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Purpose Smartphones have become a critical medium by which people remain in contact with family, friends, and colleagues. A variety of factors have contributed to the rapid prevalence of smartphones. The most influential factor is definitely the mobile platform or mobile operating system (OS). The purpose of this paper is to employ a theoretical framework based on an information systems success model and a theory values to examine the factors that affect smartphone users’ switching mobile OSs. Habit is regarded as a moderating variable to construct an integrated research model which helps researchers unveil the puzzle of users’ switching mobile OSs. Design/methodology/approach The proposed model was empirically evaluated using survey data collected from 424 users on their perceptions of smartphones. A structural equation modeling was used to assess the relationships of the research model. Findings The results of the study have shown that users consider the perceived switching value and satisfaction arising from their behaviors when deciding whether to switch to another mobile OS. Quality characteristics, including information quality, system quality, and service quality, are the key factors determining people’s perceived switching value and satisfaction. The participants of the study were grouped by degree of habit. The effect on satisfaction was not significant in the high-habit subgroup. Nevertheless, it has been found that the influence of the perceived switching value was stronger in the low-habit subgroup than in the high-habit subgroup. Originality/value This study contributes to a theoretical understanding of factors that explain users’ intention to switch mobile OSs.

\"Mobile commerce transactions continue to soar, driven largely by the ever-increasing adoption and use of smartphones and tablets. The use of this technology gives consumers the flexibility to shop whenever and wherever they want. This book addresses the role of industry, academia, scientists, engineers, professionals, and students in developing innovative new mobile commerce technologies and systems to further improve the consumer experience. It also discusses the impact of mobile commerce on society, economics, culture, organizations, government, industry, and our daily lives. This book brings together experts from multiple disciplines in industry and academia to stimulate new thinking in the development and application of mobile commerce technology. The book covers important mobile commerce topics, such as critical infrastructure management, mobile security issues, new applications and services, emerging development architectures, mobile business solutions, and future research opportunities. In addition to its multidisciplinary approach, the book also provides a cross-cultural approach intended to overcome cultural barriers and accelerate mobile commerce advancement in the global economy. Authors and researchers from around the world discuss a broad spectrum of methods, tools, and guidelines for designing mobile commerce systems and services in different cultures\"-- Provided by publisher.

Based on the research of XML core technology, this paper introduces the current mainstream XML parsing methods, XML DOM and SAX methods. For the current android system, which is used in android operating system. XML is researched in the android layout file, which is combined with the current android platform. The paper introduce application of XML pull parsing process, first study the parsing performance of XML, the second through an example to discuss these three methods, the last analysis XML file Size and compares these three methods.

The previous existing mobile technologies were only limited to voice and short messages, organized between several network operators and service providers. However, recent advancements in technologies, introduction, and development of the smartphones added many features such: high-speed processors, huge memory, multitasking, screens with large-resolution, utile communication hardware, and so on. Mobile devices were evolving into general-purpose computers, which resulted in the development of various technological platforms, operating systems, and platforms for the development of the applications. All these results in the occurrence of various competitive offers on the market. The above-mentioned features, processing speed and applications available on mobile devices are affected by underlying operating systems. In this paper, there will be discussed the mobile operating systems and application development platforms. 

Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device’s camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network’s training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.

Although many efforts have been made through past years, skin cancer recognition from medical images is still an active area of research aiming at more accurate results. Many efforts have been made in recent years based on deep learning neural networks. Only a few, however, are based on a single deep learning model and targeted to create a mobile application. Contributing to both efforts, first we present a summary of the required medical knowledge on skin cancer, followed by an extensive summary of the most recent related works. Afterwards, we present 11 CNN (convolutional neural network) candidate single architectures. We train and test those 11 CNN architectures, using the HAM10000 dataset, concerning seven skin lesion classes. To face the imbalance problem and the high similarity between images of some skin lesions, we apply data augmentation (during training), transfer learning and fine-tuning. From the 11 CNN architecture configurations, DenseNet169 produced the best results. It achieved an accuracy of 92.25%, a recall (sensitivity) of 93.59% and an F1-score of 93.27%, which outperforms existing state-of-the-art efforts. We used a light version of DenseNet169 in constructing a mobile android application, which was mapped as a two-class model (benign or malignant). A picture is taken via the mobile device camera, and after manual cropping, it is classified into benign or malignant type. The application can also inform the user about the allowed sun exposition time based on the current UV radiation degree, the phototype of the user’s skin and the degree of the used sunscreen. In conclusion, we achieved state-of-the-art results in skin cancer recognition based on a single, relatively light deep learning model, which we also used in a mobile application.

Many two-sided platforms, such as eBay, iOS, Android, and Twitter, invest in developer integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, in order to reduce the cost and improve the functionality of third-party content developed for the platform. Although these integration tools are crucial to platform success, they are costly to create, and therefore, managers need to understand where and when to deploy them. In particular, when the necessary integration investment is high, the advice to subsidize one side of a two-sided market while charging the other may not hold. This means that integration investment should be carefully coordinated with market pricing decisions. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in high-consumer value markets. However, there are nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that such investments are complements, not—as one might expect—substitutes. Many two-sided platforms (for example, eBay, Google, iOS, Android, Twitter, and Amazon) provide integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, to reduce the costs and improve the functionality of third-party content developed for the platform. The need for such investment is increasing with the rise of major new markets as the result of technologies, such as the “Internet of Things.” Although crucial to platform success, platform integration tools are costly to create. We develop an analytic model to explore the key tradeoffs behind investment in integration tools and how that investment interacts with pricing decisions in a two-sided market. We model these decisions for hardware/software platforms as well as hybrid retail platforms and analyze them under various scenarios, including monopoly and competition. Our results suggest that considering integration investment can create market regimes in which the standard pricing results from the extant platform literature no longer hold. For example, the tendency to reduce prices to one side of a market in response to increasing the benefit of the network to the other side may be suboptimal in the presence of integration investment. Therefore, integration investments must be well coordinated with pricing decisions made for both sides of the market. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in a market in which content providers earn a high-enough profit margin creating content that is highly valued by the consumer market. Hybrid retail platforms often show similar behavior. However, there are some nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that these investments are complements, not—as one might expect—substitutes. We conclude by discussing this work’s implications for theory and practice.

The launch of the new iPad Pro by Apple in March 2020 generated high interest and expectations for different reasons; nevertheless, one of the new features that developers and users were interested in testing was the LiDAR sensor integrated into this device (and, later on, in the iPhone 12 and 13 Pro series). The implications of using this technology are mainly related to augmented and mixed reality applications, but its deployment for surveying tasks also seems promising. In particular, the potentialities of this miniaturized and low-cost sensor embedded in a mobile device have been assessed for documentation from the cultural heritage perspective—a domain where this solution may be particularly innovative. Over the last two years, an increasing number of mobile apps using the Apple LiDAR sensor for 3D data acquisition have been released. However, their performance and the 3D positional accuracy and precision of the acquired 3D point clouds have not yet been fully validated. Among the solutions available, as of September 2021, three iOS apps (SiteScape, EveryPoint, and 3D Scanner App) were tested. They were compared in different surveying scenarios, considering the overall accuracy of the sensor, the best acquisition strategies, the operational limitations, and the 3D positional accuracy of the final products achieved.