. Learning Objective Upon completion of this session, the attendee will be able understand how a simulation of Atrial Fibrillation can be used for training and deepening the clinical understanding of atrial fibrillation.

Review on Comparative Study of Flutter App and Android App

Abstract: In today lives, mobile application can be increasing vital. In flutter is a cross platform framework and other native mobile application is one particular operating system. The time of 2015 year mobile application development increasing day by day. and also, the time of COVID-19 mobile application was very popular there was huge competition between the mobile application that available in a market. flutter can support both iOS and Android platform application. The main focused on the paper highlight the comparison between flutter mobile application and other native android mobile application. In this we can comparison on flutter SDK and android SDK on the basic of compilation, memory consumption, CPU usage, app size, etc.

Connect Brain, a Mobile App for Studying Depth Perception in Angiography Visualization: Gamification Study

Background One of the bottlenecks of visualization research is the lack of volunteers for studies that evaluate new methods and paradigms. The increased availability of web-based marketplaces, combined with the possibility of implementing volume rendering, a computationally expensive method, on mobile devices, has opened the door for using gamification in the context of medical image visualization studies. Objective We aimed to describe a gamified study that we conducted with the goal of comparing several cerebrovascular visualization techniques and to evaluate whether gamification is a valid paradigm for conducting user studies in the domain of medical imaging. Methods The study was implemented in the form of a mobile game, Connect Brain, which was developed and distributed on both Android (Google LLC) and iOS (Apple Inc) platforms. Connect Brain features 2 minigames: one asks the player to make decisions about the depth of different vessels, and the other asks the player to determine whether 2 vessels are connected. Results The gamification paradigm, which allowed us to collect many data samples (5267 and 1810 for the depth comparison and vessel connectivity tasks, respectively) from many participants (N=111), yielded similar results regarding the effectiveness of visualization techniques to those of smaller in-laboratory studies. Conclusions The results of our study suggest that the gamification paradigm not only is a viable alternative to traditional in-laboratory user studies but could also present some advantages.

PEMBUATAN APLIKASI POINT OF SALES BERBASIS IPHONE OPERATING SYSTEM UNTUK UD. SINAR MAS

UD. Sinar Mas is a distribution company selling chemicals. UD. Sinar Mas records transactions using methods that are still manual or traditional. Where is the transaction method at UD. Sinar Mas also has several kinds of transaction systems such as receivables, debts, and there is also a barter sales of jerry cans, customers bring their own drums, or leave the drums with UD. Sinar Mas. The recording method in UD transactions. Sinar Mas, namely recording supplier names, recording customer names, sales transaction dates, purchase transaction dates, maturity dates. Because many transaction systems are recorded, it causes user errors such as missing notes, viewing the due date as well as errors in recording the due date when to collect debts, then how much customer debt remains, whether the goods have been sent by the customer or not, wrong mention of the price to a particular customer , an incorrect calculation of the amount sent is sometimes too less or too much, where the most fatal is the incorrect mention of the price to the customer because there are several customers whose prices are differentiated because of certain cases such as customers who have subscribed for a long time and also the remaining stock counting system which is still manual using approximate method so that it cannot produce a definite stock. The method that will be used for solving the problem is by creating an IOS-based application to help record transactions in UD. Sinar Mas. The result of this application is in the form of a cashier application that can record expenses, income and others. The purpose of this research is to help UD companies. Sinar Mas to record transactions more easily. From this problem, the author's solution is to design and design an iOS-based POS application. The POS application is made on the iOS platform because the owner of UD. Sinar Mas uses iOS as the main device. To save the data will be stored in SQL. The test is carried out using the UMUX method. The results of the test can be concluded that the resulting score is quite good. However, in the application test, there were a few obstacles, such as users experiencing confusion when pressing buttons and so on. Apart from that it runs pretty smoothly. The features that have been implemented have met the needs of the user.

Semi-automation of keratopathy visual acuity grading of corneal events in belantamab mafodotin clinical trials: clinical decision support software.

Belantamab mafodotin (belamaf) has demonstrated clinically meaningful antimyeloma activity in patients with heavily pretreated multiple myeloma. However, it is highly active against dividing cells, contributing to off-target adverse events, particularly ocular toxicity. Changes in best corrected visual acuity (BCVA) and corneal examination findings are routinely monitored to determine Keratopathy Visual Acuity (KVA) grade to inform belamaf dose modification. We aimed to develop a semiautomated mobile app to facilitate the grading of ocular events in clinical trials involving belamaf. The paper process was semiautomated by creating a library of finite-state automaton (FSA) models to represent all permutations of KVA grade changes from baseline BCVA readings. The transition states in the FSA models operated independently of eye measurement units (e.g., Snellen, logMAR, decimal) and provided a uniform approach to determining KVA grade changes. Together with the FSA, the complex decision tree for determining the grade change based on corneal examination findings was converted into logical statements for accurate and efficient overall KVA grade computation. First, a web-based user interface, conforming to clinical practice settings, was developed to simplify the input of key KVA grading criteria. Subsequently, a mobile app was developed that included additional guided steps to assist in clinical decision-making. The app underwent a robust Good Clinical Practice validation process. Outcomes were reviewed by key stakeholders, our belamaf medical lead, and the systems integration team. The time to compute a patient's overall KVA grade using the Belamaf Eye Exam (BEE) app was reduced from a 20- to 30-min process to <1-2 min. The BEE app was well received, with most investigators surveyed selecting "satisfied" or "highly satisfied" for its accuracy and time efficiency. Our semiautomated approach provides for an accurate, simplified method of assessment of patients' corneal status that reduces errors and quickly delivers information critical for potential belamaf dose modifications. The app is currently available on the Apple iOS and Android platforms for use by investigators of the DREAMM clinical trials, and its use could easily be extended to the clinic to support healthcare providers who need to make informed belamaf treatment decisions.

Foreignness Liability of Mobile App Startups: Examining Performance in the Context of Consumer and Investor Cultural Distances

ABSTRACT Mobile platforms provide important entrepreneurial opportunities by facilitating access to a global market composed of more than a hundred countries. These opportunities are crucial for mobile app startups as they seek to reach a wider audience and accelerate growth. Nevertheless, performing in foreign countries is challenging as mobile app startups face a “foreignness liability” since they are competing against complementors (i.e., app developers) who have experience in their domestic market. We offer a research model that assesses the impact of cultural distance on the app-level performance of mobile app startups in foreign countries. We hypothesize the moderating effects of investor-consumer cultural distance and environmental characteristics within foreign countries. We test the model using a dataset of over 550 mobile app startups operating on the iOS platform. Our findings support our hypotheses and provide insights into whether mobile platforms are level playing fields. Our study sheds light on the interactions between a startup and its investors’ cultural distances highlighting configurations of cultural distance that are beneficial for app performance in foreign countries when the environment is stable or uncertain.